Linear regression

Biology

Statistics

Electronic nose

Biochemistry

Computer science

Sitophilus

Mathematics

Infestation

Artificial intelligence

Horticulture

Chemistry

Uric acid

Food Chemistry

Fuzzy controller artmap based algorithms via E-nose selective metal oxides sensor (MOS) data was applied for classification of S. oryzae infestation in rice grains. The screened defuzzified data of selective sensors was further applied to detect S. oryzae infested rice with PCA and MLR techniques. Reliability of data was cross validated with reference methods of protein and uric acid content. Out of 18 MOS, 6 sensors namely P30/2, P30/1, T30/1, P40/2, T70/2 and PA/2 showed maximum resistivity change. Defuzzified score of 62.17 for P30/2 and 59.33 for P30/1 MOS further confirmed validity studies of E-nose sensor response with reference methods. The PCA plots were able to classify up to 84.75% of rice with variable degree of S. oryzae infestation. The MLR values of predicted versus reference values of protein and uric acid content were found to be fitting with R2 of 0.972, 0.997 and RMSE values of 2.08, 1.05.

Fuzzy controller based E-nose classification of Sitophilus oryzae infestation in stored rice grain