Staff member

Gancarz, Marek

JTD

Oxidation is a major factor affecting the quality and shelf life of Extra Virgin Olive Oil (EVOO), leading to chemical degradation and loss of freshness. This study investigates the assessment of EVOO freshness using a peptide-based optoelectronic nose (OE-nose) system combined with signal processing and machine learning techniques. Volatile organic compound (VOC) profiles from fresh and oxidized EVOO samples were acquired using a multigas sensor array implemented on the Aryballe NeOse Advance platform. The oxidation status of the samples was validated using reference chemical quality analyses. Sensor signals were subjected to baseline correction and normalization, without the application of digital smoothing. Full-sequence analysis was employed to exploit desorption-phase kinetics as a volatility-driven, implicit preseparation mechanism, enabling robust discrimination without chromatographic steps. Exploratory and supervised models were evaluated, including principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and support vector machines (SVM). The SVM model achieved a classification accuracy of 100%, while PLS-DA reached 95.8% accuracy under strict validation conditions. Compared to conventional analytical methods, the proposed approach offers a rapid, nondestructive, and cost-effective solution for on-site EVOO freshness evaluation. To the authors' knowledge, this work represents the first application of a peptide-based optoelectronic nose for assessing EVOO oxidation, highlighting its potential advantages over conventional MOX- and polymer-based electronic nose systems reported in previous studies.

JTD