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Sensors 2017, 17(5), 1036; doi:10.3390/s17051036

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

1
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of National Administration of Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & College of Life Sciences and Oceanography, Shenzhen University, 518060 Shenzhen, China
2
Department of Geography, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, China
3
School of Resource and Environmental Sciences, Wuhan University, 430079 Wuhan, China
4
Suzhou Institute of Wuhan University, 215000 Suzhou, China
*
Author to whom correspondence should be addressed.
Academic Editor: Dimitrios Moshou
Received: 24 March 2017 / Revised: 24 April 2017 / Accepted: 2 May 2017 / Published: 4 May 2017
(This article belongs to the Special Issue Sensors in Agriculture)
View Full-Text   |   Download PDF [3648 KB, uploaded 4 May 2017]   |  

Abstract

This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar’s test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies. View Full-Text
Keywords: visible and near-infrared reflectance spectroscopy; heavy metal contamination; spectral pre-processing; feature selection; machine-learning visible and near-infrared reflectance spectroscopy; heavy metal contamination; spectral pre-processing; feature selection; machine-learning
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Shi, T.; Liu, H.; Chen, Y.; Fei, T.; Wang, J.; Wu, G. Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils. Sensors 2017, 17, 1036.

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