Data fusion approaches in spectroscopic characterization and classification of PDO wine vinegars

TALANTA

卷:198

Article

Rios-Reina, Rocio¹  Callejon, Raquel M.¹  Savorani, Francesco²  Arnigo, Jose M.³  Cocchi, Marina⁴ 

[1] Univ Seville, Fac Farm, Dept Nutr & Bromatol, Toxicol & Med Legal, C-P Garcia Gonzalez 2, E-41012 Seville, Spain

[2] Polytech Univ Turin, Dept Appl Sci & Technol DISAT, Corso Duca Abruzzi 24, I-10129 Turin, TO, Italy

[3] Univ Copenhagen, Dept Food Sci, Chemometr & Analyt Tech, Rolighedsvej 26, DK-1958 Frederiksberg C, Denmark

[4] Univ Modena & Reggio Emilia, Dipartimento Sci Chim & Geol, Via Campi 103, I-41125 Modena, Italy

关键词: Wine vinegars;    Food authentication;    Spectroscopy;    Classification;    Data fusion;    P-Comdim;   

DOI  :  10.1016/j.talanta.2019.01.100

来源: Elsevier

【 摘 要 】

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 (H-1-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.

【 授权许可】

Free