Validation of multivariate screening methodology. Case study: detection of food fraud

Abstract

Multivariate screening methods are increasingly being implemented but there is no worldwide harmonized criterion for their validation. This study contributes to establish protocols for validating these methodologies. We propose the following strategy: (1) Establish the multivariate classification model and use receiver operating characteristic (ROC) curves to optimize the significance level (α) for setting the model's boundaries. (2) Evaluate the performance parameter from the contingency table results and performance characteristic curves (PCC curves). The adulteration of hazelnut paste with almond paste and chickpea flour has been used as a case study. Samples were analyzed by infrared (IR) spectroscopy and the multivariate classification technique used was soft independent modeling of class analogies (SIMCA). The ROC study showed that the optimal α value for setting the SIMCA boundaries was 0.03 in both cases. The sensitivity value was 93%, specificity 100% for almond and 98% for chickpea, and efficiency 97% for almond and 93% for chickpea.

Keywords: Food fraud; Multivariate screening validation; Performance characteristic curves; Performance parameters; ROC curves.