Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis.

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.

[1]  Y. Yotsumoto,et al.  Effects of Interlayer Ion in Montmorillonite on Appearance of Decaffeinated Tea Beverage , 2018 .

[2]  F. Wachira,et al.  Determination of Residual Catechins, Polyphenolic Contents and Antioxidant Activities of Developed Theaflavin-3,3’-Digallate Rich Black Teas , 2016 .

[3]  J. Xie,et al.  Determination of Contents of Catechins in Oolong Teas by Quantitative Analysis of Multi-components Via a Single Marker (QAMS) Method , 2017, Food Analytical Methods.

[4]  N. Ohkura,et al.  Simultaneous Determination of Catechins and Caffeine in Green Tea-Based Beverages and Foods for Specified Health Uses , 2017 .

[5]  Eun-hee Kim,et al.  Metabolomic unveiling of a diverse range of green tea (Camellia sinensis) metabolites dependent on geography. , 2015, Food chemistry.

[6]  Dehan Luo,et al.  Application of ANN with extracted parameters from an electronic nose in cigarette brand identification , 2004 .

[7]  D. Xiao,et al.  Evaluation and Optimization of a Superior Extraction Method for the Characterization of the Volatile Profile of Black Tea by HS-SPME/GC-MS , 2017, Food Analytical Methods.

[8]  Jun Wang,et al.  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 , 2009, Sensors.

[9]  Chi-Tang Ho,et al.  Chemistry and Biological Activities of Processed Camellia sinensis Teas: A Comprehensive Review. , 2019, Comprehensive reviews in food science and food safety.

[10]  Ganesh Kumar Mani,et al.  Electronic noses for food quality : a review , 2015 .

[11]  Hanwen Sun,et al.  Investigation of six bioactive anthraquinones in slimming tea by accelerated solvent extraction and high performance capillary electrophoresis with diode-array detection. , 2016, Food chemistry.

[12]  Bipan Tudu,et al.  Artificial flavor perception of black tea using fusion of electronic nose and tongue response: A Bayesian statistical approach , 2014 .

[13]  Dezheng Zhang,et al.  A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment , 2017, Sensors.

[14]  Takuji Ogawa,et al.  Identification of Tobacco Types and Cigarette Brands Using an Electronic Nose Based on Conductive Polymer/Porphyrin Composite Sensors , 2018, ACS omega.

[15]  Yuanjiang Pan,et al.  Multivariate analysis of the volatile components in tobacco based on infrared-assisted extraction coupled to headspace solid-phase microextraction and gas chromatography-mass spectrometry. , 2016, Journal of separation science.

[16]  M. Peris,et al.  A 21st century technique for food control: electronic noses. , 2009, Analytica chimica acta.

[17]  Masaki Kanamori,et al.  Identification of coumarin-enriched Japanese green teas and their particular flavor using electronic nose , 2009 .

[18]  Ricard Boqué,et al.  Data fusion methodologies for food and beverage authentication and quality assessment - a review. , 2015, Analytica chimica acta.

[19]  Zhen Li,et al.  Mass spectrometry-based metabolomics and chemometric analysis of Pu-erh teas of various origins. , 2018, Food chemistry.

[20]  E. Marguí,et al.  Multielement Analysis of Tea and Mint Infusions by Total Reflection X-ray Fluorescence Spectrometry , 2017, Food Analytical Methods.

[21]  Jun Wang,et al.  Discrimination of LongJing green-tea grade by electronic nose , 2007 .

[22]  F. Zhu,et al.  Effect of black tea on antioxidant, textural, and sensory properties of Chinese steamed bread. , 2016, Food chemistry.

[23]  C. Scaman,et al.  Descriptive and hedonic analyses of low-Phe food formulations containing corn (Zea mays) seedling roots: toward development of a dietary supplement for individuals with phenylketonuria. , 2016, Journal of the science of food and agriculture.

[24]  Feng Chen,et al.  Evaluation of the synergism among volatile compounds in Oolong tea infusion by odour threshold with sensory analysis and E-nose. , 2017, Food chemistry.

[25]  Douglas N Rutledge,et al.  Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine. , 2015, Talanta.

[26]  Jânio Sousa Santos,et al.  Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing. , 2018, Comprehensive reviews in food science and food safety.

[27]  Tiina Reponen,et al.  Key determinants of the fungal and bacterial microbiomes in homes. , 2015, Environmental research.

[28]  Yang Ye,et al.  Evaluation of green tea sensory quality via process characteristics and image information , 2017 .

[29]  Quansheng Chen,et al.  Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion. , 2014, Analytica chimica acta.

[30]  N. Kuhnert,et al.  What is under the hump? Mass spectrometry based analysis of complex mixtures in processed food--lessons from the characterisation of black tea thearubigins, coffee melanoidines and caramel. , 2013, Food & function.

[31]  Jânio Sousa Santos,et al.  Multivariate effects of Chinese keemun black tea grades (Camellia sinensis var. sinensis) on the phenolic composition, antioxidant, antihemolytic and cytotoxic/cytoprotection activities. , 2019, Food research international.

[32]  Quansheng Chen,et al.  Recent developments of green analytical techniques in analysis of tea's quality and nutrition , 2015 .

[33]  Surya R. Kalidindi,et al.  Quantification and classification of microstructures in ternary eutectic alloys using 2-point spatial correlations and principal component analyses , 2016 .

[34]  M. Amini,et al.  Rapid Analysis of Styrene in Drinking Water and Tea Samples Using Dispersive Liquid-Liquid Microextraction Combined with Liquid Chromatography-Ultraviolet Detection , 2016, Food Analytical Methods.

[35]  Zhenbo Wei,et al.  Detecting internal quality of peanuts during storage using electronic nose responses combined with physicochemical methods. , 2015, Food chemistry.

[36]  Rajib Bandyopadhyay,et al.  Application of electronic nose for industrial odors and gaseous emissions measurement and monitoring--An overview. , 2015, Talanta.

[37]  Tae-Eun Kim,et al.  Quantitative Analysis of Four Catechins from Green Tea Extract in Human Plasma Using Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry for Pharmacokinetic Studies , 2018, Molecules.

[38]  D. Kuhn,et al.  Changes in flavor volatile composition of oolong tea after panning during tea processing , 2015, Food science & nutrition.

[39]  Sayan Mukherjee,et al.  Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia. , 2016, American journal of human genetics.

[40]  Sajad Kiani,et al.  Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: A review , 2016 .

[41]  A. Gulati,et al.  Fractionation and identification of minor and aroma-active constituents in Kangra orthodox black tea. , 2015, Food chemistry.

[42]  M. de la Guardia,et al.  Evaluation of Data Mining Strategies for Classification of Black Tea Based on Image-Based Features , 2018, Food Analytical Methods.

[43]  H. Horie,et al.  Application of capillary electrophoresis to tea quality estimation , 1998 .