Angelica sinensis (Oliv.) Diels (AS) is a medicinal and food plant that has long faced a persistent challenge: its quality and price are often influenced by environmental conditions and geographical origins. To achieve substantial profits, items that are not produced in primary regions, along with counterfeit products, are frequently misbranded as originating from main production areas; this leads to fraud regarding geographic origin and product tampering. Rapid, effective and feasible methods for distinguishing the geographic origin of AS are important for ensuring consumer safety and protecting their interests. This study establishes the authenticity and geographical origins of AS. Meanwhile, diverse machine learning strategies are used to identify the optimal combination by incorporating spectral pre-processing techniques, feature wavenumber selection methods and classification algorithms. The findings reveal that the backpropagation neural network (BPNN), convolutional neural network (CNN) and radial basis function neural network (RBF) excel in determining the authenticity of AS. To distinguish among different growing environments of AS, three models obtained 98.94% classification accuracy on the test set: (1) multiplicative scatter correction (MSC) pre-processing with an RBF classifier, (2) standard normalised variate (SNV) pre-processing with an RBF classifier and (3) Savitzky–Golay (SG) smoothing pre-processing, competitive adaptive reweighted sampling (CARS) for selecting features and a BPNN for classification. This study validates the feasibility of ensemble learning combined with MIR for discriminating AS from authenticity and different geographical sources.
Zhou et al. (Wed,) studied this question.