Version 1
: Received: 8 June 2024 / Approved: 10 June 2024 / Online: 11 June 2024 (10:57:31 CEST)
How to cite:
Ruiz-Canales, A.; Martínez-Muñoz, G.; Conesa-Celdrán, A.; CÁNOVAS FLORES, I.; Vertedor, D. M.; Oates, M. J. Detection of Different Lemon Pre-Harvest Additive Treatments by Means an Electronic Nose Prototype. Preprints2024, 2024060679. https://doi.org/10.20944/preprints202406.0679.v1
Ruiz-Canales, A.; Martínez-Muñoz, G.; Conesa-Celdrán, A.; CÁNOVAS FLORES, I.; Vertedor, D. M.; Oates, M. J. Detection of Different Lemon Pre-Harvest Additive Treatments by Means an Electronic Nose Prototype. Preprints 2024, 2024060679. https://doi.org/10.20944/preprints202406.0679.v1
Ruiz-Canales, A.; Martínez-Muñoz, G.; Conesa-Celdrán, A.; CÁNOVAS FLORES, I.; Vertedor, D. M.; Oates, M. J. Detection of Different Lemon Pre-Harvest Additive Treatments by Means an Electronic Nose Prototype. Preprints2024, 2024060679. https://doi.org/10.20944/preprints202406.0679.v1
APA Style
Ruiz-Canales, A., Martínez-Muñoz, G., Conesa-Celdrán, A., CÁNOVAS FLORES, I., Vertedor, D. M., & Oates, M. J. (2024). Detection of Different Lemon Pre-Harvest Additive Treatments by Means an Electronic Nose Prototype. Preprints. https://doi.org/10.20944/preprints202406.0679.v1
Chicago/Turabian Style
Ruiz-Canales, A., Daniel Martin Vertedor and Martin J Oates. 2024 "Detection of Different Lemon Pre-Harvest Additive Treatments by Means an Electronic Nose Prototype" Preprints. https://doi.org/10.20944/preprints202406.0679.v1
Abstract
This study was carried out with a low-cost electronic nose prototype based on eight metal oxide sensors (MQ) in order to characterize samples of lemons treated with 0.5% and 0.1% of sodium benzoate. The MQ sensors designed are sensitive to one or more chemicals to detect the presence of a variety of chemicals in the air. The sensor MQ135 detects ammonia, hydrogen sulphide and benzene. Signal data were studied to obtain a pattern recognition of rotten in lemon fruits. Network analysis was used to obtain a calibration of measures among the stage of lemons. In this article, an electronic nose prototype based on 8 MQ metal oxide sensors has been used in order to analyze and characterize different lemon varieties to which different chemical treatments have been applied in pre-harvest. PCA-based data analyzes were used to observe clusters in the data. Through the combined use of the data obtained by the nose and these Sequential Neural Networks (SNNs) a classification tool for lemon varieties and applied treatments has been obtained. It is shown the ability of this device to be used as a reliable discrimination method, in addition to providing low cost and optimization of time and expert resources.
Keywords
resistive sensors; monitoring; postharvest; Citrus limon
Subject
Biology and Life Sciences, Food Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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