An E-Nose Using Metal Oxide Semiconductor Sensors Array to Recognize the Odors of Fall Armyworm Pest for Its Early Detection in the Farm

Kiki, Manhougbé Probus A. F.; Ahouandjinou, Arnaud; Assogba, Kokou; Sama, Yves N.

doi:10.1007/978-3-031-23116-2_5

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…This procedure followed the protocol delineated by Attasopa, Ferrari, Chantawannakul and Bänziger. 14 The polymerase chain reaction (PCR) amplification of the cytochrome oxidase I (COI) gene fragment involved a 25 μL reaction volume, utilizing the primers LWCOI_U (5 0 -GTTTTGGGATATCTCTCA TAC-3 0 ) and LWCOI_L (5 0 -GAGCAACAACATAATAAGTATC-3 0 ) as forward and reverse primers, respectively, as described by Webster, Thomas and McCormack. 15 The PCR thermal cycling conditions included 35 cycles of: denaturation at 94 °C for 30 s, annealing at 50 °C for 30 s, and elongation at 72 °C for 1 min.…”

Section: Methodsmentioning

confidence: 99%

“…The sensitivity of MQ series odor sensors in detecting mite infestation within chicken feed was evaluated using nine distinct sensors: MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, MQ-8, MQ-9, and MQ-135. Each sensor was interfaced with an ESP32 microcontroller, connecting to pins 14,27,13,26,25,33,32,35, and 34 through their respective analog pins. The microcontroller was programmed to acquire a reading from each sensor at 1-s intervals.…”

Section: Sensitivity Of Mq Odor Sensorsmentioning

confidence: 99%

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Quantitative identification and sensor sensitivity analysis in detection of odor volatile compounds emitted by scaly grain mites, Suidasia pontifica Oudemans

Ta‐Phaisach,

Konvipasruang,

Attasopa

et al. 2024

Pest Management Science

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BACKGROUNDIn Thailand, the scaly grain mite, Suidasia pontifica Oudemans, impacts the feed industry by emitting a lemongrass‐like odor in contaminated feed, reducing its palatability. This study focused on identifying volatile organic compounds (VOCs) associated with this odor in contaminated chicken feeds and ground maize, as well as individual mites using gas chromatography–mass spectrometry (GC–MS). We explored the relationship between VOC concentration and mite population size in chicken feed cultures, aiming to detect minimal mite presence through regression models. Additionally, we tested the sensitivity of nine electronic odor sensors in detecting these VOCs.RESULTSOur results showed Z‐citral and E‐citral present in mite‐contaminated ground maize, chicken feeds, but absent in uncontaminated samples. Mite populations in chicken feed followed a normal distribution, increasing rapidly, peaking, and then declining, a pattern mirrored in the concentrations of Z‐citral and E‐citral. Simple linear regression models confirmed a positive correlation between mite density and citral concentrations. Polynomial regression models indicated Z‐citral is detectable at mite densities over 67 individuals per gram and E‐citral over 3.89 individuals per gram. Odor sensors showed varying readings across different citral concentrations, uncontaminated, and mite‐infested chicken feeds.CONCLUSIONSOur study confirms that S. pontifica are responsible for the lemongrass‐like odor in infested samples, due to Z‐citral and E‐citral. The citral concentrations increase with mite population growth and are detectable at low mite densities, suggesting their effectiveness as biomarkers for early mite infestation detection. Additionally, MQ series odor sensors detected these VOCs, indicating their potential for monitoring mite infestations in storage environments.This article is protected by copyright. All rights reserved.

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Section: Methodsmentioning

confidence: 99%

Section: Sensitivity Of Mq Odor Sensorsmentioning

confidence: 99%

Quantitative identification and sensor sensitivity analysis in detection of odor volatile compounds emitted by scaly grain mites, Suidasia pontifica Oudemans

Ta‐Phaisach,

Konvipasruang,

Attasopa

et al. 2024

Pest Management Science

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“…Finally, in 2023, Kiki et al [37] implemented an eNose system based on metal oxide semiconductor sensors to detect the fall armyworm plague, which destroys crops and considerably affects the agricultural sector. Therefore, they studied the sensors that showed the most significant response to the volatile compounds of the worm.…”

Section: Other Applications Of Mq Family Sensorsmentioning

confidence: 99%

Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques

Viciano-Tudela,

Parra,

Navarro-Garcia

et al. 2023

Sensors

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Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.

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An E-Nose Using Metal Oxide Semiconductor Sensors Array to Recognize the Odors of Fall Armyworm Pest for Its Early Detection in the Farm

Cited by 2 publications

References 32 publications

Quantitative identification and sensor sensitivity analysis in detection of odor volatile compounds emitted by scaly grain mites, Suidasia pontifica Oudemans

Quantitative identification and sensor sensitivity analysis in detection of odor volatile compounds emitted by scaly grain mites, Suidasia pontifica Oudemans

Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques

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