Abstract
The present study deals with the design of the hybrid structure of the particle swarm optimization (PSO)-based back propagation multilayer perceptron –artificial neural network (BPMLP-ANN) and technique used for the classification of monofloral honey samples. The transient response is collected from the multielectrode electronic tongue (ET) system for the rapid floral classification of honey. Forty samples of five different floral types (eucalyptus, til, leechi, pumpkin, and mustard) are recorded based on the cyclic voltammetric technique. The obtained electronic tongue response matrix has been treated with various preprocessing techniques. Principal component analysis (PCA) is done to observe the capability of cluster formation. Further, the discrete wavelet transform (DWT) method has been used for feature selection and compression of data set. The resultant compressed data is used as the input variable for classification using back propagation multilayer perceptron-based neural network model. The weights are updated using particle swarm optimization (PSO) during the training of BPMLP-ANN. The result indicates that the proposed hybrid model is effective for classification of different floral origins of honey samples with increased in the classification rate up to 97%.
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Appendix: PSO Parameters
Appendix: PSO Parameters
Population size—50, 80,100; Maximum Iterations—800, 1000, 1100; Inertia Weight (w) = 1;
Constriction factors = 1.5.
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Tiwari, K., Pain, S., Tudu, B., Bandopadhyay, R., Chatterjee, A. (2021). Classification of Different Floral Origin of Honey Using Hybrid Model of Particle Swarm Optimization and Artificial Neural Network. In: Muthukumar, P., Sarkar, D.K., De, D., De, C.K. (eds) Innovations in Sustainable Energy and Technology. Advances in Sustainability Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-16-1119-3_13
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