Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy

Abstract

Visible and near-infrared (Vis–NIR) spectroscopy is used to estimate soil organic matter (SOM). Spectral preprocessing techniques and multivariate modeling methods play important roles in the quantitative analysis of SOM. First and second derivatives (i.e., the conventional integer order derivatives) are commonly used spectral derivatives, which, however, may ignore some detailed spectral information regarding SOM. Here, we presented a fractional order derivative (FOD) method to preprocess the reflectance spectra. Robust modeling methods are still required for accurate estimation of SOM. Local modeling technique (memory-based learning, MBL) was introduced to compare with two global modeling approaches, namely, partial least square (PLS) and random forest (RF). A total of 535 topsoil samples were gathered from Hubei Province, Central China, with their reflectance spectra and SOM contents measured in the laboratory. FOD was allowed to vary from 0 to 2 with an increment of 0.25 at each step. Coefficient of determination (R2) and ratio of the performance to deviation (RPD) were employed as performance statistics during validation. Results showed that with the increase of derivative order, the baseline drifts and overlapping peaks were gradually removed but the spectral strength decreased concurrently. Higher derivative order reflectance (i.e., 1.5-order, 1.75-order, and 2-order reflectance) were more susceptible to spectral noise interferences. The correlation coefficient of SOM with FOD processed spectra at some specific wavelengths was larger than that with the original reflectance. MBL performed better than PLS and RF, regardless of FOD transformation. Calibration with 0.25-order reflectance and MBL provided the most accurate estimation of SOM, with an RPD of 2.23. Our results confirm the effectiveness of FOD and local modeling (MBL) in the development of Vis–NIR models for SOM estimation.