Data fusion approaches in spectroscopic characterization and classification of PDO wine vinegars
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Data fusion approaches in spectroscopic characterization and classification of PDO wine vinegars. / Ríos-Reina, Rocío; Callejón, Raquel M.; Savorani, Francesco; Amigo, José M.; Cocchi, Marina.
In: Talanta, Vol. 198, 2019, p. 560-572.Research output: Contribution to journal › Journal article › peer-review
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TY - JOUR
T1 - Data fusion approaches in spectroscopic characterization and classification of PDO wine vinegars
AU - Ríos-Reina, Rocío
AU - Callejón, Raquel M.
AU - Savorani, Francesco
AU - Amigo, José M.
AU - Cocchi, Marina
PY - 2019
Y1 - 2019
N2 - 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 ( 1 H-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.
AB - 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 ( 1 H-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.
KW - Classification
KW - Data fusion
KW - Food authentication
KW - P-Comdim
KW - Spectroscopy
KW - Wine vinegars
U2 - 10.1016/j.talanta.2019.01.100
DO - 10.1016/j.talanta.2019.01.100
M3 - Journal article
C2 - 30876600
AN - SCOPUS:85061546381
VL - 198
SP - 560
EP - 572
JO - Talanta
JF - Talanta
SN - 0039-9140
ER -
ID: 214750546