Work

AIC formulae in soil spectroscopy

Year: 2026

Type: article

Abstract: Partial least squares regression (PLSR) is widely used in soil spectroscopy; however, the methodology used for selection of the optimal number of components remains inconsistent. In this study, we review and compare commonly used formulations of the Akaike Information Criterion (AIC) for selecting PLSR components. Three different AIC formulations reported in the literature were evaluated. To this end, we used visible–near-infrared (vis–NIR) spectra to predict soil organic carbon (SOC; n = 310) and soil texture ( n = 50) data. Despite substantial mathematical differences among the AIC formulations and resulting differences in selected component numbers, model performances—assessed using root mean square error (RMSE), ratio of performance to interquartile distance (RPIQ), and Nash-Sutcliffe model efficiency (NSE) for cross-validation and external validation—were largely similar. Our findings highlight the need for greater awareness regarding AIC formulation and interpretation in soil spectroscopy applications.

Source: Geoderma

DOI: 10.1016/j.geoderma.2026.117833

Authors: L. Wilke, I. Greenberg, H.P. Piepho, T.K.D. Weber, C. Wachendorf

Institutions: University of Kassel, University of Hohenheim

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,320

Funders: Deutsche Forschungsgemeinschaft, Universität Kassel