Does the limited use of orthogonal signal correction pre-treatment approach to improve the prediction accuracy of soil organic carbon need attention?

Abstract Visible-Near-infrared (Vis-NIR) spectroscopy is a relatively modern method that can be used to predict soil properties such as soil organic carbon (SOC). Predictions of soil properties with Vis-NIR requires pre-processing algorithms. Applying a wrong type or applying too extreme pre-processing algorithms may result in the removal of valuable information or may even introduce unwanted variations. This can negatively affect the prediction accuracy of the property being studied. Orthogonal signal correction (OSC) pre-processing method has been used in other fields for visible and near infrared spectra improvement. However, the application of OSC application in soil science remains limited. The main idea behind OSC is the removal of only unwanted variation from the spectrum unlike some other pre-treatment methods which are believed to remove valuable information in the process of removing undesirable variation. This study verifies the effectiveness of the OSC against nine commonly used pre-treatment methods across three different agricultural fields for both lab-dry and in-field spectra. For the prediction, partial least square regression (PLSR) and support vector machine regression (SVMR) algorithms were used. In this study, the OSC method overall improved prediction accuracy the most (e.g. with OSC the best result was R2CV = 0.79, without OSC the best result was R2CV = 0.62) and is therefore a promising tool that should be included in further studies on different soils and other soil properties.