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

Carlos H Junges1, Celito C Guerra2, Adriano A Gomes1

  • 1Laboratório 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.

Food Chemistry
|October 19, 2023
PubMed
Summary

A new multiblock regression strategy effectively authenticates organic grape juice using diverse data sources. This method, employing one-class partial least squares (OC-PLS), achieves high accuracy in classifying juice authenticity.

Keywords:
AuthenticationGrape juiceMultiblock dataOne-class classificationOrganicSequentially orthogonalization

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Area of Science:

  • Food Science and Technology
  • Analytical Chemistry
  • Chemometrics

Background:

  • Ensuring the authenticity of organic grape juice is crucial for consumer trust and regulatory compliance.
  • Traditional analytical methods may struggle to integrate diverse data sources for comprehensive authenticity assessment.
  • The need for robust classification strategies capable of handling multi-source analytical data in food analysis.

Purpose of the Study:

  • To develop and evaluate a novel multiblock regression strategy for enhancing the authenticity assessment of organic grape juice.
  • To establish a robust classification method integrating data from various analytical sources.
  • To compare the performance of the proposed multiblock approach with existing methods like DD-SIMCA.

Main Methods:

  • Implementation of a multiblock regression technique, specifically the one-class partial least squares (OC-PLS) classifier.
  • Sequential calculation and orthogonalization with respect to preceding regression scores for data integration.
  • Comparative analysis using the DD-SIMCA method on visible data for benchmarking.

Main Results:

  • The proposed OC-PLS based multiblock approach demonstrated high effectiveness in detecting targeted organic grape juice samples.
  • The best models achieved up to 100% sensitivity, 89% specificity, and 83% accuracy on the test set.
  • The multiblock approach outperformed DD-SIMCA when applied to visible data, highlighting its superiority in integrating varied datasets.

Conclusions:

  • The developed multiblock regression strategy offers an efficient and effective solution for authenticating organic grape juice.
  • This method excels in evaluating and classifying organic grape juice by leveraging data from diverse analytical sources.
  • The OC-PLS classifier within a multiblock framework provides a powerful tool for food authenticity analysis.