Abstract
The aim of this study is to improve the discrimination performance of electronic noses by introducing a new method for measuring the similarity of the signals obtained from the electronic nose. We constructed abstract odor factor maps (AOFMs) as the characteristic maps of odor samples by decomposition of three-way signal data array of an electronic nose. A similarity measure for two-way data was introduced to evaluate the similarities and differences of AOFMs from different samples. The method was assessed by three types of pipe and powder tobacco samples. Comparisons were made with other techniques based on PCA, SIMCA, PARAFAC and PARAFAC2. The results showed that our method had significant advantages in discriminating odor samples with similar flavors or with high VOCs release.
Highlights
In Nature, mammals discriminate odor through a complex process
The results showed that our method had significant advantages in discriminating odor samples with similar flavors or with high volatile organic compounds (VOCs) release
The olfactory sensory neurons detected odor molecules, axons on the neurons transmit the signal to olfactory bulb, and the olfactory bulbs process the signal to obtain the information of the odor [1]
Summary
In Nature, mammals discriminate odor through a complex process. Inspired by the odor discrimination process of mammals, researchers have developed artificial olfactory systems called electronic nose systems that contains perception, signal processing and recognition sections [2,3]. Compared with the traditional odor analysis techniques, such as gas chromatography and its hyphenated techniques [21,22], electronic nose systems show good application prospects because they are fast and sensitive, need simple pre-processing and operation steps, and can obtain overall information of the volatile components in samples. Many data processing methods have been introduced to improve the discrimination performance of electronic noses [23,25,26,27,28,29,30,31,32]
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