Journal
FOOD CHEMISTRY
Volume 283, Issue -, Pages 604-610Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2019.01.076
Keywords
Electronic nose; Fuzzy analysis; Infested rice; Principal component analysis; Sensors
Funding
- Ministry of Human Resource Development (MHRD), Govt. of India under DAG project - Food Security Research (FSR) scheme at Agricultural & Food Engineering Department, IIT Kharagpur
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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 R-2 of 0.972, 0.997 and RMSE values of 2.08, 1.05.
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