Avocado oil, regarded as a health-promoting food product due to its bioactive compounds, faces a persistent problem of adulteration. Existing methods for assessing the authenticity of edible oils are often complex, expensive, and time-consuming. Furthermore, they are environmentally burdensome due to chemical wastes. This study used gas chromatography (GC) analysis, a traditional method, to evaluate 25 commercial samples for compliance with the fatty acid (FA) and phytosterol (PS) standards by Codex Alimentarius. Results showed that only 40% of the samples fully complied with the CA, confirming widespread non-authenticity, particularly among the private label brands. A low-cost electronic nose (e-nose) was employed to develop machine learning (ML) models for rapid authentication, using the GC data as reference. A principal component analysis (PCA) showed clear separation among samples based on fault levels. Significant correlations (R) were found between the e-nose sensor responses and key FAs and PSs. Classification models using artificial neural networks (ANN) achieved overall accuracies > 95%. The developed ANN regression models for individual compounds, including oleic acid and β-sitosterol, showed also high accuracy (R = 0.92-0.98). Both classification and regression models showed no signs of overfitting. Mean squared error values during training were consistently lower than those obtained during testing. These approaches represent a cost-effective, rapid, reliable, and scalable alternative for routine authenticity screening. It offers promising applications for the food industry, particularly for private-label retailers seeking to prevent food fraud and ensure product integrity. • Groundbreaking ML models using e-nose offered a novel method for oil authentication. • Approach offered a low-cost, rapid, and scalable tool to prevent food fraud. • Only 40% of 25 avocado oil brands fully met Codex Alimentarius standards.
Mayorga-Martínez et al. (2026) studied this question.