{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T20:52:40Z","timestamp":1772657560254,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Ensuring food quality and safety is a growing challenge in the food industry, where early detection of contamination or spoilage is crucial. Using gas sensors combined with Artificial Intelligence (AI) offers an innovative and effective approach to food identification, improving quality control and minimizing health risks. This study aims to evaluate food identification strategies using supervised learning techniques applied to data collected by the BME Development Kit, equipped with the BME688 sensor. The dataset includes measurements of temperature, pressure, humidity, and, particularly, gas composition, ensuring a comprehensive analysis of food characteristics. The methodology explores two strategies: a neural network model trained using Bosch BME AI-Studio software, and a more flexible, customizable approach that applies multiple predictive algorithms, including DT, LR, kNN, NB, and SVM. The experiments were conducted to analyze the effectiveness of both approaches in classifying different food samples based on gas emissions and environmental conditions. The results demonstrate that combining electronic noses (E-Noses) with machine learning (ML) provides high accuracy in food identification. While the neural network model from Bosch follows a structured and optimized learning approach, the second methodology enables a more adaptable exploration of various algorithms, offering greater interpretability and customization. Both approaches yielded high predictive performance, with strong classification accuracy across multiple food samples. However, performance variations depend on the characteristics of the dataset and the algorithm selection. A critical analysis suggests that optimizing sensor calibration, feature selection, and consideration of environmental parameters can further enhance accuracy. This study confirms the relevance of AI-driven gas analysis as a promising tool for food quality assessment.<\/jats:p>","DOI":"10.3390\/app15179687","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T09:18:57Z","timestamp":1756977537000},"page":"9687","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Potential Use of BME Development Kit and Machine Learning Methods for Odor Identification: A Case Study"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3011-5821","authenticated-orcid":false,"given":"Jos\u00e9","family":"Pereira","sequence":"first","affiliation":[{"name":"Department of Engineering, School of Sciences and Technology, Universidade de Tras-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9534-505X","authenticated-orcid":false,"given":"Afonso","family":"Mota","sequence":"additional","affiliation":[{"name":"Department of Engineering, School of Sciences and Technology, Universidade de Tras-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0859-8978","authenticated-orcid":false,"given":"Pedro","family":"Couto","sequence":"additional","affiliation":[{"name":"Department of Engineering, School of Sciences and Technology, Universidade de Tras-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"},{"name":"CITAB (Centre for the Research and Technology of Agroenvironmental and Biological Sciences), Inov4Agro, Universidade Tras-os-Montes e Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5798-1298","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Valente","sequence":"additional","affiliation":[{"name":"Department of Engineering, School of Sciences and Technology, Universidade de Tras-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"},{"name":"CRIIS (Centre for Robotics in Industry and Intelligent Systems), INESC TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4632-9664","authenticated-orcid":false,"given":"Carlos","family":"Ser\u00f4dio","sequence":"additional","affiliation":[{"name":"Department of Engineering, School of Sciences and Technology, Universidade de Tras-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"},{"name":"Center ALGORITMI, Campus de Azurem, Universidade do Minho, 4800-058 Guimaraes, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115873","DOI":"10.1016\/j.trac.2020.115873","article-title":"Miniaturised Air Sampling Techniques for Analysis of Volatile Organic Compounds in Air","volume":"126","author":"Lan","year":"2020","journal-title":"TrAC Trends Anal. 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