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Xiao H, Tian Y, Yang H, Zeng Y, Yang Y, Yuan Z, Zhou H. Are there any differences in the quality of high-mountain green tea before and after the first new leaves unfold? A comprehensive study based on E-sensors, whole metabolomics and sensory evaluation. Food Chem 2024; 457:140119. [PMID: 38936125 DOI: 10.1016/j.foodchem.2024.140119] [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: 01/12/2024] [Revised: 06/07/2024] [Accepted: 06/12/2024] [Indexed: 06/29/2024]
Abstract
High-mountain green tea, where the first new leaf hasn't yet unfurled, is prized for perceived superior quality, but this hasn't yet been verified by experimentation. Electronic sensors, whole metabolomics and sensory evaluation were employed to assess the quality of yymj (tea buds with a newly unfurled leaf) and qymj (tea buds without new leaves). The qymj proved to have significant advantages in aroma, color and shape, but still had some shortcomings in umami, bitterness and sourness. Differences in the content of volatile organic compounds (including alcohols, hydrocarbons and lipids) and nonvolatile organic compounds (flavonoids, amino acids, sugars, and phenolic acids) quality of high-mountain green teas with different maturity levels and provides well explained these quality differences. This study establishes a systematic approach to study the quality of high-mountain green tea at different maturity levels, and provides important reference information for consumers, governments and tea farmers.
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Affiliation(s)
- Hongshi Xiao
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410000, China
| | - Yun Tian
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410000, China
| | - Hui Yang
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410000, China
| | - Yajuan Zeng
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410000, China
| | - Yang Yang
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410000, China
| | - Zhihui Yuan
- College of Chemistry and Bioengineering, Hunan University of Science and Engineering, Yongzhou 425199, China.
| | - Haiyan Zhou
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, 410000, China.
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2
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Wang L, Xie J, Wang Q, Hu J, Jiang Y, Wang J, Tong H, Yuan H, Yang Y. Evaluation of the quality grade of Congou black tea by the fusion of GC- E-Nose, E-tongue, and E-eye. Food Chem X 2024; 23:101519. [PMID: 38933990 PMCID: PMC11200275 DOI: 10.1016/j.fochx.2024.101519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 05/23/2024] [Accepted: 05/27/2024] [Indexed: 06/28/2024] Open
Abstract
In the present study, the comprehensive quality of Congou black tea (CBT) including aroma, taste, and liquid color was investigated by a combination of gas chromatography electronic nose (GC-E-Nose), electronic tongue (E-tongue), and electronic eye (E-eye). An excellent discrimination of different quality grades of CBT was accomplished through the fusion of GC-E-Nose, E-tongue, and E-eye combined with orthogonal partial least squares discriminant analysis, with parameters of R2Y = 0.803 and Q2 = 0.740. Moreover, the quantitative evaluation of CBT quality was successfully achieved by partial least squares regression analysis, with the absolute error within 1.39 point, and the relative error within 1.62%. Additionally, 12 key variables were screened out by stepwise multiple linear regression analysis, which significantly contributed to the comprehensive quality score of CBT. Our results suggest that the fusion of multiple intelligent sensory technologies offers great potential and practicability in the quality evaluation of black tea.
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Affiliation(s)
- Lilei Wang
- Key Laboratory of Biology, Genetics and breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
- College of Food Science, Southwest University, Beibei District, Chongqing 400715, China
| | - Jialing Xie
- Key Laboratory of Biology, Genetics and breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Qiwei Wang
- Key Laboratory of Biology, Genetics and breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Jiajing Hu
- Key Laboratory of Biology, Genetics and breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Yongwen Jiang
- Key Laboratory of Biology, Genetics and breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Jinjin Wang
- Key Laboratory of Biology, Genetics and breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Huarong Tong
- College of Food Science, Southwest University, Beibei District, Chongqing 400715, China
| | - Haibo Yuan
- Key Laboratory of Biology, Genetics and breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Yanqin Yang
- Key Laboratory of Biology, Genetics and breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
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3
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Jin X, Wang Z, Ma J, Liu C, Bai X, Lan Y. Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of Astragalus. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:5930-5943. [PMID: 38459895 DOI: 10.1002/jsfa.13450] [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: 10/10/2023] [Revised: 01/23/2024] [Accepted: 03/09/2024] [Indexed: 03/11/2024]
Abstract
BACKGROUND Astragalus is a widely used traditional Chinese medicine material that is easily confused due to its quality, price and other factors derived from different origins. This article describes a novel method for the rapid tracing and detection of Astragalus via the joint application of an electronic tongue (ET) and an electronic eye (EE) combined with a lightweight convoluted neural network (CNN)-transformer model. First, ET and EE systems were employed to measure the taste fingerprints and appearance images, respectively, of different Astragalus samples. Three spectral transform methods - the Markov transition field, short-time Fourier transform and recurrence plot - were utilized to convert the ET signals into 2D spectrograms. Then, the obtained ET spectrograms were fused with the EE image to obtain multimodal information. A lightweight hybrid model, termed GETNet, was designed to achieve pattern recognition for the Astragalus fusion information. The proposed model employed an improved transformer module and an improved Ghost bottleneck as its backbone network, complementarily utilizing the benefits of CNN and transformer architectures for local and global feature representation. Furthermore, the Ghost bottleneck was further optimized using a channel attention technique, which boosted the model's feature extraction effectiveness. RESULTS The experiments indicate that the proposed data fusion strategy based on ET and EE devices has better recognition accuracy than that attained with independent sensing devices. CONCLUSION The proposed method achieved high precision (99.1%) and recall (99.1%) values, providing a novel approach for rapidly identifying the origin of Astragalus, and it holds great promise for applications involving other types of Chinese herbal medicines. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Xinning Jin
- School of Computer Science and Technology, Shandong University of Technology, Zibo, China
| | - Zhiqiang Wang
- School of Computer Science and Technology, Shandong University of Technology, Zibo, China
| | - Jingyu Ma
- School of Computer Science and Technology, Shandong University of Technology, Zibo, China
| | - Chuanzheng Liu
- School of Computer Science and Technology, Shandong University of Technology, Zibo, China
| | - Xuerui Bai
- School of Computer Science and Technology, Shandong University of Technology, Zibo, China
| | - Yubin Lan
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, China
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4
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Bai L, Zhang ZT, Guan H, Liu W, Chen L, Yuan D, Chen P, Xue M, Yan G. Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network. Talanta 2024; 275:126098. [PMID: 38640523 DOI: 10.1016/j.talanta.2024.126098] [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: 02/02/2024] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/21/2024]
Abstract
The authentic traditional Chinese medicines (TCMs) including Angelicae Sinensis Radix (ASR) are the representative of high-quality herbals in China. However, ASR from authentic region being adulterated or counterfeited is frequently occurring, and there is still a lack of rapid quality evaluation methods for identifying the authentic ASR. In this study, the color features of ASR were firstly characterized. The results showed that the authentic ASR cannot be fully identified by color characteristics. Then near-infrared (NIR) spectroscopy combined with Bayesian optimized long short-term memory (BO-LSTM) was used to evaluate the quality of ASR, and the performance of BO-LSTM with common classification and regression algorithms was compared. The results revealed that following the pretreatment of NIR spectra, the optimal NIR spectra combined with BO-LSTM not only successfully distinguished authentic, non-authentic, and adulterated ASR with 100 % accuracy, but also accurately predicted the adulteration concentration of authentic ASR (R2 > 0.99). Moreover, BO-LSTM demonstrated excellent performance in classification and regression compared with common algorithms (ANN, SVM, PLSR, etc.). Overall, the proposed strategy could quickly and accurately evaluate the quality of ASR, which provided a reference for other TCMs.
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Affiliation(s)
- Lei Bai
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Zhi-Tong Zhang
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Huanhuan Guan
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Wenjian Liu
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Li Chen
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Dongping Yuan
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Pan Chen
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Mei Xue
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing 210023, China.
| | - Guojun Yan
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China.
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5
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Li N, Xu J, Zhao Y, Zhao M, Liu Z, Wang K, Huang J, Zhu M. The influence of processing methods on polyphenol profiling of tea leaves from the same large-leaf cultivar (Camellia sinensis var. assamica cv. Yunkang-10): nontargeted/targeted polyphenomics and electronic sensory analysis. Food Chem 2024; 460:140515. [PMID: 39067433 DOI: 10.1016/j.foodchem.2024.140515] [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: 04/24/2024] [Revised: 07/07/2024] [Accepted: 07/16/2024] [Indexed: 07/30/2024]
Abstract
Tea polyphenols transform under processing methods, but a systematic study on their changes in the same large-leaf tea cultivar is lacking. Here, Camellia sinensis var. assamica cv. Yunkang-10 leaves underwent six processing methods and were assessed using optimized nontargeted (UHPLC-Q-Exactive Orbitrap-MS) and targeted (UHPLC-QqQ-MS) polyphenomics, along with molecular networking analysis. 903 and 52 polyphenolic compounds (catechins, flavones and flavonols, and phenolic acids) were respectively relatively and absolutely quantified for the first time. Dark and black teas, with the lowest polyphenol content, differed from the other four tea types, although variations existed among these four teas. However, some flavonol and flavone aglycones (e.g. kaempferol, apigenin), as well as some phenolic acids (e.g. ellagic acid, gallic acid), exhibited higher levels in dark and black teas. Correlations between polyphenolic composition and electronic sensory characteristics were observed using E-tongue and E-eye. This study enriches understanding of polyphenol profiles in Chinese teas post diverse processing.
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Affiliation(s)
- Na Li
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China
| | - Junren Xu
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China
| | - Yiqiao Zhao
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China
| | | | - Zhonghua Liu
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China
| | - Kunbo Wang
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China.
| | - Jianan Huang
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China.
| | - Mingzhi Zhu
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China.
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6
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Diab H, Calle A, Thompson J. Rapid and Online Microvolume Flow-Through Dialysis Probe for Sample Preparation in Veterinary Drug Residue Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:3971. [PMID: 38931755 PMCID: PMC11207326 DOI: 10.3390/s24123971] [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: 05/22/2024] [Revised: 06/13/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024]
Abstract
A rapid and online microvolume flow-through dialysis probe designed for sample preparation in the analysis of veterinary drug residues is introduced. This study addresses the need for efficient and green sample preparation methods that reduce chemical waste and reagent use. The dialysis probe integrates with liquid chromatography and mass spectrometry (LC-MS) systems, facilitating automated, high-throughput analysis. The dialysis method utilizes minimal reagent volumes per sample, significantly reducing the generation of solvent waste compared to traditional sample preparation techniques. Several veterinary drugs were spiked into tissue homogenates and analyzed to validate the probe's efficacy. A diagnostic sensitivity of >97% and specificity of >95% were obtained for this performance evaluation. The results demonstrated the effective removal of cellular debris and particulates, ensuring sample integrity and preventing instrument clogging. The automated dialysis probe yielded recovery rates between 27 and 77% for multiple analytes, confirming its potential to streamline veterinary drug residue analysis, while adhering to green chemistry principles. The approach highlights substantial improvements in both environmental impact and operational efficiency, presenting a viable alternative to conventional sample preparation methods in regulatory and research applications.
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Affiliation(s)
| | | | - Jonathan Thompson
- School of Veterinary Medicine, Texas Tech University, Amarillo, TX 79106, USA
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Liu T, Yu M, Dai Y, Xiao Y, Li L. Traditional method of rhubarb processing optimized by combining flavor analysis with anthraquinone content determination. Front Nutr 2024; 11:1406430. [PMID: 38933883 PMCID: PMC11199713 DOI: 10.3389/fnut.2024.1406430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024] Open
Abstract
Introduction Rhubarb is a popular food that relieves constipation and aids with weight loss. The traditional method of preparation, includes steaming and sun-drying rhubarb nine times (SDR-9) to reduce its toxicity and increase efficacy. Methods Flavor analysis includes odor analysis by gas chromatography-ion mobility spectrometry and taste characterization using an electronic tongue. Results Odor analysis of the samples prepared through SDR-9 identified 61 volatile compounds, including aldehydes, esters, alcohols, ketones, acids, alkenes, and furans. Of these, 13 volatile components were the key substances associated with odor. This enabled the process to be divided into two stages: 1-5 times of steaming and sun-drying and 6-9 times. In the second stage, SDR-6 and SDR-9 were grouped together in terms of odor. Analysis using electronic tongue revealed that the most prominent taste was bitterness. A radar map indicated that the bitterness response was the highest for raw rhubarb, whereas that for processed (steamed and sun-dried) rhubarb decreased. Orthogonal partial least squares discriminant analysis (OPLS-DA) clustering results for SDR-6 and SDR-9 samples indicated that their tastes were similar. Anthraquinones were analyzed via high-performance liquid chromatography; moreover, analysis of the taste and components of the SDR samples revealed a significant correlation. Discussion These results indicate that there are similarities between SDR-6 and SDR-9 in terms of smell, taste, and composition, indicating that the steaming and sun-drying cycles can be conducted six times instead of nine.
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Affiliation(s)
| | | | | | | | - Li Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
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8
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Hu Y, Chen W, Gouda M, Yao H, Zuo X, Yu H, Zhang Y, Ding L, Zhu F, Wang Y, Li X, Zhou J, He Y. Fungal fermentation of Fuzhuan brick tea: A comprehensive evaluation of sensory properties using chemometrics, visible near-infrared spectroscopy, and electronic nose. Food Res Int 2024; 186:114401. [PMID: 38729704 DOI: 10.1016/j.foodres.2024.114401] [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: 01/10/2024] [Revised: 04/17/2024] [Accepted: 04/20/2024] [Indexed: 05/12/2024]
Abstract
Fuzhuan brick tea (FBT) fungal fermentation is a key factor in achieving its unique dark color, aroma, and taste. Therefore, it is essential to develop a rapid and reliable method that could assess its quality during FBT fermentation process. This study focused on using electronic nose (e-nose) and spectroscopy combination with sensory evaluations and physicochemical measurements for building machine learning (ML) models of FBT. The results showed that the fused data achieved 100 % accuracy in classifying the FBT fermentation process. The SPA-MLR method was the best prediction model for FBT quality (R2 = 0.95, RMSEP = 0.07, RPD = 4.23), and the fermentation process was visualized. Where, it was effectively detecting the degree of fermentation relationship with the quality characteristics. In conclusion, the current study's novelty comes from the established real-time method that could sensitively detect the unique post-fermentation quality components based on the integration of spectral, and e-nose and ML approaches.
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Affiliation(s)
- Yan Hu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Wei Chen
- Tea Research Institute, Zhejiang University, Hangzhou 310058, China.
| | - Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Department of Nutrition and Food Science, National Research Centre, Dokki, Gizah 12622, Egypt
| | - Huan Yao
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xinxin Zuo
- Tea Research Institute, Zhejiang University, Hangzhou 310058, China
| | - Huahao Yu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yuying Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Lejia Ding
- Tea Research Institute, Zhejiang University, Hangzhou 310058, China
| | - Fengle Zhu
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yuefei Wang
- Tea Research Institute, Zhejiang University, Hangzhou 310058, China
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Jihong Zhou
- Tea Research Institute, Zhejiang University, Hangzhou 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
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9
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Lu L, Wang L, Liu R, Zhang Y, Zheng X, Lu J, Wang X, Ye J. An efficient artificial intelligence algorithm for predicting the sensory quality of green and black teas based on the key chemical indices. Food Chem 2024; 441:138341. [PMID: 38176147 DOI: 10.1016/j.foodchem.2023.138341] [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: 10/30/2023] [Revised: 12/20/2023] [Accepted: 12/29/2023] [Indexed: 01/06/2024]
Abstract
The key components dominating the quality of green tea and black tea are still unclear. Here, we respectively produced green and black teas in March and June, and investigated the correlations between sensory quality and chemical compositions of dry teas by multivariate statistics, bioinformatics and artificial intelligence algorithm. The key chemical indices were screened out to establish tea sensory quality-prediction models based on the result of OPLS-DA and random forest, namely 4 flavonol glycosides of green tea and 8 indices of black tea (4 pigments, epigallocatechin, kaempferol-3-O-rhamnosyl-glucoside, ratios of caffeine/total catechins and epi/non-epi catechins). Compared with OPLS-DA and random forest, the support vector machine model had good sensory quality-prediction performance for both green tea and black tea (F1-score > 0.92), even based on the indices of fresh tea leaves. Our study explores the potential of artificial intelligence algorithm in classification and prediction of tea products with different sensory quality.
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Affiliation(s)
- Lu Lu
- Zhejiang University Tea Research Institute, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Lu Wang
- Key Laboratory of Biology, Genetics and Breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, National Center for Tea Plant Improvement, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Ruyi Liu
- Zhejiang University Tea Research Institute, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Yingbin Zhang
- Key Laboratory of Biology, Genetics and Breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, National Center for Tea Plant Improvement, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Xinqiang Zheng
- Zhejiang University Tea Research Institute, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Jianliang Lu
- Zhejiang University Tea Research Institute, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Xinchao Wang
- Key Laboratory of Biology, Genetics and Breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, National Center for Tea Plant Improvement, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.
| | - Jianhui Ye
- Zhejiang University Tea Research Institute, 866 Yuhangtang Road, Hangzhou 310058, China.
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10
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Huang X, Li Y, Zhou F, Xiao T, Shang B, Niu L, Huang J, Liu Z, Wang K, Zhu M. Insight into the chemical compositions of Anhua dark teas derived from identical tea materials: A multi-omics, electronic sensory, and microbial sequencing analysis. Food Chem 2024; 441:138367. [PMID: 38199099 DOI: 10.1016/j.foodchem.2024.138367] [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: 11/02/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 01/12/2024]
Abstract
Anhua dark teas (DTs), including Tianjian tea, Qianliang tea, Hei brick tea, and Fu brick tea, are unique fermented teas from China's Anhua County; yet their chemical composition differences remain unclear. Herein, metabolomics, volatolomics, and electronic sensory assessments were employed to analyze and compare chemical compositions and sensory characteristics of five types of Anhua DTs. All of these teas were derived from identical tea materials. Chemical compositions differed significantly among Anhua DTs, with Tianjian tea remarkable. Long-lasting fermentation and complex processing methods led to transformation of multiple compounds, particularly catechins. Eighteen volatile compounds with OVA > 1 were key aroma contributors in Anhua DTs. Internal transcribed spacer and 16S ribosomal DNA sequencing showed that Eurotium, Pseudomonas, and Bacillus are dominant microorganisms in Anhua DTs. Furthermore, this study unveiled notable differences in chemical compositions between Anhua DTs and five other traditional types of tea. This research enhances our understanding of Anhua DTs processing.
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Affiliation(s)
- Xiangxiang Huang
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China.
| | - Yilong Li
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China.
| | - Fang Zhou
- School of Chemistry and Environmental Science, Xiangnan University, Chenzhou 423000, China.
| | - Tian Xiao
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China.
| | - Bohao Shang
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China.
| | - Li Niu
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China.
| | - Jianan Huang
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China.
| | - Zhonghua Liu
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China.
| | - Kunbo Wang
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China.
| | - Mingzhi Zhu
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients & Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China; Key Laboratory of Tea Science of Ministry of Education, Key Laboratory for Evaluation and Utilization of Gene Resources of Horticultural Crops, Ministry of Agriculture and Rural Affairs of China, Hunan Agricultural University, Changsha 410128, China.
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11
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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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12
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Zhang JB, Wang B, Zhang YF, Wu Y, Li MX, Gao T, Lu TL, Bian ZH, Su LL. E-eye and FT-NIR combined with multivariate algorithms to rapidly evaluate the dynamic changes in the quality of Gastrodia elata during steaming process. Food Chem 2024; 439:138148. [PMID: 38064826 DOI: 10.1016/j.foodchem.2023.138148] [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: 09/21/2023] [Revised: 12/01/2023] [Accepted: 12/03/2023] [Indexed: 01/10/2024]
Abstract
Gastrodia elata (GE) is traditionally subjected to steaming, and steaming duration plays a crucially important role in determining GE quality. This study examined the variations in bioactive components during the steaming process and proposed the utilization of electronic eye and Fourier Transform near-infrared (FT-NIR) spectroscopy for quality assessment. The findings revealed that the levels of parishin E parishin B, parishin A, and gastrodin initially rose and subsequently declined, while 4-Hydroxybenzyl alcohol exhibited a rapid decrease followed by stabilization. With prolonged steaming, the brightness of GE decreased, while the red and yellow tones became more pronounced and the color saturation increased. FT-NIR divided the steaming process into three stages: 0 min (raw GE), 0-9 min (partially steamed GE), and 9-30 min (fully steamed GE), and the partial least squares regression models effectively predicted the levels of five components. Overall, this study provided valuable insights into quality control in food processing.
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Affiliation(s)
- Jiu-Ba Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Bin Wang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yun-Fei Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yi Wu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Ming-Xuan Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Ting Gao
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Tu-Lin Lu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Zhen-Hua Bian
- Department of Pharmacy, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214071, China; Jiangsu CM Clinical Innovation Center of Degenerative Bone & Joint Disease, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214071, China.
| | - Lian-Lin Su
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China; Jiangsu Provincial Technology Engineering Research Center of TCM Health Preservation, Nanjing 210023, China.
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13
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Wang X, Sun K, Liao X, Zhang Y, Ban Y, Zhang X, Song Z. Physicochemical, antibacterial and aromatic qualities of herbaceous peony ( Paeonia lactiflora pall) tea with different varieties. RSC Adv 2024; 14:14303-14310. [PMID: 38690105 PMCID: PMC11060045 DOI: 10.1039/d3ra08144c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/22/2024] [Indexed: 05/02/2024] Open
Abstract
The aim of this study was to evaluate the effect of five varieties on the quality of herbaceous peony tea by physicochemical analysis, sensory evaluation, antimicrobial capacity analysis and a combination of gas chromatography with quadruple time of flight mass spectrometry (GC-QTOF). Antibacterial and antioxidant analyses revealed that the ABTS free radical scavenging rate of HPT was high, ranging from 82.20% to 87.40% overall. 'Madame Claude Tain' had the strongest inhibitory ability against Staphylococcus aureus with an inhibitory effect of 12.65 mm. The sensory evaluation showed that 'Angel cheeks' had the highest overall sensory score. GC-QTOF combined with orthogonal projections to latent structures discriminant analysis showed that 22 volatile components were the key aroma components of herbaceous peony tea. Different varieties of herbaceous peony tea had a unique characteristic aroma. 'Angel cheeks' imparted lily-like and chestnut fragrances, which were attributed to linalool and 3,5-octadien-2-one. 'Sea Shell', 'Mother's Choice' and 'Angel Cheek' had a medicinal aroma, which may be due to the presence of o-cymene. Overall, 'Angel cheeks' was the most suitable for developing high-quality herbaceous peony tea in five varieties. This study provided a theoretical basis and technical guidance for the development of herbaceous peony.
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Affiliation(s)
- Xiaoxiao Wang
- College of Landscape Architecture and Forestry, Qingdao Agricultural University Qingdao Shandong 266109 China
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences Beijing 100081 China
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs Beijing 100081 China
- College of Engineering, China Agricultural University Beijing 100083 China
| | - Kairong Sun
- College of Horticulture, China Agricultural University Beijing 100193 China
| | - Xueping Liao
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs Beijing 100081 China
| | - Yanli Zhang
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences Beijing 100081 China
| | - Yuqian Ban
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences Beijing 100081 China
| | - Xiuxin Zhang
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs Beijing 100081 China
| | - Zihan Song
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences Beijing 100081 China
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14
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Li Y, Li Y, Xiao T, Jia H, Xiao Y, Liu Z, Wang K, Zhu M. Integration of non-targeted/targeted metabolomics and electronic sensor technology reveals the chemical and sensor variation in 12 representative yellow teas. Food Chem X 2024; 21:101093. [PMID: 38268841 PMCID: PMC10805769 DOI: 10.1016/j.fochx.2023.101093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/09/2023] [Accepted: 12/20/2023] [Indexed: 01/26/2024] Open
Abstract
Yellow tea is a lightly fermented tea with unique sensory qualities and health benefits. However, chemical composition and sensory quality of yellow tea products have rarely been studied. 12 representative yellow teas, which were basically covered the main products of yellow tea, were chosen in this study. Combined analysis of non-targeted/targeted metabolomics and electronic sensor technologies (E-eye, E-nose, E-tongue) revealed the chemical and sensor variation. The results showed that yellow big tea differed greatly from yellow bud teas and yellow little teas, but yellow bud teas could not be effectively distinguished from yellow little teas based on chemical constituents and electronic sensory characteristics. Sensor variation of yellow teas might be attributed to some compounds related to bitterness and aftertaste-bitterness (4'-dehydroxylated gallocatechin-3-O-gallate, dehydrotheasinensin C, myricitin 3-O-galactoside, phloroglucinol), aftertaste-astringency (methyl gallate, 1,5-digalloylglucose, 2,6-digalloylglucose), and sweetness (maltotriose). This study provided a comprehensive understanding of yellow tea on chemical composition and sensory quality.
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Affiliation(s)
- Yuan Li
- Key Laboratory of Tea Science of Ministry of Education, National Research Center of Engineering Technology for Utilization of Functional Ingredients from Botanicals, Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
- Hunan Provincial Key Lab of Dark Tea and Jin-hua, Hunan City University, Yiyang 413000, China
| | - Yilong Li
- Key Laboratory of Tea Science of Ministry of Education, National Research Center of Engineering Technology for Utilization of Functional Ingredients from Botanicals, Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Tian Xiao
- Key Laboratory of Tea Science of Ministry of Education, National Research Center of Engineering Technology for Utilization of Functional Ingredients from Botanicals, Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Huimin Jia
- Key Laboratory of Tea Science of Ministry of Education, National Research Center of Engineering Technology for Utilization of Functional Ingredients from Botanicals, Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Yu Xiao
- College of Food Science and Technology, Hunan Agricultural University, Changsha 410128, China
| | - Zhonghua Liu
- Key Laboratory of Tea Science of Ministry of Education, National Research Center of Engineering Technology for Utilization of Functional Ingredients from Botanicals, Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Kunbo Wang
- Key Laboratory of Tea Science of Ministry of Education, National Research Center of Engineering Technology for Utilization of Functional Ingredients from Botanicals, Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Mingzhi Zhu
- Key Laboratory of Tea Science of Ministry of Education, National Research Center of Engineering Technology for Utilization of Functional Ingredients from Botanicals, Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
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15
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Zhao Q, Ye Z, Deng Y, Chen J, Chen J, Liu D, Ye X, Huan C. An advance in novel intelligent sensory technologies: From an implicit-tracking perspective of food perception. Compr Rev Food Sci Food Saf 2024; 23:e13327. [PMID: 38517017 DOI: 10.1111/1541-4337.13327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 02/19/2024] [Accepted: 03/01/2024] [Indexed: 03/23/2024]
Abstract
Food sensory evaluation mainly includes explicit and implicit measurement methods. Implicit measures of consumer perception are gaining significant attention in food sensory and consumer science as they provide effective, subconscious, objective analysis. A wide range of advanced technologies are now available for analyzing physiological and psychological responses, including facial analysis technology, neuroimaging technology, autonomic nervous system technology, and behavioral pattern measurement. However, researchers in the food field often lack systematic knowledge of these multidisciplinary technologies and struggle with interpreting their results. In order to bridge this gap, this review systematically describes the principles and highlights the applications in food sensory and consumer science of facial analysis technologies such as eye tracking, facial electromyography, and automatic facial expression analysis, as well as neuroimaging technologies like electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and functional near-infrared spectroscopy. Furthermore, we critically compare and discuss these advanced implicit techniques in the context of food sensory research and then accordingly propose prospects. Ultimately, we conclude that implicit measures should be complemented by traditional explicit measures to capture responses beyond preference. Facial analysis technologies offer a more objective reflection of sensory perception and attitudes toward food, whereas neuroimaging techniques provide valuable insight into the implicit physiological responses during food consumption. To enhance the interpretability and generalizability of implicit measurement results, further sensory studies are needed. Looking ahead, the combination of different methodological techniques in real-life situations holds promise for consumer sensory science in the field of food research.
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Affiliation(s)
- Qian Zhao
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
| | - Zhiyue Ye
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
| | - Yong Deng
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
| | - Jin Chen
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
| | - Jianle Chen
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Zhongyuan Institute, Zhejiang University, Zhengzhou, China
- Ningbo Innovation Center, Zhejiang University, Ningbo, China
| | - Donghong Liu
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
- Zhongyuan Institute, Zhejiang University, Zhengzhou, China
- Ningbo Innovation Center, Zhejiang University, Ningbo, China
| | - Xingqian Ye
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Zhongyuan Institute, Zhejiang University, Zhengzhou, China
- Ningbo Innovation Center, Zhejiang University, Ningbo, China
| | - Cheng Huan
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
- Zhongyuan Institute, Zhejiang University, Zhengzhou, China
- Ningbo Innovation Center, Zhejiang University, Ningbo, China
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16
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Xu K, Zhang Z, Jiang K, Yang A, Wang T, Xu L, Li X, Zhang X, Meng F, Wang B. Elucidating the effect of different processing methods on the sensory quality of chestnuts based on multi-scale molecular sensory science. Food Chem 2024; 431:136989. [PMID: 37572488 DOI: 10.1016/j.foodchem.2023.136989] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/13/2023] [Accepted: 07/23/2023] [Indexed: 08/14/2023]
Abstract
Chestnuts are known for their unique flavor and nutritional value. However, the flavor changes in chestnuts after processing remain unclear. Multi-intelligent sensory technologies and headspace solid-phase microextraction-arrow gas chromatography-mass spectrometry (HS-SPME-Arrow-GC-MS) combined with multivariate statistical analysis were applied to evaluate the effect of packaging and heat sterilization procedures on the sensory quality of chestnuts. The results showed that the significant variations (p < 0.05) between the different chestnut processing methods were revealed via the electronic eye (E-eye), electronic nose (E-nose), and electronic tongue (E-tongue). The packaging had a more significant influence on the sensory quality of the chestnuts than heat sterilization procedures. HS-SPME-Arrow-GC-MS identified 83 volatile compounds. The processed chestnuts exhibited higher aldehyde, ester, and alkene concentrations, while N2 packaging was more favorable to flavor elicitation and retention. Therefore, combining intelligent sensory techniques with GC-MS can rapidly determine the chestnut quality and guide industrial production.
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Affiliation(s)
- Kunli Xu
- School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
| | - Zheting Zhang
- School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
| | - Kexin Jiang
- School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
| | - Aolin Yang
- School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
| | - Tielong Wang
- Chinese Academy of Inspection and Quarantine, Beijing 100176, China
| | - Lingyun Xu
- Chinese Academy of Inspection and Quarantine, Beijing 100176, China
| | - Xiaodong Li
- Shimadzu CO., LTD., China Innovation Center, Beijing 100020, China
| | - Xiaoli Zhang
- Shimadzu CO., LTD., China Innovation Center, Beijing 100020, China
| | - Fanyu Meng
- School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
| | - Bei Wang
- School of Food and Health, Beijing Technology and Business University, Beijing 100048, China.
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17
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Oroian M, Dranca F, Ropciuc S, Pauliuc D. A comparative study regarding the adulteration detection of honey: Physicochemical parameters vs. impedimetric data. Curr Res Food Sci 2023; 7:100642. [PMID: 38115897 PMCID: PMC10728335 DOI: 10.1016/j.crfs.2023.100642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/02/2023] [Accepted: 11/13/2023] [Indexed: 12/21/2023] Open
Abstract
Honey adulteration is a major issue for European Union and its members because of an unfair practice of different producers and beekeepers, many adulterations involve the addition of sweet, concentrated syrups which may appear like honey. In our study we analysed the influence of adulteration of tilia honey with different syrups (such as corn, rice, inverted sugar, agave, maple syrups) in different percentages (5%, 10%, and 20% respectively) on physicochemical parameters (moisture content, L*, hab,cab, pH, free acidity, electrical conductivity (EC), 5-hydroxymetilfurfural (HMF), fructose, glucose, sucrose, turanose, trehalose, melesitose and raffinose) and impedimetric properties using electrochemical impedance spectroscopy. The impedimetric sensing was made using an electrochemical cell composed of two gold electrodes, and the frequency ranged between 0.1 kHz and 100 kHz. The impedimetric parameters (Z', Z″ and phase) and Randal circuit parameters can distinguish the authentic honeys from the adulterated ones (based on the adulteration agent and adulteration percentage, respectively). The partial least squares - discriminant analysis (PLS-DA) and support vector machines (SVM) were used in a binary mode to separate the authentic honeys from the adulterated ones, and the SVM proved to separate much better than PLS-DA.
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Affiliation(s)
- Mircea Oroian
- Faculty of Food Engineering, Stefan cel Mare University of Suceava, Romania
| | - Florina Dranca
- Faculty of Food Engineering, Stefan cel Mare University of Suceava, Romania
| | - Sorina Ropciuc
- Faculty of Food Engineering, Stefan cel Mare University of Suceava, Romania
| | - Daniela Pauliuc
- Faculty of Food Engineering, Stefan cel Mare University of Suceava, Romania
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18
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Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Review of technology advances to assess rice quality traits and consumer perception. Food Res Int 2023; 172:113105. [PMID: 37689840 DOI: 10.1016/j.foodres.2023.113105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/02/2023] [Accepted: 06/09/2023] [Indexed: 09/11/2023]
Abstract
The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.
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Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia; Department of Agrotechnology, Faculty of Mechanical Engineering and Technology, Universiti Malaysia Perlis, 02600 Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia; Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., México 64849, Mexico.
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19
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Anker M, Yousefi-Darani A, Zettel V, Paquet-Durand O, Hitzmann B, Krupitzer C. Online Monitoring of Sourdough Fermentation Using a Gas Sensor Array with Multivariate Data Analysis. SENSORS (BASEL, SWITZERLAND) 2023; 23:7681. [PMID: 37765737 PMCID: PMC10536588 DOI: 10.3390/s23187681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/22/2023] [Accepted: 08/26/2023] [Indexed: 09/29/2023]
Abstract
Sourdough can improve bakery products' shelf life, sensory properties, and nutrient composition. To ensure high-quality sourdough, the fermentation has to be monitored. The characteristic process variables for sourdough fermentation are pH and the degree of acidity measured as total titratable acidity (TTA). The time- and cost-intensive offline measurement of process variables can be improved by utilizing online gas measurements in prediction models. Therefore, a gas sensor array (GSA) system was used to monitor the fermentation process of sourdough online by correlation of exhaust gas data with offline measurement values of the process variables. Three methods were tested to utilize the extracted features from GSA to create the models. The most robust prediction models were achieved using a PCA (Principal Component Analysis) on all features and combined two fermentations. The calibrations with the extracted features had a percentage root mean square error (RMSE) from 1.4% to 12% for the pH and from 2.7% to 9.3% for the TTA. The coefficient of determination (R2) for these calibrations was 0.94 to 0.998 for the pH and 0.947 to 0.994 for the TTA. The obtained results indicate that the online measurement of exhaust gas from sourdough fermentations with gas sensor arrays can be a cheap and efficient application to predict pH and TTA.
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Affiliation(s)
- Marvin Anker
- Department of Food Informatics and Computational Science Hub, University of Hohenheim, 70599 Stuttgart, Germany;
| | - Abdolrahim Yousefi-Darani
- Department of Process Analytics and Cereal Science, University of Hohenheim, 70599 Stuttgart, Germany; (A.Y.-D.); (O.P.-D.); (B.H.)
| | - Viktoria Zettel
- Department of Process Analytics and Cereal Science, University of Hohenheim, 70599 Stuttgart, Germany; (A.Y.-D.); (O.P.-D.); (B.H.)
| | - Olivier Paquet-Durand
- Department of Process Analytics and Cereal Science, University of Hohenheim, 70599 Stuttgart, Germany; (A.Y.-D.); (O.P.-D.); (B.H.)
| | - Bernd Hitzmann
- Department of Process Analytics and Cereal Science, University of Hohenheim, 70599 Stuttgart, Germany; (A.Y.-D.); (O.P.-D.); (B.H.)
| | - Christian Krupitzer
- Department of Food Informatics and Computational Science Hub, University of Hohenheim, 70599 Stuttgart, Germany;
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20
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Anwar H, Anwar T, Murtaza S. Review on food quality assessment using machine learning and electronic nose system. BIOSENSORS AND BIOELECTRONICS: X 2023; 14:100365. [DOI: 10.1016/j.biosx.2023.100365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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21
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Yang H, Wang Y, Zhao J, Li P, Li L, Wang F. A machine learning method for juice human sensory hedonic prediction using electronic sensory features. Curr Res Food Sci 2023; 7:100576. [PMID: 37691694 PMCID: PMC10485034 DOI: 10.1016/j.crfs.2023.100576] [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: 07/02/2023] [Revised: 08/03/2023] [Accepted: 08/24/2023] [Indexed: 09/12/2023] Open
Abstract
This study proposed a method that combines fused electronic sensory analysis technology with artificial neural network to predict the human sensory hedonic of fruit juice. Quantitative descriptive analysis (QDA) and the scoring test method were utilized for human sensory evaluation. The first step involved modeling the fused e-sensory features with human sensory attributes, followed by establishing a fitting model of human sensory attributes and acceptance. The R2 and RMSE values obtained were 0.77 and 0.42 (QDA method), and 0.63 and 0.63 (scoring test method). Finally, the relationship between the fusion e-sensory features and the human sensory hedonic was established. Model-1 achieved an R2 of 0.95 and an RMSE of 0.04, while model-2 achieved an R2 value of 0.88 and an RMSE value of 0.21. This study demonstrates the potential of fusing e-sensory technologies to replace human senses, which may lead to the development of devices with simultaneous multiple senses.
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Affiliation(s)
- Huihui Yang
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China
- Weifang Institute of Food Science and Processing Technology, Weifang, 261000, PR China
| | - Yutang Wang
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China
| | - Jinyong Zhao
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China
| | - Ping Li
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China
| | - Long Li
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China
- Weifang Institute of Food Science and Processing Technology, Weifang, 261000, PR China
| | - Fengzhong Wang
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China
- Weifang Institute of Food Science and Processing Technology, Weifang, 261000, PR China
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22
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Chen Y, Lai L, You Y, Gao R, Xiang J, Wang G, Yu W. Quantitative Analysis of Bioactive Compounds in Commercial Teas: Profiling Catechin Alkaloids, Phenolic Acids, and Flavonols Using Targeted Statistical Approaches. Foods 2023; 12:3098. [PMID: 37628097 PMCID: PMC10453493 DOI: 10.3390/foods12163098] [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: 06/06/2023] [Revised: 08/10/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Tea, an extensively consumed and globally popular beverage, has diverse chemical compositions that ascertain its quality and categorization. In this investigation, we formulated an analytical and quantification approach employing reversed-phase ultra-high-performance liquid chromatography (UHPLC) methodology coupled with diode-array detection (DAD) to precisely quantify 20 principal constituents within 121 tea samples spanning 6 distinct variants. The constituents include alkaloids, catechins, flavonols, and phenolic acids. Our findings delineate that the variances in chemical constitution across dissimilar tea types predominantly hinge upon the intricacies of their processing protocols. Notably, green and yellow teas evinced elevated concentrations of total chemical moieties vis à vis other tea classifications. Remarkably divergent levels of alkaloids, catechins, flavonols, and phenolic acids were ascertained among the disparate tea classifications. By leveraging random forest analysis, we ascertained gallocatechin, epigallocatechin gallate, and epicatechin gallate as pivotal biomarkers for effective tea classification within the principal cadre of tea catechins. Our outcomes distinctly underscore substantial dissimilarities in the specific compounds inherent to varying tea categories, as ascertained via the devised and duly validated approach. The implications of this compositional elucidation serve as a pertinent benchmark for the comprehensive assessment and classification of tea specimens.
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Affiliation(s)
- Yuan Chen
- Fujian Academy of Agricultural Sciences, Fuzhou 350003, China; (Y.C.); (R.G.); (J.X.)
| | - Lingling Lai
- Fujian Tea Science Society, Fuzhou 350013, China;
| | - Youli You
- Yongchun County Cultivation Service Center, Quanzhou 362699, China;
| | - Ruizhen Gao
- Fujian Academy of Agricultural Sciences, Fuzhou 350003, China; (Y.C.); (R.G.); (J.X.)
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Jiaxin Xiang
- Fujian Academy of Agricultural Sciences, Fuzhou 350003, China; (Y.C.); (R.G.); (J.X.)
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Guojun Wang
- Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, FL 34946, USA;
| | - Wenquan Yu
- Fujian Academy of Agricultural Sciences, Fuzhou 350003, China; (Y.C.); (R.G.); (J.X.)
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23
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Tian H, Wu D, Chen B, Yuan H, Yu H, Lou X, Chen C. Rapid identification and quantification of vegetable oil adulteration in raw milk using a flash gas chromatography electronic nose combined with machine learning. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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24
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Bai F, Chen G, Niu H, Zhu H, Huang Y, Zhao M, Hou R, Peng C, Li H, Wan X, Cai H. The types of brewing water affect tea infusion flavor by changing the tea mineral dissolution. Food Chem X 2023; 18:100681. [PMID: 37215200 PMCID: PMC10192933 DOI: 10.1016/j.fochx.2023.100681] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 04/07/2023] [Accepted: 04/12/2023] [Indexed: 05/24/2023] Open
Abstract
The effects of different brewing water samples, including natural drinking water (NDW), pure water (PW), mineral water (MW), distilled water (DW), and tap water (TW) on flavor and quality of green tea infusion were investigated. The results showed the dissolution rate of mineral substances varied greatly depend on the type of water used. Notably, the tea infusion brewed with MW showed the highest taste response and darker but higher brightness in color. Furthermore, the content of volatile compounds was highest in tea infusion brewed with NDW and lowest in tea infusion brewed with MW. The mineral substances content and pH were the main factors affecting volatile compounds in green tea infusion. Thereinto, Ca2+ and Fe3+ remarkably affected the content of alcohols and aldehydes in volatile compounds. These results suggested that water with a neutral pH value and lower mineral substance content is more conducive for brewing green tea.
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25
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Wang Q, Qiu Z, Chen Y, Song Y, Zhou A, Cao Y, Xiao J, Xiao H, Song M. Review of recent advances on health benefits, microbial transformations, and authenticity identification of Citri reticulatae Pericarpium bioactive compounds. Crit Rev Food Sci Nutr 2023:1-29. [PMID: 37326362 DOI: 10.1080/10408398.2023.2222834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The extensive health-promoting effects of Citri Reticulatae Pericarpium (CRP) have attracted researchers' interest. The difference in storage time, varieties and origin of CRP are closely related to the content of bioactive compounds they contain. The consitituent transformation mediated by environmental microorganisms (bacteria and fungi) and the production of new bioactive components during the storage process may be the main reason for 'the older, the better' of CRP. In addition, the gap in price between different varieties can be as large as 8 times, while the difference due to age can even reach 20 times, making the 'marketing young-CRP as old-CRP and counterfeiting origin' flood the entire market, seriously harming consumers' interests. However, so far, the research on CRP is relatively decentralized. In particular, a summary of the microbial transformation and authenticity identification of CRP has not been reported. Therefore, this review systematically summarized the recent advances on the main bioactive compounds, the major biological activities, the microbial transformation process, the structure, and content changes of the active substances during the transformation process, and authenticity identification of CRP. Furthermore, challenges and perspectives concerning the future research on CRP were proposed.
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Affiliation(s)
- Qun Wang
- Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, College of Food Science, South China Agricultural University, Guangzhou, China
| | - Zhenyuan Qiu
- Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, College of Food Science, South China Agricultural University, Guangzhou, China
| | - Yilu Chen
- Department of Food Science, University of Massachusetts, Amherst, Massachusetts, USA
| | - Yuqing Song
- Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, College of Food Science, South China Agricultural University, Guangzhou, China
| | - Aimei Zhou
- Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, College of Food Science, South China Agricultural University, Guangzhou, China
| | - Yong Cao
- Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, College of Food Science, South China Agricultural University, Guangzhou, China
| | - Jie Xiao
- Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, College of Food Science, South China Agricultural University, Guangzhou, China
| | - Hang Xiao
- Department of Food Science, University of Massachusetts, Amherst, Massachusetts, USA
| | - Mingyue Song
- Guangdong Provincial Key Laboratory of Nutraceuticals and Functional Foods, College of Food Science, South China Agricultural University, Guangzhou, China
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26
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Hou F, Fan X, Gui X, Li H, Li H, Wang Y, Shi J, Zhang L, Yao J, Li X, Liu R. Development of a variety and quality evaluation method for Amomi fructus using GC, electronic tongue, and electronic nose. Front Chem 2023; 11:1188219. [PMID: 37398979 PMCID: PMC10310405 DOI: 10.3389/fchem.2023.1188219] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/07/2023] [Indexed: 07/04/2023] Open
Abstract
Amomi fructus is rich in volatile components and valuable as a medicine and edible spice. However, the quality of commercially available A. fructus varies, and issues with mixed sources and adulteration by similar products are common. In addition, due to incomplete identification methods, rapid detection of the purchased A. fructus quality is still an issue. In this study, we developed qualitative and quantitative evaluation models to assess the variety and quality of A. fructus using GC, electronic tongue, and electronic nose to provide a rapid and accurate variety and quality evaluation method of A. fructus. The models performed well; the qualitative authenticity model had an accuracy of 1.00 (n = 64), the accuracy of the qualitative origin model was 0.86 (n = 44), and the quantitative model was optimal on the sensory fusion data from the electronic tongue and electronic nose combined with borneol acetate content, with R 2 = 0.7944, RMSEF = 0.1050, and RMSEP = 0.1349. The electronic tongue and electronic nose combined with GC quickly and accurately evaluated the variety and quality of A. fructus, and the introduction of multi-source information fusion technology improved the model prediction accuracy. This study provides a useful tool for quality evaluation of medicine and food.
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Affiliation(s)
- Fuguo Hou
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Xuehua Fan
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Xinjing Gui
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Han Li
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Haiyang Li
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Yanli Wang
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Junhan Shi
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Lu Zhang
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Jing Yao
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Xuelin Li
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Ruixin Liu
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
- Engineering Research Center for Pharmaceutics of Chinese Materia Medica and New Drug Development, Ministry of Education, Beijing, China
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27
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Banerjee A, Ghosh R, Singh S, Adhikari A, Mondal S, Roy L, Midya S, Mukhopadhyay S, Shyam Chowdhury S, Chakraborty S, Das R, Al-Fahemi JH, Moussa Z, Kumar Mallick A, Chattopadhyay A, Ahmed SA, Kumar Pal S. Spectroscopic studies on a natural biomarker for the identification of origin and quality of tea extracts for the development of a portable and field deployable prototype. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 299:122842. [PMID: 37216816 DOI: 10.1016/j.saa.2023.122842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/03/2023] [Accepted: 05/06/2023] [Indexed: 05/24/2023]
Abstract
Even in the era of smart technologies and IoT enabled devices, tea testing technique continues to be a person specific subjective task. In this study, we have employed optical spectroscopy-based detection technique for the quantitative validation of tea quality. In this regard, we have employed the external quantum yield of quercetin at 450 nm (λex = 360 nm), which is an enzymatic product generated by the activity of β-glucosidase on rutin, a naturally occurring metabolite responsible for tea-flavour (quality). We have found that a specific point in a graph representing Optical Density and external Quantum Yield as independent and dependent variables respectively of an aqueous tea extract objectively indicates a specific variety of the tea. A variety of tea samples from various geographical origin have been analysed with the developed technique and found to be useful for the tea quality assessment. The principal component analysis distinctly showed the tea samples originated from Nepal and Darjeeling having similar external quantum yield, while the tea samples from Assam region had a lower external quantum yield. Furthermore, we have employed experimental and computational biology techniques for the detection of adulteration and health benefit of the tea extracts. In order to assure the portability/field use, we have also developed a prototype which confirms the results obtained in the laboratory. We are of the opinion that the simple user interface and almost zero maintenance cost of the device will make it useful and attractive with minimally trained manpower at low resource setting.
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Affiliation(s)
- Amrita Banerjee
- Department of Physics, Jadavpur University, 188, Raja S.C. Mallick Rd, Kolkata 700032, India; Technical Research Centre, S. N. Bose National Centre for Basic Sciences, Block JD, Sector III, Salt Lake, Kolkata, West Bengal 700106, India
| | - Ria Ghosh
- Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Block JD, Sector 3, Salt Lake, Kolkata-700106, India
| | - Soumendra Singh
- Technical Research Centre, S. N. Bose National Centre for Basic Sciences, Block JD, Sector III, Salt Lake, Kolkata, West Bengal 700106, India; Neo Care Inc, 9, Parkstone Road, Dartmouth, NS B3A 4J1, Canada
| | - Aniruddha Adhikari
- Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Block JD, Sector 3, Salt Lake, Kolkata-700106, India; Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095, USA
| | - Susmita Mondal
- Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Block JD, Sector 3, Salt Lake, Kolkata-700106, India
| | - Lopamudra Roy
- Technical Research Centre, S. N. Bose National Centre for Basic Sciences, Block JD, Sector III, Salt Lake, Kolkata, West Bengal 700106, India
| | - Suman Midya
- Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Block JD, Sector 3, Salt Lake, Kolkata-700106, India
| | - Subhadipta Mukhopadhyay
- Department of Physics, Jadavpur University, 188, Raja S.C. Mallick Rd, Kolkata 700032, India
| | - Sudeshna Shyam Chowdhury
- Department of Microbiology, St. Xavier's College, 30, Mother Teresa Sarani, Kolkata 700016, India
| | - Subhananda Chakraborty
- Department of Electrical Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai 400076, India
| | - Ranjan Das
- Department of Chemistry, West Bengal State University, Barasat, North 24 PGS, Kolkata 700126, India
| | - Jabir H Al-Fahemi
- Department of Chemistry, Faculty of Applied Science, Umm Al-Qura University, 21955 Makkah Saudi Arabia
| | - Ziad Moussa
- Department of Chemistry, College of Science, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Asim Kumar Mallick
- Department of Paediatric Medicine, Nil RatanSircar Medical College & Hospital, 138, AJC Bose Road, Sealdah, Raja Bazar, Kolkata 700014, India
| | - Arpita Chattopadhyay
- Department of Basic science and humanities Techno International New Town Block - DG 1/1, Action Area 1 New Town, Rajarhat, Kolkata 700156, India.
| | - Saleh A Ahmed
- Department of Chemistry, Faculty of Applied Science, Umm Al-Qura University, 21955 Makkah Saudi Arabia; Chemistry Department, Faculty of Science, Assiut University, 71516 Assiut, Egypt.
| | - Samir Kumar Pal
- Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Block JD, Sector 3, Salt Lake, Kolkata-700106, India.
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28
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Li M, Dong S, Cao S, Cui Q, Chen Q, Ning J, Li L. A rapid aroma quantification method: Colorimetric sensor-coupled multidimensional spectroscopy applied to black tea aroma. Talanta 2023; 263:124622. [PMID: 37267888 DOI: 10.1016/j.talanta.2023.124622] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/27/2023] [Accepted: 04/30/2023] [Indexed: 06/04/2023]
Abstract
Aroma affects the quality of black tea, and the rapid evaluation of aroma quality is the key to realize the intelligent processing of black tea. A simple colorimetric sensor array coupled with a hyperspectral system was proposed for the rapid quantitative detection of key volatile organic compounds (VOCs) in black tea. Feature variables were screened based on competitive adaptive reweighted sampling (CARS). Furthermore, the performance of the models for VOCs quantitative prediction was compared. For the quantitative prediction of linalool, benzeneacetaldehyde, hexanal, methyl salicylate, and geraniol, the CARS-least-squares support vector machine model's correlation coefficients were 0.89, 0.95, 0.88, 0.80, and 0.78, respectively. The interaction mechanism of array dyes with VOCs was based on density flooding theory. The optimized highest occupied molecular orbital levels, lowest unoccupied molecular orbital energy levels, dipole moments, and intermolecular distances were determined to be strongly correlated with interactions between array dyes and VOCs.
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Affiliation(s)
- Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education,Anhui Provincial Laboratory, Hefei, 230036, Anhui, China
| | - Shuai Dong
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education,Anhui Provincial Laboratory, Hefei, 230036, Anhui, China
| | - Shuci Cao
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education,Anhui Provincial Laboratory, Hefei, 230036, Anhui, China
| | - Qingqing Cui
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education,Anhui Provincial Laboratory, Hefei, 230036, Anhui, China
| | - Quansheng Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, 361021, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education,Anhui Provincial Laboratory, Hefei, 230036, Anhui, China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education,Anhui Provincial Laboratory, Hefei, 230036, Anhui, China.
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29
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Amsaraj R, Mutturi S. Rapid detection of sunset yellow adulteration in tea powder with variable selection coupled to machine learning tools using spectral data. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2023; 60:1530-1540. [PMID: 37033304 PMCID: PMC10076470 DOI: 10.1007/s13197-023-05694-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 01/29/2023] [Accepted: 02/07/2023] [Indexed: 02/21/2023]
Abstract
In the present study sunset yellow (SY), a synthetic colour, which is a common adulterant in tea powders has been analysed using FT-IR spectral data coupled to machine learning tools for efficient classification and quantification of the SY adulteration. Earlier established real coded genetic algorithm (RCGA) was used as variable selection method to predict the key fingerprints of SY in the FT-IR spectra. Here, RCGA was used to select 20, 30, 40, 50 and 60 characteristic wavenumbers for SY. Classification was carried using support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGB) classifiers. SVM classifier using 50 variables could give an accuracy of 0.90 amongst the three. Quantification of SY based on PLS (partial least squares), LS-SVM (least squares-SVM), RF and XGBoost were built on characteristic wavenumbers. Both RF and LS-SVM models were observed to be superior to PLS when coupled to RCGA obtained 20 fingerprint variables. Overall, RCGA-LS-SVM model resulted in lowest RMSECV (0.1956) with regression co-efficient values RC 2 = 0.9989 and RP 2 = 0.9979, when 50 fingerprint variables were used. These results demonstrated that FT-IR combined with RCGA-LS-SVM procedure could be a robust technique for rapid detection of SY in tea powder. Supplementary Information The online version contains supplementary material available at 10.1007/s13197-023-05694-3.
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Affiliation(s)
- Rani Amsaraj
- Microbiology and Fermentation Technology Department, CSIR-Central Food Technological Research Institute, Mysore, Karnataka India
| | - Sarma Mutturi
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
- Microbiology and Fermentation Technology Department, CSIR-Central Food Technological Research Institute, Mysore, Karnataka India
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30
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Zhang JB, Li MX, Zhang YF, Qin YW, Li Y, Su LL, Li L, Bian ZH, Lu TL. E-eye, flash GC E-nose and HS-GC-MS combined with chemometrics to identify the adulterants and geographical origins of Ziziphi Spinosae Semen. Food Chem 2023; 424:136270. [PMID: 37207600 DOI: 10.1016/j.foodchem.2023.136270] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 04/14/2023] [Accepted: 04/27/2023] [Indexed: 05/21/2023]
Abstract
Ziziphi Spinosae Semen (ZSS), a valuable seed food, has faced increasing authenticity issues. In this study, the adulterants and geographical origins of ZSS were successfully identified by electronic eye, flash gas chromatography electronic nose (Flash GC e-nose) and headspace gas chromatography-mass spectrometry (HS-GC-MS). As a result, there were color differences between ZSS and adulterants, mainly represented by the a* value of ZSS was less than adulterants. In ZSS, 29 and 32 compounds were detected by Flash GC e-nose and HS-GC-MS. Spicy, sweety, fruity and herbal were the main flavor of ZSS. Five compounds were determined to be responsible for flavor differences between different geographical origins. In the HS-GC-MS analysis, the relative content of Hexanoic acid was the highest in ZSS from Hebei and Shandong, while 2,4-Decadien-1-ol was the highest in Shaanxi. Overall, this study provided a meaningful strategy for addressing authenticity problems of ZSS and other seed foods.
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Affiliation(s)
- Jiu-Ba Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Ming-Xuan Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yun-Fei Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yu-Wen Qin
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yu Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Lian-Lin Su
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Lin Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Zhen-Hua Bian
- Department of Pharmacy, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214071, China.
| | - Tu-Lin Lu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
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31
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Chen J, Lin B, Zheng FJ, Fang XC, Ren EF, Wu FF, Verma KK, Chen GL. Characterization of the Pure Black Tea Wine Fermentation Process by Electronic Nose and Tongue-Based Techniques with Nutritional Characteristics. ACS OMEGA 2023; 8:12538-12547. [PMID: 37033789 PMCID: PMC10077554 DOI: 10.1021/acsomega.3c00862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
Wine is an alcoholic beverage, consisting of several compounds in various ranges of concentrations. Wine quality is usually assessed by a sensory panel of trained personnel. Electronic tongues (e-tongues) and electronic noses (e-noses) have been established in recent years to assess the quality of beverages and foods. Response surface and electronic analysis tools were used to examine the quality of black tea wine. The results indicated the optimum initial sugar level (25 °Brix), yeast addition (0.5%), and fermentation temperature (25 °C) for Golden Peony black tea wine. The black tea wine produced under these conditions with 14.0% vol alcohol has as an orange-red color, full wine and tea flavor, and mild and mellow taste. The sourness of the wine was most affected by fermentation factors-yeast addition, fermentation temperature, and initial sugar level. Alcohols, aldehydes, ketones, and alkanes contributed to most of the volatile components under the influence of yeast addition and fermentation temperature. In contrast, nitrogen oxides, aromatics, and organic sulfides contributed under the influence of the initial sugar level. This study provided a facilitated strategy for obtaining the optimum black tea wine fermentation process through electronic nose and tongue-based techniques. The analysis of wines requires new technologies able to detect various different compounds simultaneously, providing worldwide information about the sample instead of information about specific compounds.
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Affiliation(s)
- Jing Chen
- Guangxi
South Subtropical Agricultural Research Institute, Longzhou 532400, Guangxi, China
- Institute
of Agro-Products Processing Science and Technology, Guangxi Academy of Agricultural Sciences, Nanning 530 007, Guangxi, China
| | - Bo Lin
- Institute
of Agro-Products Processing Science and Technology, Guangxi Academy of Agricultural Sciences, Nanning 530 007, Guangxi, China
- Guangxi
Key Laboratory of Fruits and Vegetables Storage-Processing Technology, Nanning 530 007, Guangxi, China
| | - Feng-Jin Zheng
- Institute
of Agro-Products Processing Science and Technology, Guangxi Academy of Agricultural Sciences, Nanning 530 007, Guangxi, China
- Guangxi
Key Laboratory of Fruits and Vegetables Storage-Processing Technology, Nanning 530 007, Guangxi, China
| | - Xiao-Chun Fang
- Institute
of Agro-Products Processing Science and Technology, Guangxi Academy of Agricultural Sciences, Nanning 530 007, Guangxi, China
- Guangxi
Key Laboratory of Fruits and Vegetables Storage-Processing Technology, Nanning 530 007, Guangxi, China
| | - Er-Fang Ren
- Guangxi
Subtropical Crops Research Institute, Guangxi
Subtropical Fruits Processing Research Center of Engineering Technology, Nanning 530001, Guangxi, China
| | - Fei-Fei Wu
- Guangxi
South Subtropical Agricultural Research Institute, Longzhou 532400, Guangxi, China
- Institute
of Agro-Products Processing Science and Technology, Guangxi Academy of Agricultural Sciences, Nanning 530 007, Guangxi, China
| | - Krishan K. Verma
- Key
Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi),
Ministry of Agriculture and Rural Affairs Guangxi Key Laboratory of
Sugarcane Genetic Improvement Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 530 007, Guangxi, China
| | - Gan-Lin Chen
- Institute
of Agro-Products Processing Science and Technology, Guangxi Academy of Agricultural Sciences, Nanning 530 007, Guangxi, China
- Guangxi
Key Laboratory of Fruits and Vegetables Storage-Processing Technology, Nanning 530 007, Guangxi, China
- School
of
Chemistry and Chemical Engineering, Guangxi
Minzu University, Nanning 530 006, Guangxi, China
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32
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Li X, Shi C, Wang S, Wang S, Wang X, Lü X. Uncovering the effect of Moringa oleifera Lam. leaf addition to Fuzhuan Brick Tea on sensory properties, volatile profiles and anti-obesity activity. Food Funct 2023; 14:2404-2415. [PMID: 36786051 DOI: 10.1039/d2fo03531f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
As a nutritious plant with valuable potential, the Moringa oleifera Lam. leaf addition to Fuzhuan Brick Tea (FBT) for co-fermentation is an industrial innovation and a new route to make full use of Moringa oleifera Lam. leaves. However, the sensory properties, volatile profiles and anti-obesity activity of Fuzhuan Brick (Moringa oleifera Lam.) tea (MFBT) are still unknown. The results demonstrated that MFBT has richer and more complex smell and taste, better color and higher overall acceptance scores. In total, 57 volatile flavor compounds, consisting of 3 acids, 16 hydrocarbons, 5 esters, 8 ketones, 13 aldehydes, 6 alcohols and others, were identified using HS-SPME-GC-MS. The characteristic odor components in MFBT were 3-buten-2-one, 4-(2,6,6-trimethyl-1-cyclohexen-1-yl)- and 1-cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl-, which gave it a floral, woody, sweet, herbal and fruity aroma. 2-Octenal, (E) contributed significantly to the aroma of FBT, which could impart fresh, fatty and green aromas. In addition, MFBT could better regulate lipid accumulation, glucose tolerance, insulin tolerance and inflammation response more effectively than FBT. The mechanism is that MFBT could better regulate the dysbiosis of gut microbiota induced by HFFD, mainly increasing the abundance of beneficial bacteria such as SCFA-producing bacteria (Bacteroidetes, Lactobacillaceae, Bacteroidales_S24-7_group and Clostridiaceae_1) and decreasing the abundance of harmful bacteria such as pro-inflammatory/obesity and metabolic syndrome-related bacteria (Proteobacteria, Deferribacteres, Desulfovibrio, Catenibacterium and Helicobacter), which in turn increased feces short-chain fatty acids and lowered circulating lipopolysaccharides. These results suggested that co-fermentation with Moringa oleifera Lam. leaf could significantly improve the quality and enhance the anti-obesity effect of FBT.
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Affiliation(s)
- Xin Li
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China.
| | - Caihong Shi
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China.
| | - Shuxuan Wang
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China.
| | - Shuang Wang
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China.
| | - Xin Wang
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China.
| | - Xin Lü
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China.
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Li X, Guo J, Xu W, Cao J. Optimization of the Mixed Gas Detection Method Based on Neural Network Algorithm. ACS Sens 2023; 8:822-828. [PMID: 36701636 DOI: 10.1021/acssensors.2c02450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Real-time mixed gas detection has attracted significant interest for being a key factor for applications of the electronic nose (E-nose). However, mixed gas detection still faces the challenge of long detection time and a large amount of training data. Therefore, in this work, we propose a feasible way to realize low-cost fast detection of mixed gases, which uses only the part response data of the adsorption process as the training set. Our results indicated that the proposed method significantly reduced the number of training sets and the prediction time of mixed gas. Moreover, it can achieve new concentration prediction of mixed gas using only the response data of the first 10 s, and the training set proportion can reduce to 60%. In addition, the convolutional neural network model can realize both the smaller training set but also the higher accuracy of mixed gas. Our findings provide an effective way to improve the detection efficiency and accuracy of E-noses for the experimental measurement.
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Affiliation(s)
- Xiulei Li
- Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan411105, PR China
| | - Jiayi Guo
- Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan411105, PR China
| | - Wangping Xu
- Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan411105, PR China
| | - Juexian Cao
- Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan411105, PR China
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Naku W, Nambisan AK, Roman M, Zhu C, Gerald RE, Huang J. Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning. ACS OMEGA 2023; 8:4597-4607. [PMID: 36777572 PMCID: PMC9909791 DOI: 10.1021/acsomega.2c05451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
In this paper, we report an array of fiber-optic sensors based on the Fabry-Perot interference principle and machine learning-based analyses for identifying volatile organic liquids (VOLs). Three optical fiber tip sensors with different surfaces were included in the array of sensors to improve the accuracy for identifying liquids: an intrinsic (unmodified) flat cleaved endface, a hydrophobic-coated endface, and a hydrophilic-coated endface. The time-transient responses of evaporating droplets from the optical fiber tip sensors were monitored and collected following the controlled immersion tests of 11 different organic liquids. A continuous wavelet transform was used to convert the time-transient response signal into images. These images were then utilized to train convolution neural networks for classification (identification of VOLs). We show that diversity in the information collected using the array of three sensors helps machine learning-based methods perform significantly better. We explore different pipelines for combining the information from the array of sensors within a machine learning framework and their effect on the robustness of models. The results showed that the machine learning-based methods achieved high accuracy in their classification of different liquids based on their droplet evaporation time-transient events.
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Affiliation(s)
- Wassana Naku
- Department
of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri 65409, United States
| | - Anand K. Nambisan
- Department
of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri 65409, United States
| | - Muhammad Roman
- Department
of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri 65409, United States
| | - Chen Zhu
- Research
Center for Optical Fiber Sensing, Zhejiang Laboratory, Hangzhou 311100, China
| | - Rex E. Gerald
- Department
of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri 65409, United States
| | - Jie Huang
- Department
of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri 65409, United States
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35
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Zhao H, Xue D, Zhang L. Electrochemical fingerprints identification of tea based on one-dimensional convolutional neural network. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01812-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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36
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The Impact of Wet Fermentation on Coffee Quality Traits and Volatile Compounds Using Digital Technologies. FERMENTATION-BASEL 2023. [DOI: 10.3390/fermentation9010068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Fermentation is critical for developing coffee’s physicochemical properties. This study aimed to assess the differences in quality traits between fermented and unfermented coffee with four grinding sizes of coffee powder using multiple digital technologies. A total of N = 2 coffee treatments—(i) dry processing and (ii) wet fermentation—with grinding levels (250, 350, 550, and 750 µm) were analysed using near-infrared spectrometry (NIR), electronic nose (e-nose), and headspace/gas chromatography–mass spectrometry (HS-SPME-GC-MS) coupled with machine learning (ML) modelling. Most overtones detected by NIR were within the ranges of 1700–2000 nm and 2200–2396 nm, while the enhanced peak responses of fermented coffee were lower. The overall voltage of nine e-nose sensors obtained from fermented coffee (250 µm) was significantly higher. There were two ML classification models to classify processing and brewing methods using NIR (Model 1) and e-nose (Model 2) values as inputs that were highly accurate (93.9% and 91.2%, respectively). Highly precise ML regression Model 3 and Model 4 based on the same inputs for NIR (R = 0.96) and e-nose (R = 0.99) were developed, respectively, to assess 14 volatile aromatic compounds obtained by GC-MS. Fermented coffee showed higher 2-methylpyrazine (2.20 ng/mL) and furfuryl acetate (2.36 ng/mL) content, which induces a stronger fruity aroma. This proposed rapid, reliable, and low-cost method was shown to be effective in distinguishing coffee postharvest processing methods and evaluating their volatile compounds, which has the potential to be applied for coffee differentiation and quality assurance and control.
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37
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Ma R, Shen H, Cheng H, Zhang G, Zheng J. Combining e-nose and e-tongue for improved recognition of instant starch noodles seasonings. Front Nutr 2023; 9:1074958. [PMID: 36698480 PMCID: PMC9868914 DOI: 10.3389/fnut.2022.1074958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 12/19/2022] [Indexed: 01/12/2023] Open
Abstract
Seasonings play a key role in determining sensory attributes of instant starch noodles. Controlling and improving the quality of seasoning is becoming important. In this study, five different brands along with fifteen instant starch noodles seasonings (seasoning powder, seasoning mixture sauce and the mixture of powder and sauce) were characterized by electronic nose (e-nose) and electronic tongue (e-tongue). Feature-level fusion for the integration of the signals was introduced to integrate the e-nose and e-tongue signals, aiming at improving the performances of identification and prediction models. Principal component analysis (PCA) explained over 85.00% of the total variance in e-nose data and e-tongue data, discriminated all samples. Multilayer perceptron neural networks analysis (MLPN) modeling demonstrated that the identification rate of the combined data was basically 100%. PCA, cluster analysis (CA), and MLPN proved that the classification results acquired from the combined e-nose and e-tongue data were better than individual e-nose and e-tongue result. This work demonstrated that in combination e-nose and e-tongue provided more comprehensive information about the seasonings compared to each individual e-nose and e-tongue. E-nose and e-tongue technologies hold great potential in the production, quality control, and flavor detection of instant starch noodles seasonings.
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38
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Gui XJ, Li H, Ma R, Tian LY, Hou FG, Li HY, Fan XH, Wang YL, Yao J, Shi JH, Zhang L, Li XL, Liu RX. Authenticity and species identification of Fritillariae cirrhosae: a data fusion method combining electronic nose, electronic tongue, electronic eye and near infrared spectroscopy. Front Chem 2023; 11:1179039. [PMID: 37188096 PMCID: PMC10175593 DOI: 10.3389/fchem.2023.1179039] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
This paper focuses on determining the authenticity and identifying the species of Fritillariae cirrhosae using electronic nose, electronic tongue, and electronic eye sensors, near infrared and mid-level data fusion. 80 batches of Fritillariae cirrhosae and its counterfeits (including several batches of Fritillaria unibracteata Hsiao et K.C. Hsia, Fritillaria przewalskii Maxim, Fritillaria delavayi Franch and Fritillaria ussuriensis Maxim) were initially identified by Chinese medicine specialists and by criteria in the 2020 edition of Chinese Pharmacopoeia. After obtaining the information from several sensors we constructed single-source PLS-DA models for authenticity identification and single-source PCA-DA models for species identification. We selected variables of interest by VIP value and Wilk's lambda value, and we subsequently constructed the three-source fusion model of intelligent senses and the four-source fusion model of intelligent senses and near-infrared spectroscopy. We then explained and analyzed the four-source fusion models based on the sensitive substances detected by key sensors. The accuracies of single-source authenticity PLS-DA identification models based on electronic nose, electronic eye, electronic tongue sensors and near-infrared were respectively 96.25%, 91.25%, 97.50% and 97.50%. The accuracies of single-source PCA-DA species identification models were respectively 85%, 71.25%, 97.50% and 97.50%. After three-source data fusion, the accuracy of the authenticity identification of the PLS-DA identification model was 97.50% and the accuracy of the species identification of the PCA-DA model was 95%. After four-source data fusion, the accuracy of the authenticity of the PLS-DA identification model was 98.75% and the accuracy of the species identification of the PCA-DA model was 97.50%. In terms of authenticity identification, four-source data fusion can improve the performance of the model, while for the identification of the species the four-source data fusion failed to optimize the performance of the model. We conclude that electronic nose, electronic tongue, electronic eye data and near-infrared spectroscopy combined with data fusion and chemometrics methods can identify the authenticity and determine the species of Fritillariae cirrhosae. Our model explanation and analysis can help other researchers identify key quality factors for sample identification. This study aims to provide a reference method for the quality evaluation of Chinese herbs.
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Affiliation(s)
- Xin-Jing Gui
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Han Li
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Rui Ma
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Liang-Yu Tian
- Zhengzhou Traditional Chinese Hospital of Orthopedics, Zhengzhou, China
| | - Fu-Guo Hou
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Hai-Yang Li
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Xue-Hua Fan
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Yan-Li Wang
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Jing Yao
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Jun-Han Shi
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Lu Zhang
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Xue-Lin Li
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
- *Correspondence: Rui-Xin Liu, ; Xue-Lin Li,
| | - Rui-Xin Liu
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
- Engineering Research Center for Pharmaceutics of Chinese Materia Medica and New Drug Development, Ministry of Education, Beijing, China
- *Correspondence: Rui-Xin Liu, ; Xue-Lin Li,
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39
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Jiang X, McPhedran KN, Hou X, Chen Y, Huang R. Assessment of the trace level metal ingredients that enhance the flavor and taste of traditionally crafted rice-based products. Lebensm Wiss Technol 2023. [DOI: 10.1016/j.lwt.2023.114435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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40
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Jiang L, Zheng K. Towards the intelligent antioxidant activity evaluation of green tea products during storage: A joint cyclic voltammetry and machine learning study. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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41
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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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42
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Rapid assessment of citrus fruits freshness by fuzzy mathematics combined with E-tongue and GC–MS. Eur Food Res Technol 2022. [DOI: 10.1007/s00217-022-04177-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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43
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Liu X, Wang X, Cheng Y, Wu Y, Yan Y, Li Z. Variations in volatile organic compounds in Zhenyuan Daocai samples at different storage durations evaluated using E-nose, E-tongue, gas chromatography, and spectrometry. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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44
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Qualitative and quantitative assessment of flavor quality of Chinese soybean paste using multiple sensor technologies combined with chemometrics and a data fusion strategy. Food Chem 2022; 405:134859. [DOI: 10.1016/j.foodchem.2022.134859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 10/23/2022] [Accepted: 11/02/2022] [Indexed: 11/08/2022]
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45
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Lin Y, Ma J, Wang Q, Sun DW. Applications of machine learning techniques for enhancing nondestructive food quality and safety detection. Crit Rev Food Sci Nutr 2022; 63:1649-1669. [PMID: 36222697 DOI: 10.1080/10408398.2022.2131725] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.
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Affiliation(s)
- Yuandong Lin
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.,State Key Laboratory of Luminescent Materials and Devices, Center for Aggregation-Induced Emission, South China University of Technology, Guangzhou 510641, China
| | - Qijun Wang
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.,Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Dublin 4, Ireland
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46
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Mao Y, Li H, Wang Y, Fan K, Song Y, Han X, Zhang J, Ding S, Song D, Wang H, Ding Z. Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging. Foods 2022; 11:foods11162537. [PMID: 36010536 PMCID: PMC9407140 DOI: 10.3390/foods11162537] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/02/2022] [Accepted: 08/17/2022] [Indexed: 11/16/2022] Open
Abstract
The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.
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Affiliation(s)
- Yilin Mao
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - He Li
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - Yu Wang
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - Kai Fan
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - Yujie Song
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - Xiao Han
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - Jie Zhang
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
| | - Shibo Ding
- Tea Research Institute, Rizhao Academy of Agricultural Sciences, Rizhao 276800, China
| | - Dapeng Song
- Tea Research Institute, Rizhao Academy of Agricultural Sciences, Rizhao 276800, China
| | - Hui Wang
- Tea Research Institute, Rizhao Academy of Agricultural Sciences, Rizhao 276800, China
| | - Zhaotang Ding
- Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China
- Correspondence:
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47
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Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics. Foods 2022; 11:foods11152344. [PMID: 35954110 PMCID: PMC9368096 DOI: 10.3390/foods11152344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 07/27/2022] [Accepted: 08/03/2022] [Indexed: 12/04/2022] Open
Abstract
Oolong tea is a semi-fermented tea that is popular among people. This study aims to establish a classification method for oolong tea based on fluorescence hyperspectral technology(FHSI) combined with chemometrics. First, the spectral data of Tieguanyin, Benshan, Maoxie and Huangjingui were obtained. Then, standard normal variation (SNV) and multiple scatter correction (MSC) were used for preprocessing. Principal component analysis (PCA) was used for data visualization, and with tolerance ellipses that were drawn according to Hotelling, outliers in the spectra were removed. Variable importance for the projection (VIP) > 1 in partial least squares discriminant analysis (PLS−DA) was used for feature selection. Finally, the processed spectral data was entered into the support vector machine (SVM) and PLS−DA. MSC_VIP_PLS−DA was the best model for the classification of oolong tea. The results showed that the use of FHSI could accurately distinguish these four types of oolong tea and was able to identify the key wavelengths affecting the tea classification, which were 650.11, 660.29, 665.39, 675.6, 701.17, 706.31, 742.34 and 747.5 nm. In these wavelengths, different kinds of tea have significant differences (p < 0.05). This study could provide a non-destructive and rapid method for future tea identification.
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Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review. Molecules 2022; 27:molecules27154846. [PMID: 35956791 PMCID: PMC9369811 DOI: 10.3390/molecules27154846] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 12/03/2022] Open
Abstract
The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.
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Zhao LQ, Shan CM, Shan TY, Li QL, Ma KL, Deng WW, Wu JW. Comparative transcriptomic analysis reveals the regulatory mechanisms of catechins synthesis in different cultivars of Camellia sinensis. Food Res Int 2022; 157:111375. [DOI: 10.1016/j.foodres.2022.111375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 05/04/2022] [Accepted: 05/10/2022] [Indexed: 11/28/2022]
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50
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Tian H, Chen B, Yu H, Lou X, Li Y, Yu H, Chen L, Chen C. Rapid detection of neutralising acid adulterants in raw milk using a milk component analyser and chemometrics. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2022; 39:1501-1511. [PMID: 35767628 DOI: 10.1080/19440049.2022.2093985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
This study focused on the development of a method for the rapid detection of acid-neutralising adulterants in raw milk using a milk composition analyser. Qualitative analysis for the discrimination of different acid-neutralising acid adulterants in raw milk and quantification of NaSCN in adulterated raw milk were conducted, combined with chemometrics. The results showed that the milk component analyser combined with principal component analysis (PCA) could judge whether raw milk samples were adulterated but cannot identify the types of adulterated substances. Although partial least squares discrimination analysis (PLS-DA) can distinguish some adulterated raw milk samples, the accuracy rate was only 56.3%; the random forest (RF) model could recognise most adulterated raw milk samples with an accuracy rate of 97.5% and the F1-score was 0.9638. In the prediction model of NaSCN adulteration concentration in raw milk constructed by RF, the coefficient of determination (R2) was 0.9889, and the root means square error (RMSE) was 3.28 × 10-4, suggesting a high prediction performance of the model. The effectiveness of the method for the detection of real samples in practical production was also proved. Based on the above results, it could conclude that the milk component analyser, combined with chemometrics, effectively distinguished acid-neutralising adulterants in raw milk. These findings provide a reference for the rapid detection of adulterants and the quality control of raw milk.
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Affiliation(s)
- Huaixiang Tian
- Department of Food Science and Technology, Shanghai Institute of Technology, Shanghai, China
| | - Bin Chen
- Department of Food Science and Technology, Shanghai Institute of Technology, Shanghai, China
| | - Hongbin Yu
- Department of Food Science and Technology, Shanghai Institute of Technology, Shanghai, China
| | - Xinman Lou
- Department of Food Science and Technology, Shanghai Institute of Technology, Shanghai, China
| | - Yong Li
- Department of Food Science and Technology, Shanghai Institute of Technology, Shanghai, China
| | - Haiyan Yu
- Department of Food Science and Technology, Shanghai Institute of Technology, Shanghai, China
| | - Liqiong Chen
- School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China
| | - Chen Chen
- Department of Food Science and Technology, Shanghai Institute of Technology, Shanghai, China
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