The potential of hyperspectral imaging (HSI) in the visible-near infrared (445–945 nm) wavelength range to discriminate between casing soil, enzymatic browning and undamaged tissue on mushroom (Agaricus bisporus) surfaces was investigated. A calibration set of 108 damage free mushrooms, grown under controlled conditions in a research station, were first tested as undamaged class (U) and then were divided into 2 groups of 54 samples. The first group was smeared with casing soil and designated as casing soil class (C) and the second group was subjected to vibrational damage resulting in enzymatic browning and designated as damaged class (D). Partial least squares discriminant analysis (PLS-DA) models were developed to classify mushroom tissue as one of the three classes investigated (U, C and D) using pixel spectra from each class. Prediction maps were obtained by applying the developed models to the hyperspectral images of candidate mushrooms. Percentages of pixels classified into each class were also calculated for the mushrooms studied in the calibration set. Results obtained showed that the developed models performed satisfactorily to discriminate between the 3 classes studied. Comparison of red–green–blue (RGB) and hyperspectral image analysis showed that HSI was better able to identify the regions containing casing soil. Model validation was performed using 3 different test sets of mushrooms obtained from a commercial producer. It was found that the developed PLS-DA models were satisfactorily capable of identifying undamaged regions, casing soil and enzymatic damaged areas on mushrooms from the validation sets.

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