Evidence map›Paper›PMID 40291476›Full record

ArticleApplied intelligence (Dordrecht, Netherlands)2025

AI-driven 5G IoT e-nose for whiskey classification.

Jaume Segura-Garcia, Rafael Fayos-Jordan, Mohammad Alselek, Sergi Maicas, Miguel Arevalillo-Herraez, Enrique A Navarro-Camba, Jose M Alcaraz-Calero

Abstract read
In one paragraph

Article in Applied intelligence (Dordrecht, Netherlands), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jaume Segura-GarciaComputer Science Dpt, Universitat de València, Avda de la Universitat, s/n, Burjassot, 46100 Valencia Spain.
Rafael Fayos-JordanSchool of Computing, Engineering and Physics, University of the West of Scotland, High St, Paisley, PA1 2BE Scotland UK.
Mohammad AlselekSchool of Computing, Engineering and Physics, University of the West of Scotland, High St, Paisley, PA1 2BE Scotland UK.
Sergi MaicasMicrobiology and Ecology Dpt, Universitat de València, C/ Dr Moliner 50, Burjassot, 46100 Valencia Spain.
Miguel Arevalillo-HerraezComputer Science Dpt, Universitat de València, Avda de la Universitat, s/n, Burjassot, 46100 Valencia Spain.
Enrique A Navarro-CambaComputer Science Dpt, Universitat de València, Avda de la Universitat, s/n, Burjassot, 46100 Valencia Spain.
Jose M Alcaraz-CaleroSchool of Computing, Engineering and Physics, University of the West of Scotland, High St, Paisley, PA1 2BE Scotland UK.ORCID 0000-0002-2654-7595

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The main contribution is the design, implementation and validation of a complete AI-driven electronic nose architecture to perform the classification of whiskey and acetones. This classification is of paramount important in the distillery production line of whiskey in order to predict the quality of the final product. In this work, we investigate the application of an e-nose (based on arrays of single-walled carbon nanotubes) to the distinction of two different substances, such as whiskey and acetone (as a subproduct of the distillation process), and discrimination of three different types of the same substance, such as three types of whiskies. We investigated different strategies to classify the odor data and provided a suitable approach based on random forest with accuracy of 99% and with inference times under 1.8 seconds. In the case of clearly different substances, as subproducts of the whiskey distillation process, the procedure presented achieves a high accuracy in the classification process, with an accuracy around 96%.

Indexed as

5G IoTe-noseMLOdor discriminationPCA

Identifiers

PMID40291476
PMCPMC12021964

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.