Metric-Based Meta-Learning Combined with Hyperspectral Imaging for Rapid Detection of Adulteration in Domain-Shifted Camel Milk Powder

Abstract

Camel milk powder possesses high nutritional and economic value. The adulteration of camel milk powder with other varieties seriously compromises consumer rights. In the practical application of hyperspectral analysis for camel milk powder detection, the sample categories used for testing often differ from those used to construct the model. As a learning approach adept at domain-shifted and few shot scenarios, meta-learning is employed to tackle this issue. In this study, we used camel milk powder adulterated with cow milk powder as training samples and adulterated with goat milk powder as test samples. In the detection of eleven adulteration levels, the detection accuracy for pure camel milk powder reached 98.92%. Notably, the detection accuracy for the less conspicuous 70% adulteration level achieved 77.69%. The comprehensive detection accuracy of meta-learning reached 84.4%, showcasing notable improvements compared to SVM, BP, and CNN, which saw increases of 24.67%, 28.16%, and 18.4%, respectively. The detailed analysis of feature vectors and contributions substantiates the reliability and stability of the meta-learning-based qualitative analysis. The introduction of meta-learning methods is poised to make significant contributions to rapid detection by relevant testing agencies and the protection of consumer rights.