Data fusion strategy for rapid prediction of moisture content during drying of black tea based on micro-NIR spectroscopy and machine vision

Drying is an important process in black tea processing, meanwhile, the moisture content is an important factor in determining the drying quality. However, at present, there is a lack of effective real-time detection methods for the moisture content of black tea during the drying. Therefore, we explored the analysis of spectral data and image color and texture feature data based on micro-near-infrared spectroscopy(micro-NIRS) and machine vision data fusion tech-nology, and analyzed the spectral data and image color and texture feature data based on micro-NIR spectroscopy and machine vision data fusion technology, a quantitative prediction model for the drying moisture content of black tea was developed by LS-SVM. The results showed that the prediction accuracy of the prediction model established based on the middle-level data fusion achieved the best results and the correlation coefficient (Rp) and root mean square error of prediction (RMSEP) of the prediction set were 0. 9696 and 0.0016, respectively, and the relative deviation (RPD) was 4.0846. This study shows that data fusion based on the fusion of spectral and image technologies has a strong predictive capability for the drying process of black tea, and has some guiding significance for controlling the drying quality of black tea.