Abstract
When an electronic nose (e-nose) is used for prediction, extracting more useful information from the original response curve is of great importance. However, the most traditional feature extraction models in e-nose only sample a few data during the process of extracting features. To use more data and acquire more information to improve e-nose’s classification accuracy, we present a new feature extraction method called “weighted summation” (WS). In addition, this method was compared with other exiting methods, including maximum value of the steady-state response (MAX), curve fitting (CF), dynamic moments of the phase space (MD2), maximum value of the first-order derivative (Dmax), and Db1 wavelet transformation (WT). Dingfeng pig farm located at Changchun (Jilin Province, China) was used as odor source. Four kinds of odors taken from inside of pig barn in the morning and in the evening, and outside of pig barn in the morning and in the evening were used as the original response of e-nose. The reasons why we choose these four classes are as follows: to start with, the smell of the house has a great influence on the health of pigs; then, outdoor odors affect residents’ comfort level; and morning and evening are the most odorous hours. Experimental results demonstrated that for WS, MAX, CF, MD2, Dmax, and WT methods, accuracy in training set was 88.33%, 85%, 83.33%, 83.33%, 46.67%, and 51.67%, respectively, and accuracy in testing set was 100%, 100%, 91.67%, 91.67%, 41.67%, and 41.67%, respectively, suggesting that novel feature extraction method outperformed other methods. Moreover, a simple monitor system based on WS method was established to monitor the real environment in pig farm.
Highlights
Gaseous pollution generated by pig farms can originate from animals and the decomposition of piggery waste manure
The aforementioned feature extraction methods did not take full use of the information that embedded in the original response curve
The feature extraction method we proposed can obtain features from the original response since it doesn’t need complex calculation and transform
Summary
Gaseous pollution generated by pig farms can originate from animals and the decomposition of piggery waste manure. MAX is usually used as the most common and simple feature, because it represents the final steady-state feature of the entire dynamic response process, which reflects the maximum reaction degree change of sensors responding to odors [25], [27]. Each sensor has its own behavior in response to an odor presentation that is stored along the response [30] To solve this problem, some researchers extract feature based on curve fitting and transform domains. The aforementioned feature extraction methods did not take full use of the information that embedded in the original response curve. We proposed a weighted summation (WS) method to extract features from the original response curve. The gas data from pig house have not yet been studied using electronic nose response values obtained at different times, and synthesized the response data and got the final feature space.
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