In this study, different food and beverage samples were analysed without chromatographic separation by direct transfer of their analytes into a mass spectrometer or by disregarding chromatographic separation. Three different sample introduction techniques – Headspace, Solid Phase MicroExtraction (SPME) [1], and HeadSpace Sorptive Extraction (HSSE) – were used. Mass spectral fingerprints were compared using pattern recognition software. Multivariate statistics were used to create models that classify samples or detect spoilage and adulteration. Exploratory analysis such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) indicated the viability of the data sets for classification models. Soft-independent-modelling-of-class-analogy (SIMCA) and K Nearest Neighbours (KNN) were used to create classification models. Both SIMCA and KNN provided a quick and accurate classification of the foods and beverages with and without spoilage or adulterations.
Yoghurt, olive oil and coffee were examined under different aspects. Results indicate the successful identifi cation of bad yoghurts lots although using different flavours. Also olive oils could be classified between degassed or not. Coffee samples could be differentiated in Robusta and Arabica as well as into there country of origin.
