Low-cost optical approach for noncontact predicting moisture content of apple slices during hot air drying

Absorption and scattering properties of product change as moisture content is reduced, but it has not been investigated how these changes are correlated. This study was aimed to measure and test the feasibility of using optical properties in predicting the moisture content of sliced apple samples during hot air drying. In this investigation, the noninvasive backscattering laser imaging technique at three wavelengths (650, 780, and 880nm) and Farrell's diffusion theory were used to determine absorption and reduced scattering coefficients. Artificial neural network model was applied to correlate the optical coefficients and moisture content of samples. The highest correlation between above-mentioned parameters was found at 780nm. The best moisture content prediction result was obtained when absorption and reduced scattering coefficients were combined at three wavelengths with R-p=0.984. The results suggested that this method can be effectively used to predict the moisture content and control the drying process.