Hyperspectral Imaging for Quality Detection of Agaricus bisporus Under Vibration Stress

In order to quickly and effectively identify the slight damage of white button mushroom (Agaricus bisporus), the near-infrared hyperspectral images (400‒1000 nm) of the intact mushroom and the mushroom with different degrees of damage caused by vibration for 60 and 120 s were recorded. It was found that the spectra of the three types of samples in the wavelength range 450‒750 nm were obviously different. Compared to standard normal variable transformation and multivariate scattering correction, Savitzky Golay (SG) smoothing was determined as a better pretreatment method. The successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were applied to extract the vibration-specific wavelengths. Based on the gray-level co-occurrence matrix, the texture features of the region of interest of the feature image at 500 nm were extracted. A model to discriminate the degree of damage of mushrooms was established using partial least square discriminant analysis (PLS-DA), BP neural network and extreme learning machine (ELM) based on the spectral information and the texture feature information, separately. The results showed that the PLS-DA model with each of the two feature sets as input had better performance than the other two models. With the spectral information as input, the average recognition accuracies for the training set and the test set were 93.33% and 91.11%, while those with the texture feature information as input were 88.89% and 86.67%, respectively. Finally, a PLS-DA classification model was established based on the spectrum-texture fusion information, whose overall classification accuracies for the training set and the test set were 97.78% and 95.56%, respectively. The predictive performance of the model based on the spectrum-texture fusion information was better than the model based on the single information. Therefore, the application of spectrum-texture fusion information-based modeling can improve the detection accuracy of white button mushrooms with different degrees of damage, which provides theoretical support for the storage and classification of white button mushrooms.