ROC curves for the optimization of one-class model parameters. A case study: Authenticating extra virgin olive oil from a Catalan protected designation of origin

This paper proposes a ROC curve-based methodology to find optimal classification model parameters. ROC curves are implemented to set the optimal number of PCs to build a one-class SIMCA model and to set the threshold class value that optimizes both the sensitivity and specificity of the model. The authentication of the geographical origin of extra-virgin olive oils of Arbequina botanical variety is presented. The model was developed for samples from Les Garrigues, target class, Samples from Siurana were used as the non-target class. Samples were measured by FT-Raman with no pretreatment. PCA was used as exploratory technique. Spectra underwent pre-treatment and variables were selected based on their VIP score values. ROC curve and others already known criteria were applied to set the threshold class value. The results were better when the ROC curve was used, obtaining performance values higher than 82%, 75% and 77% for sensitivity, specificity and efficiency, respectively.

Automated summary

A methodology based on ROC curves is proposed to optimize classification model parameters, successfully applied to authenticate the geographical origin of extra-virgin olive oils. By setting the threshold class value, the sensitivity and specificity of the model were improved.