Verification of authenticity is a crucial aspect of food quality control, and also important to regulatory organizations. In this study, two wines of known pure varietal along with some commercial wines were examined using a mass spectrometry based chemical sensor. The fast analysis times obtained using this instrument makes this technology ideal for detection of adulteration.
Multivariate statistics were used to create models that discriminate between wine varieties. Exploratory analysis such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) indicated the viability of the data set for classifi cation models. Soft-independent-modeling-of-class-analogy (SIMCA) and K Nearest Neighbors (KNN) were used to create two classification models.
Both SIMCA and KNN provided a quick identification of unknown samples. Overall, the fast identification of wine varieties demonstrates the usefulness of the MS chemical sensor in detecting samples with close chemical composition.
