Exploring Informative Response Features of Two Temperature Modulated Gas Sensors at a Wide Range
of Relative Humidity
Hannaneh Mahdavi Department of Electrical Engineering
Shahed University Tehran, Iran [email protected]
Seyed Mohsen Hosseini-Golgoo Department of Electrical Engineering
University of Guilan Rasht, Iran [email protected]
Saeideh Rahbarpour Department of Electrical Engineering
Shahed University Tehran, Iran [email protected]
Hamidreza Jamaati
Chronic Respiratory Diseases Research Center, NRITLD Shahid Beheshti University of Medical Sciences
Tehran, Iran [email protected] m
Abstract—The response signals of temperature modulated gas sensors contain essential information about measured target gas that must be separated from other correlated, redundant, or noisy data. This issue becomes more critical when variations in environmental factors such as relative humidity of target gas or background odors affect the sensor response. Conductance values of two electronic noses based on a single TGS -2602 and a single FIS S P-53B sensors to four gases and clean air at a wide range of relative humidity levels were measured for analyzing the response features. The role of each feature and increasing the number of features in the accuracy of an S VM classifier are investigated. A method is proposed based on removing non - informative features and compared to four conventional feature selection techniques. It is shown that our proposed scheme with a simple S VM classifier results in 96.7% detection accuracy for TGS -2602 and 98.8% for FIS S P-53B, which is up to the accuracy value of common or advanced methods of selecting features. It is concluded that employing feature selection techniques is more beneficial for the TGS -2602 dataset, which had more destructive features than FIS S P-53B. In conclusion, when working with an E-Nose dataset, it is first necessary to explore the important features to find out whether feature selection is required or not, and if needed, which feature selection method provides the best accuracy.
Keywords—Electronic Nose (E-Nose); Feature selection;
Informative Response Features, Metal oxide gas sensor;
Temperature modulation
I. INT RODUCT ION
Electronic Noses (E-Noses) have been known as portable, reliable, and simple devices measuring and recognizing volatile
gases. They are used in various applications such as food and agricultural industries [1], environment controlling [2], medicine [3], etc. Such systems are equipped with an array of sensors to convert odors into olfactory signals.
One of the most common gas sensors is the metal oxide sensor [4]. Metal oxide semiconductors are often sensitive to a group of volatile gases and non-selective [5]. Nevertheless, researchers have shown that only a single metal oxide sensor can be selective by temperature modulation [6] and act virtually as a set of selective sensors in an electronic nose [7- 9]. In this method, programmed temperature variations are applied to the surface of the metal oxide pallet. This variable temperature is created by applying a specific voltage waveform such as pulse, sinusoidal, triangular, saw-tooth, etc., to the sensor heater. As the reaction rate of gas molecules at the sensor surface is strongly dependent on the temperature of the pallet surface [10], temperature modulation leads the sensor to produce a dynamic signal. The shape of this signal varies depending on the type of target gas because various volatile gases tend to react with the sensor at specific surface temperatures [11]. Such sensor responses are used for designing an accurate classification model by statistical or machine learning algorithms. After that, the classifier model is capable of further gas identifications.
Different time segments of the sensor signal do not have the same discriminate information. For example, in the case of a staircase temperature modulated sensor in [12], it has been observed that the sensor response has more effective detection information whenever the sensor temperature is close to its optimum temperature. Moreover, there are many correlated and
redundant features that affect the model performance.
Furthermore, some parts of the signal may have destructive information for the separation of gases and should be removed.
One reason for creating destructive features is the relative humidity content of target gases. Humidity variations can change the response of the metal oxide sensors and cause a difference in the obtained pattern for test gas compared to the training pattern [13,14]. As a result, the accuracy of detecting electronic nose or single-sensor temperature-modulated systems is reduced [15]. So, finding discriminate information becomes more complex in applications with a wide range of relative humidity variations. Additionally, to avoid the curse of dimensionality, reducing the number of features is needed [16].
The principal component analysis (PCA) method is the most common approach for reducing the feature size and obtaining important information. However, PCA is a feature extraction method that increases the complexity of pattern recognition calculations and eliminates the physical meaning of features [17]. Feature selection is another common method for utilizing the most helpful information from the response of gas sensors by preserving the physical sense of features. The main goals of selecting the best features are avoiding overfitting, simplifying the model, reducing the training time, removing non-informative features, and improving prediction accuracy, especially while working with high dimensional data. Also, optimization methods such as particle swarm optimization [18, 19], genetic algorithm [20], space optimization [21], and etc.
can be applied to s elect features with significant importance.
Researchers have been used different feature selection techniques and shown their good results in increasing the detection accuracy of gas detection systems. Most feature selection methods can be divided into three significant schemes of Filter-based, Wrapper-based, and Embedded. Filters assess features by some metrics but are irrelevant to the classification model. Wrappers consider different sets of features, calculate the classifier accuracy for each group, and determine the best feature set. Therefore, however wrappers take more time, they are more reliable than filter methods, as the classification model and selected features are relevant to each other. Then, embedded methods use algorithms that have built -in feature selection methods and learn features while training the classifier [22]. Therefore, according to a variety of feature reduction methods, when working with an E-Nose dataset, it is first necessary to check whether utilizing a feature selection method is required or not, and second, which type of feature reduction techniques provides better classification accuracy.
In this paper, the responses of two electronic noses based on two commercial single temperature-modulated sensors are explored in terms of essential and informative parts of signals.
Then, a method for removing non-instructive features is suggested, followed by four standard feature selection methods. The aim is to examine the need for feature selection in each sensor while working with it and possibly increase its classification accuracy.
II. MAT ERIALS AND MET HODS A. Measurement and data collection
In the present work, two separate E-Noses were used that each of them containing a single temperature modulated gas sensor. The gas sensors were the commercial FIS SP-53B (FIS Co., Japan) and TGS-2602 (Figaro Co., Japan). To better compare the behavior of the two sensors in the detection of gases, both sensors were placed inside one test chamber, and the experiments were performed simultaneously. Respons e of sensors to four reducing gases of acetone, ethanol, 1-propanol, and 1-butanol at seven concentrations (0.6, 0.9, 1.2, 1.8, 2.4, 0.3, and 3.5 ppm), and three relative humidity levels (70%, 50%, and 30%) were recorded. Moreover, eight experiments were performed in clean air, and 92 datasets were collected for each sensor. The FIS SP-53B dataset was previously used in [23].
The measurement system is shown in Fig. 1. In this type of E-Nose, instead of commonly using constant voltage to stimulate the sensor heater, programmed variation of the sensor operating temperature was used to increase the sensor's selectivity. This system includes a gas mixture chamber, a test chamber containing gas sensors, a vacuum pump, solenoid valves, fittings, and pipes. It also has an interface circuit with a data acquisition card and a PCB board connected to a computer for generating the voltage waveform of the sensor heater and recording the sensor response. The conductance of each sensor was recorded by 10 sample per second.
Before experiments, the sensors were preheated with a constant 5 V for at least one hour. The target gas was prepared in the gas mixing chamber by injecting and evaporating its liquid form in each test. Then the humidity of the target gas was adjusted. After two minutes of circulating clean air along the entire route, the vacuum pump circulates the target pollutant at a flow rate of 40 mL/s in the system. Precisely at the same time as the gas is transferred to the analysis chamber, a waveform consisting of 5 step staircase and half sinusoidal on each step is applied to the heater of sensors, and responses were recorded. All experiments were done at the ambient temperature of 27 °C. The thermal modulation waveform applied to the heater of sensors, along with the conductivity of the sensors, are shown in Fig. 2 for typical experiments.
B. Pre-processing
For each experiment and sensor, 1000 conductance values were recorded from 0 to 100 s, while applying the temperature modulation waveform (see Fig. 2). Normalization was performed according to (1) to reduce the effect of baseline changes and eliminate the impact of gas concentration.
Fig. 1. T he schematic diagram of the experimental setup.
min ( )
max ( ) min ( )
t t
t t t
G G
S G G
in which, Gt is the sensor conductance value at time t. No other preprocessing method such as filtering or target noise, etc. was performed on the data.
C. Feature selection and classification
For the detection of five types of gases , including four reducing gases and clean air by two single temperature modulated sensors, the Support Vector Machines (SVM) classification method with Radial Bias Function (RBF) kernel was used. As mentioned previously, for each of the two single- sensor E-noses, a matrix of 92×1000 was obtained. The detection accuracies of classifiers were calculated based on the 5-fold cross-validation.
Feature analysis was done for both sensors , and the most informative or destructive parts of signals of these sensors were recognized. The effect of each individual feature and increasing the number of features on the accuracy of the classification task was examined. We also utilized a method to remove non - informative features and reduce the dimension of the data. In this scheme, we select a specific part of the original signal that contains a set of features. In Section III, we will discuss the details and explain the procedure required for selecting the most important part of our sensors’ signals.
Four common feature selection techniques were applied, and SVM classifier models were designed and validated. These feature selection methods were Chi-Squared, mutual information regression, Recursive Feature Elimination (RFE), and tree-based feature selection. The Chi-square is a filter- based method that selects features based on a Chi-square score.
Mutual information regression is another filter-based method that estimates the mutual information for a continuous target variable and measures the dependency between the variables.
RFE is a wrapper scheme that selects features by recursively considering smaller and smaller sets of features. The tree-based feature selection method is an embedded one and calculates the feature importance using node impurities in each decision tree.
The performances of these feature selection methods were compared with our proposed scheme for two datasets.
III. RESULT S AND DISCUSSION A. Feature analysis of two datasets
The classification accuracy of two E-Noses based on single temperature modulated gas sensors, TGS-2602 and FIS SP- 53B, were investigated and compared in terms of important and informative parts of signals of these sensors.
To this end, the role of each feature in the accuracy of the classification task was examined. Fig. 3 shows the SVM classification accuracy (in percent) for the TGS-2602 dataset in five-class separation. Each plot point corresponds to the validation accuracy of a classifier designed by only one feature of signals generated by this sensor. As shown in this figure, the most important features that increase the classification accuracy, are the beginning features.
Similarly, Fig. 4 shows the classification accuracy of FIS SP-53B, while we have used only one feature of the generated signals of this sensor. This figure also indicates that the features in the middle part of this sensor’s signals play an important role in classification accuracy.
According to the introduction, the difference between Fig. 3 and Fig. 4 in terms of the time of occurrence of the best features in their signals is due to the time when the sensors reach the optimum temperature. In fact, there is an optimal temperature for each sensor to react with target gas molecules,
(a)
(b)
(c)
Fig. 2. a) T he temperature modulation waveform applied to the heater of gas sensors, and the typical conductance of the b) FIS SP -53B, and c) T GS-2602 different concentrations of ethanol with 30% RH. T. M. is an abbreviation for temperature modulation.
and the best characteristics are created near those temperatures.
The time of occurrence of this optimum temperature in signal is related to various factors such as sensor material, target gases, temperature modulation profile, ambient temperature, and measurement conditions in experiments [24].
As revealed in both Fig. 3 and Fig. 4, by selecting only one feature, the detection accuracy of both E-Noses is not high enough, so feature numbers enhancement is essential.
Therefore, the role of increasing the number of features and neighboring features was inspected in classification accuracy.
In Fig. 5, the SVM classification accuracy is presented by considering that each plot point is the accuracy of a classifier designed by the first N number of features for each signal of the TGS-2602 sensor. As depicted in Fig. 5, as the number of features increases, the classification accuracy gradually decreases for this dataset of the TGS-2602 sensor. Therefore, Fig. 5 confirms the results of Fig. 3, and it is concluded that some destructive pieces of information at the middle or end of TGS-2602 signals reduce the accuracy of this E-Nose if all the features are given to the classifier input.
Also, Fig. 6. visualizes the same analysis of Fig. 5 for the FIS SP-53B sensor. It can be seen that if the beginning features of signals of this sensor are used, the first 475 features, the SVM accuracy is about 92%. However, it can be perceived that adding the features of the middle part has improved the accuracy up to 98%. In addition, the ending features provide duplicate information that is neither informative to improve nor destructive to reduce the accuracy. Therefore, similar to results of Fig. 4, the middle part of this sensor’s signals has sufficient
Fig. 3. T he classification accuracy (in percent) of T GS-2602 for each individual feature.
Fig. 4. T he classification accuracy (in percent) of FIS SP-53B for each individual feature.
features to accurately detect the target gases.
B. Examining different feature selection methods
Finally, to demonstrate the importance of feature analysis on the detection performance of these single temperature modulated gas sensors, based on the obtained knowledge about the informative features of each sensor, one feature set was selected for each sensor dataset. So, in our proposed method, we used the first 30 features of each signal generated by TGS- 2602, and the 400 features (including features 475-875) for FIS SP-53B sensor dataset. Furthermore, different feature selection methods, represented in Section III, were used and the detection accuracies were obtained. The number of selected features is limited to 200 features for these methods.
In Table. 1, the accuracy achieved by these methods was summarized for both datasets. As shown in this table, through
our proposed method which was based on feature analysis, for the TGS-2602 dataset, the average accuracy of the SVM classifier is increased from 92.6% to 96.7%. Besides, for the FIS SP-53B dataset, the accuracy has small improvement, from 97.9% to 98.8%. Also, other feature selection methods have also almost led to increased accuracy. However, by Chi- Squared, the accuracies were decreased because such filter- based methods are selecting features without the relationship of the features to the classification model.
Additionally, for TGS-2602 unlike FIS SP-53B, the method of feature selection has a more significant effect on accuracy.
Because according to Fig. 3 and 5, this dataset had more destructive features. Conclusively, whether feature selection is
Fig. 5. T he classification accuracy (in percent) of T GS-2602 for varioous number of features.
Fig. 6. T he classification accuracy (in percent) of FIS SP-53B for various number of features.
beneficial and necessary or which method is more appropriate, depends on the sensor type and the measurements conditions.
TABLE I. THE AVERAGE CLASSIFICATION ACCURACY FOR DIFFERENT FEATURE SELECTION METHODS USING SVM.
Me thod TGS-2602 FIS SP-53B
Without feature selection 92.6% 97.9%
Chi-Squared test 93.4% 91.3%
Mutual information regression 94.5% 98.9%
Recursive feature elimination 95.7% 97.8%
T ree-based feature selection 91.4% 98.9%
Our method 96.7% 98.8%
IV. CONCLUSION
The features of two temperature modulated gas sensors were investigated to find the most informative part of generated signals. Feature engineering and feature selection are critical parts of any machine learning pipeline. We strive for increasing the classification accuracy in our models, and one cannot get to a good accuracy without revisiting these feature selection methods. We tried to explain some of the most used feature selection techniques as well as our method for removing non- informative features. The classification accuracy of an electronic nose system increases, something up to the accuracy of the common and advanced techniques of feature selection, by Using this method of analyzing and choosing informative features. Other machine learning techniques such as neural network architectures [25] or extreme value machine [26] can be investigated in the future works.
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