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Garrido-Cuevas MDM, Garrido-Varo AM, Oliveri P, Sánchez MT, Pérez-Marín D. In-house validation of a visible and near infrared spectroscopy non-targeted method to support panel test of virgin olive oils. Food Res Int 2024; 192:114799. [PMID: 39147500 DOI: 10.1016/j.foodres.2024.114799] [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: 04/15/2024] [Revised: 07/15/2024] [Accepted: 07/17/2024] [Indexed: 08/17/2024]
Abstract
In this study, an in-house validation of Visible and Near Infrared Spectroscopy was performed to distinguish between extra virgin olive oil (EVOO) and virgin olive oil (VOO). A total of 161 samples of olive oil of three different categories (EVOO, VOO and lampante (LOO)) were analysed by transflectance using a monochromator instrument. One-class models were initially developed using Partial Least Squares (PLS) Density Modelling to characterize EVOO and VOO category. Once the LOO samples were discriminated, linear and non-linear discriminant models were built to classify EVOO and VOO. Different data pre-treatments and variable selection algorithms were evaluated to establish the best models in terms of Correct Classification Rate (CCR). The best model, obtained after variable selection using PLS Discriminant Analysis, yielded CCR values of 82.35 % for EVOO and 66.67 % for VOO in external validation. These results confirmed that VIS + NIRS technology may be used to provide rapid, non-destructive preliminary screening of olive oil samples for categorization; suspect samples may then be analysed by official analytical methods.
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Affiliation(s)
- María-Del-Mar Garrido-Cuevas
- Faculty of Agriculture and Forestry Engineering (ETSIAM), University of Cordoba, Campus de Rabanales, 14071 Cordoba, Spain.
| | - Ana-María Garrido-Varo
- Faculty of Agriculture and Forestry Engineering (ETSIAM), University of Cordoba, Campus de Rabanales, 14071 Cordoba, Spain
| | - Paolo Oliveri
- Department of Pharmacy (DIFAR), University of Genova, Viale Cembrano 4, 16148 Genova, Italy
| | - María-Teresa Sánchez
- Faculty of Agriculture and Forestry Engineering (ETSIAM), University of Cordoba, Campus de Rabanales, 14071 Cordoba, Spain
| | - Dolores Pérez-Marín
- Faculty of Agriculture and Forestry Engineering (ETSIAM), University of Cordoba, Campus de Rabanales, 14071 Cordoba, Spain.
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2
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Schloegl S, Kamleitner J, Kroell N, Chen X, Vollprecht D, Tischberger-Aldrian A. Developing a prediction model in a lightweight packaging waste sorting plant using sensor-based sorting data combined with data of external near-infrared and LiDAR sensors. WASTE MANAGEMENT & RESEARCH : THE JOURNAL OF THE INTERNATIONAL SOLID WASTES AND PUBLIC CLEANSING ASSOCIATION, ISWA 2024; 42:747-758. [PMID: 38686663 PMCID: PMC11370209 DOI: 10.1177/0734242x241237184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 02/09/2024] [Indexed: 05/02/2024]
Abstract
Sensor-based material flow monitoring allows for continuously high output qualities, through quality management and process control. The implementation in the waste management sector, however, is inhibited by the heterogeneity of waste and throughput fluctuations. In this study, challenges and possibilities of using different types of sensors in a lightweight packaging waste sorting plant are investigated. Three external sensors have been mounted on different positions in an Austrian sorting plant: one Light Detection and Ranging (LiDAR) sensor for monitoring the volume flow and two near-infrared (NIR) sensors for measuring the pixel-based material composition and occupation density. Additionally, the data of an existing sensor-based sorter (SBS) were evaluated. To predict the newly introduced parameter material-specific occupation density (MSOD) of multi-coloured polyethylene terephthalate (PET) preconcentrate, different machine learning models were evaluated. The results indicate that using SBS data for both monitoring of throughput fluctuations caused by different bag opener settings as well as monitoring the material composition is feasible, if the pre-set teach-in is suitable. The ridge regression model based on SBS was comparable (RMSE = 3.59 px%, R² = 0.57) to the one based on NIR and LiDAR (RMSE = 3.1 px%, R² = 0.68). The demonstrated feasibility of the implementation at plant scale highlights the opportunities of sensor-based material flow monitoring for the waste management sector and paves the way towards a more circular plastics economy.
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Affiliation(s)
- Sabine Schloegl
- Chair of Waste Processing Technology and Waste Management, Montanuniversität Leoben, Leoben, Austria
| | | | - Nils Kroell
- Department of Anthropogenic Material Cycles, RWTH Aachen University, Aachen, Germany
| | | | - Daniel Vollprecht
- Institute for Materials Resource Management, Chair of Resource and Chemical Engineering, University of Augsburg, Augsburg, Germany
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3
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Biswas A, Chaudhari SR. Exploring the role of NIR spectroscopy in quantifying and verifying honey authenticity: A review. Food Chem 2024; 445:138712. [PMID: 38364494 DOI: 10.1016/j.foodchem.2024.138712] [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: 11/29/2023] [Revised: 01/19/2024] [Accepted: 02/06/2024] [Indexed: 02/18/2024]
Abstract
Honey, recognized for its diverse flavors and nutritional benefits, confronts challenges in maintaining authenticity and quality due to factors like adulteration and mislabelling. This review undertakes a comprehensive exploration of the utility of Near-Infrared (NIR) spectroscopy as a non-destructive analytical method for concurrently evaluating both honey quantity and authenticity. The primary purpose of this investigation is to delve into the various applications of NIR spectroscopy in honey analysis, with a specific focus on its capability to identify and quantify significant quality parameters such as sugar content, moisture levels, 5-HMF, and proline content. Results from the study underscore the effectiveness of NIR spectroscopy, especially when integrated with advanced chemometrics models. This combination not only facilitates quantification of diverse quality parameters but also enhances the classification of honey based on geographical and botanical origin. The technology emerges as a potent tool for detecting adulteration, addressing critical challenges in preserving the authenticity and quality of honey products. The impact of this critical analysis extends to shedding light on the current state, challenges, and future prospects of applying NIR spectroscopy in the honey industry. This analysis outlines the current challenges and future prospects of NIR spectroscopy in the honey industry. Emphasizing its potential to improve consumer confidence and food safety, the research has broader implications for authenticity and quality assurance in honey. Integrating NIR spectroscopy into industry practices could establish stronger quality control measures, benefiting both producers and consumers globally.
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Affiliation(s)
- Anisha Biswas
- Department of Plantation Products, Spices and Flavour Technology, CSIR-Central Food Technological Research Institute, Mysuru, Karnataka 570020, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Sachin R Chaudhari
- Department of Plantation Products, Spices and Flavour Technology, CSIR-Central Food Technological Research Institute, Mysuru, Karnataka 570020, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
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4
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Wang Y, Xing L, He HJ, Zhang J, Chew KW, Ou X. NIR sensors combined with chemometric algorithms in intelligent quality evaluation of sweetpotato roots from 'Farm' to 'Table': Progresses, challenges, trends, and prospects. Food Chem X 2024; 22:101449. [PMID: 38784692 PMCID: PMC11112285 DOI: 10.1016/j.fochx.2024.101449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/26/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024] Open
Abstract
NIR sensors, in conjunction with advanced chemometric algorithms, have proven to be a powerful and efficient tool for intelligent quality evaluation of sweetpotato roots throughout the entire supply chain. By leveraging NIR data in different wavelength ranges, the physicochemical, nutritional and antioxidant compositions, as well as variety classification of sweetpotato roots during the different stages were adequately evaluated, and all findings involving quantitative and qualitative investigations from the beginning to the present were summarized and analyzed comprehensively. All chemometric algorithms including both linear and nonlinear employed in NIR analysis of sweetpotato roots were introduced in detail and their calibration performances in terms of regression and classification were assessed and discussed. The challenges and limitations of current NIR application in quality evaluation of sweetpotato roots are emphasized. The prospects and trends covering the ongoing advancements in software and hardware are suggested to support the sustainable and efficient sweetpotato processing and utilization.
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Affiliation(s)
- Yuling Wang
- School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Longzhu Xing
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Jie Zhang
- Henan Xinlianxin Chemical Industry Co., Ltd., Xinxiang 453003, China
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Xingqi Ou
- School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China
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Qin Y, Zhao Q, Zhou D, Shi Y, Shou H, Li M, Zhang W, Jiang C. Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae. Food Chem X 2024; 21:101220. [PMID: 38384686 PMCID: PMC10879671 DOI: 10.1016/j.fochx.2024.101220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/04/2024] [Accepted: 02/08/2024] [Indexed: 02/23/2024] Open
Abstract
Pericarpium citri reticulatae (PCR) is the dried mature fruit peel of Citrus reticulata Blanco and its cultivated varieties in the Brassicaceae family. It can be used as both food and medicine, and has the effect of relieving cough and phlegm, and promoting digestion. The smell and medicinal properties of PCR are aged over the years; only varieties with aging value can be called "Chenpi". That is to say, the storage year of PCR has a great influence on its quality. As the color and smell of PCR of different storage years are similar, some unscrupulous merchants often use PCRs of low years to pretend to be PCRs of high years, and make huge profits. Therefore, we did this study with the aim of establishing a rapid and nondestructive method to identify the counterfeiting of PCR storage year, so as to protect the legitimate rights and interests of consumers. In this study, a classification model of PCR was established by e-eye, flash GC e-nose, and Fourier transform near-infrared (FT-NIR) combined with machine learning algorithms, which can quickly and accurately distinguish PCRs of different storage years. DFA and PLS-DA models were established by flash GC e-nose to distinguish PCRs of different ages, and 8 odor components were identified, among which (+)-limonene and γ-terpinene were the key components to distinguish PCRs of different ages. In addition, the classification and calibration model of PCRs were established by the combination of FT-NIR and machine learning algorithms. The classification models included SVM, KNN, LSTM, and CNN-LSTM, while the calibration models included PLSR, LSTM, and CNN-LSTM. Among them, the CNN-LSTM model built by internal capsule had significantly better classification and calibration performance than the other models. The accuracy of the classification model was 98.21 %. The R2P of age, (+)-limonene and γ-terpinene was 0.9912, 0.9875 and 0.9891, respectively. These results showed that the combination of flash GC e-nose and FT-NIR combined with deep learning algorithm could quickly and accurately distinguish PCRs of different ages. It also provided an effective and reliable method to monitor the quality of PCR in the market.
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Affiliation(s)
- Yuwen Qin
- College of Pharmacy, Wenzhou Medical University, Wenzhou 325035, China
- Jiuhuashan Polygonati Rhizoma Research Institute, Chizhou 247100, China
| | - Qi Zhao
- College of Pharmacy, Wenzhou Medical University, Wenzhou 325035, China
- Jiuhuashan Polygonati Rhizoma Research Institute, Chizhou 247100, China
| | - Dan Zhou
- College of Pharmacy, Wenzhou Medical University, Wenzhou 325035, China
- Jiuhuashan Polygonati Rhizoma Research Institute, Chizhou 247100, China
| | - Yabo Shi
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Haiyan Shou
- College of Pharmacy, Wenzhou Medical University, Wenzhou 325035, China
- Jiuhuashan Polygonati Rhizoma Research Institute, Chizhou 247100, China
| | - Mingxuan Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Wei Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
- College of Pharmacy, Anhui University of Chinese Medicine, Anhui 230012, China
- Anhui Province Key Laboratory of Traditional Chinese Medicine Decoction Pieces of New Manufacturing Technology, China
| | - Chengxi Jiang
- College of Pharmacy, Wenzhou Medical University, Wenzhou 325035, China
- Jiuhuashan Polygonati Rhizoma Research Institute, Chizhou 247100, China
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Kumar Y, Koul A, Kamini, Woźniak M, Shafi J, Ijaz MF. Automated detection and recognition system for chewable food items using advanced deep learning models. Sci Rep 2024; 14:6589. [PMID: 38504098 PMCID: PMC10951243 DOI: 10.1038/s41598-024-57077-z] [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: 07/03/2023] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.
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Affiliation(s)
- Yogesh Kumar
- Department of CSE, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India
| | - Apeksha Koul
- Department of Computer Science and Engineering, Punjabi University, Patiala, Punjab, India
| | - Kamini
- Southern Alberta Institute of Technology, Calgary, Alberta, Canada
| | - Marcin Woźniak
- Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44100, Gliwice, Poland.
| | - Jana Shafi
- Department of Computer Engineering and Information, College of Engineering in Wadi Al Dawasir, Prince Sattam Bin Abdulaziz University, 11991, Wadi Al Dawasir, Saudi Arabia
| | - Muhammad Fazal Ijaz
- School of IT and Engineering, Melbourne Institute of Technology, Melbourne, 3000, Australia.
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7
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Liu Y, Xiong J, Qiao F, Xu L, Xu Z. Detection of paralytic shellfish toxins by near-infrared spectroscopy based on a near-Bayesian SVM classifier with unequal misclassification costs. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:1984-1991. [PMID: 37899531 DOI: 10.1002/jsfa.13086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 09/25/2023] [Accepted: 10/30/2023] [Indexed: 10/31/2023]
Abstract
BACKGROUND Paralytic shellfish poisoning caused by human consumption of shellfish fed on toxic algae is a public health hazard. It is essential to implement shellfish monitoring programs to minimize the possibility of shellfish contaminated by paralytic shellfish toxins (PST) reaching the marketplace. RESULTS This paper proposes a rapid detection method for PST in mussels using near-infrared spectroscopy (NIRS) technology. Spectral data in the wavelength range of 950-1700 nm for PST-contaminated and non-contaminated mussel samples were used to build the detection model. Near-Bayesian support vector machines (NBSVM) with unequal misclassification costs (u-NBSVM) were applied to solve a classification problem arising from the fact that the quantity of non-contaminated mussels was far less than that of PST-contaminated mussels in practice. The u-NBSVM model performed adequately on imbalanced datasets by combining unequal misclassification costs and decision boundary shifts. The detection performance of the u-NBSVM did not decline as the number of PST samples decreased due to adjustments to the misclassification costs. When the number of PST samples was 20, the G-mean and accuracy reached 0.9898 and 0.9944, respectively. CONCLUSION Compared with the traditional support vector machines (SVMs) and the NBSVM, the u-NBSVM model achieved better detection performance. The results of this study indicate that NIRS technology combined with the u-NBSVM model can be used for rapid and non-destructive PST detection in mussels. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Yao Liu
- School of Electronic and Electrical Engineering, Lingnan Normal University, Zhanjiang, China
| | - Jianfang Xiong
- School of Computer Science and Intelligence Education, Lingnan Normal University, Zhanjiang, China
| | - Fu Qiao
- School of Computer Science and Intelligence Education, Lingnan Normal University, Zhanjiang, China
- Mangrove Institute, Lingnan Normal University, Zhanjiang, China
| | - Lele Xu
- School of Life Science and Technology, Lingnan Normal University, Zhanjiang, China
| | - Zhen Xu
- Science and Technology Extension Department, Heilongjiang Academy of Agricultural Sciences, Harbin, China
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8
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Jiang D, Wang K, Li H, Zhang Y. Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign. SENSORS (BASEL, SWITZERLAND) 2024; 24:1245. [PMID: 38400402 PMCID: PMC10893441 DOI: 10.3390/s24041245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/11/2024] [Accepted: 02/11/2024] [Indexed: 02/25/2024]
Abstract
This study systematically developed a deep transfer network for near-infrared spectrum detection using convolutional neural network modules as key components. Through meticulous evaluation, specific modules and structures suitable for constructing the near-infrared spectrum detection model were identified, ensuring its effectiveness. This study extensively analyzed the basic network components and explored three unsupervised domain adaptation structures, highlighting their applications in the nondestructive testing of wood. Additionally, five transfer networks were strategically redesigned to substantially enhance their performance. The experimental results showed that the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network outperformed the Direct Standardization, Piecewise Direct Standardization, and Spectral Space Transformation models. The coefficients of determination for the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network are 82.11% and 83.59%, respectively, with root mean square error prediction values of 12.237 and 11.582, respectively. These achievements represent considerable advancements toward the practical implementation of an efficient and reliable near-infrared spectrum detection system using a deep transfer network.
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Affiliation(s)
- Dapeng Jiang
- College of Computer and Control Engineering, Northeast Forestry University, 26 Hexing Rd., Harbin 150040, China; (D.J.); (K.W.)
| | - Keqi Wang
- College of Computer and Control Engineering, Northeast Forestry University, 26 Hexing Rd., Harbin 150040, China; (D.J.); (K.W.)
| | - Hongbo Li
- College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China;
| | - Yizhuo Zhang
- College of Computer Science and Artificial Intelligence, Changzhou University, 1 Gehu Middle Rd., Changzhou 213164, China
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9
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Li R, Liu Y, Xia Z, Wang Q, Liu X, Gong Z. Discriminating geographical origins and determining active substances of water caltrop shells through near-infrared spectroscopy and chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 303:123198. [PMID: 37531683 DOI: 10.1016/j.saa.2023.123198] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/28/2023] [Accepted: 07/24/2023] [Indexed: 08/04/2023]
Abstract
Near-infrared spectroscopy (NIRS) combined with chemometric methods were used to discriminate the geographical origins of the water caltrop shells from different regions of China. Two active substances, the total phenolic content (TPC) and total flavonoid content (TFC) in the water caltrop shells were determined through the technique as well. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was adopted to build the geographical discriminant model. Quantitative analysis models of TPC and TFC were built using partial least squares (PLS) regression. 1st derivative and randomization test (RT) methods were used to optimize the quantitative analysis models. It was found that the geographical discriminant model can correctly recognize the water caltrop shells from different regions of China with a total accuracy of 93.33%. The values of TPC and TFC obtained by the optimized models and the standard method are close. The coefficient of determination (R2) and the ratio of prediction to deviation for the two substances were 0.91, 0.89 and 3.02, 3.02, respectively. The results demonstrated the feasibility of NIRS combined with chemometric methods for the geographical discrimination of water caltrop shells and the quantitative analysis of TPC and TFC in water caltrop shells.
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Affiliation(s)
- Rui Li
- College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China
| | - Yan Liu
- College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China; Key Laboratory for Deep Processing of Major Grain and Oil (Wuhan Polytechnic University), Ministry of Education, College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China; Hubei Key Laboratory for Processing and Transformation of Agricultural Products (Wuhan Polytechnic University), College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China; Center of Food Safety, Hubei Key Research Base of Humanities and Social Science, College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China.
| | - Zhenzhen Xia
- Institute of Agricultural Quality Standards and Testing Technology Research, Hubei Academy of Agricultural Science, Wuhan 430064, PR China
| | - Qiao Wang
- College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China
| | - Xin Liu
- College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China
| | - Zhiyong Gong
- College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China
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10
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Chen Y, Xie X, Wen Z, Zuo Y, Bai Z, Wu Q. Estimating the sensory-associated metabolites profiling of matcha based on PDO attributes as elucidated by NIRS and MS approaches. Heliyon 2023; 9:e21920. [PMID: 38027626 PMCID: PMC10654251 DOI: 10.1016/j.heliyon.2023.e21920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 10/31/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Matcha has been globally valued by consumers for its distinctive fragrance and flavor since ancient times. Currently, the protected designation of origin (PDO) certified matcha, characterized by unique sensory attributes, has garnered renewed interest from consumers and the industry. Given the challenges associated with assessing sensory perceptions, the origin of PDO-certified matcha samples from Guizhou was determined using NIRS and LC-MS platforms. Notably, the accuracy of our established attribute models, based on informative wavelengths selected by the CARS-PLS method, exceeds 0.9 for five sensory attributes, particularly the particle homogeneity attribute (with a validation correlation coefficient of 0.9668). Moreover, an LC-MS method was utilized to analyze non-target matcha metabolites to identify the primary flavor compounds associated with each flavor attribute and to pinpoint the key constituents responsible for variations in grade and flavor intensity. Additionally, high three-way intercorrelations between descriptive sensory attributes, metabolites, and the selected informative wavelengths were observed through network analysis, with correlation coefficients calculated to quantify these relationships. In this research, the integration of matcha chemical composition and sensory panel data was utilized to develop predictive models for assessing the flavor profile of matcha based on its chemical properties.
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Affiliation(s)
- Yan Chen
- Guizhou Key Laboratory of Information and Computing Science, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou, 550001, China
| | - Xiaoyao Xie
- Guizhou Key Laboratory of Information and Computing Science, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou, 550001, China
| | - Zhirui Wen
- Guizhou Key Laboratory of Information and Computing Science, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou, 550001, China
| | - Yamin Zuo
- School of Basic Medical Sciences, Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, 30 Renmin South Rd, Shiyan, Hubei, 442000, China
| | - Zhiwen Bai
- The Guizhou Gui Tea (Group) Co. Ltd., Huaxi District, Guiyang, Guizhou, 550001, China
| | - Qing Wu
- Guizhou Key Laboratory of Information and Computing Science, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou, 550001, China
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou, 550001, China
- Innovation Laboratory, The Third Experiment Middle School in Guiyang, Guiyang, Guizhou, 550001, China
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11
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Wang T, Tan Y, Chen YZ, Tan C. Infrared Spectral Analysis for Prediction of Functional Groups Based on Feature-Aggregated Deep Learning. J Chem Inf Model 2023; 63:4615-4622. [PMID: 37531205 DOI: 10.1021/acs.jcim.3c00749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
Infrared (IR) spectroscopy is a powerful and versatile tool for analyzing functional groups in organic compounds. A complex and time-consuming interpretation of massive unknown spectra usually requires knowledge of chemistry and spectroscopy. This paper presents a new deep learning method for transforming IR spectral features into intuitive imagelike feature maps and prediction of major functional groups. We obtained 8272 gas-phase IR spectra from the NIST Chemistry WebBook. Feature maps are constructed using the intrinsic correlation of spectral data, and prediction models are developed based on convolutional neural networks. Twenty-one major functional groups for each molecule are successfully identified using binary and multilabel models without expert guidance and feature selection. The multilabel classification model can produce all prediction results simultaneously for rapid characterization. Further analysis of the detailed substructures indicates that our model is capable of obtaining abundant structural information from IR spectra for a comprehensive investigation. The interpretation of our model reveals that the peaks of most interest are similar to those often considered by spectroscopists. In addition to demonstrating great potential for spectral identification, our method may contribute to the development of automated analyses in many fields.
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Affiliation(s)
- Tianyi Wang
- The State Key Laboratory of Chemical Oncogenomics, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, P. R. China
- Open FIESTA, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, P. R. China
| | - Ying Tan
- The State Key Laboratory of Chemical Oncogenomics, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, P. R. China
- Open FIESTA, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, P. R. China
| | - Yu Zong Chen
- The State Key Laboratory of Chemical Oncogenomics, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, P. R. China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, P.R. China
| | - Chunyan Tan
- The State Key Laboratory of Chemical Oncogenomics, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, P. R. China
- Open FIESTA, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, P. R. China
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12
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He S, Zhang B, Dong X, Wei Y, Li H, Tang B. Differentiation of Goat Meat Freshness Using Gas Chromatography with Ion Mobility Spectrometry. Molecules 2023; 28:molecules28093874. [PMID: 37175284 PMCID: PMC10179894 DOI: 10.3390/molecules28093874] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/17/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
To investigate the flavor changes in goat meat upon storage, the volatile components observed in goat meat after different storage periods were determined using gas chromatography-ion mobility spectrometry (GC-IMS). A total of 38 volatile organic compounds (VOCs) were determined from the goat meat samples, including alcohols, ketones, aldehydes, esters, hydrocarbons, ethers, and amine compounds. 1-Hexanol, 3-Hydroxy-2-butanone, and Ethyl Acetate were the main volatile substances in fresh goat meat, and they rapidly decreased with increasing storage time and can be used as biomarkers for identifying fresh meat. When combined with the contents of total volatile basic-nitrogen (TVB-N) and the total numbers of bacterial colonies observed in physical and chemical experiments, the characteristic volatile components of fresh, sub-fresh, and spoiled meat were determined by principal component analysis (PCA). This method will help with the detection of fraudulent production dates in goat meat sales.
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Affiliation(s)
- Shan He
- College of Food and Bioengineering, Bengbu University, Bengbu 233000, China
| | - Bin Zhang
- College of Food and Bioengineering, Bengbu University, Bengbu 233000, China
| | - Xuan Dong
- College of Food and Bioengineering, Bengbu University, Bengbu 233000, China
| | - Yuqing Wei
- College of Food and Bioengineering, Bengbu University, Bengbu 233000, China
| | - Hongtu Li
- College of Food and Bioengineering, Bengbu University, Bengbu 233000, China
| | - Bo Tang
- College of Food and Bioengineering, Bengbu University, Bengbu 233000, China
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13
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Jiang C, Zhao J, Ding Y, Li G. Vis-NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai-Tibet Plateau. SENSORS (BASEL, SWITZERLAND) 2023; 23:3686. [PMID: 37050746 PMCID: PMC10098562 DOI: 10.3390/s23073686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/22/2023] [Accepted: 03/29/2023] [Indexed: 06/19/2023]
Abstract
Soil nutrients play vital roles in vegetation growth and are a key indicator of land degradation. Accurate, rapid, and non-destructive measurement of the soil nutrient content is important for ecological conservation, degradation monitoring, and precision farming. Currently, visible and near-infrared (Vis-NIR) spectroscopy allows for rapid and non-destructive monitoring of soil nutrients. However, the performance of Vis-NIR inversion models is extremely dependent on the number of samples. Limited samples may lead to low prediction accuracy of the models. Therefore, modeling and prediction based on a small sample size remain a challenge. This study proposes a method for the simultaneous augmentation of soil spectral and nutrient data (total nitrogen (TN), soil organic matter (SOM), total potassium oxide (TK2O), and total phosphorus pentoxide (TP2O5)) using a generative adversarial network (GAN). The sample augmentation range and the level of accuracy improvement were also analyzed. First, 42 soil samples were collected from the pika disturbance area on the QTP. The collected soils were measured in the laboratory for Vis-NIR and TN, SOM, TK2O, and TP2O5 data. A GAN was then used to augment the soil spectral and nutrient data simultaneously. Finally, the effect of adding different numbers of generative samples to the training set on the predictive performance of a convolutional neural network (CNN) was analyzed and compared with another data augmentation method (extended multiplicative signal augmentation, EMSA). The results showed that a GAN can generate data very similar to real data and with better diversity. A total of 15, 30, 60, 120, and 240 generative samples (GAN and EMSA) were randomly selected from 300 generative samples to be included in the real data to train the CNN model. The model performance first improved and then deteriorated, and the GAN was more effective than EMSA. Further shortening the interval for adding GAN data revealed that the optimal ranges were 30-40, 50-60, 30-35, and 25-35 for TK2O, TN, TP2O5, and SOM, respectively, and the validation set accuracy was maximized in these ranges. Therefore, the above method can compensate to some extent for insufficient samples in the hyperspectral prediction of soil nutrients, and can quickly and accurately estimate the content of soil TK2O, TN, TP2O5, and SOM.
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Affiliation(s)
- Chuanli Jiang
- Department of Geologic Engineering, Qinghai University, Xining 810016, China
| | - Jianyun Zhao
- Department of Geologic Engineering, Qinghai University, Xining 810016, China
- Key Lab of Cenozoic Resource & Environment in North Margin of the Tibetan Plateau, Xining 810016, China
| | - Yuanyuan Ding
- Department of Geologic Engineering, Qinghai University, Xining 810016, China
| | - Guorong Li
- Department of Geologic Engineering, Qinghai University, Xining 810016, China
- Key Lab of Cenozoic Resource & Environment in North Margin of the Tibetan Plateau, Xining 810016, China
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14
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Rayavel P, Murukesh C. Comparative analysis of deep learning classifiers for diabetic retinopathy identification and detection. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2168851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Affiliation(s)
- P. Rayavel
- Department of Computer Science and Engineering (Cybersecurity), Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India
| | - C. Murukesh
- Department of Electronics and Communication Engineering, Velammal Engineering College, Chennai, Tamil Nadu, India
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15
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Ren S, Jia Y. Near-Infrared data classification at phone terminal based on the combination of PCA and CS-RBFSVC algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 287:122080. [PMID: 36370633 DOI: 10.1016/j.saa.2022.122080] [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: 04/18/2022] [Revised: 09/30/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Near-infrared (NIR) spectroscopy is a non-destructive, efficient and convenient detection technology, with the emergence of portable NIR spectrometers, NIR mobile applications (APPs) come into being. The popularity of intelligent mobile phones provides an impetus to the research and development of NIR APPs, however, the primary functions such as operating the NIR spectrometers and collecting data cannot satisfy NIR users in the field of data processing. Herein, we propose an APP processing NIR data locally at the mobile terminal, by the comprehensive utilization of Principal Component Analysis (PCA) and Cuckoo Search algorithm optimized Support Vector Classifier with radial basis function (RBFSVC) kernel (CS-RBFSVC). 738 NIR samples of four drugs (Cydiodine Buccal Tablets, Sulfasalazine Enteric-coated Tablets, Dexamethasone Acetate Tablets, Vecuronium Bromide for Injection) were used as the validation objects to train and test the data classification model. Firstly, the original data were subjected to dimensional reduction through PCA for the purpose of compressing calculation amount. Secondly, the CS-RBFSVC model was utilized to classify the types of drugs and their manufacturers, moreover, the improved accuracy and efficiency by introducing Cuckoo Search (CS) algorithm into RBFSVC were proven in comparison with the conventional grid optimized RBFSVC (Grid-RBFSVC) and Linear Support Vector Classifier (Linear-SVC). Last but not least, an APP based on the proposed PCA and CS-RBFSVC model is developed and demonstrated to be able to classify the type of drugs with an accuracy of 100%, the accuracies of classifying the drugs' manufacturers were 100%, 100%, 98.3% and 90.7%, respectively. Conclusively, the proposed PCA and CS-RBFSVC based model can provide a low-consumption, high accuracy and quick strategy for NIR data classification and overcome the limitations of internal storage and operating speed at phone terminals, in conjunction with the portable NIR spectrometer, it is believed to push forward NIR technology into the instant detection and on-site inspection.
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Affiliation(s)
- Shuhui Ren
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300071, PR China
| | - Yunfang Jia
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300071, PR China.
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16
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Wu T, Phacharapan S, Inoue N, Kamitani Y. Antioxidant Activity Enhancement Effect of Silver-Ionized Water: Silver Cation Prepared by Electrolysis. Antioxidants (Basel) 2023; 12:antiox12020467. [PMID: 36830025 PMCID: PMC9952159 DOI: 10.3390/antiox12020467] [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: 01/13/2023] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 02/15/2023] Open
Abstract
In the present study, tap water, alkaline electrolyzed water (AlEW) and tourmaline water (TMW) were used as the electrolytes to generated the silver-ionized water (SIW), AlEW-SIW and TMW-SIW, respectively. The antioxidant properties of the samples containing ascorbic acid (AsA) were investigated by WST-kit method. The results showed that the SOD activity of AsA (2 mmol/L) dissolved in SIW (66.0%) was enhanced by about 8% compared to that of the tap water (57.9%). The SOD activity of the AlEW-SIW solution (77.3%), which was higher than that of the SIW solution, and lower than that of the AlEW solution (83.6%). The SOD activity of the TMW-SIW solution (83.0%) was similar to that of the TMW solution (82.5%). Furthermore, to classify the sample solutions, discriminant analyses were performed based on near infrared (NIR) spectral data, which was consistent with the results of the WST-kit method. The SOD activity of the AlEW-SIW and TMW-SIW solutions decreased slowly with storage time, and their SOD activities were higher than that of AlEW, TMW and the tap water solutions at storage time of 14 days. In summary, AlEW-SIW and TMW-SIW showed similar antioxidant activity enhancement as AlEW and TMW, and they also maintained the stability of the antioxidant activity of AsA during storage.
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Affiliation(s)
- Tongjiao Wu
- The United Graduate School of Agricultural Sciences, Kagoshima University, 1-21-24 Korimoto, Kagoshima 890-0065, Japan
| | - Santudprom Phacharapan
- The United Graduate School of Agricultural Sciences, Kagoshima University, 1-21-24 Korimoto, Kagoshima 890-0065, Japan
| | - Natsuki Inoue
- Graduate School of Agricultural, Forestry and Fisheries, Kagoshima University, 1-21-24 Korimoto, Kagoshima 890-0065, Japan
| | - Yoshinori Kamitani
- The United Graduate School of Agricultural Sciences, Kagoshima University, 1-21-24 Korimoto, Kagoshima 890-0065, Japan
- Graduate School of Agricultural, Forestry and Fisheries, Kagoshima University, 1-21-24 Korimoto, Kagoshima 890-0065, Japan
- Correspondence:
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17
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Zhang F, Zhang Y, Shi L, Li L, Cui X, Gao Y. Application of portable near‐infrared spectroscopy technology for grade identification of Panax notoginseng slices. J Food Saf 2023. [DOI: 10.1111/jfs.13033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Fujie Zhang
- Faculty of Modern Agricultural Engineering Kunming University of Science and Technology Kunming China
| | - Yu Zhang
- Faculty of Modern Agricultural Engineering Kunming University of Science and Technology Kunming China
| | - Lei Shi
- Faculty of Modern Agricultural Engineering Kunming University of Science and Technology Kunming China
| | - Lixia Li
- Faculty of Modern Agricultural Engineering Kunming University of Science and Technology Kunming China
| | - Xiuming Cui
- Yunnan Key Laboratory of Sustainable Utilization of Panax Notoginseng Kunming University of Science and Technology Kunming China
| | - Yongping Gao
- Yixintang Pharmaceutical Group Ltd. Kunming China
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18
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Szabo G, Vitalis F, Horvath-Mezofi Z, Gob M, Aguinaga Bosquez JP, Gillay Z, Zsom T, Nguyen LLP, Hitka G, Kovacs Z, Friedrich L. Application of Near Infrared Spectroscopy to Monitor the Quality Change of Sour Cherry Stored under Modified Atmosphere Conditions. SENSORS (BASEL, SWITZERLAND) 2023; 23:479. [PMID: 36617077 PMCID: PMC9824794 DOI: 10.3390/s23010479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/27/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
Determining and applying ‘good’ postharvest and quality control practices for otherwise highly sensitive fruits, such as sour cherry, is critical, as they serve as excellent media for a wide variety of microbial contaminants. The objective of this research was to report two series of experiments on the modified atmosphere storage (MAP) of sour cherries (Prunus cerasus L. var. Kántorjánosi, Újfehértói fürtös). Firstly, the significant effect of different washing pre-treatments on various quality indices was examined (i.e., headspace gas composition, weight loss, decay rate, color, firmness, soluble solid content, total plate count) in MAP-packed fruits. Subsequently, the applicability of near infrared (NIR) spectroscopy combined with chemometrics was investigated to detect the effect of various storage conditions (packed as control or MAP, stored at 3 or 5 °C) on sour cherries of different perceived ripeness. Significant differences were found for oxygen concentration when two perforations were applied on the packages of ‘Kántorjánosi’ (p < 0.01); weight loss when ‘Kánorjánosi’ (p < 0.001) and ‘Újfehértói fürtös’ (p < 0.01) were packed in MAP; SSC when ‘Újfehértói fürtös’ samples were ozone-treated (p < 0.05); and total plate count when ‘Kántorjánosi’ samples were ozone-treated (p < 0.01). The difference spectra reflected the high variability in the samples, and the detectable effects of different packaging. Based on the investigations with the soft independent modelling of class analogies (SIMCA), different packaging and storage resulted in significant differences in most of the cases even on the first storage day, which in many cases increased by the end of storage. The soft independent modelling of class analogies proved to be suitable for classification with apparent error rates between 0 and 0.5 during prediction regardless of ripeness. The research findings suggest the further correlation of NIR spectroscopic and reference parameters to support postharvest handling and fast quality control.
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Affiliation(s)
- Gergo Szabo
- Department of Postharvest, Commerce, Supply Chain and Sensory Science, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), H-1118 Budapest, Hungary
| | - Flora Vitalis
- Department of Food Measurement and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), H-1118 Budapest, Hungary
| | - Zsuzsanna Horvath-Mezofi
- Department of Postharvest, Commerce, Supply Chain and Sensory Science, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), H-1118 Budapest, Hungary
| | - Monika Gob
- Department of Postharvest, Commerce, Supply Chain and Sensory Science, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), H-1118 Budapest, Hungary
| | - Juan Pablo Aguinaga Bosquez
- Department of Food Measurement and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), H-1118 Budapest, Hungary
| | - Zoltan Gillay
- Department of Food Measurement and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), H-1118 Budapest, Hungary
| | - Tamás Zsom
- Department of Postharvest, Commerce, Supply Chain and Sensory Science, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), H-1118 Budapest, Hungary
| | - Lien Le Phuong Nguyen
- Department of Livestock Product and Preservation Technology, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), H-1118 Budapest, Hungary
| | - Geza Hitka
- Department of Postharvest, Commerce, Supply Chain and Sensory Science, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), H-1118 Budapest, Hungary
| | - Zoltan Kovacs
- Department of Food Measurement and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), H-1118 Budapest, Hungary
| | - Laszlo Friedrich
- Department of Livestock Product and Preservation Technology, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), H-1118 Budapest, Hungary
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19
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Pretreatment to terahertz absorption curves by narrow undulation constraint and Its quick implementation suggested by convex hull. Sci Rep 2022; 12:17806. [PMID: 36280682 PMCID: PMC9592622 DOI: 10.1038/s41598-022-21770-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 09/30/2022] [Indexed: 01/19/2023] Open
Abstract
In this work, a method of pretreating THz absorption curve is proposed, which leads to minimal range in absorption, reserving necessary undulation of curve for identification by convolutional neural network. The kernel thought of proposed method is about confining the undulation of curve with a pair of narrow parallel lines and solving their optimal position by consecutively rotation of normalized curve at two fixed points. A fast algorithm is further proposed based on features of convex hull, whose procedure is described in detail. The algorithm involves definition of some important point sets, calculating and comparing slopes and determining best choice out of 4 potential rotations. The rationality of searching critical point is illustrated in a geometric way. Additionally, the adaption of the method is discussed and real examples are given to show the capacity of method to extract nonlinear information of a curve. The study suggests that methods regarding computer graphics also contributes to feature extraction with respect to THz curve and pattern recognition.
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20
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Huang J, He H, Lv R, Zhang G, Zhou Z, Wang X. Non-destructive detection and classification of textile fibres based on hyperspectral imaging and 1D-CNN. Anal Chim Acta 2022; 1224:340238. [PMID: 35998989 DOI: 10.1016/j.aca.2022.340238] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 06/28/2022] [Accepted: 08/01/2022] [Indexed: 11/27/2022]
Abstract
Textile fibre is very common in daily life, and its classification and identification play an important role in textile recycling, archaeology, public security, and other industries. However, traditional identification methods are time-consuming, laborious, and often destructive to the samples. In order to quickly, accurately, and nondestructively classify and recognize textile fibres, this study established a textile fibre classification and recognition method based on hyperspectral imaging (HSI) and a one-dimensional convolutional neural network (1D-CNN) model. Hyperspectral images of 25 kinds of commercial textile fibres were collected and denoised by pixel fusion. Four traditional machine learning classification models, k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and partial least squares-discriminant analysis (PLS-DA), were used to identify the data. The results show that RF has the highest classification accuracy, reaching 91.4%. Then a back propagation neural network (BPNN) model and a one-dimensional convolutional neural network (1D-CNN) model were constructed and compared with the traditional machine learning methods. The results show that the 1D-CNN models have 97.9% and 98.6% accuracy on the training and test sets, respectively. The precision (Pr), sensitivity (Se), specificity (Sp), and F1 score (F1 score) of the models reached 98.7%, 98.6%, 99.9%, and 98.6%, respectively, which were significantly better than the four traditional machine learning models. It seems that 1D-CNN combined with the HSI technique may be a potential method in the detection and classification of textile fibres.
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Affiliation(s)
- Jiadong Huang
- School of Criminal Investigation, People's Public Security University of China, Beijing, China
| | - Hongyuan He
- School of Criminal Investigation, People's Public Security University of China, Beijing, China.
| | - Rulin Lv
- School of Criminal Investigation, People's Public Security University of China, Beijing, China
| | - Guangteng Zhang
- School of Criminal Investigation, People's Public Security University of China, Beijing, China
| | - Zongxian Zhou
- School of Criminal Investigation, People's Public Security University of China, Beijing, China
| | - Xiaobin Wang
- School of Criminal Investigation, People's Public Security University of China, Beijing, China
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21
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Study on Extraction and Antioxidant Activity of Flavonoids from Hemerocallis fulva (Daylily) Leaves. Molecules 2022; 27:molecules27092916. [PMID: 35566266 PMCID: PMC9104616 DOI: 10.3390/molecules27092916] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 04/29/2022] [Accepted: 05/02/2022] [Indexed: 11/16/2022] Open
Abstract
Hemerocallis fulva is a medical and edible plant. In this study, we optimized the ultrasound-assisted extraction (UAE) process of extracting flavonoids from Hemerocallis fulva leaves by single-factor experiments and response surface methodology (RSM). The optimum extraction conditions generating the maximal total flavonoids content was as follows: 70.6% ethanol concentration; 43.9:1 mL/g solvent to sample ratio; 61.7 °C extraction temperature. Under the optimized extraction conditions, the total flavonoid content (TFC) in eight Hemerocallis fulva varieties were determined, and H. fulva (L.) L. var. kwanso Regel had the highest TFC. The cytotoxicity of the extract was studied using the Cell Counting Kit-8 (CCK-8 assay). When the concentration was less than 1.25 mg/mL, the extract had no significant cytotoxicity to HaCaT cells. The antioxidant activity was measured via chemical antioxidant activity methods in vitro and via cellular antioxidant activity methods. The results indicated that the extract had a strong ABTS and •OH radical scavenging activity. Additionally, the extract had an excellent protective effect against H2O2-induced oxidative damage at a concentration of 1.25 mg/mL, which could effectively reduce the level of ROS to 106.681 ± 9.733% (p < 0.001), compared with the 163.995 ± 6.308% of the H2O2 group. We identified five flavonoids in the extracts using high-performance liquid chromatography (HPLC). Infrared spectroscopy indicated that the extract contained the structure of flavonoids. The results showed that the extract of Hemerocallis fulva leaves had excellent biocompatibility and antioxidant activity, and could be used as a cheap and potential source of antioxidants in the food, cosmetics, and medicine industries.
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22
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Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies. Foods 2022; 11:1181. [PMID: 35563907 PMCID: PMC9105373 DOI: 10.3390/foods11091181] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/18/2022] [Accepted: 04/18/2022] [Indexed: 12/10/2022] Open
Abstract
Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.
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Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
- Faculty of Chemical Engineering Technology, University Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
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23
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Ami D, Mereghetti P, Natalello A. Contribution of Infrared Spectroscopy to the Understanding of Amyloid Protein Aggregation in Complex Systems. Front Mol Biosci 2022; 9:822852. [PMID: 35463965 PMCID: PMC9023755 DOI: 10.3389/fmolb.2022.822852] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Infrared (IR) spectroscopy is a label-free and non-invasive technique that probes the vibrational modes of molecules, thus providing a structure-specific spectrum. The development of infrared spectroscopic approaches that enable the collection of the IR spectrum from a selected sample area, from micro- to nano-scale lateral resolutions, allowed to extend their application to more complex biological systems, such as intact cells and tissues, thus exerting an enormous attraction in biology and medicine. Here, we will present recent works that illustrate in particular the applications of IR spectroscopy to the in situ characterization of the conformational properties of protein aggregates and to the investigation of the other biomolecules surrounding the amyloids. Moreover, we will discuss the potential of IR spectroscopy to the monitoring of cell perturbations induced by protein aggregates. The essential support of multivariate analyses to objectively pull out the significant and non-redundant information from the spectra of highly complex systems will be also outlined.
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Affiliation(s)
- Diletta Ami
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, Italy
- *Correspondence: Diletta Ami, ; Antonino Natalello,
| | | | - Antonino Natalello
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, Italy
- *Correspondence: Diletta Ami, ; Antonino Natalello,
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24
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Antônio DC, de Assis DCS, Botelho BG, Sena MM. Detection of adulterations in a valuable Brazilian honey by using spectrofluorimetry and multiway classification. Food Chem 2022; 370:131064. [PMID: 34537433 DOI: 10.1016/j.foodchem.2021.131064] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 07/29/2021] [Accepted: 09/03/2021] [Indexed: 11/04/2022]
Abstract
Spectrofluorimetry combined with multiway chemometric tools were applied to discriminate pure Aroeira honey samples from samples adulterated with corn syrup, sugar cane molasses and polyfloral honey. Excitation emission spectra were acquired for 232 honey samples by recording excitation from 250 to 500 nm and emission from 270 to 640 nm. Parallel factor analysis (PARAFAC), partial least squares discriminant analysis (PLS-DA), unfolded PLS-DA (UPLS-DA) and multilinear PLS-DA (NPLS-DA) methods were used to decompose the spectral data and build classification models. PLS-DA models presented poor classification rates, demonstrating the limitation of the traditional two-way methods for this dataset, and leading to the development of three-way classification models. Overall, UPLS-DA provided the best classification results with misclassification rates of 4% and 8% for the training and test sets, respectively. These results showed the potential of the proposed method for routine laboratory analysis as a simple, reliable, and affordable tool.
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Affiliation(s)
- Daphne Chiara Antônio
- Departamento de Química, Instituto de Ciências Exatas (ICEx), Universidade Federal de Minas Gerais (UFMG), 31270-901 Belo Horizonte, MG, Brazil
| | | | - Bruno Gonçalves Botelho
- Departamento de Química, Instituto de Ciências Exatas (ICEx), Universidade Federal de Minas Gerais (UFMG), 31270-901 Belo Horizonte, MG, Brazil
| | - Marcelo Martins Sena
- Departamento de Química, Instituto de Ciências Exatas (ICEx), Universidade Federal de Minas Gerais (UFMG), 31270-901 Belo Horizonte, MG, Brazil; Instituto Nacional de Ciência e Tecnologia em Bioanalítica, 13083-970 Campinas, SP, Brazil.
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Kabir MH, Guindo ML, Chen R, Liu F. Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques. Foods 2021; 10:foods10112767. [PMID: 34829048 PMCID: PMC8623769 DOI: 10.3390/foods10112767] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/09/2021] [Accepted: 11/09/2021] [Indexed: 01/12/2023] Open
Abstract
Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.
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Affiliation(s)
- Muhammad Hilal Kabir
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.)
- Department of Agricultural and Bioresource Engineering, Abubakar Tafawa Balewa University, Bauchi PMB 0248, Nigeria
| | - Mahamed Lamine Guindo
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.)
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.)
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.)
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
- Correspondence: ; Tel.: +86-571-88982825
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Lu Q. Molecular structure recognition by blob detection. RSC Adv 2021; 11:35879-35886. [PMID: 35492772 PMCID: PMC9043223 DOI: 10.1039/d1ra05752a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/31/2021] [Indexed: 11/23/2022] Open
Abstract
Molecular structure recognition is fundamental in computational chemistry. The most common approach is to calculate the root mean square deviation (RMSD) between two sets of molecular coordinates. However, this method does not perform well for large molecules. In this work, a new method is proposed for structure comparison. Blob detection is used for recognizing structural features. Fragmentation of molecules is proposed as the pre-treatment. Mapping between blobs and atoms is developed as the post-treatment. A set of key parameters important for blob detections are determined. The dissimilarity is quantified by calculating the Euclidean metric of the blob vectors. The overall algorithm is found to be accurate to distinguish structural dissimilarity. The method has potential to be combined with other pattern recognition techniques for new chemistry discoveries. Molecular structure recognition is fundamental in computational chemistry.![]()
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Affiliation(s)
- Qing Lu
- Beijing National Laboratory for Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences Beijing 100190 China
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Comparison between Mid-Infrared (ATR-FTIR) Spectroscopy and Official Analysis Methods for Determination of the Concentrations of Alcohol, SO2, and Total Acids in Wine. SEPARATIONS 2021. [DOI: 10.3390/separations8100191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The determination of alcohol, SO2, and total acids in wine through conventional laboratory techniques have some limitations related to the amount of the samples, analytical preparation of laboratory staff, and time to carry out the analysis. In recent years, spectroscopic and chromatographic methods have been proposed to determinate simultaneously multiple analytical parameters. The new methods claim the speed of analysis and easy execution. However, they need a validation process that guarantees the reliability of the results to be used in official determinations. This study aimed to evaluate the usefulness of FT-infrared reflectance (FT-IR) to quantify total acid, alcohol, and SO2 concentration in the wines. For this purpose, 156 DOC Italian wines were tested with IR technology, and results were compared to those obtained by official analysis methods. The comparison was performed using two non-parametric statistical methods: the Bland & Altman test and Passing & Bablok regression. Our results showed that the spectrophotometric methods make errors due to interfering contaminants in the sample that can be corrected by blank determination. Therefore, the spectrophotometric methods that use the infrared region of the electromagnetic spectrum can be used by the wine industry and regulators for the wine routine as an alternative to official methodologies.
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