Abstract
Two multivariate screening strategies (untargeted and targeted modelling) have been developed to compare their ability to detect food fraud. As a case study, possible adulteration of hazelnut paste is considered. Two different adulterants were studied, almond paste and chickpea flour. The models were developed from near-infrared (NIR) data coupled with soft independent modelling of class analogy (SIMCA) as a classification technique. Regarding the untargeted strategy, only unadulterated samples were modelled, obtaining 96.3% of correct classification. The prediction of adulterated samples gave errors between 5.5% and 2%. Regarding targeted modelling, two classes were modelled: Class 1 (unadulterated samples) and Class 2 (almond adulterated samples). Samples adulterated with chickpea were predicted to prove its ability to deal with non-modelled adulterants. The results show that samples adulterated with almond were mainly classified in their own class (90.9%) and samples with chickpea were classified in Class 2 (67.3%) or not in any class (30.9%), but no one only as unadulterated.
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