ArticleFrontiers in nutrition2022
Discrimination of Geographical Origin of Agricultural Products From Small-Scale Districts by Widely Targeted Metabolomics With a Case Study on Pinggu Peach.
Article in Frontiers in nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Metabolomic fingerprinting of Indonesian beetroot across Java agroecological zones: A tool for geographical authentication and quality assessment.Food chemistry: X · 2025Article
- Classification of Crab-Field Rice and Conventional Rice Based on Multi-Element, Stable Isotope, and Non-Targeted Metabolome Combined with Chemometrics.Foods (Basel, Switzerland) · 2025Article
- Metabolomic analysis of Yunnan cigar tobacco leaves: impact of geography and climate on flavor characteristics and machine learning-based origin traceability.Frontiers in plant science · 2025Article
- The authentication of Yanchi tan lamb based on lipidomic combined with particle swarm optimization-back propagation neural network.Food chemistry: X · 2024Article
- A General Strategy for Food Traceability and Authentication Based on Assembly-Tunable Fluorescence Sensor Arrays.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Comprehensive widely targeted metabolomics to decipher the molecular mechanisms ofFood chemistry: X · 2024Article
- Metabolomics: A suitable foodomics approach to the geographical origin traceability of Ethiopian Arabica specialty coffees.Food science & nutrition · 2023Review
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5 authors.
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Abstract
Geographical indications of agricultural products are characterized by high quality and regional attributes, while they are more likely to be counterfeited by similar products from nearby regions. Accurate discrimination of origin on small geographical scales is extremely important for geographical indications of agricultural products to avoid food fraud. In this study, a widely targeted metabolomics based on ultra-high-performance liquid chromatography-tandem mass spectrometry combined with multivariate statistical analysis was used to distinguish the geographical origin of Pinggu Peach of Beijing and its two surrounding areas in Heibei province (China). Orthogonal partial least squares-discriminant analysis (OPLS-DA) based on 159 identified metabolites showed significant separation from Pinggu and the other adjacent regions. The number of the most important discriminant variables (VIP value >1) was up to 62, which contributed to the differentiation model. The results demonstrated that the metabolic fingerprinting combined with OPLS-DA could be successfully implemented to differentiate the geographical origin of peach from small-scale origins, thus providing technical support to further ensure the authenticity of geographical indication products. The greenness of the developed method was assessed using the Analytical GREEnness Metric Approach and Software (ARGEE) tool. It was a relatively green analytical method with room for improvement.
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