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

Commercial printers based on fused deposition modeling (FDM) are widely adopted for 3D printing applications. This method consists of the heating of polymeric filaments over the melting point followed by their deposition onto a solid base to create the desirable 3D structure. Prior investigation using chromatographic techniques has shown that chemical compounds (e.g. VOCs), which can be harmful to users, are emitted during the printing process, producing adverse effects to human health and contributing to indoor air pollution. In this study, we present a simple, inexpensive and disposable paperbased optoelectronic nose (i.e. colorimetric sensor array) to identify the gaseous emission fingerprint of five different types of thermoplastic filaments (ABS, TPU, PETG, TRITAN and PLA) in the indoor environment. The optoelectronic nose is comprised of selected 15 dyes with different chemical properties deposited onto a microfluidic paper-based device with spots of 5 mm in diameter each. Digital images were obtained from an ordinary flatbed scanner, and the RGB information collected before and after air exposure was extracted by using an automated routine designed in MATLAB, in which the color changes provide a unique fingerprint for each filament in 5 min of printing. Reproducibility was obtained in the range of 2.5-10% (RSD). Hierarchical clustering analysis (HCA) and principal component analysis (PCA) were successfully employed, showing suitable discrimination of all studied filaments and the non-polluted air. Besides, air spiked with vapors of the most representative VOCs were analyzed by the optoelectronic nose and visually compared to each filament. The described study shows the potential of the paper-based optoelectronic nose to monitor possible hazard emissions from 3D printers. (C) 2020 Elsevier B.V. All rights reserved.

JTD Keywords: 3d printing, colorimetric sensor array, indoor air pollution, optoelectronic nose, paper-based, 3d printing, Colorimetric sensor array, Emissions, Indoor air pollution, Optoelectronic nose, Paper-based, Thermoplastic filaments