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Zhang W, Bai X, Guo J, Yang J, Yu B, Chen J, Wang J, Zhao D, Zhang H, Liu M. Hyperspectral imaging for in situ visual assessment of Industrial-Scale ginseng. SPECTROCHIMICA ACTA PART A: MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 321:124700. [DOI: doi.org/10.1016/j.saa.2024.124700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
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Lu Y, Nie L, Guo X, Pan T, Chen R, Liu X, Li X, Li T, Liu F. Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 282:116704. [PMID: 38996646 DOI: 10.1016/j.ecoenv.2024.116704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/14/2024] [Accepted: 07/06/2024] [Indexed: 07/14/2024]
Abstract
Hyperaccumulators are the material basis and key to the phytoremediation of heavy metal contaminated soils. Conventional methods for screening hyperaccumulators are highly dependent on the time- and labor-consuming sampling and chemical analysis. In this study, a novel spectral approach assisted with multi-task deep learning was proposed to streamline accumulating ecotype screening, heavy metal stress discrimination, and heavy metals quantification in plants. The significant Cd/Zn co-hyperaccumulator Sedum alfredii and its non-accumulating ecotype were stressed by Cd, Zn, and Pb. Spectral images of leaves were rapidly acquired by hyperspectral imaging. The self-designed deep learning architecture was composed of a shallow network (ENet) for accumulating ecotype identification, and a multi-task network (HMNet) for heavy metal stress type and accumulation prediction simultaneously. To further assess the robustness of the networks, they were compared with conventional machine learning models (i.e., partial least squares (PLS) and support vector machine (SVM)) on a series of evaluation metrics of classification, multi-label classification, and regression. S. alfredii with heavy metals accumulation capability was identified by ENet with 100 % accuracy. HMNet reduced overfitting and outperformed machine learning models with the average exact match ratio (EMR) of heavy metal stress discrimination increased by 7.46 %, and residual prediction deviations (RPD) of heavy metal concentrations prediction increased by 53.59 %. The method succeeded in rapidly and accurately discriminating heavy metal stress with EMRs over 91 % and accuracies over 96 %, and in predicting heavy metals accumulation with an average RPD of 3.29 for Zn, 2.57 for Cd, and 2.53 for Pb, indicating the satisfactory practicability and potential for sensing heavy metals accumulation. This study provides a relatively novel spectral method to facilitate hyperaccumulator screening and heavy metals accumulation prediction in the phytoremediation process.
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Affiliation(s)
- Yi Lu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Linjie Nie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Xinyu Guo
- Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Tiantian Pan
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Xunyue Liu
- College of Advanced Agricultural Sciences, Zhejiang A & F University, Hangzhou 311300, China
| | - Xiaolong Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Tingqiang Li
- Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
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Yuan W, Zhou H, Zhou Y, Zhang C, Jiang X, Jiang H. In-field and non-destructive determination of comprehensive maturity index and maturity stages of Camellia oleifera fruits using a portable hyperspectral imager. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124266. [PMID: 38599024 DOI: 10.1016/j.saa.2024.124266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/27/2024] [Accepted: 04/06/2024] [Indexed: 04/12/2024]
Abstract
To efficiently detect the maturity stages of Camellia oleifera fruits, this study proposed a non-invasive method based on hyperspectral imaging technology. First, a portable hyperspectral imager was used for the in-field image acquisition of Camellia oleifera fruits at three maturity stages, and ten quality indexes were measured as reference standards. Then, factor analysis was performed to obtain the comprehensive maturity index (CMI) by analyzing the change trends and correlations of different indexes. To reduce the high dimensionality of spectral data, the successive projection algorithm (SPA) was employed to select effective feature wavelengths. The prediction models for CMI, including partial least squares regression (PLSR), support vector regression (SVR), extreme learning machine (ELM), and convolutional neural network regression (CNNR), were constructed based on full spectra and feature wavelengths; for CNNR, only the raw spectra were used as input. The SPA-CNNR model exhibited more promising performance (RP = 0.839, RMSEP = 0.261, and RPD = 1.849). Furthermore, PLS-DA models for maturity discrimination of Camellia oleifera fruits were developed using full wavelength, characteristic wavelengths and their fusion CMI, respectively. The PLS-DA model using the fused dataset achieved the highest maturity classification accuracy, with the best simplified model achieving 88.6 % accuracy in prediction set. This study indicated that a portable hyperspectral imager can be used for in-field determination of the internal quality and maturity stages of Camellia oleifera fruits. It provides strong support for non-destructive quality inspection and timely harvesting of Camellia oleifera fruits in the field.
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Affiliation(s)
- Weidong Yuan
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Hongping Zhou
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Yu Zhou
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Cong Zhang
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Xuesong Jiang
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Hongzhe Jiang
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
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He M, Jin C, Li C, Cai Z, Peng D, Huang X, Wang J, Zhai Y, Qi H, Zhang C. Simultaneous determination of pigments of spinach ( Spinacia oleracea L.) leaf for quality inspection using hyperspectral imaging and multi-task deep learning regression approaches. Food Chem X 2024; 22:101481. [PMID: 38840724 PMCID: PMC11152701 DOI: 10.1016/j.fochx.2024.101481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 06/07/2024] Open
Abstract
Rapid and accurate determination of pigment content is important for quality inspection of spinach leaves during storage. This study aimed to use hyperspectral imaging at two spectral ranges (visible/near-infrared, VNIR: 400-1000 nm; NIR: 900-1700 nm) to simultaneously determine the pigment (chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids) content in spinach stored at different durations and conditions (unpackaged and packaged). Partial least squares (PLS), back propagation neural network (BPNN) and convolutional neural network (CNN) were used to establish single-task and multi-task regression models. Single-task CNN (STCNN) models and multi-task CNN (MTCNN) models obtained better performances than the other models. The models using VNIR spectra were superior to those using NIR spectra. The overall results indicated that hyperspectral imaging with multi-task learning could predict the quality attributes of spinach simultaneously for spinach quality inspection under various storage conditions. This research will guide food quality inspection by simultaneously inspecting multiple quality attributes.
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Affiliation(s)
- Mengyu He
- School of Information Engineering, Huzhou University, 313000 Huzhou, China
| | - Chen Jin
- School of Information Engineering, Huzhou University, 313000 Huzhou, China
| | - Cheng Li
- School of Information Engineering, Huzhou University, 313000 Huzhou, China
| | - Zeyi Cai
- School of Information Engineering, Huzhou University, 313000 Huzhou, China
| | - Dongdong Peng
- School of Information Engineering, Huzhou University, 313000 Huzhou, China
| | - Xiang Huang
- School of Information Engineering, Huzhou University, 313000 Huzhou, China
| | - Jun Wang
- School of Information Engineering, Huzhou University, 313000 Huzhou, China
| | - Yuanning Zhai
- School of Information Engineering, Huzhou University, 313000 Huzhou, China
| | - Hengnian Qi
- School of Information Engineering, Huzhou University, 313000 Huzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, 313000 Huzhou, China
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Zhong Y, Wen W, Fan X, Cheng N. An intelligent process analysis method for rapidly evaluating the quality of Chinese medicine with near-infrared non-contact hyperspectral imaging: A case study of Weifuchun concentrate. PHYTOCHEMICAL ANALYSIS : PCA 2024. [PMID: 38924197 DOI: 10.1002/pca.3408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 06/04/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024]
Abstract
INTRODUCTION The quality of Chinese medicine preparations can be greatly influenced by the quality of the intermediates such as extracts or concentrates. However, it is highly challenging to evaluate the quality in a rapid and non-contact manner during manufacturing. Here, we introduce an intelligent hyperspectral analysis method integrating a self-built abnormal region removal algorithm with machine learning and demonstrate its utility using the concentrate of Weifuchun (WFC), a traditional Chinese medicine preparation made from Ginseng Radix et Rhizoma Rubra, Rabdosia Amethystoides, and Aurantii Fructus. OBJECTIVE To rapidly and non-destructively detect quality attributes of the intermediates in the manufacturing processes of Chinese medicine, an intelligent hyperspectral analysis method was developed for simultaneously quantifying the contents of naringin, neohesperidin, rosmarinic acid, and relative density of WFC concentrates. METHODOLOGY Samples were evenly spread on solid white flat bottom containers, which were batch placed on a horizontal sample stage. Subsequent to the acquisition of near-infrared (NIR) hyperspectral images, abnormal pixels such as large/small bubbles and fine solids were first removed according to the differential pixel values in the binary grayscale map and the Mahalanobis distance metric. Then, partial least squares (PLS) and support vector machine (SVM) algorithms were used to construct hyperspectral quantitative calibration models for quality attributes. The hyperspectral images were reconstructed based on these models to visually evaluate the quality of the concentrates during manufacturing. RESULTS As a case study, quality attributes of the WFC concentrates including contents of naringin, neohesperidin, rosmarinic acid, and relative density were determined simultaneously, and coefficients of determination of these quantitative correction models were 0.900, 0.891, 0.851, and 0.920, respectively. CONCLUSION The method proposed in this study favors real-time determination of multiple attributes in viscous samples with industrial application prospects.
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Affiliation(s)
- Yi Zhong
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Wu Wen
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Ningtao Cheng
- School of Medicine, Zhejiang University, Hangzhou, China
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Zhang W, Bai X, Guo J, Yang J, Yu B, Chen J, Wang J, Zhao D, Zhang H, Liu M. Hyperspectral imaging for in situ visual assessment of Industrial-Scale ginseng. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 321:124700. [PMID: 38925038 DOI: 10.1016/j.saa.2024.124700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 06/13/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024]
Abstract
In industrial production, the timely assessment of ginseng-derived ingredients is crucial and requires nondestructive techniques for identifying and analyzing composition. Hyperspectral imaging (HSI) effectively visualizes the three-dimensional spatial distribution of phytochemicals in dried ginseng. This study explores the in-situ prediction and visualization of moisture content (MC) and ginsenoside content (GC) in thermally processed ginseng using dual-band HSI. We collected hyperspectral images from 216 raw ginseng samples, which underwent dimensionality reduction, noise reduction, and feature enhancement via Principal Component Analysis (PCA) and Minimum Noise Separation (MNF). Linear regression models were developed following these pretreatments and evaluated using a validation set. The PCA-based models demonstrated superior performance over those based on MNF, especially in predicting GC in the near-infrared (NIR) spectrum. Similarly, models predicting MC in the visible spectrum showed favorable results. HSI enables rapid generation of distribution maps, facilitating real-time imaging for commercial applications. Repeated drying cycles and increased duration primarily affect the textural characteristics and visible color of the ginseng surface, without significantly altering its intrinsic properties. The deployment of this predictive model alongside real-time content inversion using HSI technology holds promise for integrating visual and intelligent quality monitoring in the trade of valuable herbal commodities.
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Affiliation(s)
- Wei Zhang
- 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
| | - Jin Yang
- Changchun Institute of Optics, Precision Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
| | - Bo Yu
- Baishan Lincun Traditional Chinese Medicine Development CO., Ltd, Jingyu, China
| | - Jiaqi Chen
- Changchun Institute of Optics, Precision Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
| | - Jinyu Wang
- Changchun Institute of Optics, Precision Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
| | - Daqing Zhao
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - He Zhang
- The Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, China
| | - Meichen Liu
- Northeast Asia Research Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
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Xu ML, He YF, Xie L, Qu LB, Xu GR, Cui CX. Research Progress on Active Ingredients and Product Development of Lycium ruthenicum Murray. Molecules 2024; 29:2269. [PMID: 38792130 PMCID: PMC11123928 DOI: 10.3390/molecules29102269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/05/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Lycium ruthenicum Murray possesses significant applications in both food and medicine, including antioxidative, anti-tumor, anti-fatigue, anti-inflammatory, and various other effects. Consequently, there has been a surge in research endeavors dedicated to exploring its potential benefits, necessitating the organization and synthesis of these findings. This article systematically reviews the extraction and content determination methods of active substances such as polysaccharides, anthocyanins, flavonoids, and polyphenols in LRM in the past five years, as well as some active ingredient composition determination methods, biological activities, and product development. This review is divided into three main parts: extraction and determination methods, their bioactivity, and product development. Building upon prior research, we also delve into the economic and medicinal value of Lycium ruthenicum Murray, thereby contributing significantly to its further exploration and development. It is anticipated that this comprehensive review will serve as a valuable resource for advancing research on Lycium ruthenicum Murray.
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Affiliation(s)
- Ming-Lu Xu
- School of Chemistry and Chemical Engineering, Institute of Computational Chemistry, Henan Institute of Science and Technology, Xinxiang 453003, China; (M.-L.X.); (Y.-F.H.); (L.X.)
| | - Yun-Feng He
- School of Chemistry and Chemical Engineering, Institute of Computational Chemistry, Henan Institute of Science and Technology, Xinxiang 453003, China; (M.-L.X.); (Y.-F.H.); (L.X.)
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Liang Xie
- School of Chemistry and Chemical Engineering, Institute of Computational Chemistry, Henan Institute of Science and Technology, Xinxiang 453003, China; (M.-L.X.); (Y.-F.H.); (L.X.)
| | - Ling-Bo Qu
- School of Chemistry and Chemical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Guang-Ri Xu
- School of Chemistry and Chemical Engineering, Institute of Computational Chemistry, Henan Institute of Science and Technology, Xinxiang 453003, China; (M.-L.X.); (Y.-F.H.); (L.X.)
| | - Cheng-Xing Cui
- School of Chemistry and Chemical Engineering, Institute of Computational Chemistry, Henan Institute of Science and Technology, Xinxiang 453003, China; (M.-L.X.); (Y.-F.H.); (L.X.)
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Li W, Zhang X, Wang S, Gao X, Zhang X. Research Progress on Extraction and Detection Technologies of Flavonoid Compounds in Foods. Foods 2024; 13:628. [PMID: 38397605 PMCID: PMC10887530 DOI: 10.3390/foods13040628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Flavonoid compounds have a variety of biological activities and play an essential role in preventing the occurrence of metabolic diseases. However, many structurally similar flavonoids are present in foods and are usually in low concentrations, which increases the difficulty of their isolation and identification. Therefore, developing and optimizing effective extraction and detection methods for extracting flavonoids from food is essential. In this review, we review the structure, classification, and chemical properties of flavonoids. The research progress on the extraction and detection of flavonoids in foods in recent years is comprehensively summarized, as is the application of mathematical models in optimizing experimental conditions. The results provide a theoretical basis and technical support for detecting and analyzing high-purity flavonoids in foods.
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Affiliation(s)
- Wen Li
- Jiangxi Key Laboratory for Mass Spectrometry and Instrumentation, East China University of Technology, Nanchang 330013, China
| | - Xiaoping Zhang
- Jiangxi Key Laboratory for Mass Spectrometry and Instrumentation, East China University of Technology, Nanchang 330013, China
| | - Shuanglong Wang
- Jiangxi Key Laboratory for Mass Spectrometry and Instrumentation, East China University of Technology, Nanchang 330013, China
| | - Xiaofei Gao
- Jiangxi Key Laboratory for Mass Spectrometry and Instrumentation, East China University of Technology, Nanchang 330013, China
| | - Xinglei Zhang
- Jiangxi Key Laboratory for Mass Spectrometry and Instrumentation, East China University of Technology, Nanchang 330013, China
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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] [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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Bai R, Zhou J, Wang S, Zhang Y, Nan T, Yang B, Zhang C, Yang J. Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning. Foods 2024; 13:498. [PMID: 38338633 PMCID: PMC10855119 DOI: 10.3390/foods13030498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/29/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model's classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model's accuracy. Upon validating the model's performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years.
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Affiliation(s)
- 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, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Junhui Zhou
- 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, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - 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, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Yue Zhang
- 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, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - 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, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Bin 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, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, 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, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
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Zhang T, Lu L, Song Y, Yang M, Li J, Yuan J, Lin Y, Shi X, Li M, Yuan X, Zhang Z, Zeng R, Song Y, Gu L. Non-destructive identification of Pseudostellaria heterophylla from different geographical origins by Vis/NIR and SWIR hyperspectral imaging techniques. FRONTIERS IN PLANT SCIENCE 2024; 14:1342970. [PMID: 38288409 PMCID: PMC10822997 DOI: 10.3389/fpls.2023.1342970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 12/27/2023] [Indexed: 01/31/2024]
Abstract
The composition of Pseudostellaria heterophylla (Tai-Zi-Shen, TZS) is greatly influenced by the growing area of the plants, making it significant to distinguish the origins of TZS. However, traditional methods for TZS origin identification are time-consuming, laborious, and destructive. To address this, two or three TZS accessions were selected from four different regions of China, with each of these resources including distinct quality grades of TZS samples. The visible near-infrared (Vis/NIR) and short-wave infrared (SWIR) hyperspectral information from these samples were then collected. Fast and high-precision methods to identify the origins of TZS were developed by combining various preprocessing algorithms, feature band extraction algorithms (CARS and SPA), traditional two-stage machine learning classifiers (PLS-DA, SVM, and RF), and an end-to-end deep learning classifier (DCNN). Specifically, SWIR hyperspectral information outperformed Vis/NIR hyperspectral information in detecting geographic origins of TZS. The SPA algorithm proved particularly effective in extracting SWIR information that was highly correlated with the origins of TZS. The corresponding FD-SPA-SVM model reduced the number of bands by 77.2% and improved the model accuracy from 97.6% to 98.1% compared to the full-band FD-SVM model. Overall, two sets of fast and high-precision models, SWIR-FD-SPA-SVM and SWIR-FD-DCNN, were established, achieving accuracies of 98.1% and 98.7% respectively. This work provides a potentially efficient alternative for rapidly detecting the origins of TZS during actual production.
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Affiliation(s)
- Tingting Zhang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Long Lu
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Yihu Song
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Minyu Yang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Jing Li
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Jiduan Yuan
- Pharmaceutical Development Board of Zherong County, Ningde, China
| | - Yuquan Lin
- Huzhou Wuxing Jinnong Ecological Agriculture Development Co., Ltd, Huzhou, China
| | - Xingren Shi
- Huzhou Wuxing Jinnong Ecological Agriculture Development Co., Ltd, Huzhou, China
| | - Mingjie Li
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Xiaotan Yuan
- Pharmaceutical Development Board of Zherong County, Ningde, China
| | - Zhongyi Zhang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Rensen Zeng
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Yuanyuan Song
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Li Gu
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
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12
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Qi H, Li H, Chen L, Chen F, Luo J, Zhang C. Hyperspectral Imaging Using a Convolutional Neural Network with Transformer for the Soluble Solid Content and pH Prediction of Cherry Tomatoes. Foods 2024; 13:251. [PMID: 38254552 PMCID: PMC10814136 DOI: 10.3390/foods13020251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/19/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Cherry tomatoes are cultivated worldwide and favored by consumers of different ages. The soluble solid content (SSC) and pH are two of the most important quality attributes of cherry tomatoes. The rapid and non-destructive measurement of the SSC and pH of cherry tomatoes is of great significance to their production and consumption. In this research, hyperspectral imaging combined with a convolutional neural network with Transformer (CNN-Transformer) was utilized to analyze the SSC and pH of cherry tomatoes. Conventional machine learning and deep learning models were established for the determination of the SSC and pH. The findings demonstrated that CNN-Transformer yielded outstanding results in predicting the SSC, with the coefficient of determination of calibration (R2C), validation (R2V), and prediction (R2P) for the SSC being 0.83, 0.87, and 0.83, respectively. Relatively worse results were obtained for the pH value prediction, with R2C, R2V, and R2P values of 0.74, 0.68, and 0.60, respectively. Furthermore, the visualization of the CNN-Transformer model revealed the wavelength weight distributions, indicating that the 1380-1650 nm range served as the characteristic band for the SSC, while the spectral range at 945-1280 nm was the characteristic band for pH. In conclusion, integrating spectral information features with the attention mechanism of Transformer through a convolutional neural network can enhance the accuracy of predicting the SSC and pH for cherry tomatoes.
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Affiliation(s)
- Hengnian Qi
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Hongyang Li
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Liping Chen
- Huzhou Agricultural Science and Technology Development Center, Huzhou 313000, China
| | - Fengnong Chen
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Jiahao Luo
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
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13
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Zeng S, Zhang Z, Cheng X, Cai X, Cao M, Guo W. Prediction of soluble solids content using near-infrared spectra and optical properties of intact apple and pulp applying PLSR and CNN. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123402. [PMID: 37738767 DOI: 10.1016/j.saa.2023.123402] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 09/06/2023] [Accepted: 09/11/2023] [Indexed: 09/24/2023]
Abstract
Soluble solids content (SSC) is one of the most important internal quality attributes of fruit and could be predicted using near-infrared (NIR) spectra and optical properties. Partial least squares regression (PLSR) is a conventional regression method in SSC prediction. In recent years, deep learning methods represented by convolutional neural network (CNN) was suggested to be implied in spectral analysis. However, researchers are inevitably facing problems with regard to the selection of spectral pretreatment methods and the evaluation of the performance of the chosen regression. This study employed PLSR and CNN regression to predict SSC of apple based on the collected diffuse reflectance spectra of intact apple, total reflectance and total transmittance spectra of apple pulp, and the calculated optical property spectra, i.e., absorption coefficient and reduced scattering coefficient spectra of apple pulp. Five different spectral pretreatment methods were exerted on these spectra. Results showed that at a given regression (PLSR or CNN), the built models based on the diffuse reflectance spectra of intact apple had the best SSC prediction, and the built models based on pulp's reduced scattering coefficient spectra had the poorest prediction performance. The best prediction performance was achieved by PLSR models using Savitzky-Golay with multiple scattering correction (Rp = 0.96, RMSEP = 0.54 %) and CNN regressions using Savitzky-Golay with standard normal variational transformation (Rp = 0.95, RMSEP = 0.59 %), respectively. Additionally, when the unknown original spectra were used for modeling, CNN had a better performance compared to PLSR, indicating the outstanding preponderance of CNN in spectral analysis. This study provides an effective reference for the selection of chemometric method based on NIR spectra.
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Affiliation(s)
- Shuochong Zeng
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Zongyi Zhang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiaodong Cheng
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiao Cai
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Mengke Cao
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Wenchuan Guo
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
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14
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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: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [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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15
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Pang T, Chen C, Fu R, Wang X, Yu H. An end-to-end seed vigor prediction model for imbalanced samples using hyperspectral image. FRONTIERS IN PLANT SCIENCE 2023; 14:1322391. [PMID: 38192695 PMCID: PMC10773811 DOI: 10.3389/fpls.2023.1322391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 11/24/2023] [Indexed: 01/10/2024]
Abstract
Hyperspectral imaging is a key technology for non-destructive detection of seed vigor presently due to its capability to capture variations of optical properties in seeds. As the seed vigor data depends on the actual germination rate, it inevitably results in an imbalance between positive and negative samples. Additionally, hyperspectral image (HSI) suffers from feature redundancy and collinearity due to its inclusion of hundreds of wavelengths. It also creates a challenge to extract effective wavelength information in feature selection, however, which limits the ability of deep learning to extract features from HSI and accurately predict seed vigor. Accordingly, in this paper, we proposed a Focal-WAResNet network to predict seed vigor end-to-end, which improves the network performance and feature representation capability, and improves the accuracy of seed vigor prediction. Firstly, the focal loss function is utilized to adjust the loss weights of different sample categories to solve the problem of sample imbalance. Secondly, a WAResNet network is proposed to select characteristic wavelengths and predict seed vigor end-to-end, focusing on wavelengths with higher network weights, which enhance the ability of seed vigor prediction. To validate the effectiveness of this method, this study collected HSI of maize seeds for experimental verification, providing a reference for plant breeding. The experimental results demonstrate a significant improvement in classification performance compared to other state-of-the-art methods, with an accuracy up to 98.48% and an F1 score of 95.9%.
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Affiliation(s)
- Tiantian Pang
- College of Computer Science and Technology, Jilin University, Changchun, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jlin University, Changchun, China
| | - Chengcheng Chen
- School of Computer Science, Shenyang Aerospace University, Shenyang, China
| | - Ronghao Fu
- College of Computer Science and Technology, Jilin University, Changchun, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jlin University, Changchun, China
| | - Xianchang Wang
- College of Computer Science and Technology, Jilin University, Changchun, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jlin University, Changchun, China
- Chengdu Kestrel Artificial Intelligence Institute, Chengdu, China
| | - Helong Yu
- College of Information Technology, Jilin Agricultural University, Changchun, China
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16
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Ai J, Zhao W, Yu Q, Qian X, Zhou J, Huo X, Tang F. SR-Unet: A Super-Resolution Algorithm for Ion Trap Mass Spectrometers Based on the Deep Neural Network. Anal Chem 2023; 95:17407-17415. [PMID: 37963290 DOI: 10.1021/acs.analchem.3c04172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
The mass spectrometer is an important tool for modern chemical analysis and detection. Especially, the emergence of miniature mass spectrometers has provided new tools for field analysis and detection. The resolution of a mass spectrometer reflects the ability of the instrument to discriminate between adjacent mass-to-charge ratio ions, and the higher the resolution, the better the discrimination of complex mixtures. Quadrupole ion traps are generally considered as a low-resolution mass spectrometry method, but they have gained wide attention and development in recent years because of their suitability for miniaturization and high qualitative capability. For an ion trap mass spectrometer, the mass sensitivity and resolution can be mutually constrained and need to be balanced by setting an appropriate scanning speed. In this study, a super-resolution U-net algorithm (SR-Unet) is proposed for ion trap mass spectrometry, which can estimate the possible ions from the overlapping ion peaks of low-resolution spectra and improve the equivalent resolution while ensuring sufficient sensitivity and analysis speed of the instrument. By determining the mass spectra of a linear ion trap mass spectrometer (LTQ XL) in Turbo and Normal scan modes, the same unit mass resolution as that at a scan speed of 16,667 Da/s was successfully obtained at 125,000 Da/s. Also, the experiments demonstrated that the algorithm is capable of the mass-to-charge ratio and instrument migration. SR-Unet can be migrated and applied to a miniature mass spectrometer for cruise detection of volatile organic compounds (VOCs), and the identification of VOC species in Photochemical Assessment Monitoring Stations (PAMS) was improved from 31 to 50 species with the same monitoring and analysis speed requirement. Further, super-unit mass resolution peptide detection was achieved on a miniature mass spectrometer with the help of the SR-Unet algorithm, which reduced the full width at half-maxima (FWHM) of bradykinin divalent ions (m/z 531) from 0.35 to 0.15 Da at a scan speed of 375 Da/s and improved the equivalent resolution to 3540. The proposed method provides a new idea to enhance the field mixture detection capability of miniature ion trap mass spectrometers.
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Affiliation(s)
- Jiawen Ai
- Division of Advanced Manufacturing, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Weize Zhao
- Division of Advanced Manufacturing, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Quan Yu
- Division of Advanced Manufacturing, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
| | - Xiang Qian
- Division of Advanced Manufacturing, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
| | - Jianhua Zhou
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen 518107, China
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Xinming Huo
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen 518107, China
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Fei Tang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
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17
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Hu H, Xu Z, Wei Y, Wang T, Zhao Y, Xu H, Mao X, Huang L. The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism. Foods 2023; 12:4153. [PMID: 38002210 PMCID: PMC10670081 DOI: 10.3390/foods12224153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/14/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated characteristic bands, which can adversely impact sample identification. To address this issue, we proposed an enhanced one-dimensional convolutional neural network (1DCNN) with an attention mechanism. Given an intermediate feature map, two attention modules are constructed along two separate dimensions, channel and spectral, and then combined to enhance relevant features and to suppress irrelevant ones. Validated by Fritillaria datasets, the results demonstrate that an attention-enhanced 1DCNN model outperforms several machine learning algorithms and shows consistent improvements over a vanilla 1DCNN. Notably under VNIR and SWIR lenses, the model obtained 98.97% and 99.35% for binary classification between Fritillariae Cirrhosae Bulbus (FCB) and other non-FCB species, respectively. Additionally, it still achieved an extraordinary accuracy of 97.64% and 98.39% for eight-category classification among Fritillaria species. This study demonstrated the application of HSI with artificial intelligence can serve as a reliable, efficient, and non-destructive quality control method for authenticating Fritillaria species. Moreover, our findings also illustrated the great potential of the attention mechanism in enhancing the performance of the vanilla 1DCNN method, providing reference for other HSI-related quality controls of plants with medicinal and edible uses.
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Affiliation(s)
- Huiqiang Hu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Zhenyu Xu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Yunpeng Wei
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Tingting Wang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Yuping Zhao
- China Academy of Chinese Medical Sciences, Beijing 100070, China
| | - Huaxing Xu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Xiaobo Mao
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Luqi Huang
- China Academy of Chinese Medical Sciences, Beijing 100070, China
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18
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Hu H, Wang T, Wei Y, Xu Z, Cao S, Fu L, Xu H, Mao X, Huang L. Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix. FRONTIERS IN PLANT SCIENCE 2023; 14:1271320. [PMID: 37954990 PMCID: PMC10634472 DOI: 10.3389/fpls.2023.1271320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 10/03/2023] [Indexed: 11/14/2023]
Abstract
Accurate assessment of isoflavone and starch content in Puerariae Thomsonii Radix (PTR) is crucial for ensuring its quality. However, conventional measurement methods often suffer from time-consuming and labor-intensive procedures. In this study, we propose an innovative and efficient approach that harnesses hyperspectral imaging (HSI) technology and deep learning (DL) to predict the content of isoflavones (puerarin, puerarin apioside, daidzin, daidzein) and starch in PTR. Specifically, we develop a one-dimensional convolutional neural network (1DCNN) model and compare its predictive performance with traditional methods, including partial least squares regression (PLSR), support vector regression (SVR), and CatBoost. To optimize the prediction process, we employ various spectral preprocessing techniques and wavelength selection algorithms. Experimental results unequivocally demonstrate the superior performance of the DL model, achieving exceptional performance with mean coefficient of determination (R2) values surpassing 0.9 for all components. This research underscores the potential of integrating HSI technology with DL methods, thereby establishing the feasibility of HSI as an efficient and non-destructive tool for predicting the content of isoflavones and starch in PTR. Moreover, this methodology holds great promise for enhancing efficiency in quality control within the food industry.
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Affiliation(s)
- Huiqiang Hu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
- Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou, Henan, China
| | - Tingting Wang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Yunpeng Wei
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Zhenyu Xu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Shiyu Cao
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Ling Fu
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Huaxing Xu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Xiaobo Mao
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
- Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou, Henan, China
| | - Luqi Huang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
- Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou, Henan, China
- 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, China
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19
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Zhang L, Guan Y, Wang N, Ge F, Zhang Y, Zhao Y. Identification of growth years for Puerariae Thomsonii Radix based on hyperspectral imaging technology and deep learning algorithm. Sci Rep 2023; 13:14286. [PMID: 37653027 PMCID: PMC10471754 DOI: 10.1038/s41598-023-40863-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 08/17/2023] [Indexed: 09/02/2023] Open
Abstract
Puerariae Thomsonii Radix (PTR) is not only widely used in disease prevention and treatment but is also an important raw material as a source of starch and other food. The growth years of PTR are closely related to its quality. The rapid and nondestructive identification of growth year is essential for the quality control of PTR and other traditional Chinese medicines. In this study, we proposed a convolutional neural network (CNN)-based classification framework in conjunction with hyperspectral imaging (HSI) technology for the rapid identification of the growth years of PTRs. Traditional treatment methods (i.e., multiplicative scatter correction, standard normal variate, and Savitzky-Golay smoothing) combined with machine learning algorithms (i.e., random forest, logistic regression, naive Bayes, and eXtreme gradient boost) were used as baseline models. Among them, the F1-score of CNN-based models based on PTRs' outer surfaces was over 90%, outperforming all the other baseline models. These results showed that it was feasible to use a deep learning algorithm in conjunction with HSI technology to identify the growth years of PTR. This method provides a fast, nondestructive, and simple method of identifying the growth years of PTR. It can be easily applied to other scenarios, such as for the identification of the locality or years of growth for other traditional Chinese herbs.
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Affiliation(s)
- Lei Zhang
- China Academy of Chinese Medical Sciences, No.16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, People's Republic of China
- School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, 300004, People's Republic of China
| | - Yu Guan
- GAP Center, Heilongjiang University of Chinese Medicine, Harbin, 150040, People's Republic of China
| | - Ni Wang
- School of Materials Science and Engineering, Zhejiang University, No.866, Yuhangtang, Xihu District, Hangzhou, 310058, People's Republic of China.
| | - Fei Ge
- School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, 300004, People's Republic of China
| | - Yan Zhang
- China Academy of Chinese Medical Sciences, No.16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, People's Republic of China
| | - Yuping Zhao
- China Academy of Chinese Medical Sciences, No.16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, People's Republic of China.
- School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, 300004, People's Republic of China.
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Li T, Wei W, Xing S, Min W, Zhang C, Jiang S. Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation. Foods 2023; 12:3145. [PMID: 37685077 PMCID: PMC10487018 DOI: 10.3390/foods12173145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/16/2023] [Accepted: 08/20/2023] [Indexed: 09/10/2023] Open
Abstract
The limited nutritional information provided by external food representations has constrained the further development of food nutrition estimation. Near-infrared hyperspectral imaging (NIR-HSI) technology can capture food chemical characteristics directly related to nutrition and is widely used in food science. However, conventional data analysis methods may lack the capability of modeling complex nonlinear relations between spectral information and nutrition content. Therefore, we initiated this study to explore the feasibility of integrating deep learning with NIR-HSI for food nutrition estimation. Inspired by reinforcement learning, we proposed OptmWave, an approach that can perform modeling and wavelength selection simultaneously. It achieved the highest accuracy on our constructed scrambled eggs with tomatoes dataset, with a determination coefficient of 0.9913 and a root mean square error (RMSE) of 0.3548. The interpretability of our selection results was confirmed through spectral analysis, validating the feasibility of deep learning-based NIR-HSI in food nutrition estimation.
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Affiliation(s)
- Tianhao Li
- The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wensong Wei
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
- Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Beijing 100193, China
| | - Shujuan Xing
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
- Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Beijing 100193, China
| | - Weiqing Min
- The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chunjiang Zhang
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
- Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Beijing 100193, China
| | - Shuqiang Jiang
- The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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21
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Saha D, Senthilkumar T, Singh CB, Manickavasagan A. Quantitative detection of metanil yellow adulteration in chickpea flour using line-scan near-infrared hyperspectral imaging with partial least square regression and one-dimensional convolutional neural network. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
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22
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Singh T, Garg NM, Iyengar SRS, Singh V. Near-infrared hyperspectral imaging for determination of protein content in barley samples using convolutional neural network. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01892-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Cui F, Zheng S, Wang D, Tan X, Li Q, Li J, Li T. Recent advances in shelf life prediction models for monitoring food quality. Compr Rev Food Sci Food Saf 2023; 22:1257-1284. [PMID: 36710649 DOI: 10.1111/1541-4337.13110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/30/2022] [Accepted: 01/10/2023] [Indexed: 01/31/2023]
Abstract
Each year, 1.3 billion tons of food is lost due to spoilage or loss in the supply chain, accounting for approximately one third of global food production. This requires a manufacturer to provide accurate information on the shelf life of the food in each stage. Various models for monitoring food quality have been developed and applied to predict food shelf life. This review classified shelf life models and detailed the application background and characteristics of commonly used models to better understand the different uses and aspects of the commonly used models. In particular, the structural framework, application mechanisms, and numerical relationships of commonly used models were elaborated. In addition, the study focused on the application of commonly used models in the food field. Besides predicting the freshness index and remaining shelf life of food, the study addressed aspects such as food classification (maturity and damage) and content prediction. Finally, further promotion of shelf life models in the food field, use of multivariate analysis methods, and development of new models were foreseen. More reliable transportation, processing, and packaging methods could be screened out based on real-time food quality monitoring.
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Affiliation(s)
- Fangchao Cui
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Shiwei Zheng
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Dangfeng Wang
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
- College of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Xiqian Tan
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Qiuying Li
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Jianrong Li
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Tingting Li
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, College of Life Science, Dalian Minzu University, Dalian, China
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Bendjedou H, Benamar H, Bennaceur M, Rodrigues MJ, Pereira CG, Trentin R, Custódio L. New Insights into the Phytochemical Profile and Biological Properties of Lycium intricatum Bois. (Solanaceae). PLANTS (BASEL, SWITZERLAND) 2023; 12:996. [PMID: 36903857 PMCID: PMC10004830 DOI: 10.3390/plants12050996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/13/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
This work aimed to boost the valorisation of Lycium intricatum Boiss. L. as a source of high added value bioproducts. For that purpose, leaves and root ethanol extracts and fractions (chloroform, ethyl acetate, n-butanol, and water) were prepared and evaluated for radical scavenging activity (RSA) on 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radicals, ferric reducing antioxidant power (FRAP), and metal chelating potential against copper and iron ions. Extracts were also appraised for in vitro inhibition of enzymes implicated on the onset of neurological diseases (acetylcholinesterase: AChE and butyrylcholinesterase: BuChE), type-2 diabetes mellitus (T2DM, α-glucosidase), obesity/acne (lipase), and skin hyperpigmentation/food oxidation (tyrosinase). The total content of phenolics (TPC), flavonoids (TFC), and hydrolysable tannins (THTC) was evaluated by colorimetric methods, while the phenolic profile was determined by high-performance liquid chromatography, coupled to a diode-array ultraviolet detector (HPLC-UV-DAD). Extracts had significant RSA and FRAP, and moderate copper chelation, but no iron chelating capacity. Samples had a higher activity towards α-glucosidase and tyrosinase, especially those from roots, a low capacity to inhibit AChE, and no activity towards BuChE and lipase. The ethyl acetate fraction of roots had the highest TPC and THTC, whereas the ethyl acetate fraction of leaves had the highest flavonoid levels. Gallic, gentisic, ferulic, and trans-cinnamic acids were identified in both organs. The results suggest that L. intricatum is a promising source of bioactive compounds with food, pharmaceutical, and biomedical applications.
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Affiliation(s)
- Houaria Bendjedou
- Faculty of Natural Sciences and Life, Department of Biology, University of Oran1, El M’Naouer, P.O. Box 1524, Oran 31000, Algeria
- Laboratory of Research in Arid Areas, University of Science and Technology Houari Boumediene, P.O. Box 32, Algiers 16111, Algeria
| | - Houari Benamar
- Faculty of Natural Sciences and Life, Department of Biology, University of Oran1, El M’Naouer, P.O. Box 1524, Oran 31000, Algeria
- Laboratory of Research in Arid Areas, University of Science and Technology Houari Boumediene, P.O. Box 32, Algiers 16111, Algeria
| | - Malika Bennaceur
- Faculty of Natural Sciences and Life, Department of Biology, University of Oran1, El M’Naouer, P.O. Box 1524, Oran 31000, Algeria
- Laboratory of Research in Arid Areas, University of Science and Technology Houari Boumediene, P.O. Box 32, Algiers 16111, Algeria
| | - Maria João Rodrigues
- Centre of Marine Sciences (CCMAR), Faculdade de Ciências e Tecnologia, Universidade do Algarve, Ed. 7, Campus de Gambelas, 8005-139 Faro, Portugal
| | - Catarina Guerreiro Pereira
- Centre of Marine Sciences (CCMAR), Faculdade de Ciências e Tecnologia, Universidade do Algarve, Ed. 7, Campus de Gambelas, 8005-139 Faro, Portugal
| | - Riccardo Trentin
- Centre of Marine Sciences (CCMAR), Faculdade de Ciências e Tecnologia, Universidade do Algarve, Ed. 7, Campus de Gambelas, 8005-139 Faro, Portugal
- Department of Biology, University of Padova, Via U. Bassi, 58/B 35131 Padova, Italy
| | - Luísa Custódio
- Centre of Marine Sciences (CCMAR), Faculdade de Ciências e Tecnologia, Universidade do Algarve, Ed. 7, Campus de Gambelas, 8005-139 Faro, Portugal
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Rapid and Nondestructive Identification of Origin and Index Component Contents of Tiegun Yam Based on Hyperspectral Imaging and Chemometric Method. J FOOD QUALITY 2023. [DOI: 10.1155/2023/6104038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
Tiegun yam is a typical food and medicine agricultural product, which has the effects of nourishing the kidney and benefitting the lungs. The quality and price of Tiegun yam are affected by its origin, and counterfeiting and adulteration are common. Therefore, it is necessary to establish a method to identify the origin and index component contents of Tiegun yam. Hyperspectral imaging combined with chemometrics was used, for the first time, to explore and implement the identification of origin and index component contents of Tiegun yam. The origin identification models were established by partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF) using full wavelength and feature wavelength. Compared with other models, MSC-PLS-DA is the best model, and the accuracy of the training set and prediction set is 100% and 98.40%. Partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR) models were used to predict the contents of starch, polysaccharide, and protein in Tiegun yam powder. The optimal residual predictive deviation (RPD) values of starch, polysaccharide, and protein prediction models selected in this study were 5.21, 3.21, and 2.94, respectively. The characteristic wavelength extracted by the successive projections algorithm (SPA) method can achieve similar results as the full-wavelength model. These results confirmed the application of hyperspectral imaging (HSI) in the identification of the origin and the rapid nondestructive prediction of starch, polysaccharide, and protein contents of Tiegun yam powder. Therefore, the HSI combined with the chemometric method was available for conveniently and accurately determining the origin and index component contents of Tiegun yam, which can expect to be an attractive alternative method for identifying the origin of other food.
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Adnouni M, Jiang L, Zhang X, Zhang L, Pathare PB, Roskilly A. Computational modelling for decarbonised drying of agricultural products: Sustainable processes, energy efficiency, and quality improvement. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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27
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Golpelichi F, Parastar H. Quantitative Mass Spectrometry Imaging Using Multivariate Curve Resolution and Deep Learning: A Case Study. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:236-244. [PMID: 36594891 DOI: 10.1021/jasms.2c00268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
In the present contribution, a novel approach based on multivariate curve resolution and deep learning (DL) is proposed for quantitative mass spectrometry imaging (MSI) as a potent technique for identifying different compounds and creating their distribution maps in biological tissues without need for sample preparation. As a case study, chlordecone as a carcinogenic pesticide was quantitatively determined in mouse liver using matrix-assisted laser desorption ionization-MSI (MALDI-MSI). For this purpose, data from seven standard spots containing 0 to 20 picomoles of chlordecone and four unknown tissues from the mouse livers infected with chlordecone for 1, 5, and 10 days were analyzed using a convolutional neural network (CNN). To solve the lack of sufficient data for CNN model training, each pixel was considered as a sample, the designed CNN models were trained by pixels in training sets, and their corresponding amounts of chlordecone were obtained by multivariate curve resolution-alternating least-squares (MCR-ALS). The trained models were then externally evaluated using calibration pixels in test sets for 1, 5, and 10 days of exposure, respectively. Prediction R2 for all three data sets ranged from 0.93 to 0.96, which was superior to support vector machine (SVM) and partial least-squares (PLS). The trained CNN models were finally used to predict the amount of chlordecone in mouse liver tissues, and their results were compared with MALDI-MSI and GC-MS methods, which were comparable. Inspection of the results confirmed the validity of the proposed method.
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Affiliation(s)
- Fatemeh Golpelichi
- Department of Chemistry, Sharif University of Technology, P.O. Box 11155-9516, 1458889694Tehran, Iran
| | - Hadi Parastar
- Department of Chemistry, Sharif University of Technology, P.O. Box 11155-9516, 1458889694Tehran, Iran
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28
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Qiu R, Zhao Y, Kong D, Wu N, He Y. Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121838. [PMID: 36108407 DOI: 10.1016/j.saa.2022.121838] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/26/2022] [Accepted: 09/02/2022] [Indexed: 06/15/2023]
Abstract
Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique. Principal component analysis was used to process the full spectral information and three wavelength selection methods (competitive adaptive reweighted sampling, genetic algorithm, and successive projections algorithm) were applied to extract effective wavelengths. These methods were combined with the support vector machine algorithm to develop conventional classification models, respectively. In addition, a convolutional neural network model based on deep learning was designed and trained for comparison. The performance of the convolutional neural network model was significantly better than that of conventional classification models. The overall accuracy for chicken sample classifications was improved from 83.88% to 91.38%. The results demonstrated that deep learning can effectively extract spectral features and promote the application of hyperspectral imaging in foodborne pathogens detection of chicken products.
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Affiliation(s)
- Ruicheng Qiu
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yinglei Zhao
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310000, China
| | - Dandan Kong
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Na Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
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29
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Che S, Du G, Zhong X, Mo Z, Wang Z, Mao Y. Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0012. [PMID: 37040513 PMCID: PMC10076050 DOI: 10.34133/plantphenomics.0012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/17/2022] [Indexed: 06/19/2023]
Abstract
Phycobilisomes and chlorophyll-a (Chla) play important roles in the photosynthetic physiology of red macroalgae and serve as the primary light-harvesting antennae and reaction center for photosystem II. Neopyropia is an economically important red macroalga widely cultivated in East Asian countries. The contents and ratios of 3 main phycobiliproteins and Chla are visible traits to evaluate its commercial quality. The traditional analytical methods used for measuring these components have several limitations. Therefore, a high-throughput, nondestructive, optical method based on hyperspectral imaging technology was developed for phenotyping the pigments phycoerythrin (PE), phycocyanin (PC), allophycocyanin (APC), and Chla in Neopyropia thalli in this study. The average spectra from the region of interest were collected at wavelengths ranging from 400 to 1000 nm using a hyperspectral camera. Following different preprocessing methods, 2 machine learning methods, partial least squares regression (PLSR) and support vector machine regression (SVR), were performed to establish the best prediction models for PE, PC, APC, and Chla contents. The prediction results showed that the PLSR model performed the best for PE (R Test 2 = 0.96, MAPE = 8.31%, RPD = 5.21) and the SVR model performed the best for PC (R Test 2 = 0.94, MAPE = 7.18%, RPD = 4.16) and APC (R Test 2 = 0.84, MAPE = 18.25%, RPD = 2.53). Two models (PLSR and SVR) performed almost the same for Chla (PLSR: R Test 2 = 0.92, MAPE = 12.77%, RPD = 3.61; SVR: R Test 2 = 0.93, MAPE = 13.51%, RPD =3.60). Further validation of the optimal models was performed using field-collected samples, and the result demonstrated satisfactory robustness and accuracy. The distribution of PE, PC, APC, and Chla contents within a thallus was visualized according to the optimal prediction models. The results showed that hyperspectral imaging technology was effective for fast, accurate, and noninvasive phenotyping of the PE, PC, APC, and Chla contents of Neopyropia in situ. This could benefit the efficiency of macroalgae breeding, phenomics research, and other related applications.
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Affiliation(s)
- Shuai Che
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Guoying Du
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Xuefeng Zhong
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Zhaolan Mo
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Zhendong Wang
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Yunxiang Mao
- Key Laboratory of Utilization and Conservation of Tropical Marine Bioresource (Ministry of Education), College of Fisheries and Life Science, Hainan Tropical Ocean University, Sanya, 572002, China
- Yazhou Bay Innovation Institute, Hainan Tropical Ocean University, Sanya, 572025, China
- Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266073, China
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30
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Avula B, Katragunta K, Osman AG, Ali Z, John Adams S, Chittiboyina AG, Khan IA. Advances in the Chemistry, Analysis and Adulteration of Anthocyanin Rich-Berries and Fruits: 2000-2022. Molecules 2023; 28:560. [PMID: 36677615 PMCID: PMC9865467 DOI: 10.3390/molecules28020560] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/09/2023] Open
Abstract
Anthocyanins are reported to exhibit a wide variety of remedial qualities against many human disorders, including antioxidative stress, anti-inflammatory activity, amelioration of cardiovascular diseases, improvement of cognitive decline, and are touted to protect against neurodegenerative disorders. Anthocyanins are water soluble naturally occurring polyphenols containing sugar moiety and are found abundantly in colored fruits/berries. Various chromatographic (HPLC/HPTLC) and spectroscopic (IR, NMR) techniques as standalone or in hyphenated forms such as LC-MS/LC-NMR are routinely used to gauge the chemical composition and ensure the overall quality of anthocyanins in berries, fruits, and finished products. The major emphasis of the current review is to compile and disseminate various analytical methodologies on characterization, quantification, and chemical profiling of the whole array of anthocyanins in berries, and fruits within the last two decades. In addition, the factors affecting the stability of anthocyanins, including pH, light exposure, solvents, metal ions, and the presence of other substances, such as enzymes and proteins, were addressed. Several sources of anthocyanins, including berries and fruit with their botanical identity and respective yields of anthocyanins, were covered. In addition to chemical characterization, economically motivated adulteration of anthocyanin-rich fruits and berries due to increasing consumer demand will also be the subject of discussion. Finally, the health benefits and the medicinal utilities of anthocyanins were briefly discussed. A literature search was performed using electronic databases from PubMed, Science Direct, SciFinder, and Google Scholar, and the search was conducted covering the period from January 2000 to November 2022.
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Affiliation(s)
- Bharathi Avula
- National Center for Natural Products Research, University, MS 38677, USA
| | - Kumar Katragunta
- National Center for Natural Products Research, University, MS 38677, USA
| | - Ahmed G. Osman
- National Center for Natural Products Research, University, MS 38677, USA
| | - Zulfiqar Ali
- National Center for Natural Products Research, University, MS 38677, USA
| | | | | | - Ikhlas A. Khan
- National Center for Natural Products Research, University, MS 38677, USA
- Division of Pharmacognosy, Department of BioMolecular Sciences, School of Pharmacy, University of Mississippi, University, MS 38677, USA
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31
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Ru C, Wen W, Zhong Y. Raman spectroscopy for on-line monitoring of botanical extraction process using convolutional neural network with background subtraction. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 284:121494. [PMID: 35715369 DOI: 10.1016/j.saa.2022.121494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 06/15/2023]
Abstract
Aqueous extraction is the most common and cost-effective means of obtaining active ingredients from medicinal plants. However, botanical extracts generally contain high pigment content and complex chemical composition posing a challenge for the process analysis of aqueous extraction. Here, we employed Raman spectroscopy to monitor the physical and chemical properties during the extraction process using convolution neural network (CNN) with background subtraction. Real-time spectra were first preprocessed to eliminate fluorescence background interference. Next, two types of CNN models, the one-dimensional CNN (1D-CNN) based on one preprocessing method, and two-dimensional CNN (2D-CNN) based on a concatenation of differentially pretreated data blocks, were used to receive the preprocessed spectra data. Two case studies were conducted for 1D- and 2D-CNN: the extraction of Aurantii fructus, and the co-extraction of Radix Salvia miltiorrhiza and Rhizoma Ligusticum chuanxiong. Furthermore, partial least squares (PLS) models and sequential preprocessing through orthogonalization (SPORT) models were developed and compared with 1D-CNN and 2D-CNN, respectively. CNN-based methods were superior to other models in terms of prediction accuracy, with 2D-CNN yielding the best results. These results indicated that preprocessing and CNN methods were highly complementary, and could effectively remove the fluorescence effect and artefacts introduced by pretreatment in spectral profile. To the best of our knowledge, this is the first study to demonstrate that a combination of preprocessing and CNN leads to improved prediction performance of analytes when using Raman spectroscopy for online monitoring high-pigmented samples.
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Affiliation(s)
- Chenlei Ru
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.
| | - Wu Wen
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yi Zhong
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Zhang Boli Intelligent Health Innovation Lab, Hangzhou 311121, China
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32
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Luo X, Gouda M, Perumal AB, Huang Z, Lin L, Tang Y, Sanaeifar A, He Y, Li X, Dong C. Using surface-enhanced Raman spectroscopy combined with chemometrics for black tea quality assessment during its fermentation process. SENSORS AND ACTUATORS B: CHEMICAL 2022; 373:132680. [DOI: 10.1016/j.snb.2022.132680] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
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33
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Xu M, Sun J, Cheng J, Yao K, Wu X, Zhou X. Non‐destructive prediction of total soluble solids and titratable acidity in Kyoho grape using hyperspectral imaging and deep learning algorithm. Int J Food Sci Technol 2022. [DOI: 10.1111/ijfs.16173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Min Xu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
- School of Electronic Engineering, Changzhou College of Information Technology Changzhou 213164 Jiangsu China
| | - Jun Sun
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
| | - Jiehong Cheng
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
| | - Kunshan Yao
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
| | - Xiaohong Wu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
| | - Xin Zhou
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
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A Review of The Application of Spectroscopy to Flavonoids from Medicine and Food Homology Materials. Molecules 2022; 27:molecules27227766. [PMID: 36431869 PMCID: PMC9696260 DOI: 10.3390/molecules27227766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 11/16/2022] Open
Abstract
Medicinal and food homology materials are a group of drugs in herbal medicine that have nutritional value and can be used as functional food, with great potential for development and application. Flavonoids are one of the major groups of components in pharmaceutical and food materials that have been found to possess a variety of biological activities and pharmacological effects. More and more analytical techniques are being used in the study of flavonoid components of medicinal and food homology materials. Compared to traditional analytical methods, spectroscopic analysis has the advantages of being rapid, economical and free of chemical waste. It is therefore widely used for the identification and analysis of herbal components. This paper reviews the application of spectroscopic techniques in the study of flavonoid components in medicinal and food homology materials, including structure determination, content determination, quality identification, interaction studies, and the corresponding chemometrics. This review may provide some reference and assistance for future studies on the flavonoid composition of other medicinal and food homology materials.
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Ma P, Zhang Z, Jia X, Peng X, Zhang Z, Tarwa K, Wei CI, Liu F, Wang Q. Neural network in food analytics. Crit Rev Food Sci Nutr 2022; 64:4059-4077. [PMID: 36322538 DOI: 10.1080/10408398.2022.2139217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.
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Affiliation(s)
- Peihua Ma
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Zhikun Zhang
- CISPA Helmholtz Center for Information Security, Saarbrucken, Germany
| | - Xiaoxue Jia
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Xiaoke Peng
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, PR China
| | - Zhi Zhang
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Kevin Tarwa
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Cheng-I Wei
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Fuguo Liu
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, PR China
| | - Qin Wang
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
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Wang Y, Yang J, Yu S, Fu H, He S, Yang B, Nan T, Yuan Y, Huang L. Prediction of chemical indicators for quality of Zanthoxylum spices from multi-regions using hyperspectral imaging combined with chemometrics. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2022. [DOI: 10.3389/fsufs.2022.1036892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Fruits of Zanthoxylum bungeanum Maxim (Red “Huajiao,” RHJ) and Z. schinifolium Sieb. et Zucc. (Green “Huajiao,” GHJ) are famous spices around the world. Antioxidant capability (AOC), total alkylamides content (TALC) and volatile oil content (VOC) in HJ are three important quality indicators and lack rapid and effective methods for detection. Non-destructive, time-saving, and effective technology of hyperspectral imaging (HSI) combined with chemometrics was adopted to improve the indicators prediction in this study. Results showed that the three chemical indexes exhibited significant differences between different regions and varieties (P < 0.05). Specifically, the mass percentages of TALC were 11–22% in RHJ group and 21–36% in GHJ group. The mass percentages of VOC content were 23–31% and 16–24% in RHJ and GHJ groups, respectively. More importantly, these indicators could be well predicted based on the full or effective HSI wavelengths via model adaptive space shrinkage (MASS) and iteratively variable subset optimization (IVSO) selections combined with wavelet transform (WT) method for noise reduction. The best prediction results of AOC, TALC, and VOC indicators were achieved with the highest residual predictive deviation (RPD) values of 7.43, 7.82, and 3.73 for RHJ, respectively, and 6.82, 2.66, and 4.64 for GHJ, respectively. The above results highlight the great potential of HSI assisted with chemometrics in the rapid and effective prediction of chemical indicators of Zanthoxylum spices.
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Li J, He L, Liu M, Chen J, Xue L. Hyperspectral dimension reduction and navel orange surface disease defect classification using independent component analysis-genetic algorithm. Front Nutr 2022; 9:993737. [PMID: 36337614 PMCID: PMC9626814 DOI: 10.3389/fnut.2022.993737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/28/2022] [Indexed: 11/17/2022] Open
Abstract
Canker is a common disease of navel oranges that is visible before harvest, and penicilliosis is a common disease occurring after harvest and storage. In this research, the typical fruit surface, canker spots, penicillium spore, and hypha of navel oranges were, respectively, identified by hyperspectral imaging. First, the light intensity on the edge of samples in hyperspectral images was improved by spherical correction. Then, independent component images and weight coefficients were obtained using independent component analysis. This approach, combined with use of a genetic algorithm, was used to select six characteristic wavelengths. The method achieved dimension reduction of hyperspectral data, and the testing time was reduced from 46.21 to 1.26 s for a self-developed online detection system. Finally, a deep learning neural network model was established, and the four kinds of surface pixels were identified accurately.
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Affiliation(s)
- Jing Li
- Jiangxi Key Laboratory of Modern Agricultural Equipment, College of Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Liang He
- Jiangxi Key Laboratory of Modern Agricultural Equipment, College of Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Muhua Liu
- Jiangxi Key Laboratory of Modern Agricultural Equipment, College of Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Jinyin Chen
- Collaborative Innovation Center of Postharvest Key Technology and Quality Safety of Fruits and Vegetables in Jiangxi Province, Nanchang, China
| | - Long Xue
- Jiangxi Key Laboratory of Modern Agricultural Equipment, College of Engineering, Jiangxi Agricultural University, Nanchang, China
- Key Laboratory of Optics-Electrics Application of Biomaterials of Jiangxi Province, Jiangxi Agricultural University, Nanchang, China
- *Correspondence: Long Xue,
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Wang Y, Zhang Y, Yuan Y, Zhao Y, Nie J, Nan T, Huang L, Yang J. Nutrient content prediction and geographical origin identification of red raspberry fruits by combining hyperspectral imaging with chemometrics. Front Nutr 2022; 9:980095. [DOI: 10.3389/fnut.2022.980095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/30/2022] [Indexed: 11/13/2022] Open
Abstract
The geographical origin and the important nutrient contents greatly affect the quality of red raspberry (RRB, Rubus idaeus L.), a popular fruit with various health benefits. In this study, a chemometrics-assisted hyperspectral imaging (HSI) method was developed for predicting the nutrient contents, including pectin polysaccharides (PPS), reducing sugars (RS), total flavonoids (TF) and total phenolics (TP), and identifying the geographical origin of RRB fruits. The results showed that these nutrient contents in RRB fruits had significant differences between regions (P < 0.05) and could be well predicted based on the HSI full or effective wavelengths selected through competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA). The best prediction results of PPS, RS, TF, and TP contents were achieved with the highest residual predictive deviation (RPD) values of 3.66, 3.95, 2.85, and 4.85, respectively. The RRB fruits from multi-regions in China were effectively distinguished by using the first derivative-partial least squares discriminant analysis (DER-PLSDA) model, with an accuracy of above 97%. Meanwhile, the fruits from three protected geographical indication (PGI) regions were successfully classified by using the orthogonal partial least squares discrimination analysis (OPLSDA) model, with an accuracy of above 98%. The study results indicate that HSI assisted with chemometrics is a promising method for predicting the important nutrient contents and identifying the geographical origin of red raspberry fruits.
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Lin Y, Ma J, Wang Q, Sun DW. Applications of machine learning techniques for enhancing nondestructive food quality and safety detection. Crit Rev Food Sci Nutr 2022; 63:1649-1669. [PMID: 36222697 DOI: 10.1080/10408398.2022.2131725] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.
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Affiliation(s)
- Yuandong Lin
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.,State Key Laboratory of Luminescent Materials and Devices, Center for Aggregation-Induced Emission, South China University of Technology, Guangzhou 510641, China
| | - Qijun Wang
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.,Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Dublin 4, Ireland
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40
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Chakraborty SK, Chandel NS, Jat D, Tiwari MK, Rajwade YA, Subeesh A. Deep learning approaches and interventions for futuristic engineering in agriculture. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07744-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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41
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Combined hyperspectral imaging technology with 2D convolutional neural network for near geographical origins identification of wolfberry. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01552-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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42
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Tao M, He Y, Bai X, Chen X, Wei Y, Peng C, Feng X. Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification. FRONTIERS IN PLANT SCIENCE 2022; 13:973745. [PMID: 36003818 PMCID: PMC9393615 DOI: 10.3389/fpls.2022.973745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 07/22/2022] [Indexed: 06/15/2023]
Abstract
Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other's advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection.
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Affiliation(s)
- Mingzhu Tao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Xiaoyun Chen
- Key Laboratory of Traceability for Agricultural Genetically Modified Organisms, Ministry of Agriculture and Rural Affairs, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Yuzhen Wei
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Cheng Peng
- Key Laboratory of Traceability for Agricultural Genetically Modified Organisms, Ministry of Agriculture and Rural Affairs, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xuping Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
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43
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Duan C, Liu X, Cai W, Shao X. Spectral Encoder to Extract the Features of Near-Infrared Spectra for Multivariate Calibration. J Chem Inf Model 2022; 62:3695-3703. [PMID: 35916486 DOI: 10.1021/acs.jcim.2c00786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
An autoencoder architecture was adopted for near-infrared (NIR) spectral analysis by extracting the common features in the spectra. Three autoencoder-based networks with different purposes were constructed. First, a spectral encoder was established by training the network with a set of spectra as the input. The features of the spectra can be encoded by the nodes in the bottleneck layer, which in turn can be used to build a sparse and robust model. Second, taking the spectra of one instrument as the input and that of another instrument as the reference output, the common features in both spectra can be obtained in the bottleneck layer. Therefore, in the prediction step, the spectral features of the second can be predicted by taking the reverse of the decoder as the encoder. Furthermore, transfer learning was used to build the model for the spectra of more instruments by fine-tuning the trained network. NIR datasets of plant, wheat, and pharmaceutical tablets measured on multiple instruments were used to test the method. The multi-linear regression (MLR) model with the encoded features was found to have a similar or slightly better performance in prediction compared with the partial least-squares (PLS) model.
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Affiliation(s)
- Chaoshu Duan
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Xuyang Liu
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Wensheng Cai
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Xueguang Shao
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
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Manzoor MF, Hussain A, Naumovski N, Ranjha MMAN, Ahmad N, Karrar E, Xu B, Ibrahim SA. A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products. Front Nutr 2022; 9:901342. [PMID: 35928834 PMCID: PMC9343702 DOI: 10.3389/fnut.2022.901342] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/31/2022] [Indexed: 01/10/2023] Open
Abstract
Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.
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Affiliation(s)
| | - Abid Hussain
- Department of Agriculture and Food Technology, Faculty of Life Science, Karakoram International University, Gilgit-Baltistan, Pakistan
| | - Nenad Naumovski
- School of Rehabilitation and Exercise Science, Faculty of Health, University of Canberra, Canberra, ACT, Australia
- Functional Foods and Nutrition Research (FFNR) Laboratory, University of Canberra, Bruce, ACT, Australia
| | | | - Nazir Ahmad
- Department of Nutritional Sciences, Faculty of Medical Sciences, Government College University Faisalabad, Faisalabad, Pakistan
| | - Emad Karrar
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Bin Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- *Correspondence: Bin Xu
| | - Salam A. Ibrahim
- Food Microbiology and Biotechnology Laboratory, North Carolina Agricultural and Technical State University, Greensboro, NC, United States
- Salam A. Ibrahim
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Liu J, Wang FF, Jiang ZM, Liu EH. Identification of antidiabetic components in Uncariae Rammulus Cum Uncis based on phytochemical isolation and spectrum-effect relationship analysis. PHYTOCHEMICAL ANALYSIS : PCA 2022; 33:659-669. [PMID: 35261095 DOI: 10.1002/pca.3118] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/17/2022] [Accepted: 02/19/2022] [Indexed: 06/14/2023]
Abstract
OBJECTIVES Uncariae Rammulus Cum Uncis (URCU) is a commonly used herbal medicine to treat diabetes. This work is aimed to discover and identify the antidiabetic components from URCU extract. METHODS Column chromatography and recrystallisation were used to separate individual compounds from URCU extract, and the obtained individual compounds were used for determination of α-glucosidase inhibitory activity. Molecular docking was applied to predict the molecular interactions. High-performance liquid chromatography (HPLC) was used for fingerprint analysis of 12 batches of URCU. HPLC fingerprints were assessed by the similarity analysis (SA) and hierarchical clustering analysis (HCA). The spectrum-effect relationship analysis of URCU was assessed by orthogonal partial least squares (OPLS) and bivariate correlation analysis (BCA). RESULTS A total of 10 potential bioactive compounds were isolated and six of them showed potent α-glucosidase inhibitory activity (IC50 = 4.21-166.10 μM). The molecular docking results revealed that the binding energy was consistent with the results of α-glucosidase inhibition activity analysis (-8.55 to -4.84 kcal/mol). The ethanol extracts of the 12 batches of URCU showed inhibitory effect on α-glucosidase in a dose-dependent manner, and the IC50 values ranged from 0.94 μg/mL to 12.57 μg/mL. The spectrum-effect relationship analysis results indicated that 13 peaks might be potential antidiabetic compounds in URCU, including 18 (hyperoside) and 19 (rutin). CONCLUSION A comprehensive connection between URCU chemical components and α-glucosidase inhibitory activity was established for the first time by using a spectrum-effect relationship model, which might be applicable to the quality control of URCU.
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Affiliation(s)
- Jie Liu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Fang-Fang Wang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Zheng-Meng Jiang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - E-Hu Liu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
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Zhang L, An D, Wei Y, Liu J, Wu J. Prediction of oil content in single maize kernel based on hyperspectral imaging and attention convolution neural network. Food Chem 2022; 395:133563. [PMID: 35763927 DOI: 10.1016/j.foodchem.2022.133563] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/07/2022] [Accepted: 06/21/2022] [Indexed: 11/04/2022]
Abstract
An attention (A) based convolutional neural network regression (CNNR) model, namely ACNNR, was proposed to combine hyperspectral imaging to predict oil content in single maize kernel. During the period, a reflectance HSI system was used to collect hyperspectral images of embryo side and non-embryo side of single maize kernel, and the performances of CNNR (without attention mechanism), ACNNR and partial least squares regression (PLSR) were compared. For PLSR, a series of spectral preprocessing and dimensionality reduction methods were used to finally determine the optimal hybrid PLSR model. Whereas for CNNR and ACNNR, only raw spectra were used as their inputs. The results showed that embryo side was more suitable for developing regression models; the attentional mechanism was helpful to reduce the error of prediction, making ACNNR performed best (coefficient of determination of prediction = 0.9198). Overall, the proposed method did not require additional processing on raw spectra, and performed well.
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Affiliation(s)
- Liu Zhang
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Dong An
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
| | - Yaoguang Wei
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Jincun Liu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Jianwei Wu
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
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Navarro PJ, Miller L, Díaz-Galián MV, Gila-Navarro A, Aguila DJ, Egea-Cortines M. A novel ground truth multispectral image dataset with weight, anthocyanins, and Brix index measures of grape berries tested for its utility in machine learning pipelines. Gigascience 2022; 11:6608507. [PMID: 35701377 PMCID: PMC9197681 DOI: 10.1093/gigascience/giac052] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/11/2022] [Accepted: 05/02/2022] [Indexed: 11/21/2022] Open
Abstract
Background The combination of computer vision devices such as multispectral cameras coupled with artificial intelligence has provided a major leap forward in image-based analysis of biological processes. Supervised artificial intelligence algorithms require large ground truth image datasets for model training, which allows to validate or refute research hypotheses and to carry out comparisons between models. However, public datasets of images are scarce and ground truth images are surprisingly few considering the numbers required for training algorithms. Results We created a dataset of 1,283 multidimensional arrays, using berries from five different grape varieties. Each array has 37 images of wavelengths between 488.38 and 952.76 nm obtained from single berries. Coupled to each multispectral image, we added a dataset with measurements including, weight, anthocyanin content, and Brix index for each independent grape. Thus, the images have paired measures, creating a ground truth dataset. We tested the dataset with 2 neural network algorithms: multilayer perceptron (MLP) and 3-dimensional convolutional neural network (3D-CNN). A perfect (100% accuracy) classification model was fit with either the MLP or 3D-CNN algorithms. Conclusions This is the first public dataset of grape ground truth multispectral images. Associated with each multispectral image, there are measures of the weight, anthocyanins, and Brix index. The dataset should be useful to develop deep learning algorithms for classification, dimensionality reduction, regression, and prediction analysis.
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Affiliation(s)
- Pedro J Navarro
- Escuela Técnica Superior de Ingeniería de Telecomunicación (DSIE), Campus Muralla del Mar, s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Leanne Miller
- Escuela Técnica Superior de Ingeniería de Telecomunicación (DSIE), Campus Muralla del Mar, s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - María Victoria Díaz-Galián
- Genética Molecular, Instituto de Biotecnología Vegetal, Edificio I+D+I, Plaza del Hospital s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Alberto Gila-Navarro
- Genética Molecular, Instituto de Biotecnología Vegetal, Edificio I+D+I, Plaza del Hospital s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Diego J Aguila
- Sociedad Cooperativa Las Cabezuelas, 30840 Alhama de Murcia, Spain
| | - Marcos Egea-Cortines
- Genética Molecular, Instituto de Biotecnología Vegetal, Edificio I+D+I, Plaza del Hospital s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
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Kong D, Sun D, Qiu R, Zhang W, Liu Y, He Y. Rapid and nondestructive detection of marine fishmeal adulteration by hyperspectral imaging and machine learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 273:120990. [PMID: 35183858 DOI: 10.1016/j.saa.2022.120990] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/26/2022] [Accepted: 01/31/2022] [Indexed: 06/14/2023]
Abstract
Pure fishmeal (PFM) from whole marine-origin fish is an expensive and indispensable protein ingredient in most aquaculture feeds. In China, the supply shortage of domestically produced PFM has caused frequent PFM adulteration with low-cost protein sources such as feather meal (FTM) and fishmeal from by-products (FBP). The aim of this study was to develop a rapid and nondestructive detection method using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms for the identification of PFM adulterated with FTM, FBP, and the binary adulterant (composed of FTM and FBP). A hierarchical modelling strategy was adopted to acquire a better classification accuracy. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) coupled with four spectral preprocessing methods were employed to construct classification models. The SVM with baseline offset (BO-SVM) model using 20 effective wavelengths selected by successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) achieved classification accuracy of 100% and 99.43% for discriminating PFM from the adulterants (FTM, FBP) and adulterated fishmeal (AFM), respectively. This study confirmed that NIR-HSI offered a promising technique for feed mills to identify AFM containing FTM, FBP, or binary adulterants.
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Affiliation(s)
- Dandan Kong
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Dawei Sun
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Ruicheng Qiu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Wenkai Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
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Li F, Zhang J, Wang Y. Vibrational Spectroscopy Combined with Chemometrics in Authentication of Functional Foods. Crit Rev Anal Chem 2022; 54:333-354. [PMID: 35533108 DOI: 10.1080/10408347.2022.2073433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Many foods have both edible and medical importance and are appreciated as functional foods, preventing diseases. However, due to unscrupulous vendors and imperfect market supervision mechanisms, curative foods are prone to adulteration or some other events that harm the interests of consumers. However, traditional analytical methods are unsuitable and expensive for a broad and complex application. Therefore, people urgently need a fast, efficient, and accurate detection method to protect self-interests. Recently, the study of target samples by vibration spectrum shows strong qualitative and quantitative ability. The model established by platform technology combined with the stoichiometric analysis method can obtain better parameters, which it has good robustness and can detect functional food efficiently, quickly and nondestructive. The review compared and prospect five different vibrational spectroscopic techniques (near-infrared, Fourier transform infrared, Raman, hyperspectral imaging spectroscopy and Terahertz spectroscopy). In order to better solve some of the actual situations faced by certification, we explore and through relevant research and investigation to appropriately highlight the applicability and importance of technology combined with chemometrics in functional food authentication. There are four categories of authentication discussed: functional food authenticated in source, processing method, fraud and ingredient ratio. This paper provides an innovative process for the authentication of functional food, which has a meaningful reference value for future review or scientific research of relevant departments.
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Affiliation(s)
- Fengjiao Li
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
- School of Agriculture, Yunnan University, Kunming, China
| | - Jinyu Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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A Long Short-Term Memory Neural Network Based Simultaneous Quantitative Analysis of Multiple Tobacco Chemical Components by Near-Infrared Hyperspectroscopy Images. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10050164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Near-infrared (NIR) spectroscopy has been widely used in agricultural operations to obtain various crop parameters, such as water content, sugar content, and different indicators of ripeness, as well as other potential information concerning crops that cannot be directly obtained by human observation. The chemical compositions of tobacco play an important role in the quality of cigarettes. The NIR spectroscopy-based chemical composition analysis has recently become one of the most effective methods in tobacco quality analysis. Existing NIR spectroscopy-related solutions either have relatively low analysis accuracy, or are only able to analyze one or two chemical components. Thus, a precise prediction model is needed to improve the analysis accuracy of NIR data. This paper proposes a tobacco chemical component analysis method based on a neural network (TCCANN) to quantitatively analyze the chemical components of tobacco leaves by using NIR spectroscopy, including nicotine, total sugar, reducing sugar, total nitrogen, potassium, chlorine, and pH value. The proposed TCCANN consists of both residual network (ResNet) and long short-term memory (LSTM) neural network. ResNet is applied to the feature extraction of high-dimension NIR spectroscopy, which can effectively avoid the gradient-disappearance issue caused by the increase of network depth. LSTM is used to quantitatively analyze the multiple chemical compositions of tobacco leaves in a simultaneous manner. LSTM selectively allows information to pass through by a gated unit, thereby comprehensively analyzing the correlation between multiple chemical components and corresponding spectroscopy. The experimental results confirm that the proposed TCCANN not only predicts the corresponding values of seven chemical components simultaneously, but also achieves better prediction performance than other existing machine learning methods.
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