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Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods

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Abstract

One of the most important problems associated with the rice industry is the authenticity, mainly the identification of varieties by providing a reliable, fast, yet accurate method. To overcome these limitations, the development of fast and non-destructive methodologies for different rice type classification is, nowadays, a huge challenge for producers. The near-infrared (NIR) spectroscopy associated to principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and support vector machines (SVM) for discrimination and classification of rice varieties (Indica and Japonica) were explored after different spectra processing steps such as multiplicative scatter correction (MSC), first derivative and second derivative. The SVM model developed after the MSC processing procedure, showed a significant fitting accuracy (97%), cross-validation (93%) and prediction (91%). These data support the robustness of the model for efficient rice types classification. In terms of spectral analysis, the major differences between both rice types are present at range 7476–7095 cm−1, 7046 cm−1 and 4264–4153 cm−1, which can be used for its discrimination. This study showed that NIR spectroscopy associated to PLS-DA and SVM techniques allowed an efficient discrimination of rice samples, being considered as a suitable strategy for a competent system for fully automated classification and sorting of rice types grouping with a high level of accuracy, representing a valuable approach for discrimination and anti-fraud procedure for food control as well as in terms of security issues of any product.

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Acknowledgements

Funding for this research has been received from the Portuguese Fundação para a Ciência e Tecnologia (FCT) under the grant agreement number RECI/AGR-TEC/0285/2012, BEST-RICE-4-LIFE project and P.N. Sampaio acknowledges the financial support of Post-Doc research Grant included in this project. A. Castanho acknowledges the financial support from the FCT PhD grant SFRH/BD/120929/2016.

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Authors and Affiliations

  1. Instituto Nacional de Investigação Agrária E Veterinária (INIAV), Av. da República, Quinta Do Marquês, 2780-157, Oeiras, Portugal

    Pedro Sousa Sampaio, Ana Castanho, Ana Sofia Almeida & Carla Brites

  2. DREAMS—Interdisciplinary Center for Development and Research in Environment, Applied Management and Space, Faculty of Engineering, Lusophone University of Humanities and Technologies, Campo Grande, 376, 1749-024, Lisbon, Portugal

    Pedro Sousa Sampaio

  3. School of Engineering, University College Cork, Cork, T12 YN60, Ireland

    Jorge Oliveira

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  1. Pedro Sousa Sampaio
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  2. Ana Castanho
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  3. Ana Sofia Almeida
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  4. Jorge Oliveira
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  5. Carla Brites
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Correspondence to Pedro Sousa Sampaio.

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The authors (Pedro N. Sampaio, Ana Castanho, Ana Sofia Almeida, Jorge Oliveira and Carla Brites) declare that they do not have any relationship or interest with other people, organisations or financial entities that could inappropriately influence and that prevent the disclosure and publication of the experimental results of this work.

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Sampaio, P.S., Castanho, A., Almeida, A.S. et al. Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods. Eur Food Res Technol 246, 527–537 (2020). https://doi.org/10.1007/s00217-019-03419-5

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  • DOI: https://doi.org/10.1007/s00217-019-03419-5

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