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A Survey on Statistical and Machine Learning Algorithms Used in Electronic Noses for Food Quality Assessment

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Abstract

A deeper assessment of the nutritional and organoleptic potential of the items we consume is the first step toward knowing their capacity. Research is becoming more and more interested in developing quick and accurate instruments to assess touch, vision, hearing, and other senses under this impetus. This paper examines a variety of statistical and machine learning algorithms used for food quality applications with electronic nose technology. The latter was discussed, allowing us to say that different data collection devices and data analysis methods were used for each study that was undertaken. Setting up an experimental e-nose based on commercially accessible sensors would be simpler, quicker, and less expensive in a West African setting than buying a commercial e-nose to forecast local crop condition. There is a scarcity of studies that examine the connection between e-nose technologies and food quality in Africa.

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Acknowledgements

This publication was made possible through the Digital Science and Technology Network (DSTN) supported by Institut de Recherche pour le Développement (IRD) and Agence Française de Développement (AFD).

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  1. Institut de Mathématiques et de Sciences Physiques, University of Abomey-Calavi, Dangbo, Benin

    Lionel Affognon & Eugène C. Ezin

  2. Institut de Formation et de Recherche en Informatique, University of Abomey-Calavi, Abomey-Calavi, Benin

    Eugène C. Ezin

  3. UFR des Sciences Appliquées et de Technologies, University of Gaston Berger, Saint-Louis, Senegal

    Abdoulaye Diallo & Chérif Diallo

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Affognon, L., Diallo, A., Diallo, C. et al. A Survey on Statistical and Machine Learning Algorithms Used in Electronic Noses for Food Quality Assessment. SN COMPUT. SCI. 4, 590 (2023). https://doi.org/10.1007/s42979-023-02052-0

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