Prediction of various soil properties for a national spatial dataset of Scottish soils based on four different chemometric approaches: a comparison of near infrared and mid-infrared spectroscopy.

R. Haghi; E. Perez Fernandez; A.H.J. Robertson

doi:10.1016/j.geoderma.2021.115071

Prediction of various soil properties for a national spatial dataset of Scottish soils based on four different chemometric approaches: a comparison of near infrared and mid-infrared spectroscopy.

R. Haghi
, E. Perez Fernandez
, A.H.J. Robertson

Environmental & Biochemical Sciences

Büchi Labortechnik

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	115071
Journal	Geoderma
Volume	396
DOIs	https://doi.org/10.1016/j.geoderma.2021.115071
Publication status	Published - 1 Aug 2021

Keywords

Chemometric
Machine learning
Spectroscopy

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10.1016/j.geoderma.2021.115071Licence: Unspecified licence

Cite this

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title = "Prediction of various soil properties for a national spatial dataset of Scottish soils based on four different chemometric approaches: a comparison of near infrared and mid-infrared spectroscopy.",

keywords = "Chemometric, Machine learning, Spectroscopy",

author = "R. Haghi and \{Perez Fernandez\}, E. and A.H.J. Robertson",

year = "2021",

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language = "English",

volume = "396",

journal = "Geoderma",

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AU - Haghi, R.

AU - Perez Fernandez, E.

AU - Robertson, A.H.J.

PY - 2021/8/1

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KW - Machine learning

KW - Spectroscopy

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JO - Geoderma

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