Sirven J-B, Bousquet B, Canioni L, Sarger L
Centre de Physique Moléculaire Optique et Hertzienne (CPMOH), 351 Cours de la Libération, 33405 Talence Cedex, France.
Anal Chem. 2006 Mar 1;78(5):1462-9. doi: 10.1021/ac051721p.
Laser-induced breakdown spectroscopy is used to measure chromium concentration in soil samples. A comparison is carried out between the calibration curve method and two chemometrics techniques: partial least-squares regression and neural networks. The three quantitative techniques are evaluated in terms of prediction accuracy, prediction precision, and limit of detection. The influence of several parameters specific to each method is studied in detail, as well as the effect of different pretreatments of the spectra. Neural networks are shown to correctly model nonlinear effects due to self-absorption in the plasma and to provide the best results. Subsequently, principal components analysis is used for classifying spectra from two different soils. Then simultaneous prediction of chromium concentration in the two matrixes is successfully performed through partial least-squares regression and neural networks.
激光诱导击穿光谱法用于测量土壤样品中的铬浓度。在校准曲线法与两种化学计量学技术（偏最小二乘回归和神经网络）之间进行了比较。从预测准确性、预测精密度和检测限方面对这三种定量技术进行了评估。详细研究了每种方法特有的几个参数的影响，以及光谱不同预处理的效果。结果表明，神经网络能够正确模拟等离子体中自吸收引起的非线性效应，并提供最佳结果。随后，主成分分析用于对两种不同土壤的光谱进行分类。然后通过偏最小二乘回归和神经网络成功地对两种基质中的铬浓度进行了同时预测。
