Signal Preprocessing in Instrument-Based Electronic Noses Leads to Parsimonious Predictive Models: Application to Olive Oil Quality Control Article Swipe
Luis Fernández
,
Sergio Oller Moreno
,
Jordi Fonollosa
,
Rocío Garrido‐Delgado
,
Lourdes Arce
,
Andrés Martín-Gómez
,
Santiago Marco
,
Antonio Pardo
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25030737
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25030737
- OA Status
- gold
- References
- 88
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406910389
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406910389Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s25030737Digital Object Identifier
- Title
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Signal Preprocessing in Instrument-Based Electronic Noses Leads to Parsimonious Predictive Models: Application to Olive Oil Quality ControlWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-25Full publication date if available
- Authors
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Luis Fernández, Sergio Oller Moreno, Jordi Fonollosa, Rocío Garrido‐Delgado, Lourdes Arce, Andrés Martín-Gómez, Santiago Marco, Antonio PardoList of authors in order
- Landing page
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https://doi.org/10.3390/s25030737Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/s25030737Direct OA link when available
- Concepts
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Electronic nose, Overfitting, Workflow, Computer science, Preprocessor, Machine learning, Artificial intelligence, Predictive modelling, Data mining, Process (computing), Artificial neural network, Operating system, DatabaseTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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88Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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