On-line monitoring of moisture ratio for apple during vacuum freeze-drying based on image texture analysis

Based on image texture analysis, this paper provides a new experimental method on real-time monitoring of moisture content. The images of original apple slice sample and sample dyed by CuSO4 solution were captured by high-speed CCD and annular light source with polariscope during vacuum freeze-drying process. Mean value, standard deviation, smoothness, the third moment, uniformity and entropy were analyzed statistically by principal component analysis method, and the relationship between PC1 and moisture ratio W1 of original apple slice, PC1 and moisture ratio W2 of dyed apple slice were analyzed using non-linear regression method. In addition, the relationship between W1 and moisture ratio W2 of dyed apple slice was carried out the same computation with non-linear regression method. The results indicate that the p values of models are both <0.0001, whose coefficient of determination are 0.9376, 0.9289 and 0.9930, respectively. In addition, most relative errors to the moisture ratio are limited 3%. Therefore, the mentioned models not only provide a new monitoring method of moisture ratio, but also give foundation of monitoring moisture ratio for other drying processes.