Spain is one of the major producers of high-quality wine vinegars having three protected designations of origin (a.k.a. PDOs): "Vinagre de Jerez", "Vinagre de Condado de Huelva" and "Vinagre de Montilla-Moriles". Their high prices due to their high quality and their high production costs explain the need for developing an adequate quality control technique and the interest in extensive characterization in order to capture the identity of each denomination. In this framework, methodologies based on non-targeted techniques, such as spectroscopies, are becoming popular in food authentication. Thus, for improving vinegar quality assessment, fusion of data blocks obtained from the same samples but different analytical techniques could be a good strategy, since the quantity and quality of sample knowledge could be enhanced providing new insights into the differentiation of vinegars. Therefore, the aim of this manuscript is the development of a multi-platform methodology and a model able to classify the Spanish wine vinegar PDOs. Sixty-five PDO wine vinegars were analyzed by four spectroscopic techniques: Fourier transform mid-infrared spectroscopy (MIR), near infrared spectroscopy (NIR), multidimensional fluorescence spectroscopy (EEM) and proton nuclear magnetic resonance (1H-NMR). Two different data fusion strategies were evaluated: Mid-level data fusion with different preprocessing, and Common Component and Specific Weights analysis multiblock method. Exploratory and classification analysis on the data from individual techniques were also performed and compared with data fusion models. The data fusion models improved the classification, providing a more efficient differentiation, than the models based on single methods, and supporting the approach to combine these methods to achieve synergies for an optimized PDO differentiation.

DATA FUSION APPROACHES IN SPECTROSCOPIC CHARACTERIZATION AND CLASSIFICATION OF PDO WINE VINEGARS / Rios-Reina, R.; Callejón, R. M.; Savorani, F.; Amigo, J. M.; Cocchi, M.. - In: TALANTA. - ISSN 0039-9140. - (2019), pp. 1-1. [10.1016/j.talanta.2019.01.100]

DATA FUSION APPROACHES IN SPECTROSCOPIC CHARACTERIZATION AND CLASSIFICATION OF PDO WINE VINEGARS

Savorani, F.;Cocchi, M.
2019

Abstract

Spain is one of the major producers of high-quality wine vinegars having three protected designations of origin (a.k.a. PDOs): "Vinagre de Jerez", "Vinagre de Condado de Huelva" and "Vinagre de Montilla-Moriles". Their high prices due to their high quality and their high production costs explain the need for developing an adequate quality control technique and the interest in extensive characterization in order to capture the identity of each denomination. In this framework, methodologies based on non-targeted techniques, such as spectroscopies, are becoming popular in food authentication. Thus, for improving vinegar quality assessment, fusion of data blocks obtained from the same samples but different analytical techniques could be a good strategy, since the quantity and quality of sample knowledge could be enhanced providing new insights into the differentiation of vinegars. Therefore, the aim of this manuscript is the development of a multi-platform methodology and a model able to classify the Spanish wine vinegar PDOs. Sixty-five PDO wine vinegars were analyzed by four spectroscopic techniques: Fourier transform mid-infrared spectroscopy (MIR), near infrared spectroscopy (NIR), multidimensional fluorescence spectroscopy (EEM) and proton nuclear magnetic resonance (1H-NMR). Two different data fusion strategies were evaluated: Mid-level data fusion with different preprocessing, and Common Component and Specific Weights analysis multiblock method. Exploratory and classification analysis on the data from individual techniques were also performed and compared with data fusion models. The data fusion models improved the classification, providing a more efficient differentiation, than the models based on single methods, and supporting the approach to combine these methods to achieve synergies for an optimized PDO differentiation.
2019
30-gen-2019
1
1
DATA FUSION APPROACHES IN SPECTROSCOPIC CHARACTERIZATION AND CLASSIFICATION OF PDO WINE VINEGARS / Rios-Reina, R.; Callejón, R. M.; Savorani, F.; Amigo, J. M.; Cocchi, M.. - In: TALANTA. - ISSN 0039-9140. - (2019), pp. 1-1. [10.1016/j.talanta.2019.01.100]
Rios-Reina, R.; Callejón, R. M.; Savorani, F.; Amigo, J. M.; Cocchi, M.
File in questo prodotto:
File Dimensione Formato  
TALANTA_DFvinegars2019.pdf

Open access

Tipologia: Versione dell'autore revisionata e accettata per la pubblicazione
Dimensione 1.79 MB
Formato Adobe PDF
1.79 MB Adobe PDF Visualizza/Apri
VQR_1-s2.0-S0039914019301201-main.pdf

Accesso riservato

Tipologia: Versione pubblicata dall'editore
Dimensione 5.52 MB
Formato Adobe PDF
5.52 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1169977
Citazioni
  • ???jsp.display-item.citation.pmc??? 11
  • Scopus 60
  • ???jsp.display-item.citation.isi??? 50
social impact