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Tytuł pozycji:

Chemometric tools for food fraud detection: The role of target class in non-targeted analysis.

Tytuł :
Chemometric tools for food fraud detection: The role of target class in non-targeted analysis.
Autorzy :
Rodionova OY; N.N. Semenov Federal Research Center for Chemical Physics, RAS, Kosygin 4, 119991 Moscow, Russia. Electronic address: .
Pomerantsev AL; N.N. Semenov Federal Research Center for Chemical Physics, RAS, Kosygin 4, 119991 Moscow, Russia.
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Źródło :
Food chemistry [Food Chem] 2020 Jul 01; Vol. 317, pp. 126448. Date of Electronic Publication: 2020 Feb 19.
Typ publikacji :
Journal Article
Język :
English
Imprint Name(s) :
Publication: Barking : Elsevier Applied Science Publishers
Original Publication: Barking, Eng., Applied Science Publishers.
MeSH Terms :
Fraud*
Food Analysis/*methods
Food Contamination/*analysis
Origanum/*chemistry
Discriminant Analysis ; Food Analysis/statistics & numerical data ; Food Contamination/statistics & numerical data ; Least-Squares Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; Spectroscopy, Near-Infrared/methods
Contributed Indexing :
Keywords: DD-SIMCA; Food authentication; Multiclass PLS-DA; NIR; Non-targeted analysis; Oregano herbs
Entry Date(s) :
Date Created: 20200302 Date Completed: 20200511 Latest Revision: 20200511
Update Code :
20210623
DOI :
10.1016/j.foodchem.2020.126448
PMID :
32114274
Czasopismo naukowe
The chemometric issues related to the application of non-targeted analysis for the detection of food frauds were analyzed employing discriminant analysis and a one-class classifier. The similarities and differences between the two methods were investigated. The results of classification are characterized by a set of indices called figures of merit. They comprehensively characterized the quality and reliability of classification. The principle is illustrated using an actual example of Oregano herbs adulteration. The informative region 9000-4000 cm -1 of near-Infrared spectroscopy is used as analytical means. The results of the application of each method for Oregano data collection are presented. It is shown that the discriminant method is only partially appropriate for solving the authentication problem. One class classifier is a powerful and devoted for non-targeted analysis. The step by step analysis introduced in the paper can also be successfully utilized in apply for revealing of forgeries of various food products.
(Copyright © 2020 Elsevier Ltd. All rights reserved.)

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