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The potential of hyperspectral imaging (500–1010 nm) was evaluated for monitoring of the quality of potato slices (var. Anuschka) of 5, 7 and 9 mm thickness subjected to air drying at 50 °C. The study investigated three different feature selection methods for the prediction of dry basis moisture content and colour of potato slices using partial least squares regression (PLS).

The feature selection strategies tested include interval PLS regression (iPLS), and differences and ratios between raw reflectance values for each possible pair of wavelengths (R[λ₁]–R[λ₂] and R[λ₁]:R[λ₂], respectively). Moreover, the combination of spectral and spatial domains was tested. Excellent results were obtained using the iPLS algorithm. However, features from both datasets of raw reflectance differences and ratios represent suitable alternatives for development of low‐complex prediction models. Finally …