In this study, the potential application of hyperspectral imaging (HSI) for quality prediction of white mushroom slices during storage at 4 C and 15 C was investigated. Mushroom slice quality was measured in terms of moisture content, colour (CIE Lightness, L* and yellowness, b*) and texture (hardness, H and chewiness, Ch). Hyperspectral images were obtained using a pushbroom line-scanning HSI instrument, operating in the wavelength range of 400–1,000 nm with spectroscopic resolution of 5 nm. Multiple linear regression (MLR) and Principal Component Regression (PCR) models were developed to investigate the relationship between reflectance and the various quality parameters measured. 20 optimal wavelengths for quality prediction were selected after performing an exhaustive search for the best subsets of predictor variables on a calibration set of 84 samples. PCR applied to the set of optimal wavelengths gave the best performance as compared to MLR and PCR on the entire wavelength range. When applied to an independent validation set of samples, PCR models developed on the calibration set were capable of predicting moisture content with RMSEP of 0.74% w.b. and R2 of 0.75, L* with RMSEP of 0.47 and R2 of 0.95, b* with RMSEP of 0.66 and R2 of 0.75, H with RMSEP of 0.49 N and R2 of 0.77 and Ch with RMSEP of 0.27 N and R2 of 0.72. Virtual images showing the distribution of moisture content on the mushroom surface were generated from the estimated PCR model. Results from this study could be used for the development of a non-destructive monitoring system for prediction of sliced mushroom quality.

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