Physicochemical indicators coupled with multivariate analysis for comprehensive evaluation of matcha sensory quality

The sensory quality of matcha is a pivotal factor in determining consumer acceptance. However, the human sensory panel test is difficult to popularize by virtue of professional requirements and inability to evaluate large samples. The analysis showed that physicochemical indicators of matcha were significantly related to sensory quality. Hence, principal component analysis (PCA) based on selected key physicochemical indicators was proposed to evaluate the sensory quality of matcha in this research. The eight key indicators were selected from twenty-four physicochemical indicators based on least absolute shrinkage and selection operator (LASSO) for the establishment of the PCA comprehensive evaluation model. The results demonstrated that the PCA comprehensive evaluation model achieved superior performance, with -0.895 rc (correlation coefficient in calibration set) and -0.883 rp (correlation coefficient in prediction set) for overall sensory quality. This work demonstrated that LASSO-PCA comprehensive evaluation as an objective protocol has great potential in predicting matcha sensory quality.