Quantitative prediction and visualization of key physical and chemical components in black tea fermentation using hyperspectral imaging

Abstract Fermentation is a key process that affects the quality of black tea. In this study, we discussed the changes and influencing factors of key endoplasmic components at different positions of stacked fermented leaves, and the effects of different preprocessing, variable selection and intelligent algorithm on the model performance are compared, the quantitative prediction model of main endoplasmic components of Congou black tea under different fermentation time series was established, finally, the content distribution is depicted in different colors. The results show that the RPD values of the random forest (RF) prediction model constructed using the optimal variables of theafuscin, thearubigin, catechin, caffeine, and soluble sugar were 3.40, 2.21, 5.71, 1.46, and 2.89, respectively. The RPD values of the support vector machine (SVR) prediction model constructed using the optimal variables of theaflavin and the phenol ammonia ratio were 3.78 and 2.91, respectively. Furthermore, the visualization process successfully displayed the distribution of various quality indicators of the samples at different time periods. These research results lay a theoretical foundation for advancing the judicious processing of black tea.

[1]  Tong Lei,et al.  Rapid classification of commercial Cheddar cheeses from different brands using PLSDA, LDA and SPA–LDA models built by hyperspectral data , 2019, Journal of Food Measurement and Characterization.

[2]  Luqing Li,et al.  Quality assessment of instant green tea using portable NIR spectrometer. , 2020, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[3]  Xiaoxia Zhou,et al.  Classification detection of saccharin jujube based on hyperspectral imaging technology , 2020 .

[4]  Bipan Tudu,et al.  Preemptive identification of optimum fermentation time for black tea using electronic nose , 2008 .

[5]  Yi Yang,et al.  Hyperspectral imaging for a rapid detection and visualization of duck meat adulteration in beef , 2019, Food Analytical Methods.

[6]  J. Qin,et al.  Raman hyperspectral imaging and spectral similarity analysis for quantitative detection of multiple adulterants in wheat flour , 2019, Biosystems Engineering.

[7]  Chunwang Dong,et al.  Application of machine learning algorithms in quality assurance of fermentation process of black tea-- based on electrical properties , 2019, Journal of Food Engineering.

[8]  Caixia Wang,et al.  Combination of spectra and texture data of hyperspectral imaging for prediction and visualization of palmitic acid and oleic acid contents in lamb meat. , 2020, Meat science.

[9]  Bin Hu,et al.  Rapid Sensing of Key Quality Components in Black Tea Fermentation Using Electrical Characteristics Coupled to Variables Selection Algorithms , 2020, Scientific Reports.

[10]  K. Tu,et al.  Quantitative Visualization of Fungal Contamination in Peach Fruit Using Hyperspectral Imaging , 2020, Food Analytical Methods.

[11]  Hewei Meng,et al.  Rapid determination by near infrared spectroscopy of theaflavins-to-thearubigins ratio during Congou black tea fermentation process. , 2018, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[12]  Takashi Tanaka,et al.  Oxidation mechanism of black tea pigment theaflavin by peroxidase , 2015 .

[13]  Quansheng Chen,et al.  Prediction of black tea fermentation quality indices using NIRS and nonlinear tools , 2017, Food Science and Biotechnology.

[14]  Luqing Li,et al.  Intelligent evaluation of black tea fermentation degree by FT-NIR and computer vision based on data fusion strategy , 2020 .

[15]  Jun Sun,et al.  Nondestructive detection for egg freshness grade based on hyperspectral imaging technology , 2020 .

[16]  Md. Palash Uddin,et al.  Effective subspace detection based on the measurement of both the spectral and spatial information for hyperspectral image classification , 2020, International Journal of Remote Sensing.

[17]  K. Tu,et al.  Information fusion of hyperspectral imaging and electronic nose for evaluation of fungal contamination in strawberries during decay , 2019, Postharvest Biology and Technology.

[18]  Phuong Thao Thi Ngo,et al.  Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron , 2020 .

[19]  R. Bandyopadhyay,et al.  Detection of optimum fermentation time for black tea manufacturing using electronic nose , 2007 .

[20]  Jun Sun,et al.  Quantitative detection of moisture content in rice seeds based on hyperspectral technique , 2018, Journal of Food Process Engineering.

[21]  Jie-wen Zhao,et al.  Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools , 2017, Journal of Zhejiang University-SCIENCE B.