Recognition of Process Degree for Gardeniae Fructus Praeparatus Based on Hyperspectral Data and Neural Network

This study aims to address the urgent problem of recognizing the process degree for gardeniae fructus praeparatus (GFP), which is a processed product of gardeniae fructus (GF), a widely used traditional Chinese medicine. In this study, a hyperspectral (400–1000 nm) system was utilized to acquire hyperspectral images of GF and construct a dataset containing 3146 spectral data. The spectral data was preprocessed by using standard orthogonal transform (SNV) and multiple scattering corrections (MSC), with successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) methods for feature wavelength selection in full waveband; additionally, two neural network structures, GFP-impulse detection convolutional neural network (1D-CNN) and GFP-long short-term memory (LSTM), were built to train the spectral data. The results showed that both neural network models gave satisfactory recognition accuracy in the recognition of the process degree for GFP, and the training accuracy was much higher than the traditional support vector machines (SVM) and partial least square discriminant analysis (PLS-DA) spectral data classification methods. Remarkably, the full-band data processed by SNV reached the highest accuracy of 98.23% after LSTM training. These findings underscore the feasibility and effectiveness of using hyperspectral data and neural networks for recognizing the processing degree of GFP, offering potential advancements in the quality control of traditional Chinese medicine.