Rapid Identification Method for CH4/CO/CH4-CO Gas Mixtures Based on Electronic Nose

Abstract

The inherent cross-sensitivity of semiconductor gas sensors makes them extremely challenging to accurately detect mixed gases. In order to solve this problem, this paper designed an electronic nose (E-nose) with seven gas sensors and proposed a rapid method for identifying CH4, CO, and their mixtures. Most reported methods for E-nose were based on analyzing the entire response process and employing complex algorithms, such as neural network, which result in long time-consuming processes for gas detection and identification. To overcome these shortcomings, this paper firstly proposes a way to shorten the gas detection time by analyzing only the start stage of the E-nose response instead of the entire response process. Subsequently, two polynomial fitting methods for extracting gas features are designed according to the characteristics of the E-nose response curves. Finally, in order to shorten the time consumption of calculation and reduce the complexity of the identification model, linear discriminant analysis (LDA) is introduced to reduce the dimensionality of the extracted feature datasets, and an XGBoost-based gas identification model is trained using the LDA optimized feature datasets. The experimental results show that the proposed method can shorten the gas detection time, obtain sufficient gas features, and achieve nearly 100% identification accuracy for CH4, CO, and their mixed gases.

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

“…Finally, the SVM-based identification model is trained using the constructed dataset to realize the identification of adulterants. Because there is inevitable noise in the E-nose original response signals [13,18,24], in order to facilitate the feature extraction and improve the identification accuracy, noise reduction and filtering is conducted during the data acquisition process.…”

“…The gas sensor array of the E-nose consists of 10 metal oxide semiconductor gas sen sors. More details of the material preparation process and the characterization of these gas sensors have been described in the literature [24,36]. When the sensitive material of the sensor is exposed in the detected gas, the change in its resistance value will be converted into the corresponding electrical signal, which is collected by the personal computer with a sampling frequency of 1 Hz.…”

“…Extracting effective odor features from the response signals and then training models are the two key steps for odor identification by using the E-nose. At present, the commonly used ways to extract the odor features are time-domain features [18,20,21], frequency domain features [22,23], curve fitting [24][25][26], and data compression [27,28]. However, the effectiveness of the feature-extraction methods is affected by many factors, such as the target gases and the application scenarios.…”

“…Although a neural network model can achieve a high identification accuracy, it inevitably takes a relatively long computational time. This shortcoming limits its implementation in rapid identification and its application on portable devices [24,32].…”

Classification and Identification of Contaminants in Recyclable Containers Based on a Recursive Feature Elimination-Light Gradient Boosting Machine Algorithm Using an Electronic Nose

“…Figure 3 shows the schematic of the electronic nose system used in this work, which consists of three components: a gas sensing array (10 homemade MEMS metal oxide sensors; because different sensors have different sensitivities to different gases, it is better to choose sensors with high sensitivity for different practical applications) [34,35]; a gas collection module; and a data acquisition module. The sensor parameters of the gas sensing array are listed in Table 1.…”