Literature DB >> 33813204

Discrimination of geographical origin and species of China's cattle bones based on multi-element analyses by inductively coupled plasma mass spectrometry.

Hongru Zhang1, Wenyuan Liu2, Qingshan Shen1, Laiyu Zhao3, Chunhui Zhang4, Aurore Richel5.   

Abstract

Consumers have an increasing concern in the provenance of the foods they consume. Methods for discriminating geographical origins and species of cattle bone product are essential to provide veracious information for consumers and avoid the adulteration and inferior problems. In this study, 50 element contents of a total of 143 cattle bone samples from eight producing regions in China, were determined by inductively coupled plasma mass spectrometry (ICP-MS). Element contents were used as chemical indicators to discriminate species and geographical origins of cattle bone samples by multivariate data analysis, including hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). The K-fold cross validation accuracy for species and geographical origin discrimination was 99.3% and 94.5%, respectively. This study reveals that multi-element analysis accompanied by LDA is an effective technique to ensure the information reliability of cattle bone samples, and this strategy may be a potential tool for standardizing market.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cattle bone; Discrimination; Geographical origin; ICP-MS; Multi-element; Species

Year:  2021        PMID: 33813204     DOI: 10.1016/j.foodchem.2021.129619

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


  1 in total

1.  Machine-learning assisted modelling of multiple elements for authenticating edible animal blood food.

Authors:  Fangkai Han; Joshua H Aheto; Marwan M A Rashed; Xingtao Zhang
Journal:  Food Chem X       Date:  2022-03-07
  1 in total

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