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

Abstract

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 Chimiométrie 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.