Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

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 correcti...

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Detalles Bibliográficos
Autores principales: Shi, Tiezhu, Liu, Huizeng, Chen, Yiyun, Fei, Teng, Wang, Junjie, Wu, Guofeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5469641/
https://www.ncbi.nlm.nih.gov/pubmed/28471412
http://dx.doi.org/10.3390/s17051036

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5469641/
https://www.ncbi.nlm.nih.gov/pubmed/28471412
http://dx.doi.org/10.3390/s17051036

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