University of Tehran Press Iranian Journal of Biosystem Engineering 2008-4803 55 1 2024 03 20 The Use of Gradient Boost Regression Model to Modeling of Gas Sensors in Diagnosis of Sun-dried, Sulphurous and Acidic solution dried Raisins The Use of Gradient Boost Regression Model to Modeling of Gas Sensors in Diagnosis of Sun-dried, Sulphurous and Acidic solution dried Raisins 1 18 98244 10.22059/ijbse.2024.370678.665534 FA Mohammad Ghoushchian PhD student, Department of Agricultural Machinery Engineering, Faculty of Agriculture, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran 0009-0008-0110-7164 Seyed Saeid Mohtasebi Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran 0000-0002-4031-1095 Shahin Rafiee Professor, Department of Agricultural Machinery Engineering, Faculty of Agriculture, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran 0000-0003-2647-3984 Journal Article 2024 01 06 Machine learning modeling can help overcome some of the limitations of gas sensors, such as high operational conditions, drift errors, limited selectivity, the need for a large amount of labeled data, and cost and fabrication challenges. In this research, an electronic nose system was developed for the detection of sulfur dioxide and acetic acid. three treatments, including sunny, acetic, and sulfuric, were prepared in three repetitions, and each was exposed to olfactory sensors for 60 minutes to record the sensor responses to each treatment. Then, the data obtained from the sensor responses were examined by machine learning models to determine the modeling accuracy of each method. The results showed that the utilized Gradient Boost Regression model with a determination coefficient of 0.9972, root mean square error of 0.0209, mean absolute error of 0.0026, and relative root mean square error of 0.0209 was able to model the gas sensor responses well for the introduced treatments. Furthermore, by analyzing the results, the type and degree of correlation between the sensor responses to each other and over time were determined to evaluate their behavior prediction. Then, based on the conducted modeling, it was revealed that MQ9, MQ3, MQ5, and TGS2620 sensors, with determination coefficients of 0.8668, 0.8786, 0.9458, and 0.9074, and root mean square errors of 0.0163, 0.0168, 0.0083, and 0.0227, respectively, provided more accurate and predictable responses compared to MQ135, TGS822, TGS810, and MQ4 sensors. Machine learning modeling can help overcome some of the limitations of gas sensors, such as high operational conditions, drift errors, limited selectivity, the need for a large amount of labeled data, and cost and fabrication challenges. In this research, an electronic nose system was developed for the detection of sulfur dioxide and acetic acid. three treatments, including sunny, acetic, and sulfuric, were prepared in three repetitions, and each was exposed to olfactory sensors for 60 minutes to record the sensor responses to each treatment. Then, the data obtained from the sensor responses were examined by machine learning models to determine the modeling accuracy of each method. The results showed that the utilized Gradient Boost Regression model with a determination coefficient of 0.9972, root mean square error of 0.0209, mean absolute error of 0.0026, and relative root mean square error of 0.0209 was able to model the gas sensor responses well for the introduced treatments. Furthermore, by analyzing the results, the type and degree of correlation between the sensor responses to each other and over time were determined to evaluate their behavior prediction. Then, based on the conducted modeling, it was revealed that MQ9, MQ3, MQ5, and TGS2620 sensors, with determination coefficients of 0.8668, 0.8786, 0.9458, and 0.9074, and root mean square errors of 0.0163, 0.0168, 0.0083, and 0.0227, respectively, provided more accurate and predictable responses compared to MQ135, TGS822, TGS810, and MQ4 sensors. https://ijbse.ut.ac.ir/article_98244_8f86b68f99f724ea46381f3bf012f6fc.pdf