Validation of multivariate screening methodology. Case study: detection of food fraud.
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.
Chemometrics, Qualimetric and Nanosensors Grup, Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcel·lí Domingo s/n, 43007 Tarragona, Spain.
M Isabel
MI
López
Chemometrics, Qualimetric and Nanosensors Grup, Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcel·lí Domingo s/n, 43007 Tarragona, Spain.
Núria
N
Colomer
Chemometrics, Qualimetric and Nanosensors Grup, Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcel·lí Domingo s/n, 43007 Tarragona, Spain. Electronic address: itziar.ruisanchez@urv.cat.
Itziar
I
Ruisánchez
Chemometrics, Qualimetric and Nanosensors Grup, Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcel·lí Domingo s/n, 43007 Tarragona, Spain.
M Pilar
MP
Callao
D005504:Food Analysis / Q000379:methods*; D005607:Fraud; D015999:Multivariate Analysis; D012372:ROC Curve; D013055:Spectrophotometry, Infrared
Food fraud; Multivariate screening validation; Performance characteristic curves; Performance parameters; ROC curves