Classification of Spanish extra virgin olive oils by data fusion of visible spectroscopic fingerprints and chemical descriptors

► The potential of visible fingerprints was examined to classify Spanish olive oils. ► Results were compared with those obtained by a set of chemical descriptors. ► The synergy among the information from the two different sources was also examined. ► Combination of data matrices resulted in an improved geographical discrimination. The potential of visible fingerprints and physical–chemical parameters in combination with multivariate data analysis was examined to classify extra virgin olive oils (EVOOs) from different Spanish regions according to their geographical origin. Firstly, spectral and quality parameters matrices were processed separately and subsequently were joined to evaluate the effect of synergy on the information obtained from the different methodologies. Linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) were performed as classification methods. The results revealed a perfect discrimination between the defined categories after performing PLS-DA on the Fused matrix, reaching 100% of correct classifications and showed a clear improvement in the overall prediction rates (92.5%), so that the effect of synergy was confirmed. These accurate results emphasise the feasibility of the proposed strategy and encourage the development of similar approaches based on visible spectroscopy in olive oil quality and traceability determination.