Evaluating aroma quality of black tea by an olfactory visualization system: Selection of feature sensor using particle swarm optimization.

Aroma is an important index to evaluate the quality and grade of black tea. This work innovatively proposed the sensory evaluation of black tea aroma quality based on an olfactory visual sensor system. Firstly, the olfactory visualization system, which can visually represent the aroma quality of black tea, was assembled using a lab-made color sensitive sensor array including eleven porphyrins and one pH indicator for data acquisition and color components extraction. Then, the color components from different color sensitive spots were optimized using the particle swarm optimization (PSO) algorithm. Finally, the back propagation neural network (BPNN) model was developed using the optimized characteristic color components for the sensory evaluation of black tea aroma quality. Results demonstrated that the BPNN models, which were developed using three color components from FTPPFeCl (component G), MTPPTE (component B) and BTB (component B), can get better results based on comprehensive consideration of the generalization performance of the model and the fabrication cost of the sensor. In the validation set, the average of correlation coefficient (RP) value was 0.8843 and the variance was 0.0362. The average of root mean square error of prediction (RMSEP) was 0.3811 and the variance was 0.0525. The overall results sufficiently reveal that the optimized sensor array has promising applications for the sensory evaluation of black tea products in the process of practical production.

[1]  Jiewen Zhao,et al.  Classification of rice wine according to different marked ages using a novel artificial olfactory technique based on colorimetric sensor array. , 2013, Food chemistry.

[2]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[3]  Jun Sun,et al.  Detection of submerged fermentation ofTremella aurantialbausing data fusion of electronic nose and tongue , 2019, Journal of Food Process Engineering.

[4]  Margaret E. Kosal,et al.  Seeing smells: Development of an optoelectronic nose , 2007 .

[5]  Wei Liu,et al.  Design of A Portable Electronic Nose system and Application in K Value Prediction for Large Yellow Croaker (Pseudosciaena crocea) , 2016, Food Analytical Methods.

[6]  Quansheng Chen,et al.  Evaluation of chicken freshness using a low-cost colorimetric sensor array with AdaBoost–OLDA classification algorithm , 2014 .

[7]  Nabarun Bhattacharyya,et al.  Electronic Nose for Black Tea Classification and Correlation of Measurements With “Tea Taster” Marks , 2008, IEEE Transactions on Instrumentation and Measurement.

[8]  Hui Jiang,et al.  Chemometric Models for the Quantitative Descriptive Sensory Properties of Green Tea (Camellia sinensis L.) Using Fourier Transform Near Infrared (FT-NIR) Spectroscopy , 2015, Food Analytical Methods.

[9]  O. Abd-Elkader,et al.  Levels of Trace Elements in Black Teas Commercialized in Saudi Arabia Using Inductively Coupled Plasma Mass Spectrometry , 2016, Biological Trace Element Research.

[10]  Jiewen Zhao,et al.  Classification of different varieties of Oolong tea using novel artificial sensing tools and data fusion , 2015 .

[11]  Yu Gu,et al.  Convenient and accurate method for the identification of Chinese teas by an electronic nose , 2019 .

[12]  Quansheng Chen,et al.  Classification of tea category using a portable electronic nose based on an odor imaging sensor array. , 2013, Journal of pharmaceutical and biomedical analysis.

[13]  Quansheng Chen,et al.  Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems. , 2018, Journal of the science of food and agriculture.

[14]  B. Tudu,et al.  Portable Electronic Nose System for Aroma Classification of Black Tea , 2008, 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems.

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

[16]  Weiqi Wang,et al.  Study of sensitivity evaluation on ridgetail white prawn (Exopalaemon carinicauda) quality examination methods , 2019, International Journal of Food Properties.

[17]  Runu Banerjee Roy,et al.  Black tea classification employing feature fusion of E-Nose and E-Tongue responses , 2019, Journal of Food Engineering.

[18]  Hui Jiang,et al.  Monitoring of Cell Concentration during Saccharomyces cerevisiae Culture by a Color Sensor: Optimization of Feature Sensor Using ACO , 2019, Sensors.

[19]  W. U. Anake,et al.  Concentrations, sources and risk characterisation of polycyclic aromatic hydrocarbons (PAHs) in green, herbal and black tea products in Nigeria , 2018 .

[20]  Tong Liu,et al.  Qualitative discrimination of yeast fermentation stages based on an olfactory visualization sensor system integrated with a pattern recognition algorithm , 2019, Analytical Methods.

[21]  Jiewen Zhao,et al.  Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: A comparison of spectra and color data information. , 2017, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[22]  Jiewen Zhao,et al.  Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. , 2014, Food chemistry.

[23]  L. Jian,et al.  Electronic nose system fabrication and application in large yellow croaker (Pseudosciaena crocea) fressness prediction , 2017, Journal of Food Measurement and Characterization.

[24]  I. Kakar,et al.  Pharmacological values and therapeutic properties of black tea (Camellia sinensis): A comprehensive overview. , 2018, Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie.

[25]  Xingyi Huang,et al.  Evaluating quality of tomato during storage using fusion information of computer vision and electronic nose , 2018, Journal of Food Process Engineering.

[26]  R. Bandyopadhyay,et al.  Monitoring of black tea fermentation process using electronic nose , 2007 .

[27]  Liyong Luo,et al.  Characterization of Volatile Compounds and Sensory Analysis of Jasmine Scented Black Tea Produced by Different Scenting Processes. , 2018, Journal of food science.

[28]  Jiewen Zhao,et al.  Characterization of Volatile Organic Compounds of Vinegars with Novel Electronic Nose System Combined with Multivariate Analysis , 2014, Food Analytical Methods.

[29]  Harry T. Lawless,et al.  Sensory Evaluation of Food: Principles and Practices , 1998 .

[30]  Guohua Hui,et al.  Ridgetail White Prawn (Exopalaemon carinicauda) K Value Predicting Method by Using Electronic Nose Combined with Non-linear Data Analysis Model , 2018, Food Analytical Methods.

[31]  Hui Guohua,et al.  Optimization of eigenvalue selection in Chinese liquors discrimination based on electronic nose , 2014, Journal of Food Measurement and Characterization.

[32]  F. Zheng,et al.  Rapid freshness analysis of mantis shrimps (Oratosquilla oratoria) by using electronic nose , 2016, Journal of Food Measurement and Characterization.

[33]  M. Serafini,et al.  Antioxidants from black and green tea: from dietary modulation of oxidative stress to pharmacological mechanisms , 2017, British journal of pharmacology.

[34]  Jiewen Zhao,et al.  Quantifying Total Viable Count in Pork Meat Using Combined Hyperspectral Imaging and Artificial Olfaction Techniques , 2016, Food Analytical Methods.

[35]  Zbigniew Michalewicz,et al.  Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review , 2017, Evolutionary Computation.