Total Phosphorus Determination in Soils Using Laser-Induced Breakdown Spectroscopy: Evaluating Different Sources of Matrix Effects.

Laser-induced breakdown spectroscopy (LIBS) is a potential alternative to wet chemical methods for total soil phosphorus determination, but matrix effects related to physical and chemical sample properties need to be further understood. The aim of this study was to explore matrix effects linked to particle size distribution and chemical form of phosphorus on LIBS response and the ability of LIBS to predict total phosphorus in a range of different soil types. Univariate calibration curves were developed by spiking the soils with increasing doses of phosphorus, and limits of detection for LIBS determined phosphorous (P) (LIBS-P) were calculated. Different particle size distributions in otherwise identical soils were obtained by four milling treatments and effects of chemical form of phosphorus were examined by spiking soils with identical amounts of phosphorus in different chemical compounds. The LIBS-P response showed a high correlation (R2 > 0.99) with total phosphorus for all soils. Yet, the sensitivity of LIBS differed significantly among soils, as the slope of the calibration curves increased with increasing sand content, resulting in estimated limits of detection of 10 mg kg-1 for the sandiest and 122 mg · kg-1 for the most clayey soils. These limits indicate that quantitative evaluation of total phosphorus in sandy and loamy sandy soils by LIBS is feasible, since they are lower than typical total phosphorus concentrations in soil. A given milling treatment created different particle size distributions depending on soil type, and consequently different LIBS-P results. Thus, procedures that specify the required degree of homogenization of soil samples prior to analysis are needed. Sieving after milling could be an option, but that should be tested. The soils spiked with Fe(III) phosphate, potassium phosphate and phytic acid had similar LIBS-P, except for soils with hydroxyapatite, which resulted in markedly lower response. These results suggested that matrix effects related to the chemical nature of phosphorus would be minor for non-calcareous soils in humid regions, where apatites comprise only a small fraction of total phosphorus. Strategies to overcome matrix effects related to particle size and content of apatite-phosphorus by combining multivariate models and soil type groupings should be further investigated.

Combining Laser-Induced Breakdown Spectroscopy (LIBS) and Visible Near-Infrared Spectroscopy (Vis-NIRS) for Soil Phosphorus Determination.

Conventional wet chemical methods for the determination of soil phosphorus (P) pools, relevant for environmental and agronomic purposes, are labor-intensive. Therefore, alternative techniques are needed, and a combination of the spectroscopic techniques-in this case, laser-induced breakdown spectroscopy (LIBS)-and visible near-infrared spectroscopy (vis-NIRS) could be relevant. We aimed at exploring LIBS, vis-NIRS and their combination for soil P estimation. We analyzed 147 Danish agricultural soils with LIBS and vis-NIRS. As reference measurements, we analyzed water-extractable P (Pwater), Olsen P (Polsen), oxalate-extractable P (Pox) and total P (TP) by conventional wet chemical protocols, as proxies for respectively leachable, plant-available, adsorbed inorganic P, and TP in soil. Partial least squares regression (PLSR) models combined with interval partial least squares (iPLS) and competitive adaptive reweighted sampling (CARS) variable selection methods were tested, and the relevant wavelengths for soil P determination were identified. LIBS exhibited better results compared to vis-NIRS for all P models, except for Pwater, for which results were comparable. Model performance for both the LIBS and vis-NIRS techniques as well as the combined LIBS-vis-NIR approach was significantly improved when variable selection was applied. CARS performed better than iPLS in almost all cases. Combined LIBS and vis-NIRS models with variable selection showed the best results for all four P pools, except for Pox where the results were comparable to using the LIBS model with CARS. Merging LIBS and vis-NIRS with variable selection showed potential for improving soil P determinations, but larger and independent validation datasets should be tested in future studies.