激光诱导击穿光谱
土壤水分
光谱学
土工试验
校准
材料科学
分析化学(期刊)
化学计量学
土壤碳
环境科学
稳健性(进化)
土壤科学
化学
环境化学
数学
物理
统计
基因
量子力学
生物化学
色谱法
作者
Kleydson Stenio,Alfredo Augusto Pereira Xavier,Carla Pereira de Morais,D. M. B. P. Milori
出处
期刊:Analytical Methods
[Royal Society of Chemistry]
日期:2022-01-01
卷期号:14 (42): 4219-4229
被引量:9
摘要
Soil carbon (C) determinations have been widely studied due to soil C sequestration that contributes to the mitigation of greenhouse gas emissions and improves soil quality. However, traditional chemical processes for large-scale analysis generate waste, are time-consuming, and have a high cost per measurement. Laser-induced breakdown spectroscopy (LIBS) is a multi-element spectroanalytical technique that allows fast and low-cost analysis, almost no sample preparation is required, and does not generate hazardous chemical waste. Two emission lines are commonly used for LIBS C determination, 193.03 and 247.85 nm. However, Brazilian soils have a high concentration of aluminum (Al) and iron (Fe), directly interfering in those C emission lines. Furthermore, multiple soil textures increase the difficulty of building calibration models due to matrix effects. In the present work, a mathematical model is proposed to quantify the total C in soil samples having different textures bypassing spectral interferences. A LIBS-specific method for removing outliers has been developed with 6% spectrum removal. From the univariate analysis, it was noticed that some results were projections of a 3D surface in a 2D space, so a 3D plane model was obtained with good fits for the evaluated C emission lines, R2 > 0.91, with limits of detection of 0.11% and 0.13% and limits of quantitation of 0.11% and 0.32% for lines 193.03 and 247.85 nm, respectively. Three repetitions were used to test the robustness of the methods and presented an R2 of 0.95 and 0.93, a mean error of about 20.38% and 24.12% for lines 193.03 and 247.85 nm, respectively, and a root mean square error of prediction lower than 0.40% for both lines.
科研通智能强力驱动
Strongly Powered by AbleSci AI