Inorganic and organic constituent analysis: A data fusion strategy to differentiate between wines of different origins

Abstract

The authenticity of wine is a significant concern in the global market due to widespread fraudulent activities involving wine origins. In this study, the inorganic and organic constituent profiles of 102 wine samples from five different growing regions were analyzed. Low- and mid-level data fusion coupled with a multivariate analysis approach was applied to three platforms of mass spectrometry data sets using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), Isotopic Ratio Mass Spectrometry (IRMS), and Ultra-high Performance Liquid Chromatography coupled with Ion Mobility Quadrupole Time-Of-Flight Mass Spectrometry (UPLC/IM-QTOF-MS) to determine the correct classification of wine origin. Significant difference of organic and inorganic compounds from five regions were selected for low- and mid-level data fusion. In the low-level data fusion, Partial Least Squares Discriminant Analysis (PLS-DA) under the positive ionization modes (ESI+) achieved an optimal prediction rate of 74.4%. By extracting features from low-level data fusion for mid-level data fusion, linear discriminant and nonlinear models were established, resulting in an optimal accuracy of 98.7%. The mid-level data fusion multivariate analysis strategy significantly enhanced the accuracy of identifying the geographical origin of wine and presents a new approach for identifying origins in the origin of other foods.