Article (Scientific journals)
Discrimination of geographical origin and species of China's cattle bones based on multi-element analyses by inductively coupled plasma mass spectrometry.
Zhang, Hongru; Liu, Wenyuan; Shen, Qingshan et al.
2021In Food Chemistry, 356, p. 129619
Peer Reviewed verified by ORBi
 

Files


Full Text
1-s2.0-S0308814621006257-main.pdf
Publisher postprint (1.85 MB)
Request a copy

All documents in ORBi are protected by a user license.

Send to



Details



Keywords :
Cattle bone; Discrimination; Geographical origin; ICP-MS; Multi-element; Species; Biological Products; Minerals; bone meal; Animals; Biological Products/chemistry; Cattle; China; Cluster Analysis; Discriminant Analysis; Mass Spectrometry; Minerals/chemistry; Multivariate Analysis; Reproducibility of Results; Element contents; Geographical origins; Inductively coupled plasma-mass spectrometry; Linear discriminant analysis; Multielement analysis; Multivariate data analysis; Analytical Chemistry; Food Science; General Medicine
Abstract :
[en] 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.
Disciplines :
Chemistry
Author, co-author :
Zhang, Hongru  ;  Université de Liège - ULiège > TERRA Research Centre
Liu, Wenyuan;  Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China, Hulunbuir Muyuankangtai Biotechnology Co. LTD, Arongqi Logistics Business Park, Hulunbuir Inner Mongolia, Hulunbuir 021000, China
Shen, Qingshan ;  Université de Liège - ULiège > Gembloux Agro-Bio Tech > Gembloux Agro-Bio Tech
Zhao, Laiyu;  Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
Zhang, Chunhui;  Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China. Electronic address: dr_zch@163.com
Richel, Aurore  ;  Université de Liège - ULiège > TERRA Research Centre > Smart Technologies for Food and Biobased Products (SMARTECH)
Language :
English
Title :
Discrimination of geographical origin and species of China's cattle bones based on multi-element analyses by inductively coupled plasma mass spectrometry.
Publication date :
15 September 2021
Journal title :
Food Chemistry
ISSN :
0308-8146
eISSN :
1873-7072
Publisher :
Elsevier Ltd, England
Volume :
356
Pages :
129619
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
NSCF - National Natural Science Foundation of China [CN]
Funding text :
This work was supported by the National Natural Science Foundation of China (32072156); National Agricultural Science and Technology Innovation Project (CAAS-ASTIP-2020-IFST-05); Innovation Program of Inner Mongolia Science and Technology Major Project.
Available on ORBi :
since 07 October 2022

Statistics


Number of views
15 (0 by ULiège)
Number of downloads
0 (0 by ULiège)

Scopus citations®
 
16
Scopus citations®
without self-citations
14
OpenCitations
 
6

Bibliography


Similar publications



Contact ORBi