Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy

Pedometrics is the use of quantitative methods for the study of soil distribution and genesis and as a sustainable resource. A common research area in pedometrics and chemometrics is the calibration and prediction of soil properties from diffuse infrared reflectance spectra. The most common method is using partial least-squares regression (PLS). In this paper we present an alternative method in the form of regression rules. The regression-rules model consists of a set of rules, in which each rule is a linear model of the predictors. It is also analogous to piecewise linear functions. The accuracy is tested for prediction of soil properties from their mid-infrared (2500-25000 nm) diffuse reflectance spectra. In addition, we also tested it with the Chimiometrie 2006 challenge data which used the near-infrared spectra to predict soil properties. The results showed that, in comparison with PLS with spectra pretreatment and another data-mining technique, the regression-rules model provides greater accuracy, is simpler and more parsimonious, produces comprehensible equations, provides an optimal variable selection, and respects the upper and lower limits of the data. (C) 2008 Elsevier B.V. All rights reserved.