Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy

Abstract

The selection of calibration method is one of the main factors influencing the measurement accuracy with visible (vis) and near infrared (NIR) spectroscopy. This paper compared the performance of three calibration methods, namely, principal component regression (PCR), partial least squares regression (PLSR) and back propagation neural network (BPNN) analyses for the accuracy of measurement of selected soil properties, namely, organic carbon (OC) and extractable forms of potassium (K), sodium (Na), magnesium (Mg) and phosphorous (P). A total of 168 soil samples collected from Belgium and Northern France were used as the data set for the calibration–validation procedure. Optical scanning was carried out on fresh soil samples with a fibre-type, vis–NIR (LabSpec®Pro Near Infrared Analyzer, Analytical Spectral Devices, Inc, USA) with a measurement range of 350–2500 nm. The entire data set was split randomly into 3 replicates of 90% and 10% for the cross-validation set and prediction set, respectively. The input of BPNN was the first 5 principal components (PCs) resulted from the principal component analysis (PCA) and the optimal number of latent variables (LVs) obtained from PLSR. Both the leave-one-out cross validation and prediction for the three replicates showed that all BPNN-LV models outperformed PCR, PLSR and BPNN-PCs models. Furthermore, BPNN-PCs and PLSR provided, respectively, better performance than PCR. The best predictions were obtained with BPNN-LVs modelling for OC ( R pre 2 = 0.84 and residual prediction deviation (RPD) = 2.54) and Mg ( R pre 2 = 0.82 and RPD = 2.54), which were classified as excellent model predictions. The prediction of K, P and Na was classified as good ( R pre 2 = 0.68–0.74 and RPD = 1.77–1.94), where quantitative predictions were considered possible. It is recommended to adopt BPNN-LVs modelling technique for higher accuracy measurement of the selected soil properties with vis–NIR spectroscopy, in comparison with PCR, PLS and BPNN-PCs modelling techniques.