Multiblock data applied in organic grape juice authentication by one-class classification OC-PLS.

Autor: Junges CH; Laboratório de Quimiometria e Instrumentação Analítica (LAQIA), Instituto de Química, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, Rio Grande do Sul (RS), CEP 91501-970, Brazil. Electronic address: carlosjunges88@gmail.com., Guerra CC; Laboratório de Cromatografia e Espectrometria de Massas (LACEM), Unidade Uva e Vinho, Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA), Rua Livramento, 515, Bento Gonçalves, Rio Grande do Sul, CEP 95701-008, Brazil., Gomes AA; Laboratório de Quimiometria e Instrumentação Analítica (LAQIA), Instituto de Química, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, Rio Grande do Sul (RS), CEP 91501-970, Brazil., Ferrão MF; Laboratório de Quimiometria e Instrumentação Analítica (LAQIA), Instituto de Química, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, Rio Grande do Sul (RS), CEP 91501-970, Brazil; Instituto Nacional de Ciência e Tecnologia-Bioanalítica (INCT-Bioanalítica), Cidade Universitária Zeferino Vaz, s/n, Campinas, São Paulo (SP), CEP 13083-970, Brazil.
Jazyk: angličtina
Zdroj: Food chemistry [Food Chem] 2024 Mar 15; Vol. 436, pp. 137695. Date of Electronic Publication: 2023 Oct 17.
DOI: 10.1016/j.foodchem.2023.137695
Abstrakt: A new strategy has been developed to enhance the assessment of the authenticity of whole grape juice within the organic class. This approach is based on the analysis of data from different analytical sources. The novel method employs a multiblock regression technique, specifically the one-class partial least squares (OC-PLS) classifier, to establish a relationship between each predictor block and the response variable. Sequential calculations are performed after orthogonalization with respect to the preceding regression scores. The proposed method has demonstrated effectiveness in detecting targeted samples. The results achieved of the best models for the test set had rates of up to 100 % sensitivity, 89 % specificity, and 83 % accuracy. To compare with the multiblock models, the DD-SIMCA method was employed, but it yielded inferior results when applied to visible data. The multiblock approach proved to be efficient in evaluating from different datasets of varied sources to classification of organic grape juice.
Competing Interests: 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.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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