Quantitative prediction and visualization of matcha color physicochemical indicators using hyperspectral microscope imaging technology

Color physicochemical indicators of matcha are related to sensory quality, and the distributions of color physicochemical indicators can reflect the uniformity of matcha powder. This study aimed to explore the feasibility of using hyperspectral microscope imaging (HMI) technology to predict and visualize matcha color physicochemical indicators. The average spectra (400-998 nm) were extracted from the regions of interest (ROI). Subsequently, competitive adaptive reweighted sampling (CARS), interval random frog (iRF), successive projections algorithm (SPA), and the combination of iRF and SPA (iRF-SPA) were adopted to screen the characteristic spectra variables that were associated with specific color physicochemical indicators. Partial least squares (PLS) regression was applied to develop the calibration models for predicting the color physicochemical indicators. The results showed that the iRF-SPA-PLS models obtained the optimal prediction results, with predictive correlation coefficients (Rp) of 0.9262 for L*, 0.8826 for a*, 0.8583 for b*, 0.8243 for chlorophyll a, 0.7518 for chlorophyll b, and 0.8093 for chlorophyll total. Finally, the distribution maps of matcha color physicochemical indicators were visualized using these optimized models. The results suggested that the HMI technology coupled with chemometrics has enormous potential to predict and visualize the color physicochemical indicators of matcha, which could reflect the uniformity of matcha powder to ensure the stability of matcha quality. The approach presented in this study can also be applied to quality control of other powdery foods.