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Qing X, Lu G, Zhang X, Chen Q, Zhou X, He W, Xu L, Zhang J. Essential spectral pixels-based improvement of UMAP classifying hyperspectral imaging data to identify minor compounds in food matrix. Talanta 2024; 273:125845. [PMID: 38442566 DOI: 10.1016/j.talanta.2024.125845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/31/2024] [Accepted: 02/28/2024] [Indexed: 03/07/2024]
Abstract
Classifying big data in hyperspectral imaging (HSI) can be challenging when minor (low-concentrated) compounds are present in actual samples, as for chemical additives and adulterants in food matrix. Herein, we propose a new strategy to classify HSI data for the identification of adulterants in food material for the first time. This strategy is based on the selection of essential spectral pixels of full HSI data followed by the feature space construction using uniform manifold approximation and projection as well as the data clustering utilizing hierarchical clustering analysis on the reduced data (named ESPs-UMAP-HCA). We apply our approach to analyze two real NIR datasets and four new Raman datasets. Compared with non-ESPs UMAP-HCA and t-distributed stochastic neighbor embedding combined with ESPs and HCA (ESPs-t-SNE-HCA), the developed strategy provides well-separated clusters for major and minor compounds in food matrix. Finally, the adulterants as minor compounds are accurately identified, which is confirmed by the fact that the extracted spectra of them perfectly match with their pure spectra. In addition, their locations are found in the contribution map even though they are present in a few pixels. What's more, the proposed strategy does not need any a priori knowledge of the data structure and the class memberships and therefore reduced the studied difficulty and confirmation bias in the analysis of big HSI datasets. Overall, the proposed ESPs-UMAP-HCA method could be a potential approach for food adulteration detection.
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Affiliation(s)
- Xiangdong Qing
- Hunan Provincial Key Laboratory of Dark Tea and Jin-hua, College of Materials and Chemical Engineering, Hunan City University, Yiyang, 413000, PR China.
| | - Guiying Lu
- National Center of Dark Tea Product Quality Inspection and Testing, Yiyang Testing Institute of Product and Commodity Quality Supervision, Yiyang, 413000, PR China
| | - Xiaohua Zhang
- Department of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang, 414006, PR China
| | - Qingling Chen
- Analytical Instrumentation Center of Hunan University, Changsha, 410082, PR China
| | - Xiaohong Zhou
- Hunan Provincial Key Laboratory of Dark Tea and Jin-hua, College of Materials and Chemical Engineering, Hunan City University, Yiyang, 413000, PR China
| | - Wei He
- Hunan Provincial Key Laboratory of Dark Tea and Jin-hua, College of Materials and Chemical Engineering, Hunan City University, Yiyang, 413000, PR China
| | - Ling Xu
- Hunan Provincial Key Laboratory of Dark Tea and Jin-hua, College of Materials and Chemical Engineering, Hunan City University, Yiyang, 413000, PR China
| | - Jin Zhang
- Hunan Provincial Key Laboratory of Dark Tea and Jin-hua, College of Materials and Chemical Engineering, Hunan City University, Yiyang, 413000, PR China.
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Qin X, Quan Y, Ji H. Enhanced deep unrolling networks for snapshot compressive hyperspectral imaging. Neural Netw 2024; 174:106250. [PMID: 38531122 DOI: 10.1016/j.neunet.2024.106250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 02/01/2024] [Accepted: 03/18/2024] [Indexed: 03/28/2024]
Abstract
Snapshot compressive hyperspectral imaging necessitates the reconstruction of a complete hyperspectral image from its compressive snapshot measurement, presenting a challenging inverse problem. This paper proposes an enhanced deep unrolling neural network, called EDUNet, to tackle this problem. The EDUNet is constructed via the deep unrolling of a proximal gradient descent algorithm and introduces two innovative modules for gradient-driven update and proximal mapping reflectivity. The gradient-driven update module leverages a memory-assistant descent approach inspired by momentum-based acceleration techniques, for enhancing the unrolled reconstruction process and improving convergence. The proximal mapping is modeled by a sub-network with a cross-stage spectral self-attention, which effectively exploits the inherent self-similarities present in hyperspectral images along the spectral axis. It also enhances feature flow throughout the network, contributing to reconstruction performance gain. Furthermore, we introduce a spectral geometry consistency loss, encouraging EDUNet to prioritize the geometric layouts of spectral curves, leading to a more precise capture of spectral information in hyperspectral images. Experiments are conducted using three benchmark datasets including KAIST, ICVL, and Harvard, along with some real data, comprising a total of 73 samples. The experimental results demonstrate that EDUNet outperforms 15 competing models across four metrics including PSNR, SSIM, SAM, and ERGAS.
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Affiliation(s)
- Xinran Qin
- School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.
| | - Yuhui Quan
- School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China; Pazhou Lab, Guangzhou 510335, China.
| | - Hui Ji
- Department of Mathematics, National University of Singapore, 119076, Singapore.
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Sun J, Yao K, Cheng J, Xu M, Zhou X. Nondestructive detection of saponin content in Panax notoginseng powder based on hyperspectral imaging. J Pharm Biomed Anal 2024; 242:116015. [PMID: 38364344 DOI: 10.1016/j.jpba.2024.116015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/16/2024] [Accepted: 02/03/2024] [Indexed: 02/18/2024]
Abstract
This study investigated the feasibility of using hyperspectral imaging (HSI) technique to detect the saponin content in Panax notoginseng (PN) powder. The reflectance hyperspectral images of PN powder samples were collected in the spectral range of 400.6-999.9 nm. Savitzky-golay (SG) smoothing combined with detrending correction was utilized to preprocess the original spectral data. Two model population analysis (MPA) based methods, namely bootstrapping soft shrinkage (BOSS) and iteratively retains informative variables (IRIV) were employed to extract feature wavelengths from the full spectra. A generalized normal distribution optimization based extreme learning machine (GNDO-ELM) model was proposed to establish calibration model between spectra and saponin content, and compared with existing methods (GA-ELM, PSO-ELM and SSA-ELM). The result showed that the IRIV-GNDO-ELM model gave the best performance, with coefficient of determination for prediction (R2P) of 0.953 and root mean square error for prediction (RMSEP) of 0.115%. Therefore, it is possible to determine the saponin content of PN powder by using HSI technique.
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Affiliation(s)
- Jun Sun
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
| | - Kunshan Yao
- School of Electrical and Information Engineering of Changzhou Institute of Technology, Changzhou 213032, China.
| | - Jiehong Cheng
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Min Xu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
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Dong F, Bi Y, Hao J, Liu S, Yi W, Yu W, Lv Y, Cui J, Li H, Xian J, Chen S, Wang S. A new comprehensive quantitative index for the assessment of essential amino acid quality in beef using Vis-NIR hyperspectral imaging combined with LSTM. Food Chem 2024; 440:138040. [PMID: 38103505 DOI: 10.1016/j.foodchem.2023.138040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/31/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023]
Abstract
The quality of beef is usually predicted by measuring a single index rather than a comprehensive index. To precisely determine the essential amino acid (EAA) contents in 360 beef samples, the feasibility of optimized spectral detection techniques based on the comprehensive EAA index (CEI) and comprehensive weight index (CWI) constructed by factor analysis was explored. Two-dimensional correlation spectroscopy (2D-COS) was used to analyse the mechanisms of spectral peak shifts in complex disturbance systems with CEI and CWI contents, and 15 sensitive feature variables were extracted to establish a quantitative analysis model of a long short-term memory network (LSTM). The results indicated that 2D-COS had good predictive performance in both CEI-LSTM (R2P of 0.9095 and RPD of 2.76) and CWI-LSTM (R2P of 0.8449 and RPD of 2.45), which reduced data information by 88%. This indicates that utilizing 2D-COS can eliminate collinearity and redundant information among variables while achieving data dimensionality reduction and simplification of calibration models. Furthermore, a spatial distribution map of the comprehensive EAA content was generated by combining the optimal prediction model. This study demonstrated that the comprehensive index method furnishes a new approach to rapidly evaluate EAA content.
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Affiliation(s)
- Fujia Dong
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Yongzhao Bi
- Beijing Key Laboratory of Flavor Chemistry, Beijing Technology and Business University (BTBU), Beijing 100048, China
| | - Jie Hao
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Sijia Liu
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Weiguo Yi
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Wenjie Yu
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Yu Lv
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Jiarui Cui
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Hui Li
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Jinhua Xian
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Sichun Chen
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Songlei Wang
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China.
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Kania A, Branchi V, Braun L, Verrel F, Kalff JC, Vilz TO. [Indications and surgical strategy for bowel resection in mesenteric ischemia : Resection margins considering current guidelines and literature as well as the influence of new technical possibilities]. Chirurgie (Heidelb) 2024; 95:367-374. [PMID: 38378936 DOI: 10.1007/s00104-024-02041-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/16/2024] [Indexed: 02/22/2024]
Abstract
Acute mesenteric ischemia (AMI) is still a time-critical and life-threatening clinical picture. If exploration of the abdominal cavity is necessary during treatment, an intraoperative assessment of which segments of the intestines have a sufficient potential for recovery must be made. These decisions are mostly based on purely clinical parameters, which are subject to high level of uncertainty. This review article provides an overview of how this decision-making process and the determination of resection margins can be improved using technical aids, such as laser Doppler flowmetry (LDF), indocyanine green (ICG) fluorescence angiography or hyperspectral imaging (HSI). Furthermore, this article compiles guideline recommendations on the role of laparoscopy and the value of a planned second-look laparotomy. In addition, an overview of strategies for preventing short bowel syndrome is given and other aspects, such as the timing and technical aspects of placement of a preternatural anus and an anastomosis are highlighted.
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Affiliation(s)
- Alexander Kania
- Klinik und Poliklinik für Allgemein‑, Viszeral‑, Thorax- und Gefäßchirurgie, Universitätsklinikum Bonn, Venusberg Campus 1, 53127, Bonn, Deutschland.
| | - Vittorio Branchi
- Klinik und Poliklinik für Allgemein‑, Viszeral‑, Thorax- und Gefäßchirurgie, Universitätsklinikum Bonn, Venusberg Campus 1, 53127, Bonn, Deutschland
| | - Lara Braun
- Klinik und Poliklinik für Allgemein‑, Viszeral‑, Thorax- und Gefäßchirurgie, Universitätsklinikum Bonn, Venusberg Campus 1, 53127, Bonn, Deutschland
| | - Frauke Verrel
- Klinik und Poliklinik für Allgemein‑, Viszeral‑, Thorax- und Gefäßchirurgie, Universitätsklinikum Bonn, Venusberg Campus 1, 53127, Bonn, Deutschland
| | - Jörg C Kalff
- Klinik und Poliklinik für Allgemein‑, Viszeral‑, Thorax- und Gefäßchirurgie, Universitätsklinikum Bonn, Venusberg Campus 1, 53127, Bonn, Deutschland
| | - Tim O Vilz
- Klinik und Poliklinik für Allgemein‑, Viszeral‑, Thorax- und Gefäßchirurgie, Universitätsklinikum Bonn, Venusberg Campus 1, 53127, Bonn, Deutschland
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Amariei G, Henriksen ML, Klarskov P, Hinge M. Quantification of aluminium trihydrate flame retardant in polyolefins via in-line hyperspectral imaging and machine learning for safe sorting. Spectrochim Acta A Mol Biomol Spectrosc 2024; 311:123984. [PMID: 38320471 DOI: 10.1016/j.saa.2024.123984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 01/24/2024] [Accepted: 01/29/2024] [Indexed: 02/08/2024]
Abstract
The extensive use of aluminium trihydrate (ATH) flame retardant in plastics poses challenges and hazards in plastic waste recycling, thus it is crucial to accurately identify ATH. This study demonstrates the effectiveness of an industrial in-line shortwave infrared (SWIR) hyperspectral imaging system and principal component analysis (PCA) for detecting and quantifying ATH in low-density polyethylene (LDPE) and polypropylene (PP). The samples were characterized by elemental analysis, ATR-FTIR, DSC, and TGA. A quantitative estimation model was developed by analysing spectra with varying ATH concentrations. PCA and SWIR band area ratio were fitted to estimate the ATH concentration. The PCA model outperformed the SWIR band area ratio model and achieved good predictions between measured and predicted ATH concentrations ranging from 22.9 to 1.6 wt% (R2LDPE = 0.95) in LDPE and from 24.0 to 2.5 wt% in PP (R2PP = 0.94). The obtained in-line control tool is relevant to the recycling industries, enabling real-time assessment of additives.
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Affiliation(s)
- Georgiana Amariei
- Plastic and Polymer Engineering, Department of Biological and Chemical Engineering, Aarhus University, Aabogade 40, DK-8200 Aarhus N., Denmark
| | - Martin Lahn Henriksen
- Plastic and Polymer Engineering, Department of Biological and Chemical Engineering, Aarhus University, Aabogade 40, DK-8200 Aarhus N., Denmark
| | - Pernille Klarskov
- Terahertz Photonics, Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, DK-8200 Aarhus N., Denmark
| | - Mogens Hinge
- Plastic and Polymer Engineering, Department of Biological and Chemical Engineering, Aarhus University, Aabogade 40, DK-8200 Aarhus N., Denmark.
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Wu N, Weng S, Xiao Q, Jiang H, Zhao Y, He Y. Rapid and accurate identification of bakanae pathogens carried by rice seeds based on hyperspectral imaging and deep transfer learning. Spectrochim Acta A Mol Biomol Spectrosc 2024; 311:123889. [PMID: 38340442 DOI: 10.1016/j.saa.2024.123889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 02/12/2024]
Abstract
Bakanae disease is a common seed-borne disease of rice. Rapid and accurate detection of bakanae pathogens carried by rice seeds is essential for the health of rice germplasm resources and the safety of rice production. This study aims to propose a general framework for species identification of major bakanae pathogens carried by rice seeds based on hyperspectral imaging and deep transfer learning. Seven varieties of rice seeds and four kinds of bakanae pathogens were analyzed. One-dimensional deep convolution neural networks (DCNNs) were first constructed using complete datasets. They achieved accuracies larger than 96.5% on the testing sets of most datasets, exceeding the conventional SVM and PLS-DA models. Then the developed DCNNs were transferred to detect other complete training sets. Most of the deep transferred models achieved comparable or even better performance than the original DCNNs. Two smaller target training sets were further constructed by randomly selecting spectra from the complete training sets. As the size of the target training sets reduced, the accuracies of all models on the corresponding testing sets also decreased gradually. Visualization analysis were conducted using the t-distribution stochastic neighbor embedding (t-SNE) algorithm and a proposed gradient-weighted activation wavelength (Grad-AW) method. They all showed that deep transfer learning could utilize the representation patterns in the source datasets to improve the target tasks. The overall results indicated that the bakanae pathogens were all identified accurately under our proposed framework. Hyperspectral imaging combined with deep transfer learning provided a new idea for the quality detection of large-scale seeds in modern seed industry.
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Affiliation(s)
- Na Wu
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
| | - Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Hubiao Jiang
- School of Plant Protection, Anhui Agricultural University, Hefei, China
| | - Yun Zhao
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
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Mulowayi AM, Shen ZH, Nyimbo WJ, Di ZF, Fallah N, Zheng SH. Quantitative measurement of internal quality of carrots using hyperspectral imaging and multivariate analysis. Sci Rep 2024; 14:8514. [PMID: 38609452 PMCID: PMC11014857 DOI: 10.1038/s41598-024-59151-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/08/2024] [Indexed: 04/14/2024] Open
Abstract
The study aimed to measure the carotenoid (Car) and pH contents of carrots using hyperspectral imaging. A total of 300 images were collected using a hyperspectral imaging system, covering 472 wavebands from 400 to 1000 nm. Regions of interest (ROIs) were defined to extract average spectra from the hyperspectral images (HIS). We developed two models: least squares support vector machine (LS-SVM) and partial least squares regression (PLSR) to establish a quantitative analysis between the pigment amounts and spectra. The spectra and pigment contents were predicted and correlated using these models. The selection of EWs for modeling was done using the Successive Projections Algorithm (SPA), regression coefficients (RC) from PLSR models, and LS-SVM. The results demonstrated that hyperspectral imaging could effectively evaluate the internal attributes of carrot cortex and xylem. Moreover, these models accurately predicted the Car and pH contents of the carrot parts. This study provides a valuable approach for variable selection and modeling in hyperspectral imaging studies of carrots.
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Affiliation(s)
- Arcel Mutombo Mulowayi
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China
| | - Zhen Hui Shen
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
- Engineering College, Fujian Jiangxia University, Fuzhou, 350108, China
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China
| | - Witness Joseph Nyimbo
- Fujian Provincial Key Laboratory of Agro-Ecological Processing and Safety Monitoring, College of Life Sciences, Agriculture and Forestry University, Fuzhou, 350002, China
| | - Zhi Feng Di
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China
| | - Nyumah Fallah
- Fujian Provincial Key Laboratory of Agro-Ecological Processing and Safety Monitoring, College of Life Sciences, Agriculture and Forestry University, Fuzhou, 350002, China
| | - Shu He Zheng
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China.
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Matenda RT, Rip D, Marais J, Williams PJ. Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review. Spectrochim Acta A Mol Biomol Spectrosc 2024; 315:124261. [PMID: 38608560 DOI: 10.1016/j.saa.2024.124261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.
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Affiliation(s)
- Rumbidzai T Matenda
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Diane Rip
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Jeannine Marais
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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Baffa MDFO, Zezell DM, Bachmann L, Pereira TM, Deserno TM, Felipe JC. Deep neural networks can differentiate thyroid pathologies on infrared hyperspectral images. Comput Methods Programs Biomed 2024; 247:108100. [PMID: 38442622 DOI: 10.1016/j.cmpb.2024.108100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/12/2024] [Accepted: 02/23/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND AND OBJECTIVE The thyroid is a gland responsible for producing important body hormones. Several pathologies can affect this gland, such as thyroiditis, hypothyroidism, and thyroid cancer. The visual histological analysis of thyroid specimens is a valuable process that enables pathologists to detect diseases with high efficiency, providing the patient with a better prognosis. Existing computer vision systems developed to aid in the analysis of histological samples have limitations in distinguishing pathologies with similar characteristics or samples containing multiple diseases. To overcome this challenge, hyperspectral images are being studied to represent biological samples based on their molecular interaction with light. METHODS In this study, we address the acquisition of infrared absorbance spectra from each voxel of histological specimens. This data is then used for the development of a multiclass fully-connected neural network model that discriminates spectral patterns, enabling the classification of voxels as healthy, cancerous, or goiter. RESULTS Through experiments using the k-fold cross-validation protocol, we obtained an average accuracy of 93.66 %, a sensitivity of 93.47 %, and a specificity of 96.93 %. Our results demonstrate the feasibility of using infrared hyperspectral imaging to characterize healthy tissue and thyroid pathologies using absorbance measurements. The proposed deep learning model has the potential to improve diagnostic efficiency and enhance patient outcomes.
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Affiliation(s)
| | | | - Luciano Bachmann
- Department of Physics, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Thiago Martini Pereira
- Department of Science and Technology, Federal University of São Paulo, São José dos Campos, SP, Brazil
| | - Thomas Martin Deserno
- Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig, Braunschweig, Germany
| | - Joaquim Cezar Felipe
- Department of Computing and Mathematics, University of São Paulo, Ribeirão Preto, SP, Brazil
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Wang Y, Ou X, He HJ, Kamruzzaman M. Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review. Food Chem X 2024; 21:101235. [PMID: 38420503 PMCID: PMC10900407 DOI: 10.1016/j.fochx.2024.101235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/07/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
The potential of hyperspectral imaging technology (HIT) for the determination of physicochemical and nutritional components, evaluation of fungal/mycotoxins contamination, wheat varieties classification, identification of non-mildew-damaged wheat kernels, as well as detection of flour adulteration is comprehensively illustrated and reviewed. The latest findings (2018-2023) of HIT in wheat quality evaluation through internal and external attributes are compared and summarized in detail. The limitations and challenges of HIT to improve assessment accuracy are clearly described. Additionally, various practical recommendations and strategies for the potential application of HIT are highlighted. The future trends and prospects of HIT in evaluating wheat quality are also mentioned. In conclusion, HIT stands as a cutting-edge technology with immense potential for revolutionizing wheat quality evaluation. As advancements in HIT continue, it will play a pivotal role in shaping the future of wheat quality assessment and contributing to a more sustainable and efficient food supply chain.
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Affiliation(s)
- Yuling Wang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Xingqi Ou
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Mohammed Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Youssef A, Moa B, El-Sharkawy YH. A novel visible and near-infrared hyperspectral imaging platform for automated breast-cancer detection. Photodiagnosis Photodyn Ther 2024; 46:104048. [PMID: 38484830 DOI: 10.1016/j.pdpdt.2024.104048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 03/01/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Breast cancer is a leading cause of cancer-related deaths among women worldwide. Early and accurate detection is crucial for improving patient outcomes. Our study utilizes Visible and Near-Infrared Hyperspectral Imaging (VIS-NIR HSI), a promising non-invasive technique, to detect cancerous regions in ex-vivo breast specimens based on their hyperspectral response. METHODS In this paper, we present a novel HSI platform integrated with fuzzy c-means clustering for automated breast cancer detection. We acquire hyperspectral data from breast tissue samples, and preprocess it to reduce noise and enhance hyperspectral features. Fuzzy c-means clustering is then applied to segment cancerous regions based on their spectral characteristics. RESULTS Our approach demonstrates promising results. We evaluated the quality of the clustering using metrics like Silhouette Index (SI), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The clustering metrics results revealed an optimal number of 6 clusters for breast tissue classification, and the SI values ranged from 0.68 to 0.72, indicating well-separated clusters. Moreover, the CHI values showed that the clusters were well-defined, and the DBI values demonstrated low cluster dispersion. Additionally, the sensitivity, specificity, and accuracy of our system were evaluated on a dataset of breast tissue samples. We achieved an average sensitivity of 96.83%, specificity of 93.39%, and accuracy of 95.12%. These results indicate the effectiveness of our HSI-based approach in distinguishing cancerous and non-cancerous regions. CONCLUSIONS The paper introduces a robust hyperspectral imaging platform coupled with fuzzy c-means clustering for automated breast cancer detection. The clustering metrics results support the reliability of our approach in effectively segmenting breast tissue samples. In addition, the system shows high sensitivity and specificity, making it a valuable tool for early-stage breast cancer diagnosis. This innovative approach holds great potential for improving breast cancer screening and, thereby, enhancing our understanding of the disease and its detection patterns.
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Affiliation(s)
- Ahmed Youssef
- Radar Department, Military Technical Collage, Cairo, Egypt.
| | - Belaid Moa
- Electrical and Computer Engineering Department, University of Victoria, Victoria, Canada
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Wang J, Zhang B, Wang Y, Zhou C, Vonsky MS, Mitrofanova LB, Zou D, Li Q. CrossU-Net: Dual-modality cross-attention U-Net for segmentation of precancerous lesions in gastric cancer. Comput Med Imaging Graph 2024; 112:102339. [PMID: 38262134 DOI: 10.1016/j.compmedimag.2024.102339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 10/20/2023] [Accepted: 01/15/2024] [Indexed: 01/25/2024]
Abstract
Gastric precancerous lesions (GPL) significantly elevate the risk of gastric cancer, and precise diagnosis and timely intervention are critical for patient survival. Due to the elusive pathological features of precancerous lesions, the early detection rate is less than 10%, which hinders lesion localization and diagnosis. In this paper, we provide a GPL pathological dataset and propose a novel method for improving the segmentation accuracy on a limited-scale dataset, namely RGB and Hyperspectral dual-modal pathological image Cross-attention U-Net (CrossU-Net). Specifically, we present a self-supervised pre-training model for hyperspectral images to serve downstream segmentation tasks. Secondly, we design a dual-stream U-Net-based network to extract features from different modal images. To promote information exchange between spatial information in RGB images and spectral information in hyperspectral images, we customize the cross-attention mechanism between the two networks. Furthermore, we use an intermediate agent in this mechanism to improve computational efficiency. Finally, we add a distillation loss to align predicted results for both branches, improving network generalization. Experimental results show that our CrossU-Net achieves accuracy and Dice of 96.53% and 91.62%, respectively, for GPL lesion segmentation, providing a promising spectral research approach for the localization and subsequent quantitative analysis of pathological features in early diagnosis.
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Affiliation(s)
- Jiansheng Wang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China; Engineering Research Center of Nanophotonics & Advanced Instrument, Ministry of Education, East China Normal University, Shanghai, China
| | - Benyan Zhang
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Wang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
| | - Chunhua Zhou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Maxim S Vonsky
- D.I. Mendeleev Institute for Metrology, Moskovsky Pr 19, St Petersburg, Russia; Almazov National Medical Research Centre, Saint-Petersburg, Russia
| | | | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qingli Li
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China; Engineering Research Center of Nanophotonics & Advanced Instrument, Ministry of Education, East China Normal University, Shanghai, China; Engineering Center of SHMEC for Space Information and GNSS, Shanghai, China.
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Felinska EA, Studier-Fischer A, Özdemir B, Willuth E, Wise PA, Müller-Stich B, Nickel F. Effects of endoluminal vacuum sponge therapy on the perfusion of gastric conduit in a porcine model for esophagectomy. Surg Endosc 2024; 38:1422-1431. [PMID: 38180542 PMCID: PMC10881612 DOI: 10.1007/s00464-023-10647-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/10/2023] [Indexed: 01/06/2024]
Abstract
BACKGROUND After esophagectomy, the postoperative rate of anastomotic leakage is up to 30% and is the main driver of postoperative morbidity. Contemporary management includes endoluminal vacuum sponge therapy (EndoVAC) with good success rates. Vacuum therapy improves tissue perfusion in superficial wounds, but this has not been shown for gastric conduits. This study aimed to assess gastric conduit perfusion with EndoVAC in a porcine model for esophagectomy. MATERIAL AND METHODS A porcine model (n = 18) was used with gastric conduit formation and induction of ischemia at the cranial end of the gastric conduit with measurement of tissue perfusion over time. In three experimental groups EndoVAC therapy was then used in the gastric conduit (- 40, - 125, and - 200 mmHg). Changes in tissue perfusion and tissue edema were assessed using hyperspectral imaging. The study was approved by local authorities (Project License G-333/19, G-67/22). RESULTS Induction of ischemia led to significant reduction of tissue oxygenation from 65.1 ± 2.5% to 44.7 ± 5.5% (p < 0.01). After EndoVAC therapy with - 125 mmHg a significant increase in tissue oxygenation to 61.9 ± 5.5% was seen after 60 min and stayed stable after 120 min (62.9 ± 9.4%, p < 0.01 vs tissue ischemia). A similar improvement was seen with EndoVAC therapy at - 200 mmHg. A nonsignificant increase in oxygenation levels was also seen after therapy with - 40 mmHg, from 46.3 ± 3.4% to 52.5 ± 4.3% and 53.9 ± 8.1% after 60 and 120 min respectively (p > 0.05). An increase in tissue edema was observed after 60 and 120 min of EndoVAC therapy with - 200 mmHg but not with - 40 and - 125 mmHg. CONCLUSIONS EndoVAC therapy with a pressure of - 125 mmHg significantly increased tissue perfusion of ischemic gastric conduit. With better understanding of underlying physiology the optimal use of EndoVAC therapy can be determined including a possible preemptive use for gastric conduits with impaired arterial perfusion or venous congestion.
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Affiliation(s)
- Eleni Amelia Felinska
- Department of General, Visceral and Transplant Surgery, University of Heidelberg, Heidelberg, Germany
| | - Alexander Studier-Fischer
- Department of General, Visceral and Transplant Surgery, University of Heidelberg, Heidelberg, Germany
| | - Berkin Özdemir
- Department of General, Visceral and Transplant Surgery, University of Heidelberg, Heidelberg, Germany
| | - Estelle Willuth
- Department of General, Visceral and Transplant Surgery, University of Heidelberg, Heidelberg, Germany
| | - Philipp Anthony Wise
- Department of General, Visceral and Transplant Surgery, University of Heidelberg, Heidelberg, Germany
| | - Beat Müller-Stich
- Department of General, Visceral and Transplant Surgery, University of Heidelberg, Heidelberg, Germany
- Department of Surgery, Clarunis University Center for Gastrointestinal and Liver Disease, University Hospital and St. Clara Hospital Basel, Basel, Switzerland
| | - Felix Nickel
- Department of General, Visceral and Transplant Surgery, University of Heidelberg, Heidelberg, Germany.
- Department of General, Visceral, and Thoracic Surgery, University Medical Center Hamburg Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany.
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Chen Y, Chen Z, Yan Q, Liu Y, Wang Q. Non-destructive detection of egg white and yolk morphology transformation and salt content of salted duck eggs in salting by hyperspectral imaging. Int J Biol Macromol 2024; 262:130002. [PMID: 38331060 DOI: 10.1016/j.ijbiomac.2024.130002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 01/17/2024] [Accepted: 02/04/2024] [Indexed: 02/10/2024]
Abstract
Salt content is a crucial indicator of the maturity and internal quality of salted duck eggs (SDEs) during the pickling process. However, there is currently no valid and rapid method available for accurately detecting salt content. In the present study, we utilized hyperspectral imaging to no-destructively determine the salt content in egg yolks, egg whites, and whole eggs during the curing period. Firstly, principal component analysis was applied to explain the characteristics of egg yolk and white morphology transformation of SDEs with different maturities during curing. Secondly, sensitive spectral factors representative of changes in the salt content of SDEs were extracted by three spectral transformations (Savitzky-Golay SG, continuum removal CR, and first-order derivation FD) and three approaches of selecting characteristic wavelengths (successive projection algorithm SPA, uninformative variables elimination UVE and competitive adaptive reweighting sampling algorithm CARS). The results of the PLSR model suggested that the optimal models for predicting salt content in egg yolks, whites, and whole eggs were SG-UVE-PLSR (predicted coefficient of determination Rp2=0.912, predicted standard deviation SEp=0.151, residual prediction deviation RPD = 3.371), CR-CARS-PLSR (Rp2=0.873, SEp=0.862, RPD = 2.806), and CR-UVE-PLSR (Rp2=0.877, SEp=0.680, RPD = 2.851), respectively. Eventually, the optimal prediction model for the salt content of the whole egg was employed to a pixel spectral matrix to calculate the salt content values of pixel points on the hyperspectral image of SDEs. Additionally, pseudo-color techniques were employed to visualize the spatial distribution of predicted salt content. This work will provide a theoretical foundation for rapidly detecting maturity and enabling high-throughput quality sorting of SDEs.
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Affiliation(s)
- Yuanzhe Chen
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
| | - Zhuoting Chen
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
| | - Qian Yan
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuming Liu
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
| | - Qiaohua Wang
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; National Research and Development Center for Egg Processing, Huazhong Agricultural University, Wuhan 430070, China; Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River, Ministry of Agriculture and Rural Agriculture, Wuhan 430070, China.
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Selvander M, Alexander J, Guenot D. Naevi Characterization Using Hyperspectral Imaging: A Pilot Study. Curr Eye Res 2024:1-7. [PMID: 38407145 DOI: 10.1080/02713683.2024.2314602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 01/29/2024] [Indexed: 02/27/2024]
Abstract
PURPOSE The prevalence of choroidal naevi is common and has been found to be up to 10%. Little is known regarding the optical properties of choroidal naevi. A novel hyperspectral eye fundus camera was used to investigate choroidal naevi's optical density spectra in the retina. METHODS In an ophthalmology clinic setting, patients with choroidal naevi were included in the study. Visual acuity and pressure were tested. Following mydriatics, optical coherence tomography and fundus photography were taken as a reference, after which a hyperspectral image with 12 nm spectral resolution at 450-700 nm was taken. The optical density spectra was measured across the area of the naevus. RESULTS Nine patients with 11 naevi were examined. The visual acuity was not affected by any of the naevi. All the naevi were flat as measured either with the optical coherence tomography and/or on inspection, and only one naevi had a risk factor (orange pigmentation). The Wasserstein distance between the background and the naevi was higher at 695 nm compared to 555 nm (p = .002). The naevi could be grouped into three clusters based on the extracted optical density spectra. CONCLUSION Choroidal naevi are better visible in longer wavelengths compared to shorter wavelengths. This finding can be used to contour and follow choroidal naevi. Choroidal naevi expose different optical density spectra that can be grouped into three different clusters. One of these clusters has an optical density spectra resembling the absorption spectra of lipofuscin, which may indicate the content of this pigment.
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Affiliation(s)
- Madeleine Selvander
- Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Sundets Ögonläkare, Helsingborg, Sweden
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Cheng R, Bai X, Guo J, Huang L, Zhao D, Liu Z, Zhang W. Hyperspectral discrimination of ginseng variety and age from Changbai Mountain area. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2024; 307:123613. [PMID: 37976570 DOI: 10.1016/j.saa.2023.123613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/12/2023] [Accepted: 11/01/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND The efficacy and market value of Panax ginseng Meyer are significantly influenced by its diversity and age. Traditional identification methods are prone to subjective biases and necessitate the use of destructive sample processing, leading to the loss and wastage of ginseng. Consequently, non-destructive in-situ identification has emerged as a crucial subject of interest for both researchers and the ginseng industry. The advancement of technology and the expansion of research have introduced spectral technology and image processing technology as novel approaches and concepts for non-destructive in-situ identification. METHODS Hyperspectral imaging (HSI) is a methodology that combines conventional spectroscopy and imaging to acquire comprehensive spectral and spatial data from various samples. In this study, we investigated the use of Support Vector Machine (SVM) and Spectral Angle Mapper (SAM) classifier algorithms, in conjunction with HSI classification technology, for quasi-Artificial Intelligence (quasi-AI) ginseng identification. To enhance the hyperspectral images prior to SVM classification, we compared the efficacy of Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). RESULTS The classification of ginseng based on age was accomplished through the utilization of Radial Basis Function (RBF) kernel SVM and SAM algorithm, which was trained on feature enhanced images. The classification of WMG, MCG, and GG is primarily based on age, with the endmember spectrum serving as the foundation for SAM and SVM. CONCLUSION The "endmember spectrum set" derived from the classification outcomes can serve as the "mutation point" for identifying ginseng of different ages.
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Affiliation(s)
- Ruiyang Cheng
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Xueyuan Bai
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Jianying Guo
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Luqi Huang
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Daqing Zhao
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Zhaojian Liu
- Department of Cell Biology, School of Basic Medical Science, Shandong University, Jinan, China.
| | - Wei Zhang
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China.
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Bonifazi G, Capobianco G, Serranti S, Trotta O, Bellagamba S, Malinconico S, Paglietti F. Asbestos detection in construction and demolition waste by different classification methods applied to short-wave infrared hyperspectral images. Spectrochim Acta A Mol Biomol Spectrosc 2024; 307:123672. [PMID: 37995651 DOI: 10.1016/j.saa.2023.123672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 11/17/2023] [Accepted: 11/19/2023] [Indexed: 11/25/2023]
Abstract
In this study, different multivariate classification methods were applied to hyperspectral images acquired, in the short-wave infrared range (SWIR: 1000-2500 nm), to define and evaluate quality control actions applied to construction and demolition waste (C&DW) flow streams, with particular reference to the detection of hazardous material as asbestos. Three asbestos fibers classes (i.e., amosite, chrysotile and crocidolite) inside asbestos-containing materials (ACM) were investigated. Samples were divided into two groups: calibration and validation datasets. The acquired hyperspectral images were first explored by Principal Component Analysis (PCA). The following multivariate classification methods were selected in order to verify and compare their efficiency and robustness: Hierarchical Partial Least Squares-Discriminant Analysis (Hi-PLSDA), Principal Component Analysis k-Nearest Neighbors (PCA-kNN) and Error Correcting Output Coding with Support Vector Machines (ECOC-SVM). The classification results obtained for the three models were evaluated by prediction maps and the values of performance parameters (Sensitivity and Specificity). Micro-X-ray fluorescence (micro-XRF) maps confirmed the correctness of classification results. The results demonstrate how SWIR-HSI technology, coupled with multivariate analysis modelling, is a promising approach to develop both "off-line" and "online" fast, reliable and robust quality control strategies, finalized to perform a quick assessment of ACM presence.
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Affiliation(s)
- G Bonifazi
- Department of Chemical Engineering Materials & Environment, Sapienza University of Rome, Rome, Italy
| | - G Capobianco
- Department of Chemical Engineering Materials & Environment, Sapienza University of Rome, Rome, Italy.
| | - S Serranti
- Department of Chemical Engineering Materials & Environment, Sapienza University of Rome, Rome, Italy
| | - O Trotta
- Department of Chemical Engineering Materials & Environment, Sapienza University of Rome, Rome, Italy
| | - S Bellagamba
- Italian Workers Compensation Authority (INAIL), Department of Technological Innovations and Safety of Plants, Products and Anthropic Settlements, Rome, Italy
| | - S Malinconico
- Italian Workers Compensation Authority (INAIL), Department of Technological Innovations and Safety of Plants, Products and Anthropic Settlements, Rome, Italy
| | - F Paglietti
- Italian Workers Compensation Authority (INAIL), Department of Technological Innovations and Safety of Plants, Products and Anthropic Settlements, Rome, Italy
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Yang S, Cao Y, Li C, Castagnini JM, Barba FJ, Shan C, Zhou J. Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis. Curr Res Food Sci 2024; 8:100695. [PMID: 38362161 PMCID: PMC10867766 DOI: 10.1016/j.crfs.2024.100695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/13/2024] [Accepted: 02/07/2024] [Indexed: 02/17/2024] Open
Abstract
This study proposes a recognition model for different drying methods of grain using hyperspectral imaging technology (HSI) and multivariate analysis. Fresh harvested grain samples were dried using three different methods: rotating ventilation drying, mechanical drying, and natural drying. Hyperspectral images of the samples were collected within the 388-1065 nm band range. The spectral features of the samples were extracted using principal component analysis (PCA), while the texture features were extracted using second-order probability statistical filtering. Partial least squares regression (PLSR) drying models with different characteristics were established. At the same time, a BPNN (Back-propagation neural network, BPNN) based on spectral texture fusion features was established to compare the recognition effects of different models. Texture analysis indicated that the mean-image had the clearest contour, and the texture characteristics of mechanical drying were smaller than those of rotating ventilation drying and natural drying. The BPNN model established using spectral-texture feature variables showed the best performance in distinguishing grain in different drying modes, with a prediction model obtained based on the correlation coefficients of special variables. The spectral and texture feature values were fused for pseudo-color visualization expression, and the three drying methods of grain showed different colors. This study provides a reference for non-destructive and rapid detection of grain with different drying methods.
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Affiliation(s)
- Sicheng Yang
- Huanggang Public Testing Center, No.128 Huangzhou Avenue, Huanggang City, Hubei Province, China
| | - Yang Cao
- Academy of State Administration of Grain, Beijing, 100037, China
| | - Chuanjie Li
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural Reclamation University, Daqing, 163319, Heilongjiang, China
| | - Juan Manuel Castagnini
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, Spain
| | - Francisco Jose Barba
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, Spain
| | - Changyao Shan
- College of Science, Health, Engineering and Education, Murdoch University, Perth, 6150, Australia
| | - Jianjun Zhou
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, Spain
- Department of Biotechnology, Institute of Agrochemistry and Food Technology-National Re-search Council (IATA-CSIC), Agustin Escardino 7, 46980, Paterna, Spain
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Sim W, Song SW, Park S, Jang JI, Kim JH, Cho YM, Kim HM. Unveiling microplastics with hyperspectral Raman imaging: From macroscale observations to real-world applications. J Hazard Mater 2024; 463:132861. [PMID: 37939557 DOI: 10.1016/j.jhazmat.2023.132861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/10/2023]
Abstract
The widespread use of plastic materials, owing to their several advantageous properties, has resulted in a considerable increase in plastic consumption. Consequently, the production of primary and secondary microplastics has also increased. To identify, categorize, and quantify microplastics, several analytical methods, such as thermal analysis and spectroscopic methods, have been developed. They generally offer little insight into the size and shape of microplastics, require time-consuming sample preparation and classification, and are susceptible to background interference. Herein, we created a macroscale hyperspectral Raman method to quickly quantify and characterize large volumes of plastics. Using this approach, we successfully obtained Raman spectra of five different types of microplastics scattered over an area of 12.4 mm × 12.4 mm within just 550 s and perfectly classified these microplastics using a machine learning method. Additionally, we demonstrated that our system is effective for obtaining Raman spectra, even when the microplastics are suspended in aquatic environments or bound to metal-mesh nets. These results highlight the considerable potential of our proposed method for real-world applications.
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Affiliation(s)
- Wooseok Sim
- Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea
| | - Si Won Song
- Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea
| | - Subeen Park
- Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea; Sensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Jin Il Jang
- Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea
| | - Jae Hun Kim
- Sensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Yeo-Myoung Cho
- Department of Civil and Environmental Engineering, Stanford University, CA 94305, United States
| | - Hyung Min Kim
- Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea.
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Malounas I, Vierbergen W, Kutluk S, Zude-Sasse M, Yang K, Zhao M, Argyropoulos D, Van Beek J, Ampe E, Fountas S. SpectroFood dataset: A comprehensive fruit and vegetable hyperspectral meta-dataset for dry matter estimation. Data Brief 2024; 52:110040. [PMID: 38287951 PMCID: PMC10823050 DOI: 10.1016/j.dib.2024.110040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 01/31/2024] Open
Abstract
In the dataset presented in this article, samples belonging to one of the following crops, apple, broccoli, leek, and mushroom, were measured by hyperspectral cameras in the visible/near-infrared spectral domain (430-900 nm). The dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models. In particular, this dataset focuses on estimating dry matter content across various crops by a single model in a non-destructive way using hyperspectral measurements. This dataset contains extracted mean reflectance spectra for each sample (n=1028) and their respective dry matter content (%).
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Affiliation(s)
- Ioannis Malounas
- Agricultural University of Athens (AUA), Iera Odos 75, 11855 Athens, Greece
| | - Wout Vierbergen
- Technology and Food Science Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Burgemeester Van Gansberghelaan 115 bus 1, 9820 Merelbeke, Belgium
| | - Sezer Kutluk
- Department of Datascience in Bioeconomy, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam-Bornim, Germany
| | - Manuela Zude-Sasse
- Department of Agromechatronic, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam-Bornim, Germany
| | - Kai Yang
- School of Biosystems and Food Engineering, University College Dublin (UCD), Stillorgan Rd, Belfield, Dublin 4, Ireland
| | - Ming Zhao
- School of Biosystems and Food Engineering, University College Dublin (UCD), Stillorgan Rd, Belfield, Dublin 4, Ireland
| | - Dimitrios Argyropoulos
- School of Biosystems and Food Engineering, University College Dublin (UCD), Stillorgan Rd, Belfield, Dublin 4, Ireland
| | - Jonathan Van Beek
- Technology and Food Science Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Burgemeester Van Gansberghelaan 115 bus 1, 9820 Merelbeke, Belgium
| | - Eva Ampe
- INAGRO VZW, Ieperseweg 87, 8800 Rumbeke-Beitem, Belgium
| | - Spyros Fountas
- Agricultural University of Athens (AUA), Iera Odos 75, 11855 Athens, Greece
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Wu J. Hyperspectral imaging for non-invasive blood oxygen saturation assessment. Photodiagnosis Photodyn Ther 2024; 45:104003. [PMID: 38336148 DOI: 10.1016/j.pdpdt.2024.104003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/27/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
Hyperspectral Imaging (HSI) seamlessly integrates imaging and spectroscopy, capturing both spatial and spectral data concurrently. With widespread applications in medical diagnostics, HSI serves as a noninvasive tool for gaining insights into tissue characteristics. The distinctive spectral profiles of biological tissues set HSI apart from traditional microscopy in enabling in vivo tissue analysis. Despite its potential, existing HSI techniques face challenges such as alignment issues, low light throughput, and tissue heating due to intense illumination. This study introduces an innovative HSI system featuring active sequential bandpass illumination seamlessly integrated into conventional optical instruments. The primary focus is on analyzing oxyhemoglobin and deoxyhemoglobin saturation in animal tissue samples using multivariate linear regression. This approach holds promise for enhancing noninvasive medical diagnostics. A key feature of the system, active bandpass illumination, effectively prevents tissue overheating, thereby bolstering its suitability for medical applications.
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Affiliation(s)
- Jiangbo Wu
- School of Information Science and Technology, Fudan University, Shanghai 200433, China.
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23
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Liu L, Zhang L, Zhang X, Dong X, Jiang X, Huang X, Li W, Xie X, Qiu X. Analysis of cellular response to drugs with a microfluidic single-cell platform based on hyperspectral imaging. Anal Chim Acta 2024; 1288:342158. [PMID: 38220290 DOI: 10.1016/j.aca.2023.342158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 12/07/2023] [Accepted: 12/16/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND Cellular response to pharmacological action of drugs is significant for drug development. Traditional detection method for cellular response to drugs normally rely on cell proliferation assay and metabolomics examination. In principle, these analytical methods often required cell labeling, invasion analysis, and hours of co-culture with drugs, which are relatively complex and time-consuming. Moreover, these methods can only indicate the drug effectiveness on cell colony rather than single cells. Thus, to meet the requirements of personal precision medicine, the development of drug response analysis on the high resolution of single cell is demanded. RESULTS To provide precise result for drug response on single-cell level, a microfluidic platform coupled with the label-free hyperspectral imaging was developed. With the help of horizontal single-cell trapping sieves, hundreds of single cells were trapped independently in microfluidic channels for the purposes of real-time drug delivery and single-cell hyperspectral image recording. To significantly identify the cellular hyperspectral change after drug stimulation, the differenced single-cell spectrum was proposed. Compared with the deep learning classification method based on hyperspectral images, an optimal performance can be achieved by the classification strategy based on differenced spectra. And the cellular response to different reagents, for example, K+, Epidermal Growth Factor (EGF), and Gefitinib at different concentrations can be accurately characterized by the differenced single-cell spectra analysis. SIGNIFICANCE AND NOVELTY The high-throughput, rapid analysis of cellular response to drugs at the single-cell level can be accurately performed by our platform. After systematically analyzing the materials and the structures of the single-cell microfluidic chip, the optimal single-cell trapping method was proposed to contribute to the further application of hyperspectral imaging on microfluidic single-cell analysis. And the hyperspectral characterization of single-cell with cancer drug stimulation proved the application potential of our method in personal cancer medication.
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Affiliation(s)
- Luyao Liu
- Institute of Microfluidic Chip Development in Biomedical Engineering, School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Lulu Zhang
- Institute of Microfluidic Chip Development in Biomedical Engineering, School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Xueyu Zhang
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Xiaobin Dong
- Institute of Microfluidic Chip Development in Biomedical Engineering, School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Xiaodan Jiang
- Institute of Microfluidic Chip Development in Biomedical Engineering, School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Xiaoqi Huang
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Wei Li
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Xiaoming Xie
- School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Xianbo Qiu
- Institute of Microfluidic Chip Development in Biomedical Engineering, School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
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24
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Yang Z, Zhang M, Li X, Xu Z, Chen Y, Xu X, Chen D, Meng L, Si X, Wang J. Fluorescence spectroscopic profiling of urine samples for predicting kidney transplant rejection. Photodiagnosis Photodyn Ther 2024; 45:103984. [PMID: 38244654 DOI: 10.1016/j.pdpdt.2024.103984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/22/2024]
Abstract
Rejection is the primary factor affecting the functionality of a kidney post-transplant, where its prompt prediction of risk significantly influences therapeutic strategies and clinical outcomes. Current graft health assessment methods, including serum creatinine measurements and transplant kidney puncture biopsies, possess considerable limitations. In contrast, urine serves as a direct indicator of the graft's degenerative stage and provides a more accurate measure than peripheral blood analysis, given its non-invasive collection of kidney-specific metabolite. This research entailed collecting fluorescent fingerprint data from 120 urine samples of post-renal transplant patients using hyperspectral imaging, followed by the development of a learning model to detect various forms of immunological rejection. The model successfully identified multiple rejection types with an average diagnostic accuracy of 95.56 %.Beyond proposing an innovative approach for predicting the risk of complications post-kidney transplantation, this study heralds the potential introduction of a non-invasive, rapid, and accurate supplementary method for risk assessment in clinical practice.
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Affiliation(s)
- Zhe Yang
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Minrui Zhang
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Xianduo Li
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Zhipeng Xu
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Yi Chen
- Shandong Medical College, Jinan 250000, China
| | - Xiaoyu Xu
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Dongdong Chen
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Lingquan Meng
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Xiaoqing Si
- Department of dermatology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China.
| | - Jianning Wang
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China.
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25
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Li S, Dixit Y, Reis MM, Singh H, Ye A. Movements of moisture and acid in gastric milk clots during gastric digestion: Spatiotemporal mapping using hyperspectral imaging. Food Chem 2024; 431:137094. [PMID: 37586231 DOI: 10.1016/j.foodchem.2023.137094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/25/2023] [Accepted: 08/02/2023] [Indexed: 08/18/2023]
Abstract
Ruminant milk is known to coagulate into structured clots during gastric digestion. This study investigated the movements of moisture and acid in skim milk clots formed during dynamic gastric digestion and the effects of milk type (regular or calcium-rich) and the presence/absence of pepsin. We conducted hyperspectral imaging analysis and successfully modelled the moisture contents based on the spectral information using partial least squares regression. We generated prediction maps of the spatiotemporal distribution of moisture within the samples at different stages of gastric digestion. Simultaneously to acid uptake, the moisture in the milk clots tended to decrease over the digestion time; this was significantly promoted by pepsin. Moisture mapping by hyperspectral imaging demonstrated that the high and low moisture zones were centralized within the clot and at the surface respectively. A structural compaction process promoted by pepsinolysis and acidification probably contributed to the water expulsion from the clots during digestion.
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Affiliation(s)
- Siqi Li
- Riddet Institute, Massey University, Private Bag 11 222, Palmerston North 4442, New Zealand
| | - Yash Dixit
- AgResearch Ltd, Te Ohu Rangahau Kai, Private Bag 11 008, Palmerston North, New Zealand
| | - Marlon M Reis
- AgResearch Ltd, Te Ohu Rangahau Kai, Private Bag 11 008, Palmerston North, New Zealand.
| | - Harjinder Singh
- Riddet Institute, Massey University, Private Bag 11 222, Palmerston North 4442, New Zealand
| | - Aiqian Ye
- Riddet Institute, Massey University, Private Bag 11 222, Palmerston North 4442, New Zealand.
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26
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Guo Z, Zhang J, Sun J, Dong H, Huang J, Geng L, Li S, Jing X, Guo Y, Sun X. A multivariate algorithm for identifying contaminated peanut using visible and near-infrared hyperspectral imaging. Talanta 2024; 267:125187. [PMID: 37722342 DOI: 10.1016/j.talanta.2023.125187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/29/2023] [Accepted: 09/07/2023] [Indexed: 09/20/2023]
Abstract
In this study, a novel uniform manifold approximation and projection combined-improved simultaneous optimization genetic algorithm-convolutional neural network (UMAP-ISOGA-CNN) algorithm was proposed. The improved simultaneous optimization genetic algorithm (ISOGA) combined with convolutional neural network (CNN) to optimize the architecture, hyperparameters, and optimizer of the CNN model simultaneously. Additionally, a uniform manifold approximation and projection (UMAP) method was used to visualize the feature space of all feature layers of the ISOGA-CNN model. The UMAP-ISOGA-CNN algorithm combined with visible and near-infrared hyperspectral imaging was used to identify peanut kernels contaminated with Aspergillus flavus and to distinguish their storage time, which is essential for the food industry to monitor the freshness of products. Overall, the UMAP-ISOGA-CNN algorithm provides useful insights into the feature space of the ISOGA-CNN model, contributing to a better understanding of the model's internal mechanisms. This study has practical implications for the food industry and future research on deep learning optimization.
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Affiliation(s)
- Zhen Guo
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Jing Zhang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Jiashuai Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Haowei Dong
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Jingcheng Huang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Lingjun Geng
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Shiling Li
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Xiangzhu Jing
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Yemin Guo
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China.
| | - Xia Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China.
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27
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Xing C, Liu C, Lin J, Tan W, Liu T. VOCs hyperspectral imaging: A new insight into evaluate emissions and the corresponding health risk from industries. J Hazard Mater 2024; 461:132573. [PMID: 37729711 DOI: 10.1016/j.jhazmat.2023.132573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 09/05/2023] [Accepted: 09/16/2023] [Indexed: 09/22/2023]
Abstract
The harm of VOCs emitted from industries to surrounding atmospheric environment and human health was well known and had received continuous attention. In order to improve the quality of urban atmospheric environment and the living environment of urban residents, a large number of original urban industries had been relocated to economically underdeveloped suburbs, which has significantly deteriorated the atmospheric environment in these areas and brought potential health risks to local vulnerable residents, which is actually an unfair manifestation under the background of economic development and ecological civilization construction. There were many residents near industrial parks, but there was a significant lack of VOCs monitoring equipment and data. At present, the time resolution of the most commonly used in situ method was seriously insufficient, and it was unable to quantify the diffusion/transport process of VOCs. It was urgent to have effective detection methods for industrial VOCs plume concentration and diffusion/transport process. In this study, we proposed a hyperspectral imaging technology, which can realize long-term continuous imaging monitoring on plume concentrations of formaldehyde (HCHO), glyoxal (CHOCHO) and benzaldehyde (C6H5CHO) and their corresponding diffusion processes. The deviation between the imaging and in situ sampling concentrations in the outlet was 4-19 %. The spatial resolution of this technique reached meter level, and the temporal resolution of one pixel was better than 20 s. In this study, we carried out hyperspectral imaging of aldehyde VOCs for a chemical facility, a petrochemical facility and an industrial park containing various types of enterprises in the Yangtze River Delta. The maximum observed concentration of HCHO was 120.44 ± 12.14 ug/m3 with the emission flux of 39.27 ± 3.97 g/h, which was emitted from a petrochemical facility in Shanghai. A diffusion/transport model was established, and we found that the spatial distribution of HCHO, CHOCHO and C6H5CHO for the chemical facility case in Shanghai were all mainly along the southeast-northwest direction during one year. The health risk assessment emphasized that residents within 10 km north of the outlet of the chemical facility in Shanghai should pay more attention to the health risks caused by industrial HCHO emissions. More systematically and comprehensively hyperspectral imaging of VOCs emissions for different types of enterprises and different processes were expected to performed to greatly promote the establishment of a dynamic emission inventory and an effective health risk evaluation system in the future.
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Affiliation(s)
- Chengzhi Xing
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Cheng Liu
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei 230026, China.
| | - Jinan Lin
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Wei Tan
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Ting Liu
- School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China
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28
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Capobianco G, Pronti L, Gorga E, Romani M, Cestelli-Guidi M, Serranti S, Bonifazi G. Methodological approach for the automatic discrimination of pictorial materials using fused hyperspectral imaging data from the visible to mid-infrared range coupled with machine learning methods. Spectrochim Acta A Mol Biomol Spectrosc 2024; 304:123412. [PMID: 37741099 DOI: 10.1016/j.saa.2023.123412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/30/2023] [Accepted: 09/12/2023] [Indexed: 09/25/2023]
Abstract
Hyperspectral imaging represents a powerful tool for the study of artwork's materials since it permits to obtain simultaneously information about the spectral behavior of the materials and their spatial distribution. By combining hyperspectral images performed on several spectral intervals (visible, near infrared and mid-infrared ranges) through chemometric methods it is possible to clearly identify most of the materials used in painting (i.e., pigments, dyes, varnishes, and binders). Moreover, in the last decade, the development of machine learning algorithms coupled with comprehensive and continuously updated databases opens new perspective on the automatic recognition of pictorial materials. In this work, we propose a novel procedure to support the automatic discrimination of pictorial materials consisting in a mid-level data fusion on imaging datasets coming from two commercial hyperspectral cameras, in the 400-1000 nm and 1000-2500 nm spectral ranges, respectively, and a MAcroscopic Fourier Transform InfRared scanning in reflection mode (MA-rFTIR), in the 7000 to 350 cm-1 (1428 nm - 28 μm) spectral range. The automatic recognition of 102 pictorial mock-ups from the fused data is performed by testing the performance of ECOC-SVM (error-correcting output coding and support vector machine) model obtaining a good predictive result with only few pixels that are confused with other classes. The methodology described in this paper demonstrates that an accurate paint layer multiclass recognition is feasible, and the use of chemometric approaches solves some challenges involving the study of materials.
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Affiliation(s)
- G Capobianco
- Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy
| | - Lucilla Pronti
- National Laboratories of Frascati - INFN, via Enrico Fermi 54, 00044 Frascati, Rome, Italy.
| | - E Gorga
- National Laboratories of Frascati - INFN, via Enrico Fermi 54, 00044 Frascati, Rome, Italy
| | - M Romani
- National Laboratories of Frascati - INFN, via Enrico Fermi 54, 00044 Frascati, Rome, Italy
| | - M Cestelli-Guidi
- National Laboratories of Frascati - INFN, via Enrico Fermi 54, 00044 Frascati, Rome, Italy
| | - Silvia Serranti
- Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy
| | - G Bonifazi
- Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy
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29
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Wang Y, Wang S, Bai R, Li X, Yuan Y, Nan T, Kang C, Yang J, Huang L. Prediction performance and reliability evaluation of three ginsenosides in Panax ginseng using hyperspectral imaging combined with a novel ensemble chemometric model. Food Chem 2024; 430:136917. [PMID: 37557029 DOI: 10.1016/j.foodchem.2023.136917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/30/2023] [Accepted: 07/15/2023] [Indexed: 08/11/2023]
Abstract
Panax ginseng C. A. Meyer (PG) is a health-promoting food, and its ginsenosides (Rb1, Rg1, Re) content, as the quality indicator, is affected by the planting modes (garden or forest ginsengs) and years. Effective prediction of this content remains to be investigated. In this study, hyperspectral (HSI) combined with ensemble model (CGRU-GPR) including the convolutional neural network (CNN), gate recurrent unit (GRU), and Gaussian process regression (GPR) realized a comprehensive evaluation of the prediction performance and predictive uncertainty. With effective wavelengths, the proposed CGRU-GPR model improved operation efficiency and obtained satisfactory prediction results with relative percent deviation (RPD) values all higher than 2.70 in three ginsenosides. Meanwhile, the interval prediction with a high prediction interval coverage probability (PICP) of 0.97 - 1.0 and a low mean width percentage (MWP) of 0.7 - 1.66 indicated a low prediction uncertainty. This study provides a rapid and reliable method for predicting ginsenosides contents in PG.
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Affiliation(s)
- Youyou Wang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, PR China
| | - Siman Wang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, PR China
| | - Ruibin Bai
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, PR China; Dexing Research and Training Center of Chinese Medical Sciences, Dexing 334220, PR China
| | - Xiaoyong Li
- State SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, PR China
| | - Yuwei Yuan
- Institute of Agro-product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310021, PR China
| | - Tiegui Nan
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, PR China
| | - Chuanzhi Kang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, PR China; Dexing Research and Training Center of Chinese Medical Sciences, Dexing 334220, PR China
| | - Jian Yang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, PR China; Dexing Research and Training Center of Chinese Medical Sciences, Dexing 334220, PR China.
| | - Luqi Huang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, PR China.
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30
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Zhang B, Ou Y, Yu S, Liu Y, Liu Y, Qiu W. Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging. Plant Methods 2023; 19:148. [PMID: 38115023 PMCID: PMC10729489 DOI: 10.1186/s13007-023-01123-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/04/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND Gray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected and healthy leaves are very similar, making the early identification of strawberry gray mold and anthracnose still a challenge. RESULTS Based on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases. The competitive adaptive reweighted sampling (CARS) algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and VIs, respectively. Three machine learning models, Backpropagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF), were developed for the early identification of strawberry gray mold and anthracnose, using spectral fingerprint, VIs and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and VIs had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers (BPNN, SVM and RF) were 97.78%, 94.44%, and 93.33%, respectively, which indicate that the fusion features approach proposed in this study can effectively improve the early detection performance of strawberry leaves diseases. CONCLUSIONS This study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.
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Affiliation(s)
- Baohua Zhang
- College of Engineering, Nanjing Agricultural University, No. 40, Dianjiangtai Road, Taishan Street, Pukou District, Nanjing, Jiangsu, 210031, P.R. China
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Yunmeng Ou
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Shuwan Yu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Yuchen Liu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Ying Liu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Wei Qiu
- College of Engineering, Nanjing Agricultural University, No. 40, Dianjiangtai Road, Taishan Street, Pukou District, Nanjing, Jiangsu, 210031, P.R. China.
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Fulladosa E, Barnés-Calle C, Cruz J, Martínez B, Giró-Candanedo M, Comaposada J, Font-I-Furnols M, Gou P. Near infrared sensors for the precise characterization of salt content in canned tuna fish. Spectrochim Acta A Mol Biomol Spectrosc 2023; 303:123217. [PMID: 37544216 DOI: 10.1016/j.saa.2023.123217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/11/2023] [Accepted: 07/27/2023] [Indexed: 08/08/2023]
Abstract
Non-invasive technologies could help to guarantee quality standards of canned tuna fish. The aim of this study was to investigate the ability of bench-top (FT-NIR) and low-cost (LC-NIR) near infrared spectrometers to determine salt content and texture in canned tuna. Salt content distribution was also investigated using hyperspectral imaging (HSI) and computed tomography. Spectra were acquired on canned tuna and reference analysis performed. Partial least squares regression and discriminant analysis were used to develop salt content predictive and texture classification models. Salt content predictive errors were 0.10%, 0.22% and 0.22% for FT-NIR, LC-NIR and HSI, respectively. Salt content was not always homogeneously distributed in the can which was attributed to the salt content differences between internal and external parts of the tuna fish. Low-cost sensors could be a suitable solution to standardise the production and enable precise nutritional labelling, but more sophisticated algorithms are needed to identify textural defects.
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Affiliation(s)
- E Fulladosa
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain.
| | - C Barnés-Calle
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain
| | - J Cruz
- Escola Universitària Salesiana de Sarrià, Passeig Sant Joan Bosco, 74, 08017 Barcelona, Spain
| | - B Martínez
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain
| | - M Giró-Candanedo
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain
| | - J Comaposada
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain
| | - M Font-I-Furnols
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain
| | - P Gou
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain
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Cucuzza P, Serranti S, Capobianco G, Bonifazi G. Multi-level color classification of post-consumer plastic packaging flakes by hyperspectral imaging for optimizing the recycling process. Spectrochim Acta A Mol Biomol Spectrosc 2023; 302:123157. [PMID: 37481925 DOI: 10.1016/j.saa.2023.123157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/25/2023] [Accepted: 07/13/2023] [Indexed: 07/25/2023]
Abstract
In a circular economy perspective, the development of fast and efficient sensor-based recognition strategies of plastic waste, not only by polymer but also by color, plays a crucial role for the production of high quality secondary raw materials in recycling plants. In this work, mixed colored flakes of high-density polyethylene (HDPE) from packaging waste were simultaneously classified by hyperspectral imaging working in the visible range (400-750 nm), combined with machine learning. Two classification models were built and compared: (1) Partial Least Square-Discriminant Analysis (PLS-DA) for 6 HDPE macro-color classes identification (i.e., white, blue, green, red, orange and yellow) and (2) hierarchical PLS-DA for a more accurate discrimination of the different HDPE color tones, providing as output 14 color classes. The obtained classification results were excellent for both models, with values of Recall, Specificity, Accuracy, and F-score in prediction close to 1. The proposed methodological approach can be utilized as sensor-based sorting logic in plastic recycling plants, tuning the output based on the required needs of the recycling plant, allowing to obtain a high-quality recycled HDPE of different colors, optimizing the plastic recycling process, in agreement with the principles of circular economy.
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Affiliation(s)
- Paola Cucuzza
- Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, Rome, Italy
| | - Silvia Serranti
- Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, Rome, Italy.
| | - Giuseppe Capobianco
- Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Bonifazi
- Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, Rome, Italy
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Liu W, Hou X, Li Y, Wang Z. Hyperspectral imaging to predict the effect of cyclophosphamide in primary membranous nephropathy. Photodiagnosis Photodyn Ther 2023; 44:103751. [PMID: 37634608 DOI: 10.1016/j.pdpdt.2023.103751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 08/03/2023] [Accepted: 08/14/2023] [Indexed: 08/29/2023]
Abstract
BACKGROUND Presently, there is a lack of accurate predictors of the efficacy of primary membranous nephropathy. The aim of this study is to explore the application value of hyperspectral imaging in predicting the efficacy of cyclophosphamide treatment in primary membranous nephropathy. METHOD A total of 30 patients with primary membranous nephropathy who were treated with glucocorticoid combined with cyclophosphamide were collected. Hematoxylin-eosin stained renal pathological images were acquired by hyperspectral imaging system at the spectral range of 400-1000 nm. The remission group data set contained 23 samples, while the non-remission group data set contained 28 samples. A one-dimensional convolutional neural network model was established to train and test the hyperspectral data, and the performance of the model was evaluated. RESULT From the spectral curve, the spectral difference between the remission group and the non-remission group was obvious between 525 and 700 nm. The spectral data in this band were analyzed by one-dimensional convolutional neural network, and the confusion matrix showed that the remission group and the non-remission group were successfully classified. The precision and recall were 0.89 and 0.81 for the non-response group and 0.83 and 0.90 for the response group, respectively, with an F1 score of 0.85 in both groups. The area under the AUC curve of the classification model reached 0.857. CONCLUSION In this study, a one-dimensional convolutional neural network model was used to analyze the hyperspectral images of renal pathology of PMN patients, and the patients in remission group and non-remission group were successfully classified after glucocorticoid combined with cyclophosphamide treatment.
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Affiliation(s)
- Wen Liu
- Department of Nephrology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Nephrology, No.16766 Jingshi Road, Jinan, Shandong 250014, China
| | - Xiangyu Hou
- Department of Nephrology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Nephrology, No.16766 Jingshi Road, Jinan, Shandong 250014, China
| | - Yang Li
- Department of Nephrology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Nephrology, No.16766 Jingshi Road, Jinan, Shandong 250014, China
| | - Zunsong Wang
- Department of Nephrology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Nephrology, No.16766 Jingshi Road, Jinan, Shandong 250014, China.
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Dutta A, Chaudhary P, Sharma S, Lall B. Satellite hyperspectral imaging technology as a potential rapid pollution assessment tool for urban landfill sites: case study of Ghazipur and Okhla landfill sites in Delhi, India. Environ Sci Pollut Res Int 2023; 30:116742-116750. [PMID: 35982385 DOI: 10.1007/s11356-022-22421-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 08/02/2022] [Indexed: 06/15/2023]
Abstract
Hyperspectral imaging technology has been used for biochemical analysis of Earth's surface exploiting the spectral reflectance signatures of various materials. The new-generation Italian PRISMA (PRecursore IperSpettrale dellaMissione Applicativa) hyperspectral satellite launched by the Italian space agency (ASI) provides a unique opportunity to map various materials through spectral signature analysis for recourse management and sustainable development. In this study PRISMA hyperspectral satellite imagery-based multiple spectral indices were generated for rapid pollution assessment at Ghazipur and Okhla landfill sites in Delhi, India. It was found that the combined risk score for Okhla landfill site was higher than the Ghazipur landfill site. Various manmade materials identified, exploiting the hyperspectral imagery and spectral signature libraries, indicated presence of highly saline water, plastic (black, ABS, pipe, netting, etc.), asphalt tar, black tar paper, kerogen BK-Cornell, black paint and graphite, chalcocite minerals, etc. in large quantities in both the landfill sites. The methodology provides a rapid pollution assessment tool for municipal landfill sites.
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Affiliation(s)
- Amitava Dutta
- School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, India
| | - Priya Chaudhary
- University of Queensland (UQ)-IITD Academy of Research, Indian Institute of Technology Delhi, New Delhi, India
| | - Shilpi Sharma
- School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India
| | - Brejesh Lall
- School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, India.
- Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India.
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Kieu HT, Pak HY, Trinh HL, Pang DSC, Khoo E, Law AWK. UAV-based remote sensing of turbidity in coastal environment for regulatory monitoring and assessment. Mar Pollut Bull 2023; 196:115482. [PMID: 37864857 DOI: 10.1016/j.marpolbul.2023.115482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 10/23/2023]
Abstract
The adoption of Unmanned Aerial Vehicle (UAV) remote sensing for the regulatory monitoring of turbidity plumes induced by land reclamation operations remains a difficult task. Compared to UAV remote sensing on ambient turbidity in estuaries and rivers, such monitoring of construction-induced turbidity plumes requires significantly higher spatial resolutions and accuracy as well as wider turbidity ranges with nonlinear reflectance. In this study, a pilot-scale deployment of UAV-based hyperspectral sensing is carried out for this objective, with specific new elements developed to overcome the challenges and minimise the uncertainties involved. In particular, Machine learning (ML) models for the turbidity determination were trained by the large dataset collected to better capture the non-linearity of the relationship between the water leaving reflectance and turbidity level. The models achieve a good accuracy with a R2 score of 0.75 that is deemed acceptable in view of the uncertainties associated with construction and land reclamation work.
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Affiliation(s)
- Hieu Trung Kieu
- Environmental Process Modelling Centre, Nanyang Environment and Water Research Institute, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Hui Ying Pak
- Environmental Process Modelling Centre, Nanyang Environment and Water Research Institute, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore; Interdisciplinary Graduate Programme, Graduate College, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Ha Linh Trinh
- Environmental Process Modelling Centre, Nanyang Environment and Water Research Institute, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Dawn Sok Cheng Pang
- Environmental Process Modelling Centre, Nanyang Environment and Water Research Institute, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Eugene Khoo
- Engineering and Project Management Division, Maritime and Port Authority of Singapore, Singapore 119963, Singapore
| | - Adrian Wing-Keung Law
- Environmental Process Modelling Centre, Nanyang Environment and Water Research Institute, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore; School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.
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Merdasa A, Berggren J, Tenland K, Stridh M, Hernandez-Palacios J, Gustafsson N, Sheikh R, Malmsjö M. Oxygen saturation mapping during reconstructive surgery of human forehead flaps with hyperspectral imaging and spectral unmixing. Microvasc Res 2023; 150:104573. [PMID: 37390964 DOI: 10.1016/j.mvr.2023.104573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/20/2023] [Accepted: 06/26/2023] [Indexed: 07/02/2023]
Abstract
BACKGROUND Optical spectroscopy is commonly used clinically to monitor oxygen saturation in tissue. The most commonly employed technique is pulse oximetry, which provides a point measurement of the arterial oxygen saturation and is commonly used for monitoring systemic hemodynamics, e.g. during anesthesia. Hyperspectral imaging (HSI) is an emerging technology that enables spatially resolved mapping of oxygen saturation in tissue (sO2), but needs to be further developed before implemented in clinical practice. The aim of this study is to demonstrate the applicability of HSI for mapping the sO2 in reconstructive surgery and demonstrate how spectral analysis can be used to obtain clinically relevant sO2 values. METHODS Spatial scanning HSI was performed on cutaneous forehead flaps, raised as part of a direct brow lift, in eight patients. Pixel-by-pixel spectral analysis, accounting for the absorption from multiple chromophores, was performed and compared to previous analysis techniques to assess sO2. RESULTS Spectral unmixing using a broad spectral range, and accounting for the absorption of melanin, fat, collagen, and water, provided a more clinically relevant estimate of sO2 than conventional techniques, where typically only spectral features associated with absorption of oxygenated (HbO2) and deoxygenated (HbR) hemoglobin are considered. We demonstrate its clinical applicability by generating sO2 maps of partially excised forehead flaps showed a gradual decrease in sO2 along the length of the flap from 95 % at the flap base to 85 % at the flap tip. After being fully excised, sO2 in the entire flap decreased to 50 % within a few minutes. CONCLUSIONS The results demonstrate the capability of sO2 mapping in reconstructive surgery in patients using HSI. Spectral unmixing, accounting for multiple chromophores, provides sO2 values that are in accordance with physiological expectations in patients with normal functioning microvascularization. Our results suggest that HSI methods that yield reliable spectra are to be preferred, so that the analysis can produce results that are of clinical relevance.
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Affiliation(s)
- Aboma Merdasa
- Department of Ophthalmology, Clinical Sciences Lund, Lund University, Sweden.
| | - Johanna Berggren
- Department of Ophthalmology, Clinical Sciences Lund, Lund University, Sweden; Skåne University Hospital, Department of Clinical Sciences Lund, Ophthalmology Lund, Sweden
| | - Kajsa Tenland
- Department of Ophthalmology, Clinical Sciences Lund, Lund University, Sweden; Skåne University Hospital, Department of Clinical Sciences Lund, Ophthalmology Lund, Sweden
| | - Magne Stridh
- Department of Ophthalmology, Clinical Sciences Lund, Lund University, Sweden; Skåne University Hospital, Department of Clinical Sciences Lund, Ophthalmology Lund, Sweden
| | | | - Nils Gustafsson
- Skåne University Hospital, Department of Clinical Sciences Lund, Ophthalmology Lund, Sweden
| | - Rafi Sheikh
- Department of Ophthalmology, Clinical Sciences Lund, Lund University, Sweden; Skåne University Hospital, Department of Clinical Sciences Lund, Ophthalmology Lund, Sweden
| | - Malin Malmsjö
- Department of Ophthalmology, Clinical Sciences Lund, Lund University, Sweden; Skåne University Hospital, Department of Clinical Sciences Lund, Ophthalmology Lund, Sweden
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Dung CD, Trueman SJ, Wallace HM, Farrar MB, Gama T, Tahmasbian I, Bai SH. Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields. Environ Sci Pollut Res Int 2023; 30:114166-114182. [PMID: 37858016 PMCID: PMC10663281 DOI: 10.1007/s11356-023-30344-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023]
Abstract
Managing the nutritional status of strawberry plants is critical for optimizing yield. This study evaluated the potential of hyperspectral imaging (400-1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield. Partial least squares regression (PLSR) models were developed to estimate nutrient concentrations. The determination coefficient of prediction (R2P) and ratio of performance to deviation (RPD) were used to evaluate prediction accuracy, which often proved to be greater for leaves, flowers, and unripe fruit than for ripe fruit. The prediction accuracies for N concentration were R2P = 0.64, 0.60, 0.81, and 0.30, and RPD = 1.64, 1.59, 2.64, and 1.31, for leaves, flowers, unripe fruit, and ripe fruit, respectively. Prediction accuracies for Ca concentrations were R2P = 0.70, 0.62, 0.61, and 0.03, and RPD = 1.77, 1.63, 1.60, and 1.15, for the same respective plant parts. Yield and fruit mass only had significant linear relationships with the Difference Vegetation Index (R2 = 0.256 and 0.266, respectively) among the eleven vegetation indices tested. Hyperspectral imaging showed potential for estimating nutrient status in strawberry crops. This technology will assist growers to make rapid nutrient-management decisions, allowing for optimal yield and quality.
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Affiliation(s)
- Cao Dinh Dung
- Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- School of Science, Technology and Engineering, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- Potato, Vegetable and Flower Research Center - Institute of Agricultural Science for Southern Vietnam, Thai Phien Village, Ward 12, Da Lat, Lam Dong, Vietnam
| | - Stephen J Trueman
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia
| | - Helen M Wallace
- Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- School of Science, Technology and Engineering, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia
| | - Michael B Farrar
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia
| | - Tsvakai Gama
- Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- School of Science, Technology and Engineering, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
| | - Iman Tahmasbian
- Department of Agriculture and Fisheries, Queensland Government, Toowoomba, QLD, 4350, Australia
| | - Shahla Hosseini Bai
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia.
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Schimunek L, Schöpp K, Wagner M, Brucker SY, Andress J, Weiss M. Hyperspectral imaging as a new diagnostic tool for cervical intraepithelial neoplasia. Arch Gynecol Obstet 2023; 308:1525-1530. [PMID: 37574506 PMCID: PMC10520109 DOI: 10.1007/s00404-023-07171-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 07/20/2023] [Indexed: 08/15/2023]
Abstract
PURPOSE Cervical cancer screening by visual inspection with acetic acid (VIA) during colposcopy can be challenging and is highly dependent on the clinical experience of the examiner. Health-care systems lack qualified physicians able to perform the examination in both industrialized and low- and middle-income countries. Previous work has shown the general potential of hyperspectral imaging (HSI) to discriminate CIN from normal tissue, but clinical translation has been limited due to the lack of medically approved HSI systems. METHODS In this study, we evaluate the feasibility of a commercially available HSI system for CIN detection in a prospective monocentric clinical trial. RESULTS By obtaining spectral fingerprints of 41 patients with CIN 1-3 we show that HSI-based differentiation between CIN and normal tissue is possible with high statistical significance. Major spectral differences were seen in the 555-585 wavelength area. CONCLUSION HSI advances tissue differentiation by associating each pixel with high-dimensional spectra and thereby obtains morphological and biochemical information of the observed tissue. Currently available and medically approved HSI systems may represent a contact- and marker-free examiner-independent method for the diagnosis of CIN.
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Affiliation(s)
- Lukas Schimunek
- Department of Women's Health, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Katharina Schöpp
- Department of Women's Health, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Michael Wagner
- Department of Women's Health, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Sara Y Brucker
- Department of Women's Health, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Jürgen Andress
- Department of Women's Health, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Martin Weiss
- Department of Women's Health, University Hospital Tübingen, 72076, Tübingen, Germany.
- NMI Naturwissenschaftlich Medizinisches Institut an der Eberhard Karls Universität Tübingen, 72770, Reutlingen, Germany.
- Section for Plasmamedicine and Medical Technology, Department of Women's Health, University Hospital Tübingen, Calwerstrasse 7, 72076, Tübingen, Germany.
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Solovchenko A, Shurygin B, Nesterov DA, Sorokin DV. Towards the synthesis of spectral imaging and machine learning-based approaches for non-invasive phenotyping of plants. Biophys Rev 2023; 15:939-946. [PMID: 37975015 PMCID: PMC10643738 DOI: 10.1007/s12551-023-01125-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 08/23/2023] [Indexed: 11/19/2023] Open
Abstract
High-throughput phenotyping is now central to the progress of plant sciences, accelerated breeding, and precision farming. The power of phenotyping comes from the automated, rapid, non-invasive collection of large datasets describing plant objects. In this context, the goal of extracting relevant information from different kinds of images is of paramount importance. We review both the spectral and machine learning-based approaches to imaging of plants for the purpose of their phenotyping. The advantages and drawbacks of both approaches will be discussed with a focus on the monitoring of plants. We argue that an emerging approach combining the strengths of the spectral and the machine learning-based approaches will remain a promising direction in plant phenotyping in the nearest future.
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Affiliation(s)
- Alexei Solovchenko
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
- Institute of Natural Sciences, Derzhavin Tambov State University, Tambov, Russia
| | - Boris Shurygin
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
- Institute of Natural Sciences, Derzhavin Tambov State University, Tambov, Russia
| | - Dmitry A. Nesterov
- Faculty of Computer Science and Cybernetics, Lomonosov Moscow State University, Moscow, Russia
| | - Dmitry V. Sorokin
- Faculty of Computer Science and Cybernetics, Lomonosov Moscow State University, Moscow, Russia
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Amariei G, Lahn Henriksen M, Klarskov P, Hinge M. In-line quantitative estimation of ammonium polyphosphate flame retardant in polyolefins via industrial hyperspectral imaging system and machine learning. Waste Manag 2023; 170:1-7. [PMID: 37531740 DOI: 10.1016/j.wasman.2023.07.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/04/2023] [Accepted: 07/23/2023] [Indexed: 08/04/2023]
Abstract
Due to developments in European legislation, several halogenated flame retardants are banned due to their toxicity, and the use of phosphor-based flame retardants in plastics is increasing. A revision of ammonium polyphosphate (APP) flame retardant revealed that it is an eye irritant and toxic, thus posing a health issue. Hence APP identification is needed for enabling safe recycling of plastic waste streams. Herein an industrial in-line method for quantitative estimation of APP in low density polyethylene (LDPE) and polypropylene (PP) is demonstrated, by using an industrial hyperspectral imaging system (955 to 1700 nm) and principal component analysis (PCA). Spectra of plastic samples with varying concentrations of APP were applied to build and calibrate a quantitative determination method. PCA and band area ratios (of selected bands) were made and fitted with continuous functions for concentration determination. The plastic samples were characterised by elemental analysis, attenuated total reflection, differential scanning calorimetry, and thermogravimetric analysis. The PCA model outperforms the band area ratio model and predicts APP concentrations between 24.3 and 1.5 wt% in LDPE (R2 = 0.98) and 20.0 and 1.7 wt% in PP (R2 = 0.97). Unknown samples with APP ranging from 23.7 to 2.7 wt% in LDPE and from 18.6 to 2.3 wt% in PP were predicted and correlated to the actual concentrations. The proposed approach is valuable for the plastic recyclers and waste management industries where inline concentration determination of flame retardants is key.
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Affiliation(s)
- Georgiana Amariei
- Plastic and Polymer Engineering, Department of Biological and Chemical Engineering, Aarhus University, Aabogade 40, DK-8200 Aarhus N., Denmark
| | - Martin Lahn Henriksen
- Plastic and Polymer Engineering, Department of Biological and Chemical Engineering, Aarhus University, Aabogade 40, DK-8200 Aarhus N., Denmark
| | - Pernille Klarskov
- Terahertz Photonics, Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, DK-8200 Aarhus N, Denmark
| | - Mogens Hinge
- Plastic and Polymer Engineering, Department of Biological and Chemical Engineering, Aarhus University, Aabogade 40, DK-8200 Aarhus N., Denmark.
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Abstract
BACKGROUND Accurate diagnosis of breast cancer (BC) plays a crucial role in clinical pathology analysis and ensuring precise surgical margins to prevent recurrence. METHODS Laser-induced fluorescence (LIF) technology offers high sensitivity to tissue biochemistry, making it a potential tool for noninvasive BC identification. In this study, we utilized hyperspectral (HS) imaging data of stimulated BC specimens to detect malignancies based on altered fluorescence characteristics compared to normal tissue. Initially, we employed a HS camera and broadband spectrum light to assess the absorbance of BC samples. Notably, significant absorbance differences were observed in the 440-460 nm wavelength range. Subsequently, we developed a specialized LIF system for BC detection, utilizing a low-power blue laser source at 450 nm wavelength for ten BC samples. RESULTS Our findings revealed that the fluorescence distribution of breast specimens, which carries molecular-scale structural information, serves as an effective marker for identifying breast tumors. Specifically, the emission at 561 nm exhibited the greatest variation in fluorescence signal intensity for both tumor and normal tissue, serving as an optical predictive biomarker. To enhance BC identification, we propose an advanced image classification technique that combines image segmentation using contour mapping and K-means clustering (K-mc, K = 8) for HS emission image data analysis. CONCLUSIONS This exploratory work presents a potential avenue for improving "in-vivo" disease characterization using optical technology, specifically our LIF technique combined with the advanced K-mc approach, facilitating early tumor diagnosis in BC.
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Affiliation(s)
- Alaaeldin Mahmoud
- Optoelectronics and automatic control systems department, Military Technical College, Kobry El-Kobba, Cairo, Egypt.
| | - Yasser H El-Sharkawy
- Optoelectronics and automatic control systems department, Military Technical College, Kobry El-Kobba, Cairo, Egypt
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42
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Feng L, Chen S, Chu H, Zhang C, Hong Z, He Y, Wang M, Liu Y. Machine-learning-facilitated prediction of heavy metal contamination in distiller's dried grains with solubles. Environ Pollut 2023; 333:122043. [PMID: 37328124 DOI: 10.1016/j.envpol.2023.122043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/16/2023] [Accepted: 06/13/2023] [Indexed: 06/18/2023]
Abstract
Excessive heavy metal contamination often occurs in feed due to natural or anthropogenic activity, leading to poisoning and other health problems in animals. In this study, a visible/near-infrared hyperspectral imaging system (Vis/NIR HIS) was used to reveal the different characteristics of spectral reflectance of Distillers Dried Grains with Solubles (DDGS) doped with various heavy metals and to effectively predict metal concentrations. Two types of sample treatment were used, namely tablet and bulk. Three quantitative analysis models were constructed based on the full wavelength, and through comparison the support vector regression (SVR) model was found to show the best performance. As typical heavy metal contaminants, copper (Cu) and zinc (Zn) were used for modeling and prediction. The prediction set accuracy of the tablet samples doped with Cu and Zn was 94.9% and 86.2%, respectively. In addition, a novel characteristic wavelength selection model based on SVR (SVR-CWS) was proposed to filter characteristic wavelengths, which improved the detection performance. The regression accuracy of the SVR model on the prediction set of tableted samples with different Cu and Zn concentrations was 94.7% and 85.9%, respectively. The accuracy of bulk samples with different Cu and Zn concentrations was 81.3% and 80.3%, respectively, which indicated that the detection method can reduce the pretreatment steps and verify its practicability. The overall results suggested the potential of Vis/NIR-HIS in the detection of feed safety and quality.
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Affiliation(s)
- Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Sishi Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Hangjian Chu
- Zhejiang Academy of Agricultural Sciences, Hangzhou, 310058, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, 313000, China
| | - Zhiqi Hong
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Mengcen Wang
- Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Ministry of Agriculture, Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou, 310058, China
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
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43
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Zhang H, Pan Y, Liu X, Chen Y, Gong X, Zhu J, Yan J, Zhang H. Recognition of the rhizome of red ginseng based on spectral-image dual-scale digital information combined with intelligent algorithms. Spectrochim Acta A Mol Biomol Spectrosc 2023; 297:122742. [PMID: 37098315 DOI: 10.1016/j.saa.2023.122742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/08/2023] [Accepted: 04/11/2023] [Indexed: 05/14/2023]
Abstract
Red ginseng is a widely used and extensively researched food and medicinal product with high nutritional value, derived from steamed fresh ginseng. The components in various parts of red ginseng differ significantly, resulting in distinct pharmacological activities and efficacies. This study proposed to establish a hyperspectral imaging technology combined with intelligent algorithms for the recognition of different parts of red ginseng based on the dual-scale of spectrum and image information. Firstly, the spectral information was processed by the best combination of first derivative as pre-processing method and partial least squares discriminant analysis (PLS-DA) as classification model. The recognition accuracy of the rhizome and the main root of red ginseng is 96.79% and 95.94% respectively. Then, the image information was processed by the You Only Look Once version 5 small (YOLO v5s) model. The best parameter combination is epoch = 30, learning rate = 0.01, and activation function is leaky ReLU. In the red ginseng dataset, the highest accuracy, recall and mean Average Precision at IoU (Intersection over Union) threshold 0.5 (mAP@0.5) were 99.01%, 98.51% and 99.07% respectively. The application of spectrum-image dual-scale digital information combined with intelligent algorithms in the recognition of red ginseng is successful, which provides a positive significance for the online and on-site quality control and authenticity identification of crude drugs or fruits.
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Affiliation(s)
- HongXu Zhang
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China
| | - YiXia Pan
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China
| | - XiaoYi Liu
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China
| | - Yuan Chen
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China
| | - XingChu Gong
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - JieQiang Zhu
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China
| | - JiZhong Yan
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China.
| | - Hui Zhang
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China.
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Lantin S, McCourt K, Butcher N, Puri V, Esposito M, Sanchez S, Ramirez-Loza F, McLamore E, Correll M, Singh A. SPOT: Scanning plant IoT facility for high-throughput plant phenotyping. HardwareX 2023; 15:e00468. [PMID: 37693634 PMCID: PMC10485143 DOI: 10.1016/j.ohx.2023.e00468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 07/21/2023] [Accepted: 08/24/2023] [Indexed: 09/12/2023]
Abstract
Many plant phenotyping platforms have been kept out of the reach of smaller labs and institutions due to high cost and proprietary software. The Scanning Plant IoT (SPOT) Facility, located at the University of Florida, is a mobile, laboratory-based platform that facilitates open-source collection of high-quality, interoperable plant phenotypic data. It consists of three main sensors: a hyperspectral sensor, a thermal camera, and a LiDAR camera. Real-time data from the sensors can be collected in its 10 ft. × 10 ft. scanning region. The mobility of the device allows its use in large growth chambers, environmentally controlled rooms, or greenhouses. Sensors are oriented nadir and positioned via computer numerical control of stepper motors. In a preliminary experiment, data gathered from SPOT was used to autonomously and nondestructively differentiate between cultivars.
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Affiliation(s)
- Stephen Lantin
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Kelli McCourt
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
- Department of Environmental Engineering and Earth Science, Clemson University, SC 29634, USA
| | - Nicholas Butcher
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Varun Puri
- Department of Computer and Information Sciences and Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Martha Esposito
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Sasha Sanchez
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Francisco Ramirez-Loza
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Eric McLamore
- Department of Agricultural Sciences, Clemson University, SC 29634, USA
| | - Melanie Correll
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Aditya Singh
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
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45
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Li X, Feng X, Fang H, Yang N, Yang G, Yu Z, Shen J, Geng W, He Y. Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network. Plant Methods 2023; 19:82. [PMID: 37563698 PMCID: PMC10413611 DOI: 10.1186/s13007-023-01057-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 07/17/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Pumpkin seeds are major oil crops with high nutritional value and high oil content. The collection and identification of different pumpkin germplasm resources play a significant role in the realization of precision breeding and variety improvement. In this research, we collected 75 species of pumpkin from the Zhejiang Province of China. 35,927 near-infrared hyperspectral images of 75 types of pumpkin seeds were used as the research object. RESULTS To realize the rapid classification of pumpkin seed varieties, position attention embedded three-dimensional convolutional neural network (PA-3DCNN) was designed based on hyperspectral image technology. The experimental results showed that PA-3DCNN had the best classification effect than other classical machine learning technology. The classification accuracy of 99.14% and 95.20% were severally reached on the training and test sets. We also demonstrated that the PA-3DCNN model performed well in next year's classification with fine-tuning and met with 94.8% accuracy. CONCLUSIONS The model performance improved by introducing double convolution and pooling structure and position attention module. Meanwhile, the generalization performance of the model was verified, which can be adopted for the classification of pumpkin seeds in multiple years. This study provided a new strategy and a feasible technical approach for identifying germplasm resources of pumpkin seeds.
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Affiliation(s)
- Xiyao Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Xuping Feng
- The Rural Development Academy, Zhejiang University, Hangzhou, 310058, China
| | - Hui Fang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Ningyuan Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Guofeng Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Zeyu Yu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Jia Shen
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310000, China.
| | - Wei Geng
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310000, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
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46
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García MJ, Kamaid A, Malacrida L. Label-free fluorescence microscopy: revisiting the opportunities with autofluorescent molecules and harmonic generations as biosensors and biomarkers for quantitative biology. Biophys Rev 2023; 15:709-719. [PMID: 37681086 PMCID: PMC10480099 DOI: 10.1007/s12551-023-01083-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/19/2023] [Indexed: 09/09/2023] Open
Abstract
Over the past decade, the utilization of advanced fluorescence microscopy technologies has presented numerous opportunities to study or re-investigate autofluorescent molecules and harmonic generation signals as molecular biomarkers and biosensors for in vivo cell and tissue studies. The label-free approaches benefit from the endogenous fluorescent molecules within the cell and take advantage of their spectroscopy properties to address biological questions. Harmonic generation can be used as a tool to identify the occurrence of fibrillar or lipid deposits in tissues, by using second and third-harmonic generation microscopy. Combining autofluorescence with novel techniques and tools such as fluorescence lifetime imaging microscopy (FLIM) and hyperspectral imaging (HSI) with model-free analysis of phasor plots has revolutionized the understanding of molecular processes such as cellular metabolism. These tools provide quantitative information that is often hidden under classical intensity-based microscopy. In this short review, we aim to illustrate how some of these technologies and techniques may enable investigation without the need to add a foreign fluorescence molecule that can modify or affect the results. We address some of the most important autofluorescence molecules and their spectroscopic properties to illustrate the potential of these combined tools. We discuss using them as biomarkers and biosensors and, under the lens of this new technology, identify some of the challenges and potentials for future advances in the field.
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Affiliation(s)
- María José García
- Departamento de Fisiopatología, Hospital de Clínicas, Facultad de Medicina, Universidad de La República, Montevideo, Uruguay
- Advanced Bioimaging Unit, Institut Pasteur de Montevideo & Universidad de la República, Montevideo, Uruguay
| | - Andrés Kamaid
- Advanced Bioimaging Unit, Institut Pasteur de Montevideo & Universidad de la República, Montevideo, Uruguay
| | - Leonel Malacrida
- Departamento de Fisiopatología, Hospital de Clínicas, Facultad de Medicina, Universidad de La República, Montevideo, Uruguay
- Advanced Bioimaging Unit, Institut Pasteur de Montevideo & Universidad de la República, Montevideo, Uruguay
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47
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Giulietti N, Discepolo S, Castellini P, Martarelli M. Neural network based hyperspectral imaging for substrate independent bloodstain age estimation. Forensic Sci Int 2023; 349:111742. [PMID: 37331047 DOI: 10.1016/j.forsciint.2023.111742] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/20/2023]
Abstract
Being able to determine the age of a bloodstain can be a key element in a crime scene investigation. Many techniques exploit reflectance spectroscopy because it is very versatile and can be used in the field with ease. However, there are no methods for estimating bloodstain age with adequate uncertainty, and the problem of substrate influence is not yet fully resolved. We develop a hyperspectral imaging based technique for the substrate-independent age estimation of a bloodstain. Once the hyperspectral image is acquired, a neural network model recognizes the pixels belonging to the bloodstain. The reflectance spectra belonging to the bloodstain are then processed by an artificial intelligence model that removes the effect of the substrate on the bloodstain and then estimates its age. The method is trained on bloodstains deposited on 9 different substrates over a time period of 0-385 h obtaining an absolute mean error of 6.9 h over the period considered. Within two days of age, the method achieves a mean absolute error of 1.1 h. The method is finally tested on a new material (i.e., red cardboard) never used to test or validate the neural network models. Also in this case the bloodstain age is identified with the same accuracy.
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Affiliation(s)
- Nicola Giulietti
- Department of Mechanical Engineering, Politecnico di Milano, Via La Masa 1, Milan 60131, Italy.
| | - Silvia Discepolo
- Department of Industrial Engineering and Mathematical Science, Universit'a Politecnica delle Marche, Via Brecce Bianche 12, Ancona 20156, Italy
| | - Paolo Castellini
- Department of Industrial Engineering and Mathematical Science, Universit'a Politecnica delle Marche, Via Brecce Bianche 12, Ancona 20156, Italy
| | - Milena Martarelli
- Department of Industrial Engineering and Mathematical Science, Universit'a Politecnica delle Marche, Via Brecce Bianche 12, Ancona 20156, Italy
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48
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Xue R, Lan R, Su W, Wang Z, Li X, Zhao J, Ma C, Xing B. Mechanistic Understanding toward the Maternal Transfer of Nanoplastics in Daphnia magna. ACS Nano 2023. [PMID: 37449792 DOI: 10.1021/acsnano.3c01847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Nanoplastics induce transgenerational toxicity to aquatic organisms, but the specific pathways for the maternal transfer of nanoplastics remain unclear. Herein, laser scanning confocal microscopy (LSCM) observations identified the specific pathways on the maternal transfer of polystyrene (PS) nanoplastics (25 nm) in Daphnia magna. In vivo and in vitro experiments showed that PS nanoplastics could enter the brood chamber through its opening and then be internalized to eggs and embryos using LSCM imaging (pathway I). In addition, PS nanoplastics were observed in the oocytes of the ovary, demonstrating gut-ovary-oocyte transfer (pathway II). Furthermore, label-free hyperspectral imaging was used to detect the distribution of nanoplastics in the embryos and ovary of Daphnia, again confirming the maternal transfer of nanoplastics through the two pathways mentioned above. The contribution from pathway I (88%) was much higher than pathway II (12%) based on nanoflow cytometry quantification. In addition, maternal transfer in Daphnia depended on the particle size of PS nanoplastics, as demonstrated by using LSCM and hyperspectral imaging. Unlike 25 nm nanoplastics, 50 nm PS nanoplastics could enter the brood chamber and the eggs/embryos (pathway I), but were not detected in the ovary (pathway II); 100 nm PS nanoplastics were difficult to be internalized by eggs/embryos and could not enter the ovary either. These findings provide insight into the maternal transfer mechanisms of nanoplastics in Daphnia, and are critical for better understanding the transgenerational toxicity of aquatic organisms.
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Affiliation(s)
- Runze Xue
- Institute of Coastal Environmental Pollution Control, Key Laboratory of Marine Environment and Ecology (Ministry of Education), Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China, Qingdao 266100, China
- Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Ruyi Lan
- Institute of Coastal Environmental Pollution Control, Key Laboratory of Marine Environment and Ecology (Ministry of Education), Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China, Qingdao 266100, China
| | - Wenli Su
- Institute of Coastal Environmental Pollution Control, Key Laboratory of Marine Environment and Ecology (Ministry of Education), Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China, Qingdao 266100, China
| | - Zhenyu Wang
- Institute of Environmental Processes and Pollution Control, and School of Environmental and Civil Engineering, Jiangnan University, Wuxi 214122, China
| | - Xinyu Li
- Institute of Coastal Environmental Pollution Control, Key Laboratory of Marine Environment and Ecology (Ministry of Education), Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China, Qingdao 266100, China
| | - Jian Zhao
- Institute of Coastal Environmental Pollution Control, Key Laboratory of Marine Environment and Ecology (Ministry of Education), Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China, Qingdao 266100, China
- Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Chuanxin Ma
- Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
| | - Baoshan Xing
- Stockbridge School of Agriculture, University of Massachusetts, Amherst, Massachusetts 01003, United States
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da Silveira Estevão PL, Lemes LFR, Soares FLF, Nagata N. Raman mapping for determination of TiO 2 in different solid food samples by multivariate curve resolution with alternating least squares. Anal Bioanal Chem 2023:10.1007/s00216-023-04839-9. [PMID: 37438565 DOI: 10.1007/s00216-023-04839-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 06/28/2023] [Accepted: 06/30/2023] [Indexed: 07/14/2023]
Abstract
Titanium dioxide is a food additive commonly used as a white food coloring (E171). Its wide use by the food industry associated with the nanometric size distribution of the particles of this pigment has shown high genotoxicity associated with recurrent exposure by ingestion. Therefore, the use of E171 in food products has already been banned by some industries and in the European Union. Such banishment should soon be extended to other countries around the world, making it important to establish techniques for the efficient determination of TiO2 in different food products. The association between hyperspectral images and chemometric tools can be useful in this sense, aiming to enable the use of a single method for sample preparation and analysis of different types of food. Thus, the present work aims to evaluate the use of Raman mapping associated with the resolution of multivariate curves with alternating least squares (MCR-ALS) for the determination of titanium dioxide in solid food samples with different compositions, without the need to introduce specific sample preparation. The proposed method allowed for the first-time quantification of TiO2 in different food matrices without specific sample preparation, with a simple, rapid, accurate (93% of recovery), low detection limits (0.0111% m/m) and quantification (0.0370% m/m) and adequate linearity (r = 0.9990) and precise (standard deviation around 0.020-0.030% w/w) methodology. Such results highlight the potential use of Raman mapping associated with the MCR-ALS for quantification of the nano-TiO2 in commercial samples.
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Affiliation(s)
| | | | | | - Noemi Nagata
- Chemistry Department, Federal University of Parana, Curitiba, Parana State, Brazil
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50
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An T, Wang Z, Li G, Fan S, Huang W, Duan D, Zhao C, Tian X, Dong C. Monitoring the major taste components during black tea fermentation using multielement fusion information in decision level. Food Chem X 2023; 18:100718. [PMID: 37397207 PMCID: PMC10314168 DOI: 10.1016/j.fochx.2023.100718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/14/2023] [Accepted: 05/18/2023] [Indexed: 07/04/2023] Open
Abstract
Hitherto, the intelligent detection of black tea fermentation quality is still a thought-provoking problem because of one-side sample information and poor model performance. This study proposed a novel method for the prediction of major chemical components including total catechins, soluble sugar and caffeine using hyperspectral imaging technology and electrical properties. The multielement fusion information were used to establish quantitative prediction models. The performance of model using multielement fusion information was better than that of model using single information. Subsequently, the stacking combination model using fusion data combined with feature selection algorithms for evaluating the fermentation quality of black tea. Our proposed strategy achieved better performance than classical linear and nonlinear algorithms, with the correlation coefficient of the prediction set (Rp) for total catechins, soluble sugar and caffeine being 0.9978, 0.9973 and 0.9560, respectively. The results demonstrated that our proposed strategy could effectively evaluate the fermentation quality of black tea.
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Affiliation(s)
- Ting An
- College of Engineering and Technology, Southwest University, Chongqing 400715, China
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250033, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Zheli Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Guanglin Li
- College of Engineering and Technology, Southwest University, Chongqing 400715, China
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Dandan Duan
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Chunjiang Zhao
- College of Engineering and Technology, Southwest University, Chongqing 400715, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Xi Tian
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Chunwang Dong
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250033, China
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