Discrimination of Geographical Origin of Agricultural Products From Small-Scale Districts by Widely Targeted Metabolomics With a Case Study on Pinggu Peach

Jie Zhao; An Li; Xinxin Jin; Gang Liang; Ligang Pan

doi:10.3389/fnut.2022.891302

Discrimination of Geographical Origin of Agricultural Products From Small-Scale Districts by Widely Targeted Metabolomics With a Case Study on Pinggu Peach

Front Nutr. 2022 May 24:9:891302. doi: 10.3389/fnut.2022.891302. eCollection 2022.

Authors

Jie Zhao^{1

2}, An Li^{1

2}, Xinxin Jin^{1

2}, Gang Liang^{1

2}, Ligang Pan^{1

2}

Affiliations

¹ Institute of Quality Standard and Testing Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
² Risk Assessment Lab for Agro-Products, Ministry of Agriculture, Beijing, China.

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.

Keywords: Analytical GREEnness metric approach; metabolic fingerprint; origin discrimination; peach; small-scale districts; widely targeted metabolomics.