Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721302, West Bengal, India.
Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721302, West Bengal, India.
Food Chem. 2019 Jun 15;283:604-610. doi: 10.1016/j.foodchem.2019.01.076. Epub 2019 Jan 19.
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 of 0.972, 0.997 and RMSE values of 2.08, 1.05.
基于模糊控制器 artmap 的算法通过电子鼻选择性金属氧化物传感器 (MOS) 数据应用于稻米中苏云金芽孢杆菌侵染的分类。进一步将选择性传感器的筛选出的去模糊数据应用于基于 PCA 和 MLR 技术检测受苏云金芽孢杆菌侵染的稻米。数据的可靠性通过与蛋白质和尿酸含量的参考方法进行交叉验证。在 18 个 MOS 中，有 6 个传感器，即 P30/2、P30/1、T30/1、P40/2、T70/2 和 PA/2，表现出最大的电阻率变化。P30/2 和 P30/1 MOS 的去模糊分数分别为 62.17 和 59.33，进一步证实了电子鼻传感器响应与参考方法的有效性研究。PCA 图能够对不同程度受苏云金芽孢杆菌侵染的稻米进行分类，达到 84.75%。预测值与蛋白质和尿酸含量参考值的 MLR 值拟合良好，R 值分别为 0.972、0.997，RMSE 值分别为 2.08、1.05。
