This study compares the performance of partial least-squares (PLS) regression analysis for the prediction of a wide range of soil chemical and physical properties from their mid-infrared (MIR) spectra with those from a combination of PLS regression and neural networks (NN). A combination of PLS and NN, referred to as PLSNN, uses a relatively few PLS scores as inputs to the NN. This has the advantages of the robustness and qualitative and quantitative features of PLS and the non-linear capabilities of neural networks. In this study, the PLS-NN method outperformed the basic PLS regression for the prediction of some soil properties from MIR spectra of soils from throughout New South Wales, Australia. The coefficient of determination (R2) and root-mean standard error of prediction (RMSEP) for total organic carbon (TOC) were improved from an R2=0.87 and RMSEP=0.7 by PLS, to an R2=0.94 and RMSEP=0.5 by PLS-NN. Predictions appeared to be most improved where PLS regression curvature was apparent or when the analytical value distributions were heavily skewed to low values with many negative predictions, e.g. for exchangeable-Al and Mg, TOC, CEC, Psorption and moisture content. For other soil properties there appeared to be a significant improvement in the lower 25 percentile of analyte values but the prediction errors increased at high analyte values and no net improvement was achieved, or were only marginal for the full range of data. In cases where PLS failed to give a viable model, NN also could not derive a converging model. The use of PLS-NN over the usual PLS method for routine soil analytical applications must be questioned with regard to a tradeoff between some possible limited improvement versus the added computational complexity and additional software requirement for NN.
Janik, L. J., Forrester, S. T., & Rawson, G. (2009). The prediction of soil chemical and physical properties from mid-infrared spectroscopy and combined partial least-squares regression and neural networks (PLS-NN) analysis. Chemometrics and Intelligent Laboratory Systems, 97(2), 179-188. https://doi.org/10.1016/j.chemolab.2009.04.005