Signal Preprocessing in Instrument-Based Electronic Noses Leads to Parsimonious Predictive Models: Application to Olive Oil Quality Control

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 3; p. 737
• Main Authors: Fernandez, Luis, Oller-Moreno, Sergio, Fonollosa, Jordi, Garrido-Delgado, Rocío, Arce, Lourdes, Martín-Gómez, Andrés, Marco, Santiago, Pardo, Antonio
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.01.2025; MDPI
• Subjects: Analytical chemistry; Chromatography; Comparative analysis; Design and construction; Electronic Nose; Environmental monitoring; Gas-detectors; Humidity; MCC-IMS-based e-nose; Olive oil; Olive Oil - analysis; Olive Oil - chemistry; Outdoor air quality; parsimony; Pattern recognition systems; preprocessing; Quality Control; Quality management; Reproducibility; Scientific imaging; Sensors; Signal processing; signal processing workflow; Signal Processing, Computer-Assisted; Technology application; validation; Spain; olive oil; signal processing workflow; parsimony; preprocessing; validation; MCC-IMS-based e-nose
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25030737

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.