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Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

Fu J, Huang C, Xing J, Zheng J - Sensors (Basel) (2012)

Bottom Line: The principal component analysis technique was applied for feature selection and dimension reduction.In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector.We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou, China. junfu@zjgsu.edu.cn

ABSTRACT
Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

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Related in: MedlinePlus

Photo of the experimental set-up with the customized electronic nose system.
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f4-sensors-12-02818: Photo of the experimental set-up with the customized electronic nose system.

Mentions: A homemade electronic nose system has been developed for data acquisition, as illustrated in Figure 4. Eight MOS Taguchi-type gas sensors (TGS832, TGS880, TGS800, TGS822, TGS826, TGS825, TGS816 and TGS812) were purchased from Figaro Engineering Inc. (Osaka, Japan). They are arranged in two PCB boards with corresponding voltage divider circuits, and then fixed in a sealed gas chamber with a volume of 315 mL. Two micro-bumps (0.5 L/min) are connected to the chamber for aroma sampling and exhausting. The measurement are controlled, monitored and recorded by homemade Delphi software in a computer which connected to a microprocessor-based circuit (MSP430 with a 12-bit ADC) via a RS-232 cable.


Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

Fu J, Huang C, Xing J, Zheng J - Sensors (Basel) (2012)

Photo of the experimental set-up with the customized electronic nose system.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376605&req=5

f4-sensors-12-02818: Photo of the experimental set-up with the customized electronic nose system.
Mentions: A homemade electronic nose system has been developed for data acquisition, as illustrated in Figure 4. Eight MOS Taguchi-type gas sensors (TGS832, TGS880, TGS800, TGS822, TGS826, TGS825, TGS816 and TGS812) were purchased from Figaro Engineering Inc. (Osaka, Japan). They are arranged in two PCB boards with corresponding voltage divider circuits, and then fixed in a sealed gas chamber with a volume of 315 mL. Two micro-bumps (0.5 L/min) are connected to the chamber for aroma sampling and exhausting. The measurement are controlled, monitored and recorded by homemade Delphi software in a computer which connected to a microprocessor-based circuit (MSP430 with a 12-bit ADC) via a RS-232 cable.

Bottom Line: The principal component analysis technique was applied for feature selection and dimension reduction.In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector.We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou, China. junfu@zjgsu.edu.cn

ABSTRACT
Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Show MeSH
Related in: MedlinePlus