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 KU Leuven
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) ...