Mango slices were dried by microwave-vacuum drying using a domestic microwave oven equipped with a
vacuum desiccator inside. Two lab-scale hyperspectral imaging (HSI) systems were employed for moisture
prediction. The Page and the Two-term thin-layer drying models were suitable to describe the current drying
process with a fitting goodness of R2 = 0.978. Partial least square (PLS) was applied to correlate the
mean spectrum of each slice and reference moisture content. With three waveband selection strategies,
optimal wavebands corresponding to moisture prediction were identified. The best model RC-PLS-2
(Rp
2 = 0.972 and RMSEP = 4.611%) was implemented into the moisture visualization procedure. Moisture
distribution map clearly showed that the moisture content in the central part of the mango slices was lower
than that of other parts. The present study demonstrated that hyperspectral imaging was a useful tool for
non-destructively and rapidly measuring and visualizing the moisture content during drying process.