Application of machine learning algorithms in quality assurance of fermentation process of black tea: based on electrical properties
Research output: Contribution to journal › Journal article › Research › peer-review
Fermentation process directly determines the product quality of black tea. This work aimed to develop a rapid method for detecting the degree of fermentation of black tea based on electrical properties of tea leaves. An LCR meter employed to identify 11 electrical parameters of tea leaves during the fermentation process, and the content of catechins and tea pigments in tea leaves were measured by using HPLC and UV-Vis spectrometer, respectively. Principal component analysis and hierarchical clustering analysis applied to divide samples into different groups in the degree of fermentation. Correlation analysis used to characterize the responding strength of electrical parameters on the variation of catechins and pigments. Finally, multilayer perceptron, random forest, and support vector machine algorithm used to build discrimination models of fermentation degree, and the average accuracy rate on the testing set reached to 88.90%, 100%, and 76.92%, respectively.
|Journal||Journal of Food Engineering|
|Number of pages||8|
|Publication status||Published - 2019|
- Black tea, Electrical properties, Fermentation, Machine learning algorithms, Quality components, Random forest