摘要
光谱测量数据需要对其进行波长选择以提高模型预测精度和简化模型。文章提出了一种基于PLS投影相关系数的快速、准确的波段选择方法,它计算某波长点光谱数据对待测组分浓度向量的影响时,考虑了该波长点光谱数据的变化量与光谱PLS回归系数向量在该波长点的投影分量的共同影响。与传统相关系数方法想比较,该方法明显地改善了分析模型的稳健性并大幅度地压缩了建模所需的波长点数。对208个汽油样本的实验表明,经过PLS投影相关系数方法进行光谱波段选择后的光谱波长点数占全谱的比例下降至30%,交互验证均方根偏差(root mean square error of cross validation,RMSECV)由未经过波长选择时的0.44降至0.34。该方法可广泛应用于各类光谱定量分析中的波长选择与数据压缩。
In order to enhance the prediction accuracy of spectral analysis models and reduce their input number, this paper presents a simple and rapid wavelength selection method based on PLS projection correlation coefficients. These correlation coefficients are decided by both the changes in spectra data and the PLS regression coefficients between spectra matrix and concentration vector. Compared with the traditional wavelength selection method based on correlation analysis, the novel proposed method obviously improves the robustness of spectral analysis models and reduces their input number sharply. Applying the proposed method to 208 gasoline samples, the experimental results show that the number of calibration model input decreases to 30% of the original wavelength number, and the root mean square error of cross validation is reduced from 0. 44 to 0.34. This method can be widely used in wavelength selection and data compression in spectral quantitative analysis.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2009年第2期351-354,共4页
Spectroscopy and Spectral Analysis
基金
国家"863"计划项目(2006AA040309)资助
