The effect of local samples in the accuracy of mid-infrared (MIR) and X-ray fluorescence (XRF) -based spectral prediction models

Abstract

AbstractWithin the soil spectroscopy community, there is an ongoing discussion addressing the comparison of the performance of prediction models built on a global calibration database, versus a local calibration database. In this study, this issue is addressed by spiking of global databases with local samples. The soil samples were analysed with MIR and XRF sensors. The samples were further measured using traditional wet chemistry methods to build the prediction models for seventeen major parameters. The prediction models applied by AgroCares, the company that assisted in this study, combine spectral information from MIR and XRF into a single ‘fused-spectrum’. The local dataset of 640 samples was split into 90% train and 10% test samples. To illustrate the benefits of using local calibration samples, three separate prediction models were built per element. For each model, 0%, 50% (randomly selected) and 100% of the local training samples were added to the global dataset. The remaining 10% local samples were used for validation. Seventeen soil parameters were selected to illustrate the differences in performance across a range of soil qualities, using the validation set to measure performance. The results showed that many models already exhibit an excellent level of performance (R2 ≥ 0.95) even without local samples. However, there was a clear trend that, as more local calibration samples were added, both R2 and ratio of performance to interquantile distance (RPIQ) increase.