Moisture content monitoring in withering leaves during black tea processing based on electronic eye and near infrared spectroscopy

Monitoring the moisture content of withering leaves in black tea manufacturing remains a difficult task because the external and internal information of withering leaves cannot be simultaneously obtained. In this study, the spectral data and the color/texture information of withering leaves were obtained using near infrared spectroscopy (NIRS) and electronic eye (E-eye), respectively, and then fused to predict the moisture content. Subsequently, the low- and middle-level fusion strategy combined with support vector regression (SVR) was applied to detect the moisture level of withering leaves. In the middle-level fusion strategy, the principal component analysis (PCA) and random frog (RF) were employed to compress the variables and select effective information, respectively. The middle-level-RF (cutoff line=0.8) displayed the best performance because this model used fewer variables and still achieved a satisfactory result, with 0.9883 and 5.5596 for the correlation coefficient of the prediction set (R-p) and relative percent deviation (RPD), respectively. Hence, our study demonstrated that the proposed data fusion strategy could accurately predict the moisture content during the withering process.