Publications

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 Keywords: Evoo, Feature extraction, Fraud detection, Nondestructive quality control, Optoelectronic nose (oe-nose), Peace, justice and strong institutions, Reduced inequalities, Time-resolved headspace analysis

Gas sensor-based electronic noses (e-noses) have gained considerable attention over the past thirty years, leading to the publication of numerous research studies focused on both the development of these instruments and their various applications. Nonetheless, the limited specificity of gas sensors, along with the common requirement for chemical identification, has led to the adaptation and incorporation of analytical chemistry instruments into the e-nose framework. Although instrument-based e-noses exhibit greater specificity to gasses than traditional ones, they still produce data that require correction in order to build reliable predictive models. In this work, we introduce the use of a multivariate signal processing workflow for datasets from a multi-capillary column ion mobility spectrometer-based e-nose. Adhering to the electronic nose philosophy, these workflows prioritized untargeted approaches, avoiding dependence on traditional peak integration techniques. A comprehensive validation process demonstrates that the application of this preprocessing strategy not only mitigates overfitting but also produces parsimonious models, where classification accuracy is maintained with simpler, more interpretable structures. This reduction in model complexity offers significant advantages, providing more efficient and robust models without compromising predictive performance. This strategy was successfully tested on an olive oil dataset, showcasing its capability to improve model parsimony and generalization performance.

JTD Keywords: Breath, Chromatography, Classification, Electronic nose, Hs-gc-ims, Ion mobility spectrometry, Mcc-ims-based e-nose, Olive oi, Olive oil, Parsimony, Preprocessing, Quality control, Selection, Signal processing workflow, System, Too, Validation, Wavelet