ArticleSensors (Basel, Switzerland)2020
Odor Detection Using an E-Nose With a Reduced Sensor Array.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Optimization of Temperature Modulation for Gas Classification Based on Bayesian Optimization.Sensors (Basel, Switzerland) · 2024Article
- Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry.Sensors (Basel, Switzerland) · 2024Review
- Development of a Low-Cost Electronic Nose with an Open Sensor Chamber: Application to Detection ofSensors (Basel, Switzerland) · 2023Article
- Classification of Three Volatiles Using a Single-Type eNose with Detailed Class-Map Visualization.Sensors (Basel, Switzerland) · 2022Article
- Review
- Advances in the Detection of Emerging Tree Diseases by Measurements of VOCs andPathogens (Basel, Switzerland) · 2021Article
- Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It toSensors (Basel, Switzerland) · 2021Article
- Electronic Nose Differentiation betweenMolecules (Basel, Switzerland) · 2021Article
- Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic OomycetesSensors (Basel, Switzerland) · 2021Article
- Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques.Sensors (Basel, Switzerland) · 2020Article
- Collaborative Filtering to Predict Sensor Array Values in Large IoT Networks.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Recent advances in the field of electronic noses (e-noses) have led to new developments in both sensors and feature extraction as well as data processing techniques, providing an increased amount of information. Therefore, feature selection has become essential in the development of e-nose applications. Sophisticated computation techniques can be applied for solving the old problem of sensor number optimization and feature selections. In this way, one can find an optimal application-specific sensor array and reduce the potential cost associated with designing new e-nose devices. In this paper, we examine a procedure to extract and select modeling features for optimal e-nose performance. The usefulness of this approach is demonstrated in detail. We calculated the model's performance using cross-validation with the standard leave-one-group-out and group shuffle validation methods. Our analysis of wine spoilage data from the sensor array shows when a transient sensor response is considered, both from gas adsorption and desorption phases, it is possible to obtain a reasonable level of odor detection even with data coming from a single sensor. This requires adequate extraction of modeling features and then selection of features used in the final model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.