Probabilistic artificial neural network and E-nose based classification of Rhyzopertha dominica infestation in stored rice grains

The versatility of artificial neural network with back propagation multilayer perceptron approach could entitle an easy and methodical interpretation of results corresponding to multiple metal oxides sensor in an electronic nose. Three algorithms discriminant factorial analysis (DFA), soft independent modeling by class analogy (SIMCA), probabilistic artificial neural network (PANN) with back propagation multilayer perceptron (BPNN) were used for the classification of R. dominica infested rice stored for 225 days via 18 metal oxide sensors in E-nose. The coefficient of correlation for the three approaches were 88 (DFA), 96 (SIMCA), 98.96 (BPNN) %, respectively. The percentage discrimination index was more distinct between 0 and 225 days R. dominica infested rice (98%) than 0-180 days (93%), and 0-135 days (88%). The residual errors of validation and cross validation for SIMCA were 1.04 x 10(-3) and 1.26 x 10(-3) respectively. Major metal oxide sensors responsible for the production of volatiles were P30/1, T 30/1, PA/2, P30/2, T70/2, P40/1, and P40/2. The overall relative errors during artificial neural network training and testing were 0.092 and 0.286 respectively. The artificial neural network relative error for scale dependents in response to metal oxide sensors for mean, SD, % RSD were 0.033, 0.162, 0.081, respectively. The applicability of E-nose with neural network could help in securing the data analysis time without loss of information and can also work well for noxious odors which might not be able to be categorized by human olfactory.