Stable isotope and multi-elemental analysis combined with statistical modeling for saffron origin authentication

Saffron (Crocus sativus L.) is among the world's most valuable spices, yet its high market price and complex supply chains make it particularly vulnerable to mislabeling and fraud. To address this, we integrated stable isotope ratio analysis (δ2H, δ13C, δ15N, δ18O, δ34S) with multi-elemental profiling and advanced statistical modeling to authenticate saffron geographical origin. A total of 75 saffron samples—48 authentic—sourced from Morocco, Greece, Slovenia, Spain, Iran, and the United Kingdom were analyzed using isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS). These were combined with two previously published datasets, including 163 authentic samples in total. The incorporation of external datasets was possible due to their strong methodological and analytical comparability with the present study, ensuring directly compatible results. Orthogonal partial least squares discriminant analysis (OPLS-DA) achieved 94 % classification accuracy, identifying δ2H, δ15N, K, Ca, δ13C, Sr, Mn, Co, and Zn as key variables for discriminating geographical origins. Incorporation of elemental ratios (Rb/Sr, K/Rb, Ca/Sr) and/or δ18O and δ34S further refined classification, particularly improving accuracy for Moroccan saffron. Class-modeling via Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) confirmed high sensitivity and specificity across target sets and revealed potential mislabeling in ∼15 % of commercial samples, with the highest discrepancy (24 %) observed in Iranian-labeled products. These findings underscore the power of combined stable isotope and multi-elemental approaches for saffron origin authentication and highlight the need for robust analytical verification to strengthen transparency and combat fraud in the saffron trade