Research of moldy tea identification based on RF-RFE-Softmax model and hyperspectra

Abstract

To provide consumers with high-quality and safe tea, an accurate, fast and effective method of discriminating moldy tea based on hyperspectral technology was put forward. Moldy tea with three different degrees was studied in this paper. Hyperspectral images of all samples were firstly acquired by the hyperspectral imaging system, and then the spectral data was extracted from the images. Savitzky-Golay and SNV-Detrending were respectively used to pretreat the spectral data. After that, the random forest-recursive feature elimination and the principal component analysis were adopted for feature selection and feature extraction. By combining different pretreatment and feature screening methods, the softmax model optimised by the gradient descent algorithm was established to identify the tea with different moldy degrees. Comparing the classification accuracy and cost function value of all models, the softmax model based on Savitzky-Golay and SNV-Detrending preprocessing and random forest-recursive feature elimination feature selection performed best, which achieved the identification accuracy of 100% for the training set and 98.5% for the test set. Therefore, it is feasible to identify moldy tea with different degrees by using RF-RFE-softmax model and hyperspectral technology.