MLSP: Machine Learning Models for Soil Properties from Spectroscopy
Soil spectroscopy studies use many different combinations of
spectral preprocessing and calibration methods, which makes results
difficult to compare or generalize across studies. For visible and
near-infrared (VNIR) spectra (350-2500 nm), runs every combination of
preprocessing and calibration methods on identical repeated calibration
and validation splits. The 26 preprocessing methods are raw reflectance,
standard normal variate, pseudo-absorbance, and 23 Savitzky-Golay
smoothers, first derivatives, and second derivatives with linear,
quadratic, or cubic polynomials and 3- to 11-point windows, excluding
settings that give mathematically identical filters. The calibration
methods are principal component regression (PCR), partial least squares
regression (PLSR), least absolute shrinkage and selection operator
(LASSO), 'Cubist', and random forest. Model complexity is tuned by
cross-validation within each calibration set, and combinations are ranked
by the coefficient of determination (R2), root mean squared error (RMSE),
ratio of performance to interquartile distance (RPIQ), or bias, reporting
the top 10 by default. The methods are those most commonly used in
published soil spectroscopy studies, following Savitzky and Golay (1964)
<doi:10.1021/ac60214a047>, Barnes et al. (1989)
<doi:10.1366/0003702894202201>, Rinnan et al. (2009)
<doi:10.1016/j.trac.2009.07.007>, Bellon-Maurel et al. (2010)
<doi:10.1016/j.trac.2010.05.006>, and Clingensmith et al. (2019)
<doi:10.1111/ejss.12753>.
| Version: |
0.2.0 |
| Imports: |
stats, utils, gsignal, pls, glmnet, Cubist, randomForest |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2026-10-29 |
| DOI: |
10.32614/CRAN.package.MLSP |
| Author: |
Pengyuan Chen [aut],
Christopher Clingensmith [aut],
Chenglong Ye [aut],
Sabine Grunwald [aut],
Katsutoshi Mizuta [aut, cre] |
| Maintainer: |
Katsutoshi Mizuta <toshi.m at uky.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
MLSP results |
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