Da Chen, Wang Fang, Shao Xueguang, Su Qingde
Department of Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China.
Analyst. 2003 Sep;128(9):1200-3. doi: 10.1039/b305023h.
A new hybrid algorithm is proposed to eliminate the interference information for multivariate calibration of near-infrared (NIR) spectra that includes noise, background and systemic spectral variation irrelevant to concentration. The method consists of two parts: approximate derivative based on continuous wavelet transform (CWT) and orthogonal signal correction (OSC). After the approximate derivative calculated by CWT, OSC was performed. It was successfully applied to real complex NIR spectral data to eliminate the interference information. Correction for the interference of NIR spectra resulted in a substantial improvement in the predicted precision, and a more concise calibration model was obtained. The proposed procedure also compared favourably with several pretreatment methods, and the new method appears to provide a high-performance pretreatment tool for multivariate calibration of NIR spectra. In addition, the strategy proposed here can be applied to various other spectral data for quantitative purposes as well.
提出了一种新的混合算法,用于消除近红外(NIR)光谱多元校准中的干扰信息,这些干扰信息包括噪声、背景以及与浓度无关的系统光谱变化。该方法由两部分组成:基于连续小波变换(CWT)的近似导数和正交信号校正(OSC)。通过CWT计算出近似导数后,进行OSC。该算法成功应用于实际复杂的近红外光谱数据,以消除干扰信息。对近红外光谱干扰的校正显著提高了预测精度,并获得了更简洁的校准模型。所提出的方法也优于几种预处理方法,该新方法似乎为近红外光谱多元校准提供了一种高性能的预处理工具。此外,这里提出的策略也可应用于其他各种光谱数据的定量分析。