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Wang S, Chen X, Wang E, Zhang Y, Tang Y, Wei Y, He W. Comparison of Pivot Profile (PP), Rate-All-That-Apply (RATA), and Pivot-CATA for the sensory profiling of commercial Chinese tea products. Food Res Int 2023; 173:113419. [PMID: 37803757 DOI: 10.1016/j.foodres.2023.113419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 08/24/2023] [Accepted: 08/28/2023] [Indexed: 10/08/2023]
Abstract
Rapid sensory profiling methods relying on consumers' perceptions are getting prevalent and broadly utilized by labs and companies to supersede conventional sensory profiling methodologies. Till now, various intensity-based sensory methods such as the newly proposed Pivot-Check-All-That-Apply (CATA) are limitedly developed and compared. In this investigation, Pivot Profile (PP), Rate-All-That-Apply (RATA), and Pivot-CATA methods were applied and validated using tea consumers and commercial Chinese tea products as samples. Data from three approaches were collected, analyzed by correspondence analysis (CA), and used to compare the three methods assessing the panel assessment process, sensory maps, confidence ellipses, and practical applications. Pivot-CATA exhibited a high similarity with RATA (RV = 0.873), and a lower similarity with PP (RV = 0.629). Of the three intensity-related methods, confidence ellipses on the RATA sensory map were the smallest and overlapped the least. However, Pivot-CATA consumed less time in collecting data and its questionnaire was more friendly to participants compared with PP and made the difference in intensity of samples more noticeable to the participants than RATA due to the existence of the pivot sample. Its experimental versatility also allows for a wide range of applications, indicating that the Pivot-CATA is an approach with great promise for routine use.
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Affiliation(s)
- Shiqin Wang
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, Guangdong, China
| | - Xinlei Chen
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, Guangdong, China
| | - Enze Wang
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, Guangdong, China
| | - Yifang Zhang
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, Guangdong, China
| | - Yihang Tang
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, Guangdong, China
| | - Yujia Wei
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, Guangdong, China
| | - Wenmeng He
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, Guangdong, China.
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2
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An T, Wang Z, Li G, Fan S, Huang W, Duan D, Zhao C, Tian X, Dong C. Monitoring the major taste components during black tea fermentation using multielement fusion information in decision level. Food Chem X 2023; 18:100718. [PMID: 37397207 PMCID: PMC10314168 DOI: 10.1016/j.fochx.2023.100718] [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: 03/21/2023] [Revised: 05/14/2023] [Accepted: 05/18/2023] [Indexed: 07/04/2023] Open
Abstract
Hitherto, the intelligent detection of black tea fermentation quality is still a thought-provoking problem because of one-side sample information and poor model performance. This study proposed a novel method for the prediction of major chemical components including total catechins, soluble sugar and caffeine using hyperspectral imaging technology and electrical properties. The multielement fusion information were used to establish quantitative prediction models. The performance of model using multielement fusion information was better than that of model using single information. Subsequently, the stacking combination model using fusion data combined with feature selection algorithms for evaluating the fermentation quality of black tea. Our proposed strategy achieved better performance than classical linear and nonlinear algorithms, with the correlation coefficient of the prediction set (Rp) for total catechins, soluble sugar and caffeine being 0.9978, 0.9973 and 0.9560, respectively. The results demonstrated that our proposed strategy could effectively evaluate the fermentation quality of black tea.
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Affiliation(s)
- Ting An
- College of Engineering and Technology, Southwest University, Chongqing 400715, China
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250033, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Zheli Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Guanglin Li
- College of Engineering and Technology, Southwest University, Chongqing 400715, China
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Dandan Duan
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Chunjiang Zhao
- College of Engineering and Technology, Southwest University, Chongqing 400715, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Xi Tian
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Chunwang Dong
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250033, China
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3
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Silva Fernandes J, de Sousa Fernandes DD, Pistonesi MF, Gonçalves Dias Diniz PH. Tea authentication and determination of chemical constituents using digital image-based fingerprint signatures and chemometrics. Food Chem 2023; 421:136164. [PMID: 37099954 DOI: 10.1016/j.foodchem.2023.136164] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/29/2023] [Accepted: 04/11/2023] [Indexed: 04/28/2023]
Abstract
Tea (Camellia sinensis) fraud has been frequently identified and involves tampering with the labelling of inferior products or without geographical origin certification and even mixing them with superior quality teas to mask an adulteration. Consequently, economic losses and health damage to consumers are observed. Thus, a Chemometrics-assisted Color Histogram-based Analytical System (CACHAS) was employed a simple, cost-effective, reliable, and green analytical tool to screen the quality of teas. Data-Driven Soft Independent Modeling of Class Analogy was used to authenticate their geographical origin and category simultaneously, recognizing correctly all Argentinean and Sri Lankan black teas and Argentinean green teas. For the determination of moisture, total polyphenols, and caffeine, Partial Least Squares obtained satisfactory predictive abilities, with values of root mean squared error of prediction (RMSEP) of 0.50, 0.788, and 0.25 mg kg-1, rpred of 0.81, 0.902, and 0.81, and relative error of prediction (REP) of 6.38, 9.031, and 14.58%., respectively. CACHAS proved to be a good alternative tool for environmentally-friendly non-destructive chemical analysis.
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Affiliation(s)
- Jéssica Silva Fernandes
- Programa de Pós-Graduação em Química Pura e Aplicada, Universidade Federal do Oeste da Bahia, CEP 47810-059, Barreiras Bahia, Brasil
| | - David Douglas de Sousa Fernandes
- Departamento de Química, Centro de Ciências Exatas e da Natureza, Universidade Federal da Paraíba, CEP 58051-970 João Pessoa, Paraíba, Brasil
| | - Marcelo Fabián Pistonesi
- Universidad Nacional del Sur, INQUISUR, Departamento de Química, Zip Code 8000, Bahía Blanca, Buenos Aires, Argentina
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4
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Identifying the “Dangshan” Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image. Foods 2023; 12:foods12061178. [PMID: 36981105 PMCID: PMC10048714 DOI: 10.3390/foods12061178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/06/2023] [Accepted: 03/08/2023] [Indexed: 03/14/2023] Open
Abstract
The “Dangshan” pear woolliness response is a physiological disease that causes large losses for fruit farmers and nutrient inadequacies.The cause of this disease is predominantly a shortage of boron and calcium in the pear and water loss from the pear. This paper used the fusion of near-infrared Spectroscopy (NIRS) and Computer Vision Technology (CVS) to detect the woolliness response disease of “Dangshan” pears. This paper employs the merging of NIRS features and image features for the detection of “Dangshan” pear woolliness response disease. Near-infrared Spectroscopy (NIRS) reflects information on organic matter containing hydrogen groups and other components in various biochemical structures in the sample under test, and Computer Vision Technology (CVS) captures image information on the disease. This study compares the results of different fusion models. Compared with other strategies, the fusion model combining spectral features and image features had better performance. These fusion models have better model effects than single-feature models, and the effects of these models may vary according to different image depth features selected for fusion modeling. Therefore, the model results of fusion modeling using different image depth features are further compared. The results show that the deeper the depth model in this study, the better the fusion modeling effect of the extracted image features and spectral features. The combination of the MLP classification model and the Xception convolutional neural classification network fused with the NIR spectral features and image features extracted, respectively, was the best combination, with accuracy (0.972), precision (0.974), recall (0.972), and F1 (0.972) of this model being the highest compared to the other models. This article illustrates that the accuracy of the “Dangshan” pear woolliness response disease may be considerably enhanced using the fusion of near-infrared spectra and image-based neural network features. It also provides a theoretical basis for the nondestructive detection of several techniques of spectra and pictures.
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ZHANG Y, WANG J, LUO H, YANG J, WU X, WU Q, ZHONG Y. Rapid prediction of Yongchuan Xiuya tea quality by using near infrared spectroscopy coupled with chemometric methods. FOOD SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1590/fst.101122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Ying ZHANG
- Chongqing Academy of Agricultural Sciences, China
| | - Jie WANG
- Chongqing Academy of Agricultural Sciences, China
| | - Hongyu LUO
- Chongqing Academy of Agricultural Sciences, China
| | - Juan YANG
- Chongqing Academy of Agricultural Sciences, China
| | - Xiuhong WU
- Chongqing Academy of Agricultural Sciences, China
| | - Quan WU
- Chongqing Academy of Agricultural Sciences, China
| | - Yingfu ZHONG
- Chongqing Academy of Agricultural Sciences, China
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6
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Yin XL, Fu WJ, Chen Y, Zhou RF, Sun W, Ding B, Peng XT, Gu HW. GC-MS-based untargeted metabolomics reveals the key volatile organic compounds for discriminating grades of Yichang big-leaf green tea. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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7
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Wei K, Chen B, Li Z, Chen D, Liu G, Lin H, Zhang B. Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2022; 22:7764. [PMID: 36298114 PMCID: PMC9609479 DOI: 10.3390/s22207764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/25/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.
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Affiliation(s)
- Kaihua Wei
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Bojian Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Zejian Li
- Zhejiang Key Laboratory of Design and Intelligence and Digital Creativity, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Dongmei Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Guangyu Liu
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Hongze Lin
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
- Shangyu Institute of Science and Engineering, Hangzhou Dianzi University, Shaoxing 312000, China
| | - Baihua Zhang
- School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou 325000, China
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8
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Dissipation and processing factors of emamectin benzoate and tolfenpyrad in tea (Camellia Sinensis). JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01639-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2022]
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9
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Liu CT, Tzen JT. Exploring The Relative Astringency of Tea Catechins and Distinct Astringent Sensation of Catechins and Flavonol Glycosides via an In Vitro Assay Composed of Artificial Oil Bodies. Molecules 2022; 27:molecules27175679. [PMID: 36080445 PMCID: PMC9457659 DOI: 10.3390/molecules27175679] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/31/2022] [Accepted: 08/31/2022] [Indexed: 12/03/2022] Open
Abstract
Artificial oil bodies covered by a recombinant surface protein, caleosin fused with histatin 3 (a major human salivary peptide), were employed to explore the relative astringency of eight tea catechins. The results showed that gallate-type catechins were more astringent than non-gallate-type catechins, with an astringency order of epicatechin gallate > epigallocatechin gallate > gallocatechin gallate > catechin gallate > epigallocatechin > epicatechin > gallocatechin > catechin. As expected, the extension of brewing time led to an increase in catechin content in the tea infusion, thus elevating tea astringency. Detailed analysis showed that the enhanced proportion of gallate-type catechins was significantly higher than that of non-gallate-type catechins, indicating that tea astringency was elevated exponentially, rather than proportionally, when brewing time was extended. Rough surfaces were observed on artificial oil bodies when they were complexed with epigallocatechin gallate (a catechin), while a smooth surface was observed on those complexed with rutin (a flavonol glycoside) under an atomic force microscope and a scanning electron microscope. The results indicate that catechins and flavonol glycosides induce the sensation of rough (puckering) and smooth (velvety) astringency in tea, respectively.
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Affiliation(s)
| | - Jason T.C. Tzen
- Correspondence: ; Tel.: +886-4-22840328 (ext. 776); Fax: +886-4-22853527
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10
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11
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Song Y, Wang X, Xie H, Li L, Ning J, Zhang Z. Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 252:119522. [PMID: 33582437 DOI: 10.1016/j.saa.2021.119522] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 01/19/2021] [Accepted: 01/20/2021] [Indexed: 06/12/2023]
Abstract
Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a single-sensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades.
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Affiliation(s)
- Yan Song
- School of Engineering, Anhui Agricultural University, Hefei 230036, China
| | - Xiaozhong Wang
- School of Engineering, Anhui Agricultural University, Hefei 230036, China
| | - Hanlei Xie
- School of Engineering, Anhui Agricultural University, Hefei 230036, China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
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12
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A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose. Foods 2021; 10:foods10040795. [PMID: 33917735 PMCID: PMC8068162 DOI: 10.3390/foods10040795] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 03/20/2021] [Accepted: 03/30/2021] [Indexed: 11/16/2022] Open
Abstract
Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.
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14
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Zhang W, Zhang B, Ye Y, Zhu H. Methylglyoxal-hydroimidazolones (MG-Hs) instead of Nɛ-(carboxymethyl)-l-lysine (CML) is the major advanced glycation end-product during drying process in black tea. Food Chem 2020; 333:127499. [PMID: 32673957 DOI: 10.1016/j.foodchem.2020.127499] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 07/02/2020] [Accepted: 07/03/2020] [Indexed: 12/24/2022]
Abstract
This study was to examine the formation of advanced glycation end-products (AGEs) in black tea during drying process at 90, 120, and 150 °C for 1 h. Nine AGEs including Nɛ-(carboxyethyl)-l-lysine (CEL), Nɛ-(carboxymethyl)-l-lysine (CML), three isomers of methylglyoxal-hydroimidazolones (MG-Hs), three isomers of glyoxal-hydroimidazolones (GO-Hs), and argpyrimidine were quantified by using HPLC-MS/MS with isotope-labelled internal standard. Results showed that each AGE during the drying process of 150 °C was significantly higher (p < 0.05) than at 90 and 120 °C, and argpyrimidine was only found in the treatment of 150 °C. MG-H1/3 was first quantified as the major AGE during drying at 120-150 °C, the content respectively reached to (39.66 ± 2.61) μg/g and (58.88 ± 1.76) μg/g after 1 h drying, where CML content only had (19.86 ± 1.02) μg/g and (23.71 ± 1.40) μg/g. This study indicated that arginine derived-AGEs are the key components of black tea AGEs.
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Affiliation(s)
- Wei Zhang
- Department of Molecular Biology, Umeå University, Umeå 90187, Sweden
| | - Beibei Zhang
- Meitan Shengxing Tea Industry Co. Ltd, Zunyi 564100, China
| | - Yang Ye
- Tea Research Institute, China Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Hongkai Zhu
- Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg, Denmark.
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15
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Vahid-Dastjerdi L, Jazaeri S, Hadaegh H, Razeghi Jahromi S, Hosseini SM, Farhoodi M, Momken I. Sucrose substitution by polyols for the production of shelf stable macaroon: attribution of their molecular weight and synergy. Eur Food Res Technol 2020. [DOI: 10.1007/s00217-020-03542-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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16
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An T, Yu H, Yang C, Liang G, Chen J, Hu Z, Hu B, Dong C. Black tea withering moisture detection method based on convolution neural network confidence. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13428] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Ting An
- Tea Research InstituteThe Chinese Academy of Agricultural Sciences Hangzhou China
- College of Mechanical and Electrical EngineeringShihezi University Shihezi China
| | - Huan Yu
- Tea Research InstituteThe Chinese Academy of Agricultural Sciences Hangzhou China
| | - Chongshan Yang
- Tea Research InstituteThe Chinese Academy of Agricultural Sciences Hangzhou China
- College of Mechanical and Electrical EngineeringShihezi University Shihezi China
| | - Gaozhen Liang
- Tea Research InstituteThe Chinese Academy of Agricultural Sciences Hangzhou China
- College of Mechanical and Electrical EngineeringShihezi University Shihezi China
| | - Jiayou Chen
- Tea Research InstituteThe Chinese Academy of Agricultural Sciences Hangzhou China
- Fujian Jiayu Tea Machinery Intelligent Technology Co., Ltd Anxi China
| | - Zonghua Hu
- Tea Research InstituteThe Chinese Academy of Agricultural Sciences Hangzhou China
| | - Bin Hu
- College of Mechanical and Electrical EngineeringShihezi University Shihezi China
| | - Chunwang Dong
- Tea Research InstituteThe Chinese Academy of Agricultural Sciences Hangzhou China
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17
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Ren G, Fan Q, He X, Li W, Tang X. Applicability of multifunctional preprocessing device for simultaneous estimation of spreading of green tea, withering of black tea and shaking of oolong tea. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2020; 100:560-569. [PMID: 31588555 DOI: 10.1002/jsfa.10046] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/23/2019] [Accepted: 09/12/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Preprocessing technologies of fresh tea leaves have a great influence on tea quality. A multifunctional preprocessing device for tea raw materials has been designed and utilized as a novel item of equipment to synchronously meet the process needs of spreading of green tea, withering of black tea and shaking of oolong tea. RESULTS The preprocessing parameters of fresh tea leaves for spreading of green tea, withering of black tea and shaking of oolong tea were optimized by orthogonal experiments. Sensory assessment combined with statistical tools was employed as an analytical method to evaluate the pretreatment effect of processing different sorts of tea. The range analysis and variance analysis of tea sensory evaluation combined with chemical components (total polyphenols, free amino acids and soluble sugar) showed that A3 B2 C3 (70%, 25 °C, 8 h), A1 B3 C1 (60%, 28 °C, 18 h) and A1 B1 C3 (5 min, 20 °C, discontinuous leaf turning c) were considered to be the optimum schemes for the best pretreatment conditions of the above three major processing types of tea, respectively. The verification experiment of the proposed schemes was performed with satisfactory performance. CONCLUSION This study demonstrated that a multifunctional preprocessing device for fresh tea leaves can be successfully applied to simultaneously estimate spreading of green tea, withering of black tea and shaking of oolong tea. © 2019 Society of Chemical Industry.
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Affiliation(s)
- Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Qiye Fan
- Hangzhou Tea Research Institute, All China Federation of Supply and Marketing Cooperatives, Hangzhou, China
| | - Xuejun He
- State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, China
| | - Wencui Li
- Hangzhou Tea Research Institute, All China Federation of Supply and Marketing Cooperatives, Hangzhou, China
| | - Xiaolin Tang
- Hangzhou Tea Research Institute, All China Federation of Supply and Marketing Cooperatives, Hangzhou, China
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Ye Y, Dong C, Luo F, Cui J, Liao X, Lu A, Yan J, Mao S, Li M, Fang C, Tong H. Effects of withering on the main physical properties of withered tea leaves and the sensory quality of congou black tea. J Texture Stud 2019; 51:542-553. [PMID: 31769870 DOI: 10.1111/jtxs.12498] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 10/17/2019] [Accepted: 11/17/2019] [Indexed: 11/28/2022]
Abstract
To explore the relationship between the moisture content of withered tea leaves and their physical properties (i.e., elasticity, plasticity, flexibility, and texture) during withering, texture analyzer was employed to test the elasticity and flexibility of withered tea leaves with different moisture contents. The texture was evaluated by computer vision technology. The withered tea leaves with different moisture contents were used to process congou black tea, which was then subjected to sensory evaluation. Results showed that good elasticity, optimal flexibility, and plasticity were achieved when the moisture content of the withered tea leaves of Fudingdabai comprising two leaves and one bud varied arranging from 65.51 to 61.48%. The sensory evaluation of congou black tea revealed that moderate withering was better than long-term withering and that both moderate and long-term withering were better than no withering during processing. The moisture content was significantly correlated with the flexibility and plasticity of the withered tea leaves. Fresh tea leaves undergoing moderate withering with moisture content of 65.51-61.48% to process congou black tea, good tea shape and liquor color were achieved. This study provided new evidence that the moisture content of withered tea leaves significantly affected the quality of black tea.
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Affiliation(s)
- Yulong Ye
- College of Food Science, Southwest University, Chongqing, China.,Tea Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu, Sichuan, China
| | - Chunwang Dong
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, China
| | - Fan Luo
- Tea Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu, Sichuan, China
| | - Jilai Cui
- College of Life Science, Xinyang Normal University, Xinyang, Henan, China
| | - Xueli Liao
- College of Food Science, Southwest University, Chongqing, China
| | - Anxia Lu
- College of Food Science, Southwest University, Chongqing, China
| | - Jingna Yan
- College of Food Science, Southwest University, Chongqing, China
| | - Shihong Mao
- College of Food Science, Southwest University, Chongqing, China
| | - Meifeng Li
- College of Food Science, Southwest University, Chongqing, China
| | - Chunyan Fang
- State Key Laboratory of Silkworm Genome Biology, Southwest University, Chongqing, China
| | - Huarong Tong
- College of Food Science, Southwest University, Chongqing, China
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19
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Yuan H, Chen X, Shao Y, Cheng Y, Yang Y, Zhang M, Hua J, Li J, Deng Y, Wang J, Dong C, Jiang Y, Xie Z, Wu Z. Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis. J Food Sci 2019; 84:3411-3417. [PMID: 31750940 DOI: 10.1111/1750-3841.14917] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 10/01/2019] [Accepted: 10/07/2019] [Indexed: 01/01/2023]
Abstract
Aroma assessment remains difficult and uncertain in the present sensory assessment system. It is highly desirable to develop a new assessment method to discriminate the quality of various teas in the tea market. In the present work, based on linear discriminant analysis and principal component analysis, the aroma of dry and wet samples of different Xi-hu Longjing and Pu-erh teas were tested and differentiated by electronic noses (e-nose). The results confirm that e-nose can discriminate different priced Xi-hu Longjing tea samples in the range of 80-800 RMB/500 g and varying storage years of Pu-erh tea samples. Furthermore, for the detection of both dry and wet samples of Longjing and Pu-erh teas, the results reveal that all samples have specific aroma characteristics that e-nose can recognize. More importantly, contribution analysis in sensors indicates that nitrogen oxides, methane and alcohols are the characteristic components that contribute to the fragrances of different priced Xi-hu Longjing teas, while nitrogen oxides, aromatic benzene and amines make the fragrances of Pu-erh teas with different storage years disparate. PRACTICAL APPLICATION: This work demonstrates that e-nose can rapidly distinguish tea products with different price levels and varying storage years. With the advantages of ease of use, high portability and flexibility, e-nose will be widely expanded and applied in refined processing and the development of flavored foods.
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Affiliation(s)
- Haibo Yuan
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Inst., Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China
| | - Xiaoqiang Chen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural Univ., 130 Changjiang West Rd., Hefei, 230036, Anhui, China.,Natl. "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei Univ. of Technology, Wuhan, 430068, China
| | - Yundong Shao
- Zhejiang Skyherb Biotechnologies Co., Ltd., Anji, 313300, China
| | - Yong Cheng
- Zhejiang Skyherb Biotechnologies Co., Ltd., Anji, 313300, China
| | - Yanqin Yang
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Inst., Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China
| | - Mingming Zhang
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Inst., Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China
| | - Jinjie Hua
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Inst., Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China
| | - Jia Li
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Inst., Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China
| | - Yuliang Deng
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Inst., Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China
| | - Jinjin Wang
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Inst., Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China
| | - Chunwang Dong
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Inst., Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China
| | - Yongwen Jiang
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Inst., Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China
| | - Zhongwen Xie
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural Univ., 130 Changjiang West Rd., Hefei, 230036, Anhui, China
| | - Zhengqi Wu
- Natl. "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei Univ. of Technology, Wuhan, 430068, China
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20
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Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages. BEVERAGES 2019. [DOI: 10.3390/beverages5040062] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.
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21
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Aboulwafa MM, Youssef FS, Gad HA, Altyar AE, Al-Azizi MM, Ashour ML. A Comprehensive Insight on the Health Benefits and Phytoconstituents of Camellia sinensis and Recent Approaches for Its Quality Control. Antioxidants (Basel) 2019; 8:E455. [PMID: 31590466 PMCID: PMC6826564 DOI: 10.3390/antiox8100455] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 09/30/2019] [Accepted: 10/03/2019] [Indexed: 12/18/2022] Open
Abstract
Tea, Camellia sinensis, which belongs to the family Theaceae, is a shrub or evergreen tree up to 16 m in height. Green tea is very popular because of its marked health benefits comprising its anticancer, anti-oxidant, and antimicrobial activities, as well as its effectiveness in reducing body weight. Additionally, it was recognized by Chinese people as an effective traditional drink required for the prophylaxis against many health ailments. This is due to the complex chemical composition of green tea, which comprises different classes of chemical compounds, such as polyphenols, alkaloids, proteins, minerals, vitamins, amino acids, and others. The beneficial health effects of green tea ultimately led to its great consumption and increase its liability to be adulterated by either low-quality or non-green tea products with concomitant decrease in activity. Thus, in this review, green tea was selected to highlight its health benefits and phytoconstituents, as well as recent approaches for its quality-control monitoring that guarantee its incorporation in many pharmaceutical industries. More research is needed to find out other more biological activities, active constituents, and other simple and cheap techniques for its quality assurance that ascertain the prevention of its adulteration.
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Affiliation(s)
- Maram M Aboulwafa
- Department of Pharmacognosy, Faculty of Pharmacy, Ain-Shams University, Cairo-11566, Egypt.
| | - Fadia S Youssef
- Department of Pharmacognosy, Faculty of Pharmacy, Ain-Shams University, Cairo-11566, Egypt.
| | - Haidy A Gad
- Department of Pharmacognosy, Faculty of Pharmacy, Ain-Shams University, Cairo-11566, Egypt.
| | - Ahmed E Altyar
- Department of Pharmacy Practice, Faculty of Pharmacy, King Abdulaziz University, P.O. Box 80260 Jeddah-21589, Saudi Arabia.
| | - Mohamed M Al-Azizi
- Department of Pharmacognosy, Faculty of Pharmacy, Ain-Shams University, Cairo-11566, Egypt.
| | - Mohamed L Ashour
- Department of Pharmacognosy, Faculty of Pharmacy, Ain-Shams University, Cairo-11566, Egypt.
- Department of Pharmaceutical Sciences, Pharmacy Program, Batterjee Medical College, P.O. Box 6231, Jeddah-21442, Saudi Arabia.
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22
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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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23
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Li H, Luo L, Ma M, Zeng L. Characterization of Volatile Compounds and Sensory Analysis of Jasmine Scented Black Tea Produced by Different Scenting Processes. J Food Sci 2018; 83:2718-2732. [PMID: 30339723 DOI: 10.1111/1750-3841.14340] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 08/05/2018] [Accepted: 08/09/2018] [Indexed: 11/28/2022]
Abstract
Jasmine scented black tea is widely popular in China and the scenting process is the most crucial issue affecting its aroma quality. This study aimed to analyze the influence of different processes on aroma quality and the specific contribution of volatiles to aroma quality. To achieve it, the headspace solid-phase microextraction method combined with gas-chromatography mass-spectrometry was used to identify and quantify the volatile compounds, and further collected the characteristics according to odor activity values or contents. The quantitative descriptive analysis (QDA) and traditional sensory analysis (TSA) were used to evaluate its aroma quality and the correlation analysis between QDA and characteristic aroma compounds was carry out to analyze the specific contribution of characteristics to sensory quality. The GC/MS results showed that 130 aroma components and 20 characteristics were identified in 6 tea samples and more new volatile components appeared and less original volatile components disappeared in traditional scenting process (TSP) compared with continuous scenting process (CSP). The QDA and TSA results showed that TSP samples had better "overall" and "jasmine" than CSP samples. The correlation analysis between QDA and characteristics showed that nerolidol and methylbenzoate were related to "overall," linalool and methyl anthranilate to "jasmine," and decanal and benzeneacetaldehyde to "sweet floral," "roasted," and "fermented." This study suggested that TSP is a favorable scenting process for black tea, and QDA is an objective and digital sensory evaluation method for jasmine scented black tea. PRACTICAL APPLICATION: Compare the effect of scenting technology on the aroma quality of jasmine scented black tea based on sensory analysis and chemical analysis, and suggested a favorable scenting process for processing jasmine scented black tea with better qualities. Explore the correlation between aroma composition and specific aroma properties to provide some useful information about guiding the scenting process to acquire a product with target aroma qualities. And the application of QDA for evaluation of jasmine scented black tea was better for consumers to understand the quality attributes of product.
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Affiliation(s)
- Huanhuan Li
- College of Food Science, Southwest Univ., Chongqing, 400715, China
| | - Liyong Luo
- College of Food Science, Southwest Univ., Chongqing, 400715, China.,Tea Research Inst., Southwest Univ., Chongqing, 400715, China
| | - Mengjun Ma
- Xianning Academy of Agricultural Sciences, Xianning, 437100, Hubei, China
| | - Liang Zeng
- College of Food Science, Southwest Univ., Chongqing, 400715, China.,Tea Research Inst., Southwest Univ., Chongqing, 400715, China
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24
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Chen Q, Chen M, Liu Y, Wu J, Wang X, Ouyang Q, Chen X. Application of FT-NIR spectroscopy for simultaneous estimation of taste quality and taste-related compounds content of black tea. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2018; 55:4363-4368. [PMID: 30228436 PMCID: PMC6133835 DOI: 10.1007/s13197-018-3353-1] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 07/03/2018] [Accepted: 07/17/2018] [Indexed: 12/20/2022]
Abstract
Fourier transform near-infrared spectroscopy (FT-NIR) coupled to chemometric algorithms such as back propagation (BP)-AdaBoost and synergy interval partial least square (Si-PLS) were deployed for the rapid prediction taste quality and taste-related components in black tea. Eight main taste-related components were determined via chemical analysis and Pearson correlations. The achieved chemical results of the eight taste-related components in black tea infusion were predicted based on 160 tea samples obtained from different countries. Prediction results revealed BP-AdaBoost models gave superior predictions, with all the correlation coefficients of the prediction set (Rp) > 0.76, and the root mean square error values of the prediction set (RMSEP) < 1.7% compared with Si-PLS models (0.71 ≤ Rp ≤ 0.94, 0.08% ≤ RMSEP ≤ 1.73%). This implies that FT-NIR combined to BP-AdaBoostis capable of being deployed for the rapid evaluation of black tea taste quality and taste-related components content simultaneously.
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Affiliation(s)
- Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013 People’s Republic of China
| | - Min Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013 People’s Republic of China
| | - Yan Liu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013 People’s Republic of China
| | - Jizhong Wu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013 People’s Republic of China
| | - Xinyu Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013 People’s Republic of China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013 People’s Republic of China
| | - Xiaohong Chen
- Zhenjiang Jinshan Cuiya Tea Industry Co. Ltd, Zhenjiang, 212021 People’s Republic of China
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25
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Dong CW, Zhu HK, Zhao JW, Jiang YW, Yuan HB, Chen QS. Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools. J Zhejiang Univ Sci B 2018; 18:544-548. [PMID: 28585431 DOI: 10.1631/jzus.b1600423] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Tea is one of the three greatest beverages in the world. In China, green tea has the largest consumption, and needle-shaped green tea, such as Maofeng tea and Sparrow Tongue tea, accounts for more than 40% of green tea (Zhu et al., 2017). The appearance of green tea is one of the important indexes during the evaluation of green tea quality. Especially in market transactions, the price of tea is usually determined by its appearance (Zhou et al., 2012). Human sensory evaluation is usually conducted by experts, and is also easily affected by various factors such as light, experience, psychological and visual factors. In the meantime, people may distinguish the slight differences between similar colors or textures, but the specific levels of the tea are hard to determine (Chen et al., 2008). As human description of color and texture is qualitative, it is hard to evaluate the sensory quality accurately, in a standard manner, and objectively. Color is an important visual property of a computer image (Xie et al., 2014; Khulal et al., 2016); texture is a visual performance of image grayscale and color changing with spatial positions, which can be used to describe the roughness and directivity of the surface of an object (Sanaeifar et al., 2016). There are already researchers who have used computer visual image technologies to identify the varieties, levels, and origins of tea (Chen et al., 2008; Xie et al., 2014; Zhu et al., 2017). Most of their research targets are crush, tear, and curl (CTC) red (green) broken tea, curly green tea (Bilochun tea), and flat-typed green tea (West Lake Dragon-well green tea) as the information sources. However, the target of the above research is to establish a qualitative evaluation method on tea quality (Fu et al., 2013). There is little literature on the sensory evaluation of the appearance quality of needle-shaped green tea, especially research on a quantitative evaluation model (Zhou et al., 2012; Zhu et al., 2017).
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Affiliation(s)
- Chun-Wang Dong
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.,Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Hong-Kai Zhu
- Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Jie-Wen Zhao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Yong-Wen Jiang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.,Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Hai-Bo Yuan
- Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Quan-Sheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
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26
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Evaluation of Data Mining Strategies for Classification of Black Tea Based on Image-Based Features. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-1075-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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