Work

Integrated Metabolomics-KPCA-Machine Learning framework: a solution for geographical traceability of Chinese Jujube

Year: 2025

Type: article

Abstract: Due to widespread product adulteration, Chinese jujube (CJ), a crop of global economic importance with nutritional and medicinal properties, struggles with geographical traceability. The study introduced a Metabolomics-Kernel Principal Component Analysis (KPCA)-Machine Learning (ML) framework to set up an origin identification system for CJ from six production regions in China (Xinjiang, Gansu, Shaanxi, Henan, Shandong, and Hebei). Using LC-MS/MS for untargeted metabolomics, researchers identified 312 metabolites. Multivariate analysis revealed 37 key discriminant variables (VIP > 1). KPCA compressed these features into 28 principal components (retaining 90.59 % information). Compared with the traditional method, the K-means clustering after dimensionality reduction of KPCA greatly improves the sample differentiation ability: the origin samples with original data overlap with fuzzy boundaries; while after dimensionality reduction, the six origin samples form a clear and compact cluster, which achieves accurate classification. This study pioneers a “Metabolomics-KPCA-ML” paradigm, offering a solution for traceability of geographical indication agricultural products. • Developed a pioneering “Metabolomics-KPCA-Machine Learning” framework. • Identified 312 metabolites, revealing region-specific metabolic fingerprints of CJ. • KPCA-driven model greatly improves the CJ samples differentiation ability. • This study provides a technological solution for combating geographic mislabelling.

Source: Food Chemistry X

DOI: 10.1016/j.fochx.2025.103069