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Food Chem. 2020 Jan 22;316:126278. doi: 10.1016/j.foodchem.2020.126278. [Epub ahead of print]

The application of pseudotargeted metabolomics method for fruit juices discrimination.

Author information

1
Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing Key Laboratory for Food Nonthermal Processing, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing 100083, China; Institute of Quality Standard & Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-food Safety and Quality, Ministry of Agriculture and Rural Affairs, Beijing 100081, China.
2
Institute of Quality Standard & Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-food Safety and Quality, Ministry of Agriculture and Rural Affairs, Beijing 100081, China. Electronic address: xuzhenzhen@caas.cn.
3
Institute of Quality Standard & Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-food Safety and Quality, Ministry of Agriculture and Rural Affairs, Beijing 100081, China.
4
Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing Key Laboratory for Food Nonthermal Processing, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.

Abstract

To optimize and evaluate the pseudotargeted metabolomics for juice discrimination and authentication, five widely consumed fruit (apple, orange, pear, purple grape and mandarin) juices were selected. SWATH-MS data was acquired by various windows being calculated based on total ion current, and then 2310 and 2292 MRM transitions were generated. Most of them (1522 and 1872) were detected in positive and negative modes. Distinctive separation among these juices could be observed from principal component analysis and hierarchical clustering analysis. After analysis of variance, fold change analysis and orthogonal projection to latent structures discriminant analysis, 57 potential markers were defined. Subsequently, 33 markers were putatively annotated, which could be used for juice discrimination and authentication. And 7 markers including l-phenylalanine, ascorbic acid, adenosine, epicatechin, glutathione, chlorogenic acid and nobiletin, were confirmed by standards. It is clearly indicated that pseudotargeted metabolomics could make great contribution to food industry as a new emerging technique.

KEYWORDS:

Fruit juice authenticity; Pseudotargeted metabolomics; SWATHtoMRM; Variable SWATH window

Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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