Chen Da, Hu Bin, Shao Xueguang, Su Qingde
Department of Chemistry, University of Science and Technology of China, 230026 Hefei, Anhui, People's Republic of China.
Anal Bioanal Chem. 2004 May;379(1):143-8. doi: 10.1007/s00216-004-2569-2. Epub 2004 Mar 18.
This work describes a hybrid procedure for eliminating major interference sources in aqueous near-infrared (NIR) spectra, that include aqueous influence, noise, and systemic variations irrelevant to concentration. The scheme consists of two parts: extension of wavelet prism (WPe) and orthogonal signal correction (OSC). First, WPe is employed to remove variations due to aqueous absorbance and noise; then OSC is applied to remove systemic spectral variations irrelevant to concentration. Although water possesses strong absorption bands that overshadow and overlap the absorption bands of analytes, along with noise and systematic interference, successful calibration models can be generated by employing the method proposed here. We show that the elimination of major interference sources from the aqueous NIR spectra results in a substantial improvement in the precision of prediction, and reduces the required number of PLS components in the model. In addition, the strategy proposed here can be applied to various analytical data for quantitative purposes as well.
这项工作描述了一种用于消除近红外(NIR)水溶液光谱中主要干扰源的混合程序,这些干扰源包括水的影响、噪声以及与浓度无关的系统变化。该方案由两部分组成:小波棱镜扩展(WPe)和正交信号校正(OSC)。首先,使用WPe去除由于水的吸光度和噪声引起的变化;然后应用OSC去除与浓度无关的系统光谱变化。尽管水具有强烈的吸收带,这些吸收带会掩盖和重叠分析物的吸收带,同时还存在噪声和系统干扰,但通过采用本文提出的方法仍可生成成功的校准模型。我们表明,从近红外水溶液光谱中消除主要干扰源可显著提高预测精度,并减少模型中所需的偏最小二乘(PLS)分量数量。此外,本文提出的策略也可应用于各种用于定量目的的分析数据。