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Giussani B, Gorla G, Riu J. Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview. Crit Rev Anal Chem 2024; 54:11-43. [PMID: 35286178 DOI: 10.1080/10408347.2022.2047607] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.
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Affiliation(s)
- Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Giulia Gorla
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Jordi Riu
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Tarragona, Spain
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2
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Johnson NAN, Adade SYSS, Haruna SA, Ekumah JN, Ma Y. Quantitative assessment of phytochemicals in chickpea beverages using NIR spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 307:123623. [PMID: 37989004 DOI: 10.1016/j.saa.2023.123623] [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: 06/12/2023] [Revised: 09/17/2023] [Accepted: 11/04/2023] [Indexed: 11/23/2023]
Abstract
The prospects of near-infrared (NIR) spectroscopy combined with effective variable selection algorithms for quantifying phytochemical compounds in chickpea beverages were investigated in this study. As reference measurement analysis, the phytochemicals were extracted and identified via high-performance liquid chromatography. Multivariate algorithms were then applied, analyzed, and evaluated using correlation coefficients of validation set (Rp), root mean square error of prediction (RMSEP), and residual predictive deviations (RPDs). Accordingly, the competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model achieved superior performance for biochanin A (Rp = 0.933, RPD = 3.63), chlorogenic acid (Rp = 0.928, RPD = 3.52), p-coumaric acid (Rp = 0.900, RPD = 2.37), and stigmasterol (Rp = 0.932, RPD = 3.15), respectively. Hence, this study demonstrated that NIR spectroscopy paired with CARS-PLS could be used for nondestructive quantitative prediction of phytochemicals in chickpea beverages during manufacture and storage.
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Affiliation(s)
- Nana Adwoa Nkuma Johnson
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China
| | - Selorm Yao-Say Solomon Adade
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Nutrition and Dietetics, Ho Teaching Hospital, Ho, Ghana.
| | - Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Food Science and Technology, Kano University of Science andTechnology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - John-Nelson Ekumah
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Nutrition and Food Science, College of Basic and Applied Sciences, University of Ghana, P. O. Box LG 134, Legon, Ghana
| | - Yongkun Ma
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China.
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3
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Xu Y, Zhou X, Lei W. Identifying the Producer and Grade of Matcha Tea through Three-Dimensional Fluorescence Spectroscopy Analysis and Distance Discrimination. Foods 2023; 12:3614. [PMID: 37835269 PMCID: PMC10572704 DOI: 10.3390/foods12193614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/23/2023] [Accepted: 08/30/2023] [Indexed: 10/15/2023] Open
Abstract
The three-dimensional fluorescence spectroscopy features the advantage of obtaining emission spectra at different excitation wavelengths and providing more detailed information. This study established a simple method to discriminate both the producer and grade of matcha tea by coupling three-dimensional fluorescence spectroscopy analysis and distance discrimination. The matcha tea was extracted three times and three-dimensional fluorescence spectroscopies of these tea infusions were scanned; then, the dimension of three-dimensional fluorescence spectroscopies was reduced by the integration at three specific areas showing local peaks of fluorescence intensity, and a series of vectors were constructed based on a combination of integrated vectors of the three tea infusions; finally, four distances were used to discriminate the producer and grade of matcha tea, and two discriminative patterns were compared. The results indicated that proper vector construction, appropriate discriminative distance, and correct steps are three key factors to ensure the high accuracy of the discrimination. The vector based on the three-dimensional fluorescence spectroscopy of all three tea infusions resulted in a higher accuracy than those only based on spectroscopy of one or two tea infusions, and the first tea infusion was more sensitive than the other tea infusion. The Mahalanobis distance had a higher accuracy that was up to 100% when the vector is appropriate, while the other three distances were about 60-90%. The two-step discriminative pattern, identifying the producer first and the grade second, showed a higher accuracy and a smaller uncertainty than the one-step pattern of identifying both directly. These key conclusions above help discriminate the producer and grade of matcha in a quick, accurate, and green method through three-dimensional fluorescence spectroscopy, as well as in quality inspections and identifying the critical parameters of the producing process.
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Affiliation(s)
- Yue Xu
- College of Tea Science, Guizhou University, Guiyang 550025, China;
| | - Xiangyang Zhou
- College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China;
- Key Laboratory of Karst Geological Resources and Environment, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Wenjuan Lei
- College of Tea Science, Guizhou University, Guiyang 550025, China;
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4
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Phuah YQ, Chang SK, Ng WJ, Lam MQ, Ee KY. A review on matcha: Chemical composition, health benefits, with insights on its quality control by applying chemometrics and multi-omics. Food Res Int 2023; 170:113007. [PMID: 37316075 DOI: 10.1016/j.foodres.2023.113007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 05/15/2023] [Accepted: 05/17/2023] [Indexed: 06/16/2023]
Abstract
This review discussed the origin, manufacturing process, chemical composition, factors affecting quality and health benefits of matcha (Camellia sinensis), and the application of chemometrics and multi-omics in the science of matcha. The discussion primarily distinguishes between matcha and regular green tea with processing and compositional factors, and demonstrates beneficial health effects of consuming matcha. Preferred Reporting Items for Systematic Reviews and Meta-Analyses was adopted to search for relevant information in this review. Boolean operators were incorporated to explore related sources in various databases. Notably, climate, cultivar, maturity of tea leaves, grinding process and brewing temperature impact on the overall quality of matcha. Besides, sufficient shading prior to harvesting significantly increases the contents of theanine and chlorophyll in the tea leaves. Furthermore, the ground whole tea leaf powder delivers matcha with the greatest benefits to the consumers. The health promoting benefits of matcha are mainly contributed by its micro-nutrients and the antioxidative phytochemicals, specifically epigallocatechin-gallate, theanine and caffeine. Collectively, the chemical composition of matcha affected its quality and health benefits significantly. To this end, more studies are required to elucidate the biological mechanisms of these compounds for human health. Chemometrics and multi-omics technologies are useful to fill up the research gaps identified in this review.
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Affiliation(s)
- Yi Qian Phuah
- Department of Agricultural and Food Science, Faculty of Science, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia
| | - Sui Kiat Chang
- Department of Allied Health Sciences, Faculty of Science, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia; Centre for Biomedical and Nutrition Research, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia
| | - Wen Jie Ng
- Department of Allied Health Sciences, Faculty of Science, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia; Centre for Biomedical and Nutrition Research, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia
| | - Ming Quan Lam
- Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia; Centre for Agriculture and Food Research, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia
| | - Kah Yaw Ee
- Department of Agricultural and Food Science, Faculty of Science, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia; Centre for Agriculture and Food Research, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia.
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5
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Liu S, Wang S, Hu C, Kong D, Yuan Y. Series fusion of scatter correction techniques coupled with deep convolution neural network as a promising approach for NIR modeling. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 291:122371. [PMID: 36669242 DOI: 10.1016/j.saa.2023.122371] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 06/17/2023]
Abstract
Deep convolution neural network (CNN) with one-dimensional (1D) convolution structure is a potential and effective nonlinear method for near infrared (NIR) spectroscopy analysis. However, it is also a challenge to build a reliable CNN calibration model since industrial NIR data present serious scattering effect which will seriously interfere with important information. Thus, this paper proposed a promising approach, namely series fusion of scatter correction technologies (SCSF), where CNN built on the series splicing data of normalized raw spectra, standard normal variable (SNV) spectra and first derivative (1d) spectra. Two real NIR cases (one is the identification of alcohols/diesel blends and the other is the prediction of methanol and ethanol content in alcohols/diesel blends) were introduced to explore the feasibility and effectiveness of the presented model. Through the comparative analysis with CNN based on raw spectra, SNV spectra and 1d spectra, as well as common support vector machine (SVM) and BP neural network, the proposed SCSF coupled with CNN cannot only achieve 97.73 % recognition rate for three types of diesel, but also significantly improve the prediction accuracy of methanol and ethanol. Satisfactory results show that SCSF approach can be regarded as series boosting of multiple scatter correction technologies to improve overall performance without mastering data prior information and professional knowledge. Further, the proposed SCSF applied to CNN deep learning is simple and efficient, and can be recommended for actual implementation in industrial NIR applications.
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Affiliation(s)
- Shiyu Liu
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Shutao Wang
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China.
| | - Chunhai Hu
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Deming Kong
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Yuanyuan Yuan
- School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China
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6
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Ye JH, Fang QT, Zeng L, Liu RY, Lu L, Dong JJ, Yin JF, Liang YR, Xu YQ, Liu ZH. A comprehensive review of matcha: production, food application, potential health benefits, and gastrointestinal fate of main phenolics. Crit Rev Food Sci Nutr 2023; 64:7959-7980. [PMID: 37009832 DOI: 10.1080/10408398.2023.2194419] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
Abstract
Matcha, a powder processed from tea leaves, has a unique green tea flavor and appealing color, in addition to many other sought after functional properties for a wide range of formulated food applications (e.g., dairy products, bakery products, and beverage). The properties of matcha are influenced by cultivation method and processing post-harvest. The transition from drinking tea infusion to eating whole leaves provides a healthy option for the delivery of functional component and tea phenolics in various food matrix. The aim of this review is to describe the physico-chemical properties of matcha, the specific requirements for tea cultivation and industrial processing. The quality of matcha mainly depends on the quality of fresh tea leaves, which is affected by preharvest factors including tea cultivar, shading treatment, and fertilization. Shading is the key measure to increase greenness, reduce bitterness and astringency, and enhance umami taste of matcha. The potential health benefits of matcha and the gastrointestinal fate of main phenolics in matcha are covered. The chemical compositions and bioactivities of fiber-bound phenolics in matcha and other plant materials are discussed. The fiber-bound phenolics are considered promising components which endow matcha with boosted bioavailability of phenolics and health benefits through modulating gut microbiota.
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Affiliation(s)
- Jian-Hui Ye
- Tea Research Institute, Zhejiang University, Hangzhou, China
| | - Qi-Ting Fang
- Tea Research Institute, Zhejiang University, Hangzhou, China
| | - Lin Zeng
- Tea Research Institute Chinese Academy of Agricultural Sciences, Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Hangzhou, China
| | - Ru-Yi Liu
- Tea Research Institute, Zhejiang University, Hangzhou, China
| | - Lu Lu
- Tea Research Institute, Zhejiang University, Hangzhou, China
| | - Jun-Jie Dong
- Research and Development Department, Zhejiang Camel Transworld (Organic Food) Co., Ltd, Hangzhou, China
| | - Jun-Feng Yin
- Tea Research Institute Chinese Academy of Agricultural Sciences, Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Hangzhou, China
| | - Yue-Rong Liang
- Tea Research Institute, Zhejiang University, Hangzhou, China
| | - Yong-Quan Xu
- Tea Research Institute Chinese Academy of Agricultural Sciences, Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Hangzhou, China
| | - Zhong-Hua Liu
- Key Laboratory of Ministry of Education for Tea Science, Hunan Agricultural University, Changsha, China
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7
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Pu H, Yu J, Sun DW, Wei Q, Shen X, Wang Z. Distinguishing Fresh and Frozen-thawed Beef Using Hyperspectral Imaging Technology Combined with Convolutional Neural Networks. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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8
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Rapid determination of free amino acids and caffeine in matcha using near-infrared spectroscopy: A comparison of portable and benchtop systems. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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9
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Gharibzahedi SMT, Barba FJ, Zhou J, Wang M, Altintas Z. Electronic Sensor Technologies in Monitoring Quality of Tea: A Review. BIOSENSORS 2022; 12:bios12050356. [PMID: 35624658 PMCID: PMC9138728 DOI: 10.3390/bios12050356] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/14/2022] [Accepted: 05/19/2022] [Indexed: 05/27/2023]
Abstract
Tea, after water, is the most frequently consumed beverage in the world. The fermentation of tea leaves has a pivotal role in its quality and is usually monitored using the laboratory analytical instruments and olfactory perception of tea tasters. Developing electronic sensing platforms (ESPs), in terms of an electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye) equipped with progressive data processing algorithms, not only can accurately accelerate the consumer-based sensory quality assessment of tea, but also can define new standards for this bioactive product, to meet worldwide market demand. Using the complex data sets from electronic signals integrated with multivariate statistics can, thus, contribute to quality prediction and discrimination. The latest achievements and available solutions, to solve future problems and for easy and accurate real-time analysis of the sensory-chemical properties of tea and its products, are reviewed using bio-mimicking ESPs. These advanced sensing technologies, which measure the aroma, taste, and color profiles and input the data into mathematical classification algorithms, can discriminate different teas based on their price, geographical origins, harvest, fermentation, storage times, quality grades, and adulteration ratio. Although voltammetric and fluorescent sensor arrays are emerging for designing e-tongue systems, potentiometric electrodes are more often employed to monitor the taste profiles of tea. The use of a feature-level fusion strategy can significantly improve the efficiency and accuracy of prediction models, accompanied by the pattern recognition associations between the sensory properties and biochemical profiles of tea.
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Affiliation(s)
- Seyed Mohammad Taghi Gharibzahedi
- Institute of Chemistry, Faculty of Natural Sciences and Maths, Technical University of Berlin, Straße des 17. Juni 124, 10623 Berlin, Germany;
- Institute of Materials Science, Faculty of Engineering, Kiel University, 24143 Kiel, Germany
| | - Francisco J. Barba
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, University of Valencia, 46100 Valencia, Spain; (F.J.B.); (J.Z.); (M.W.)
| | - Jianjun Zhou
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, University of Valencia, 46100 Valencia, Spain; (F.J.B.); (J.Z.); (M.W.)
| | - Min Wang
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, University of Valencia, 46100 Valencia, Spain; (F.J.B.); (J.Z.); (M.W.)
| | - Zeynep Altintas
- Institute of Chemistry, Faculty of Natural Sciences and Maths, Technical University of Berlin, Straße des 17. Juni 124, 10623 Berlin, Germany;
- Institute of Materials Science, Faculty of Engineering, Kiel University, 24143 Kiel, Germany
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10
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Beć KB, Grabska J, Huck CW. Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives. Foods 2022; 11:foods11101465. [PMID: 35627034 PMCID: PMC9140213 DOI: 10.3390/foods11101465] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/05/2022] [Accepted: 05/13/2022] [Indexed: 01/27/2023] Open
Abstract
The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.
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11
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Rezaeian FM, Zimmermann BF. Simplified analysis of flavanols in matcha tea. Food Chem 2022; 373:131628. [PMID: 34863606 DOI: 10.1016/j.foodchem.2021.131628] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 11/14/2021] [Indexed: 11/04/2022]
Abstract
Matcha tea contains only the softer parts of the tea leaves and is finely ground. Therefore, extraction of the flavanols for analysis by HPLC is possible by a simpler protocol compared to the ISO 14502-2 method. 21 different simplified extraction methods were screened and five of them gave equal results as the ISO 14502-2 method. The simplest and fastest method consists of extraction by ethanol + water (7 + 3, v + v) at room temperature with ultrasonication. This method was validated by determining accuracy, intraday and interday repeatability. The simplified method was successfully applied to four traditional matcha teas and two powdered green teas from Japan. This method paves the way for time-saving, energy-saving and accurate analyses of flavanols in matcha tea.
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Affiliation(s)
- Flora M Rezaeian
- Department of Nutritional and Food Sciences - Food Science, University of Bonn, Meckenheimer Allee 166a, 53115 Bonn, Germany.
| | - Benno F Zimmermann
- Department of Nutritional and Food Sciences - Food Science, University of Bonn, Meckenheimer Allee 166a, 53115 Bonn, Germany.
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12
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General model of multi-quality detection for apple from different origins by Vis/NIR transmittance spectroscopy. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01375-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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13
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Huang Y, Goh RMV, Pua A, Liu SQ, Sakumoto S, Oh HY, Ee KH, Sun J, Lassabliere B, Yu B. Effect of three milling processes (cyclone-, bead- and stone-millings) on the quality of matcha: Physical properties, taste and aroma. Food Chem 2022; 372:131202. [PMID: 34607047 DOI: 10.1016/j.foodchem.2021.131202] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 09/13/2021] [Accepted: 09/17/2021] [Indexed: 11/16/2022]
Abstract
Analysis of three matcha (cyclone-, bead- and stone-milled) revealed differences in their sizes and surface morphologies. Using liquid chromatography, 4 sugars, 6 organic acids, 18 amino acids and 9 polyphenols were detected in all matcha samples and shown to present in different amount. Moreover, 108 volatile compounds were detected and 30 potential flavour-contributing compounds were quantified by gas chromatography time-of-flight mass spectrometry using headspace-stir bar sorptive extraction-thin-film solid-phase microextraction (HS-SBSE-TFSPME). Sensory evaluation by a trained panel found that the matcha samples possess different notes (cyclone-milled: leafy; bead-milled: fishy; and stone-milled: roasty) which is supported by the volatile compound analysis. Finally, the three matcha were differentiated based on non-volatile and volatile components using principal component analysis, and the correlation between chemical composition and sensory evaluation data was carried out using partial least square regression. In conclusion, milling processes clearly affected the physical, chemical and sensory qualities of matcha.
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Affiliation(s)
- Yunle Huang
- Mane SEA PTE LTD, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Singapore; Department of Food Science and Technology, National University of Singapore, S14 Level 5, Science Drive 2, Singapore 117542, Singapore
| | - Rui Min Vivian Goh
- Mane SEA PTE LTD, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Singapore; Department of Food Science and Technology, National University of Singapore, S14 Level 5, Science Drive 2, Singapore 117542, Singapore
| | - Aileen Pua
- Mane SEA PTE LTD, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Singapore; Department of Food Science and Technology, National University of Singapore, S14 Level 5, Science Drive 2, Singapore 117542, Singapore
| | - Shao Quan Liu
- Department of Food Science and Technology, National University of Singapore, S14 Level 5, Science Drive 2, Singapore 117542, Singapore
| | - Shunichi Sakumoto
- Fukujuen Co. Ltd, 3-1-1 Saganakadai, Kizugawa-shi, Kyoto 619-0223, Japan
| | - Hong Yun Oh
- Mane SEA PTE LTD, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Singapore
| | - Kim Huey Ee
- Mane SEA PTE LTD, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Singapore
| | - Jingcan Sun
- Mane SEA PTE LTD, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Singapore
| | - Benjamin Lassabliere
- Mane SEA PTE LTD, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Singapore
| | - Bin Yu
- Mane SEA PTE LTD, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Singapore.
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14
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Qi Z, Wu X, Yang Y, Wu B, Fu H. Discrimination of the Red Jujube Varieties Using a Portable NIR Spectrometer and Fuzzy Improved Linear Discriminant Analysis. Foods 2022; 11:foods11050763. [PMID: 35267396 PMCID: PMC8909659 DOI: 10.3390/foods11050763] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/17/2022] [Accepted: 02/25/2022] [Indexed: 02/06/2023] Open
Abstract
In order to quickly, nondestructively, and effectively distinguish red jujube varieties, based on the combination of fuzzy theory and improved LDA (iLDA), fuzzy improved linear discriminant analysis (FiLDA) algorithm was proposed to classify near-infrared reflectance (NIR) spectra of red jujube samples. FiLDA shows performs better than iLDA in dealing with NIR spectra containing noise. Firstly, the portable NIR spectrometer was employed to gather the NIR spectra of five kinds of red jujube, and the initial NIR spectra were pretreated by standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (S-G smoothing), mean centering (MC) and Savitzky-Golay filter (S-G filter). Secondly, the high-dimensional spectra were processed for dimension reduction by principal component analysis (PCA). Then, linear discriminant analysis (LDA), iLDA and FiLDA were applied to extract features from the NIR spectra, respectively. Finally, K nearest neighbor (KNN) served as a classifier for the classification of red jujube samples. The highest classification accuracy of this identification system for red jujube, by using FiLDA and KNN, was 94.4%. These results indicated that FiLDA combined with NIR spectroscopy was an available method for identifying the red jujube varieties and this method has wide application prospects.
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Affiliation(s)
- Zuxuan Qi
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Z.Q.); (X.W.); (H.F.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Z.Q.); (X.W.); (H.F.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Yangjian Yang
- Research Institute of Zhejiang University-Taizhou, Taizhou 317700, China
- Correspondence:
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China;
| | - Haijun Fu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Z.Q.); (X.W.); (H.F.)
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15
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Haruna SA, Li H, Zareef M, Mehedi Hassan M, Arslan M, Geng W, Wei W, Abba Dandago M, Yao-Say Solomon Adade S, Chen Q. Application of NIR spectroscopy for rapid quantification of acid and peroxide in crude peanut oil coupled multivariate analysis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 267:120624. [PMID: 34824004 DOI: 10.1016/j.saa.2021.120624] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 10/11/2021] [Accepted: 11/10/2021] [Indexed: 06/13/2023]
Abstract
Two key parameters (acidity and peroxide content) for evaluation of the oxidation level in crude peanut oil have been studied. The titrimetric analysis was carried out for reference data collection. Then, near-infrared spectroscopy in combination with chemometric algorithms such as partial least square (PLS); bootstrapping soft shrinkage-PLS (BOSS-PLS); uninformative variable elimination-PLS (UVE-PLS), and competitive-adaptive reweighted sampling-PLS (CARS-PLS) were attempted and assessed. The correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to individually evaluate the performance of the models. Optimum results were noticed with CARS-PLS, 0.9517 ≤ Rc ≤ 0.9670, 0.9503 ≤ Rp ≤ 0.9637, 0.0874 ≤ RMSEP ≤ 0.5650, and 3.14 ≤ RPD ≤ 3.64. Therefore, this affirmed that the near-infrared spectroscopy coupled with CARS-PLS could be used as a simple, fast, and non-invasive technique for quantifying acid value and peroxide value in crude peanut oil.
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Affiliation(s)
- Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; Department of Food Science and Technology, Kano University of Science and Technology, Wudil, P.M.B 3244, Kano, Kano State, Nigeria
| | - Huanhuan Li
- 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
| | - Md Mehedi Hassan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Muhammad Arslan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Wenhui Geng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Munir Abba Dandago
- Department of Food Science and Technology, Kano University of Science and Technology, Wudil, P.M.B 3244, Kano, Kano State, Nigeria
| | | | - 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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16
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Shao J, Wang C, Shen Y, Shi J, Ding D. Electrochemical Sensors and Biosensors for the Analysis of Tea Components: A Bibliometric Review. Front Chem 2022; 9:818461. [PMID: 35096777 PMCID: PMC8795770 DOI: 10.3389/fchem.2021.818461] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 12/28/2021] [Indexed: 12/25/2022] Open
Abstract
Tea is a popular beverage all around the world. Tea composition, quality monitoring, and tea identification have all been the subject of extensive research due to concerns about the nutritional value and safety of tea intake. In the last 2 decades, research into tea employing electrochemical biosensing technologies has received a lot of interest. Despite the fact that electrochemical biosensing is not yet the most widely utilized approach for tea analysis, it has emerged as a promising technology due to its high sensitivity, speed, and low cost. Through bibliometric analysis, we give a systematic survey of the literature on electrochemical analysis of tea from 1994 to 2021 in this study. Electrochemical analysis in the study of tea can be split into three distinct stages, according to the bibliometric analysis. After chromatographic separation of materials, electrochemical techniques were initially used only as a detection tool. Many key components of tea, including as tea polyphenols, gallic acid, caffeic acid, and others, have electrochemical activity, and their electrochemical behavior is being investigated. High-performance electrochemical sensors have steadily become a hot research issue as materials science, particularly nanomaterials, and has progressed. This review not only highlights these processes, but also analyzes and contrasts the relevant literature. This evaluation also provides future views in this area based on the bibliometric findings.
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Affiliation(s)
- Jinhua Shao
- School of Chemistry and Bioengineering, Hunan University of Science and Engineering, Yongzhou, China
| | - Chao Wang
- School of Chemistry and Bioengineering, Hunan University of Science and Engineering, Yongzhou, China
| | - Yiling Shen
- School of Chemistry and Bioengineering, Hunan University of Science and Engineering, Yongzhou, China
| | - Jinlei Shi
- School of Chemistry and Bioengineering, Hunan University of Science and Engineering, Yongzhou, China
| | - Dongqing Ding
- School of Chemistry and Bioengineering, Hunan University of Science and Engineering, Yongzhou, China
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17
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Zheng J, Gong Z, Yin S, Wang W, Wang M, Lin P, Zhou H, Yang Y. Rapid determination of lambda-cyhalothrin residues on Chinese cabbage based on MIR spectroscopy and a Gustafson–Kessel noise clustering algorithm. RSC Adv 2022; 12:18457-18465. [PMID: 35799918 PMCID: PMC9218965 DOI: 10.1039/d2ra01557a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 05/23/2022] [Indexed: 11/29/2022] Open
Abstract
Pesticide residues exceeding the standard in Chinese cabbage is harmful to human health. In order to quickly, non-destructively and effectively qualitatively analyze lambda-cyhalothrin residues on Chinese cabbage, a method involving a Gustafson–Kessel noise clustering (GKNC) algorithm was proposed to cluster the mid-infrared (MIR) spectra. A total of 120 Chinese cabbage samples with three different lambda-cyhalothrin residue levels (no lambda-cyhalothrin, and cases where the ratios of lambda-cyhalothrin and water were 1 : 500 and 1 : 100) were scanned using an Agilent Cary 630 FTIR spectrometer for collecting the MIR spectra. Next, multiple scatter correction (MSC) was employed to eliminate the effects of light scattering. Furthermore, principal component analysis (PCA) and linear discriminant analysis (LDA) were utilized to reduce the dimensionality and extract the feature information from the MIR spectra. Finally, fuzzy c-means (FCM) clustering, Gustafson–Kessel (GK) clustering, noise clustering (NC) and the GKNC algorithm were applied to cluster the MIR spectral data, respectively. The experimental results showed that the GKNC algorithm gave the best classification performance compared against the other three fuzzy clustering algorithms, and its highest clustering accuracy reached 93.3%. Therefore, the GKNC algorithm coupled with MIR spectroscopy is an effective method for detecting lambda-cyhalothrin residues on Chinese cabbage. Pesticide residues exceeding the standard in Chinese cabbage is harmful to human health.![]()
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Affiliation(s)
- Jun Zheng
- Department of Electrical and Control Engineering, Research Institute of Zhejiang University-Taizhou, Taizhou 318000, China
| | - Zhe Gong
- Department of Electrical and Control Engineering, Research Institute of Zhejiang University-Taizhou, Taizhou 318000, China
| | - Shaojie Yin
- Department of Electrical and Control Engineering, Research Institute of Zhejiang University-Taizhou, Taizhou 318000, China
| | - Wei Wang
- Department of Electrical and Control Engineering, Research Institute of Zhejiang University-Taizhou, Taizhou 318000, China
| | - Meng Wang
- Department of Electrical and Control Engineering, Research Institute of Zhejiang University-Taizhou, Taizhou 318000, China
| | - Peng Lin
- Department of Electrical and Control Engineering, Research Institute of Zhejiang University-Taizhou, Taizhou 318000, China
| | - Haoxiang Zhou
- Department of Electrical and Control Engineering, Research Institute of Zhejiang University-Taizhou, Taizhou 318000, China
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212000, China
| | - Yangjian Yang
- Department of Electrical and Control Engineering, Research Institute of Zhejiang University-Taizhou, Taizhou 318000, China
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18
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Liang S, Granato D, Zou C, Gao Y, Zhu Y, Zhang L, Yin JF, Zhou W, Xu YQ. Processing technologies for manufacturing tea beverages: From traditional to advanced hybrid processes. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.10.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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19
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Guo Z, Chen P, Wang M, Zuo M, El-Seedi HR, Chen Q, Shi J, Zou X. Rapid enrichment detection of patulin and alternariol in apple using surface enhanced Raman spectroscopy with coffee-ring effect. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.112333] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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20
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Recent advances in assessing qualitative and quantitative aspects of cereals using nondestructive techniques: A review. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.08.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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21
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Wu J, Ouyang Q, Park B, Kang R, Wang Z, Wang L, Chen Q. Physicochemical indicators coupled with multivariate analysis for comprehensive evaluation of matcha sensory quality. Food Chem 2021; 371:131100. [PMID: 34537612 DOI: 10.1016/j.foodchem.2021.131100] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 09/05/2021] [Accepted: 09/06/2021] [Indexed: 01/12/2023]
Abstract
The sensory quality of matcha is a pivotal factor in determining consumer acceptance. However, the human sensory panel test is difficult to popularize by virtue of professional requirements and inability to evaluate large samples. The analysis showed that physicochemical indicators of matcha were significantly related to sensory quality. Hence, principal component analysis (PCA) based on selected key physicochemical indicators was proposed to evaluate the sensory quality of matcha in this research. The eight key indicators were selected from twenty-four physicochemical indicators based on least absolute shrinkage and selection operator (LASSO) for the establishment of the PCA comprehensive evaluation model. The results demonstrated that the PCA comprehensive evaluation model achieved superior performance, with -0.895 rc (correlation coefficient in calibration set) and -0.883 rp (correlation coefficient in prediction set) for overall sensory quality. This work demonstrated that LASSO-PCA comprehensive evaluation as an objective protocol has great potential in predicting matcha sensory quality.
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Affiliation(s)
- Jizhong Wu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Bosoon Park
- United States Department of Agriculture, Agricultural Research Services, U.S. National Poultry Research Center, Athens, GA 30605, USA
| | - Rui Kang
- Center of Information, Jiangsu Academy of Agricultural Science, Nanjing 210031, PR China
| | - Zhen Wang
- National Research and Development Center for Matcha Processing Technology, Jiangsu Xinpin Tea Co., Ltd, Changzhou 213254, PR China
| | - Li Wang
- 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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22
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Liu S, Yu H, Sui Y, Zhou H, Zhang J, Kong L, Dang J, Zhang L. Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance. PLoS One 2021; 16:e0257008. [PMID: 34478465 PMCID: PMC8415606 DOI: 10.1371/journal.pone.0257008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 08/20/2021] [Indexed: 11/19/2022] Open
Abstract
In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1-DS14) and used as inputs to build the classification models. Models' performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).
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Affiliation(s)
- Shuang Liu
- College of Biological and Agricultural Engineering, Jilin University, Changchun, Jilin, China
| | - Haiye Yu
- College of Biological and Agricultural Engineering, Jilin University, Changchun, Jilin, China
| | - Yuanyuan Sui
- College of Biological and Agricultural Engineering, Jilin University, Changchun, Jilin, China
| | - Haigen Zhou
- College of Biological and Agricultural Engineering, Jilin University, Changchun, Jilin, China
| | - Junhe Zhang
- College of Biological and Agricultural Engineering, Jilin University, Changchun, Jilin, China
| | - Lijuan Kong
- College of Biological and Agricultural Engineering, Jilin University, Changchun, Jilin, China
| | - Jingmin Dang
- College of Biological and Agricultural Engineering, Jilin University, Changchun, Jilin, China
| | - Lei Zhang
- College of Biological and Agricultural Engineering, Jilin University, Changchun, Jilin, China
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23
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Cysteamine-mediated upconversion sensor for lead ion detection in food. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-021-01054-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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24
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Ong P, Chen S, Tsai CY, Chuang YK. Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 255:119657. [PMID: 33744842 DOI: 10.1016/j.saa.2021.119657] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 02/16/2021] [Accepted: 02/28/2021] [Indexed: 06/12/2023]
Abstract
In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R2) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R2 values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.
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Affiliation(s)
- Pauline Ong
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan; Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia.
| | - Suming Chen
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Chao-Yin Tsai
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Yung-Kun Chuang
- Master Program in Food Safety, College of Nutrition, Taipei Medical University, Taipei, Taiwan; School of Food Safety, College of Nutrition, Taipei Medical University, Taipei, Taiwan; Nutrition Research Center, Taipei Medical University Hospital, Taipei, Taiwan
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25
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Jiang H, He Y, Chen Q. Determination of acid value during edible oil storage using a portable NIR spectroscopy system combined with variable selection algorithms based on an MPA-based strategy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:3328-3335. [PMID: 33222172 DOI: 10.1002/jsfa.10962] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/12/2020] [Accepted: 11/22/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND The acid value is an important indicator for evaluating the quality of edible oil during storage. This study employs a portable near-infrared (NIR) spectroscopy system to determine the acid value during edible oil storage. Four MPA-based variable selection methods, namely competitive adaptive reweighted sampling (CARS), the variable iterative space shrinkage approach (VISSA), iteratively variable subset optimization (IVSO), and bootstrapping soft shrinkage (BOSS) were introduced to optimize the preprocessed NIR spectra. Support vector machine (SVM) models based on characteristic spectra obtained by different selection methods were then established to achieve quantitative detection of the acid value during edible oil storage. RESULTS The results revealed that, compared with the full-spectrum SVM model, the SVM models established by the characteristic wavelengths optimized by the variable selection methods based on the MPA strategy exhibit a significant improvement in complexity and generalization performance. Furthermore, compared with the CARS, VISSA, and IVSO methods, the BOSS method obtained the least number of characteristic wavelength variables, and the SVM model established based on the optimized features of this method exhibited the optimal prediction performance. The root mean square error of prediction (RMSEP) was 0.11 mg g-1, the coefficient of determination (Rp2) was 0.92 and the ratio performance deviation (RPD) was 2.82, respectively. CONCLUSION The overall results indicate that the variable selection methods based on the MPA strategy can select more targeted characteristic variables. This has good application prospects in NIR spectra feature optimization. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Yingchao He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
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26
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Zareef M, Arslan M, Mehedi Hassan M, Ali S, Ouyang Q, Li H, Wu X, Muhammad Hashim M, Javaria S, Chen Q. Application of benchtop NIR spectroscopy coupled with multivariate analysis for rapid prediction of antioxidant properties of walnut (Juglans regia). Food Chem 2021; 359:129928. [PMID: 33957331 DOI: 10.1016/j.foodchem.2021.129928] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 04/17/2021] [Accepted: 04/19/2021] [Indexed: 11/26/2022]
Abstract
Benchtop near-infrared (NIR) spectroscopy coupled with multivariate analysis was used for the classification and prediction of antioxidant properties of walnut. Total phenolic content (TPC), total flavonoid content (TFC), ABTS assay and FRAP assay were performed spectrophotometrically. The synergy interval partial least square coupled competitive adaptive reweighted sampling (Si-CARS-PLS) was used for the prediction. A decent discrimination using principal component analysis (PCA) was observed by mean of spectroscopic and antioxidant properties data with total cumulative variance of 99.26% (PC1 = 95.07%, PC2 = 2.98%, PC3 = 1.21%) and 96.60% (PC1 = 64.28%, PC2 = 32.32%) respectively. The Si-CARS-PLS yielded optimal performance, RP = 0.9616, RPD = 3.807 for TPC, RP = 0.9657, RPD = 3.367 for TFC, RP = 0.9683, RPD = 2.728 for ABTS assay, and RP = 0.914, RPD = 2.669 for FRAP assay. These findings revealed that NIR integrated with Si-CARS-PLS could be used for the prediction of antioxidant properties of walnut.
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Affiliation(s)
- Muhammad Zareef
- School of Food and Biological Engineering Jiangsu University, Xuefu Road 301, Zhenjiang 213013, China
| | - Muhammad Arslan
- School of Food and Biological Engineering Jiangsu University, Xuefu Road 301, Zhenjiang 213013, China
| | - Md Mehedi Hassan
- School of Food and Biological Engineering Jiangsu University, Xuefu Road 301, Zhenjiang 213013, China
| | - Shujat Ali
- School of Food and Biological Engineering Jiangsu University, Xuefu Road 301, Zhenjiang 213013, China; College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China
| | - Qin Ouyang
- School of Food and Biological Engineering Jiangsu University, Xuefu Road 301, Zhenjiang 213013, China.
| | - Huanhuan Li
- School of Food and Biological Engineering Jiangsu University, Xuefu Road 301, Zhenjiang 213013, China
| | - Xiangyang Wu
- School of Environment and Safety Engineering Jiangsu University, Xuefu Road 301, Zhenjiang 213013, China
| | | | - Sadaf Javaria
- Institute of Food Science and Nutrition, Gomal University D.I Khan, Pakistan
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University, Xuefu Road 301, Zhenjiang 213013, China.
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27
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Wang J, Chen Q, Belwal T, Lin X, Luo Z. Insights into chemometric algorithms for quality attributes and hazards detection in foodstuffs using Raman/surface enhanced Raman spectroscopy. Compr Rev Food Sci Food Saf 2021; 20:2476-2507. [DOI: 10.1111/1541-4337.12741] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 02/08/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Affiliation(s)
- Jingjing Wang
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University Zhenjiang People's Republic of China
| | - Tarun Belwal
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Xingyu Lin
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Zisheng Luo
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
- Ningbo Research Institute Zhejiang University Ningbo People's Republic of China
- Fuli Institute of Food Science Hangzhou People's Republic of China
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28
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Qiu G, Tao D, Xiao Q, Li G. Simultaneous sex and species classification of silkworm pupae by NIR spectroscopy combined with chemometric analysis. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:1323-1330. [PMID: 32830318 DOI: 10.1002/jsfa.10740] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 07/17/2020] [Accepted: 08/23/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Most studies only focus on the sex discrimination of silkworm pupae. However, species differentiation of silkworm pupae is also needed in sericulture. To classify the sex and species at the same time, the present study adopts near infrared (NIR) spectroscopy combined with multivariate analysis. RESULTS First, spectra samples were acquired using an NIR sensor, comprising female and male silkworm pupae from three species. Second, three different variables selection approaches were used, including a successive projections algorithm, competitive adaptive reweighted sampling (CARS) and interval partial least squares (iPLS). Third, identification models were built based on random forest and partial least squares discriminant analysis (PLSDA). The experimental results show that iPLS-PLSDA model (95.24%) gives a high performance when using the one of the three variable selection methods alone. To further increase the performance, the variable selection methods are optimized. The accuracy of the iPLS-CARS-PLSDA model is as high as 98.41%. CONCLUSION The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for sex and species identification of silkworm pupae. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Guangying Qiu
- Rail Transportation Technology Innovation Center, East China Jiao Tong University, Nanchang, China
| | - Dan Tao
- College of Electrical and Automation Engineering, East China Jiao Tong University, Nanchang, China
| | - Qian Xiao
- Rail Transportation Technology Innovation Center, East China Jiao Tong University, Nanchang, China
| | - Guanglin Li
- College of Engineering and Technology, Southwest University, Chongqing, China
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29
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Intelligent evaluation of taste constituents and polyphenols-to-amino acids ratio in matcha tea powder using near infrared spectroscopy. Food Chem 2021; 353:129372. [PMID: 33725540 DOI: 10.1016/j.foodchem.2021.129372] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 02/01/2021] [Accepted: 02/12/2021] [Indexed: 12/29/2022]
Abstract
Matcha tea is rich in taste and bioactive constituents, quality evaluation of matcha tea is important to ensure flavor and efficacy. Near-infrared spectroscopy (NIR) in combination with variable selection algorithms was proposed as a fast and non-destructive method for the quality evaluation of matcha tea. Total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio (TP/FAA) were assessed as the taste quality indicators. Successive projections algorithm (SPA), genetic algorithm (GA), and simulated annealing (SA) were subsequently developed from the synergy interval partial least squares (SiPLS). The overall results revealed that SiPLS-SPA and SiPLS-SA models combined with NIR exhibited higher predictive capabilities for the effective determination of TP, FAA and TP/FAA with correlation coefficient in the prediction set (Rp) of Rp > 0.97, Rp > 0.98 and Rp > 0.98 respectively. Therefore, this simple and efficient technique could be practically exploited for tea quality control assessment.
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Xu Y, Hassan MM, Ali S, Li H, Ouyang Q, Chen Q. Self-Cleaning-Mediated SERS Chip Coupled Chemometric Algorithms for Detection and Photocatalytic Degradation of Pesticides in Food. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:1667-1674. [PMID: 33522812 DOI: 10.1021/acs.jafc.0c06513] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Pesticide residues in food have been a grave concern to consumers. Herein, we have developed a dual-mode SERS chip using Cu2O mesoporous spheres decorated with Ag nanoparticles (MCu2O@Ag NPs) as both sensing and degradation/clearing unit for rapid detection of pymetrozine and thiram pesticides in tea samples. Three kinds of chemometric algorithms were comparatively applied to analyze the collected SERS spectra of pesticides. In comparison, random frog-partial least squares achieved the best performance with root mean square error of prediction and residual predictive deviation values of 0.9871, 6.17, and 0.9873, 6.64 for pymetrozine and thiram, respectively. Additionally, the prepared SERS chip showed great photocatalytic activity to degrade pesticides under visible light irradiation. Through a facile method, this work presented a novel dual-functional SERS chip for the rapid detection and degradation of low-concentration pesticides in both environmental and food samples.
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Affiliation(s)
- Yi Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang212013, People's Republic of China
| | - Md Mehedi Hassan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang212013, People's Republic of China
| | - Shujat Ali
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang212013, People's Republic of China
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang212013, People's Republic of China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang212013, People's Republic of China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang212013, People's Republic of China
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Beć KB, Grabska J, Huck CW. Principles and Applications of Miniaturized Near-Infrared (NIR) Spectrometers. Chemistry 2021; 27:1514-1532. [PMID: 32820844 PMCID: PMC7894516 DOI: 10.1002/chem.202002838] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 08/20/2020] [Indexed: 12/16/2022]
Abstract
This review article focuses on the principles and applications of miniaturized near-infrared (NIR) spectrometers. This technology and its applicability has advanced considerably over the last few years and revolutionized several fields of application. What is particularly remarkable is that the applications have a distinctly diverse nature, ranging from agriculture and the food sector, through to materials science, industry and environmental studies. Unlike a rather uniform design of a mature benchtop FTNIR spectrometer, miniaturized instruments employ diverse technological solutions, which have an impact on their operational characteristics. Continuous progress leads to new instruments appearing on the market. The current focus in analytical NIR spectroscopy is on the evaluation of the devices and associated methods, and to systematic characterization of their performance profiles.
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Affiliation(s)
- Krzysztof B. Beć
- Institute of Analytical Chemistry and RadiochemistryCCB-Center for Chemistry and BiomedicineLeopold-Franzens UniversityInnrain 80/826020InnsbruckAustria
| | - Justyna Grabska
- Institute of Analytical Chemistry and RadiochemistryCCB-Center for Chemistry and BiomedicineLeopold-Franzens UniversityInnrain 80/826020InnsbruckAustria
| | - Christian W. Huck
- Institute of Analytical Chemistry and RadiochemistryCCB-Center for Chemistry and BiomedicineLeopold-Franzens UniversityInnrain 80/826020InnsbruckAustria
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Li H, Zhu J, Jiao T, Wang B, Wei W, Ali S, Ouyang Q, Zuo M, Chen Q. Development of a novel wavelength selection method VCPA-PLS for robust quantification of soluble solids in tomato by on-line diffuse reflectance NIR. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 243:118765. [PMID: 32861202 DOI: 10.1016/j.saa.2020.118765] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 07/15/2020] [Accepted: 07/18/2020] [Indexed: 06/11/2023]
Abstract
This work was attempted to evaluate the feasibility of a constructed on-line NIR platform coupled with efficient algorithms for rapid and robust quantification of quality parameter in cherry tomato. Specifically, a system was developed based on shortwave NIR spectroscopy for on-line quality inspection of cherry tomatoes. The spectra were recorded in diffuse reflectance mode from 900 to 1700 nm, and the conveyor belt speed was fixed to five samples per second. Three novel methods, namely variable combination population analysis (VCPA), uninformative variable elimination (UVE) and competitive adaptive reweighed sampling algorithm (CARS) were coupled with partial least square (PLS) for selecting optimal dataset, and modeling. The obtained results showed that under the optimal tuning parameters (N = 100, k = 500, ω = 14, σ = 10%), a total of 512 original variables, only 9 variables (1.75%) were extracted by VCPA. Subsequently, VCPA-PLS yielded outstanding performance in predicting soluble solid content in cherry tomatoes, with a higher correlation coefficient (RP = 0.9053), and lower root mean square errors (RMSEP = 0.382) in prediction set. This methodology demonstrated the versatile potential of the proposed installation coupled with VCPA methods for on-line detection of total soluble solids in cherry tomatoes.
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Affiliation(s)
- Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Jiaji Zhu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Tianhui Jiao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Bing Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Shujat Ali
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Min Zuo
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, 100048 Beijing, PR China; School of Computer and Information Engineering, Beijing Technology and Business University, 100048, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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Jiang H, Liu T, Chen Q. Dynamic monitoring of fatty acid value in rice storage based on a portable near-infrared spectroscopy system. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 240:118620. [PMID: 32599483 DOI: 10.1016/j.saa.2020.118620] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Accepted: 06/13/2020] [Indexed: 06/11/2023]
Abstract
The fatty acid value of rice is one of the important indexes to reflect its freshness. A portable near-infrared spectroscopy (NIRS) system was designed and assembled to dynamically monitor fatty acid values in rice storage in this study. First, the near-infrared (NIR) spectra of rice samples in different storage periods were obtained using the portable NIRS system. Then, a weighted multiplicative scatter correction with variable selection (WMSCVS) algorithm was applied to the original spectra for scattering correction, and to compress variable space for achieving characteristic wavelengths. Finally, a partial least square (PLS) regression model was established using the characteristic wavelengths to realize the rapid monitoring of fatty acid values in rice storage. The results showed that the performance of the optimal PLS model based on the features by the WMSCVS algorithm was significantly better than those of the optimal PLS models based on SNV and MSC pre-processing spectra, with the determination coefficient (RP2) of 0.9615 and the root mean square error of prediction (RMSEP) of 0.3626 in the predictive process. The overall results demonstrate that it is feasible to use the portable NIRS system developed by our team to quickly monitor the fatty acid values in rice 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
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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Sun Y, Wang Y, Huang J, Ren G, Ning J, Deng W, Li L, Zhang Z. Quality assessment of instant green tea using portable NIR spectrometer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 240:118576. [PMID: 32535491 DOI: 10.1016/j.saa.2020.118576] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 06/01/2020] [Accepted: 06/02/2020] [Indexed: 06/11/2023]
Abstract
Caffeine and catechin are two main components of instant green tea, and are essential components of tea quality. This paper mainly focuses on the feasibility of rapidly determining instant green tea components by using a portable near infrared (NIR) spectrometer. The two main components (caffeine and catechin) were studied. In addition, the instrument performance levels of portable and benchtop NIR spectrometers were studied and compared. Quantitative models developed using portable and benchtop spectrometers for measuring caffeine, total catechins, and four individual catechins were established and compared. After preprocessing using standard normal variate (SNV), the Rp values of the caffeine, total catechins, (-)-epigallocatechin, (-)-epigallocatechin 3-gallate, (-)-epicatechin, and (-)-epicatechin gallate in the partial least squares models for a portable NIR spectrometer were 0.974, 0.962, 0.669, 0.945, 0.942 and 0.905, respectively. For a benchtop NIR spectrometer, Rp values were 0.993, 0.958, 0.883, 0.955, 0.966 and 0.936, respectively. Passing-Bablok regression method results indicated no significant differences between the two instruments. A genetic algorithm (GA) and the successive projections algorithm (SPA) were used to screen the wavelength of the NIR spectrum and establish the model. The GA obtained more robust modeling results. This study concludes that the developed portable spectroscopy system combined with appropriate variable selection methods can be effectively used for rapid determination of caffeine, total catechins, and four individual catechins in instant green tea.
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Affiliation(s)
- Yemei Sun
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jing Huang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Weiwei Deng
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.
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Rapid Identification of Different Grades of Huangshan Maofeng Tea Using Ultraviolet Spectrum and Color Difference. Molecules 2020; 25:molecules25204665. [PMID: 33066248 PMCID: PMC7587389 DOI: 10.3390/molecules25204665] [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: 09/01/2020] [Revised: 10/03/2020] [Accepted: 10/12/2020] [Indexed: 12/31/2022] Open
Abstract
Tea is an important beverage in humans’ daily lives. For a long time, tea grade identification relied on sensory evaluation, which requires professional knowledge, so is difficult and troublesome for laypersons. Tea chemical component detection usually involves a series of procedures and multiple steps to obtain the final results. As such, a simple, rapid, and reliable method to judge the quality of tea is needed. Here, we propose a quick method that combines ultraviolet (UV) spectra and color difference to classify tea. The operations are simple and do not involve complex pretreatment. Each method requires only a few seconds for sample detection. In this study, famous Chinese green tea, Huangshan Maofeng, was selected. The traditional detection results of tea chemical components could not be used to directly determine tea grade. Then, digital instrument methods, UV spectrometry and colorimetry, were applied. The principal component analysis (PCA) plots of the single and combined signals of these two instruments showed that samples could be arranged according to grade. The combined signal PCA plot performed better with the sample grade descending in clockwise order. For grade prediction, the random forest (RF) model produced a better effect than the support vector machine (SVM) and the SVM + RF model. In the RF model, the training and testing accuracies of the combined signal were all 1. The grades of all samples were correctly predicted. From the above, the UV spectrum combined with color difference can be used to quickly and accurately classify the grade of Huangshan Maofeng tea. This method considerably increases the convenience of tea grade identification.
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36
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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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Aouadi B, Zaukuu JLZ, Vitális F, Bodor Z, Fehér O, Gillay Z, Bazar G, Kovacs Z. Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5479. [PMID: 32987908 PMCID: PMC7583984 DOI: 10.3390/s20195479] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/15/2020] [Accepted: 09/21/2020] [Indexed: 01/28/2023]
Abstract
Amid today's stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.
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Affiliation(s)
- Balkis Aouadi
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - John-Lewis Zinia Zaukuu
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - Flora Vitális
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - Zsanett Bodor
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - Orsolya Fehér
- Institute of Agribusiness, Faculty of Economics and Social Sciences, Szent István University, H-2100 Gödöllő, Hungary;
| | - Zoltan Gillay
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - George Bazar
- Department of Nutritional Science and Production Technology, Faculty of Agricultural and Environmental Sciences, Szent István University, H-7400 Kaposvár, Hungary;
- ADEXGO Kft., H-8230 Balatonfüred, Hungary
| | - Zoltan Kovacs
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
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Ouyang Q, Wang L, Zareef M, Chen Q, Guo Z, Li H. A feasibility of nondestructive rapid detection of total volatile basic nitrogen content in frozen pork based on portable near-infrared spectroscopy. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105020] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Li H, Mehedi Hassan M, Wang J, Wei W, Zou M, Ouyang Q, Chen Q. Investigation of nonlinear relationship of surface enhanced Raman scattering signal for robust prediction of thiabendazole in apple. Food Chem 2020; 339:127843. [PMID: 32889134 DOI: 10.1016/j.foodchem.2020.127843] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/02/2020] [Accepted: 08/12/2020] [Indexed: 12/12/2022]
Abstract
Thiabendazole (TBZ) is extensively used in agriculture to control molds; residue of TBZ may pose a threat to humans. Herein, surface-enhanced Raman spectroscopy (SERS) coupled variable selected regression methods have been proposed as simple and rapid TBZ quantification technique. The nonlinear correlation between the TBZ and SERS data was first diagnosed by augmented partial residual plots method and calculated by runs test. Au@Ag NPs with strong enhancement factor (EF = 4.07 × 106) of Raman signal was used as SERS active material to collect spectra from TBZ. Subsequently, three nonlinear regression models were comparatively investigated and the competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) achieved a higher correlation coefficient (Rp2 = 0.9406) and the lower root-mean-square-error of prediction (RMSEP = 0.5233 mg/L). Finally, recoveries of TBZ in apple samples were 83.02-93.54% with relative standard deviation (RSD) value < 10%. Therefore, SERS coupled CARS-ELM could be employed as a rapid and sensitive approach for TBZ detection in Fuji apples.
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Affiliation(s)
- Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Md Mehedi Hassan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Jingjing Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Min Zou
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, 100048 Beijing, China; School of Computer and Information Engineering, Beijing Technology and Business University, 100048, China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
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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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Wu X, Zhou H, Wu B, Fu H. Determination of apple varieties by near infrared reflectance spectroscopy coupled with improved possibilistic Gath–Geva clustering algorithm. J FOOD PROCESS PRES 2020. [DOI: 10.1111/jfpp.14561] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- 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
| | - Haoxiang Zhou
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Bin Wu
- Department of Information Engineering Chuzhou Polytechnic Chuzhou China
| | - Haijun Fu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
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He Y, Jiang H, Chen Q. High-precision identification of the actual storage periods of edible oil by FT-NIR spectroscopy combined with chemometric methods. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2020; 12:3722-3728. [PMID: 32729876 DOI: 10.1039/d0ay00779j] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The actual storage period of edible oil is one of the important indicators of edible oil quality. A high-precision identification method based on the near-infrared (NIR) spectroscopy technique for the actual storage period of edible oil is proposed in this study. Firstly, a Fourier transform NIR (FT-NIR) spectrometer was used to collect NIR spectra of edible oil samples in different storage periods, and the obtained spectra were pretreated by standard normal transformation (SNV). Then, the characteristics of the pretreated spectra were analyzed by principal component analysis (PCA), and the spatial distribution of edible oil samples in different storage periods was visually presented using a PCA score plot. Finally, three pattern recognition methods, which were K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were compared to establish a qualitative identification model of edible oil in different storage periods. The results showed that the recognition performance of the SVM model was significantly superior to that of the KNN and RF models, especially in terms of generalization performance, and the SVM model had a recognition rate of 100% when predicting independent samples in the prediction set. It is suggested that FT-NIR spectroscopy combined with appropriate chemometric methods is feasible to realize fast and high-precision identification of actual storage periods of edible oil and provided an effective analysis tool for edible oil storage quality detection.
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Affiliation(s)
- Yingchao He
- 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.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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Guo Z, Wang M, Shujat A, Wu J, El-Seedi HR, Shi J, Ouyang Q, Chen Q, Zou X. Nondestructive monitoring storage quality of apples at different temperatures by near-infrared transmittance spectroscopy. Food Sci Nutr 2020; 8:3793-3805. [PMID: 32724641 PMCID: PMC7382128 DOI: 10.1002/fsn3.1669] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 04/15/2020] [Accepted: 05/07/2020] [Indexed: 12/26/2022] Open
Abstract
Apple is the most widely planted fruit in the world and is popular in consumers because of its rich nutritional value. In this study, the portable near-infrared (NIR) transmittance spectroscopy coupled with temperature compensation and chemometric algorithms was applied to detect the storage quality of apples. The postharvest quality of apples including soluble solids content (SSC), vitamin C (VC), titratable acid (TA), and firmness was evaluated, and the portable spectrometer was used to obtain near-infrared transmittance spectra of apples in the wavelength range of 590-1,200 nm. Mixed temperature compensation method (MTC) was used to reduce the influence of temperature on the models and to improve the adaptability of the models. Then, variable selection methods, such as uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA), were developed to improve the performance of the models by determining characteristic variables and reducing redundancy. Comparing the full spectral models with the models established on variables selected by different variable selection methods, the CARS combined with partial least squares (PLS) showed the best performance with prediction correlation coefficient (R p) and residual predictive deviation (RPD) values of 0.9236, 2.604 for SSC; 0.8684, 2.002 for TA; 0.8922, 2.087 for VC; and 0.8207, 1.992 for firmness, respectively. Results showed that NIR transmittance spectroscopy was feasible to detect postharvest quality of apples during storage.
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Affiliation(s)
- Zhiming Guo
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Mingming Wang
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Ali Shujat
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Jingzhu Wu
- Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China
| | - Hesham R El-Seedi
- Division of Pharmacognosy Department of Medicinal Chemistry Uppsala University Uppsala Sweden
| | - Jiyong Shi
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Qin Ouyang
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Xiaobo Zou
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
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44
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Evaluation of portable and benchtop NIR for classification of high oleic acid peanuts and fatty acid quantitation. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.109398] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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45
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A near-infrared azadipyrromethene dye: Photophysical properties under different acidity conditions. INORG CHEM COMMUN 2020. [DOI: 10.1016/j.inoche.2020.107942] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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46
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Tea chemistry – What do and what don’t we know? – A micro review. Food Res Int 2020; 132:109120. [DOI: 10.1016/j.foodres.2020.109120] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 02/18/2020] [Accepted: 02/19/2020] [Indexed: 01/08/2023]
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47
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Ouyang Q, Wang L, Park B, Kang R, Wang Z, Chen Q, Guo Z. Assessment of matcha sensory quality using hyperspectral microscope imaging technology. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.109254] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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48
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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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49
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Jiang H, Xu W, Ding Y, Chen Q. Quantitative analysis of yeast fermentation process using Raman spectroscopy: Comparison of CARS and VCPA for variable selection. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 228:117781. [PMID: 31740120 DOI: 10.1016/j.saa.2019.117781] [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: 05/08/2019] [Revised: 11/05/2019] [Accepted: 11/09/2019] [Indexed: 06/10/2023]
Abstract
Yeast is one of the most widely used microbial species in the field of microbiology, and it is crucial that rapid and accurate monitoring of its process. Therefore, this study presents a method using Raman spectroscopy for quantitative analysis of yeast fermentation process. First, a ProSP-Micro2000K Raman measuring system used to obtain the Raman spectra of eight batches of yeast samples during fermentation, and the spectra obtained were pretreated using Savitzky-Golay (SG) smoothing filter and standard normal variate (SNV). Then, two variable selection methods, which were competitive adaptive reweighted sampling (CARS) and variable combination population analysis (VCPA), were compared to search the preprocessed Raman spectroscopy characteristic wavenumber. Finally, support vector machine (SVM) was employed to construct a quantitative monitoring model of yeast fermentation process based on variables from the selected characteristic wavenumbers. The results revealed that the VCPA-SVM model showed the best prediction result with 14 selected characteristic wavelength variables. The coefficient of determination (RP2) of the optimal model was 0.979, while the root mean square error of prediction (RMSEP) was 0.108 in the validation set. The overall results demonstrate that the Raman spectroscopy integrated with chemometric approaches could be utilized as a rapid method to monitor the process of yeast cultivations.
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Affiliation(s)
- Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Weidong Xu
- School of Electrical and Information 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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50
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Identification of tea varieties by mid‐infrared diffuse reflectance spectroscopy coupled with a possibilistic fuzzy c‐means clustering with a fuzzy covariance matrix. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13298] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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