Abstract: In order to rapidly detect the moisture content in tea leaves during green tea fixation, a quantitative prediction model for moisture content changes during green tea fixation was constructed by using machine vision and near infrared spectroscopy. Spectral and image information of samples at different stage of fixation was collected. The characteristic wavelengths were extracted by four different variable selection methods, competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), variable combination population analysis, iterative retained information variable algorithm (VCPA-IRIV), and random frog (RF) algorithm, and the prediction models were developed by using linear partial least squares regression (PLSR) or non-linear support vector regression (SVR) as well as fusing 15 color and texture features in the image. The results showed that the model based on data fusion had improved prediction accuracy compared with that based on single data. The SVR model developed based on the spectral feature wavelengths extracted by CARS algorithm and fusion of 15 color feature using normalization pretreatment combined with principal component analysis (PCA) was the best among all established models. The correlation coefficient of the calibration set (Rc) for the model was 0.974 2, prediction set correlation coefficient (Rp) value 0.971 9, and relative percent deviation (RPD) value 4.154 6, indicating the model had excellent prediction performance. In conclusion, this study proved the feasibility of integrating spectroscopy and imaging technology to predict the moisture content during the process of green tea fixation, which overcomes the problem of the low prediction accuracy of a single sensor, and lays a theoretical foundation for rapid nondestructive detection of the moisture content of fixed tea leaves for green tea and accurately controlling tea fixation quality.

Key words: machine vision; near-infrared spectroscopy; moisture content; green tea fixation; data fusion