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Banerjee S, Mandal S, Jesubalan NG, Jain R, Rathore AS. NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation. Biotechnol Bioeng 2024; 121:1803-1819. [PMID: 38390805 DOI: 10.1002/bit.28681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.
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Affiliation(s)
- Shantanu Banerjee
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India
| | - Shyamapada Mandal
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India
| | - Naveen G Jesubalan
- School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, Delhi, India
| | - Rijul Jain
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India
| | - Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India
- School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, Delhi, India
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Revilla I, Hernández Jiménez M, Martínez-Martín I, Valderrama P, Rodríguez-Fernández M, Vivar-Quintana AM. The Potential Use of Near Infrared Spectroscopy (NIRS) to Determine the Heavy Metals and the Percentage of Blends in Tea. Foods 2024; 13:450. [PMID: 38338587 PMCID: PMC10855971 DOI: 10.3390/foods13030450] [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: 12/21/2023] [Revised: 01/26/2024] [Accepted: 01/27/2024] [Indexed: 02/12/2024] Open
Abstract
The following study analyzed the potential of Near Infrared Spectroscopy (NIRS) to predict the metal composition (Al, Pb, As, Hg and Cu) of tea and for establishing discriminant models for pure teas (green, red, and black) and their different blends. A total of 322 samples of pure black, red, and green teas and binary blends were analyzed. The results showed that pure red teas had the highest content of As and Pb, green teas were the only ones containing Hg, and black teas showed higher levels of Cu. NIRS allowed to predict the content of Al, Pb, As, Hg, and Cu with ratio performance deviation values > 3 for all of them. Additionally, it was possible to discriminate pure samples from their respective blends with an accuracy of 98.3% in calibration and 92.3% in validation. However, when the samples were discriminated according to the percentage of blending (>95%, 95-85%, 85-75%, or 75-50% of pure tea) 100% of the samples of 10 out of 12 groups were correctly classified in calibration, but only the groups with a level of pure tea of >95% showed 100% of the samples as being correctly classified as to validation.
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Affiliation(s)
- Isabel Revilla
- Food Technology, Universidad de Salamanca, E.P.S. de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.J.); (I.M.-M.)
| | - Miriam Hernández Jiménez
- Food Technology, Universidad de Salamanca, E.P.S. de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.J.); (I.M.-M.)
| | - Iván Martínez-Martín
- Food Technology, Universidad de Salamanca, E.P.S. de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.J.); (I.M.-M.)
| | - Patricia Valderrama
- Department of Chemistry, Universidade Tecnológica Federal do Paraná (UTFPR), Via Rosalina Maria dos Santos 1233, Campo Mourão 87301-899, Paraná, Brazil
| | - Marta Rodríguez-Fernández
- Food Technology, Universidad de Salamanca, E.P.S. de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.J.); (I.M.-M.)
| | - Ana M. Vivar-Quintana
- Food Technology, Universidad de Salamanca, E.P.S. de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.J.); (I.M.-M.)
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Ye S, Weng H, Xiang L, Jia L, Xu J. Synchronously Predicting Tea Polyphenol and Epigallocatechin Gallate in Tea Leaves Using Fourier Transform-Near-Infrared Spectroscopy and Machine Learning. Molecules 2023; 28:5379. [PMID: 37513250 PMCID: PMC10384235 DOI: 10.3390/molecules28145379] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 07/05/2023] [Accepted: 07/09/2023] [Indexed: 07/30/2023] Open
Abstract
Tea polyphenol and epigallocatechin gallate (EGCG) were considered as key components of tea. The rapid prediction of these two components can be beneficial for tea quality control and product development for tea producers, breeders and consumers. This study aimed to develop reliable models for tea polyphenols and EGCG content prediction during the breeding process using Fourier Transform-near infrared (FT-NIR) spectroscopy combined with machine learning algorithms. Various spectral preprocessing methods including Savitzky-Golay smoothing (SG), standard normal variate (SNV), vector normalization (VN), multiplicative scatter correction (MSC) and first derivative (FD) were applied to improve the quality of the collected spectra. Partial least squares regression (PLSR) and least squares support vector regression (LS-SVR) were introduced to establish models for tea polyphenol and EGCG content prediction based on different preprocessed spectral data. Variable selection algorithms, including competitive adaptive reweighted sampling (CARS) and random forest (RF), were further utilized to identify key spectral bands to improve the efficiency of the models. The results demonstrate that the optimal model for tea polyphenols calibration was the LS-SVR with Rp = 0.975 and RPD = 4.540 based on SG-smoothed full spectra. For EGCG detection, the best model was the LS-SVR with Rp = 0.936 and RPD = 2.841 using full original spectra as model inputs. The application of variable selection algorithms further improved the predictive performance of the models. The LS-SVR model for tea polyphenols prediction with Rp = 0.978 and RPD = 4.833 used 30 CARS-selected variables, while the LS-SVR model build on 27 RF-selected variables achieved the best predictive ability with Rp = 0.944 and RPD = 3.049, respectively, for EGCG prediction. The results demonstrate a potential of FT-NIR spectroscopy combined with machine learning for the rapid screening of genotypes with high tea polyphenol and EGCG content in tea leaves.
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Affiliation(s)
- Sitan Ye
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Haiyong Weng
- Fujian Key Laboratory of Agricultural Information Sensoring Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- School of Future Technology, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Lirong Xiang
- Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27606, USA
| | - Liangquan Jia
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Jinchai Xu
- Fujian Key Laboratory of Agricultural Information Sensoring Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- School of Future Technology, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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Li L, Sheng X, Zan J, Yuan H, Zong X, Jiang Y. Monitoring the dynamic change of catechins in black tea drying by using near-infrared spectroscopy and chemometrics. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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Chen Y, Wu H, Liu Y, Wang Y, Lu C, Li T, Wei Y, Ning J. Monitoring green tea fixation quality by intelligent sensors: comparison of image and spectral information. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:3093-3101. [PMID: 36418909 DOI: 10.1002/jsfa.12350] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/12/2022] [Accepted: 11/24/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Intelligent monitoring of fixation quality is a prerequisite for automated green tea processing. To meet the requirements of intelligent monitoring of fixation quality in large-scale production, fast and non-destructive detection means are urgently needed. Here, smartphone-coupled micro near-infrared spectroscopy and a self-built computer vision system were used to perform rapid detection of the fixation quality in green tea processing lines. RESULTS Spectral and image information from green tea samples with different fixation degrees were collected at-line by two intelligent monitoring sensors. Competitive adaptive reweighted sampling and correlation analysis were employed to select feature variables from spectral and color information as the target data for modeling, respectively. The developed least squares support vector machine (LS-SVM) model by spectral information and the LS-SVM model by image information achieved the best discriminations of sample fixation degree, with both prediction set accuracies of 100%. Compared to the spectral information, the image information-based support vector regression model performed better in moisture prediction, with a correlation coefficient of prediction of 0.9884 and residual predictive deviation of 6.46. CONCLUSION The present study provided a rapid and low-cost means of monitoring fixation quality, and also provided theoretical support and technical guidance for the automation of the green tea fixation process. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Yuyu Chen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Huiting Wu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Ying Liu
- 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
| | - Chengye Lu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Tiehan Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yuming Wei
- 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
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Hubli GB, Banerjee S, Rathore AS. Near-infrared spectroscopy based monitoring of all 20 amino acids in mammalian cell culture broth. Talanta 2023; 254:124187. [PMID: 36549134 DOI: 10.1016/j.talanta.2022.124187] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/02/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022]
Abstract
The biopharmaceutical industry extensively employs Chinese hamster ovary (CHO) cell culture for monoclonal antibody production. Amino acids represent an essential source of nutrients in all CHO cell culture media, and their concentration is known to significantly impact cell viability, titre, and monoclonal antibody critical quality attributes. In this study, a robust Fourier transform near-infrared spectroscopy (FT-NIR) based quantification method has been developed for of all 20 amino acids (0-24 mM), as well as concentrations of glucose (0-6.7 mg mL-1), lactate (0-2.7 mg mL-1), and trastuzumab (0-2.5 mg mL-1) in the CHO cell culture. Near infra-red absorbance spectrum in the range of 4000-11,000 cm-1 were acquired, and spectra pre-processing through smoothening and derivatives were employed to enhance key characteristic signals. High-performance liquid chromatography with pre-column derivatization was used as the orthogonal analytical tool for quantification. Principal component analysis and partial least squares regression were employed for region selection and calibration model development, respectively. The results demonstrate that a good calibration statistic with the acceptable coefficient of determinations for both calibration (Rc2 = 0.94-0.99) and prediction (Rp2 = 0.83-0.98) could be achieved, along with high RPD values (>3) for all components except alanine (2.4). The external validation study also exhibited a satisfactory outcome (REV2 = 0.89-0.99, RMSE = 0.04-1.04), validating the model's ability to predict the concentrations of the respective species. The calibration models were successfully applied for at-line monitoring of two perfusion runs on a 10 L scale. To our knowledge, this is the first application where NIR spectroscopy-based measurement of all 20 amino acids in mammalian cell culture samples has been demonstrated. The proposed tool can play a critical role as biopharma manufacturers implement continuous processing as well as for facilitating process analytical technology-based control of mammalian cell culture processes.
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Affiliation(s)
| | - Shantanu Banerjee
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India.
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Wang Y, Ren Z, Li M, Lu C, Deng WW, Zhang Z, Ning J. From lab to factory: A calibration transfer strategy from HSI to online NIR optimized for quality control of green tea fixation. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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8
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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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Wang J, Li X, Wang W, Wang F, Liu Q, Yan L. Research on Rapid and Low-Cost Spectral Device for the Estimation of the Quality Attributes of Tea Tree Leaves. SENSORS (BASEL, SWITZERLAND) 2023; 23:571. [PMID: 36679372 PMCID: PMC9865718 DOI: 10.3390/s23020571] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/03/2023] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700-1000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R2) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R2 values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations.
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Affiliation(s)
- Jinghua Wang
- School of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Beijing 100083, China
| | - Xiang Li
- School of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Beijing 100083, China
| | - Wancheng Wang
- Bureau of Ecology and Environment of Hanting District, No. 1507 Fenghua Road, Weifang 261100, China
| | - Fan Wang
- School of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Beijing 100083, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Quancheng Liu
- School of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Beijing 100083, China
| | - Lei Yan
- School of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Beijing 100083, China
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10
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Estimation of the sensory properties of black tea samples using non-destructive near-infrared spectroscopy sensors. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109260] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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11
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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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12
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Man Z, Feng Y, Xiao J, Yang H, Wu X. Structural changes and molecular mechanism study on the inhibitory activity of epigallocatechin against α-glucosidase and α-amylase. Front Nutr 2022; 9:948027. [PMID: 36438757 PMCID: PMC9682078 DOI: 10.3389/fnut.2022.948027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 10/20/2022] [Indexed: 09/29/2023] Open
Abstract
In this study, the inhibition and mechanism of epigallocatechin (EGC) on two key glycoside hydrolases (α-glucosidase, α-amylase) were explored from the molecular structure level. The chemical structure of EGC was characterized by X-ray diffraction, Fourier transform infrared (FTIR) spectroscopy, and proton nuclear magnetic resonance spectroscopy. EGC's inhibition on these enzymes was colorimetrically determined. The effects of EGC on the chemical structure and spatial configuration of the enzymes were explored via FTIR spectroscopy, fluorescence spectroscopy, and molecular docking techniques. The results showed that EGC exhibited the inhibition of α-glucosidase and α-amylase in a non-competitive manner, showing a continuous upward trend as EGC's concentration increased. There was a fluorescence quenching effect of EGC on α-glucosidase and α-amylase. Molecular docking confirmed that EGC can bind to amino acid residues in the enzyme through intermolecular hydrogen bonds and hydrophobic interactions, resulting in the changed chemical structure and spatial conformation of the enzymes. This decreased enzyme activity. This result suggested that EGC has the potential to inhibit two key glycoside hydrolases, and it would be beneficial to incorporate EGC into functional foods for diabetics.
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Affiliation(s)
| | | | | | | | - Xiangting Wu
- College of Life and Environmental Sciences, Wenzhou University, Wenzhou, China
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A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman. Foods 2022; 11:foods11182928. [PMID: 36141056 PMCID: PMC9498461 DOI: 10.3390/foods11182928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/05/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022] Open
Abstract
Fixation is the most critical step in the green tea process. Hence, this study developed a rapid and accurate moisture content detection for the green tea fixation process based on near-infrared spectroscopy and computer vision. Specifically, we created a quantitative moisture content prediction model appropriate for the processing of green tea fixation. First, we collected spectrum and image information of green tea fixation leaves, utilizing near-infrared spectroscopy and computer vision. Then, we applied the partial least squares regression (PLSR), support vector regression (SVR), Elman neural network (ENN), and Elman neural network based on whale optimization algorithm (WOA-ENN) methods to build the prediction models for single data (data from a single sensor) and mid-level data fusion, respectively. The results revealed that the mid-level data fusion strategy combined with the WOA-ENN model attained the best effect. Namely, the prediction set correlation coefficient (Rp) was 0.9984, the root mean square error of prediction (RMSEP) was 0.0090, and the relative percent deviation (RPD) was 17.9294, highlighting the model’s excellent predictive performance. Thus, this study identified the feasibility of predicting the moisture content in the process of green tea fixation by miniaturized near-infrared spectroscopy. Moreover, in establishing the model, the whale optimization algorithm was used to overcome the defect whereby the Elman neural network falls into the local optimum. In general, this study provides technical support for rapid and accurate moisture content detection in green tea fixation.
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14
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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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15
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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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16
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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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17
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Shen S, Hua J, Zhu H, Yang Y, Deng Y, Li J, Yuan H, Wang J, Zhu J, Jiang Y. Rapid and real-time detection of moisture in black tea during withering using micro-near-infrared spectroscopy. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112970] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Zhang S, Zuo Y, Wu Q, Wang J, Ban L, Yang H, Bai Z. Development and Validation of Near-Infrared Methods for the Quantitation of Caffeine, Epigallocatechin-3-gallate, and Moisture in Green Tea Production. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2021; 2021:9563162. [PMID: 34820146 PMCID: PMC8608528 DOI: 10.1155/2021/9563162] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 10/18/2021] [Accepted: 10/20/2021] [Indexed: 06/13/2023]
Abstract
The quality of tea leaves (e.g., their color, appearance, and taste) can be directly influenced by the tea production process, which is closely connected with the content of a number of chemical components formed during the production of the tea leaves. However, the production process is now controlled by people's experience, making its quality significantly different. NIRS is a time-saving, cost-saving, and nondestructive method. Therefore, it is necessary to introduce NIRS technology into the quality control of the tea production process. In this study, a quantitative analysis model of caffeine, epigallocatechin-3-gallate (EGCG), and moisture content was established by near-infrared spectroscopy (NIRS) which was united simultaneously with partial least squares (PLSR) for online process monitoring of tea production. The model parameters show that the established model has fine robustness and outstanding measuring accuracy. Then, the feasibility of the established method is verified by the traditional method. Through the verification of the precision of the instrument and the stability of the sample, it is clarified that the model can be further utilized to monitor tea product quality online in a productive process.
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Affiliation(s)
- Shengsheng Zhang
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Yamin Zuo
- School of Basic Medical Sciences, Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, 30 Renmin South Rd, Shiyan, Hubei 442000, China
| | - Qing Wu
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Jiao Wang
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Lin Ban
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Huili Yang
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Zhiwen Bai
- The Guizhou Gui Tea (Group) Co. Ltd, Huaxi District, Guiyang, Guizhou 550001, China
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19
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Zuo Y, Tan G, Xiang D, Chen L, Wang J, Zhang S, Bai Z, Wu Q. Development of a novel green tea quality roadmap and the complex sensory-associated characteristics exploration using rapid near-infrared spectroscopy technology. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 258:119847. [PMID: 33940571 DOI: 10.1016/j.saa.2021.119847] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 04/11/2021] [Accepted: 04/13/2021] [Indexed: 06/12/2023]
Abstract
Nondestructive instrumental identification of the green tea quality instead of professional human panel tests is highly desired for industrial application recently. The special flavor is a key quality-trait that influence consumer preference. However, flavonoids, as well as sensory-associated compounds, which play a critical role in the quality-traits profile of green tea samples have been poorly investigated. In this study, we were proposing an objective and accurate near infrared spectroscopy (NIRS) profile to support quality control within the entire green tea sensory evaluation chain, the complexity of green tea samples' sensory analysis was performed by two complementary methods: the standard calculation and the novel NIRS roadmap coupled with chemometrics. The green tea samples' physical quality, gustatory index, and nutritional index were measured respectively, which taking into consideration the gustatory evaluation of green tea for five commercially representative overall quality ("very bad", "bad", "regular", "good" and "excellent"). Our findings highlight the underexplored role of NIRS in chemical-to-sensory relationships and its widespread importance and utility in green tea quality improvement. Collectively, the comprehensive characterization of sensory-associated attribution allowed the identification of a wide array of spectrometric features, mostly related to moisture, soluble solids (SS), tea polyphenol (TPP), epigallocatechin gallate (EGCG), epicatechin (EC) and tea polysaccharide (TPS), which can be used as putative biomarkers to rapidly evaluate the green tea flavor variations related to rank differences. Otherwise, the NIRS' data were split into the calibration (n = 80) and prediction (n = 40) set independently, which showed high correlation coefficient with Rp-values of 0.9024, 0.9020 in physical and total cup quality, respectively. In this research, we demonstrated that NIRS was an easily-generated strategy and able to close the loop to feedback into the process for advanced process control. However, the established models should be improved by more green tea samples from different regions.
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Affiliation(s)
- Yamin Zuo
- School of Basic Medical Sciences, Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, 30 Renmin South Rd, Shiyan, Hubei 442000, China; Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Gaohao Tan
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Di Xiang
- The Yunnan Tea Chamber of Commerce, Panlong District, Kunming, Yunnan 650051, China
| | - Ling Chen
- The Department of Tea, Guizhou Vocational College of Agriculture, 3 Huangshi Rd, Qingzhen, Guizhou 551400, China
| | - Jiao Wang
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Shengsheng Zhang
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Zhiwen Bai
- The Guizhou Gui Tea (Group) Co. Ltd, Huaxi District, Guiyang, Guizhou 550001, China.
| | - Qing Wu
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China; Innovation Laboratory, the Third Experiment Middle School in Guiyang, Guiyang, Guizhou 550001, China.
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20
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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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21
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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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22
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Kasemsumran S, Apiwatanapiwat W, Ngowsuwan K, Jungtheerapanich S. Rapid selection of Andrographis paniculata medicinal plant materials based on major bioactive using near-infrared spectroscopy. ACTA ACUST UNITED AC 2021; 75:5633-5644. [PMID: 34177074 PMCID: PMC8215322 DOI: 10.1007/s11696-021-01746-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 06/17/2021] [Indexed: 11/25/2022]
Abstract
Abstract The quantitative analysis of andrographolides in Andrographis paniculata plant materials is essential for pharmaceutical factories. This analysis cannot be done for all samples due to the conventional process using the extraction and HPLC methods requires a long analysis time and sample destruction. Therefore, near-infrared spectroscopy (NIRS) was employed to classify the class of A. paniculata and to determine the content of two active ingredients, andrographolide (AP1) and dehydroandrographolide (AP3) in A. paniculata, rapidly and non-destructively. One hundred twenty dried powder samples were obtained from aerial parts, branches, leaves, and branches mixed with leaves. The NIR absorption scans were collected from a broad spectral region (1000–2500 nm). Then, the scanned samples were extracted and analyzed for their AP1 and AP3 contents using an HPLC reference method. The success classification model based on AP1 level was developed using the second derivative pretreated NIR spectra of the entire wavelength region using the Partial Least Squares-Discriminant Analysis (PLS-DA) method. The NIR calibration models were developed and tested for quantitative analysis with 50 independent samples. The models were identified for the analysis of the AP1 content with excellent performance (correlation coefficient (R) = 0.98; standard error of validation (SEV) = 0.24%) and for the analysis of the AP3 content at a good level of efficiency (R = 0.93; SEV = 0.15%). This study showed that NIR spectroscopic method offers rapid analysis for the selection of A. paniculata that meets the requirement in bioactive amount. Graphic Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1007/s11696-021-01746-0.
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Affiliation(s)
- Sumaporn Kasemsumran
- Kasetsart Agricultural and Agro-Industrial Product Improvement Institute, Kasetsart University, 50, Lat Yao, Chatuchak, Bangkok, 10900 Thailand
| | - Waraporn Apiwatanapiwat
- Kasetsart Agricultural and Agro-Industrial Product Improvement Institute, Kasetsart University, 50, Lat Yao, Chatuchak, Bangkok, 10900 Thailand
| | - Krairuek Ngowsuwan
- Kasetsart Agricultural and Agro-Industrial Product Improvement Institute, Kasetsart University, 50, Lat Yao, Chatuchak, Bangkok, 10900 Thailand
| | - Sunee Jungtheerapanich
- Kasetsart Agricultural and Agro-Industrial Product Improvement Institute, Kasetsart University, 50, Lat Yao, Chatuchak, Bangkok, 10900 Thailand
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23
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Woodley SB, Mould RR, Sahuri-Arisoylu M, Kalampouka I, Booker A, Bell JD. Mitochondrial Function as a Potential Tool for Assessing Function, Quality and Adulteration in Medicinal Herbal Teas. Front Pharmacol 2021; 12:660938. [PMID: 33981240 PMCID: PMC8107435 DOI: 10.3389/fphar.2021.660938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 03/29/2021] [Indexed: 11/13/2022] Open
Abstract
Quality control has been a significant issue in herbal medicine since herbs became widely used to heal. Modern technologies have improved the methods of evaluating the quality of medicinal herbs but the methods of adulterating them have also grown in sophistication. In this paper we undertook a comprehensive literature search to identify the key analytical techniques used in the quality control of herbal medicine, reviewing their uses and limitations. We also present a new tool, based on mitochondrial profiling, that can be used to measure medicinal herbal quality. Besides being fundamental to the energy metabolism required for most cellular activities, mitochondria play a direct role in cellular signalling, apoptosis, stress responses, inflammation, cancer, ageing, and neurological function, mirroring some of the most common reasons people take herbal medicines. A fingerprint of the specific mitochondrial effects of medicinal herbs can be documented in order to assess their potential efficacy, detect adulterations that modulate these effects and determine the relative potency of batches. Furthermore, through this method it will be possible to assess whole herbs or complex formulas thus avoiding the issues inherent in identifying active ingredients which may be complex or unknown. Thus, while current analytical methods focus on determining the chemical quality of herbal medicines, including adulteration and contamination, mitochondrial functional analysis offers a new way of determining the quality of plant derived products that is more closely linked to the biological activity of a product and its potential clinical effectiveness.
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Affiliation(s)
- Steven B Woodley
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, United Kingdom
| | - Rhys R Mould
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, United Kingdom
| | - Meliz Sahuri-Arisoylu
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, United Kingdom.,Health Innovation Ecosystem, University of Westminster, London, United Kingdom
| | - Ifigeneia Kalampouka
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, United Kingdom
| | - Anthony Booker
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, United Kingdom.,Research Group 'Pharmacognosy and Phytotherapy', UCL School of Pharmacy, London, United Kingdom
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London, United Kingdom
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24
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Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves. Sci Rep 2021; 11:4169. [PMID: 33603126 PMCID: PMC7892543 DOI: 10.1038/s41598-021-83847-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 02/09/2021] [Indexed: 01/31/2023] Open
Abstract
Spectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.
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25
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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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26
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Wang YJ, Li TH, Li LQ, Ning JM, Zhang ZZ. Evaluating taste-related attributes of black tea by micro-NIRS. J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2020.110181] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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27
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Chen G, Zhang X, Wu Z, Su J, Cai G. An efficient tea quality classification algorithm based on near infrared spectroscopy and random Forest. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13604] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Guikun Chen
- Computer Engineering College Jimei University Xiamen China
| | | | - Zebiao Wu
- Computer Engineering College Jimei University Xiamen China
| | - Jinhe Su
- Computer Engineering College Jimei University Xiamen China
| | - Guorong Cai
- Computer Engineering College Jimei University Xiamen China
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28
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Macavilca EA, Condezo-Hoyos L. Assessment of total antioxidant capacity of altiplano colored quinoa (Chenopodium quinoa willd) by visible and near-infrared diffuse reflectance spectroscopy and chemometrics. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.110182] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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29
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Sanaeifar A, Huang X, Chen M, Zhao Z, Ji Y, Li X, He Y, Zhu Y, Chen X, Yu X. Nondestructive monitoring of polyphenols and caffeine during green tea processing using Vis-NIR spectroscopy. Food Sci Nutr 2020; 8:5860-5874. [PMID: 33282238 PMCID: PMC7684591 DOI: 10.1002/fsn3.1861] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 08/12/2020] [Accepted: 08/12/2020] [Indexed: 11/06/2022] Open
Abstract
Increasing consumption of green tea is attributed to the beneficial effects of its constituents, especially polyphenols, on human health, which can be varied during leaf processing. Processing technology has the most important effect on green tea quality. This study investigated the system dynamics of eight catechins, gallic acid, and caffeine in the processing of two varieties of tea, from fresh leaves to finished tea. It was found that complex biochemical changes can occur through hydrolysis under different humidity and heating conditions during the tea processing. This process had a significant effect on catechin composition in the finished tea. The potential application of visible and near-infrared (Vis-NIR) spectroscopy for fast monitoring polyphenol and caffeine contents in tea leaves during the processing procedure has been investigated. It was found that a combination of PCA (principal component analysis) and Vis-NIR spectroscopy can successfully classify the two varieties of tea samples and the five tea processing procedures, while quantitative determination of the constituents was realized by combined regression analysis and Vis-NIR spectra. Furthermore, successive projections algorithm (SPA) was proposed to extract and optimize spectral variables that reflected the molecular characteristics of the constituents for the development of determination models. Modeling results showed that the models had good predictability and robustness based on the extracted spectral characteristics. The coefficients of determination for all calibration sets and prediction sets were higher than 0.862 and 0.834, respectively, which indicated high capability of Vis-NIR spectroscopy for the determination of the constituents during the leaf processing. Meanwhile, this analytical method could quickly monitor quality characteristics and provide feedback for real-time controlling of tea processing machines. Furthermore, the study on complex biochemical changes that occurred during the tea processing would provide a theoretical basis for improving the content of quality components and effective controlling processes.
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Affiliation(s)
- Alireza Sanaeifar
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Xinyao Huang
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Mengyuan Chen
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Zhangfeng Zhao
- College of Mechanical EngineeringZhejiang University of TechnologyHangzhouChina
| | - Yifan Ji
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Xiaoli Li
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Yong He
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Yi Zhu
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Xi Chen
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Xinxin Yu
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
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30
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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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31
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Hao Z, Zheng Q, Jin L, Zhou S, Chen H, Liu X, Lu C. Rapid measurement of total polyphenol content in tea by kinetic matching approach on microfluidic paper-based analytical devices. Food Chem 2020; 342:128368. [PMID: 33158681 DOI: 10.1016/j.foodchem.2020.128368] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 09/13/2020] [Accepted: 10/09/2020] [Indexed: 11/25/2022]
Abstract
In this work, a facile kinetic matching approach for total polyphenol content (TPC) measurement was developed based on the adoption of microfluidic paper-based analytical devices with symmetric channel distribution. A set of Folin-Ciocalteu reactions performed on the same paper chip were activated all at the same time through synchronized filling of sodium carbonate solution among individual channels. Gallic acid was found valid as a standard compound for kinetic matching measurement of tea samples. TPC of tea infusions was successfully measured within ten minutes without any complexed time control procedure needed. Under the optimized conditions, the new developed method showed good linearity in the TPC range of 10-100 mg/L (r > 0.9955) and the inter-chip precision was 5.6% (n = 11). The results measured with the new developed approach were in good agreement with those with the conventional FC assay.
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Affiliation(s)
- Zhenxia Hao
- Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; Key Laboratory of Tea Quality and Safety Control, Ministry of Agriculture and Rural Affairs, Hangzhou 310008, China; Laboratory of Quality and Safety Risk Assessment for Tea Products (Hangzhou), Ministry of Agriculture and Rural Affairs, Hangzhou 310008, China.
| | - Qinqin Zheng
- Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; Graduate School of Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| | - Lili Jin
- Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; Key Laboratory of Tea Quality and Safety Control, Ministry of Agriculture and Rural Affairs, Hangzhou 310008, China; Laboratory of Quality and Safety Risk Assessment for Tea Products (Hangzhou), Ministry of Agriculture and Rural Affairs, Hangzhou 310008, China.
| | - Sujuan Zhou
- Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; Laboratory of Quality and Safety Risk Assessment for Tea Products (Hangzhou), Ministry of Agriculture and Rural Affairs, Hangzhou 310008, China.
| | - Hongping Chen
- Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; Key Laboratory of Tea Quality and Safety Control, Ministry of Agriculture and Rural Affairs, Hangzhou 310008, China; Laboratory of Quality and Safety Risk Assessment for Tea Products (Hangzhou), Ministry of Agriculture and Rural Affairs, Hangzhou 310008, China.
| | - Xin Liu
- Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; Key Laboratory of Tea Quality and Safety Control, Ministry of Agriculture and Rural Affairs, Hangzhou 310008, China; Laboratory of Quality and Safety Risk Assessment for Tea Products (Hangzhou), Ministry of Agriculture and Rural Affairs, Hangzhou 310008, China.
| | - Chengyin Lu
- Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; Key Laboratory of Tea Quality and Safety Control, Ministry of Agriculture and Rural Affairs, Hangzhou 310008, China; Laboratory of Quality and Safety Risk Assessment for Tea Products (Hangzhou), Ministry of Agriculture and Rural Affairs, Hangzhou 310008, China.
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32
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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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Quantitative Analysis and Discrimination of Partially Fermented Teas from Different Origins Using Visible/Near-Infrared Spectroscopy Coupled with Chemometrics. SENSORS 2020; 20:s20195451. [PMID: 32977413 PMCID: PMC7582835 DOI: 10.3390/s20195451] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/16/2020] [Accepted: 09/20/2020] [Indexed: 12/24/2022]
Abstract
Partially fermented tea such as oolong tea is a popular drink worldwide. Preventing fraud in partially fermented tea has become imperative to protect producers and consumers from possible economic losses. Visible/near-infrared (VIS/NIR) spectroscopy integrated with stepwise multiple linear regression (SMLR) and support vector machine (SVM) methods were used for origin discrimination of partially fermented tea from Vietnam, China, and different production areas in Taiwan using the full visible NIR wavelength range (400-2498 nm). The SMLR and SVM models achieved satisfactory results. Models using data from chemical constituents' specific wavelength ranges exhibited a high correlation with the spectra of teas, and the SMLR analyses improved discrimination of the types and origins when performing SVM analyses. The SVM models' identification accuracies regarding different production areas in Taiwan were effectively enhanced using a combination of the data within specific wavelength ranges of several constituents. The accuracy rates were 100% for the discrimination of types, origins, and production areas of tea in the calibration and prediction sets using the optimal SVM models integrated with the specific wavelength ranges of the constituents in tea. NIR could be an effective tool for rapid, nondestructive, and accurate inspection of types, origins, and production areas of teas.
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Wang YJ, Li TH, Li LQ, Ning JM, Zhang ZZ. Micro-NIR spectrometer for quality assessment of tea: Comparison of local and global models. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 237:118403. [PMID: 32361319 DOI: 10.1016/j.saa.2020.118403] [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: 03/06/2020] [Revised: 04/10/2020] [Accepted: 04/20/2020] [Indexed: 05/25/2023]
Abstract
Near-infrared (NIR) spectroscopy is an effective tool for analyzing components relevant to tea quality, especially catechins and caffeine. In this study, we predicted catechins and caffeine content in green and black tea, the main consumed tea types worldwide, by using a micro-NIR spectrometer connected to a smartphone. Local models were established separately for green and black tea samples, and these samples were combined to create global models. Different spectral preprocessing methods were combined with linear partial-least squares regression and nonlinear support vector machine regression (SVR) to obtain accurate models. Standard normal variate (SNV)-based SNV-SVR models exhibited accurate predictive performance for both catechins and caffeine. For the prediction of quality components of tea, the global models obtained results comparable to those of the local models. The optimal global models for catechins and caffeine were SNV-SVR and particle swarm optimization (PSO)-simplified SNV-PSO-SVR, which achieved the best predictive performance with correlation coefficients in prediction (Rp) of 0.98 and 0.93, root mean square errors in prediction of 9.83 and 2.71, and residual predictive deviations of 4.44 and 2.60, respectively. Therefore, the proposed low-price, compact, and portable micro-NIR spectrometer connected to smartphones is an effective tool for analyzing tea quality.
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Affiliation(s)
- Yu-Jie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Tie-Han Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Lu-Qing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Jing-Ming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
| | - Zheng-Zhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
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35
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Guo Z, Barimah AO, Shujat A, Zhang Z, Ouyang Q, Shi J, El-Seedi HR, Zou X, Chen Q. Simultaneous quantification of active constituents and antioxidant capability of green tea using NIR spectroscopy coupled with swarm intelligence algorithm. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.109510] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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36
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FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples. Foods 2020; 9:foods9060827. [PMID: 32599832 PMCID: PMC7353657 DOI: 10.3390/foods9060827] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 06/06/2020] [Accepted: 06/08/2020] [Indexed: 11/17/2022] Open
Abstract
Daily consumption of caffeine in coffee, tea, chocolate, cocoa, and soft drinks has gained wide and plentiful public and scientific attention over the past few decades. The concentration of caffeine in vivo is a crucial indicator of some disorders—for example, kidney malfunction, heart disease, increase of blood pressure and alertness—and can cause some severe diseases including type 2 diabetes mellitus (DM), stroke risk, liver disease, and some cancers. In the present study, near-infrared spectroscopy (NIRS) coupled with partial least-squares regression (PLSR) was proposed as an alternative method for the quantification of caffeine in 25 commercially available tea samples consumed in Oman. This method is a fast, complementary technique to wet chemistry procedures as well as to high-performance liquid chromatography (HPLC) methods for the quantitative analysis of caffeine in tea samples because it is reagent-less and needs little or no pre-treatment of samples. In the current study, the partial least-squares (PLS) algorithm was built by using the near-infrared NIR spectra of caffeine standards prepared in tea samples scanned by a Frontier NIR spectrophotometer (L1280034) by PerkinElmer. Spectra were collected in the absorption mode in the wavenumber range of 10,000–4000 cm−1, using a 0.2 mm path length and CaF2 sealed cells with a resolution of 2 cm−1. The NIR results for the contents of caffeine in tea samples were also compared with results obtained by HPLC analysis. Both techniques provided good results for predicting the caffeine contents in commercially available tea samples. The results of the proposed study show that the suggested FT-NIRS coupled with PLS regression algorithun has a high potential to be routinely used for the quick and reproducible analysis of caffeine contents in tea samples. For the NIR method, the limit of quantification (LOQ) was estimated as 10 times the error of calibration (root mean square error of calibration (RMSECV)) of the model; thus, RMSEC was calculated as 0.03 ppm and the LOQ as 0.3 ppm.
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Wei Q, Liu T, Pu H, Sun D. Development of a fluorescent m
icrowave‐assisted
synthesized carbon dots/Cu
2+
probe for rapid detection of tea polyphenols. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13419] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Qingyi Wei
- School of Food Science and EngineeringSouth China University of Technology Guangzhou China
- Academy of Contemporary Food EngineeringSouth China University of Technology, Guangzhou Higher Education Mega Center Guangzhou China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre Guangzhou China
| | - Ting Liu
- School of Food Science and EngineeringSouth China University of Technology Guangzhou China
- Academy of Contemporary Food EngineeringSouth China University of Technology, Guangzhou Higher Education Mega Center Guangzhou China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre Guangzhou China
| | - Hongbin Pu
- School of Food Science and EngineeringSouth China University of Technology Guangzhou China
- Academy of Contemporary Food EngineeringSouth China University of Technology, Guangzhou Higher Education Mega Center Guangzhou China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre Guangzhou China
| | - Da‐Wen Sun
- School of Food Science and EngineeringSouth China University of Technology Guangzhou China
- Academy of Contemporary Food EngineeringSouth China University of Technology, Guangzhou Higher Education Mega Center Guangzhou China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre Guangzhou China
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science CentreUniversity College Dublin, National University of Ireland Dublin Ireland
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Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery. SENSORS 2019; 20:s20010050. [PMID: 31861804 PMCID: PMC6983139 DOI: 10.3390/s20010050] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 12/15/2019] [Accepted: 12/18/2019] [Indexed: 11/17/2022]
Abstract
Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.
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Xia J, Wang D, Liang P, Zhang D, Du X, Ni D, Yu Z. Vibrational (FT-IR, Raman) analysis of tea catechins based on both theoretical calculations and experiments. Biophys Chem 2019; 256:106282. [PMID: 31756664 DOI: 10.1016/j.bpc.2019.106282] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 11/10/2019] [Accepted: 11/12/2019] [Indexed: 12/27/2022]
Abstract
Structural investigations, based on density functional theory (DFT) calculations, are performed on tea catechins, including 4-aminobutyric acid (GABA), L-theanine (Thea), caffeine (CAF), theobromine (TB), theophylline (TP), catechin (C), epicatechin (EC), gallocatechin (GC), epigallocatechin (EGC), catechin gallate (CG), epicatechin gallate (ECG), gallocatechin gallate (GCG) and epigallocatechin gallate (EGCG). With an identified lowest energy conformer of investigated molecules, FTIR and FT-Raman spectra have been assigned according to DFT calculations in the way of B3LYP/6-31 + G (d, p). Normal spectra of these catechin powders are also measured by Raman spectrometers. There is a kind of everlasting correlation between experimental results and theoretical data. And our research has also obtained a clear evidence for reliable assignments of vibrational bands, bringing great feasibility to the rapid tea catechin detection.
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Affiliation(s)
- Jing Xia
- Key Laboratory of Horticultural Plant Biology, Ministry of Education, College of Horticulture & Forestry Sciences, Huazhong Agricultural University, 430070 Wuhan, China; College of Optical and Electronic Technology, China Jiliang University, 310018 Hangzhou, China
| | - Dan Wang
- College of Optical and Electronic Technology, China Jiliang University, 310018 Hangzhou, China
| | - Pei Liang
- College of Optical and Electronic Technology, China Jiliang University, 310018 Hangzhou, China.
| | - De Zhang
- Key Laboratory of Horticultural Plant Biology, Ministry of Education, College of Horticulture & Forestry Sciences, Huazhong Agricultural University, 430070 Wuhan, China
| | - Xiaoqing Du
- School of Materials Science and Energy Engineering, Foshan University, Foshan, Guangdong 528000, China
| | - Dejiang Ni
- Key Laboratory of Horticultural Plant Biology, Ministry of Education, College of Horticulture & Forestry Sciences, Huazhong Agricultural University, 430070 Wuhan, China
| | - Zhi Yu
- Key Laboratory of Horticultural Plant Biology, Ministry of Education, College of Horticulture & Forestry Sciences, Huazhong Agricultural University, 430070 Wuhan, China.
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Wang J, Zareef M, He P, Sun H, Chen Q, Li H, Ouyang Q, Guo Z, Zhang Z, Xu D. Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2019; 99:5019-5027. [PMID: 30977141 DOI: 10.1002/jsfa.9743] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 04/07/2019] [Accepted: 04/09/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND The study reports a portable near infrared (NIR) spectroscopy system coupled with chemometric algorithms for prediction of tea polyphenols and amino acids in order to index matcha tea quality. RESULTS Spectral data were preprocessed by standard normal variate (SNV), mean center (MC) and first-order derivative (1st D) tests. The data were then subjected to full spectral partial least squares (PLS) and four variable selection algorithms, such as random frog partial least square (RF-PLS), synergy interval partial least square (Si-PLS), genetic algorithm-partial least square (GA-PLS) and competitive adaptive reweighted sampling partial least square (CARS-PLS). RF-PLS was established and identified as the optimum model based on the values of the correlation coefficients of prediction (RP ), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), which were 0.8625, 0.82% and 2.13, and 0.9662, 0.14% and 3.83, respectively, for tea polyphenols and amino acids. The content range of tea polyphenols and amino acids in matcha tea samples was 8.51-14.58% and 2.10-3.75%, respectively. The quality of matcha tea was successfully classified with an accuracy rate of 83.33% as qualified, unqualified and excellent grade. CONCLUSION The proposed method can be used as a rapid, accurate and non-destructive platform to classify various matcha tea samples based on the ratio of tea polyphenols to amino acids. © 2019 Society of Chemical Industry.
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Affiliation(s)
- Jingjing Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Peihuan He
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Hao Sun
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Zhiming Guo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Delian Xu
- National Tea Industry Technology System, Comprehensive Experimental Station of Wuxi, Wuxi, China
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41
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Recent Progress in Rapid Analyses of Vitamins, Phenolic, and Volatile Compounds in Foods Using Vibrational Spectroscopy Combined with Chemometrics: a Review. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01573-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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42
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Yeong TJ, Pin Jern K, Yao LK, Hannan MA, Hoon STG. Applications of Photonics in Agriculture Sector: A Review. Molecules 2019; 24:E2025. [PMID: 31137897 PMCID: PMC6571790 DOI: 10.3390/molecules24102025] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 05/10/2019] [Accepted: 05/12/2019] [Indexed: 11/17/2022] Open
Abstract
The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.
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Affiliation(s)
- Tan Jin Yeong
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - Ker Pin Jern
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - Lau Kuen Yao
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - M A Hannan
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - Shirley Tang Gee Hoon
- Microbiology Unit, Department of Pre-clinical, International Medical School, Management and Science University, University Drive, Off Persiaran Olahraga, Seksyen 13, Shah Alam 40100, Selangor, Malaysia.
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Páscoa RNMJ, Teixeira AM, Sousa C. Antioxidant capacity of Camellia japonica cultivars assessed by near- and mid-infrared spectroscopy. PLANTA 2019; 249:1053-1062. [PMID: 30506418 DOI: 10.1007/s00425-018-3062-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 11/24/2018] [Indexed: 06/09/2023]
Abstract
Camellia japonica antioxidant capacity highly differs among its cultivars and could be successfully predicted by near- and mid-infrared spectroscopy. Camellia japonica is a Theaceae family species which are mainly used as an ornamental plant due to its colourful flowers presenting over than 32,000 recognized cultivars. However, this species have been somehow neglected due to the popular tea source, Camellia sinensis. In this study, the antioxidant profile (total phenolic and flavonoid content and total antioxidant capacity-TPC, TFC and TAC) of 31 C. japonica cultivars leaves was determined and further assessed by near- and mid-infrared spectroscopy. The leaves' antioxidant profile was revealed to be highly dependent on the cultivars analysed being in some cases distinct even for different trees of the same cultivar. Near- and mid-infrared spectroscopy proved to be suitable techniques to predict the total phenolic and flavonoid content as well as the total antioxidant capacity. The best results were obtained with near-infrared spectroscopy whose root mean square error of the prediction set samples was of 5.7 mg of gallic acid/g dry leaf; 3.5 mg catechin/g dry leaf and 3.3 mM Trolox/g dry leaf for TPC, TFC and TAC (with coefficients of the determinations equal to or higher than 0.93). Moreover, the range error ratios were higher than 15 meaning that the developed partial least-squares models are very good for calibration and quantification determinations according to the guidelines for near-infrared models development and maintenance. In this work, the antioxidant profile of several C. japonica cultivars leaves was determined for the first time, being that a rapid and low cost spectroscopic-based method was also proposed for its determination.
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Affiliation(s)
- Ricardo N M J Páscoa
- LAQV/REQUIMTE, Departamento de Ciências Químicas, Faculdade de Farmácia, Universidade do Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313, Porto, Portugal
| | - A Margarida Teixeira
- LAQV/REQUIMTE, Departamento de Ciências Químicas, Faculdade de Farmácia, Universidade do Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313, Porto, Portugal
| | - Clara Sousa
- LAQV/REQUIMTE, Departamento de Ciências Químicas, Faculdade de Farmácia, Universidade do Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313, Porto, Portugal.
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Teixeira GHDA, Morelli L, Ma S, Stewart A, O'Keefe SF. Photoprotective effect of mycosporine-like aminoacids extracts on natamycin, saffron carotenoids and epigallocatechin gallate in acidified beverages exposed to different light sources. Int J Food Sci Technol 2019. [DOI: 10.1111/ijfs.13956] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Gustavo Henrique de Almeida Teixeira
- Faculdade de Ciências Agrárias e Veterinárias (FCAV); Campus Jaboticabal, Depto. de Produção Vegetal; Universidade Estadual Paulista (UNESP); Via de acesso Prof. Paulo Donato Castellane, s/n. CEP: 14.884-900 Jaboticabal SP Brazil
| | - Luiza Morelli
- Faculdade de Engenharia de Alimentos; Departamento de Tecnologia de Alimentos; Universidade Estadual de Campinas (UNICAMP); Rua Monteiro Lobato, 80, Cidade Universitária, CEP: 13.083-862 Campinas SP Brazil
| | - Sihui Ma
- Department of Food Science and Technology; Virginia Polytechnic Institute and State University (Virginia Tech); Zip code: 24061. Blacksburg 402A HABB1 VA USA
| | - Amanda Stewart
- Department of Food Science and Technology; Virginia Polytechnic Institute and State University (Virginia Tech); Zip code: 24061. Blacksburg 402A HABB1 VA USA
| | - Sean Francis O'Keefe
- Department of Food Science and Technology; Virginia Polytechnic Institute and State University (Virginia Tech); Zip code: 24061. Blacksburg 402A HABB1 VA USA
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Li C, Guo H, Zong B, He P, Fan F, Gong S. Rapid and non-destructive discrimination of special-grade flat green tea using Near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 206:254-262. [PMID: 30121024 DOI: 10.1016/j.saa.2018.07.085] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 07/27/2018] [Accepted: 07/30/2018] [Indexed: 06/08/2023]
Abstract
Special-grade green tea is a premium tea product with the best rank and high value. Special-grade green tea is normally classified by panel sensory evaluation which is time and sample costly. Near-infrared spectroscopy is considered as a promising rapid and non-destructive analytical technique for food quality evaluation and grading. This study established a discrimination method of special-grade flat green tea using Near-infrared spectroscopy. Full spectrum was used for partial least squares (PLS) modelling to predict the sensory scores of green tea, while specific spectral regions were used for synergy interval-partial least squares (siPLS) modelling. The best performance was achieved by the siPLS model of MSC + Mean Centering pretreatments and subintervals from 15 intervals. The optimal model was used to discriminate special-grade flat green tea with the prediction accuracy of 97% and 93% in the cross-validation and external validation respectively. The chemical compositions of green tea samples were also analyzed, including polyphenols (total polyphenols, catechins and flavonol glycosides), alkaloids and amino acids. Principal components analysis result showed that there is potential correlation between specific spectral regions and the presence of polyphenols and alkaloids. Thus, NIR technique is a practical method for rapid and non-destructive discrimination of special-grade flat green tea with chemical support.
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Affiliation(s)
- Chunlin Li
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Haowei Guo
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Bangzheng Zong
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Puming He
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Fangyuan Fan
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China.
| | - Shuying Gong
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China.
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Mishra P, Nordon A, Tschannerl J, Lian G, Redfern S, Marshall S. Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products. J FOOD ENG 2018. [DOI: 10.1016/j.jfoodeng.2018.06.015] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Wang J, Wang Y, Cheng J, Wang J, Sun X, Sun S, Zhang Z. Enhanced cross-category models for predicting the total polyphenols, caffeine and free amino acids contents in Chinese tea using NIR spectroscopy. Lebensm Wiss Technol 2018. [DOI: 10.1016/j.lwt.2018.05.012] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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Hu O, Xu L, Fu H, Yang T, Fan Y, Lan W, Tang H, Wu Y, Ma L, Wu D, Wang Y, Xiao Z, She Y. "Turn-off" fluorescent sensor based on double quantum dots coupled with chemometrics for highly sensitive and specific recognition of 53 famous green teas. Anal Chim Acta 2018; 1008:103-110. [PMID: 29420939 DOI: 10.1016/j.aca.2017.12.042] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 12/25/2017] [Accepted: 12/27/2017] [Indexed: 12/15/2022]
Abstract
Fluorescent "turn-off" sensors based on double quantum dots (QDs) has attracted increasing attention in the detection of many materials due to their properties such as more useful information, higher fluorescence efficiency and stability compared with the fluorescent "turn-off" sensors based on single QDs. In this work, highly sensitive and specific method for recognition of 53 different famous green teas was developed based on the fluorescent "turn-off" model with water-soluble ZnCdSe-CdTe double QDs. The fluorescence of the two QDs can be quenched by different teas with varying degrees, which results in the differences in positions and intensities of two peaks. By the combination of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 100% for prediction set, respectively. The fluorescent "turn-off" sensors based on the single QDs (either ZnCdSe QDs or CdTe QDs) coupled with PLSDA were also employed to recognize the 53 famous green teas with unsatisfactory results. Therefore, the fluorescent "turn-off" sensors based on the double QDs is more appropriate for the large-class-number classification (LCNC) of green teas. Herein, we have demonstrated, for the first time, that so many kinds of famous green teas can be discriminated by the "turn-off" model of double QDs combined with chemometrics, which has largely extended the capability of traditional fluorescence and chemometrics, as well as exhibits great potential to perform LCNC in other practical applications.
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Affiliation(s)
- Ou Hu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Lu Xu
- College of Material and Chemical Engineering, Tongren University, Tongren 554300, Guizhou, PR China
| | - Haiyan Fu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China.
| | - Tianming Yang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Yao Fan
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, PR China
| | - Wei Lan
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Hebing Tang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Yu Wu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Lixia Ma
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Di Wu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Yuan Wang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Zuobing Xiao
- School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, PR China
| | - Yuanbin She
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, PR China.
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Prediction of black tea fermentation quality indices using NIRS and nonlinear tools. Food Sci Biotechnol 2017; 26:853-860. [PMID: 30263613 DOI: 10.1007/s10068-017-0119-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 04/18/2017] [Accepted: 04/18/2017] [Indexed: 01/26/2023] Open
Abstract
Catechin content, the ratio of tea polyphenols and free amino acids (TP/FAA), as well as the ratio of theaflavins and thearubigins (TFs/TRs) are important biochemical indicators to evaluate fermentation quality. To achieve rapid determination of such biochemical indicators, synergy interval partial least square and extreme learning machine combined with an adaptive boosting algorithm, Si-ELM-AdaBoost algorithm, were used to establish quantitative analysis models between near infrared spectroscopy (NIRS) and catechin content and between TFs/TRs and TP/FAA, respectively. The results showed that prediction performance of the Si-ELM-AdaBoost mixed algorithm is superior than that of other models. The prediction results with root-mean-square error of prediction ranged from 0.006 to 0.563, the ratio performance deviation values exceeded 2.5, and predictive correlation coefficient values exceeded 0.9 in the prediction model of each biochemical indicator. NIRS combined with Si-ELM-AdaBoost mixed algorithm could be utilized for online monitoring of black tea fermentation. Meanwhile, the AdaBoost algorithm effectively improved the accuracy of the ELM model and could better approach the nonlinear continuous function.
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50
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Determination of Antioxidant Activity and Polyphenols Content in Chips by Raman and IR Spectroscopy. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-0972-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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