Hyperspectral imaging for the investigation of quality deterioration in sliced mushrooms (Agaricus bisporus) during storage

摘要

In this study, the potential application of hyperspectral imaging (HSI) for quality prediction of white mushroom slices during storage at 4 deg C and 15 deg 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 push-broom 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 selectedafter 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. Whenapplied 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 R~2 of 0.75, L* with RMSEP of 0.47 and R~2 of 0.95, b* with RMSEP of 0.66 and R~2 of 0.75, H with RMSEP of 0.49 N and R~2 of 0.77 and Ch with RMSEP of 0.27 N and R~2 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 beused for the development of a non-destructive monitoring system for prediction of sliced mushroom quality.