{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T03:24:46Z","timestamp":1785554686464,"version":"3.56.0"},"reference-count":46,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this study, a neural network was developed for the detection of acetone, ethanol, chloroform, and air pollutant NO2 gases using an Interdigitated Electrode (IDE) sensor-based e-nose system. A bioimpedance spectroscopy (BIS)-based interface circuit was used to measure sensor responses in the e-nose system. The sensor was fed with a sinusoidal voltage at 10 MHz frequency and 0.707 V amplitude. Sensor responses were sampled at 100 Hz frequency and converted to digital data with 16-bit resolution. The highest change in impedance magnitude obtained in the e-nose system against chloroform gas was recorded as 24.86 \u03a9 over a concentration range of 0\u201311,720 ppm. The highest gas detection sensitivity of the e-nose system was calculated as 0.7825 \u03a9\/ppm against 6.7 ppm NO2 gas. Before training with the neural network, data were filtered from noise using Kalman filtering. Principal Component Analysis (PCA) was applied to the improved signal data for dimensionality reduction, separating them from noise and outliers with low variance and non-informative characteristics. The neural network model created is multi-layered and employs the backpropagation algorithm. The Xavier initialization method was used for determining the initial weights of neurons. The neural network successfully classified NO2 (6.7 ppm), acetone (1820 ppm), ethanol (1820 ppm), and chloroform (1465 ppm) gases with a test accuracy of 87.16%. The neural network achieved this test accuracy in a training time of 239.54 milliseconds. As sensor sensitivity increases, the detection capability of the neural network also improves.<\/jats:p>","DOI":"10.3390\/s24165315","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T09:15:41Z","timestamp":1723799741000},"page":"5315","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Development of a Neural Network for Target Gas Detection in Interdigitated Electrode Sensor-Based E-Nose Systems"],"prefix":"10.3390","volume":"24","author":[{"given":"Kadir","family":"Kaya","sequence":"first","affiliation":[{"name":"Department of Electrical-Electronics Engineering, Dumlup\u0131nar University, Kutahya 43100, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet Ali","family":"Ebeo\u011flu","sequence":"additional","affiliation":[{"name":"Department of Electrical-Electronics Engineering, Dumlup\u0131nar University, Kutahya 43100, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1109\/JSEN.2015.2483901","article-title":"Temperature modulated gas sensing E-nose system for low-cost and fast detection","volume":"16","author":"Yin","year":"2015","journal-title":"IEEE Sens. 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