Analytical technology-based solutions for automation of food processing according to the quality and safety factors are of great interest to food industry. In this study, a hyperspectral imaging system with a spectral range of 380–1000 nm was used for the detection of four levels of skin browning on button mushroom. Samples were stored at standard condition (3 ± 1 °C and 92 ± 2% R.H.) to generate different levels of browning development on cap surface. After acquisition of hypercubes, the extracted spectra were pre-processed by the Savitzky-Golay and mean normalization treats. Inter- and intra-variations of different levels of browning were calculated using spectral similarity measure. The competitive adaptive reweighted sampling algorithm was applied to extract the browning-specific wavelengths, leading to the partial least square-discriminant analysis classification accuracy of 80.6 and 80.3% for calibration and testing stages, respectively. Classification maps were generated using browning-specific wavelengths and compared to the classification maps generated from the full spectral points, PCA of full spectral points, and conventional RGB imaging bands. The results illustrate that using hyperspectral imaging and chemometrics techniques for classification of different levels of browning on mushroom cap is encouraging. Within a wider context of industrial relevance, the multispectral imaging system proposed in this research has the ability to be utilized as an online and a rapid analytical tool in the mushroom processing industry.
