Department of Chemistry and Optical Science and Technology Center, University of Iowa, Iowa City, Iowa 52242, USA.
Anal Chem. 2009 Mar 15;81(6):2199-207. doi: 10.1021/ac802023w.
Multivariate calibration models are constructed through the use of Gaussian basis functions to extract relevant information from single-beam spectral data. These basis functions are related by analogy to optical filters and offer a pathway to the direct implementation of the calibration model in the spectrometer hardware. The basis functions are determined by use of a numerical optimization procedure employing genetic algorithms. This calibration methodology is demonstrated through the development of quantitative models in near-infrared spectroscopy. Calibrations are developed for the determination of physiological levels of glucose in two synthetic biological matrixes, and the resulting models are tested by application to external prediction data collected as much as 4 months outside the time frame of the calibration data used to compute the models. The calibrations developed with the Gaussian basis functions are compared to conventional calibration models computed with partial least-squares (PLS) regression. For both data sets, the models based on the Gaussian functions are observed to outperform the PLS models, particularly with respect to calibration stability over time.
多元校准模型是通过使用高斯基函数构建的，从单光束光谱数据中提取相关信息。这些基函数与光学滤波器类似，为在光谱仪硬件中直接实现校准模型提供了一种途径。基函数通过使用遗传算法的数值优化程序确定。通过近红外光谱学中定量模型的开发来演示这种校准方法。在两个合成生物基质中开发了用于测定生理葡萄糖水平的校准，并且通过将模型应用于在用于计算模型的校准数据的时间范围之外收集的外部预测数据来测试得到的模型。与使用偏最小二乘法（PLS）回归计算的传统校准模型相比，使用高斯基函数开发的校准。对于两个数据集，观察到基于高斯函数的模型优于 PLS 模型，特别是在随时间的校准稳定性方面。
