Multivariate calibration with basis functions derived from optical filters.
Multivariate calibration with basis functions derived from optical filters.
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DOI：
10.1021/ac802023w
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发表时间：
2009-02
影响因子：
7.4
通讯作者：
T. Tarumi;Yuping Wu;G. W. Small
中科院分区：
文献类型：
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作者：
T. Tarumi;Yuping Wu;G. W. Small
通过使用高斯基函数从单光束光谱数据中提取相关信息来构建多变量定标模型。这些基函数类似于滤光片，并提供了在光谱仪硬件中直接实现校准模型的途径。通过使用使用遗传算法的数值优化过程来确定基函数。这一校准方法通过近红外光谱定量模型的开发得到了证明。开发了用于确定两个合成生物基质中葡萄糖的生理水平的校准，并且通过应用于在用于计算模型的校准数据的时间框架之外收集的长达4个月的外部预测数据来测试所得到的模型。用高斯基函数建立的定标与用偏最小二乘回归计算的常规定标模型进行了比较。对于这两个数据集，基于高斯函数的模型被观察到优于偏最小二乘模型，特别是关于随时间的校准稳定性。
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.