Seattle University, Mechanical Engineering Department, Seattle, Washington 98122, USA.
Appl Spectrosc. 2010 Feb;64(2):154-60. doi: 10.1366/000370210790619500.
Laser-induced breakdown spectroscopy (LIBS) was carried out on twenty-three low to high alloy steel samples to quantify their concentrations of chromium, nickel, and manganese. LIBS spectral data were correlated to known concentrations of the samples and three calibration methods were compared. A standard LIBS calibration technique using peak area integration normalized by an internal standard was compared to peak area integration normalized by total light and the multivariate statistical technique of partial least squares. For the partial least squares analysis, the PLS-1 algorithm was used, where a predictive model is generated for each element separately. Partial least squares regression coefficients show that the algorithm correctly identifies the atomic emission peaks of interest for each of the elements. Predictive capabilities of each calibration approach were quantified by calculating the standard and relative errors of prediction. The performance of partial least squares is on par with using iron as an internal standard but has the key advantage that it can be applied to samples where the concentrations of all elements are unknown.
激光诱导击穿光谱（LIBS）对二十三份低合金钢到高合金钢样品进行了分析，以定量测量其铬、镍和锰的浓度。将 LIBS 光谱数据与样品的已知浓度相关联，并比较了三种校准方法。一种使用内部标准归一化峰面积积分的标准 LIBS 校准技术与总光归一化峰面积积分和多元统计技术偏最小二乘法进行了比较。对于偏最小二乘分析，使用了 PLS-1 算法，为每个元素分别生成一个预测模型。偏最小二乘回归系数表明，该算法正确识别了每个元素的感兴趣的原子发射峰。通过计算标准和相对预测误差来量化每种校准方法的预测能力。偏最小二乘法的性能与使用铁作为内标相当，但具有关键优势，即它可以应用于所有元素浓度未知的样品。
