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

IF 8.2 1区农林科学 Q1 CHEMISTRY, APPLIED

Food Chemistry: X Pub Date : 2025-09-23 DOI:10.1016/j.fochx.2025.103069

Xiaoli Wang , Xiaolei Ma , Yuxin Liu, Wenhan Tao, Yuting Zuo, Yueqin Zhu, Feng Hua, Chanming Liu, Wei Huang

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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.

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综合代谢组学- kpca -机器学习框架：枣树地理溯源的解决方案

由于产品普遍掺假，具有全球经济重要性的具有营养和药用特性的中国枣（CJ）在地理可追溯性方面遇到了困难。本研究采用代谢组学-核主成分分析(KPCA)-机器学习（ML）框架，建立了中国6个产区（新疆、甘肃、陕西、河南、山东和河北）CJ产地识别系统。利用LC-MS/MS进行非靶向代谢组学，研究人员鉴定出312种代谢物。多变量分析发现37个关键判别变量（VIP > 1）。KPCA将这些特征压缩为28个主成分（保留90.59%的信息）。与传统方法相比，KPCA降维后的K-means聚类大大提高了样本区分能力：原始数据的原始样本与模糊边界重叠；而在降维后，6个原始样本形成了一个清晰紧凑的聚类，实现了准确的分类。该研究开创了“代谢组学- kpca - ml”范式，为地理标志农产品的可追溯性提供了解决方案。

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来源期刊

Food Chemistry: X CHEMISTRY, APPLIED-

CiteScore

4.90

自引率

6.60%

发文量

315

审稿时长

55 days

期刊介绍： Food Chemistry: X, one of three Open Access companion journals to Food Chemistry, follows the same aims, scope, and peer-review process. It focuses on papers advancing food and biochemistry or analytical methods, prioritizing research novelty. Manuscript evaluation considers novelty, scientific rigor, field advancement, and reader interest. Excluded are studies on food molecular sciences or disease cure/prevention. Topics include food component chemistry, bioactives, processing effects, additives, contaminants, and analytical methods. The journal welcome Analytical Papers addressing food microbiology, sensory aspects, and more, emphasizing new methods with robust validation and applicability to diverse foods or regions.