Camel milk powder, as a premium product, is often diluted with inferior substitutes, specifically through the adulteration with ordinary milk powders. Due to the high similarity in composition among different types of milk powders, detecting adulterated camel milk powder presents significant challenges. This study establishes a discrimination analysis model for pure camel milk powder and mixtures containing camel, goat, and cow milk powders based on hyperspectral technology combined with an improved Black-winged Kite algorithm. Firstly, spectral data preprocessing was performed using single and combined spectral preprocessing methods; ultimately, the S-G-SNV method was selected. Secondly, a feature extraction algorithm based on one-dimensional dilated convolutional neural networks (1D Dilated CNNs) was proposed to facilitate feature fusion and enhance model accuracy. On this basis, a multi-class qualitative analysis model utilizing an improved black-winged kite algorithm (IBKA) applied to SVC was constructed. The experiment compared five qualitative analysis models; the final S-G-SNV-1D Dilated CNNs-IBKA-SVC model achieved an accuracy of 0.9493, an F1 score of 0.9478, and Cohen's kappa value of 0.947 on the test set—demonstrating superior performance over other methods. This model provides a novel and effective solution for the rapid non-destructive identification and analysis of milk powder from different sources. • .An improved BKA algorithm has been proposed. • A 1D Dilated CNNs algorithm is proposed for spectral feature extraction. • Comparative Analysis of different Preprocessing Methods (Single and Combined). • SHAP provides interpretable explanations of feature wavelength contribution rates. • Non-destructive high-precision identification of adulterated camel milk powder.
Zhang et al. (Sun,) studied this question.