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Yang C, Jiao L, Dong C, Wen X, Lin P, Duan D, Li G, Zhao C, Fu X, Dong D. Long-range infrared absorption spectroscopy and fast mass spectrometry for rapid online measurements of volatile organic compounds from black tea fermentation. Food Chem 2024; 449:139211. [PMID: 38581789 DOI: 10.1016/j.foodchem.2024.139211] [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: 12/13/2023] [Revised: 03/02/2024] [Accepted: 03/31/2024] [Indexed: 04/08/2024]
Abstract
Fermentation is the key process to determine the quality of black tea. Traditional physical and chemical analyses are time consuming, it cannot meet the needs of online monitoring. The existing rapid testing techniques cannot determine the specific volatile organic compounds (VOCs) produced at different stages of fermentation, resulting in poor model transferability; therefore, the current degree of black tea fermentation mainly relies on the sensory judgment of tea makers. This study used proton transfer reaction mass spectrometry (PTR-MS) and fourier transform infrared spectroscopy (FTIR) combined with different injection methods to collect VOCs of the samples, the rule of change of specific VOCs was clarified, and the extreme learning machine (ELM) model was established after principal component analysis (PCA), the prediction accuracy reached 95% and 100%, respectively. Finally, different application scenarios of the two technologies in the actual production of black tea are discussed based on their respective advantages.
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Affiliation(s)
- Chongshan Yang
- College of Engineering and Technology, Southwest University, Chongqing 400715, China; Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Leizi Jiao
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Chunwang Dong
- Tea Research Institute of Shandong Academy of Agricultural Sciences, Jinan 250000, China
| | - Xuelin Wen
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Peng Lin
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Dandan Duan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Guanglin Li
- College of Engineering and Technology, Southwest University, Chongqing 400715, China
| | - Chunjiang Zhao
- College of Engineering and Technology, Southwest University, Chongqing 400715, China; Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Xinglan Fu
- College of Engineering and Technology, Southwest University, Chongqing 400715, China.
| | - Daming Dong
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
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Zareef M, Arslan M, Hassan MM, Ahmad W, Chen Q. Comparison of Si-GA-PLS and Si-CARS-PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:7914-7920. [PMID: 37490702 DOI: 10.1002/jsfa.12880] [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: 03/19/2023] [Revised: 07/06/2023] [Accepted: 07/22/2023] [Indexed: 07/27/2023]
Abstract
BACKGROUND The objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si-PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables. RESULTS The collected spectral data were divided into 19 sub-interval selections totaling 246 variables, yielding the lowest root mean square error of cross-validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC ), prediction (RP ), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si-GA-PLS model produced the following results: PCs = 9; RC = 0.915; RMSECV = 1.39; RP = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si-CARS-PLS model was noted to be best at PCs = 10, while RC = 0.9723, RMSECV = 0.81, RP = 0.9114, RMSEP = 1.45 and RPD = 2.59. CONCLUSION The build model's prediction ability was amended in the order PLS < Si-PLS < CARS-PLS when full spectroscopic data were used and Si-PLS < Si-GA-PLS < Si-CARS-PLS when interval selection was performed with the Si-PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Muhammad Zareef
- School of Food and Biological Engineering Jiangsu University, Zhenjiang, People's Republic of China
| | - Muhammad Arslan
- School of Food and Biological Engineering Jiangsu University, Zhenjiang, People's Republic of China
| | - Md Mehedi Hassan
- School of Food and Biological Engineering Jiangsu University, Zhenjiang, People's Republic of China
| | - Waqas Ahmad
- School of Food and Biological Engineering Jiangsu University, Zhenjiang, People's Republic of China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University, Zhenjiang, People's Republic of China
- College of Food and Biological Engineering, Jimei University, Xiamen, People's Republic of China
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He Q, Zhang H, Li T, Zhang X, Li X, Dong C. NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States. SENSORS (BASEL, SWITZERLAND) 2023; 23:9107. [PMID: 38005495 PMCID: PMC10675699 DOI: 10.3390/s23229107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/05/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023]
Abstract
Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision agriculture in tea plantations. In recent years, spectral data have become an important tool for the non-destructive testing of soil physicochemical properties. In this study, a support vector regression (SVR) model was constructed to model the hydrolyzed nitrogen, available potassium, and effective phosphorus in tea plantation soils of different grain sizes. Then, the successful projections algorithm (SPA) and least-angle regression (LAR) and bootstrapping soft shrinkage (BOSS) variable importance screening methods were used to optimize the variables in the soil physicochemical properties. The findings demonstrated that soil particle sizes of 0.25-0.5 mm produced the best predictions for all three physicochemical properties. After further using the dimensionality reduction approach, the LAR algorithm (R2C = 0.979, R2P = 0.976, RPD = 6.613) performed optimally in the prediction model for hydrolytic nitrogen at a soil particle size of 0.25~0.5. The models using data dimensionality reduction and those that used the BOSS method to estimate available potassium (R2C = 0.977, R2P = 0.981, RPD = 7.222) and effective phosphorus (R2C = 0.969, R2P = 0.964, RPD = 5.163) had the best accuracy. In order to offer a reference for the accurate detection of soil physicochemical properties in tea plantations, this study investigated the modeling effect of each physicochemical property under various soil particle sizes and integrated the regression model with various downscaling strategies.
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Affiliation(s)
- Qinghai He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310008, China; (Q.H.); (X.L.)
- Shandong Academy of Agricultural Machinery Science, Jinan 250100, China;
| | - Haowen Zhang
- Shandong Academy of Agricultural Machinery Science, Jinan 250100, China;
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China;
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271000, China;
| | - Tianhua Li
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271000, China;
| | - Xiaojia Zhang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China;
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310008, China; (Q.H.); (X.L.)
| | - Chunwang Dong
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China;
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4
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Zhang S, Shan X, Niu L, Chen L, Wang J, Zhou Q, Yuan H, Li J, Wu T. The Integration of Metabolomics, Electronic Tongue, and Chromatic Difference Reveals the Correlations between the Critical Compounds and Flavor Characteristics of Two Grades of High-Quality Dianhong Congou Black Tea. Metabolites 2023; 13:864. [PMID: 37512571 PMCID: PMC10385030 DOI: 10.3390/metabo13070864] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Tea's biochemical compounds and flavor quality vary depending on its grade ranking. Dianhong Congou black tea (DCT) is a unique tea category produced using the large-leaf tea varieties from Yunnan, China. To date, the flavor characteristics and critical components of two grades of high-quality DCT, single-bud-grade DCT (BDCT), and special-grade DCT (SDCT) manufactured mainly with single buds and buds with one leaf, respectively, are far from clear. Herein, comparisons of two grades were performed by the integration of human sensory evaluation, an electronic tongue, chromatic differences, the quantification of major components, and metabolomics. The BDCT possessed a brisk, umami taste and a brighter infusion color, while the SDCT presented a comprehensive taste and redder liquor color. Quantification analysis showed that the levels of total polyphenols, catechins, and theaflavins (TFs) were significantly higher in the BDCT. Fifty-six different key compounds were screened by metabolomics, including catechins, flavone/flavonol glycosides, amino acids, phenolic acids, etc. Correlation analysis revealed that the sensory features of the BDCT and SDCT were attributed to their higher contents of catechins, TFs, theogallin, digalloylglucose, and accumulations of thearubigins (TRs), flavone/flavonol glycosides, and soluble sugars, respectively. This report is the first to focus on the comprehensive evaluation of the biochemical compositions and sensory characteristics of two grades of high-quality DCT, advancing the understanding of DCT from a multi-dimensional perspective.
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Affiliation(s)
- Shan Zhang
- School of Landscape Architecture and Horticulture Sciences, Southwest Forestry University, Kunming 650224, China
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Xujiang Shan
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Linchi Niu
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Le Chen
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, China
| | - Jinjin Wang
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Qinghua Zhou
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, China
| | - Haibo Yuan
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Jia Li
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Tian Wu
- School of Landscape Architecture and Horticulture Sciences, Southwest Forestry University, Kunming 650224, China
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5
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Wang Y, Ren Z, Chen Y, Lu C, Deng WW, Zhang Z, Ning J. Visualizing chemical indicators: Spatial and temporal quality formation and distribution during black tea fermentation. Food Chem 2023; 401:134090. [DOI: 10.1016/j.foodchem.2022.134090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 08/13/2022] [Accepted: 08/29/2022] [Indexed: 01/30/2023]
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6
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Effects of different acetic acid bacteria strains on the bioactive compounds, volatile compounds and antioxidant activity of black tea vinegar. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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7
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A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman. Foods 2022; 11:foods11182928. [PMID: 36141056 PMCID: PMC9498461 DOI: 10.3390/foods11182928] [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: 08/16/2022] [Revised: 09/05/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022] Open
Abstract
Fixation is the most critical step in the green tea process. Hence, this study developed a rapid and accurate moisture content detection for the green tea fixation process based on near-infrared spectroscopy and computer vision. Specifically, we created a quantitative moisture content prediction model appropriate for the processing of green tea fixation. First, we collected spectrum and image information of green tea fixation leaves, utilizing near-infrared spectroscopy and computer vision. Then, we applied the partial least squares regression (PLSR), support vector regression (SVR), Elman neural network (ENN), and Elman neural network based on whale optimization algorithm (WOA-ENN) methods to build the prediction models for single data (data from a single sensor) and mid-level data fusion, respectively. The results revealed that the mid-level data fusion strategy combined with the WOA-ENN model attained the best effect. Namely, the prediction set correlation coefficient (Rp) was 0.9984, the root mean square error of prediction (RMSEP) was 0.0090, and the relative percent deviation (RPD) was 17.9294, highlighting the model’s excellent predictive performance. Thus, this study identified the feasibility of predicting the moisture content in the process of green tea fixation by miniaturized near-infrared spectroscopy. Moreover, in establishing the model, the whale optimization algorithm was used to overcome the defect whereby the Elman neural network falls into the local optimum. In general, this study provides technical support for rapid and accurate moisture content detection in green tea fixation.
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8
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Tea Analyzer: A low-cost and portable tool for quality quantification of postharvest fresh tea leaves. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113248] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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9
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Zhang T, Wu X, Wu B, Dai C, Fu H. Rapid authentication of the geographical origin of milk using portable near‐infrared spectrometer and fuzzy uncorrelated discriminant transformation. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Tingfei Zhang
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
- High‐tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province Jiangsu University Zhenjiang China
| | - Xiaohong Wu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
- High‐tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province Jiangsu University Zhenjiang China
| | - Bin Wu
- Department of Information Engineering Chuzhou Polytechnic Chuzhou China
| | - Chunxia Dai
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Haijun Fu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
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10
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Li L, Wang Y, Cui Q, Liu Y, Ning J, Zhang Z. Qualitative and quantitative quality evaluation of black tea fermentation through noncontact chemical imaging. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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11
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Mehedi Hassan M, He P, Xu Y, Zareef M, Li H, Chen Q. Rapid detection and prediction of chloramphenicol in food employing label-free HAu/Ag NFs-SERS sensor coupled multivariate calibration. Food Chem 2021; 374:131765. [PMID: 34896956 DOI: 10.1016/j.foodchem.2021.131765] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 11/03/2021] [Accepted: 11/30/2021] [Indexed: 12/17/2022]
Abstract
Considering growing food safety issues, hollow Au/Ag nano-flower (HAu/Ag NFs) nanosensor has been synthesized for label-free and ultrasensitive detection of chloramphenicol (CP) via integrating the surface-enhanced Raman scattering (SERS) and multivariate calibration. As the anisotropic plasmonic nanomaterials, HAu/Ag NFs had numerous nano-chink on their surface, which offered huge hotspots for analytes. CP generated a strong SERS signal while adsorbed on the surface of HAu/Ag NFs and noted excellent linearity with 1st derivative-competitive adaptive reweighted sampling-partial least squares (CARS-PLS) in the range of 0.0001-1000 µg/mL among the four applied multivariate calibrations. Additionally, CARS-PLS generated the lowest prediction error (RMSEP) of 0.089 and 0.123 µg/mL for milk and water samples, respectively, and any CARS-PLS model could be used for both samples according to T-test results (P > 0.05). The intra- and interday recovery for both samples were in the range of 92.62-96.74% with CV < 10%, suggested the proposed method has excellent accuracy and precision.
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Affiliation(s)
- Md Mehedi Hassan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Peihuan He
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Yi Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Huanhuan Li
- 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; College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
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12
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Zavala-Ortiz DA, Denner A, Aguilar-Uscanga MG, Marc A, Ebel B, Guedon E. Comparison of partial least square, artificial neural network, and support vector regressions for real-time monitoring of CHO cell culture processes using in situ near-infrared spectroscopy. Biotechnol Bioeng 2021; 119:535-549. [PMID: 34821379 DOI: 10.1002/bit.27997] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/05/2021] [Accepted: 11/13/2021] [Indexed: 11/08/2022]
Abstract
The biopharmaceutical industry must guarantee the efficiency and biosafety of biological medicines, which are quite sensitive to cell culture process variability. Real-time monitoring procedures based on vibrational spectroscopy such as near-infrared (NIR) spectroscopy, are then emerging to support innovative strategies for retro-control of key parameters as substrates and by-product concentration. Whereas monitoring models are mainly constructed using partial least squares regression (PLSR), spectroscopic models based on artificial neural networks (ANNR) and support vector regression (SVR) are emerging with promising results. Unfortunately, analysis of their performance in cell culture monitoring has been limited. This study was then focused to assess their performance and suitability for the cell culture process challenges. PLSR had inferior values of the determination coefficient (R2 ) for all the monitored parameters (i.e., 0.85, 0.93, and 0.98, respectively for the PLSR, SVR, and ANNR models for glucose). In general, PLSR had a limited performance while models based on ANNR and SVR have been shown superior due to better management of inter-batch heterogeneity and enhanced specificity. Overall, the use of SVR and ANNR for the generation of calibration models enhanced the potential of NIR spectroscopy as a monitoring tool.
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Affiliation(s)
- Daniel A Zavala-Ortiz
- Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, LRGP, Vandœuvre-lès-Nancy, France.,Tecnológico Nacional de México/Instituto Tecnológico de Veracruz, Veracruz, Ver., México
| | - Aurélia Denner
- Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, LRGP, Vandœuvre-lès-Nancy, France
| | | | - Annie Marc
- Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, LRGP, Vandœuvre-lès-Nancy, France
| | - Bruno Ebel
- Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, LRGP, Vandœuvre-lès-Nancy, France
| | - Emmanuel Guedon
- Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, LRGP, Vandœuvre-lès-Nancy, France
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Zuo Y, Tan G, Xiang D, Chen L, Wang J, Zhang S, Bai Z, Wu Q. Development of a novel green tea quality roadmap and the complex sensory-associated characteristics exploration using rapid near-infrared spectroscopy technology. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 258:119847. [PMID: 33940571 DOI: 10.1016/j.saa.2021.119847] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 04/11/2021] [Accepted: 04/13/2021] [Indexed: 06/12/2023]
Abstract
Nondestructive instrumental identification of the green tea quality instead of professional human panel tests is highly desired for industrial application recently. The special flavor is a key quality-trait that influence consumer preference. However, flavonoids, as well as sensory-associated compounds, which play a critical role in the quality-traits profile of green tea samples have been poorly investigated. In this study, we were proposing an objective and accurate near infrared spectroscopy (NIRS) profile to support quality control within the entire green tea sensory evaluation chain, the complexity of green tea samples' sensory analysis was performed by two complementary methods: the standard calculation and the novel NIRS roadmap coupled with chemometrics. The green tea samples' physical quality, gustatory index, and nutritional index were measured respectively, which taking into consideration the gustatory evaluation of green tea for five commercially representative overall quality ("very bad", "bad", "regular", "good" and "excellent"). Our findings highlight the underexplored role of NIRS in chemical-to-sensory relationships and its widespread importance and utility in green tea quality improvement. Collectively, the comprehensive characterization of sensory-associated attribution allowed the identification of a wide array of spectrometric features, mostly related to moisture, soluble solids (SS), tea polyphenol (TPP), epigallocatechin gallate (EGCG), epicatechin (EC) and tea polysaccharide (TPS), which can be used as putative biomarkers to rapidly evaluate the green tea flavor variations related to rank differences. Otherwise, the NIRS' data were split into the calibration (n = 80) and prediction (n = 40) set independently, which showed high correlation coefficient with Rp-values of 0.9024, 0.9020 in physical and total cup quality, respectively. In this research, we demonstrated that NIRS was an easily-generated strategy and able to close the loop to feedback into the process for advanced process control. However, the established models should be improved by more green tea samples from different regions.
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Affiliation(s)
- Yamin Zuo
- School of Basic Medical Sciences, Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, 30 Renmin South Rd, Shiyan, Hubei 442000, China; Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Gaohao Tan
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Di Xiang
- The Yunnan Tea Chamber of Commerce, Panlong District, Kunming, Yunnan 650051, China
| | - Ling Chen
- The Department of Tea, Guizhou Vocational College of Agriculture, 3 Huangshi Rd, Qingzhen, Guizhou 551400, China
| | - Jiao Wang
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Shengsheng Zhang
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Zhiwen Bai
- The Guizhou Gui Tea (Group) Co. Ltd, Huaxi District, Guiyang, Guizhou 550001, China.
| | - Qing Wu
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China; Innovation Laboratory, the Third Experiment Middle School in Guiyang, Guiyang, Guizhou 550001, China.
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14
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Wang Y, Li L, Liu Y, Cui Q, Ning J, Zhang Z. Enhanced quality monitoring during black tea processing by the fusion of NIRS and computer vision. J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2021.110599] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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15
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Cozzolino D. From consumers' science to food functionality-Challenges and opportunities for vibrational spectroscopy. ADVANCES IN FOOD AND NUTRITION RESEARCH 2021; 97:119-146. [PMID: 34311898 DOI: 10.1016/bs.afnr.2021.03.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Current available methods used to measure or estimate the composition, functionality, and sensory properties of foods and food ingredients are destructive and time consuming. Therefore, new approaches are required by both the food industry and R&D organizations. Recent years have witnessed a steady growth on the applications and utilization of vibrational spectroscopy techniques [near (NIR), mid infrared (MIR), Raman] to analyse or estimate several properties in a wide range of foods and food ingredients. This chapter will provide with an overview of vibrational spectroscopy techniques, the combination of these techniques with multivariate data analysis, and examples on the use of these techniques to measure composition, and functional properties in a wide range of foods.
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Affiliation(s)
- Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia.
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16
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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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17
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Jin G, Wang YJ, Li M, Li T, Huang WJ, Li L, Deng WW, Ning J. Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system. Food Chem 2021; 358:129815. [PMID: 33915424 DOI: 10.1016/j.foodchem.2021.129815] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 12/13/2022]
Abstract
Intelligent identification of black tea fermentation quality is becoming a bottleneck to industrial automation. This study presents at-line rapid detection of black tea fermentation quality at industrial scale based on low-cost micro-near-infrared spectroscopy (NIRS) and laboratory-made computer vision system (CVS). High-performance liquid chromatography and a spectrophotometer were used for determining the content of catechins and theaflavins, and the color of tea samples, respectively. Hierarchical cluster analysis combined with sensory evaluation was used to group samples through different fermentation degrees. A principal component analysis-support vector machine (SVM) model was developed to discriminate the black tea fermentation degree using color, spectral, and data fusion information; high accuracy (calibration = 95.89%, prediction = 89.19%) was achieved using mid-level data fusion. In addition, SVM model for theaflavins content prediction was established. The results indicated that the micro-NIRS combined with CVS proved a portable and low-cost tool for evaluating the black tea fermentation quality.
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Affiliation(s)
- Ge Jin
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Yu-Jie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Tiehan Li
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Wen-Jing Huang
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Wei-Wei Deng
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China.
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18
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Yang C, Zhao Y, An T, Liu Z, Jiang Y, Li Y, Dong C. Quantitative prediction and visualization of key physical and chemical components in black tea fermentation using hyperspectral imaging. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.110975] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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19
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Rapid detection of catechins during black tea fermentation based on electrical properties and chemometrics. FOOD BIOSCI 2021. [DOI: 10.1016/j.fbio.2020.100855] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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20
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Fourier-transform infrared spectroscopy and machine learning to predict fatty acid content of nine commercial insects. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-020-00694-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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21
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Lin X, Sun DW. Recent developments in vibrational spectroscopic techniques for tea quality and safety analyses. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.06.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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22
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Jin G, Wang Y, Li L, Shen S, Deng WW, Zhang Z, Ning J. Intelligent evaluation of black tea fermentation degree by FT-NIR and computer vision based on data fusion strategy. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.109216] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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23
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Wang A, Sheng R, Li H, Agyekum AA, Hassan MM, Chen Q. Development of near‐infrared online grading device for long jujube. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13411] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ancheng Wang
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | - Ren Sheng
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | - Huanhuan Li
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | | | - Md Mehedi Hassan
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | - Quansheng Chen
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
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24
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Zareef M, Chen Q, Hassan MM, Arslan M, Hashim MM, Ahmad W, Kutsanedzie FYH, Agyekum AA. An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis. FOOD ENGINEERING REVIEWS 2020. [DOI: 10.1007/s12393-020-09210-7] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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25
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Visible/near infrared spectroscopy and machine learning for predicting polyhydroxybutyrate production cultured on alkaline pretreated liquor from corn stover. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.biteb.2020.100386] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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26
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Qi J, Zhao W, Kan Z, Meng H, Li Y. Parameter optimization of double-blade normal milk processing and mixing performance based on RSM and BP-GA. Food Sci Nutr 2019; 7:3501-3512. [PMID: 31741736 PMCID: PMC6848853 DOI: 10.1002/fsn3.1198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 07/18/2019] [Accepted: 08/12/2019] [Indexed: 11/11/2022] Open
Abstract
Temperature stability was taken as the evaluation index of processing performance, and the three factors that influence normal milk processing and mixing performance were optimized by response surface analysis and BP-GA neural network algorithm. Analysis results showed the influence order of the factors on temperature stability was as follows: shape > height > rotating speed. In the optimization by response surface methodology (RSM), when rotating speed was 30 r/min, height was 31 mm, and blade shape was a full trapezoid, predicted value and actual value of variable coefficient were 0.0046 and 0.0044 respectively, with relative error of 4.5%. In the optimization by BP-GA neural network algorithm, when rotating speed was 34 r/min, height was 25 mm, and blade shape was a full trapezoid, the predicted value and actual value of variable coefficient were 0.0036 and 0.0035 respectively, with relative error of 2.9%. The predicted root-mean-square error of the model by the BP-GA neural network algorithm was 0.0013, determination coefficient was 0.9960, and relative percent deviation was 8.4961, which showed better performance than the RSM model. Thus, the BP-GA neural network algorithm has better fitting performance, and then, the optimal working parameter combination was confirmed, which could provide reference to improving double-blade normal milk processing and mixing device design and milk processing quality.
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Affiliation(s)
- Jiangtao Qi
- College of Mechanical and Electrical EngineeringShihezi UniversityXinjiangChina
- Laboratory of Northwest Agricultural MachineryMinistry of AgricultureXinjiangChina
| | - Wenwen Zhao
- College of Mechanical and Electrical EngineeringShihezi UniversityXinjiangChina
- Laboratory of Northwest Agricultural MachineryMinistry of AgricultureXinjiangChina
| | - Za Kan
- College of Mechanical and Electrical EngineeringShihezi UniversityXinjiangChina
- Laboratory of Northwest Agricultural MachineryMinistry of AgricultureXinjiangChina
| | - Hewei Meng
- College of Mechanical and Electrical EngineeringShihezi UniversityXinjiangChina
- Laboratory of Northwest Agricultural MachineryMinistry of AgricultureXinjiangChina
| | - Yaping Li
- College of Mechanical and Electrical EngineeringShihezi UniversityXinjiangChina
- Laboratory of Northwest Agricultural MachineryMinistry of AgricultureXinjiangChina
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27
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Chapman J, Elbourne A, Truong VK, Newman L, Gangadoo S, Rajapaksha Pathirannahalage P, Cheeseman S, Cozzolino D. Sensomics - From conventional to functional NIR spectroscopy - Shining light over the aroma and taste of foods. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.07.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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28
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Zareef M, Chen Q, Ouyang Q, Arslan M, Hassan MM, Ahmad W, Viswadevarayalu A, Wang P, Ancheng W. Rapid screening of phenolic compounds in congou black tea (
Camellia sinensis
) during in vitro fermentation process using portable spectral analytical system coupled chemometrics. J FOOD PROCESS PRES 2019. [DOI: 10.1111/jfpp.13996] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Muhammad Zareef
- 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
| | - Qin Ouyang
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | - Muhammad Arslan
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | - Md Mehedi Hassan
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | - Waqas Ahmad
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | | | - Pingyue Wang
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | - Wang Ancheng
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
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29
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Hyperspectral imaging as a novel system for the authentication of spices: A nutmeg case study. Lebensm Wiss Technol 2019. [DOI: 10.1016/j.lwt.2019.01.045] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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30
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Arslan M, Xiaobo Z, Tahir HE, Xuetao H, Rakha A, Zareef M, Seweh EA, Basheer S. NIR Spectroscopy Coupled Chemometric Algorithms for Rapid Antioxidants Activity Assessment of Chinese Dates (Zizyphus Jujuba Mill.). INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2019. [DOI: 10.1515/ijfe-2018-0148] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractIn this work, near-infrared spectroscopy coupled the classical PLS and variable selection algorithms; synergy interval-PLS, backward interval-PLS and genetic algorithm-PLS for rapid measurement of the antioxidant activity of Chinese dates. The chemometric analysis of antioxidant activity assays was performed. The built models were investigated using correlation coefficients of calibration and prediction; root mean square error of prediction, root mean square error of cross-validation and residual predictive deviation (RPD). The correlation coefficient for calibration and prediction sets and RPD values ranged from 0.8503 to 0.9897, 0.8463 to 0.9783 and 1.86 to 4.88, respectively. In addition, variable selection algorithms based on efficient information extracted from acquired spectra were superior to classical PLS. The overall results revealed that near-infrared spectroscopy combined with chemometric algorithms could be used for rapid quantification of antioxidant content in Chinese dates samples.
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Affiliation(s)
- Muhammad Arslan
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013Zhenjiang, Jiangsu, China
| | - Zou Xiaobo
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013Zhenjiang, Jiangsu, China
| | - Haroon Elrasheid Tahir
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013Zhenjiang, Jiangsu, China
| | - Hu Xuetao
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013Zhenjiang, Jiangsu, China
| | - Allah Rakha
- National Institute of Food Science & Technology, University of Agriculture, Faisalabad38000, Pakistan
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013Zhenjiang, Jiangsu, China
| | - Emmanuel Amomba Seweh
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013Zhenjiang, Jiangsu, China
| | - Sajid Basheer
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013Zhenjiang, Jiangsu, China
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31
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Dong C, Li J, Wang J, Liang G, Jiang Y, Yuan H, Yang Y, Meng H. Rapid determination by near infrared spectroscopy of theaflavins-to-thearubigins ratio during Congou black tea fermentation process. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2018; 205:227-234. [PMID: 30029185 DOI: 10.1016/j.saa.2018.07.029] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 07/09/2018] [Accepted: 07/10/2018] [Indexed: 06/08/2023]
Abstract
The theaflavin-to-thearubigin ratio (TF/TR) is an important parameter for evaluating the degree of fermentation and quality characteristics of Congou black tea. Near infrared (NIR) spectroscopy, one of the most promising techniques for evaluating large-scale tea processing quality, in association with chemometrics, can be used as a selection tool when a fast determination of the requested parameters is required. The aim of this work is to develop a unique model for the determination of TF/TR. First, 11 key wavelength variables were screened by synergy interval partial least-squares regression (SI-PLS) and competitive adaptive reweighted sampling (CARS). Based on these characteristic variables, a new extreme learning machine (ELM) combined with an adaptive boosting (ADABOOST) algorithm (ELM-ADABOOST) was applied to construct the nonlinear prediction model for TF/TR, and an independent external set was used for the validation. A determinate coefficient (Rp2) of 0.893, root mean square error of prediction (RMSEP) of 0.0044, RSD below 10%, and RPD above 3 were acquired in the prediction model. These results demonstrate that NIR can be used to rapidly determine the TF/TR value during fermentation, and it effectively simplify the model and improve the prediction accuracy when combined with the SI-CARS variable.
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Affiliation(s)
- Chunwang Dong
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Jia Li
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Jinjin Wang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Gaozhen Liang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Yongwen Jiang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Haibo Yuan
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Yanqin Yang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.
| | - Hewei Meng
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
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