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Fusing 1H NMR and Raman experimental data for the improvement of wine recognition models.
Hategan, Ariana Raluca; David, Maria; Pirnau, Adrian; Cozar, Bogdan; Cinta-Pinzaru, Simona; Guyon, Francois; Magdas, Dana Alina.
Afiliación
  • Hategan AR; National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania; Faculty of Physics, Babeș-Bolyai University, Kogalniceanu 1, 400084 Cluj-Napoca, Romania. Electronic address: ariana.hategan@itim-cj.ro.
  • David M; National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania; Faculty of Physics, Babeș-Bolyai University, Kogalniceanu 1, 400084 Cluj-Napoca, Romania. Electronic address: maria.david@itim-cj.ro.
  • Pirnau A; National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania. Electronic address: adrian.pirnau@itim-cj.ro.
  • Cozar B; National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania. Electronic address: bogdan.cozar@itim-cj.ro.
  • Cinta-Pinzaru S; Faculty of Physics, Babeș-Bolyai University, Kogalniceanu 1, 400084 Cluj-Napoca, Romania. Electronic address: simona.pinzaru@ubbcluj.ro.
  • Guyon F; Service Commun des Laboratoires, 146 Traverse Charles Susini, 13388 Marseille, France. Electronic address: labo13@scl.finances.gouv.fr.
  • Magdas DA; National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania; Faculty of Physics, Babeș-Bolyai University, Kogalniceanu 1, 400084 Cluj-Napoca, Romania. Electronic address: alina.magdas@itim-cj.ro.
Food Chem ; 458: 140245, 2024 Nov 15.
Article en En | MEDLINE | ID: mdl-38954957
ABSTRACT
The present study proposes the development of new wine recognition models based on Artificial Intelligence (AI) applied to the mid-level data fusion of 1H NMR and Raman data. In this regard, a supervised machine learning method, namely Support Vector Machines (SVMs), was applied for classifying wine samples with respect to the cultivar, vintage, and geographical origin. Because the association between the two data sources generated an input space with a high dimensionality, a feature selection algorithm was employed to identify the most relevant discriminant markers for each wine classification criterion, before SVM modeling. The proposed data processing strategy allowed the classification of the wine sample set with accuracies up to 100% in both cross-validation and on an independent test set and highlighted the efficiency of 1H NMR and Raman data fusion as opposed to the use of a single-source data for differentiating wine concerning the cultivar and vintage.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Espectrometría Raman / Vino / Máquina de Vectores de Soporte Idioma: En Revista: Food Chem Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Espectrometría Raman / Vino / Máquina de Vectores de Soporte Idioma: En Revista: Food Chem Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido