Literature DB >> 35421655

Screening of specific quantitative peptides of beef by LC-MS/MS coupled with OPLS-DA.

Chaodi Kang1, Yingying Zhang1, Mingyue Zhang1, Jing Qi1, Wentao Zhao1, Jin Gu1, Wenping Guo1, Yingying Li2.   

Abstract

A rapid, simple, and efficient analysis methodology for screening specific quantitative peptides of beef was established based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) coupled with orthogonal partial least squares-discriminant analysis (OPLS-DA). The OPLS-DA model was built to select species-specific peptides that make a significant contribution to classification. Peptides with statistical significance were selected based on the variable importance in the projection (VIP) values and univariate P values. After the workflow of the statistical process, three specific quantitative peptides were identified by using homemade products with different beef contents. A quantification method for selected specific quantitative peptides was established by using LC-MS/MS. The quantitative results were applied to commercialized beef products. The developed method has high sensitivity, specificity, and repeatability. The results of this study proved that the integration of LC-MS/MS coupled with OPLS-DA is an efficient method for screening specific quantitative peptides and identification of the authenticity of meat products.
Copyright © 2022 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Beef products; LC–MS/MS; OPLS-DA models; Specific quantitative peptides

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Year:  2022        PMID: 35421655     DOI: 10.1016/j.foodchem.2022.132932

Source DB:  PubMed          Journal:  Food Chem        ISSN: 0308-8146            Impact factor:   7.514


  1 in total

Review 1.  Possibilities of Liquid Chromatography Mass Spectrometry (LC-MS)-Based Metabolomics and Lipidomics in the Authentication of Meat Products: A Mini Review.

Authors:  Putri Widyanti Harlina; Vevi Maritha; Ida Musfiroh; Syamsul Huda; Nandi Sukri; Muchtaridi Muchtaridi
Journal:  Food Sci Anim Resour       Date:  2022-09-01
  1 in total

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