Electronic Sensor Technologies in Monitoring Quality of Tea: A Review
Abstract
:1. Introduction
2. Non-Sensing Techniques for the Quality Assessment of Tea
3. Electronic Sensor Technologies: Advantages and Disadvantages in the Tea Industry
3.1. Electronic Nose Sensors
Tea Type | e-Nose Type a | Data Analysis b | Utilization Purpose(s) | Keynote(s) b | Reference |
---|---|---|---|---|---|
Indian black tea | A commercial e-nose, 4 tin oxide odor sensors | PCA, FCM, SOM, MLP, LVQ, RBF, PNN | Discriminating the flavors of various tea samples | The successful classification of teas with flavors released under different processing conditions using a RBF networked based MOS e-nose | [59] |
Longjing green tea | PEN2, 10 MOS sensors | PCA, LDA, ANN | Tea grade discrimination in the industry among different cultivars | The optimum discrimination using an e-nose at 60 s, and the correct classification of 90% of the total tea samples with BPNN | [60] |
13 selected tea grades | Gas sensors (Figaro Co.), 4 tin oxide sensors | MLP, RBF, CPNN | Tea quality monitoring during the tea grading process. | Tea aroma standardization in numeric terms with a classification accuracy of 90.77–93.85% | [61] |
Longjing green tea | PEN2, 10 MOS sensors | PNN, BPNN, PCA, CA | The rapid quality assessment of tea grades | The identification and classification of tea quality grade using e-nose by CA and ANN | [62] |
Longjing green tea | PEN2, 10 MOS sensors | PCA, LDA | Discriminating different grades of green teas | 100% correct classification by LDA for five different tea samples with various qualities | [63] |
Indian black tea | Gas sensors (Figaro, Japan), 5 MOS sensors | BPMLP | The quality assessment of tea via the aroma classification | Enhancing the pattern recognition accuracy of a e-nose system for black tea aroma classification | [64] |
Indian black tea | Gas sensors (Figaro, Japan), 5 MOS sensors | RBF | Standardization of the e-nose tool for black tea classification | The pattern recognition algorithm for black tea aroma classification with an e-nose using a RBF neural network with the incremental learning feature | [55] |
Longjing green tea | PEN2, 10 MOS sensors | PCA, LDA, BPNN | Grading the tea based on volatiles of dry tea leaf, beverages, and remains | Better discrimination of the tea grades based on their beverages using LDA and BPNN methods | [65] |
Longjing green tea | PEN2,10 MOS sensors | PCA, LDA, BPNN | Recognizing the volatile components emitted by differently aged tea | Better discrimination of tea samples with leaves than their beverages and residues | [46] |
Kangra orthodox black tea | Alpha M.O.S FOX 3000 EN system | SITO, MWTS | Tea classification with various fermentation times and mechanical grades | The ability improvement of an e-nose using the SITO-MWTS for online monitoring control of the tea production process | [66] |
Green, Black, and Oolong teas | Odor imaging sensors array based on the reverse gel silica flat plate and the hydrophobic porous membrane | PCA, LDA | To recognize volatile organic compounds during monitoring of tea fermentation | A high potential in tea category classification with different fermentation degrees, using an e-nose based on an odor imaging sensor array | [67] |
Black tea | Gas sensors (Figaro Co.), 5 MOS sensors | Bayesian | Artificial flavor perception of tea | Greater reduction in the classification error of different teas using combined sensory systems (e-nose + e-tongue) than an individual system | [68] |
Xihu-Longjing green tea | Fox 4000 (Alpha MOS Co., France), 18 MOS sensors | K(PCA), K(LDA) | The quality classification of Xihu-Longjing tea | 100% grade classification and recognition of tea using the KLDA-KNN model | [69] |
Indian black tea | 8 QCM sensors-based e-nose | - | The real-time monitoring of tea fermentation | Assessing the optimum fermentation time for 12 black tea samples with an accuracy of 96.27% | [70] |
Longjing green tea | Fox 4000 (Alpha MOS Co., France), 18 MOS sensors | KLDA, KNN | Better identification of tea quality | A multi-level fusion strategy, combining e-nose and e-tongue sensors to assess tea quality | [47] |
Longjing green tea | PEN3, 10 MOS sensors | KNN, SVM, MLR | Aroma compounds identification of tea | Jointly utilizing e-nose and CVS techniques to effectively identify tea quality | [44] |
Longjing green tea | PEN3, 10 MOS sensors | PCA, PLSR, SVM, RF | The qualitative discrimination of tea based on volatile compounds | The best prediction of chemical components of tea using RF based on the fusion signals | [27] |
Pu-erh tea | PEN3, 10 MOS sensors | CNN, PLSR, LDA | Finding a quick and accurate way to detect the type, blend ratio, and mix ratio of Pu’er tea in the industry | Higher detection ability of Pu-erh tea quality using a multi-source information fusion (e-nose and VIS/NIR spectrometer fusion) | [71] |
Indian black tea | Gas sensors (Figaro, Japan), 5 MOS sensors | PCA, KNN, PLS-DA | Classifying tea samples based on aromatic compounds | Tea quality classification (accuracy = 99.75%) due to the sensitivity to different chemicals (e.g., linalool, linalool oxide, β-ionone, terpeniol, and geraniol) | [72] |
Indian black tea | Gas sensors (Figaro, Japan), 5 MOS sensors | Recurrent Elman network | A rapid prediction of the optimum fermentation time of black tea | Monitoring the fermentation process of tea using an e-nose and a recurrent Elman network | [73] |
Organic green teas | PEN3, 10 MOS sensors | PCA, SVM, PLSR, RF, KRR, MBPNN | The concurrent classification of tea grade and price prediction with an excellent performance | MBPNN model: able to represent the nonlinear relationship between aroma (inputs) and quality (outputs) data of tea | [74] |
West Lake Longjing green tea | Self-developed e-nose system, 8 MOS sensors | CART | Quality level identification of tea types | The grading regulation of different teas based on the aroma components alone | [75] |
Xihu Longjing and Pu-erh teas | MOS-based PEN3 sensors | PCA, LDA | The rapid, precise determination of the difference in the overall characteristic aromas of tea varieties | The e-nose ability to discriminate different priced Xi-hu Longjing tea samples and varying storage years of Pu-erh tea samples | [76] |
Black, Green, and yellow teas | PEN3, 10 MOS sensors | Grid-SVR, XGBoost, RF | The polyphenol content in cross-category tea | Improving the estimation accuracy of tea polyphenol content for cross-category evaluation (the best model: XGBoost) | [77] |
Oolong tea | MOS-based gas sensors (Figaro, USA) | - | Accurately monitoring the smell variation during fermentation, based on online tests in a tea factory | e-nose: an efficient option to replace the sensory function of panelists in the future | [58] |
12 green teas | PEN3, 10 MOS sensors | SVM, CNN-Shi, CNN-SVM-Shi, CNN | A rapid, convenient, and effective method for classifying green teas from different geographical origins | High accuracy and strong strength of the CNN-SVM for the fine-grained classification of multiple highly-similar teas | [78] |
Green tea (fried, baked, sunburned, and steamed) | PEN2, 15 MOS sensors | PCA, LDA, KNN | The optimization of an e-nose sensor array to identify aroma compounds of tea | Eliminating redundant sensors, improving the quality of original tea aroma data A high accuracy (94.44~100%) using combined methods of LDA and KNN | [51] |
Xihu-Longjing green tea | PEN3, 10 MOS sensors | XGBoost, RF, BPNN, SVM, LightGBM | Improving the practical use of e-nose devices using TrLightGBM | TrLightGBM (transfer learning) model: the best performance for the identification of different production areas and harvest times | [79] |
Longjing green tea | PEN3, 10 MOS sensors | PCA, MDS, LDA, LR, SVM | e-nose feasibility to qualitatively and quantitatively analyze quality grades of tea | A 100% accuracy for the classification of tea infusions with SVM based on the data processed by LDA | [56] |
Dianhong black tea (44 infusions) | Heracles II fast GC-E-Nose (Alpha MOS Co., France) | PLS-DA, FDA | A innovative technical route for the quality evaluation and control of tea infusions | A supplement for the objective sensory assessment | [80] |
Green tea (Fudingdabai variety) | Heracles II gas phase e-nose (Alpha M.O.S., Toulouse, France) | PCA, PLS-DA | A framework for directional processing and quality improvement of tea | High performance of a gas-phase e-nose, to quickly and effectively characterize the dynamic changes under different drying conditions of tea | [81] |
3.2. Electronic Tongue Sensors
Tea Type | e-Tongue Type | Data Analysis a | Utilization Purpose(s) | Special Note(s) a | Reference |
---|---|---|---|---|---|
Green and black teas | Voltammetric e-tongue system, 3 noble metal-type electrodes, an Ag/AgCl reference electrode, a stainless steel counter electrode | PCA | Accurately discrimination of tea samples | Classification of tea samples based on the taste attributes detected by an online e-tongue system | [86] |
Green, black, and oolong teas | Voltammetric e-tongue (SA402 Anritsu Corp., Japan), 8 different lipid/polymer membranes, an Ag/AgCl reference electrode | PCA | The potential of combined sensors to detect taste attributes of tea samples | Improving the taste quality of tea samples by integrating a voltammetric e-tongue, and a potentiometric multichannel lipid membrane taste sensor | [87] |
Chinese green tea | Potentiometric all-solid-state e-tongue (Alpha M.O.S. Co., France), 7 sensors (ZZ, BA, BB, CA, GA, HA, and JB) | ANN, KNN | The online grading of tea | Using e-tongue technology with ANN pattern recognition to identify tea grade level | [95] |
Chinese green and black teas | Potentiometric all-solid-state e-tongue (Alpha M.O.S. Co., France), seven liquid cross-selective sensors, a reference electrode | PCA | A rapid test for diagnosing taste quality of tea samples | Predicting sensory characteristics and their relationship to the taste quality of tea assessed by professional tasters | [29] |
Indian black tea | A customized e-tongue setup | PCA | Taste recognizer by multi sensor e- tongue for tea quality classification | The classification of black tea liquor based on briskness, with a 85% rate | [108] |
Indian black tea | Voltammetry e-tongue system, 5 noble metal-type electrodes, an Ag/AgCl reference electrode, a platinum counter electrode | PCA, LDA, BP-MLP, RBF, PNN | Much better classification ability for the combined system using the combined e-nose and e-tongue | The classification possibility of tea samples with an accuracy of 85–86% with an e-tongue | [90] |
Indian black tea | Voltammetry e-tongue system, 5 noble metal-type electrodes, an Ag/AgCl reference electrode, a platinum counter electrode | PCA, FNN, BP-MLP | Tea classification using fusion of e-nose and e-tongue response using a fuzzy-based approach | FNN: the best suited model for tea classification | [68] |
Indian black tea | An e-tongue with 5 noble metal-type electrodes, an Ag/AgCl reference electrode, a platinum counter electrode | Bayesian | Artificial flavor perception of tea | Improving the artificial perception when two sensory systems are fused together rather than with an individual system | [48] |
Chinese green tea | Colorimetric artificial tongue, nanoporous ormosils as colorants | HCA, PCA | Discriminating nine Chinese green teas from various geographical origins and grade levels by integrating an e-nose | Efficient in characterizing compounds of high-water concentration using the developed colorimetric artificial tongue and nose system | [103] |
Indian black tea | An e-tongue with 5 noble metal-type electrodes, an Ag/AgCl reference electrode, a platinum counter electrode | SVM, VVRKFA | Tea quality prediction using different types of e-tongue signal measurement | The high prediction accuracy of both the applied classifiers to assess tea quality | [109] |
Black tea | An e-tongue with 5 noble metal-type electrodes, an Ag/AgCl reference electrode | FRST | A significant capability for classifying sensory properties | Better analysis of tea quality by the combined sensor response of an e-nose and e- tongue | [106] |
Indian black tea | A pulse voltammetric e-tongue, 5 noble metal-type electrodes, an Ag/AgCl reference electrode | ANN, OVO-SVM, VVRKFA, PCA | Improving the classification performance of tea | Exactly predicting the tea quality among four different samples with the e-tongue signal classification | [91] |
Green tea (Anji-white tea) | ASTREE II e-tongue (Alpha M.O.S., France), a reference electrode, 7 independent liquid sensors | PCA, PLS-DA | The specific geographical origins detection in Anji-white tea | High prediction sensitivity and specificity of PLSDA for e-tongue to diagnose tea taste | [101] |
Longjing green tea | α-ASTREE (Alpha M.O.S. Co., France), an array of seven electrodes | KLDA, KNN | An accurate identification of tea taste and odor quality | A much better classification ability for the multi-level fusion system (e-nose + e-tongue) | [47] |
Green tea | SA402B (Insent, Japan), several taste sensors array, An Ag/AgCl reference electrode | MLR, PLSR, BPNN | A theoretical reference for fast assessment of the bitter and astringent taste of green tea | The significant effect of BPNN model on the bitterness and astringency recognition of tea | [97] |
Black tea | Portable e-tongue based on glassy carbon electrode and cyclic voltammetry | Si-CARS-PLS | Improving the prediction accuracy for theaflavins in tea | A fast and cheap way to measure the total theaflavins content in black tea | [89] |
Longjing green teas | α-Astree (Alpha MOS Co., France), 7 liquid cross-sensitive electrodes (ZZ, BA, BB, CA, GA, HA, and JB), the Ag/AgCl reference electrode | SVM, RF, PLS | RF: the best performance in predicting the concentration of chemical components of tea | An accuracy of 100% for qualitative identification of tea quality grades, based on fusion signals by SVM and RF | [27] |
Tieguanyin, Biluochun, Show bud, Westlake, and Yuzhu teas | Voltammetry e-tongue hardware system, Three-electrode module | CNN-AFE | An e-tongue for more widespread use for tea grading in the future | A ~99.9% classification accuracy for tea classification using the CNN-AFE strategy | [94] |
Black tea “qi men” | Self-designed e-tongue device, 6 various cylindrical metal electrodes (outside), and a Ag/AgCl reference electrode (inside) | SRD, PLS-DA, SRD-PLS-DA | High efficiency and capability to identify the tea sample grade using e-tongue data | The potential and effectiveness of the PLS-DA-SRD model for tea grade classification | [110] |
Black tea | Cyclic voltammetry e-tongue (CVET) with an glassy carbon/platinum electrode | Si-PLS, VCPA, Si-VCPA-PLS | A fast, low-cost, efficient, and complementary approach to determining free amino acids in teas | A accurate prediction of total free amino acids content in black tea using the CVET technology | [88] |
5 dark teas: Fuzhuan, Pu-erh, Qingzhuan, Kangzhuan, Liubao | TS-5000Z (Insent, Japan), 6 taste sensors array [AAE, CAO, CTO, COO, AE1, and GL1] | PCA, HCA, OPLS-DA | Exploring the relationship between their taste quality (umami, sourness, saltiness, bitterness, astringency, and sweetness) and chemical profile | Negatively association between the bitterness and aftertaste-bitterness and the content of polyphenols, flavonoids, and polysaccharides of dark teas | [98] |
Congou black tea | SA402B (Insent, Japan), 6 taste sensors array, an Ag/AgCl electrode | ACO, ELM, LS-SVM, PLS-DA, SVM | The taste assessment potential of tea products in the actual production process | Introducing ACO optimization algorithms for the best combination of taste features of the sensor array | [96] |
Yellow tea | TS-5000Z (Insent, Japan), 5 taste sensors array | PCA, PLS-DA, HCA | The correlation determination of taste types and biochemical compositions of tea | The exact evaluation of taste properties (i.e., sweetness, umami, bitterness, astringency, and richness) | [99] |
Autumn green tea | TS-5000Z (Insent, Japan), 6 lipid membrane sensors | OPLS-DA, HCA | Detecting the improved taste of tea during fermentation | The dominant taste (strong umami taste) assessment due to the presence of theabrownins | [100] |
Pu-erh tea | A voltammetric e-tongue, 8 taste sensors array, the reference electrode of Ag/AgCl | CNN, BPNN, BOA | Discrimination of Pu-erh tea storage time (0–8 years) | Better Pu-erh tea identification performance by integrating an e-nose and e-tongue | [92] |
Pu-erh tea | A voltammetric e-tongue, 8 taste sensors array, the reference electrode of Ag/AgCl | ELM, SVM, BPNN, CNN, TL-CNN | Discriminating the storage time of Pu-erh tea | Better pattern recognition performance of the combined deep learning and transfer learning than conventional techniques for an e-tongue | [93] |
Black, White, Oolong, Green (9 samples) | A fluorescent sensor array-based e-tongue, 6 soluble conjugated polymeric nanoparticles embedded in waterborne polyurethane | LDA, SVM | Discriminating 9 tea samples with respect to tea-manufacturing | A sensing system with 100% accuracy to classify tea taste through a linear support vector machine (SVM) model | [45] |
3.3. Electronic Eye Sensors
4. Data Analysis and Classification Algorithms
5. Conclusions and Future Trends
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Wang, P.; Yu, J.; Jin, S.; Chen, S.; Yue, C.; Wang, W.; Gao, S.; Cao, H.; Zheng, Y.; Gu, M. Genetic Basis of High Aroma and Stress Tolerance in the Oolong Tea Cultivar Genome. Hort. Res. 2021, 8, 107. [Google Scholar] [CrossRef] [PubMed]
- Mei, Y.; Xie, H.; Liu, S.; Zhu, J.; Zhao, S.; Wei, C. Metabolites and Transcriptional Profiling Analysis Reveal the Molecular Mechanisms of the Anthocyanin Metabolism in the “Zijuan” Tea Plant (Camellia sinensis var. assamica). J. Agric. Food Chem. 2020, 69, 414–427. [Google Scholar] [CrossRef] [PubMed]
- Beringer, T.; Kulak, M.; Müller, C.; Schaphoff, S.; Jans, Y. First Process-Based Simulations of Climate Change Impacts on Global Tea Production Indicate Large Effects in the World’s Major Producer Countries. Environ. Res. Lett. 2020, 15, 034023. [Google Scholar] [CrossRef]
- Madiga Bala, D.; Padigapati Venkata, N.S.; Yannam, P. Global and Regional Trading Blocs of Coffee and Tea: Outlook, Trading Signals, and Policies. World Food Policy 2020, 6, 119–156. [Google Scholar] [CrossRef]
- FAO. Tea Production Quantity. 2018. Available online: http://www.fao.org/faostat/en/#data/QC (accessed on 17 December 2021).
- Patil, A.B.; Bachute, M.; Kotecha, K. Artificial Perception of the Beverages: An in Depth Review of the Tea Sample. IEEE Access 2021, 7, 82761–82785. [Google Scholar] [CrossRef]
- San Le, V.; Lesueur, D.; Herrmann, L.; Hudek, L.; Quyen, L.N.; Brau, L. Sustainable Tea Production Through Agroecological Management Practices in Vietnam: A Review. Environ. Sustain. 2021, 4, 589–604. [Google Scholar]
- Bose, S.; Sarkar, N.; Banerjee, D. Natural Medicine Delivery from Biomedical Devices for the Treatment of Bone Disorders: A Review. Acta Biomater. 2021, 126, 63–91. [Google Scholar] [CrossRef]
- Luk, H.Y.; Appell, C.; Chyu, M.C.; Chen, C.H.; Wang, C.Y.; Yang, R.S.; Shen, C.L. Impacts of Green Tea on Joint and Skeletal Muscle Health: Prospects of Translational Nutrition. Antioxidants 2020, 9, 1050. [Google Scholar] [CrossRef]
- Vural, N.; Cavuldak, Ö.A.; Akay, M.A.; Anlı, R.E. Determination of the Various Extraction Solvent Effects on Polyphenolic Profile and Antioxidant Activities of Selected Tea Samples by Chemometric Approach. J. Food Measur. Charact. 2020, 14, 1286–1305. [Google Scholar] [CrossRef]
- Yılmaz, C.; Özdemir, F.; Gökmen, V. Investigation of Free Amino Acids, Bioactive and Neuroactive Compounds in Different Types of Tea and Effect of Black Tea Processing. LWT 2020, 117, 108655. [Google Scholar] [CrossRef]
- Williams, J.; Sergi, D.; McKune, A.J.; Georgousopoulou, E.N.; Mellor, D.D.; Naumovski, N. The Beneficial Health Effects of Green Tea Amino Acid L-Theanine in Animal Models: Promises and Prospects for Human Trials. Phytother. Res. 2019, 33, 571–583. [Google Scholar] [CrossRef]
- Ma, C.; Zheng, X.; Yang, Y.; Bu, P. The Effect of Black Tea Supplementation on Blood Pressure: A Systematic Review and Dose–Response Meta-Analysis of Randomized Controlled Trials. Food Funct. 2021, 12, 41–56. [Google Scholar] [CrossRef] [PubMed]
- Abe, S.K.; Inoue, M. Green Tea and Cancer And Cardiometabolic Diseases: A Review of the Current Epidemiological Evidence. Eur. J. Clin. Nutr. 2021, 75, 865–876. [Google Scholar] [CrossRef] [PubMed]
- Turgut, S.S.; Küçüköner, E.; Karacabey, E. TeaPot: A Chemometric Tool for Tea Blend Recipe Estimation. Appl. Food Res. 2021, 1, 100006. [Google Scholar] [CrossRef]
- Yan, S.; Zhou, Z.; Wang, K.; Song, S.; Shao, H.; Yang, X. Chemical Profile and Antioxidant Potential of Extractable and Non-Extractable Polyphenols in Commercial Teas at Different Fermentation Degrees. J. Food Process. Preserv. 2020, 44, e14487. [Google Scholar] [CrossRef]
- Hu, S.; He, C.; Li, Y.; Yu, Z.; Chen, Y.; Wang, Y.; Ni, D. Changes of Fungal Community and Non-Volatile Metabolites During Pile-Fermentation of Dark Green Tea. Food Res. Int. 2021, 147, 110472. [Google Scholar] [CrossRef]
- Peredo Pozos, G.I.; Ruiz-López, M.A.; Zamora Natera, J.F.; Álvarez Moya, C.; Barrientos Ramírez, L.; Reynoso Silva, M.; Rodríguez Macías, R.; García-López, P.M.; González Cruz, R.; Salcedo Pérez, E.; et al. Antioxidant Capacity and Antigenotoxic Effect of Hibiscus sabdariffa L. Extracts Obtained with Ultrasound-Assisted Extraction Process. Appl. Sci. 2020, 10, 560. [Google Scholar] [CrossRef] [Green Version]
- Liu, Z.; Chen, F.; Sun, J.; Ni, L. Dynamic Changes of Volatile and Phenolic Components During the Whole Manufacturing Process of Wuyi Rock Tea (Rougui). Food Chem. 2022, 367, 130624. [Google Scholar] [CrossRef]
- Dai, W.; Tan, J.; Lu, M.; Zhu, Y.; Li, P.; Peng, Q.; Guo, L.; Zhang, Y.; Xie, D.; Hu, Z.; et al. Metabolomics Investigation Reveals That 8-C N-Ethyl-2-Pyrrolidinone-Substituted Flavan-3-Ols Are Potential Marker Compounds of Stored White Teas. J. Agric. Food Chem. 2018, 66, 7209–7218. [Google Scholar] [CrossRef]
- Hung, W.L.; Wang, S.; Sang, S.; Wan, X.; Wang, Y.; Ho, C.T. Quantification of Ascorbyl Adducts of Epigallocatechin Gallate and Gallocatechin Gallate in Bottled Tea Beverages. Food Chem. 2018, 261, 246–252. [Google Scholar] [CrossRef]
- Ke, J.P.; Dai, W.T.; Zheng, W.J.; Wu, H.Y.; Hua, F.; Hu, F.L.; Chu, G.X.; Bao, G.H. Two Pairs of Isomerically New Phenylpropanoidated Epicatechin Gallates with Neuroprotective Effects on H2O2-Injured Sh-Sy5y Cells from Zijuan Green Tea and Their Changes in Fresh Tea Leaves Collected from Different Months and Final Product. J. Agric. Food Chem. 2019, 67, 4831–4838. [Google Scholar] [CrossRef] [PubMed]
- Rashmi, G.; Ke, J.P.; Zhang, P.; Yang, Z.; Bao, G.H. Novel Cinnamoylated Flavoalkaloids Identified in Tea with Acetylcholinesterase Inhibition Effect. J. Agric. Food Chem. 2020, 68, 3140–3148. [Google Scholar]
- Zhang, P.; Wang, W.; Liu, X.H.; Yang, Z.; Gaur, R.; Wang, J.J.; Ke, J.P.; Bao, G.H. Detection and Quantification of Flavoalkaloids in Different Tea Cultivars and During Tea Processing Using UPLC-TOF-MS/MS. Food Chem. 2021, 339, 127864. [Google Scholar] [CrossRef]
- Wang, M.Q.; Ma, W.J.; Shi, J.; Zhu, Y.; Lin, Z.; Lv, H.P. Characterization of the Key Aroma Compounds in Longjing Tea Using Stir Bar Sorptive Extraction (SBSE) Combined with Gas Chromatography-Mass Spectrometry (GC–MS), Gas Chromatography-Olfactometry (GC-O), Odor Activity Value (OAV), and Aroma Recombination. Food Res. Int. 2020, 130, 108908. [Google Scholar] [CrossRef] [PubMed]
- Zeng, L.; Zhou, X.; Su, X.; Yang, Z. Chinese Oolong Tea: An Aromatic Beverage Produced Under Multiple Stresses. Trends Food Sci. Technol. 2020, 106, 242–253. [Google Scholar] [CrossRef]
- Xu, M.; Wang, J.; Zhu, L. The Qualitative and Quantitative Assessment of Tea Quality Based on E-nose, E-tongue and E-eye Combined with Chemometrics. Food Chem. 2019, 289, 482–489. [Google Scholar] [CrossRef]
- Gharibzahedi, S.M.T.; Altintas, Z.; Barba, F.J.; Mofid, V. Biosensing Technology in Food Production and Processing. In Advanced Sensor Technology; Barhoum, A., Altintas, Z., Eds.; Elsevier: London, UK, 2022. [Google Scholar]
- He, W.; Hu, X.; Zhao, L.; Liao, X.; Zhang, Y.; Zhang, M.; Wu, J. Evaluation of Chinese Tea by the Electronic Tongue: Correlation with Sensory Properties and Classification According to Geographical Origin and Grade Level. Food Res. Int. 2009, 42, 1462–1467. [Google Scholar] [CrossRef]
- Ye, N.S. A Minireview of Analytical Methods for the Geographical Origin Analysis of Teas (Camellia sinensis). Crit. Rev. Food Sci. Nutr. 2012, 52, 775–780. [Google Scholar] [CrossRef]
- Peres, R.G.; Tonin, F.G.; Tavares, M.F.; Rodriguez-Amaya, D.B. Determination of Catechins in Green Tea Infusions by Reduced Flow Micellar Electrokinetic Chromatography. Food Chem. 2011, 127, 651–655. [Google Scholar] [CrossRef]
- Yu, J.; Liu, Y.; Zhang, S.; Luo, L.; Zeng, L. Effect of Brewing Conditions on Phytochemicals and Sensory Profiles of Black Tea Infusions: A Primary Study on the Effects of Geraniol and Β-Ionone on Taste Perception of Black Tea Infusions. Food Chem. 2021, 354, 129504. [Google Scholar] [CrossRef]
- Sharmilan, T.; Premarathne, I.; Wanniarachchi, I.; Kumari, S.; Wanniarachchi, D. Electronic Nose Technologies in Monitoring Black Tea Manufacturing Process. J. Sens. 2020, 2020, 3073104. [Google Scholar] [CrossRef]
- Biswas, P.; Chatterjee, S.; Kumar, N.; Singh, M.; Majumder, A.B.; Bera, B. Integrated Determination of Tea Quality Based on Taster’s Evaluation, Biochemical Characterization and Use of Electronics. In Sensing Technology: Current Status And Future Trends II; Mason, A., Mukhopadhyay, S.C., Jayasundera, K.P., Eds.; Springer: Cham, Switzerland, 2014; pp. 95–117. [Google Scholar]
- Koch, W.; Kukula-Koch, W.; Komsta, Ł.; Marzec, Z.; Szwerc, W.; Głowniak, K. Green Tea Quality Evaluation Based on Its Catechins and Metals Composition in Combination with Chemometric Analysis. Molecules 2018, 23, 1689. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- 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. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2019, 206, 254–262. [Google Scholar] [CrossRef] [PubMed]
- 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. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2020, 240, 118576. [Google Scholar] [CrossRef] [PubMed]
- Wang, Y.J.; Li, T.H.; Li, L.Q.; Ning, J.M.; Zhang, Z.Z. Micro-NIR Spectrometer for Quality Assessment of Tea: Comparison of Local and Global Models. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2020, 237, 118403. [Google Scholar] [CrossRef]
- Zareef, M.; Chen, Q.; Ouyang, Q.; Kutsanedzie, F.Y.; Hassan, M.M.; Viswadevarayalu, A.; Wang, A. Prediction of Amino Acids, Caffeine, Theaflavins and Water Extract in Black Tea Using FT-NIR Spectroscopy Coupled Chemometrics Algorithms. Anal. Methods 2018, 10, 3023–3031. [Google Scholar] [CrossRef]
- Zhu, M.Z.; Wen, B.; Wu, H.; Li, J.; Lin, H.; Li, Q.; Li, Y.; Huang, J.; Liu, Z. The Quality Control of Tea by Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics. J. Spectrosc. 2019, 2019, 8129648. [Google Scholar] [CrossRef]
- Dong, C.; Li, J.; Wang, J.; Liang, G.; Jiang, Y.; Yuan, H.; Yang, Y.; Meng, H. Rapid Determination by Near Infrared Spectroscopy of Theaflavins-to-Thearubigins Ratio During Congou Black Tea Fermentation Process. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2018, 205, 227–234. [Google Scholar] [CrossRef]
- 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. J. Sci. Food Agric. 2019, 99, 5019–5027. [Google Scholar] [CrossRef]
- Guo, Z.; Barimah, A.O.; Yin, L.; Chen, Q.; Shi, J.; El-Seedi, H.R.; Zou, X. Intelligent Evaluation of Taste Constituents and Polyphenols-to-Amino Acids Ratio in Matcha Tea Powder Using Near Infrared Spectroscopy. Food Chem. 2021, 353, 129372. [Google Scholar] [CrossRef]
- Xu, M.; Wang, J.; Gu, S. Rapid Identification of Tea Quality by E-nose and Computer Vision Combining with a Synergetic Data Fusion Strategy. J. Food Eng. 2019, 241, 10–17. [Google Scholar] [CrossRef]
- Zhu, Y.; Wang, J.; Wu, Y.; Shang, Z.; Ding, Y.; Hu, A. A Fluorescent Sensor Array-Based Electronic Tongue for Chinese Tea Discrimination. J. Mater. Chem. C 2021, 9, 5676–5681. [Google Scholar] [CrossRef]
- Yu, H.; Wang, Y.; Wang, J. Identification of Tea Storage Times by Linear Discrimination Analysis and Back-Propagation Neural Network Techniques Based on The Eigenvalues of Principal Components Analysis of E-nose Sensor Signals. Sensors 2009, 9, 8073–8082. [Google Scholar] [CrossRef] [Green Version]
- Zhi, R.; Zhao, L.; Zhang, D. A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment. Sensors 2017, 17, 1007. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Roy, R.B.; Chattopadhyay, P.; Tudu, B.; Bhattacharyya, N.; Bandyopadhyay, R. Artificial Flavor Perception of Black Tea Using Fusion of Electronic Nose and Tongue Response: A Bayesian Statistical Approach. J. Food Eng. 2014, 142, 87–93. [Google Scholar]
- Wang, Y.; Liu, Y.; Cui, Q.; Li, L.; Ning, J.; Zhang, Z. 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, 300, 110534. [Google Scholar] [CrossRef]
- Shi, B.; Zhao, L.; Zhi, R.; Xi, X. Optimization of Electronic Nose Sensor Array by Genetic Algorithms in Xihu-Longjing Tea Quality Analysis. Math. Comput. Model. 2013, 58, 752–758. [Google Scholar] [CrossRef]
- Wang, J.; Zhang, C.; Chang, M.; He, W.; Lu, X.; Fei, S.; Lu, G. Optimization of Electronic Nose Sensor Array for Tea Aroma Detecting Based on Correlation Coefficient and Cluster Analysis. Chemosensors 2021, 9, 266. [Google Scholar] [CrossRef]
- Saha, P.; Ghorai, S.; Tudu, B.; Bandyopadhyay, R.; Bhattacharyya, N. Optimization Of Sensor Array In Electronic Nose By Combinational Feature Selection Method. In Sensing Technology: Current Status and Future Trends II; Springer: Cham, Switzerland, 2014; pp. 189–205. [Google Scholar]
- Borowik, P.; Adamowicz, L.; Tarakowski, R.; Siwek, K.; Grzywacz, T. Odor Detection Using an E-Nose With a Reduced Sensor Array. Sensors 2020, 20, 3542. [Google Scholar] [CrossRef]
- Cozzolino, D.; Cynkar, W.; Dambergs, R.; Smith, P. Two-Dimensional Correlation Analysis of the Effect Of Temperature on the Fingerprint of Wines Analysed by Mass Spectrometry Electronic Nose. Sens. Actuators B Chem. 2010, 145, 628–634. [Google Scholar] [CrossRef]
- Tudu, B.; Jana, A.; Metla, A.; Ghosh, D.; Bhattacharyya, N.; Bandyopadhyay, R. Electronic Nose for Black Tea Quality Evaluation by an Incremental RBF Network. Sens. Actuators B Chem. 2009, 138, 90–95. [Google Scholar] [CrossRef]
- Xu, M.; Wang, J.; Zhu, L. Tea Quality Evaluation by Applying E-Nose Combined with Chemometrics Methods. J. Food Sci. Technol. 2021, 58, 1549–1561. [Google Scholar] [CrossRef] [PubMed]
- Hidayat, S.N.; Triyana, K.; Fauzan, I.; Julian, T.; Lelono, D.; Yusuf, Y.; Ngadiman, N.; Veloso, A.C.A.; Peres, A.M. The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ. Chemosensors 2019, 7, 29. [Google Scholar] [CrossRef] [Green Version]
- Tseng, T.S.; Hsiao, M.H.; Chen, P.A.; Lin, S.Y.; Chiu, S.W.; Yao, D.J. Utilization of a Gas-Sensing System to Discriminate Smell and to Monitor Fermentation During the Manufacture of Oolong Tea Leaves. Micromachines 2021, 12, 93. [Google Scholar] [CrossRef]
- Dutta, R.; Hines, E.L.; Gardner, J.W.; Kashwan, K.R.; Bhuyan, M. Tea Quality Prediction Using a Tin Oxide-Based Electronic Nose: An Artificial Intelligence Approach. Sens. Actuators B Chem. 2003, 94, 228–237. [Google Scholar] [CrossRef]
- Yu, H.; Wang, J. Discrimination of LongJing Green-Tea Grade by Electronic Nose. Sens. Actuators B Chem. 2007, 122, 134–140. [Google Scholar] [CrossRef]
- Borah, S.; Hines, E.L.; Leeson, M.S.; Iliescu, D.D.; Bhuyan, M.; Gardner, J.W. Neural Network Based Electronic Nose for Classification of Tea Aroma. Sens. Instrum. Food Qual. Saf. 2008, 2, 7–14. [Google Scholar] [CrossRef] [Green Version]
- Yu, H.; Wang, J.; Yao, C.; Zhang, H.; Yu, Y. Quality Grade Identification of Green Tea Using E-nose by CA and ANN. LWT-Food Sci. Technol. 2008, 41, 1268–1273. [Google Scholar] [CrossRef]
- Yu, H.; Wang, J.; Zhang, H.; Yu, Y.; Yao, C. Identification of Green Tea Grade Using Different Feature of Response Signal from E-nose Sensors. Sens. Actuators B Chem. 2008, 128, 455–461. [Google Scholar] [CrossRef]
- Tudu, B.; Kow, B.; Bhattacharyya, N.; Bandyopadhyay, R. Comparison of Multivariate Normalization Techniques as Applied to Electronic Nose Based Pattern Classification for Black Tea. In Proceedings of the 2008 3rd International Conference on Sensing Technology, Taipei, Taiwan, 30 November–3 December 2008; pp. 254–258. [Google Scholar]
- Yu, H.; Wang, J.; Xiao, H.; Liu, M. Quality Grade Identification of Green Tea Using the Eigenvalues of PCA Based on the E-nose Signals. Sens. Actuators B Chem. 2009, 140, 378–382. [Google Scholar] [CrossRef]
- Kaur, R.; Kumar, R.; Gulati, A.; Ghanshyam, C.; Kapur, P.; Bhondekar, A.P. Enhancing Electronic Nose Performance: A Novel Feature Selection Approach Using Dynamic Social Impact Theory and Moving Window Time Slicing for Classification of Kangra Orthodox Black Tea (Camellia Sinensis (L.) O. Kuntze). Sens. Actuators B Chem. 2012, 166, 309–319. [Google Scholar] [CrossRef]
- Chen, Q.; Liu, A.; Zhao, J.; Ouyang, Q. Classification of Tea Category Using a Portable Electronic Nose Based on an Odor Imaging Sensor Array. J. Pharm. Biomed. Anal. 2013, 84, 77–83. [Google Scholar] [CrossRef] [PubMed]
- Roy, R.B.; Modak, A.; Mondal, S.; Tudu, B.; Bandyopadhyay, R.; Bhattacharyya, N. Fusion of Electronic Nose and Tongue Response Using Fuzzy Based Approach for Black Tea Classification. Proc. Technol. 2013, 10, 615–622. [Google Scholar]
- Dai, Y.; Zhi, R.; Zhao, L.; Gao, H.; Shi, B.; Wang, H. Longjing Tea Quality Classification by Fusion of Features Collected from E-nose. Chemom. Intell. Lab. Syst. 2015, 144, 63–70. [Google Scholar] [CrossRef]
- Sharma, P.; Ghosh, A.; Tudu, B.; Sabhapondit, S.; Baruah, B.D.; Tamuly, P.; Bhattacharyya, N.; Bandyopadhyay, R. Monitoring the Fermentation Process of Black Tea Using QCM Sensor Based Electronic Nose. Sens. Actuators B Chem. 2015, 219, 146–157. [Google Scholar] [CrossRef]
- Xu, S.; Sun, X.; Lu, H.; Zhang, Q. Detection of Type, Blended Ratio, and Mixed Ratio of Pu’er Tea by Using Electronic Nose and Visible/Near Infrared Spectrometer. Sensors 2019, 19, 2359. [Google Scholar] [CrossRef] [Green Version]
- Banerjee, M.B.; Roy, R.B.; Tudu, B.; Bandyopadhyay, R.; Bhattacharyya, N. Black Tea Classification Employing Feature Fusion of E-nose and E-tongue Responses. J. Food Eng. 2019, 244, 55–63. [Google Scholar] [CrossRef]
- Ghosh, S.; Tudu, B.; Bhattacharyya, N.; Bandyopadhyay, R. A Recurrent Elman Network in Conjunction with an Electronic Nose for Fast Prediction of Optimum Fermentation Time of Black Tea. Neural Comput. Appl. 2019, 31, 1165–1171. [Google Scholar] [CrossRef]
- Liu, H.; Yu, D.; Gu, Y. Classification and Evaluation of Quality Grades of Organic Green Teas Using an Electronic Nose Based on Machine Learning Algorithms. IEEE Access 2019, 7, 172965–172973. [Google Scholar] [CrossRef]
- Lu, X.; Wang, J.; Lu, G.; Lin, B.; Chang, M.; He, W. Quality level Identification of West Lake Longjing Green Tea Using Electronic Nose. Sens. Actuators B Chem. 2019, 301, 127056. [Google Scholar] [CrossRef]
- Yuan, H.; Chen, X.; Shao, Y.; Cheng, Y.; Yang, Y.; Zhang, M.; Hua, J.; Li, J.; Deng, Y.; Wang, J. Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis. J. Food Sci. 2019, 84, 3411–3417. [Google Scholar] [CrossRef] [PubMed]
- Yang, B.; Qi, L.; Wang, M.; Hussain, S.; Wang, H.; Wang, B.; Ning, J. Cross-category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery. Sensors 2020, 20, 50. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yu, D.; Gu, Y. A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose. Foods 2021, 10, 795. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Gu, Y.; Liu, H. A Transfer Learning Method for the Protection of Geographical Indication in China Using an Electronic Nose for the Identification of Xihu Longjing Tea. IEEE Sens. J. 2021, 21, 8065–8077. [Google Scholar] [CrossRef]
- Chen, J.; Yang, Y.; Deng, Y.; Liu, Z.; Xie, J.; Shen, S.; Yuan, H.; Jiang, Y. Aroma Quality Evaluation of Dianhong Black Tea Infusions by the Combination of Rapid Gas Phase Electronic Nose and Multivariate Statistical Analysis. LWT 2022, 153, 112496. [Google Scholar] [CrossRef]
- Yang, Y.; Chen, J.; Jiang, Y.; Qian, M.C.; Deng, Y.; Xie, J.; Li, J.; Wang, J.; Dong, C.; Yuan, H. Aroma Dynamic Characteristics During the Drying Process of Green Tea by Gas Phase Electronic Nose and Gas Chromatography-Ion Mobility Spectrometry. LWT 2022, 154, 112691. [Google Scholar] [CrossRef]
- Yan, T.; Lin, J.; Zhu, J.; Ye, N.; Huang, J.; Wang, P.; Jin, S.; Zheng, D.; Yang, J. Aroma Analysis of Fuyun 6 and Jinguanyin Black Tea in the Fu’an Area Based on E-Nose and GC–MS. Eur. Food Res. Technol. 2022, 248, 947–961. [Google Scholar] [CrossRef]
- Song, F.; Wu, J.; Liu, B.; Jiang, J.; Li, Z.; Song, C.; Li, J.; Jin, G. Intelligent Green Tea Fixation with Sensor Fusion Technology. J. Food Eng. 2022, 317, 110846. [Google Scholar] [CrossRef]
- Sanaeifar, A.; Li, X.; He, Y.; Huang, Z.; Zhan, Z. A Data Fusion Approach on Confocal Raman Microspectroscopy and Electronic Nose for Quantitative Evaluation of Pesticide Residue in Tea. Biosyst. Eng. 2021, 210, 206–222. [Google Scholar] [CrossRef]
- Tan, J.; Xu, J. Applications of Electronic Nose (E-nose) and Electronic Tongue (E-tongue) in Food Quality-Related Properties Determination: A Review. Artif. Intell. Agric. 2020, 4, 104–115. [Google Scholar] [CrossRef]
- Ivarsson, P.; Holmin, S.; Höjer, N.E.; Krantz-Rülcker, C.; Winquist, F. Discrimination of Tea by Means of a Voltammetric Electronic Tongue and Different Applied Waveforms. Sens. Actuators B Chem. 2001, 76, 449–454. [Google Scholar] [CrossRef]
- Ivarsson, P.; Kikkawa, Y.; Winquist, F.; Krantz-Rülcker, C.; Höjer, N.E.; Hayashi, K.; Toko, K.; Lundström, L. Comparison of a Voltammetric Electronic Tongue and a Lipid Membrane Taste Sensor. Anal. Chim. Acta 2001, 449, 59–68. [Google Scholar] [CrossRef]
- Ouyang, Q.; Yang, Y.; Wu, J.; Chen, Q.; Guo, Z.; Li, H. Measurement of Total Free Amino Acids Content in Black Tea Using Electronic Tongue Technology Coupled with Chemometrics. LWT 2020, 118, 108768. [Google Scholar] [CrossRef]
- Ouyang, Q.; Yang, Y.; Wu, J.; Liu, Z.; Chen, X.; Dong, C.; Chen, Q.; Zhang, Z.; Guo, Z. Rapid Sensing of Total Theaflavins Content in Black Tea Using a Portable Electronic Tongue System Coupled to Efficient Variables Selection Algorithms. J. Food Compos. Anal. 2019, 75, 43–48. [Google Scholar] [CrossRef]
- Roy, R.B.; Tudu, B.; Shaw, L.; Jana, A.; Bhattacharyya, N.; Bandyopadhyay, R. Instrumental Testing of Tea by Combining the Responses of Electronic Nose and Tongue. J. Food Eng. 2012, 110, 356–363. [Google Scholar]
- Saha, P.; Ghorai, S.; Tudu, B.; Bandyopadhyay, R.; Bhattacharyya, N. Tea Quality Prediction by Autoregressive Modeling of Electronic Tongue Signals. IEEE Sens. J. 2016, 16, 4470–4477. [Google Scholar] [CrossRef]
- Yang, Z.; Gao, J.; Wang, S.; Wang, Z.; Li, C.; Lan, Y.; Sun, X.; Lan, Y. Synergetic Application of E-Tongue and E-Eye Based on Deep Learning to Discrimination of Pu-Erh Tea Storage Time. Comput. Electron. Agric. 2021, 187, 106297. [Google Scholar] [CrossRef]
- Yang, Z.; Miao, N.; Zhang, X.; Li, Q.; Wang, Z.; Li, C.; Sun, X.; Lan, Y. Employment of An Electronic Tongue Combined with Deep Learning and Transfer Learning for Discriminating the Storage Time of Pu-Erh Tea. Food Control. 2021, 121, 107608. [Google Scholar] [CrossRef]
- Zhang, S.; He, R.; Zhang, J.; Zhou, Z.; Cheng, X.; Huang, G.; Zhang, J. A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue. Appl. Sci. 2019, 9, 2518. [Google Scholar] [CrossRef] [Green Version]
- Chen, Q.; Zhao, J.; Vittayapadung, S. Identification of the Green Tea Grade Level Using Electronic Tongue and Pattern Recognition. Food Res. Int. 2008, 41, 500–504. [Google Scholar] [CrossRef]
- Ren, G.; Li, T.; Wei, Y.; Ning, J.; Zhang, Z. Estimation of Congou Black Tea Quality by an Electronic Tongue Technology Combined with Multivariate Analysis. Microchem. J. 2021, 163, 105899. [Google Scholar] [CrossRef]
- Zou, G.; Xiao, Y.; Wang, M.; Zhang, H. Detection of Bitterness and Astringency of Green Tea with Different Taste by Electronic Nose and Tongue. PLoS ONE 2018, 13, e020651. [Google Scholar]
- Cheng, L.; Wang, Y.; Zhang, J.; Xu, L.; Zhou, H.; Wei, K.; Peng, L.; Zhang, J.; Liu, Z.; Wei, X. Integration of Non-targeted Metabolomics and E-tongue Evaluation Reveals the Chemical Variation and Taste Characteristics of Five Typical Dark Teas. LWT 2021, 150, 111875. [Google Scholar] [CrossRef]
- Wei, Y.; Li, T.; Xu, S.; Ni, T.; Deng, W.W.; Ning, J. The Profile of Dynamic Changes in Yellow Tea Quality and Chemical Composition During Yellowing Process. LWT 2021, 139, 110792. [Google Scholar] [CrossRef]
- Xiao, Y.; Li, M.; Liu, Y.; Xu, S.; Zhong, K.; Wu, Y.; Gao, H. The Effect of Eurotium cristatum (MF800948) Fermentation on the Quality of Autumn Green Tea. Food Chem. 2021, 358, 129848. [Google Scholar] [CrossRef] [PubMed]
- Yan, S.M.; Hu, Z.F.; Wu, C.X.; Jin, L.; Chen, G.; Zeng, X.Y.; Zhu, J.Q. Electronic Tongue Combined with Chemometrics to Provenance Discrimination for a Green Tea (Anji-White Tea). J. Food Qual. 2017, 2017, 3573197. [Google Scholar] [CrossRef]
- Zhang, S.F.; Zhu, D.H.; Chen, X.J. Analysis of E-tongue Data for Tea Classification Based on Semi-Supervised Learning of Generative Adversarial Network. Chin. J. Anal. Chem. 2022, 50, 77–85. [Google Scholar] [CrossRef]
- Huo, D.; Wu, Y.; Yang, M.; Fa, H.; Luo, X.; Hou, C. Discrimination of Chinese Green Tea According to Varieties and Grade Levels Using Artificial Nose and Tongue Based on Colorimetric Sensor Arrays. Food Chem. 2014, 145, 639–645. [Google Scholar] [CrossRef]
- Cheng, L.; Wang, Y.; Zhang, J.; Zhu, J.; Liu, P.; Xu, L.; Wei, K.; Zhou, H.; Peng, L.; Zhang, J.; et al. Dynamic Changes of Metabolic Profile and Taste Quality During the Long-Term Aging of Qingzhuan Tea: The Impact of Storage Age. Food Chem. 2021, 359, 129953. [Google Scholar] [CrossRef]
- Li, T.; Xu, S.; Wang, Y.; Wei, Y.; Shi, L.; Xiao, Z.; Liu, Z.; Deng, W.W.; Ning, J. Quality Chemical Analysis of Crush–Tear–Curl (CTC) Black Tea from Different Geographical Regions Based on UHPLC-Orbitrap-MS. J. Food Sci. 2021, 86, 3909–3925. [Google Scholar] [CrossRef]
- Modak, A.; Roy, R.B.; Tudu, B.; Bandyopadhyay, R.; Bhattacharyya, N. A Novel Fuzzy Based Signal Analysis Technique in Electronic Nose and Electronic Tongue for Black Tea Quality Analysis. In Proceedings of the 2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI), Kolkata, India, 8–10 January 2016; pp. 279–283. [Google Scholar]
- Liu, J.; Zuo, M.; Low, S.S.; Xu, N.; Chen, Z.; Lv, C.; Cui, Y.; Shi, Y.; Men, H. Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model. Sensors 2020, 20, 686. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sarkar, S.; Bhattacharyya, N.; Palakurthi, V.K. Taste Recognizer by Multi Sensor Electronic Tongue: A Case Study with Tea Quality Classification. In Proceedings of the 2011 Second International Conference on Emerging Applications of Information Technology, Kolkata, India, 19–20 February 2011; pp. 138–141. [Google Scholar]
- Saha, P.; Ghorai, S.; Tudu, B.; Bandyopadhyay, R.; Bhattacharyya, N. A Novel Technique of Black Tea Quality Prediction Using Electronic Tongue Signals. IEEE Trans. Instrum. Meas. 2014, 63, 2472–2479. [Google Scholar] [CrossRef]
- Chen, X.; Xu, Y.; Meng, L.; Chen, X.; Yuan, L.; Cai, Q.; Shi, W.; Huang, G. Non-Parametric Partial Least Squares–Discriminant Analysis Model Based on Sum of Ranking Difference Algorithm for Tea Grade Identification Using Electronic Tongue Data. Sens. Actuators B Chem. 2020, 311, 127924. [Google Scholar] [CrossRef]
- Xu, C. Electronic Eye for Food Sensory Evaluation. In Evaluation Technologies for Food Quality; Woodhead Publishing: John Solston, UK, 2019; pp. 37–59. [Google Scholar]
- Xu, M.; Wang, J. The Qualitative and Quantitative Assessment of Tea Quality Based on E-nose, E-tongue and E-eye Signals Combining With Chemometrics Methods. In Proceedings of the 2018 ASABE Annual International Meeting, Detroit, MI, USA, 29 July–1 August 2018; p. 1800610. [Google Scholar] [CrossRef]
- Palit, M.; Tudu, B.; Bhattacharyya, N.; Dutta, A.; Dutta, P.K.; Jana, A.; Bandyopadhyay, R.; Chatterjee, A. Comparison of Multivariate Preprocessing Techniques as Applied to Electronic Tongue Based Pattern Classification for Black Tea. Anal. Chim. Acta 2010, 675, 8–15. [Google Scholar] [CrossRef]
- Dutta, R.; Kashwan, K.R.; Bhuyan, M.; Hines, E.L.; Gardner, J.W. Electronic Nose Based Tea Quality Standardization. Neural. Netw. 2003, 16, 847–853. [Google Scholar] [CrossRef]
- Banerjee, R.; Tudu, B.; Bandyopadhyay, R.; Bhattacharyya, N. A Review on Combined Odor and Taste Sensor Systems. J. Food Eng. 2016, 190, 10–21. [Google Scholar] [CrossRef]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gharibzahedi, S.M.T.; Barba, F.J.; Zhou, J.; Wang, M.; Altintas, Z. Electronic Sensor Technologies in Monitoring Quality of Tea: A Review. Biosensors 2022, 12, 356. https://doi.org/10.3390/bios12050356
Gharibzahedi SMT, Barba FJ, Zhou J, Wang M, Altintas Z. Electronic Sensor Technologies in Monitoring Quality of Tea: A Review. Biosensors. 2022; 12(5):356. https://doi.org/10.3390/bios12050356
Chicago/Turabian StyleGharibzahedi, Seyed Mohammad Taghi, Francisco J. Barba, Jianjun Zhou, Min Wang, and Zeynep Altintas. 2022. "Electronic Sensor Technologies in Monitoring Quality of Tea: A Review" Biosensors 12, no. 5: 356. https://doi.org/10.3390/bios12050356
APA StyleGharibzahedi, S. M. T., Barba, F. J., Zhou, J., Wang, M., & Altintas, Z. (2022). Electronic Sensor Technologies in Monitoring Quality of Tea: A Review. Biosensors, 12(5), 356. https://doi.org/10.3390/bios12050356