Integrating Spectroscopic and Isotopic Data with Multi-Criteria Decision Making for Traceability and Fraud Detection in Edible Oils

Abstract

In the context of increasing food fraud risks and the need for sustainable supply chain management, this study explores a novel multi-criteria approach combining spectroscopic (ATR-FTIR) and isotopic (δ13C, δ15N, δ18O) analyses with advanced chemometric models for the authentication and traceability of sesame oils. By applying principal component analysis (PCA), hierarchical clustering, and classification algorithms (PLS-DA, Random Forest), this integrative methodology enables the robust identification of adulteration with other oils (e.g., soybean, sunflower), and discrimination of oils based on their geographical origin. The incorporation of these analytical features into multi-criteria decision-making (MCDM) frameworks allows for a more reliable and transparent system to support food quality control, consumer trust, and regulatory compliance. This chapter emphasizes the potential of coupling analytical chemistry with decision support tools to promote integrity also in the digital era where traceability and data-driven monitoring are essential