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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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Liu Y, Pan K, Liu Z, Dai Y, Duan X, Wang M, Shen Q. Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach. SENSORS (BASEL, SWITZERLAND) 2024; 24:3362. [PMID: 38894153 PMCID: PMC11174505 DOI: 10.3390/s24113362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/14/2024] [Accepted: 05/18/2024] [Indexed: 06/21/2024]
Abstract
As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.
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Affiliation(s)
| | | | | | | | | | | | - Qiang Shen
- Tea Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550025, China; (Y.L.); (K.P.); (Z.L.); (Y.D.); (X.D.); (M.W.)
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3
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Xia H, Chen W, Hu D, Miao A, Qiao X, Qiu G, Liang J, Guo W, Ma C. Rapid discrimination of quality grade of black tea based on near-infrared spectroscopy (NIRS), electronic nose (E-nose) and data fusion. Food Chem 2024; 440:138242. [PMID: 38154280 DOI: 10.1016/j.foodchem.2023.138242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/25/2023] [Accepted: 12/18/2023] [Indexed: 12/30/2023]
Abstract
For the manufacturing and sale of tea, rapid discrimination of overall quality grade is of great importance. However, present evaluation methods are time-consuming and labor-intensive. This study investigated the feasibility of combining advantages of near-infrared spectroscopy (NIRS) and electronic nose (E-nose) to assess the tea quality. We found that NIRS and E-nose models effectively identify taste and aroma quality grades, with the highest accuracies of 99.63% and 97.00%, respectively, by comparing different principal component numbers and classification algorithms. Additionally, the quantitative models based on NIRS predicted the contents of key substances. Based on this, NIRS and E-nose data were fused in the feature-level to build the overall quality evaluation model, achieving accuracies of 98.13%, 96.63% and 97.75% by support vector machine, K-nearest neighbors, and artificial neural network, respectively. This study reveals that the integration of NIRS and E-nose presents a novel and effective approach for rapidly identifying tea quality.
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Affiliation(s)
- Hongling Xia
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Wei Chen
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Die Hu
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Aiqing Miao
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Xiaoyan Qiao
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Guangjun Qiu
- Institute of Facility Agriculture of Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Jianhua Liang
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China
| | - Weiqing Guo
- GRINM (Guangdong) Institute for Advanced Materials and Technology, Foshan, Guangdong Province 528000, PR China.
| | - Chengying Ma
- Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, PR China.
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4
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Li L, Chen Y, Dong S, Shen J, Cao S, Cui Q, Song Y, Ning J. Rapid and comprehensive grade evaluation of Keemun black tea using efficient multidimensional data fusion. Food Chem X 2023; 20:100924. [PMID: 38144790 PMCID: PMC10740040 DOI: 10.1016/j.fochx.2023.100924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/08/2023] [Accepted: 10/02/2023] [Indexed: 12/26/2023] Open
Abstract
To develop a comprehensive evaluation method for Keemun black tea, we used micro-near-infrared spectroscopy, computer vision, and colorimetric sensor array to collect data. We used support vector machine, least-squares support vector machine (LS-SVM), extreme learning machine, and partial least squares discriminant analysis algorithms to qualitatively discriminate between different grades of tea. Our results indicated that the LS-SVM model with mid-level data fusion attained an accuracy of 98.57% in the testing set. To quantitatively determine flavour substances in black tea, we used support vector regression. The correlation coefficient for the predicted sets of gallic acid, caffeine, epigallocatechin, catechin, epigallocatechin gallate, epicatechin, gallocatechin gallate and total catechins were 0.84089, 0.94249, 0.94050, 0.83820, 0.81111, 0.82670, 0.93230, and 0.93608, respectively. Furthermore, all compounds exhibited residual predictive deviation values exceeding 2. Hence, combining spectral, shape, colour, and aroma data with mid-level data can provide a rapid and comprehensive assessment of Keemun black tea quality.
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Affiliation(s)
- Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
| | - Yurong Chen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
| | - Shuai Dong
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
| | - Jingfei Shen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
| | - Shuci Cao
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
| | - Qingqing Cui
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
| | - Yan Song
- School of Engineering, Anhui Agricultural University, Hefei 230036, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
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5
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Ren Z, Hou Z, Deng G, Huang L, Liu N, Ning J, Wang Y. Cost-effective colorimetric sensor for authentication of protected designation of origin (PDO) Longjing green tea. Food Chem 2023; 427:136673. [PMID: 37364316 DOI: 10.1016/j.foodchem.2023.136673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 05/29/2023] [Accepted: 06/18/2023] [Indexed: 06/28/2023]
Abstract
Traceability and authentication of protected designation of origin (PDO) tea is an important prerequisite to safeguard its production and distribution system. Here, indicator displacement array (IDA) sensors consisting of natural anthocyanidins and edible metal ions were developed to authenticate PDO and non-PDO Longjing from different origins. Five IDA elements were selected for constructing sensors, achieved by an indicator displacement reaction after adding epigallocatechin gallate solution. The obtained sensors were subsequently used for real tea samples. Unsupervised algorithms were used for data exploration among PDO and non-PDO teas. The supervised support vector machine (SVM) model further achieved accurate authentication of PDO and non-PDO Longjing with a correct classification rate of 100% for the 26 validated samples. The developed IDA sensor thus achieves accurate authentication of PDO tea in a hazard-free and cost-efficient way, providing a useful tool for origin authentication of other agricultural products.
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Affiliation(s)
- Zhengyu Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, China
| | - Zhiwei Hou
- College of Tea Science and Tea Culture, Zhejiang A&F University, China
| | - Guojian Deng
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, China
| | - Lunfang Huang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, China
| | - Nanfeng Liu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, China.
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, China.
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6
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Zhang Y, Yuan W, Ren Z, Ning J, Wang Y. Indicator displacement assay for freshness monitoring of green tea during storage. Food Res Int 2023; 167:112668. [PMID: 37087209 DOI: 10.1016/j.foodres.2023.112668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/16/2023] [Accepted: 03/05/2023] [Indexed: 03/30/2023]
Abstract
Aging of green tea leads to reductions in its flavor and health value, yet in situ testing methods for green tea freshness are lacking. A novel sensitive indicator displacement assay (IDA) sensor was constructed and applied for monitoring of green tea freshness during storage. Low-cost pH dyes and metal ions were used as indicators and receptors, respectively, for the targeted detection of catechins in tea samples. The feasibility of the IDA reaction was verified using images and UV-vis spectroscopy, respectively. IDA combined with supervised algorithms achieved accurate identification of green tea freshness with an accuracy of 86.67%, and acceptable accuracies in the prediction of catechin monomers and total catechins with ratio of prediction to deviation values over 1.5. Thus, the developed IDA sensor is capable of qualitative and quantitative monitoring of the green tea freshness during storage, providing a new option for quality evaluation and control of green teas.
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7
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Johnson JB, Walsh KB, Naiker M, Ameer K. The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review. Molecules 2023; 28:molecules28073215. [PMID: 37049978 PMCID: PMC10096661 DOI: 10.3390/molecules28073215] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/29/2023] [Accepted: 04/03/2023] [Indexed: 04/14/2023] Open
Abstract
Infrared spectroscopy (wavelengths ranging from 750-25,000 nm) offers a rapid means of assessing the chemical composition of a wide range of sample types, both for qualitative and quantitative analyses. Its use in the food industry has increased significantly over the past five decades and it is now an accepted analytical technique for the routine analysis of certain analytes. Furthermore, it is commonly used for routine screening and quality control purposes in numerous industry settings, albeit not typically for the analysis of bioactive compounds. Using the Scopus database, a systematic search of literature of the five years between 2016 and 2020 identified 45 studies using near-infrared and 17 studies using mid-infrared spectroscopy for the quantification of bioactive compounds in food products. The most common bioactive compounds assessed were polyphenols, anthocyanins, carotenoids and ascorbic acid. Numerous factors affect the accuracy of the developed model, including the analyte class and concentration, matrix type, instrument geometry, wavelength selection and spectral processing/pre-processing methods. Additionally, only a few studies were validated on independently sourced samples. Nevertheless, the results demonstrate some promise of infrared spectroscopy for the rapid estimation of a wide range of bioactive compounds in food matrices.
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Affiliation(s)
- Joel B Johnson
- School of Health, Medical & Applied Science, Central Queensland University, North Rockhampton, QLD 4701, Australia
| | - Kerry B Walsh
- School of Health, Medical & Applied Science, Central Queensland University, North Rockhampton, QLD 4701, Australia
| | - Mani Naiker
- School of Health, Medical & Applied Science, Central Queensland University, North Rockhampton, QLD 4701, Australia
| | - Kashif Ameer
- Institute of Food Science and Nutrition, University of Sargodha, Sargodha 40100, Pakistan
- Department of Integrative Food, Bioscience and Biotechnology, Chonnam National University, Gwangju 61186, Republic of Korea
- School of Food Science and Biotechnology, Kyungpook National University, Daegu 41566, Republic of Korea
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8
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Guo T, Pan F, Cui Z, Yang Z, Chen Q, Zhao L, Song H. FAPD: An Astringency Threshold and Astringency Type Prediction Database for Flavonoid Compounds Based on Machine Learning. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:4172-4183. [PMID: 36825752 DOI: 10.1021/acs.jafc.2c08822] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Astringency is a puckering or velvety sensation mainly derived from flavonoid compounds in food. The traditional experimental approach for astringent compound discovery was labor-intensive and cost-consuming, while machine learning (ML) can greatly accelerate this procedure. Herein, we propose the Flavonoid Astringency Prediction Database (FAPD) based on ML. First, the Molecular Fingerprint Similarities (MFSs) and thresholds of flavonoid compounds were hierarchically clustering analyzed. For the astringency threshold prediction, four regressions models (i.e., Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosted Decision Tree (GBDT)) were established, and the best model was RF which was interpreted by the SHapley Additive exPlanations (SHAP) approach. For the astringency type prediction, six classification models (i.e., RF, GBDT, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Stochastic Gradient Descent (SGD)) were established, and the best model was SGD. Furthermore, over 1200 natural flavonoid compounds were discovered and built into the customized FAPD. In FAPD, the astringency thresholds were achieved by RF; the astringency types were distinguished by SGD, and the real and predicted astringency types were verified by t-Distributed Stochastic Neighbor Embedding (t-SNE). Therefore, ML models can be used to predict the astringency threshold and astringency type of flavonoid compounds, which provides a new paradigm to research the molecular structure-flavor property relationship of food components.
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Affiliation(s)
- Tianyang Guo
- School of Food and Health, Beijing Technology and Business University, Beijing, 100048, China
| | - Fei Pan
- School of Food and Health, Beijing Technology and Business University, Beijing, 100048, China
- Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing, 100093, China
| | - Zhiyong Cui
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Zichen Yang
- School of Food and Health, Beijing Technology and Business University, Beijing, 100048, China
| | - Qiong Chen
- School of Food and Health, Beijing Technology and Business University, Beijing, 100048, China
| | - Lei Zhao
- School of Food and Health, Beijing Technology and Business University, Beijing, 100048, China
| | - Huanlu Song
- School of Food and Health, Beijing Technology and Business University, Beijing, 100048, China
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9
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Cost-effective and sensitive indicator-displacement array (IDA) assay for quality monitoring of black tea fermentation. Food Chem 2023; 403:134340. [DOI: 10.1016/j.foodchem.2022.134340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 09/08/2022] [Accepted: 09/16/2022] [Indexed: 11/21/2022]
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10
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Wang Y, Ren Z, Chen Y, Lu C, Deng WW, Zhang Z, Ning J. Visualizing chemical indicators: Spatial and temporal quality formation and distribution during black tea fermentation. Food Chem 2023; 401:134090. [DOI: 10.1016/j.foodchem.2022.134090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 08/13/2022] [Accepted: 08/29/2022] [Indexed: 01/30/2023]
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11
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Zhang Y, Huang L, Deng G, Wang Y. Visual Monitoring of Fatty Acid Degradation during Green Tea Storage by Hyperspectral Imaging. Foods 2023; 12:foods12020282. [PMID: 36673374 PMCID: PMC9857679 DOI: 10.3390/foods12020282] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/02/2023] [Accepted: 01/06/2023] [Indexed: 01/11/2023] Open
Abstract
The reduction in freshness during green tea storage leads to a reduction in its commercial value and consumer acceptance, which is thought to be related to the oxidation of fatty acids. Here, we developed a novel and rapid method for the assessment of green tea freshness during storage. Hyperspectral images of green tea during storage were acquired, and fatty acid profiles were detected by GC-MS. Partial least squares (PLS) analysis was used to model the association of spectral data with fatty acid content. In addition, competitive adaptive reweighted sampling (CARS) was employed to select the characteristic wavelengths and thus simplify the model. The results show that the constructed CARS-PLS can achieve accurate prediction of saturated and unsaturated fatty acid content, with residual prediction deviation (RPD) values over 2. Ultimately, chemical imaging was used to visualize the distribution of fatty acids during storage, thus providing a fast and nondestructive method for green tea freshness evaluation.
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12
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Li T, Lu C, Huang J, Chen Y, Zhang J, Wei Y, Wang Y, Ning J. Qualitative and quantitative analysis of the pile fermentation degree of Pu-erh tea. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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13
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Chen J, Yang C, Yuan C, Li Y, An T, Dong C. Moisture content monitoring in withering leaves during black tea processing based on electronic eye and near infrared spectroscopy. Sci Rep 2022; 12:20721. [PMID: 36456868 PMCID: PMC9715558 DOI: 10.1038/s41598-022-25112-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 11/24/2022] [Indexed: 12/05/2022] Open
Abstract
Monitoring the moisture content of withering leaves in black tea manufacturing remains a difficult task because the external and internal information of withering leaves cannot be simultaneously obtained. In this study, the spectral data and the color/texture information of withering leaves were obtained using near infrared spectroscopy (NIRS) and electronic eye (E-eye), respectively, and then fused to predict the moisture content. Subsequently, the low- and middle-level fusion strategy combined with support vector regression (SVR) was applied to detect the moisture level of withering leaves. In the middle-level fusion strategy, the principal component analysis (PCA) and random frog (RF) were employed to compress the variables and select effective information, respectively. The middle-level-RF (cutoff line = 0.8) displayed the best performance because this model used fewer variables and still achieved a satisfactory result, with 0.9883 and 5.5596 for the correlation coefficient of the prediction set (Rp) and relative percent deviation (RPD), respectively. Hence, our study demonstrated that the proposed data fusion strategy could accurately predict the moisture content during the withering process.
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Affiliation(s)
- Jiayou Chen
- grid.495239.00000 0004 4657 1319Liming Vocational University, Quanzhou, 362007 China ,grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China
| | - Chongshan Yang
- grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China ,grid.263906.80000 0001 0362 4044College of Engineering and Technology, Southwest University, Chongqing, 400715 China
| | - Changbo Yuan
- grid.452757.60000 0004 0644 6150Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250033 China
| | - Yang Li
- grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China
| | - Ting An
- grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China ,grid.263906.80000 0001 0362 4044College of Engineering and Technology, Southwest University, Chongqing, 400715 China
| | - Chunwang Dong
- grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China ,grid.452757.60000 0004 0644 6150Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250033 China
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14
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Improved Model for Starch Prediction in Potato by the Fusion of Near-Infrared Spectral and Textural Data. Foods 2022; 11:foods11193133. [PMID: 36230208 PMCID: PMC9563719 DOI: 10.3390/foods11193133] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/20/2022] [Accepted: 09/30/2022] [Indexed: 11/16/2022] Open
Abstract
In this study, visible-near-infrared (VIS-NIR) hyperspectral imaging was combined with a data fusion strategy for the nondestructive assessment of the starch content in intact potatoes. Spectral and textural data were extracted from hyperspectral images and transformed principal component (PC) images, respectively, and a partial least squares regression (PLSR) prediction model was then established. The results revealed that low-level data fusion could not improve accuracy in predicting starch content. Therefore, to improve prediction accuracy, key variables were selected from the spectral and textural data through competitive adaptive reweighted sampling (CARS) and correlation analysis, respectively, and mid-level data fusion was performed. With a residual predictive deviation (RPD) value > 2, the established PLSR model achieved satisfactory prediction accuracy. Therefore, this study demonstrated that appropriate data fusion can effectively improve the prediction accuracy for starch content and thus aid the sorting of potato starch content in the production line.
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15
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NIR Spectrometric Approach for Geographical Origin Identification and Taste Related Compounds Content Prediction of Lushan Yunwu Tea. Foods 2022; 11:foods11192976. [PMID: 36230052 PMCID: PMC9563823 DOI: 10.3390/foods11192976] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 11/29/2022] Open
Abstract
Lushan Yunwu Tea is one of a unique Chinese tea series, and total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio models (TP/FAA) represent its most important taste-related indicators. In this work, a feasibility study was proposed to simultaneously predict the authenticity identification and taste-related indicators of Lushan Yunwu tea, using near-infrared spectroscopy combined with multivariate analysis. Different waveband selections and spectral pre-processing methods were compared during the discriminant analysis (DA) and partial least squares (PLS) model-building process. The DA model achieved optimal performance in distinguishing Lushan Yunwu tea from other non-Lushan Yunwu teas, with a correct classification rate of up to 100%. The synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS) algorithms showed considerable advantages in improving the prediction performance of TP, FAA, and TP/FAA. The siPLS algorithms achieved the best prediction results for TP (RP = 0.9407, RPD = 3.00), FAA (RP = 0.9110, RPD = 2.21) and TP/FAA (RP = 0.9377, RPD = 2.90). These results indicated that NIR spectroscopy was a useful and low-cost tool by which to offer definitive quantitative and qualitative analysis for Lushan Yunwu tea.
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16
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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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17
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Wang F, Wang C, Song S. Rapid and Low-Cost Detection of Millet Quality by Miniature Near-Infrared Spectroscopy and Iteratively Retaining Informative Variables. Foods 2022; 11:foods11131841. [PMID: 35804657 PMCID: PMC9265786 DOI: 10.3390/foods11131841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/10/2022] [Accepted: 06/20/2022] [Indexed: 02/01/2023] Open
Abstract
Traditional chemical methods for testing the fat content of millet, a widely consumed grain, are time-consuming and costly. In this study, we developed a low-cost and rapid method for fat detection and quantification in millet. A miniature NIR spectrometer connected to a smartphone was used to collect spectral data from millet samples of different origins. The standard normal variate (SNV) and first derivative (1D) methods were used to preprocess spectral signals. Variable selection methods, including bootstrapping soft shrinkage (BOSS), the variable iterative space shrinkage approach (VISSA), iteratively retaining informative variables (IRIV), iteratively variable subset optimization (IVSO), and competitive adaptive reweighted sampling (CARS), were used to select characteristic wavelengths. The partial least squares regression (PLSR) algorithm was employed to develop the regression models aimed at predicting the fat content in millet. The results showed that the proposed 1D-IRIV-PLSR model achieved optimal accuracy for fat detection, with a correlation coefficient for prediction (Rp) of 0.953, a root mean square error for prediction (RMSEP) of 0.301 g/100 g, and a residual predictive deviation (RPD) of 3.225, by using only 18 characteristic wavelengths. This result highlights the feasibility of using this low-cost and high-portability assessment tool for millet quality testing, which provides an optional solution for in situ inspection of millet quality in different scenarios, such as production lines or sales stores.
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Affiliation(s)
- Fuxiang Wang
- School of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010000, China;
| | - Chunguang Wang
- School of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010000, China;
- Correspondence: ; Tel.: +86-0471-4304788
| | - Shiyong Song
- Mongolia Lvtao Detection Technology Company Limited, Hohhot 010000, China;
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18
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Yang Y, Zhu H, Chen J, Xie J, Shen S, Deng Y, Zhu J, Yuan H, Jiang Y. Characterization of the key aroma compounds in black teas with different aroma types by using gas chromatography electronic nose, gas chromatography-ion mobility spectrometry, and odor activity value analysis. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113492] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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19
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Liu Z, Zhang R, Yang C, Hu B, Luo X, Li Y, Dong C. Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 271:120921. [PMID: 35091181 DOI: 10.1016/j.saa.2022.120921] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 06/14/2023]
Abstract
Moisture content is an important indicator that affects green tea processing. In this study, taking Chuyeqi tea as the research object, a quantitative prediction model of the changes in moisture content during the processing of green tea was constructed based on machine vision and near-infrared spectroscopy technology. First, collect the spectrum and image information in the process of spreading, fixation, first-drying, carding, and second-drying. The competitive adaptive reweighted sampling (CARS) method is then used to extract the characteristic wavelengths in the spectrum, and the image's 9 color features and 6 texture features are combined to establish linear PLSR and nonlinear SVR prediction models by fusing the data information from the two sensors. The results show that, when compared to single data, the PLSR and SVR models based on low-level data fusion do not effectively improve the model's prediction accuracy, but rather produce poor prediction results. In contrast, the PLSR and SVR models established by middle-level data fusion have improved the prediction accuracy of moisture content in green tea processing. Among them, the established SVR model has the best effect. The correlation coefficient of the calibration set (Rc) and the root mean square error of calibration (RMSEC) are 0.9804 and 0.0425, respectively, the correlation coefficient of the prediction set (Rp) and the root mean square error of prediction (RMSEP) are 0.9777 and 0.0490 respectively, and the relative percent deviation is 4.5002. The results show that the middle data fusion based on machine vision and near-infrared spectroscopy technology can effectively predict the moisture content in the processing of green tea, which has important guiding significance for overcoming the low prediction accuracy of a single sensor.
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Affiliation(s)
- Zhongyuan Liu
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang 310008, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Rentian Zhang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang 310008, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Chongshan Yang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang 310008, China
| | - Bin Hu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Xin Luo
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Yang Li
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang 310008, China.
| | - Chunwang Dong
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang 310008, China.
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20
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Sun Y, Zhang H, Liu G, He J, Cheng L, Li Y, Pu F, Wang H. Quantitative Detection of Myoglobin Content in Tan Mutton During Cold Storage by Near-infrared Hyperspectral Imaging. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02275-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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21
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Chen C, Zhang W, Shan Z, Zhang C, Dong T, Feng Z, Wang C. Moisture contents and product quality prediction of Pu-erh tea in sun-drying process with image information and environmental parameters. Food Sci Nutr 2022; 10:1021-1038. [PMID: 35432968 PMCID: PMC9007301 DOI: 10.1002/fsn3.2699] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 10/31/2021] [Accepted: 12/02/2021] [Indexed: 11/07/2022] Open
Abstract
In this study, moisture contents and product quality of Pu-erh tea were predicted with deep learning-based methods. Images were captured continuously in the sun-drying process. Environmental parameters (EP) of air humidity, air temperature, global radiation, wind speed, and ultraviolet radiation were collected with a portable meteorological station. Sensory scores of aroma, flavor, liquor color, residue, and total scores were given by a trained panel. Convolutional neural network (CNN) and gated recurrent unit (GRU) models were constructed based on image information and EP, which were selected in advance using the neighborhood component analysis (NCA) algorithm. The evolved models based on deep-learning methods achieved satisfactory results, with RMSE of 0.4332, 0.2669, 0.7508 (also with R 2 of .9997, .9882, .9986, with RPD of 53.5894, 13.1646, 26.3513) for moisture contents prediction in each batch of tea, tea at different sampling periods, the overall samples, respectively; and with RMSE of 0.291, 0.2815, 0.162, 0.1574, 0.3931 (also with R 2 of .9688, .9772, .9752, .9741, .8906, with RPD of 5.6073, 6.5912, 6.352, 6.1428, 4.0045) for final quality prediction of aroma, flavor, liquor color, residue, total score, respectively. By analyzing and comparing the RMSE values, the most significant environmental parameters (EP) were selected. The proposed combinations of different EP can also provide a valuable reference in the development of a new sun-drying system.
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Affiliation(s)
- Cheng Chen
- Faculty of Management and Economics Kunming University of Science and Technology Kunming China
| | - Wuyi Zhang
- Faculty of Management and Economics Kunming University of Science and Technology Kunming China
| | - Zhiguo Shan
- College of Agriculture and Forestry Pu'er University Pu'er China
| | - Chunhua Zhang
- College of Agriculture and Forestry Pu'er University Pu'er China
| | - Tianwu Dong
- Pu'er Gaoshan Zuxiang Tea Garden Co., Ltd. Pu'er China
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22
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Tea Analyzer: A low-cost and portable tool for quality quantification of postharvest fresh tea leaves. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113248] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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23
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Efficient Detection of Limonoid From Citrus Seeds by Handheld NIR: Compared with Benchtop NIR. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02245-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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24
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Li L, Wang Y, Cui Q, Liu Y, Ning J, Zhang Z. Qualitative and quantitative quality evaluation of black tea fermentation through noncontact chemical imaging. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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25
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Liu Y, Huang J, Li M, Chen Y, Cui Q, Lu C, Wang Y, Li L, Xu Z, Zhong Y, Ning J. Rapid identification of the green tea geographical origin and processing month based on near-infrared hyperspectral imaging combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 267:120537. [PMID: 34740002 DOI: 10.1016/j.saa.2021.120537] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/02/2021] [Accepted: 10/22/2021] [Indexed: 06/13/2023]
Abstract
The geographical origin and processing month of green tea greatly affect its economic value and consumer acceptance. This study investigated the feasibility of combining near-infrared hyperspectral imaging (NIR-HSI) with chemometrics for the identification of green tea. Tea samples produced in three regions of Chongqing (southeastern Chongqing, northeastern Chongqing, and western Chongqing) for four months (from May to August 2020) were collected. Principal component analysis (PCA) was used to reduce data dimensionality and visualize the clustering of samples in different categories. Linear partial least squares-discriminant analysis (PLS-DA) and nonlinear support vector machine (SVM) algorithms were used to develop discriminant models. The PCA-SVM models based on the first four and first five principal components (PCs) achieved the best accuracies of 97.5% and 95% in the prediction set for geographical origin and processing month of green tea, respectively. This study demonstrated the feasibility of HSI in the identification of green tea species, providing a rapid and nondestructive method for the evaluation and control of green tea quality.
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Affiliation(s)
- Ying Liu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Junlan Huang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Yuyu Chen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Qingqing Cui
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Chengye Lu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Ze Xu
- Chongqing Academy of Agricultural Sciences Tea Research Institute, Chongqing 402160, China
| | - Yingfu Zhong
- Chongqing Academy of Agricultural Sciences Tea Research Institute, Chongqing 402160, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
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26
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Wang H, Shen S, Wang J, Jiang Y, Li J, Yang Y, Hua J, Yuan H. Novel insight into the effect of fermentation time on quality of Yunnan Congou black tea. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112939] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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27
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Chen J, Yang Y, Deng Y, Liu Z, Xie J, Shen S, Yuan H, Jiang Y. Aroma quality evaluation of Dianhong black tea infusions by the combination of rapid gas phase electronic nose and multivariate statistical analysis. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112496] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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28
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Wang Y, Li L, Liu Y, Cui Q, Ning J, Zhang Z. Enhanced quality monitoring during black tea processing by the fusion of NIRS and computer vision. J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2021.110599] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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29
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Wang Y, Liu Y, Chen Y, Cui Q, Li L, Ning J, Zhang Z. Spatial distribution of total polyphenols in multi-type of tea using near-infrared hyperspectral imaging. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.111737] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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30
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Wang F, Wang C, Song S, Xie S, Kang F. Study on starch content detection and visualization of potato based on hyperspectral imaging. Food Sci Nutr 2021; 9:4420-4430. [PMID: 34401090 PMCID: PMC8358368 DOI: 10.1002/fsn3.2415] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/20/2021] [Accepted: 06/02/2021] [Indexed: 02/06/2023] Open
Abstract
Starch is an important quality index in potato, which contributes greatly to the taste and nutritional quality of potato. At present, the determination of starch depends on chemical analysis, which is time consuming and laborious. Thus, rapid and accurate detection of the starch content of potatoes is important. This study combined hyperspectral imaging with chemometrics to predict potato starch content. Two varieties of Kexin No.1 and Holland No.15 potatoes were used as experimental samples. Hyperspectral data were collected from three sampling sites (the top, umbilicus, and middle regions). Standard normal variate (SNV) was used for spectral preprocessing, and three different methods of competitive adaptive reweighted sampling (CARS), iterative variable subset optimization (IVSO), and the variable iterative space shrinkage approach (VISSA) were used for characteristic wavelength selection. Linear partial least-squares regression (PLSR) and nonlinear support vector regression (SVR) models were then established. The results indicated that the sampling site has a considerable impact on the accuracy of the prediction model, and the umbilicus region with CARS-SVR model gave best performance with correlation coefficients in calibration (Rc) of 0.9415, in prediction (Rp) of 0.9346, root mean square errors in calibration (RMSEC) of 15.9 g/kg, in prediction (RMSEP) of 17.4 g/kg, and residual predictive deviation (RPD) of 2.69. The starch content in potatoes was visualized using the best model in combination with pseudo-color technology. Our research provides a method for the rapid and nondestructive determination of starch content in potatoes, providing a good foundation for potato quality monitoring and grading.
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Affiliation(s)
- Fuxiang Wang
- Inner Mongolia Agriculture UniversityHohhotChina
| | | | - Shiyong Song
- Inner Mongolia Agriculture UniversityHohhotChina
| | - Shengshi Xie
- Inner Mongolia Agriculture UniversityHohhotChina
| | - Feilong Kang
- Inner Mongolia Agriculture UniversityHohhotChina
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31
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Monitoring the withering condition of leaves during black tea processing via the fusion of electronic eye (E-eye), colorimetric sensing array (CSA), and micro-near-infrared spectroscopy (NIRS). J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2021.110534] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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32
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Ren G, Liu Y, Ning J, Zhang Z. Assessing black tea quality based on visible–near infrared spectra and kernel-based methods. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.103810] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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33
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Jin G, Wang YJ, Li M, Li T, Huang WJ, Li L, Deng WW, Ning J. Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system. Food Chem 2021; 358:129815. [PMID: 33915424 DOI: 10.1016/j.foodchem.2021.129815] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 12/13/2022]
Abstract
Intelligent identification of black tea fermentation quality is becoming a bottleneck to industrial automation. This study presents at-line rapid detection of black tea fermentation quality at industrial scale based on low-cost micro-near-infrared spectroscopy (NIRS) and laboratory-made computer vision system (CVS). High-performance liquid chromatography and a spectrophotometer were used for determining the content of catechins and theaflavins, and the color of tea samples, respectively. Hierarchical cluster analysis combined with sensory evaluation was used to group samples through different fermentation degrees. A principal component analysis-support vector machine (SVM) model was developed to discriminate the black tea fermentation degree using color, spectral, and data fusion information; high accuracy (calibration = 95.89%, prediction = 89.19%) was achieved using mid-level data fusion. In addition, SVM model for theaflavins content prediction was established. The results indicated that the micro-NIRS combined with CVS proved a portable and low-cost tool for evaluating the black tea fermentation quality.
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Affiliation(s)
- Ge Jin
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Yu-Jie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Tiehan Li
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Wen-Jing Huang
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Wei-Wei Deng
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China.
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34
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Rapid evaluation on pharmacodynamics of Curcumae Rhizoma based on Micro-NIR and benchtop-NIR. J Pharm Biomed Anal 2021; 200:114074. [PMID: 33873074 DOI: 10.1016/j.jpba.2021.114074] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 03/24/2021] [Accepted: 04/09/2021] [Indexed: 11/22/2022]
Abstract
It's far from enough to describe the blood circulation promoting effect of Curcumae Rhizoma (CR), which is widely grown as a functional vegetable or spice in south and southeast Asian countries, and processed Curcumae Rhizoma (PCR), only by disclosing the content of a couple of relative compounds. In this study, the thrombin inhibitory effect as well as 2,2'-azino-bis (3-ethylbenz-thiazoline-6-sulfonic acid) (ABTS) and 2,2-diphenyl-picrylhydrazyl (DPPH) free radical scavenging ability of CR/PCR extracts was investigated, and TANGO Fourier transform near-infrared (FT-NIR, Bruker, Germany)-a benchtop instrument allowing the full range NIR wavelength scanning-and handheld-NIR spectrometer (Micro-NIR, NIR-S-G1, InnoSpectra corporation, China) that can be connected to smart phones were used to realize the rapid detection of pharmacodynamic indicators. model was evaluated based on the determination coefficient (R2), root mean square error (RMSE), standard error of test set (SEP) and ratio of performance to deviation (RPD). The results of pharmacodynamics experiment confirmed for the first time that CR has significant inhibitory effect on thrombin, and the modeling results revealed that Micro-NIR had a good prediction on the antioxidant capacity (ABTS and DPPH free radical clearance) with RPD greater than 3, but showed a general predictive performance on thrombin inhibition ability (RPD = 2.434). In contrast, FT-NIR provided a good prediction for all the three indicators, with R2 greater than 0.9 and RPD greater than 4.5. Further insights into the capability of the two devices were obtained by analyzing the wavebands selection work. In the full wavelength range, wavebands related to thrombin inhibition were mainly distributed in the combination area which is out of the reach of handheld Micro-NIR, thus resulting in a decrease in the prediction ability. Therefore, compared to the benchtop-NIR, the detection range of the handheld-NIR is the main factor limiting its capability Based on an overall assessment, handheld NIR spectrometer, by greatly expanding the application scenario of NIR technology, is considered as a useful device with a satisfying predictive ability through model construction.
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35
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Ren G, Li T, Wei Y, Ning J, Zhang Z. Estimation of Congou black tea quality by an electronic tongue technology combined with multivariate analysis. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105899] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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36
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Li L, Jin S, Wang Y, Liu Y, Shen S, Li M, Ma Z, Ning J, Zhang Z. Potential of smartphone-coupled micro NIR spectroscopy for quality control of green tea. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 247:119096. [PMID: 33166782 DOI: 10.1016/j.saa.2020.119096] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 10/14/2020] [Accepted: 10/15/2020] [Indexed: 06/11/2023]
Abstract
Green tea adulterated with sugar and glutinous rice flour has an increased sensitivity to water, which affects the safety of the tea. A total of 475 samples of pure tea, sugar-adulterated tea, and glutinous-rice-flour-adulterated tea were prepared and scanned using micro near infrared spectroscopy (NIRS). The collected NIRS data were qualitatively and quantitatively detected by a multi-layer algorithm model. Principal component analysis indicated that the three sample groups had an obvious separation trend. The discriminate rate of the optimal qualitative model, namely support vector machine, was 97.47% for the prediction set. A total of three wavelength selection methods were used to improve the performances of partial least squares regression and support vector machine regression (SVR) models. The nonlinear SVR models based on characteristic wavelengths selected by iteratively retaining informative variables algorithm provided satisfactory results for the identification of sugar and glutinous rice flour adulteration. The correlation coefficients for prediction (Rp) were >0.94, and the residual prediction deviation were >3. The results indicated that smartphone-based micro NIRS can be effectively used to qualitatively and quantitatively analyze adulterants in green tea.
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Affiliation(s)
- Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Shanshan Jin
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Ying Liu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Shanshan Shen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Zhiyu Ma
- School of Information & Computer, Anhui Agricultural University, Hefei 230036, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
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37
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Li L, Wang Y, Jin S, Li M, Chen Q, Ning J, Zhang Z. Evaluation of black tea by using smartphone imaging coupled with micro-near-infrared spectrometer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 246:118991. [PMID: 33068895 DOI: 10.1016/j.saa.2020.118991] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 09/18/2020] [Accepted: 09/21/2020] [Indexed: 06/11/2023]
Abstract
Tea quality is generally assessed through panel sensory evaluation, which requires elaborate sample preparation steps. Here, a novel and low-cost evaluation method of using smartphone imaging coupled with micro-near-infrared (NIR) spectrometer based on digital light processing is proposed to classify the quality grades of Keemun black tea. RGB color information was obtained by Image J software, eight texture characteristics, including scheme, contrast, dissimilarity, entropy, correlation, second moment and variance, and homogeneity were obtained by ENVI software based on co - occurrence method from smartphone images, and spectral data were preprocessed with standard normal variate. A principal component analysis (PCA)-support vector machine (SVM) model was established to analyze the color, texture, and spectral data. Low-level and middle-level fusion strategies were introduced for analyzing the fusion data. The results indicated that the accuracy of the SVM model on mid-level data fusion (100.00%, 94.29% for calibration set and prediction set, respectively) was higher than that obtained for separate color (97.14%, 88.57%), texture (84.29%, 60%), spectrum (74.29%, 68.57%) evaluation, or low-level data fusion (88.57%, 82.86%). The best SVM model yielded satisfactory performance with 94.29% accuracy for the prediction sets. These results suggested that smartphone imaging coupled with micro-NIR spectroscopy is an effective and low-cost tool for evaluating tea quality.
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Affiliation(s)
- Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Shanshan Jin
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
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Wang Y, Li M, Li L, Ning J, Zhang Z. Green analytical assay for the quality assessment of tea by using pocket-sized NIR spectrometer. Food Chem 2020; 345:128816. [PMID: 33316713 DOI: 10.1016/j.foodchem.2020.128816] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 11/07/2020] [Accepted: 12/02/2020] [Indexed: 01/14/2023]
Abstract
Rapid and low-cost testing tools provide new methods for the evaluation of tea quality. In this study, a micro near-infrared (NIR) spectrometer was used for the qualitative and quantitative evaluation of tea. A total of 360 tea samples consisting of black, green, yellow, and oolong tea were collected from different countries. Chemometrics including linear partial least squares (PLS) regression, PLS discriminant analysis, and nonlinear radial basis function-support vector machine (RBF-SVM) were used. The RBF-SVM model achieved optimal discriminant performance for tea types with a correct classification rate of 98.33%. Wavelength selection of iteratively variable subset optimization (IVSO) exhibited considerable advantages in improving the predictive performance of catechin, caffeine, and theanine models. The IVSO-PLS regression models achieved satisfactory results for catechins and caffeine prediction, with Rp over 0.9, and RPD over 2.5. Thus, the study provided a portable and low-cost method for in-situ assessing tea quality.
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Affiliation(s)
- Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
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