Abstract

Journal of Near Infrared Spectroscopy
Volume 18 Issue 3, Pages 167–176 (2010)
doi: 10.1255/jnirs.883

Evaluation of spectral pretreatments, partial least squares, least squares support vector machines and locally weighted regression for quantitative spectroscopic analysis of soils

Benoit Igne,^a James B. Reeves, III,^b,* Gregory McCarty,^b W. Dean Hively,^b Eric Lund^c and Charles R. Hurburgh, Jr^a
aDepartment of Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa, USA
^bHydrology & Remote Sensing Laboratory, BARC West, Beltsville, Maryland, USA. E-mail: james.reeves@ars.usda.gov
^cVeris Technologies, 601 Broadway, Salina, Kansas, USA

Soil testing requires the analysis of large numbers of samples in the laboratory that is often time consuming and expensive. Mid-infrared spectroscopy (mid-IR) and near infrared (NIR) spectroscopy are fast, non-destructive and inexpensive analytical methods that have been used for soil analysis, in the laboratory and in the field, to reduce the need for measurements using complex chemical/physical analyses. A comparison of the use of spectral pretreatment as well as the implementation of linear and non-linear regression methods was performed. This study presents an overview of the use of infrared spectroscopy for the prediction of five physical (sand, silt and clay) and chemical (total carbon and total nitrogen) soil parameters with near and mid-infrared units in bench top and field set-ups. Even though no significant differences existed among pretreatment methods, models using second derivatives performed better. The implementation of partial least squares (PLS), least squares support vector machines (LS-SVM) and locally weighted regression (LWR) for the development of the calibration models showed that the LS-SVM did not out-perform linear methods for most components while LWR that creates simpler models performed well. The present results tend to show that soil models are quite sensitive to the complexity of the model. The ability of LWR to select only the appropriate samples did help in the development of robust models. Results also proved that field units performed as well as bench-top instruments. This was true for both near infrared and mid-infrared technology. Finally, analysis of field moist samples was not as satisfactory as using dried-ground samples regardless of the chemometrics methods applied.

Keywords: near infrared spectroscopy, NIR, mid-infrared spectroscopy, mid-IR, Fourier transform infrared spectroscopy, FT-IR, partial least squares regression, PLS, least squares support vector machines, LS-SVM, locally weighted regression, LWR, soil physical–chemical properties

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