The potential for Laser Induced Breakdown Spectroscopy (LIBS) technique to predict cation exchange capacity (CEC) values in agricultural soils, based on a partial least square (PLS) regression model, was investigated by analysing soils (N= 225) using a portable LIBS system. The prediction from the validation set was 0.75 and the RMSE was 5.4 cmol+/kg. Analysis of the PLS loading weights showed that emission lines that correlate with the exchangeable cations (Ca, Mg, K and Na) and clay (Al and Si) are significant. Good quality predictions could be obtained from the model as described by the ratio of performance to deviation (RPD) values calculated for calibration (RPDc =1.99) and validation (RPDv=2.02). The ratio of performance to interquartile distance (RPIQ) for calibration (RPIQc=3.20) and validation (RPIQv=3.32) indicated excellent prediction capability. It has been demonstrated that LIBS spectra coupled with multivariate data analysis could be used to predict CEC of agricultural soils which is considered fast and a chemical‐free technique.

中文翻译：

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激光诱导击穿光谱（LIBS）数据在确定农业土壤阳离子交换容量（CEC）中的潜在应用

通过使用便携式LIBS分析土壤（N = 225），研究了基于偏最小二乘（PLS）回归模型的激光诱导击穿光谱（LIBS）技术预测农业土壤中阳离子交换容量（CEC）值的潜力。系统。验证集的预测值为0.75，RMSE为5.4 cmol + / kg。对PLS负载权重的分析表明，与可交换阳离子（Ca，Mg，K和Na）和粘土（Al和Si）相关的发射谱线很重要。如通过为校准（RPDc = 1.99）和验证（RPDv = 2.02）计算出的性能与偏差（RPD）值之比所述，可以从模型中获得良好的质量预测。用于校准（RPIQc = 3.20）和验证（RPIQv = 3）的性能与四分位数距离（RPIQ）的比率。32）表示出色的预测能力。已经证明，LIBS光谱结合多元数据分析可用于预测农业土壤的CEC，这被认为是一种快速且无化学方法的技术。