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Sharma R, Nath PC, Lodh BK, Mukherjee J, Mahata N, Gopikrishna K, Tiwari ON, Bhunia B. Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges. Food Chem 2024; 454:139817. [PMID: 38805929 DOI: 10.1016/j.foodchem.2024.139817] [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: 11/25/2023] [Revised: 05/13/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024]
Abstract
Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.
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Affiliation(s)
- Ramesh Sharma
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India; Department of Food Technology, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu-641062, India.
| | - Pinku Chandra Nath
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India.
| | - Bibhab Kumar Lodh
- Department of Chemical Engineering, National Institute of Technology, Agartala-799046, India.
| | - Jayanti Mukherjee
- Department of Pharmaceutical Chemistry, CMR College of Pharmacy, Hyderabad- 501401, Telangana, India.
| | - Nibedita Mahata
- Department of Biotechnology, National Institute of Technology Durgapur, Durgapur-713209.
| | - Konga Gopikrishna
- SEED Division, Department of Science and Technology, New Delhi, 110016, India.
| | - Onkar Nath Tiwari
- Centre for Conservation and Utilisation of Blue Green Algae (CCUBGA), Division of Microbiology, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, 110012, India.
| | - Biswanath Bhunia
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India.
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Ma H, Zhao Y, He W, Wang J, Hu Q, Chen K, Yang L, Ma Y. Quantitative analysis of three ingredients in Salvia miltiorrhiza by near infrared spectroscopy combined with hybrid variable selection strategy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124273. [PMID: 38615417 DOI: 10.1016/j.saa.2024.124273] [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: 09/22/2023] [Revised: 03/25/2024] [Accepted: 04/08/2024] [Indexed: 04/16/2024]
Abstract
Rosmarinic acid (RA), Tanshinone IIA (Tan IIA), and Salvianolic acid B (Sal B) are crucial compounds found in Salvia miltiorrhiza. Quickly predicting these components can aid in ensuring the quality of S. miltiorrhiza. Spectral preprocessing and variable selection are essential processes in quantitative analysis using near infrared spectroscopy (NIR). A novel hybrid variable selection approach utilizing iVISSA was employed in this study to enhance the quantitative measurement of RA, Tan IIA, and Sal B contents in S. miltiorrhiza. The spectra underwent 108 preprocessing approaches, with the optimal method being determined as orthogonal signal correction (OSC). iVISSA was utilized to identify the intervals (feature bands) that were most pertinent to the target chemical. Various methods such as bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable combination population analysis (VCPA), successive projections algorithm (SPA), iteratively variable subset optimization (IVSO), and iteratively retained informative variables (IRIV) were used to identify significant feature variables. PLSR models were created for comparison using the given variables. The results fully demonstrated that iVISSA-SPA calibration model had the best comprehensive performance for Tan IIA, and iVISSA-BOSS had the best comprehensive performance for RA and Sal B, and correlation coefficients of cross-validation (R2cv), root mean square errors of cross-validation (RMSECV), correlation coefficients of prediction (R2p), and root mean square errors of prediction (RMSEP) were 0.9970, 0.0054, 0.9990 and 0.0033, 0.9992, 0.0016, 0.9961 and 0.0034, 0.9998, 0.0138, 0.9875 and 0.1090, respectively. The results suggest that NIR spectroscopy, along with PLSR and a hybrid variable selection method using iVISSA, can be a valuable tool for quickly quantifying RA, Sal B, and Tan IIA in S. miltiorrhiza.
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Affiliation(s)
- Hongliang Ma
- Research Center of Chinese Herbal Resource Science and Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong, China; National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China.
| | - Yu Zhao
- Research Center of Chinese Herbal Resource Science and Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong, China; National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China
| | - Wenxiu He
- Research Center of Chinese Herbal Resource Science and Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong, China; National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China
| | - Jiwen Wang
- National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China
| | - Qianqian Hu
- National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China
| | - Kehan Chen
- Research Center of Chinese Herbal Resource Science and Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong, China
| | - Lianlin Yang
- National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China
| | - Yonglin Ma
- Research Center of Chinese Herbal Resource Science and Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong, China; National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China
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Kitazoe T, Usui C, Kodaira E, Maruyama T, Kawano N, Fuchino H, Yamamoto K, Kitano Y, Kawahara N, Yoshimatsu K, Shirahata T, Kobayashi Y. Improved quantitative analysis of tenuifolin using hydrolytic continuous-flow system to build prediction models for its content based on near-infrared spectroscopy. J Nat Med 2024; 78:296-311. [PMID: 38172356 DOI: 10.1007/s11418-023-01764-0] [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: 07/26/2023] [Accepted: 11/14/2023] [Indexed: 01/05/2024]
Abstract
This study used two types of analyses and statistical calculations on powdered samples of Polygala root (PR) and Senega root (SR): (1) determination of saponin content by an independently developed quantitative analysis of tenuifolin content using a flow reactor, and (2) near-infrared spectroscopy (NIR) using crude drug powders as direct samples for metabolic profiling. Furthermore, a prediction model for tenuifolin content was developed and validated using multivariate analysis based on the results of (1) and (2). The goal of this study was to develop a rapid analytical method utilizing the saponin content and explore the possibility of quality control through a wide-area survey of crude drugs using NIR spectroscopy. Consequently, various parameters and appropriate wavelengths were examined in the regression analysis, and a model with a reasonable contribution rate and prediction accuracy was successfully developed. In this case, the wavenumber contributing to the model was consistent with that of tenuifolin, confirming that this model was based on saponin content. In this series of analyses, we have succeeded in developing a model that can quickly estimate saponin content without post-processing and have demonstrated a brief way to perform quality control of crude drugs in the clinical field and on the market.
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Affiliation(s)
- Tatsuki Kitazoe
- School of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo, 108-8641, Japan
| | - Chisato Usui
- School of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo, 108-8641, Japan
| | - Eiichi Kodaira
- School of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo, 108-8641, Japan
| | - Takuro Maruyama
- Division of Pharmacognosy, Phytochemistry and Narcotics, National Institute of Health Sciences, 3-25-26, Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa, 210-9501, Japan
| | - Noriaki Kawano
- National Institutes of Biomedical Innovation, Health and Nutrition, 1-2 Hachimandai, Tsukuba, Ibaraki, 305-0843, Japan
| | - Hiroyuki Fuchino
- National Institutes of Biomedical Innovation, Health and Nutrition, 1-2 Hachimandai, Tsukuba, Ibaraki, 305-0843, Japan
| | - Kazuhiko Yamamoto
- National Institutes of Biomedical Innovation, Health and Nutrition, 1-2 Hachimandai, Tsukuba, Ibaraki, 305-0843, Japan
| | - Yasushi Kitano
- Nippon Funmatsu Yakuhin Co., Ltd, 2-5-11, Doshomachi, Chuo-ku, Osaka, 541-0045, Japan
| | - Nobuo Kawahara
- National Institutes of Biomedical Innovation, Health and Nutrition, 1-2 Hachimandai, Tsukuba, Ibaraki, 305-0843, Japan
- The Kochi Prefectural Makino Botanical Garden, Godaisan, Kochi, 781-8125, Japan
| | - Kayo Yoshimatsu
- National Institutes of Biomedical Innovation, Health and Nutrition, 1-2 Hachimandai, Tsukuba, Ibaraki, 305-0843, Japan
| | - Tatsuya Shirahata
- School of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo, 108-8641, Japan
| | - Yoshinori Kobayashi
- School of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo, 108-8641, Japan.
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Liang Y, Lin H, Kang W, Shao X, Cai J, Li H, Chen Q. Application of colorimetric sensor array coupled with machine-learning approaches for the discrimination of grains based on freshness. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:6790-6799. [PMID: 37308777 DOI: 10.1002/jsfa.12777] [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: 08/05/2022] [Revised: 05/28/2023] [Accepted: 06/13/2023] [Indexed: 06/14/2023]
Abstract
BACKGROUND Volatile organic compounds (VOCs) in grain fluctuate depending on the degree of grain freshness. A new colorimetric sensor array (CSA) was developed as capture probes for the quantification of VOCs in grains in this work, and it was designed to monitor the variation of grain VOCs. CSA spectral data acquisition using visible-near-infrared spectroscopy and image processing of CSA's image imformation by computer were used comparatively. Then, machine-learning-based models - for example, synergistic interval partial least squares, genetic algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and ant colony optimization (ACO) algorithm - were introduced to optimize variables. Moreover, principal component analysis, and linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were used for the classification. Ultimately, quantitative models for detecting grain freshness are developed using various variable selection strategies. RESULTS Compared with the pattern recognition results of image processing, visible-near-infrared spectroscopy could better separate the grains with different freshness from principal component analysis, and the prediction set of LDA models could correctly identify 100% of rice, 96.88% of paddy, and 97.9% of soybeans. In addition, compared with CARS and ACO, the LDA model and KNN model based on genetic algorithms show the best prediction performance. The prediction set could correctly identify 100% of rice and paddy samples and 95.83% of soybean samples. CONCLUSION The method developed could be used for non-destructive detection of grain freshness. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Yue Liang
- School of Food and Biological Engineering, Jiangsu University, Jiangsu, China
| | - Hao Lin
- School of Food and Biological Engineering, Jiangsu University, Jiangsu, China
| | - Wencui Kang
- School of Food and Biological Engineering, Jiangsu University, Jiangsu, China
| | - Xiaokang Shao
- School of Food and Biological Engineering, Jiangsu University, Jiangsu, China
| | - Jianrong Cai
- School of Food and Biological Engineering, Jiangsu University, Jiangsu, China
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Jiangsu, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Jiangsu, China
- College of Food and Biological Engineering, Jimei University, Xiamen, China
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Oliveira MM, Badaró AT, Esquerre CA, Kamruzzaman M, Barbin DF. Handheld and benchtop vis/NIR spectrometer combined with PLS regression for fast prediction of cocoa shell in cocoa powder. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 298:122807. [PMID: 37148660 DOI: 10.1016/j.saa.2023.122807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/11/2023] [Accepted: 04/28/2023] [Indexed: 05/08/2023]
Abstract
The fermented and dried cocoa beans are peeled, either before or after the roasting process, as peeled nibs are used for chocolate production, and shell content in cocoa powders may result from economically motivated adulteration (EMA), cross-contamination or misfits in equipment in the peeling process. The performance of this process is carefully evaluated, as values above 5% (w/w) of cocoa shell can directly affect the sensory quality of cocoa products. In this study chemometric methods were applied to near-infrared (NIR) spectra from a handheld (900-1700 nm) and a benchtop (400-1700 nm) spectrometers to predict cocoa shell content in cocoa powders. A total of 132 binary mixtures of cocoa powders with cocoa shell were prepared at several proportions (0 to 10% w/w). Partial least squares regression (PLSR) was used to develop the calibration models and different spectral preprocessing were investigated to improve the predictive performance of the models. The ensemble Monte Carlo variable selection (EMCVS) method was used to select the most informative spectral variables. Based on the results obtained with both benchtop (R2P = 0.939, RMSEP = 0.687% and RPDP = 4.14) and handheld (R2P = 0.876, RMSEP = 1.04% and RPDP = 2.82) spectrometers, NIR spectroscopy combined with the EMCVS method proved to be a highly accurate and reliable tool for predicting cocoa shell in cocoa powder. Even with a lower predictive performance than the benchtop spectrometer, the handheld spectrometer has potential to specify whether the amount of cocoa shell present in cocoa powders is in accordance with the Codex Alimentarius specifications.
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Affiliation(s)
- M M Oliveira
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP, Brazil; Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - A T Badaró
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
| | - C A Esquerre
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - M Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - D F Barbin
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP, Brazil.
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Wang F, Hou T, Luo S, Geng C, Chen C, Liu D, Han B, Gao L. Rapid and Green Methods for Qualitative Classification of Polygonati Rhizoma and Polygonati Odorati Rhizoma Using a Handheld near Infrared Instrument. J CHEM-NY 2023. [DOI: 10.1155/2023/4888557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
The confusing use of Polygonati Rhizoma (PR) and Polygonati Odorati Rhizoma (POR) poses an unpredictable threat to the health of consumers. Sensitive, nondestructive, rapid, and multicomponent techniques for their detection are sought after. In this study, a low-cost, short-wavelength (898–1668 nm), and handheld near-infrared (NIR) spectrometer combined with multivariate spectral evaluation methods was used to establish calibration models for identifying PR and POR. NIR spectra were treated with a standard normal variate (SNV) before performing chemometric approaches. Then principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were tested for calibration model development. The PCA results showed that spectral differences existed between the two herbs. However, the evaluation techniques could not separate them with the required accuracy. The PLS-DA calibration model, on the other hand, could separate the two herbs according to their spectral information with the prediction accuracy of >98.3%. Thus, it has been proven that a rapid, green, and low-cost method to support on-site and practical inspection through a handheld NIR instrument has been established to identify PR and POR and ensure the safety of the clinical medication.
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Application of hyperspectral imaging assisted with integrated deep learning approaches in identifying geographical origins and predicting nutrient contents of Coix seeds. Food Chem 2023; 404:134503. [DOI: 10.1016/j.foodchem.2022.134503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 09/28/2022] [Accepted: 10/01/2022] [Indexed: 11/06/2022]
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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. [PMID: 36386936 PMCID: PMC9642070 DOI: 10.3389/fnut.2022.980095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/30/2022] [Indexed: 09/13/2024] 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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Affiliation(s)
- Youyou Wang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yue Zhang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
- School of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, 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 of China, Hangzhou, China
| | - Yuyang Zhao
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jing Nie
- 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 of China, Hangzhou, China
| | - Tiegui Nan
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Luqi Huang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jian Yang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
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He Y, Zeng W, Zhao Y, Zhu X, Wan H, Zhang M, Li Z. Rapid detection of adulteration of goat milk and goat infant formulas using near-infrared spectroscopy fingerprints. Int Dairy J 2022. [DOI: 10.1016/j.idairyj.2022.105536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Bowler AL, Ozturk S, Rady A, Watson N. Domain Adaptation for In-Line Allergen Classification of Agri-Food Powders Using Near-Infrared Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2022; 22:7239. [PMID: 36236338 PMCID: PMC9570570 DOI: 10.3390/s22197239] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/14/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
The addition of incorrect agri-food powders to a production line due to human error is a large safety concern in food and drink manufacturing, owing to incorporation of allergens in the final product. This work combines near-infrared spectroscopy with machine-learning models for early detection of this problem. Specifically, domain adaptation is used to transfer models from spectra acquired under stationary conditions to moving samples, thereby minimizing the volume of labelled data required to collect on a production line. Two deep-learning domain-adaptation methodologies are used: domain-adversarial neural networks and semisupervised generative adversarial neural networks. Overall, accuracy of up to 96.0% was achieved using no labelled data from the target domain moving spectra, and up to 99.68% was achieved when incorporating a single labelled data instance for each material into model training. Using both domain-adaptation methodologies together achieved the highest prediction accuracies on average, as did combining measurements from two near-infrared spectroscopy sensors with different wavelength ranges. Ensemble methods were used to further increase model accuracy and provide quantification of model uncertainty, and a feature-permutation method was used for global interpretability of the models.
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Adulteration Detection of Edible Bird’s Nests Using Rapid Spectroscopic Techniques Coupled with Multi-Class Discriminant Analysis. Foods 2022; 11:foods11162401. [PMID: 36010401 PMCID: PMC9407431 DOI: 10.3390/foods11162401] [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: 06/22/2022] [Revised: 07/30/2022] [Accepted: 08/03/2022] [Indexed: 11/25/2022] Open
Abstract
Edible bird’s nests (EBNs) are vulnerable to adulteration due to their huge demand for traditional medicine and high market price. Presently, there are pressing needs to explore field-deployable rapid screening techniques to detect adulteration of EBNs. The objective of this study is to explore the feasibility of using a handheld near-infrared (VIS/SW-NIR) spectroscopic device for the determination of EBN authenticity against the benchmark performance of a benchtop mid-infrared (MIR) spectrometer. Forty-nine authentic EBNs from the different states in Malaysia and 13 different adulterants (five types) were obtained and used to simulate the adulteration of EBNs at 1, 5 and 10% adulteration by mass (a total of 15 adulterated samples). The VIS/SW-NIR and MIR spectra collated were subsequently processed, modelled and classified using multi-class discriminant analysis. The VIS/SW-NIR results showed 100% correct classification for the collagen and nutrient agar classes in authenticity classification, while for the other classes, the lowest correct classification rate was 96.3%. For MIR analysis, only the karaya gum class had 100% correct classification whilst for the other four classes, the lowest rate of correct classification was at 94.4%. In conclusion, the combination of spectroscopic analysis with chemometrics can be a powerful screening tool to detect EBN adulteration.
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Rapid detection of Ganoderma lucidum spore powder adulterated with dyed starch by NIR spectroscopy and chemometrics. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113829] [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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13
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Calle JLP, Barea-Sepúlveda M, Ruiz-Rodríguez A, Álvarez JÁ, Ferreiro-González M, Palma M. Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data. SENSORS (BASEL, SWITZERLAND) 2022; 22:3852. [PMID: 35632260 PMCID: PMC9145498 DOI: 10.3390/s22103852] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 05/15/2022] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices.
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Affiliation(s)
- José Luis P. Calle
- Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, CeiA3, University of Cadiz, 11510 Puerto Real, Spain; (J.L.P.C.); (M.B.-S.); (A.R.-R.); (M.P.)
| | - Marta Barea-Sepúlveda
- Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, CeiA3, University of Cadiz, 11510 Puerto Real, Spain; (J.L.P.C.); (M.B.-S.); (A.R.-R.); (M.P.)
| | - Ana Ruiz-Rodríguez
- Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, CeiA3, University of Cadiz, 11510 Puerto Real, Spain; (J.L.P.C.); (M.B.-S.); (A.R.-R.); (M.P.)
| | - José Ángel Álvarez
- Department of Physical Chemistry, Faculty of Sciences, INBIO, University of Cadiz, Apartado 40, 11510 Puerto Real, Spain;
| | - Marta Ferreiro-González
- Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, CeiA3, University of Cadiz, 11510 Puerto Real, Spain; (J.L.P.C.); (M.B.-S.); (A.R.-R.); (M.P.)
| | - Miguel Palma
- Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, CeiA3, University of Cadiz, 11510 Puerto Real, Spain; (J.L.P.C.); (M.B.-S.); (A.R.-R.); (M.P.)
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14
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Zhao Z, Wang Q, Xu X, Chen F, Teng G, Wei K, Chen G, Cai Y, Guo L. Accurate Identification and Quantification of Chinese Yam Powder Adulteration Using Laser-Induced Breakdown Spectroscopy. Foods 2022; 11:1216. [PMID: 35563939 PMCID: PMC9104410 DOI: 10.3390/foods11091216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 11/16/2022] Open
Abstract
As a popular food, Chinese yam (CY) powder is widely used for healthy and commercial purposes. Detecting adulteration of CY powder has become essential. In this work, chemometric methods combined with laser-induced breakdown spectroscopy (LIBS) were developed for identification and quantification of CY powder adulteration. Pure powders (CY, rhizome of winged yam (RY) and cassava (CS)) and adulterated powders (CY adulterated with CS) were pressed into pellets to obtain LIBS spectra for identification and quantification experiments, respectively. After variable number optimization by principal component analysis and random forest (RF), the best model random forest-support vector machine (RF-SVM) decreased 48.57% of the input variables and improved the accuracy to 100% in identification. Following the better feature extraction method RF, the Gaussian process regression (GPR) method performed the best in the prediction of the adulteration rate, with a correlation coefficient of prediction (Rp2) of 0.9570 and a root-mean-square error of prediction (RMSEP) of 7.6243%. Besides, the variable importance of metal elements analyzed by RF revealed that Na and K were significant due to the high metabolic activity and maximum metal content of CY powder, respectively. These results demonstrated that chemometric methods combined with LIBS can identify and quantify CY powder adulteration accurately.
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Affiliation(s)
- Zhifang Zhao
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China; (Z.Z.); (X.X.); (G.T.); (K.W.); (G.C.)
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Qianqian Wang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China; (Z.Z.); (X.X.); (G.T.); (K.W.); (G.C.)
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314033, China
| | - Xiangjun Xu
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China; (Z.Z.); (X.X.); (G.T.); (K.W.); (G.C.)
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314033, China
| | - Feng Chen
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China;
| | - Geer Teng
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China; (Z.Z.); (X.X.); (G.T.); (K.W.); (G.C.)
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314033, China
| | - Kai Wei
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China; (Z.Z.); (X.X.); (G.T.); (K.W.); (G.C.)
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Guoyan Chen
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China; (Z.Z.); (X.X.); (G.T.); (K.W.); (G.C.)
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yu Cai
- College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430065, China;
| | - Lianbo Guo
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China;
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15
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Long W, Hu Z, Wei L, Chen H, Liu T, Wang S, Guan Y, Yang X, Yang J, Fu H. Accurate identification of the geographical origins of lily using near-infrared spectroscopy combined with carbon dot-tetramethoxyporphyrin nanocomposite and chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 271:120932. [PMID: 35123189 DOI: 10.1016/j.saa.2022.120932] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 06/14/2023]
Abstract
Near-infrared spectroscopy technique is a prevailing tool for quality control of foods and traditional Chinese medicines. However, it usually faced the problems of severe peak overlap, low classification accuracy and poor specificity. In this work, the potential of carbon dot-tetramethoxyporphyrin nanocomposite-based nano-effect near-infrared spectroscopy sensor combined with chemometric method was investigated for the accurate identification lily from different geographical origins. Partial least squares-discriminant analysis (PLS-DA) was used for differentiating geographical origins of lily based on the collected traditional and nano-effect near-infrared spectroscopy. Compared with traditional near-infrared spectroscopy, the nano-effect near-infrared spectroscopy obtains superior classification performance with 100% accuracy on the training and test set. The results showed that the proposed method based on near-infrared spectroscopy combined with nanocomposites and chemometrics could be considered as a promising tool for rapid discrimination of the authenticity of food and traditional Chinese medicine in the future.
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Affiliation(s)
- Wanjun Long
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Zikang Hu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Liuna Wei
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Hengye Chen
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Tingkai Liu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Siyu Wang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Yuting Guan
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Xiaolong Yang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Jian Yang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, PR China.
| | - Haiyan Fu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China.
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16
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Classification of pulse flours using near-infrared hyperspectral imaging. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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17
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Yan Z, Liu H, Li J, Wang Y. Application of Identification and Evaluation Techniques for Edible Mushrooms: A Review. Crit Rev Anal Chem 2021; 53:634-654. [PMID: 34435928 DOI: 10.1080/10408347.2021.1969886] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Edible mushrooms are healthy food with high nutritional value, which is popular with consumers. With the increase of the problem of mushrooms being confused with the real and pollution in the market, people pay more and more attention to food safety. More than 167 articles of edible mushroom published in the past 20 years were reviewed in this paper. The analysis tools and data analysis methods of identification and quality evaluation of edible mushroom species, origin, mineral elements were reviewed. Five techniques for identification and evaluation of edible mushrooms were introduced and summarized. The macroscopic, microscopic and molecular identification techniques can be used to identify species. Chromatography, spectroscopy technology combined with chemometrics can be used for qualitative and quantitative study of mushroom and evaluation of mushroom quality. In addition, multiple supervised pattern-recognition techniques have good classification ability. Deep learning is more and more widely used in edible mushroom, which shows its advantages in image recognition and prediction. These techniques and analytical methods can provide strong support and guarantee for the identification and evaluation of mushroom, which is of great significance to the development and utilization of edible mushroom.
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Affiliation(s)
- Ziyun Yan
- College of Resources and Environmental, Yunnan Agricultural University, Kunming, China
| | | | - Jieqing Li
- College of Resources and Environmental, Yunnan Agricultural University, Kunming, China
| | - Yuanzhong Wang
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, China
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18
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Miao X, Miao Y, Gong H, Tao S, Chen Z, Wang J, Chen Y, Chen Y. NIR spectroscopy coupled with chemometric algorithms for the prediction of cadmium content in rice samples. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 257:119700. [PMID: 33872949 DOI: 10.1016/j.saa.2021.119700] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/06/2021] [Accepted: 03/09/2021] [Indexed: 06/12/2023]
Abstract
Fast determination of heavy metals is necessary and important to ensure the safety of crops. The potential of near-infrared spectroscopy coupled with chemometric technology for quantitative analysis of cadmium in rice was investigated. A total of 825 rice samples were collected and scanned by NIRS. The Kennard-Stone method was applied to divide the samples into calibration and validation sets. Before modeling, the spectrum was preprocessed using first derivation to reduce the baseline shift. Different chemometric tools such as interval partial least squares, moving window partial least squares, synergy interval partial least squares, and backward interval partial least squares were proposed to extract and optimize spectral interval from full-spectrum data. The performance of the calibration models generated on the basis of different regression algorithms was compared and evaluated. Results showed that the PLS models based on four chemometric algorithms outperformed the full-spectrum PLS model. Among the tools, biPLS performed better with the optimal subinterval selection. The root-mean-square error of prediction and correlation coefficient (R) of the biPLS model were 0.2133 and 0.9020, respectively. In addition, the low root-mean-square error of cross-validation was obtained in biPLS, which was 0.1756. NIRS technology combined with biPLS could be considered as an effective and convenient tool for primary screening and measuring of cadmium content in rice. In comparison with classical methodologies, this new technology was beneficial because of its eco-friendliness, fast analysis, and virtually no sample preparation required.
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Affiliation(s)
- Xuexue Miao
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China
| | - Ying Miao
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
| | - Haoru Gong
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China
| | - Shuhua Tao
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China.
| | - Zuwu Chen
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China
| | - Jiemin Wang
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China
| | - Yingzi Chen
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China
| | - Yancheng Chen
- Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China
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19
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Liu Y, Li Y, Peng Y, Ma S, Yan S. A feasibility quantitative analysis of free fatty acids in polished rice by fourier transform near-infrared spectroscopy and chemometrics. J Food Sci 2021; 86:3434-3446. [PMID: 34272729 DOI: 10.1111/1750-3841.15809] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 05/11/2021] [Accepted: 05/13/2021] [Indexed: 11/30/2022]
Abstract
Free fatty acids (FFAs) are an important indicator of the freshness and quality of rice. In this study, the vibration response of C-H chemical bonds (-CH3 , -CH2 , H-C = C-H) of FFAs in the near-infrared region was determined by analyzing the standard reagent. In addition, the spectral data of different physical forms of rice and chemometrics, such as partial least squares (PLS), synergy interval-PLS, and competitive adaptive reweighted sampling (CARS), were applied to develop an optimal regression model for rice FFAs determination. The performance of the FFAs model established by using the polished rice granule spectrum (PRG) combined with CARS was the best, the correlation coefficients of the calibration set and prediction set were 0.99 (root mean squared errors of the calibration = 2.00 mg/100 g) and 0.98 (root mean squared errors of the prediction = 3.21 mg/100 g), respectively, and the ratio of performance-to-deviation was 4.50. Compared with the rice powder spectral, the PRG spectral can better retain the information of FFAs. The result shows that NIRS can rapidly, non-destructively, and accurately detect FFAs in rice granules, which will help rice business and food regulatory authorities to establish an early warning mechanism of rice aging. PRACTICAL APPLICATION: Free fatty acids (FFAs) in rice are an important indicator for evaluating the freshness of rice, and their high responsiveness to the deterioration of rice quality. The real-time detection of FFAs in rice can timely adjust the parameters of the rice storage environment, which is very meaningful to ensure the quality of rice.
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Affiliation(s)
- Yachao Liu
- College of Engineering, China Agricultural University, Beijing, China
| | - Yongyu Li
- College of Engineering, China Agricultural University, Beijing, China
| | - Yankun Peng
- College of Engineering, China Agricultural University, Beijing, China
| | - Shaojin Ma
- College of Engineering, China Agricultural University, Beijing, China
| | - Shuai Yan
- College of Engineering, China Agricultural University, Beijing, China
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20
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Liu HY, Wadood SA, Xia Y, Liu Y, Guo H, Guo BL, Gan RY. Wheat authentication:An overview on different techniques and chemometric methods. Crit Rev Food Sci Nutr 2021; 63:33-56. [PMID: 34196234 DOI: 10.1080/10408398.2021.1942783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Wheat (Triticum aestivum L.) is one of the most important cereal crops and is consumed as a staple food around the globe. Wheat authentication has become a crucial issue over the last decades. Recently, many techniques have been applied in wheat authentication including the authentication of wheat geographical origin, wheat variety, organic wheat, and wheat flour from other cereals. This paper collected related literature in the last ten years, and attempted to highlight the recent studies on the discrimination and authentication of wheat using different determination techniques and chemometric methods. The stable isotope analysis and elemental profile of wheat are promising tools to obtain information regarding the origin, and variety, and to differentiate organic from conventional farming of wheat. Image analysis, genetic parameters, and omics analysis can provide solutions for wheat variety, organic wheat, and wheat adulteration. Vibrational spectroscopy analyses, such as NIR, FTIR, and HIS, in combination with multivariate data analysis methods, such as PCA, LDA, and PLS-DA, show great potential in wheat authenticity and offer many advantages such as user-friendly, cost-effective, time-saving, and environment friendly. In conclusion, analytical techniques combining with appropriate multivariate analysis are very effective to discriminate geographical origin, cultivar classification, and adulterant detection of wheat.
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Affiliation(s)
- Hong-Yan Liu
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Syed Abdul Wadood
- Department of Food and Nutrition, University of Home Economics, Lahore, Pakistan
| | - Yu Xia
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Yi Liu
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Huan Guo
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Bo-Li Guo
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ren-You Gan
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China.,Key Laboratory of Coarse Cereal Processing (Ministry of Agriculture and Rural Affairs), Sichuan Engineering & Technology Research Center of Coarse Cereal Industrialization, Chengdu University, Chengdu, China
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21
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Woodley SB, Mould RR, Sahuri-Arisoylu M, Kalampouka I, Booker A, Bell JD. Mitochondrial Function as a Potential Tool for Assessing Function, Quality and Adulteration in Medicinal Herbal Teas. Front Pharmacol 2021; 12:660938. [PMID: 33981240 PMCID: PMC8107435 DOI: 10.3389/fphar.2021.660938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 03/29/2021] [Indexed: 11/13/2022] Open
Abstract
Quality control has been a significant issue in herbal medicine since herbs became widely used to heal. Modern technologies have improved the methods of evaluating the quality of medicinal herbs but the methods of adulterating them have also grown in sophistication. In this paper we undertook a comprehensive literature search to identify the key analytical techniques used in the quality control of herbal medicine, reviewing their uses and limitations. We also present a new tool, based on mitochondrial profiling, that can be used to measure medicinal herbal quality. Besides being fundamental to the energy metabolism required for most cellular activities, mitochondria play a direct role in cellular signalling, apoptosis, stress responses, inflammation, cancer, ageing, and neurological function, mirroring some of the most common reasons people take herbal medicines. A fingerprint of the specific mitochondrial effects of medicinal herbs can be documented in order to assess their potential efficacy, detect adulterations that modulate these effects and determine the relative potency of batches. Furthermore, through this method it will be possible to assess whole herbs or complex formulas thus avoiding the issues inherent in identifying active ingredients which may be complex or unknown. Thus, while current analytical methods focus on determining the chemical quality of herbal medicines, including adulteration and contamination, mitochondrial functional analysis offers a new way of determining the quality of plant derived products that is more closely linked to the biological activity of a product and its potential clinical effectiveness.
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Affiliation(s)
- Steven B Woodley
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, United Kingdom
| | - Rhys R Mould
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, United Kingdom
| | - Meliz Sahuri-Arisoylu
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, United Kingdom.,Health Innovation Ecosystem, University of Westminster, London, United Kingdom
| | - Ifigeneia Kalampouka
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, United Kingdom
| | - Anthony Booker
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, United Kingdom.,Research Group 'Pharmacognosy and Phytotherapy', UCL School of Pharmacy, London, United Kingdom
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, United Kingdom
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22
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Liu Z, Yang MQ, Zuo Y, Wang Y, Zhang J. Fraud Detection of Herbal Medicines Based on Modern Analytical Technologies Combine with Chemometrics Approach: A Review. Crit Rev Anal Chem 2021; 52:1606-1623. [PMID: 33840329 DOI: 10.1080/10408347.2021.1905503] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Fraud in herbal medicines (HMs), commonplace throughout human history, is significantly related to medicinal effects with sometimes lethal consequences. Major HMs fraud events seem to occur with a certain regularity, such as substitution by counterfeits, adulteration by addition of inferior production-own materials, adulteration by chemical compounds, and adulteration by addition of foreign matter. The assessment of HMs fraud is in urgent demand to guarantee consumer protection against the four fraudulent activities. In this review, three analysis platforms (targeted, non-targeted, and the combination of non-targeted and targeted analysis) were introduced and summarized. Furthermore, the integration of analysis technology and chemometrics method (e.g., class-modeling, discrimination, and regression method) have also been discussed. Each integration shows different applicability depending on their advantages, drawbacks, and some factors, such as the explicit objective analysis or the nature of four types of HMs fraud. In an attempt to better solve four typical HMs fraud, appropriate analytical strategies are advised and illustrated with several typical studies. The article provides a general workflow of analysis methods that have been used for detection of HMs fraud. All analysis technologies and chemometrics methods applied can conduce to excellent reference value for further exploration of analysis methods in HMs fraud.
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Affiliation(s)
- Zhimin Liu
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China.,School of Agriculture, Yunnan University, Kunming, China
| | - Mei Quan Yang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Yingmei Zuo
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Jinyu Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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23
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Sun X, Li H, Yi Y, Hua H, Guan Y, Chen C. Rapid detection and quantification of adulteration in Chinese hawthorn fruits powder by near-infrared spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 250:119346. [PMID: 33387806 DOI: 10.1016/j.saa.2020.119346] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 11/18/2020] [Accepted: 12/13/2020] [Indexed: 06/12/2023]
Abstract
The aim of this study is to explore the feasibility of detection and quantification of two cheap adulterants (maltodextrin and starch) in Chinese functional food, hawthorn fruits powder (HFP), by using near infrared (NIR) spectroscopy coupled with chemometrics methods. The partial least squares discriminant analysis (PLS-DA) models were developed to discriminate the adulterated HFP from the authentic HFP, while the partial least squares regression (PLSR) models were employed to determine the contents of adulterants. In order to yield the best results, various spectra pretreatment methods and wavelength selection methods were carefully investigated. The models' qualities were assessed by the self-consistency test, the independent test and the rigorous leave-one-out cross-validation test. The metrics for the PLS-DA discriminative model included error rate, true positive rate, true negative rate and F1 score, while the metrics for the PLSR quantitative model were determination coefficient, root mean square error and residual prediction deviation. Finally, very satisfying results were obtained, which indicate that our method is quite robust and applicable, and thus has great potential for rapid detection of adulteration in powder of many other herbal plants or functional foods.
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Affiliation(s)
- Xuefen Sun
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Huiling Li
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Yuan Yi
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Haimin Hua
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Ying Guan
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Chao Chen
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; Key Laboratory of Digitalization Quality Evaluation of Chinese Materia Medica of SATCM, Guangzhou 510006, PR China; Research Center for Quality Engineering & Technology of Chinese Materia Medica in Guangdong Universities, Guangzhou 510006, PR China; Research Center for Quality Engineering & Technology of Chinese Materia Medica of Guangdong Province, Guangzhou 510006, PR China.
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24
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Lin H, Jiang H, Lin J, Chen Q, Ali S, Teng SW, Zuo M. Rice Freshness Identification Based on Visible Near-Infrared Spectroscopy and Colorimetric Sensor Array. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-01963-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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25
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Sun Y, Yuan M, Liu X, Su M, Wang L, Zeng Y, Zang H, Nie L. Comparative analysis of rapid quality evaluation of Salvia miltiorrhiza (Danshen) with Fourier transform near-infrared spectrometer and portable near-infrared spectrometer. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105492] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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26
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Cruz-Tirado J, Fernández Pierna JA, Rogez H, Barbin DF, Baeten V. Authentication of cocoa (Theobroma cacao) bean hybrids by NIR-hyperspectral imaging and chemometrics. Food Control 2020. [DOI: 10.1016/j.foodcont.2020.107445] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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27
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Zareef M, Mehedi Hassan M, Arslan M, Ahmad W, Ali S, Ouyang Q, Li H, Wu X, Chen Q. Rapid prediction of caffeine in tea based on surface-enhanced Raman spectroscopy coupled multivariate calibration. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105431] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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28
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Liu Y, Li Y, Peng Y, Yang Y, Wang Q. Detection of fraud in high-quality rice by near-infrared spectroscopy. J Food Sci 2020; 85:2773-2782. [PMID: 32713030 DOI: 10.1111/1750-3841.15314] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 05/03/2020] [Accepted: 05/04/2020] [Indexed: 11/29/2022]
Abstract
A key feature of food fraud is the use of a lower value ingredient to imitate an authentic product. This study was based on near-infrared spectroscopy (NIRS) analysis technology, partial least squares discriminant analysis (PLS-DA), and a support vector machine (SVM) to detect whether high-quality rice was mixed with other varieties of rice. As an aid to qualitative discrimination, PLS was used to establish the quantitative analysis model to assist in the recognition of the degree of fraud. Due to the direct correlation between the results of NIRS analysis and the homogeneity of the samples, four groups of samples with different physical forms (full granules, 40 mesh, 70 mesh, and 100 mesh) were prepared, each group consisted of 20 pure samples and 140 mixed samples, and the mixing ratio was between 5% and 50%, with an interval of 5%. Regarding qualitative analysis, the performance of the model has no obvious relationship with the physical state of the sample, the qualitative model of PLS-DA and SVM can detect the fraudulent rice with a 5% detection limit, respectively. Regarding quantitative analysis, the performance of the prediction model was closely related to the particle size of the samples: 100 mesh > 70 mesh > 40 mesh > full grains. The determination coefficient and root mean square errors of the optimal prediction result were 0.96 and 2.93, respectively. These results demonstrate that NIRS analysis technology is a reliable and fast tool to determine whether high-quality rice contains other varieties of rice. PRACTICAL APPLICATION: The work of this article is based on the current background of increasingly serious rice fraud, using near-infrared spectroscopy to quickly identify fraudulent rice, to a certain extent, and effectively alleviate the rice fraud. This technology can serve for the supervision of food regulatory agencies on rice fraud, and can also be used in food factories to ensure the authenticity of raw materials of rice.
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Affiliation(s)
- Yachao Liu
- College of Engineering, China Agricultural University, Beijing, 100083, China
| | - Yongyu Li
- College of Engineering, China Agricultural University, Beijing, 100083, China
| | - Yankun Peng
- College of Engineering, China Agricultural University, Beijing, 100083, China
| | - Yanming Yang
- College of Engineering, China Agricultural University, Beijing, 100083, China
| | - Qi Wang
- College of Engineering, China Agricultural University, Beijing, 100083, China
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He Y, Bai X, Xiao Q, Liu F, Zhou L, Zhang C. Detection of adulteration in food based on nondestructive analysis techniques: a review. Crit Rev Food Sci Nutr 2020; 61:2351-2371. [PMID: 32543218 DOI: 10.1080/10408398.2020.1777526] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
In recent years, people pay more and more attention to food quality and safety, which are significantly relating to human health. Food adulteration is a world-wide concerned issue relating to food quality and safety, and it is difficult to be detected. Modern detection techniques (high performance liquid chromatography, gas chromatography-mass spectrometer, etc.) can accurately identify the types and concentrations of adulterants in different food types. However, the characteristics as expensive, low efficient and complex sample preparation and operation limit the use of these techniques. The rapid, nondestructive and accurate detection techniques of food adulteration is of great and urgent demand. This paper introduced the principles, advantages and disadvantages of the nondestructive analysis techniques and reviewed the applications of these techniques in food adulteration screen in recent years. Differences among these techniques, differences on data interpretation and future prospects were also discussed.
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Affiliation(s)
- Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Lei Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
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Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer. Molecules 2020; 25:molecules25071651. [PMID: 32260173 PMCID: PMC7180573 DOI: 10.3390/molecules25071651] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 04/01/2020] [Accepted: 04/01/2020] [Indexed: 11/16/2022] Open
Abstract
Sodium pyrosulfite is a browning inhibitor used for the storage of fresh-cut potato slices. Excessive use of sodium pyrosulfite can lead to sulfur dioxide residue, which is harmful for the human body. The sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentrations of sodium pyrosulfite solution was classified by near-infrared hyperspectral imaging (NIR-HSI) system and portable near-infrared (NIR) spectrometer. Principal component analysis was used to analyze the object-wise spectra, and support vector machine (SVM) model was established. The classification accuracy of calibration set and prediction set were 98.75% and 95%, respectively. Savitzky-Golay algorithm was used to recognize the important wavelengths, and SVM model was established based on the recognized important wavelengths. The final classification accuracy was slightly less than that based on the full spectra. In addition, the pixel-wise spectra extracted from NIR-HSI system could realize the visualization of different samples, and intuitively reflect the differences among the samples. The results showed that it was feasible to classify the sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentration of sodium pyrosulfite solution by NIR spectra. It provided an alternative method for the detection of sulfur dioxide residue on the surface of fresh-cut potato slices.
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31
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The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods. SENSORS 2019; 20:s20010230. [PMID: 31906139 PMCID: PMC6982964 DOI: 10.3390/s20010230] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 12/18/2019] [Accepted: 12/27/2019] [Indexed: 02/05/2023]
Abstract
Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments.
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32
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Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods. Eur Food Res Technol 2019. [DOI: 10.1007/s00217-019-03419-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Capillary electrophoresis fingerprints combined with Linear Quantitative Profiling Method to monitor the quality consistency and predict the antioxidant activity of Alkaloids of Sophora flavescens. J Chromatogr B Analyt Technol Biomed Life Sci 2019; 1133:121827. [PMID: 31756622 DOI: 10.1016/j.jchromb.2019.121827] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 10/08/2019] [Accepted: 10/08/2019] [Indexed: 01/07/2023]
Abstract
Alkaloids of Sophora flavescens (ASF) has various pharmacological effects, and it is widely used in clinical practice. The aim of this research was to develop an environmentally friendly methodology that enables not only identification but also the quality consistency assessment of Alkaloids of Sophora flavescens. A background electrolyte composed of 50 mmol/L sodium tetraborate solution, 500 mmol/L boric acid and 1.2 mmol/L citric acid was used to conduct the fingerprint analysis coupled with quantitative determination of three components. Linear quantitative profiling method was used for comprehensive quality discrimination of the test samples from both qualitative and quantitative perspectives, and the 27 batches of samples were well differentiated. In addition, the fingerprint-efficacy relationship between chemical components and antioxidant activity in vitro was established using partial least squares regression model, which provided important medicinal efficacy information for quality control.
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Leme LM, Nakamura F, Coelho Tanamati AA, Valderrama P, Março PH. Fast non-invasive screening to detect fraud in oil capsules. Lebensm Wiss Technol 2019. [DOI: 10.1016/j.lwt.2019.03.088] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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Zhang Y, Yang F, Zhang J, Sun G, Wang C, Guo Y, Wen R, Sun W. Quantitative fingerprint and quality control analysis of Compound Liquorice Tablet combined with antioxidant activities and chemometrics methods. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2019; 59:152790. [PMID: 31005815 DOI: 10.1016/j.phymed.2018.12.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 12/04/2018] [Accepted: 12/10/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND Herbal medicine (HM), as a complex system, is difficult to investigate their quality consistency effectively by chromatographic fingerprinting obtained in a single detection method. Moreover, active compound discovery affords no information about pharmacological activity until late in the discovery process, and the interaction between HMs in vitro is not yet clear, which requires sufficient practice to prove their effectiveness. PURPOSE Therefore, the purpose of this study was to improve the quality control methods of Compound Liquorice Tablet (CLT) using multi-wavelength fusion fingerprinting, explore the possible antioxidant components and assess the interaction between herbs combined with bioactivity evaluation. METHODS AND DESIGN Once the theoretical standard preparation obtained in combination of multi-wavelength fusion fingerprinting and hierarchical clustering analysis, averagely linear quantified fingerprint method could rapidly calculate the composition similarities and efficiently quantify the multiple components of CLTs without any chemical standard. Furthermore, the fingerprint-efficacy relationship was investigated by integrating high performance liquid chromatography fingerprints with antioxidant activity assessment using the partial least squares model, which was capable of directly discovering the bioactive ingredients. Hereafter, combination index value was introduced to evaluate the correlation between the two antioxidant herbs in CLT formula. RESULTS The results showed that CLT samples were effectively identified and quantified, and their quality was accurately distinguished. By analyzing the antioxidant evaluation results, it was found that CLT had strong antioxidant activity, and through the study on PLS model and antioxidant activity assay of individual compounds, it was found that the order of chemical constituents responsible for antioxidant activity in CLT was as follows: flavonoids > saponins > alkaloids. Finally, it was determined that the CI value of GE-PPCE was in the range of 1.20-1.61, indicating that the interaction of the GE-PPCE pair was a slight antagonism. CONCLUSION Thus, this study provided a preferred way for monitoring the quality consistency of HM, exploring possible bioactive components of HMs and assessing the interaction between herbs.
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Affiliation(s)
- Yujing Zhang
- School of Pharmacy, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang, Liaoning, PR China
| | - Fangliang Yang
- School of Pharmacy, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang, Liaoning, PR China
| | - Jing Zhang
- School of Pharmacy, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang, Liaoning, PR China
| | - Guoxiang Sun
- School of Pharmacy, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang, Liaoning, PR China.
| | - Chao Wang
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang, Liaoning, PR China
| | - Yong Guo
- School of Pharmacy, Fairleigh Dickinson University, Florham Park, NJ, United States of America
| | - Ran Wen
- School of Pharmacy, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang, Liaoning, PR China
| | - Wanyang Sun
- Institute of Traditional Chinese Medicine &Natural Products, College of Pharmacy, Jinan University, Guangzhou, Guangdong, PR China
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36
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Wang D, Wang Y, Zhu K, Shi L, Zhang M, Yu J, Liu Y. Detection of Cassava Component in Sweet Potato Noodles by Real-Time Loop-mediated Isothermal Amplification (Real-time LAMP) Method. Molecules 2019; 24:molecules24112043. [PMID: 31146324 PMCID: PMC6600232 DOI: 10.3390/molecules24112043] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 05/22/2019] [Accepted: 05/24/2019] [Indexed: 11/17/2022] Open
Abstract
Sweet potato (Ipomoea batatas) noodles are a traditional Chinese food with a high nutritional value; however, starch adulteration is a big concern. The objective of this study was to develop a reliable method for the rapid detection of cassava (Manihot esculenta) components in sweet potato noodles to protect consumers from commercial adulteration. Five specific Loop-mediated Isothermal Amplification (LAMP) primers targeting the internal transcribed spacer (ITS) of cassava were designed, genomic DNA was extracted, the LAMP reaction system was optimized, and the specificity of the primers was verified with genomic DNA of cassava, Ipomoea batatas, Zea mays, and Solanum tuberosum; the detection limit was determined with a serial dilution of adulterated sweet potato starch with cassava starch, and the real-time LAMP method for the detection of the cassava-derived ingredient in sweet potato noodles was established. The results showed that the real-time LAMP method can accurately and specifically detect the cassava component in sweet potato noodles with a detection limit of 1%. Furthermore, the LAMP assay was validated using commercial sweet potato noodle samples, and results showed that 57.7% of sweet potato noodle products (30/52) from retail markets were adulterated with cassava starch in China. This study provides a promising solution for facilitating the surveillance of the commercial adulteration of sweet potato noodles from retail markets.
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Affiliation(s)
- Deguo Wang
- Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety of Henan Province, Xuchang University, Xuchang 461000, China.
| | - Yongzhen Wang
- Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety of Henan Province, Xuchang University, Xuchang 461000, China.
| | - Kai Zhu
- Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety of Henan Province, Xuchang University, Xuchang 461000, China.
| | - Lijia Shi
- Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety of Henan Province, Xuchang University, Xuchang 461000, China.
| | - Meng Zhang
- Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety of Henan Province, Xuchang University, Xuchang 461000, China.
| | - Jianghan Yu
- Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety of Henan Province, Xuchang University, Xuchang 461000, China.
| | - Yanhong Liu
- Molecular Characterization of Foodborne Pathogens Research Unit, Eastern Regional Research Center, Agricultural Research Service, United States Department of Agriculture, Wyndmoor, PA 19038, USA.
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37
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Quelal-Vásconez MA, Lerma-García MJ, Pérez-Esteve É, Arnau-Bonachera A, Barat JM, Talens P. Fast detection of cocoa shell in cocoa powders by near infrared spectroscopy and multivariate analysis. Food Control 2019. [DOI: 10.1016/j.foodcont.2018.12.028] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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38
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Wang L, Hui Y, Jiang K, Yin G, Wang J, Yan Y, Wang Y, Li J, Wang P, Bi K, Wang T. Potential of near infrared spectroscopy and pattern recognition for rapid discrimination and quantification of Gleditsia sinensis thorn powder with adulterants. J Pharm Biomed Anal 2018; 160:64-72. [DOI: 10.1016/j.jpba.2018.07.036] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 06/06/2018] [Accepted: 07/19/2018] [Indexed: 11/15/2022]
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39
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Li S, Wang Z, Shao Q, Fang H, Zhu J, Wu X, Zheng B. Rapid detection of adulteration in Anoectochilus roxburghii by near-infrared spectroscopy coupled with chemometric methods. Journal of Food Science and Technology 2018; 55:3518-3525. [PMID: 30150810 DOI: 10.1007/s13197-018-3276-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 05/25/2018] [Accepted: 05/29/2018] [Indexed: 02/02/2023]
Abstract
To determine the authenticity of Anoectochilus roxburghii, this study presents an application of near-infrared spectroscopy and chemometric methods for evaluating adulteration of A. roxburghii with two cheaper adulterants, i.e. C. Goodyera schlechtendaliana and Ludisia discolor. Partial least squares discriminant analysis models were built for the accurate classification of authentic A. roxburghii and A. roxburghii adulterated at 5-100% (w/w) levels. Partial least squares regression models were used to predict the level of adulteration in the A. roxburghii. After by compared different spectral pretreatment methods, and using interval PLS and synergy interval PLS for variable selection, optimum models were developed. These results show that the NIR spectroscopy combined with chemometric methods offers a simple, fast, and reliable method for classifying and quantifying the adulteration of A. roxburghii.
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Affiliation(s)
- Shuailing Li
- 1State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, 311300 China
| | - Zhian Wang
- Zhejiang Research Institute of Traditional Chinese Medicine Co., Ltd., Hangzhou, 310023 China
| | - Qingsong Shao
- 1State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, 311300 China
| | - Hailing Fang
- 2Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, 210014 China
| | - Jianjun Zhu
- Wenzhou Academy of Agricultural Sciences, Wenzhou, 325006 China
| | - Xueqian Wu
- 1State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, 311300 China
| | - Bingsong Zheng
- 1State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, 311300 China
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40
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Zhao L, Zhang F, Ding X, Wu G, Lam YY, Wang X, Fu H, Xue X, Lu C, Ma J, Yu L, Xu C, Ren Z, Xu Y, Xu S, Shen H, Zhu X, Shi Y, Shen Q, Dong W, Liu R, Ling Y, Zeng Y, Wang X, Zhang Q, Wang J, Wang L, Wu Y, Zeng B, Wei H, Zhang M, Peng Y, Zhang C. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science 2018; 359:1151-1156. [PMID: 29590046 DOI: 10.1126/science.aao5774] [Citation(s) in RCA: 1347] [Impact Index Per Article: 224.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Accepted: 01/19/2018] [Indexed: 12/13/2022]
Abstract
The gut microbiota benefits humans via short-chain fatty acid (SCFA) production from carbohydrate fermentation, and deficiency in SCFA production is associated with type 2 diabetes mellitus (T2DM). We conducted a randomized clinical study of specifically designed isoenergetic diets, together with fecal shotgun metagenomics, to show that a select group of SCFA-producing strains was promoted by dietary fibers and that most other potential producers were either diminished or unchanged in patients with T2DM. When the fiber-promoted SCFA producers were present in greater diversity and abundance, participants had better improvement in hemoglobin A1c levels, partly via increased glucagon-like peptide-1 production. Promotion of these positive responders diminished producers of metabolically detrimental compounds such as indole and hydrogen sulfide. Targeted restoration of these SCFA producers may present a novel ecological approach for managing T2DM.
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Affiliation(s)
- Liping Zhao
- State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China. .,Department of Biochemistry and Microbiology and New Jersey Institute for Food, Nutrition, and Health, School of Environmental and Biological Sciences, Rutgers University, NJ 08901, USA
| | - Feng Zhang
- State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiaoying Ding
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Guojun Wu
- State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yan Y Lam
- Department of Biochemistry and Microbiology and New Jersey Institute for Food, Nutrition, and Health, School of Environmental and Biological Sciences, Rutgers University, NJ 08901, USA
| | - Xuejiao Wang
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Huaqing Fu
- State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xinhe Xue
- State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chunhua Lu
- Sijing Community Health Service Center of Songjiang District, Shanghai 201601, China
| | - Jilin Ma
- Sijing Community Health Service Center of Songjiang District, Shanghai 201601, China
| | - Lihua Yu
- Sijing Community Health Service Center of Songjiang District, Shanghai 201601, China
| | - Chengmei Xu
- Sijing Community Health Service Center of Songjiang District, Shanghai 201601, China
| | - Zhongying Ren
- Sijing Community Health Service Center of Songjiang District, Shanghai 201601, China
| | - Ying Xu
- Sijing Hospital of Songjiang District, Shanghai 201601, China
| | - Songmei Xu
- Sijing Hospital of Songjiang District, Shanghai 201601, China
| | - Hongli Shen
- Sijing Hospital of Songjiang District, Shanghai 201601, China
| | - Xiuli Zhu
- Sijing Hospital of Songjiang District, Shanghai 201601, China
| | - Yu Shi
- Department of Endocrinology and Metabolism, Qidong People's Hospital, Jiangsu 226200, China
| | - Qingyun Shen
- Department of Endocrinology and Metabolism, Qidong People's Hospital, Jiangsu 226200, China
| | - Weiping Dong
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Rui Liu
- State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yunxia Ling
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Yue Zeng
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Xingpeng Wang
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Qianpeng Zhang
- State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jing Wang
- State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Linghua Wang
- State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanqiu Wu
- State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Benhua Zeng
- Department of Laboratory Animal Science, College of Basic Medical Sciences, Army Medical University, Chongqing 400038, China
| | - Hong Wei
- Department of Laboratory Animal Science, College of Basic Medical Sciences, Army Medical University, Chongqing 400038, China
| | - Menghui Zhang
- State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yongde Peng
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
| | - Chenhong Zhang
- State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
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42
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Two-dimensional correlation spectroscopy reveals the underlying compositions for FT-NIR identification of the medicinal bulbs of the genus Fritillaria. J Mol Struct 2018. [DOI: 10.1016/j.molstruc.2017.11.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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43
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Wang J, Zhang X, Sun S, Sun X, Li Q, Zhang Z. Online determination of quality parameters of dried soybean protein–lipid films (Fuzhu) by NIR spectroscopy combined with chemometrics. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2018. [DOI: 10.1007/s11694-018-9762-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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44
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Key Variables Screening of Near-Infrared Models for Simultaneous Determination of Quality Parameters in Traditional Chinese Food “Fuzhu”. J FOOD QUALITY 2018. [DOI: 10.1155/2018/3136516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The traditional Chinese food Fuzhu is a dried soy protein-lipid film formed during the heating of soymilk. This study investigates whether a simple and accurate model can nondestructively determine the quality parameters of intact Fuzhu. The diffused reflectance spectra (1000–2499 nm) of intact Fuzhu were collected by a commercial near-infrared (NIR) spectrometer. Among various preprocessing methods, the derivative by wavelet transform method optimally enhanced the characteristic signals of Fuzhu spectra. Uninformative variable elimination based on Monte Carlo (MC-UVE), random frog (RF), and competitive adaptive reweighted sampling (CARS) were proposed to select key variables for partial least squares (PLS) calculation. The strong performance of the developed models is attributed to the high ratios of prediction to deviation values (3.32–3.51 for protein, 3.62–3.89 for lipid, and 4.27–4.55 for moisture). The prediction set was used to assess the performances of the best models of protein (CARS-PLS), lipid (RF-PLS), and moisture (CARS-PLS), which resulted in greater coefficients of determination of 0.958, 0.966, and 0.976, respectively, and lower root mean square errors of prediction of 0.656%, 0.442%, and 0.123%, respectively. Combined with chemometrics methods, the NIR technique is promising for simultaneous testing of quality parameters of intact Fuzhu.
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Zhang Y, Sun G, Hou Z, Yan B, Zhang J. Evaluation of the quality consistency of powdered poppy capsule extractive by an averagely linear-quantified fingerprint method in combination with antioxidant activities and two compounds analyses. J Sep Sci 2017; 40:4511-4520. [DOI: 10.1002/jssc.201700389] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 08/24/2017] [Accepted: 09/20/2017] [Indexed: 11/12/2022]
Affiliation(s)
- Yujing Zhang
- School of Pharmacy; Shenyang Pharmaceutical University; Shenyang P. R. China
| | - Guoxiang Sun
- School of Pharmacy; Shenyang Pharmaceutical University; Shenyang P. R. China
| | - Zhifei Hou
- Department of Pharmaceutical engineering; Hebei Chemical and Pharmaceutical College; Shijiazhuang P. R. China
| | - Bo Yan
- School of Pharmacy; Shenyang Pharmaceutical University; Shenyang P. R. China
| | - Jing Zhang
- School of Pharmacy; Shenyang Pharmaceutical University; Shenyang P. R. China
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Sampaio PS, Soares A, Castanho A, Almeida AS, Oliveira J, Brites C. Optimization of rice amylose determination by NIR-spectroscopy using PLS chemometrics algorithms. Food Chem 2017; 242:196-204. [PMID: 29037678 DOI: 10.1016/j.foodchem.2017.09.058] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 08/11/2017] [Accepted: 09/12/2017] [Indexed: 10/18/2022]
Abstract
Determining amylose content in rice with near infrared (NIR) spectroscopy, associated with a suitable multivariate regression method, is both feasible and relevant for the rice business to enable Process Analytical Technology applications for this critical factor, but it has not been fully exploited. Due to it being time-consuming and prone to experimental errors, it is urgent to develop a low-cost, nondestructive and 'on-line' method able to provide high accuracy and reproducibility. Different rice varieties and specific chemometrics tools, such as partial least squares (PLS), interval-PLS, synergy interval-PLS and moving windows-PLS, were applied to develop an optimal regression model for rice amylose determination. The model performance was evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The high performance of the siPLS method (R=0.94; RMSEP=1.938; 8941-8194cm-1; 5592-5045cm-1; and 4683-4335cm-1) shows the feasibility of NIR technology for determination of the amylose with high accuracy.
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Affiliation(s)
- Pedro Sousa Sampaio
- Instituto Nacional de Investigação Agrária e Veterinária (INIAV), Av. da República, Quinta do Marquês, 2780-157 Oeiras, Portugal; Faculty of Engineering, Lusophone University of Humanities and Technology, Campo Grande, 376, 1749-019 Lisbon, Portugal.
| | - Andreia Soares
- Instituto Nacional de Investigação Agrária e Veterinária (INIAV), Av. da República, Quinta do Marquês, 2780-157 Oeiras, Portugal
| | - Ana Castanho
- Instituto Nacional de Investigação Agrária e Veterinária (INIAV), Av. da República, Quinta do Marquês, 2780-157 Oeiras, Portugal
| | - Ana Sofia Almeida
- Instituto Nacional de Investigação Agrária e Veterinária (INIAV), Av. da República, Quinta do Marquês, 2780-157 Oeiras, Portugal
| | | | - Carla Brites
- Instituto Nacional de Investigação Agrária e Veterinária (INIAV), Av. da República, Quinta do Marquês, 2780-157 Oeiras, Portugal
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Lu X, Sun J, Mao H, Wu X, Gao H. Quantitative determination of rice starch based on hyperspectral imaging technology. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2017. [DOI: 10.1080/10942912.2017.1326058] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Xinzi Lu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
- Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang, China
| | - Hanping Mao
- Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang, China
| | - Xiaohong Wu
- Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang, China
| | - Hongyan Gao
- Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang, China
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Determination of Retrogradation Degree in Starch by Mid-infrared and Raman Spectroscopy during Storage. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-0932-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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