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)

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.