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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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Li L, Dong S, Cao S, Chen Y, Shen J, Li M, Cui Q, Zhang Y, Huang C, Dai Q, Ning J. E-nose and colorimetric sensor array combining homologous data fusion strategy discriminating the roasting degree of large-leaf yellow tea. Food Chem X 2024; 21:101124. [PMID: 38298355 PMCID: PMC10828643 DOI: 10.1016/j.fochx.2024.101124] [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: 06/28/2023] [Revised: 12/12/2023] [Accepted: 01/04/2024] [Indexed: 02/02/2024] Open
Abstract
Different degrees of roasting result in differences in the quality and flavor of large-leaf yellow tea. The current sensory evaluation and chemical detection methods cannot meet the requirement of online differentiation of LYT roasting degree, so an accurate and comprehensive assessment method needs to be developed urgently. First, the two aroma sensing technologies were compared. Two variable screening methods and three recognition algorithms were employed to build discriminant models. The results showed that the discrimination rate of the colorimetric sensor array (CSA) in the prediction set reached 91.89 %, outperforming that of the E-nose. Subsequently, three fusion strategies were applied to improve the discrimination accuracy. The discrimination rate of the middle fusion strategy resulted in an optimal resolution of 94.59 %. The results obtained from the homologous fusion were able to evaluate the roasting degree comprehensively and accurately, which provides a new method and idea for tea aroma quality.
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Affiliation(s)
| | | | - 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, People's Republic of China
| | - Yurong Chen
- 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, People's Republic of China
| | - Jingfei Shen
- 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, People's Republic of China
| | - 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, People's Republic of 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, People's Republic of China
| | - Ying Zhang
- 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, People's Republic of China
| | - Chuxuan Huang
- 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, People's Republic of China
| | - Qianying Dai
- 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, People's Republic of 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, People's Republic of China
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Zhu H, Wu Y, Yang G, Song R, Yu J, Zhang J. Electronic Nose Drift Suppression Based on Smooth Conditional Domain Adversarial Networks. SENSORS (BASEL, SWITZERLAND) 2024; 24:1319. [PMID: 38400477 PMCID: PMC10892276 DOI: 10.3390/s24041319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 01/31/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024]
Abstract
Anti-drift is a new and serious challenge in the field related to gas sensors. Gas sensor drift causes the probability distribution of the measured data to be inconsistent with the probability distribution of the calibrated data, which leads to the failure of the original classification algorithm. In order to make the probability distributions of the drifted data and the regular data consistent, we introduce the Conditional Adversarial Domain Adaptation Network (CDAN)+ Sharpness Aware Minimization (SAM) optimizer-a state-of-the-art deep transfer learning method.The core approach involves the construction of feature extractors and domain discriminators designed to extract shared features from both drift and clean data. These extracted features are subsequently input into a classifier, thereby amplifying the overall model's generalization capabilities. The method boasts three key advantages: (1) Implementation of semi-supervised learning, thereby negating the necessity for labels on drift data. (2) Unlike conventional deep transfer learning methods such as the Domain-adversarial Neural Network (DANN) and Wasserstein Domain-adversarial Neural Network (WDANN), it accommodates inter-class correlations. (3) It exhibits enhanced ease of training and convergence compared to traditional deep transfer learning networks. Through rigorous experimentation on two publicly available datasets, we substantiate the efficiency and effectiveness of our proposed anti-drift methodology when juxtaposed with state-of-the-art techniques.
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Affiliation(s)
| | | | | | | | | | - Jianwei Zhang
- School of Control Science and Engineering, Dalian University of Technology, Dalian 116000, China; (H.Z.); (Y.W.); (G.Y.); (R.S.); (J.Y.)
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4
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Jiang H, Zhao M, Chen Q. Determination of procymidone residues in rapeseed oil based on olfactory visualization technology. Food Chem X 2023; 19:100794. [PMID: 37780316 PMCID: PMC10534118 DOI: 10.1016/j.fochx.2023.100794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 07/13/2023] [Indexed: 10/03/2023] Open
Abstract
A new means about olfactory visualization technique for the quantitative analysis of procymidone residues in rapeseed oil has been proposed. First, an olfactory visualization system was set up to collect volatile odor information from rapeseed oil samples containing different concentrations of procymidone residues. Then, we utilized four intelligent optimization algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO) and simulated annealing (SA), to optimize the characteristics of the sensors. Finally, support vector machine regression (SVR) models employing optimized features were constructed for the quantitative detection of procymidone residues in rapeseed oil. The study demonstrated that the SA-SVR model demonstrated superior prediction results, achieving a high determination coefficient of prediction (R P 2 ) at 0.9894. As indicated by the results, it is possible to successfully conduct non-destructive detection of procymidone residues in edible oil by the olfactory visualization technology.
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Affiliation(s)
- Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Mingxing Zhao
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Quansheng Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China
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5
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Tang P, Wang JQ, Wang YF, Jin JC, Meng X, Zhu Y, Gao Y, Xu YQ. Comparison analysis of full-spectrum metabolomics revealed on the variation of potential metabolites of unscented, Chloranthus spicatus scented, and Osmanthus fragrans (Thunb.) Lour. scented Congou black teas. Front Nutr 2023; 10:1234807. [PMID: 37645629 PMCID: PMC10461629 DOI: 10.3389/fnut.2023.1234807] [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: 06/05/2023] [Accepted: 07/18/2023] [Indexed: 08/31/2023] Open
Abstract
Introduction In recent years, scented black tea has attracted much attention due to its pleasant floral aroma and mellow flavor, but little research has been carried out on its flavor metabolic profile. Methods In this study, the flavor metabolic profiles of unscented, Chloranthus spicatus scented, and Osmanthus fragrans (Thunb.) Lour. scented Congou black teas were investigated using full-spectrum metabolomics analysis method, the first time that the flavor profiles of scented black tea were characterized in detail. Results and Discussion The results revealed that a total of 3,128 metabolites were detected in the three teas. Based on the criteria of variable importance in the project >1 and fold change ≥2 or ≤ 0.5, 761 non-volatile metabolites and 509 volatile metabolites were filtered as differential metabolites. Many differential non-volatile metabolites belonged to flavonoids, phenolic acids, and terpenoids. Floral, fruity and herbaceous volatile metabolites were significantly up-regulated in Chloranthus spicatus scented Congou black tea while sweet and fruity volatile metabolites were significantly down-regulated in Osmanthus fragrans (Thunb.) Lour. scented Congou black tea. The results contribute to a better understanding of the scenting techniques on the flavor quality of scented black teas and provide some information on the flavor chemistry theory of scented black teas.
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Affiliation(s)
- Ping Tang
- Hangzhou Vocational & Technical College, Hangzhou, China
| | - Jie-Qiong 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, China
- College of Food Science, Southwest University, Chongqing, China
| | - Yong-Feng Wang
- Jingdezhen Jin Gui Yuan Agricultural Development Co Ltd, Jingdezhen, China
| | - Jian-Chang Jin
- College of Biological and Environmental Engneering, Zhejiang Shuren University, Hangzhou, China
| | - Xin Meng
- College of Food and Health, Zhejiang A&F University, Hangzhou, China
| | - Yan Zhu
- College of Food and Health, Zhejiang A&F University, Hangzhou, China
| | - Ying Gao
- 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, China
| | - Yong-Quan Xu
- 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, China
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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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7
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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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8
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Yang C, Duan D, Dong C, Li C, Li G, Zhou Y, Gu Y, Liu Y, Zhao C, Dong D. Detection of volatile organic compounds in adulterated tea using Fourier transform infrared spectroscopy and Proton-transfer-reaction mass spectrometry. Food Chem 2023; 423:136308. [PMID: 37182490 DOI: 10.1016/j.foodchem.2023.136308] [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: 02/08/2023] [Revised: 04/25/2023] [Accepted: 05/01/2023] [Indexed: 05/16/2023]
Abstract
Aroma is a key factor used to evaluate tea quality. Illegal traders usually add essence to expired or substandard tea to improve its aroma so as to gain more profit. Traditional physical and chemical testing methods are time-consuming and costly. Furthermore, rapid detection techniques, such as near-infrared spectroscopy and machine vision, can only be used to detect adulterated powdered solid essences in tea. In this study, proton-transfer reaction mass spectrometry (PTR-MS) and Fourier-transform infrared spectroscopy (FTIR) were employed to detect volatile organic compounds (VOCs) in samples, and rapid detection of different tea adulterated liquid essence was achieved. The prediction accuracies of PTR-MS and FTIR reached over 0.941 and 0.957, respectively, and the minimum detection limits were lower than the actual used values in both. In this study, the different application scenarios of the two technologies are discussed based on their performance characteristics.
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Affiliation(s)
- Chongshan Yang
- College of Engineering and Technology, Southwest University, Chongqing 400715, China; National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Dandan Duan
- National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Chunwang Dong
- Tea Research Institute of Shandong Academy of Agricultural Sciences, Jinan 250000, China
| | - Chuanxia Li
- National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Guanglin Li
- College of Engineering and Technology, Southwest University, Chongqing 400715, China
| | - Yunhai Zhou
- National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Yifan Gu
- National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Yachao Liu
- National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Chunjiang Zhao
- College of Engineering and Technology, Southwest University, Chongqing 400715, China; National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Daming Dong
- National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
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9
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Ouyang Q, Rong Y, Wu J, Wang Z, Lin H, Chen Q. Application of colorimetric sensor array combined with visible near-infrared spectroscopy for the matcha classification. Food Chem 2023; 420:136078. [PMID: 37075576 DOI: 10.1016/j.foodchem.2023.136078] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/26/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023]
Abstract
Matcha tea powder is considered as an integral part of a healthy diet due to its enormous health benefits. In the current study, visible near-infrared (Vis-NIR) and colorimetric sensor array (CSA) techniques are applied to identify the matcha grades. The color-sensitive dyes reacted with their volatile compounds and the information was recorded by Vis-NIR spectroscopy, namely Vis-NIR-CSA. Specifically, three linear and three nonlinear classification models were compared, yielding the optimal identification rate by the back-propagation artificial neural network (BPANN) model with 99% and 98% in the calibration and prediction sets, respectively. The results indicated the sensor combined with the BPANN model could be applied satisfactorily in identification of different matcha grades. Additionally, the variations in volatile compounds between different matcha grades and eight characteristic volatile compounds were identified, which verified the sensor identification results. This study provided a scientific and novel method for the stability of matcha quality in production.
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Affiliation(s)
- Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Yanna Rong
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Jiaqi Wu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Zhen Wang
- National Research and Development Center for Matcha Processing Technology, Jiangsu Xinpin Tea Co., Ltd, Changzhou 213254, PR China; Tea Industry Research Institute, Changzhou Academy of Modern Agricultural Sciences, Changzhou 213254, PR China
| | - Hao Lin
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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10
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Portable beef-freshness detection platform based on colorimetric sensor array technology and bionic algorithms for total volatile basic nitrogen (TVB-N) determination. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
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11
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Yue C, Li W, Li C, Wang Z, Peng H, Yang P. Differential characterization of volatile components and aroma sensory properties of different types of Hehong tea (Congou black tea). FLAVOUR FRAG J 2022. [DOI: 10.1002/ffj.3725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Cuinan Yue
- Jiangxi Cash Crops Research Institute Nanchang China
- Jiangxi Key Laboratory of Tea Quality and Safety Control Nanchang China
- Jiangxi Sericulture and Tea Research Institute Nanchang China
| | - Wenjin Li
- Jiangxi Cash Crops Research Institute Nanchang China
- Jiangxi Key Laboratory of Tea Quality and Safety Control Nanchang China
- Jiangxi Sericulture and Tea Research Institute Nanchang China
| | - Chen Li
- Jiangxi Cash Crops Research Institute Nanchang China
- Jiangxi Key Laboratory of Tea Quality and Safety Control Nanchang China
- Jiangxi Sericulture and Tea Research Institute Nanchang China
| | - Zhihui Wang
- Jiangxi Cash Crops Research Institute Nanchang China
- Jiangxi Key Laboratory of Tea Quality and Safety Control Nanchang China
- Jiangxi Sericulture and Tea Research Institute Nanchang China
| | - Hua Peng
- Jiangxi Cash Crops Research Institute Nanchang China
- Jiangxi Key Laboratory of Tea Quality and Safety Control Nanchang China
- Jiangxi Sericulture and Tea Research Institute Nanchang China
| | - Puxiang Yang
- Jiangxi Cash Crops Research Institute Nanchang China
- Jiangxi Key Laboratory of Tea Quality and Safety Control Nanchang China
- Jiangxi Sericulture and Tea Research Institute Nanchang China
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12
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Li J, Deng Y, Xu W, Zhao R, Chen T, Wang M, Xu E, Zhou J, Wang W, Liu D. Multiscale modeling of food thermal processing for insight, comprehension, and utilization of heat and mass transfer: A state-of-the-art review. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.11.018] [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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13
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Zhu C, Deng J, Jiang H. Parameter Optimization of Support Vector Machine to Improve the Predictive Performance for Determination of Aflatoxin B 1 in Peanuts by Olfactory Visualization Technique. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27196730. [PMID: 36235267 PMCID: PMC9573054 DOI: 10.3390/molecules27196730] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/07/2022] [Accepted: 10/08/2022] [Indexed: 11/04/2022]
Abstract
This study proposes a novel method for detection of aflatoxin B1 (AFB1) in peanuts using olfactory visualization technique. First, 12 kinds of chemical dyes were selected to prepare a colorimetric sensor to assemble olfactory visualization system, which was used to collect the odor characteristic information of peanut samples. Then, genetic algorithm (GA) with back propagation neural network (BPNN) as the regressor was used to optimize the color component of the preprocessed sensor feature image. Support vector regression (SVR) quantitative analysis model was constructed by using the optimized combination of characteristic color components to achieve determination of the AFB1 in peanuts. In this process, the optimization performance of grid search (GS) algorithm and sparrow search algorithm (SSA) on SVR parameter was compared. Compared with GS-SVR model, the model performance of SSA-SVR was better. The results showed that the SSA-SVR model with the combination of seven characteristic color components obtained the best prediction effect. Its correlation coefficients of prediction (RP) reached 0.91. The root mean square error of prediction (RMSEP) was 5.7 μg·kg-1, and ratio performance deviation (RPD) value was 2.4. The results indicate that it is reliable to use the colorimetric sensor array with strong specificity for the determination of the AFB1 in peanuts. In addition, it is necessary to properly optimize the parameters of the prediction model, which can obviously improve the generalization performance of the multivariable model.
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Affiliation(s)
- Chengyun Zhu
- School of Physics and Electronic Engineering, Yancheng Teachers University, Yancheng 224007, China
- Jiangsu Intelligent Optoelectronic Devices and Measurement and Control Engineering Research Center, Yancheng 224007, China
| | - Jihong Deng
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
- Correspondence:
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14
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Advanced sensing of volatile organic compounds in the fermentation of tea extract enabled by nano-colorimetric sensor array based on density functional theory. Food Chem 2022. [DOI: 10.1016/j.foodchem.2022.134193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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15
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Rapid monitoring of black tea fermentation quality based on a solution-phase sensor array combined with UV-visible spectroscopy. Food Chem 2022; 377:131974. [PMID: 34979395 DOI: 10.1016/j.foodchem.2021.131974] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/24/2021] [Accepted: 12/27/2021] [Indexed: 11/21/2022]
Abstract
Rapid monitoring of fermentation quality has been the key to realizing the intelligent processing of black tea. In our study, mixing ratios, sensing array components and reaction times were optimized before an optimal solution phase colorimetric sensor array was constructed. The characteristic spectral information of the array was obtained by UV-visible spectroscopy and subsequently combined with machine learning algorithms to construct a black tea fermentation quality evaluation model. The competitive adaptive reweighting algorithms (CARS)-support vector machine model discriminated the black tea fermentation degree with 100% accuracy. For quantification of catechins and four theaflavins (TF, TFDG, TF-3-G, and TF-3'-G), the correlation coefficients of the CARS least square support vector machine model prediction set were 0.91, 0.86, 0.76, 0.72 and 0.79, respectively. The results obtained within 2 min enabled accurate monitoring of the fermentation quality of black tea, which provides a new method and idea for intelligent black tea processing.
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16
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Yu XM, Wang J, Gao R, Gong BC, Ai CX. Integrated Metabolomic-Transcriptomic Analysis Reveals Diverse Resource of Functional Ingredients From Persimmon Leaves of Different Varieties. FRONTIERS IN PLANT SCIENCE 2022; 13:904208. [PMID: 35693179 PMCID: PMC9175000 DOI: 10.3389/fpls.2022.904208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/22/2022] [Indexed: 06/15/2023]
Abstract
Persimmon leaves are used for making persimmon leaf tea or as functional ingredients due to their enrichment in flavonoids, the beneficial mineral contents, and favorable flavors contributed by volatile aroma compounds. The varieties/cultivars had a significant influence on the quality and flavor of persimmon leaf tea. In this study, the integrated metabolomic-transcriptomic analysis was conducted to investigate the potential in flavonoid biosynthesis, mineral absorption, and degradation of aromatic compounds from tender leaves of "Diospyros kaki. Heishi" (HS), "Diospyros kaki Thunb. Nishimurawase" (NM), and "Diospyros kaki Thunb. Taifu" (TF), using rootstock "Diospyros Lotus Linn" (DL) as the control. The metabolomic analysis showed that 382, 391, and 368 metabolites were differentially accumulated in the comparison of DL vs. HS, DL vs. NM, and DL vs. TF, respectively, and 229 common metabolites were obtained by comparative analysis. By RNA sequencing, 182,008 unigenes with 652 bp of mean length were annotated and 2,598, 3,503, and 3,333 differentially expressed genes (DEGs) were detected from the comparison of DL vs. HS, DL vs. NM, and DL vs. TF, respectively. After the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, 6, 6, and 3 DEGs [with | log2(fold change)| ≥ 1 simultaneously in the three comparisons] involved in flavonoid biosynthesis, mineral absorption, and degradation of aromatic compounds, respectively, were selected for quantitative reverse transcription-polymerase chain reaction (qRT-PCR) validation and the consistent trends of the relative expression level of each DEG with RNA sequencing (RNA-seq) data were observed. Based on the transcriptomic analysis and qRT-PCR validation, it was observed that the leaves of HS, NM, and TF had the greatest level of mineral absorption, flavonoid biosynthesis, and degradation of aromatic compounds, respectively. In addition, a positive correlation between the 15 DEGs and their metabolites was observed by the conjoint analysis. Thus, the tender leaves of HS, NM, and TF could be recommended for the production of persimmon leaf tea rich in mineral elements, flavonoid, and aroma compounds, respectively.
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Affiliation(s)
- Xian-Mei Yu
- Shandong Institute of Pomology, Tai’an, China
| | - Jie Wang
- Shandong Institute of Pomology, Tai’an, China
| | - Rui Gao
- Shandong Institute of Pomology, Tai’an, China
| | - Bang-Chu Gong
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, China
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17
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Jiang H, Wang J, Mao W, Chen Q. Determination of aflatoxin B1 in wheat based on colourimetric sensor array technology: Optimization of sensor features and model parameters to improve the model generalization performance. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107173] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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18
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Subspace alignment based on an extreme learning machine for electronic nose drift compensation. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107664] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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19
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Mao W, Jiang H. Determination of ethanol content during simultaneous saccharification and fermentation (SSF) of cassava based on a colorimetric sensor technique. RSC Adv 2022; 12:3996-4004. [PMID: 35425420 PMCID: PMC8981118 DOI: 10.1039/d1ra07859c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 01/25/2022] [Indexed: 12/04/2022] Open
Abstract
Ethanol content is an important indicator reflecting the yield of simultaneous saccharification and fermentation (SSF) of cassava. This study proposes an innovative method based on a colorimetric sensor technique to determine the ethanol content during the SSF of cassava. First, 14 kinds of porphyrin material and one kind of pH indicator were used to form a colorimetric sensor array for collecting odor data during the SSF of cassava. Then, the ant colony algorithm (ACO) and the simulated annealing algorithm (SA) were used to optimize and reconstruct the input color feature components of the support vector regression (SVR) model. The differential evolution algorithm (DE) was used to optimize the penalty factor (c) and the kernel function (g) of the SVR model. The results obtained showed that the combined prediction model of SA-DE-SVR had the highest accuracy, and the coefficient of determination (RP2) in the prediction set was 0.9549, and the root mean square error of prediction (RMSEP) was 0.1562. The overall results reveal that the use of a colorimetric sensor technique combined with different intelligent optimization algorithms to establish a model can quantitatively determine the ethanol content in the SSF of cassava, and has broad development prospects. In this study, a colorimetric sensor was used to collect odor information from fermentation samples. The optimal model was established by introducing different combinations of intelligent optimization algorithms to determine ethanol content.![]()
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Affiliation(s)
- Wencheng Mao
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
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20
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Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves. REMOTE SENSING 2021. [DOI: 10.3390/rs13183719] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued by both producers and consumers. Rapid, nondestructive, and low-cost monitoring methods for monitoring tea quality could improve the tea quality and the economic benefits associated with tea. This research explored the possibility of monitoring tea leaf quality from multi-spectral images. Threshold segmentation and manual sampling methods were used to eliminate the image background, after which the spectral features were constructed. Based on this, the texture features of the multi-spectral images of the tea canopy were extracted. Three machine learning methods, partial least squares regression, support vector machine regression, and random forest regression (RFR), were used to construct and train multiple monitoring models. Further, the four key quality parameters of tea polyphenols, total sugars, free amino acids, and caffeine content were estimated using these models. Finally, the effects of automatic and manual image background removal methods, different regression methods, and texture features on the model accuracies were compared. The results showed that the spectral characteristics of the canopy of fresh tea leaves were significantly correlated with the tea quality parameters (r ≥ 0.462). Among the sampling methods, the EXG_Ostu sampling method was best for prediction, whereas, among the models, RFR was the best fitted modeling algorithm for three of four quality parameters. The R2 and root-mean-square error values of the built model were 0.85 and 0.16, respectively. In addition, the texture features extracted from the canopy image improved the prediction accuracy of most models. This research confirms the modeling application of a combination of multi-spectral images and chemometrics, as a low-cost, fast, reliable, and nondestructive quality control method, which can effectively monitor the quality of fresh tea leaves. This provides a scientific reference for the research and development of portable tea quality monitoring equipment that has general applicability in the future.
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21
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Kang W, Lin H, Jiang H, Yao-Say Solomon Adade S, Xue Z, Chen Q. Advanced applications of chemo-responsive dyes based odor imaging technology for fast sensing food quality and safety: A review. Compr Rev Food Sci Food Saf 2021; 20:5145-5172. [PMID: 34409725 DOI: 10.1111/1541-4337.12823] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 06/24/2021] [Accepted: 07/06/2021] [Indexed: 01/18/2023]
Abstract
Public attention to foodquality and safety has been increased significantly. Therefore, appropriate analytical tools are needed to analyze and sense the food quality and safety. Volatile organic compounds (VOCs) are important indicators for the quality and safety of food products. Odor imaging technology based on chemo-responsive dyes is one of the most promising methods for analysis of food products. This article reviews the sensing and imaging fundamentals of odor imaging technology based on chemo-responsive dyes. The aim is to give detailed outlines about the theory and principles of using odor imaging technology for VOCs detection, and to focus primarily on its applications in the field of quality and safety evaluation of food products, as well as its future applicability in modern food industries and research. The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods , poultry meat, aquatic products, fruits and vegetables, and tea. It has the potential for the rapid, reliable, and inline assessment of food safety and quality by providing odor-image-basedmonitoring tool. Practical Application: The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods, poultry meat, aquatic products, fruits and vegetables, and tea.
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Affiliation(s)
- Wencui Kang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Hao Lin
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Hao Jiang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | | | - Zhaoli Xue
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
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22
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Monitoring the withering condition of leaves during black tea processing via the fusion of electronic eye (E-eye), colorimetric sensing array (CSA), and micro-near-infrared spectroscopy (NIRS). J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2021.110534] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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23
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Ren G, Liu Y, Ning J, Zhang Z. Assessing black tea quality based on visible–near infrared spectra and kernel-based methods. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.103810] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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24
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Ren G, Li T, Wei Y, Ning J, Zhang Z. Estimation of Congou black tea quality by an electronic tongue technology combined with multivariate analysis. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105899] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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25
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Ren G, Ning J, Zhang Z. Multi-variable selection strategy based on near-infrared spectra for the rapid description of dianhong black tea quality. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 245:118918. [PMID: 32942112 DOI: 10.1016/j.saa.2020.118918] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 08/26/2020] [Accepted: 09/01/2020] [Indexed: 05/05/2023]
Abstract
The main objectives of the study are to understand and explore critical feature wavelengths of the obtained near-infrared (NIR) data relating to dianhong black tea quality categories, we propose a multi-variable selection strategy based on the variable space optimization from big to small which is the kernel idea of a variable combination of the improved genetic algorithm (IGA) and particle swarm optimization (PSO) in this study. A rapid description based on the NIR technology is implemented to assess black tea tenderness and rankings. First, 700 standard samples from dianhong black tea of seven quality classes are scanned using a NIR system. The raw spectra acquired are preprocessed by Savitzky-Golay (SG) filtering coupled with standard normal variate transformation (SNV). Then, the multi-variable selection algorithm (IGA-PSO) is applied to compare with the single method (the IGA and PSO) and search the optimal characteristic wavelengths. Finally, the identification models are developed using a decision tree (DT), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on different kernel functions combined with the effective features from the above variables screening paths for the discrimination of black tea quality. The results show that the IGA-PSO-SVM model with a radial basis function achieves the best predictive results with the correct discriminant rate (CDR) of 95.28% based on selected four characteristic variables in the prediction process. The overall results demonstrate that NIR combined with a multi-variable selection method can constitute a potential tool to understand the most important features involved in the evaluation of dianhong black tea quality helping the instrument manufacturers to achieve the development of low-cost and handheld NIR sensors.
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Affiliation(s)
- Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China.
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26
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Ren G, Gan N, Song Y, Ning J, Zhang Z. Evaluating Congou black tea quality using a lab-made computer vision system coupled with morphological features and chemometrics. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105600] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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27
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Ren G, Ning J, Zhang Z. Intelligent assessment of tea quality employing visible-near infrared spectra combined with a hybrid variable selection strategy. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105085] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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28
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Jiang H, Liu T, He P, Ding Y, Chen Q. Rapid measurement of fatty acid content during flour storage using a color-sensitive gas sensor array: Comparing the effects of swarm intelligence optimization algorithms on sensor features. Food Chem 2020; 338:127828. [PMID: 32822904 DOI: 10.1016/j.foodchem.2020.127828] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 08/05/2020] [Accepted: 08/10/2020] [Indexed: 01/09/2023]
Abstract
The fatty acid content of flour is an important indicator for determining the deterioration of flour. We propose a novel rapid measurement method for fatty acid content during flour storage based on a self-designed color-sensitive gas sensor array. First, a color-sensitive gas sensor array was prepared to capture the odor changes during flour storage. The sensor features were then optimized using genetic algorithm (GA), ant colony optimization (ACO) and particle swarm optimization (PSO). Finally, back propagation neural network (BPNN) models were established to measure the fatty acid content during flour storage. Experimental results showed that the optimization effects of the three algorithms improved in the following order: GA < ACO < PSO, for the sensor features optimization. In the validation set, the determination coefficient of the best PSO-BPNN model was 0.9837. The overall results demonstrate that the models established on the optimized features can realize rapid measurements of fatty acid content during flour storage.
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Affiliation(s)
- Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Tong Liu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Peihuan He
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Yuhan Ding
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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29
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Ren G, Sun Y, Li M, Ning J, Zhang Z. Cognitive spectroscopy for evaluating Chinese black tea grades (Camellia sinensis): near-infrared spectroscopy and evolutionary algorithms. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2020; 100:3950-3959. [PMID: 32329077 DOI: 10.1002/jsfa.10439] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 03/12/2020] [Accepted: 04/24/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Grading represents an essential criterion for the quality assurance of black tea. The main objectives of the study were to develop a highly robust model for Chinese black tea of seven grades based on cognitive spectroscopy. RESULTS Cognitive spectroscopy was proposed to combine near-infrared spectroscopy (NIRS) with machine learning and evolutionary algorithms, selected feature information from complex spectral data and show the best results without human intervention. The NIRS measuring system was used to obtain the spectra of Chinese black tea samples of seven grades. The spectra acquired were preprocessed by standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and minimum/maximum normalization (MIN/MAX), and the optimal pretreating method was implemented using principal component analysis combined with linear discriminant analysis algorithm. Three feature selection evolutionary algorithms, which were a genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO), were compared to search the best preprocessed characteristic wavelengths. Cognitive models of Chinese black tea ranks were constructed using extreme learning machine (ELM), K-nearest neighbor (KNN) and support vector machine (SVM) methods based on the selected characteristic variables. Experimental results revealed that the PSO-SVM model showed the best predictive performance with the correlation coefficients of prediction set (Rp ) of 0.9838, the root mean square error of prediction (RMSEP) of 0.0246, and the correct discriminant rate (CDR) of 98.70%. The extracted feature wavelengths were only occupying 0.18% of the origin. CONCLUSION The overall results demonstrated that cognitive spectroscopy could be utilized as a rapid strategy to identify Chinese black tea grades. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, P. R. China
| | - Yemei Sun
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, P. R. China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, P. R. China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, P. R. China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, P. R. China
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30
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Guo Z, Barimah AO, Shujat A, Zhang Z, Ouyang Q, Shi J, El-Seedi HR, Zou X, Chen Q. Simultaneous quantification of active constituents and antioxidant capability of green tea using NIR spectroscopy coupled with swarm intelligence algorithm. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.109510] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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31
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Ren G, Wang Y, Ning J, Zhang Z. Highly identification of keemun black tea rank based on cognitive spectroscopy: Near infrared spectroscopy combined with feature variable selection. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 230:118079. [PMID: 31982655 DOI: 10.1016/j.saa.2020.118079] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 01/12/2020] [Accepted: 01/16/2020] [Indexed: 06/10/2023]
Abstract
From the perspective of combating fraud issues and examining keemun black tea properties, there was a contemporary urgent demand for a keemun black tea rankings identification system. Current rapid evaluation systems had been mainly developed for green tea grade evaluation, but there was space for improvement to establish a highly robust model. The present study proposed cognitive spectroscopy that combined near infrared spectroscopy (NIRS) with multivariate calibration and feature variable selection methods. We defined "cognitive spectroscopy" as a protocol that selects characteristic information from complex spectral data and showed optimal results without human intervention. 700 samples representing keemun black tea from seven quality levels were scanned applying an NIR sensor. To differentiate which wavelength variables of the acquired NIRS data carry key and feature information regarding keemun black tea grades, there were four different variables screening approaches, namely genetic algorithm (GA), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and shuffled frog leaping algorithm (SFLA), were compared in this study. Cognitive models were developed using least squares support vector machine (LSSVM), back propagation neural network (BPNN) and random forest (RF) methods combined with the optimized characteristic variables from the above variables selection algorithms for the identification of keemun black tea rank quality. Experimental results showed that all cognitive models utilizing the SFLA approach achieved steady predictive results based on eight latent variables and selected thirteen characteristic wavelength variables. The CARS-LSSVM model with the best predictive performance was proposed based on selecting ten characteristic latent variables, and the best performance indicators of the model were as follows: the root mean square error of prediction (RMSEP) was 0.0413, the correlation coefficients of prediction set (Rp) was 0.9884, and the correct discriminant rate (CDR) was 99.01% in the validation process. This study demonstrated that cognitive spectroscopy represented a proper strategy for the highly identification of quality rankings of keemun black tea.
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Affiliation(s)
- Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China.
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32
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Huang J, Ren G, Sun Y, Jin S, Li L, Wang Y, Ning J, Zhang Z. Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics. Food Sci Nutr 2020; 8:2015-2024. [PMID: 32328268 PMCID: PMC7174226 DOI: 10.1002/fsn3.1489] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 01/11/2020] [Accepted: 02/04/2020] [Indexed: 01/24/2023] Open
Abstract
The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near-infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA-SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades.
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Affiliation(s)
- Jing Huang
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Guangxin Ren
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Yemei Sun
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Shanshan Jin
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
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“Oral” tribology study on saliva-tea compound mixtures: Correlation between sweet aftertaste (Huigan) perception and friction coefficient. Food Res Int 2019; 125:108642. [DOI: 10.1016/j.foodres.2019.108642] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 08/20/2019] [Accepted: 08/22/2019] [Indexed: 12/31/2022]
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