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Song R, Shen M, Wang Y, Sun Y, Ma J, Deng Q, Ren X, Li X, Zheng Y, He Y, Zhang F, Li M, Yao J, Sun M, Liu W, She G. Correlation analysis and modeling application from objective indicators to subjective evaluation of scented tea: A case study of rose tea. Food Chem 2025; 462:140963. [PMID: 39208739 DOI: 10.1016/j.foodchem.2024.140963] [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: 05/13/2024] [Revised: 08/11/2024] [Accepted: 08/21/2024] [Indexed: 09/04/2024]
Abstract
Different scented teas provide various choices for consumers from appearance, aroma, flavor and others. Aiming to define advantages and market positions of different scented teas and promote optimization of market structure, characteristics for scented tea favored by consumers and outstanding attributes of different scented teas should be clarified. Rose tea was taken as study object. Sensory evaluation and consumer acceptance were investigated. GC-MS and HPLC fingerprints were established. Physicochemical characteristics were determined. RGB integration analysis was inventively proposed for correlation analysis. The volatile compounds with spicy, green or herbal odor as camphene, β-phenethyl acetate, eugenol, and physicochemical parameters as antioxidant capacity, reducing sugar content, pH showed positive correlation with popular sensory properties. Six models for consumer preference by objective description were built through GA-SVR (accuracy = 1), and APP was developed. The research mode of scented tea has been successfully established to study multiple subjective characteristics with measurable objective parameters.
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Affiliation(s)
- Ruolan Song
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Meng Shen
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Yanran Wang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Youyi Sun
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Jiamu Ma
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Qingyue Deng
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Xueyang Ren
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Xianxian Li
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Yuan Zheng
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Yingyu He
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Feng Zhang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Mingxia Li
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Jianling Yao
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Mengyu Sun
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Wei Liu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Gaimei She
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
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2
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Yu Y, Chai Y, Yan Y, Li Z, Huang Y, Chen L, Dong H. Near-infrared spectroscopy combined with support vector machine for the identification of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) adulteration using wavelength selection algorithms. Food Chem 2024; 463:141548. [PMID: 39388874 DOI: 10.1016/j.foodchem.2024.141548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 09/29/2024] [Accepted: 10/03/2024] [Indexed: 10/12/2024]
Abstract
The frequent occurrence of adulterating Tartary buckwheat powder with crop flours in the market necessitates an urgent need for a simple analysis method to ensure the quality of Tartary buckwheat. This study employed near-infrared spectroscopy (NIRS) for the collection of spectral data from Tartary buckwheat samples adulterated with whole wheat, oat, soybean, barley, and sorghum flours. The competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were deployed to identify informative wavelengths. By integrating support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA), we constructed qualitative models to discern Tartary buckwheat adulteration. The PLS-DA model exhibited prediction accuracies between 89.78 % and 94.22 %, while the mean-centering (MC)-PLS-DA model showcased impressive predictive accuracy of 93.33 %. Notably, the feature-based Autoscales-CARS-CV-SVM model achieved more excellent identification accuracy. These findings exhibit the excellent potential of chemometrics as a powerful tool for detecting food product adulteration.
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Affiliation(s)
- Yue Yu
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Yinghui Chai
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Yujie Yan
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Zhanming Li
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
| | - Yue Huang
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
| | - Lin Chen
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Hao Dong
- College of Light Industry and Food Sciences, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China.
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3
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Xia H, Chen W, Hu D, Miao A, Qiao X, Qiu G, Liang J, Guo W, Ma C. Rapid discrimination of quality grade of black tea based on near-infrared spectroscopy (NIRS), electronic nose (E-nose) and data fusion. Food Chem 2024; 440:138242. [PMID: 38154280 DOI: 10.1016/j.foodchem.2023.138242] [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: 09/04/2023] [Revised: 11/25/2023] [Accepted: 12/18/2023] [Indexed: 12/30/2023]
Abstract
For the manufacturing and sale of tea, rapid discrimination of overall quality grade is of great importance. However, present evaluation methods are time-consuming and labor-intensive. This study investigated the feasibility of combining advantages of near-infrared spectroscopy (NIRS) and electronic nose (E-nose) to assess the tea quality. We found that NIRS and E-nose models effectively identify taste and aroma quality grades, with the highest accuracies of 99.63% and 97.00%, respectively, by comparing different principal component numbers and classification algorithms. Additionally, the quantitative models based on NIRS predicted the contents of key substances. Based on this, NIRS and E-nose data were fused in the feature-level to build the overall quality evaluation model, achieving accuracies of 98.13%, 96.63% and 97.75% by support vector machine, K-nearest neighbors, and artificial neural network, respectively. This study reveals that the integration of NIRS and E-nose presents a novel and effective approach for rapidly identifying tea quality.
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Affiliation(s)
- Hongling Xia
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Wei Chen
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Die Hu
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Aiqing Miao
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Xiaoyan Qiao
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Guangjun Qiu
- Institute of Facility Agriculture of Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Jianhua Liang
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Weiqing Guo
- GRINM (Guangdong) Institute for Advanced Materials and Technology, Foshan, Guangdong Province 528000, PR China.
| | - Chengying Ma
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China.
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Ni W, Yu Y, Gao X, Han Y, Zhang W, Zhang Z, Xiao W, Hu Q, Zhang Y, Huang H, Li F, Chen M, Han J. Multilocus Distance-Regulated Sensor Array for Recognition of Polyphenols via Machine Learning and Indicator Displacement Assay. Anal Chem 2024; 96:301-308. [PMID: 38102984 DOI: 10.1021/acs.analchem.3c04107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Developing new strategies to construct sensor arrays that can effectively distinguish multiple natural components with similar structures in mixtures is an exceptionally challenging task. Here, we propose a new multilocus distance-modulated indicator displacement assay (IDA) strategy for constructing a sensor array, incorporating machine learning optimization to identify polyphenols. An 8-element array, comprising two fluorophores and their six dynamic covalent complexes (C1-C6) formed by pairing two fluorophores with three distinct distance-regulated quenchers, has been constructed. Polyphenols with diverse spatial arrangements and combinatorial forms compete with the fluorophores by forming pseudocycles with quenchers within the complexes, leading to varying degrees of fluorescence recovery. The array accurately and effectively distinguished four tea polyphenols and 16 tea varieties, thereby demonstrating the broad applicability of the multilocus distance-modulated IDA array in detecting polyhydroxy foods and natural medicines.
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Affiliation(s)
- Weiwei Ni
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Yang Yu
- School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 211109, China
| | - Xu Gao
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Yang Han
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211109, China
| | - Wenhui Zhang
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Zerui Zhang
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Wenqi Xiao
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Qin Hu
- Department of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Yanliang Zhang
- Nanjing Research Center for Infectious Diseases of Integrated Traditional Chinese and Western Medicine, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing 211109, China
| | - Hui Huang
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Fei Li
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Mingqi Chen
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Jinsong Han
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
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5
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Yin L, Jayan H, Cai J, El-Seedi HR, Guo Z, Zou X. Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics. Foods 2023; 12:2968. [PMID: 37569237 PMCID: PMC10419230 DOI: 10.3390/foods12152968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 07/28/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
In the process of storage and cold chain logistics, apples are prone to physical bumps or microbial infection, which easily leads to spoilage in the micro-environment, resulting in widespread infection and serious post-harvest economic losses. Thus, development of methods for monitoring apple spoilage and providing early warning of spoilage has become the focus for post-harvest loss reduction. Thus, in this study, a spoilage monitoring and early warning system was developed by measuring volatile component production during apple spoilage combined with chemometric analysis. An apple spoilage monitoring prototype was designed to include a gas monitoring array capable of measuring volatile organic compounds, such as CO2, O2 and C2H4, integrated with the temperature and humidity sensor. The sensor information from a simulated apple warehouse was obtained by the prototype, and a multi-factor fusion early warning model of apple spoilage was established based on various modeling methods. Simulated annealing-partial least squares (SA-PLS) was the optimal model with the correlation coefficient of prediction set (Rp) and root mean square error of prediction (RMSEP) of 0.936 and 0.828, respectively. The real-time evaluation of the spoilage was successfully obtained by loading an optimal monitoring and warning model into the microcontroller. An apple remote monitoring and early warning platform was built to visualize the apple warehouse's sensors data and spoilage level. The results demonstrated that the prototype based on characteristic gas sensor array could effectively monitor and warn apple spoilage.
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Affiliation(s)
- Limei Yin
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
| | - Heera Jayan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
| | - Jianrong Cai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
| | - Hesham R. El-Seedi
- Pharmacognosy Group, Department of Pharmaceutical Biosciences, Biology Medical Center, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden;
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China
| | - Zhiming Guo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China
| | - Xiaobo Zou
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China
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6
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Ren G, Zhang X, Wu R, Yin L, Hu W, Zhang Z. Rapid Characterization of Black Tea Taste Quality Using Miniature NIR Spectroscopy and Electronic Tongue Sensors. BIOSENSORS 2023; 13:bios13010092. [PMID: 36671927 PMCID: PMC9855879 DOI: 10.3390/bios13010092] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 12/29/2022] [Accepted: 01/04/2023] [Indexed: 05/31/2023]
Abstract
The taste of tea is one of the key indicators in the evaluation of its quality and is a key factor in its grading and market pricing. To objectively and digitally evaluate the taste quality of tea leaves, miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors are considered effective sensor signals for the characterization of the taste quality of tea leaves. This study used micro-NIR spectroscopy and ET sensors in combination with data fusion strategies and chemometric tools for the taste quality assessment and prediction of multiple grades of black tea. Using NIR features and ET sensor signals as fused information, the data optimization based on grey wolf optimization, ant colony optimization (ACO), particle swarm optimization, and non-dominated sorting genetic algorithm II were employed as modeling features, combined with support vector machine (SVM), extreme learning machine and K-nearest neighbor algorithm to build the classification models. The results obtained showed that the ACO-SVM model had the highest classification accuracy with a discriminant rate of 93.56%. The overall results reveal that it is feasible to qualitatively distinguish black tea grades and categories by NIR spectroscopy and ET techniques.
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Affiliation(s)
- Guangxin Ren
- School of Biological Engineering, Institute of Digital Ecology and Health, Huainan Normal University, Huainan 232038, China
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan 232038, China
| | - Xusheng Zhang
- School of Biological Engineering, Institute of Digital Ecology and Health, Huainan Normal University, Huainan 232038, China
- Library, Huainan Normal University, Huainan 232038, China
| | - Rui Wu
- School of Biological Engineering, Institute of Digital Ecology and Health, Huainan Normal University, Huainan 232038, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan 232038, China
| | - Lingling Yin
- School of Biological Engineering, Institute of Digital Ecology and Health, Huainan Normal University, Huainan 232038, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan 232038, China
| | - Wenyan Hu
- School of Biological Engineering, Institute of Digital Ecology and Health, Huainan Normal University, Huainan 232038, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan 232038, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
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7
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Hu Y, Huang P, Wang Y, Sun J, Wu Y, Kang Z. Determination of Tibetan Tea Quality by Hyperspectral Imaging Technology and Multivariate Analysis. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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Wu R, Ren G, Yin L, Xie T, Zhang X, Zhang Z. Characterization of Congou Black Tea by an Electronic Nose with Grey Wolf Optimization (GWO) and Chemometrics. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2155833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Rui Wu
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan, China
| | - Guangxin Ren
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan, China
| | - Lingling Yin
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan, China
| | - Tian Xie
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan, China
| | - Xinyu Zhang
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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Jin G, Xu Y, Cui C, Zhu Y, Zong J, Cai H, Ning J, Wei C, Hou R. Rapid identification of the geographic origin of Taiping Houkui green tea using near-infrared spectroscopy combined with a variable selection method. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2022; 102:6123-6130. [PMID: 35474316 DOI: 10.1002/jsfa.11964] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 03/24/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Most studies focus on the geographically larger production areas in tea traceability. However, famous high-quality tea is often produced in a narrow range of origins, which makes traceability a challenge. In this study, Taiping Houkui (TPHK) green tea of narrow geographical origin was rapidly identified using Fourier-transform near-infrared (FT-NIR) spectroscopy. RESULTS First, spectral information of 114 TPHK samples from four production areas was acquired. Second, the synthetic minority over-sampling technique (SMOTE) was used to balance the sample data set, and three different spectral pre-processing methods were compared. Third, three feature variable selection algorithms were used to obtain the pre-processed spectral features. Finally, extreme learning machine (ELM) models based on the variables obtained from the selected features were established to trace the TPHK origin. The optimized ELM model achieves 95.35% classification accuracy in the test set. CONCLUSION The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for tea traceability in narrow regions. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Ge Jin
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Yifan Xu
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Chuanjian Cui
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Yuanyuan Zhu
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Jianfa Zong
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Huimei Cai
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Chaoling Wei
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Ruyan Hou
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
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Ong P, Tung IC, Chiu CF, Tsai IL, Shih HC, Chen S, Chuang YK. Determination of aflatoxin B1 level in rice (Oryza sativa L.) through near-infrared spectroscopy and an improved simulated annealing variable selection method. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108886] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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11
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Guo Z, Guo C, Sun L, Zuo M, Chen Q, El‐Seedi HR, Zou X. Identification of the apple spoilage causative fungi and prediction of the spoilage degree using electronic nose. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13816] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Zhiming Guo
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Chuang Guo
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Li Sun
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Min Zuo
- National Engineering Laboratory for Agri‐product Quality Traceability Beijing Technology and Business University Beijing China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Hesham R. El‐Seedi
- Pharmacognosy Group, Department of Pharmaceutical Biosciences, BMC Uppsala University Uppsala Sweden
- International Research Center for Food Nutrition and Safety Jiangsu University Zhenjiang China
| | - Xiaobo Zou
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
- International Research Center for Food Nutrition and Safety Jiangsu University Zhenjiang China
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Liu D, Wang E, Wang G, Wang P, Wang C, Wang Z. Non-destructive sugar content assessment of multiple cultivars of melons by dielectric properties. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:4308-4314. [PMID: 33417254 DOI: 10.1002/jsfa.11070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 12/29/2020] [Accepted: 01/08/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Non-destructive determination of the internal quality of fruit with a thick rind and of a large size is always difficult and challenging. To investigate the feasibility of the dielectric spectroscopy technique with respect to determining the sugar content of melons during the postharvest stage, three cultivars of melon samples (160 melons for each cultivar) were used to acquire dielectric spectra over the frequency range 20-4500 MHz. The three cultivars of melons were divided separately into a calibration set and a prediction set in a ratio of 3:1 by a joint x-y distance algorithm. Partial least squares (PLS) and extreme learning machine (ELM) methods were applied to develop individual-cultivar and multi-cultivar models based on full frequencies (FFs) and effective dielectric frequencies (EDFs) selected by the successive projection algorithm (SPA). RESULTS The results showed that ELM models demonstrated a better performance than PLS models for the same input dielectric variables. Most of the models built based on the EDFs selected by SPA had a slightly worse performance compared to those based on FFs. For both PLS and ELM methods, the models for multi-cultivars demonstrated a worse calibration and prediction performance compared to those for individual cultivars. When individual-cultivar and multi-cultivar samples were used to build sugar content determination models, the best model was FFs-ELM (Rp = 0.887, RMSEP = 0.986), FFs-ELM (Rp = 0.870, RMSEP = 1.028), FFs-PLS (Rp = 0.882, RMSEP = 1.010) and FFs-ELM (Rp = 0.849, RMSEP = 1.085) for 'Hongyanliang', 'Xinzaomi', 'Manao' and multi-cultivar melons, respectively. CONCLUSION The present study indicates that it is possible to develop both individual-cultivar and multi-cultivar models for determining the sugar content of melons based on the dielectric spectroscopy technique. © 2021 Society of Chemical Industry.
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Affiliation(s)
- Dayang Liu
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China
| | - Enfeng Wang
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China
| | - Guanglai Wang
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China
| | - Pan Wang
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China
| | - Congcong Wang
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China
| | - Zhuanwei Wang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
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13
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Ren G, Liu Y, Ning J, Zhang Z. Assessing black tea quality based on visible–near infrared spectra and kernel-based methods. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.103810] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Ren G, Li T, Wei Y, Ning J, Zhang Z. Estimation of Congou black tea quality by an electronic tongue technology combined with multivariate analysis. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105899] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Ren G, Wang Y, Ning J, Zhang Z. Evaluation of Dianhong black tea quality using near-infrared hyperspectral imaging technology. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:2135-2142. [PMID: 32981110 DOI: 10.1002/jsfa.10836] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/28/2020] [Accepted: 09/27/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms. RESULTS Two matrix statistical algorithms encompassing a gray-level co-occurrence matrix (GLCM) and a gradient co-occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near-infrared (NIR) spectra: iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%. CONCLUSION This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves. Sci Rep 2021; 11:4169. [PMID: 33603126 PMCID: PMC7892543 DOI: 10.1038/s41598-021-83847-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 02/09/2021] [Indexed: 01/31/2023] Open
Abstract
Spectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.
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Ren G, Gan N, Song Y, Ning J, Zhang Z. Evaluating Congou black tea quality using a lab-made computer vision system coupled with morphological features and chemometrics. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105600] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance. REMOTE SENSING 2020. [DOI: 10.3390/rs12193265] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Japanese horseradish (wasabi) grows in very specific conditions, and recent environmental climate changes have damaged wasabi production. In addition, the optimal culture methods are not well known, and it is becoming increasingly difficult for incipient farmers to cultivate it. Chlorophyll a, b and carotenoid contents, as well as their allocation, could be an adequate indicator in evaluating its production and environmental stress; thus, developing an in situ method to monitor photosynthetic pigments based on reflectance could be useful for agricultural management. Besides original reflectance (OR), five pre-processing techniques, namely, first derivative reflectance (FDR), continuum-removed (CR), de-trending (DT), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV), were compared to assess the accuracy of the estimation. Furthermore, five machine learning algorithms—random forest (RF), support vector machine (SVM), kernel-based extreme learning machine (KELM), Cubist, and Stochastic Gradient Boosting (SGB)—were considered. To classify the samples under different pH or sulphur ion concentration conditions, the end of the red edge bands was effective for OR, FDR, DT, MSC, and SNV, while a green-peak band was effective for CR. Overall, KELM and Cubist showed high performance and incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy. The best combinations were found to be DT–KELM for chl a (RPD = 1.511–5.17, RMSE = 1.23–3.62 μg cm−2) and chl a:b (RPD = 0.73–3.17, RMSE = 0.13–0.60); CR–KELM for chl b (RPD = 1.92–5.06, RMSE = 0.41–1.03 μg cm−2) and chl a:car (RPD = 1.31–3.23, RMSE = 0.26–0.50); SNV–Cubist for car (RPD = 1.63–3.32, RMSE = 0.31–1.89 μg cm−2); and DT–Cubist for chl:car (RPD = 1.53–3.96, RMSE = 0.27–0.74).
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