Analytical Chemistry Department, University of Valencia, Edificio Jeroni Muñoz, 46100, Burjassot, Valencia, Spain.
Anal Bioanal Chem. 2013 Oct;405(25):8223-32. doi: 10.1007/s00216-013-7230-5. Epub 2013 Aug 8.
The use of chemometrics in order to improve the molecular selectivity of infrared (IR) spectra has been evaluated using classic least squares (CLS), partial least squares (PLS), science-based calibration (SBC), and multivariate curve resolution-alternate least squares (MCR-ALS) techniques for improving the discriminatory and quantitative performance of infrared hollow waveguide gas sensors. Spectra of mixtures of isobutylene, methane, carbon dioxide, butane, and cyclopropane were recorded, analyzed, and validated for optimizing the prediction of associated concentrations. PLS, CLS, and SBC provided equivalent results in the absence of interferences. After addition of the spectral characteristics of water by humidifying the sample mixtures, CLS and SBC results were similar to those obtained by PLS only if the water spectrum was included in the calibration model. In the presence of an unknown interferant, CLS revealed errors up to six times higher than those obtained by PLS. However, SBC provided similar results compared to PLS by adding a measured noise matrix to the model. Using MCR-ALS provided an excellent estimation of the spectra of the unknown interference. Furthermore, this method also provided a qualitative and quantitative estimation of the components of an unknown set of samples. In summary, using the most suitable chemometrics approach could improve the selectivity and quality of the calibration model derived for a sensor system, and may avoid the need to analyze expensive calibration data sets. The results obtained in the present study demonstrated that (1) if all sample components of the system are known, CLS provides a sufficiently accurate solution; (2) the selection between PLS and SBC methods depends on whether it is easier to measure a calibration data set or a noise matrix; and (3) MCR-ALS appears to be the most suitable method for detecting interferences within a sample. However, the latter approach requires the most extensive calculations and may thus result in limited temporal resolution, if the concentration of a component should be continuously monitored.
为了提高红外（IR）光谱的分子选择性，使用经典最小二乘法（CLS）、偏最小二乘法（PLS）、基于科学的校准（SBC）和多变量曲线分辨-交替最小二乘法（MCR-ALS）技术来评估化学计量学的应用，以改善红外空心波导气体传感器的区分和定量性能。记录、分析和验证了异丁烯、甲烷、二氧化碳、丁烷和环丙烷混合物的光谱，以优化相关浓度的预测。在不存在干扰的情况下，PLS、CLS 和 SBC 提供了等效的结果。在加湿样品混合物以添加水的光谱特征后，如果将水光谱包含在校准模型中，则 CLS 和 SBC 的结果与 PLS 获得的结果相似。在存在未知干扰物的情况下，CLS 显示的误差比 PLS 高六倍。然而，通过向模型添加测量的噪声矩阵，SBC 与 PLS 提供了相似的结果。使用 MCR-ALS 可以对未知干扰的光谱进行出色的估计。此外，该方法还可以对一组未知样品的组件进行定性和定量估计。总之，使用最合适的化学计量学方法可以提高传感器系统校准模型的选择性和质量，并可能避免分析昂贵的校准数据集的需要。本研究的结果表明：（1）如果系统的所有样品成分都是已知的，CLS 提供了足够准确的解决方案；（2）在 PLS 和 SBC 方法之间的选择取决于测量校准数据集或噪声矩阵是否更容易；（3）MCR-ALS 似乎是检测样品中干扰物的最合适方法。然而，后一种方法需要最广泛的计算，如果需要连续监测组件的浓度，则可能导致时间分辨率有限。
