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Comparison of Different Multivariate Classification Methods for the Detection of Adulterations in Grape Nectars by Using Low-Field Nuclear Magnetic Resonance

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Abstract

Grape is the most consumed nectar in Brazil and a relatively expensive beverage. Therefore, it is susceptible to fraud by substitution with other less expensive fruit juices. Adulterations of grape nectars by the addition of apple juice, cashew juice, and mixtures of both were evaluated by using low-field nuclear magnetic resonance (LF-NMR) and supervised multivariate classification methods. Two different approaches were investigated using one-class (only unadulterated samples (UN) were modeled) and multiclass (three classes were modeled: UN, adulterated with cashew (CAS), and adulterated with apple (APP)) strategies. For the one-class approach, soft independent modeling of class analogy (SIMCA), one-class partial least squares (OCPLS), and data-driven SIMCA (DD-SIMCA) models were built. For the multiclass approach, partial least squares discriminant analysis (PLS-DA) and multiclass SIMCA models were built. The results obtained demonstrated good performances by all the one-class methods with efficiency rates higher than 93%. For the multiclass approach, the classification of samples containing only one type of adulterant presented efficiencies higher than 90% and 97% using SIMCA and PLS-DA, respectively. The classification of samples containing blends of two adulterants was satisfactory for the CAS class, but not for the APP class when applying PLS-DA. Nevertheless, multiclass SIMCA did not provide satisfactory predictions for either of these two classes.

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Acknowledgments

The authors acknowledge Giuliano Elias Pereira from EMBRAPA Grape & Wine/Semiarid (Petrolina, Brazil) for providing the Isabel grapes.

Funding information

This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (PhD scholarship, finance code 001).

Author information

Correspondence to Scheilla Vitorino Carvalho de Souza.

Ethics declarations

Conflict of Interest

Carolina Sheng Whei Miaw declares that she has no conflict of interest. Poliana Macedo Santos declares that she has no conflict of interest. Alessandro Rangel Carolino Sales Silva declares that he has no conflict of interest. Aline Gozzi declares that she has no conflict of interest. Nilson César Castanheira Guimarães declares that he has no conflict of interest. Maria Pilar Callao declares that she has no conflict of interest. Itziar Ruisánchez declares that she has no conflict of interest. Marcelo Martins Sena declares that he has no conflict of interest. Scheilla Vitorino Carvalho de Souza declares that she has no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Miaw, C.S.W., Santos, P.M., Silva, A.R.C.S. et al. Comparison of Different Multivariate Classification Methods for the Detection of Adulterations in Grape Nectars by Using Low-Field Nuclear Magnetic Resonance. Food Anal. Methods 13, 108–118 (2020). https://doi.org/10.1007/s12161-019-01522-7

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Keywords

  • Time-domain NMR
  • TD-NMR
  • One-class modeling
  • Discriminant analysis
  • Fruit nectar
  • Food adulteration