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Sensors (Basel)

Title:		A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System
Author(s):		Zhu L; Jia H; Chen Y; Wang Q; Li M; Huang D; Bai Y;
Address:		"Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China. Jilin Province Soil and Fertilizer Station, Changchun 130031, China. Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China. Huangdy@jlu.edu.cn. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China. Huangdy@jlu.edu.cn. College of Information, Jilin Agricultural University, Changchun 130118, China"
Journal Title:		Sensors (Basel)
Year:		2019
Volume:		20190804
Issue:		15
Page Number:		-
DOI:		10.3390/s19153417
ISSN/ISBN:		1424-8220 (Electronic) 1424-8220 (Linking)
Abstract:		"Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soil gases. From the response curve of each sensor, four features were extracted (maximum value, mean differential coefficient value, response area value, and the transient value at the 20th second). Then, soil organic matter regression prediction models were built based on back-propagation neural network (BPNN), support vector regression (SVR), and partial least squares regression (PLSR). The prediction performance of each model was evaluated using the coefficient of determination (R(2)), root-mean-square error (RMSE), and the ratio of performance to deviation (RPD). It was found that the R(2) values between prediction (from BPNN, SVR, and PLSR) and observation were 0.880, 0.895, and 0.808. RMSEs were 14.916, 14.094, and 18.890, and RPDs were 2.837, 3.003, and 2.240, respectively. SVR had higher prediction ability than BPNN and PLSR and can be used to accurately predict organic matter contents. Thus, our findings offer brand new methods for predicting SOM"
Keywords:		"Calibration *Electronic Nose Gases/chemistry Least-Squares Analysis Neural Networks, Computer Soil/*chemistry Support Vector Machine/standards Volatile Organic Compounds/analysis/standards artificial olfactory system gas sensor array prediction methods re;"
Notes:		"MedlineZhu, Longtu Jia, Honglei Chen, Yibing Wang, Qi Li, Mingwei Huang, Dongyan Bai, Yunlong eng 2016YFD070030201/National Key R&D Plan project/ 20190302116GX/Jilin Science and Technology Development Plan/ Switzerland 2019/08/07 Sensors (Basel). 2019 Aug 4; 19(15):3417. doi: 10.3390/s19153417"