Discrimination of Geographical Origin of Agricultural Products From Small-Scale Districts by Widely Targeted Metabolomics With a Case Study on Pinggu Peach
文献类型： 外文期刊
作者： Zhao, Jie 1 ; Li, An 1 ; Jin, Xinxin 1 ; Liang, Gang 1 ; Pan, Ligang 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Inst Qual Stand & Testing Technol, Beijing, Peoples R China
2.Minist Agr, Risk Assessment Lab Agroprod, Beijing, Peoples R China
关键词： Analytical GREEnness metric approach; metabolic fingerprint; origin discrimination; peach; small-scale districts; widely targeted metabolomics
期刊名称：FRONTIERS IN NUTRITION （ 影响因子：6.59； 五年影响因子：6.873 ）
ISSN： 2296-861X
年卷期： 2022 年 9 卷
页码：
收录情况： SCI
摘要： 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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