Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze)
IR@IHBT: CSIR-Institute of Himalayan Bioresource Technology, Palampur
View Archive Info| Field | Value | |
| Title |
Enhancing electronic nose performance: A novel feature selection approach
using dynamic social impact theory and moving window time slicing for
classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze)
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| Creator |
Kaur, R
Kumar, R Gulati, Ashu Ghanshyam, C Kapur, Pawan Bhondekar, Amol P |
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| Subject |
Plant sciences
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| Description |
This paper presents a novel multiobjective wrapper approach using dynamic social impact theory based
optimizer (SITO) and moving window time slicing (MWTS) for the performance enhancement of an
electronic nose (EN). SITO, in conjunction with principal component analysis (PCA) and support vector
machines (SVMs) classifier, has been used for the classification of samples collected from the single batch
production of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze). The work employs a novel SITO
assisted MWTS (SITO-MWTS) technique for identifying the optimum time intervals of the EN sensor array
response, which give the maximum classification rate. Results show that, by identifying the optimum time
slicing window positions for each sensor response, the performance of an EN can be improved. Also, the
sensor response variability is time dependent in a sniffing cycle, and hence good classification can be
obtained by selecting different time intervals for different sensors. The proposed method has also been
compared with other established techniques for EN feature extraction. The work not only demonstrates
the efficacy of SITO for feature selection owing to its simplicity in terms of few control parameters, but also
the capability of an EN to differentiate Kangra orthodox black tea samples at different production stages.
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| Date |
2012
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| Type |
Article
PeerReviewed |
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| Format |
application/pdf
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| Identifier |
http://ihbt.csircentral.net/1286/1/65_Enhancing__Ashu_Gulati.pdf
Kaur, R and Kumar, R and Gulati, Ashu and Ghanshyam, C and Kapur, Pawan and Bhondekar, Amol P (2012) Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze). Journal of Sensors and Actuators B, 166 (167). pp. 309-319. |
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| Relation |
http://ihbt.csircentral.net/1286/
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