IMPROVEMENT OF NIR RAMAN SYSTEM FOR BLOOD ANALYSIS

在大多数生物样品的光谱中，存在重叠 来自不同物种的条带。而标准的多变量校准 方法，最著名的是偏最小二乘法，可以提取单个分析物的 以惊人的精确度，他们不能利用已知的 在校准过程中分析物的光谱。我们有 最近提出了一种新的校准方法，称为混合线性 分析(HL A)，将已知光谱合并到 校准。我们已经看到，此方法可以生成 偏最小二乘法在血糖预测中的等效性或优效性 包括血清在内的各种介质。在我们即将开展的工作中，我们将 将该方法的应用扩展到其他分析物以及 继续探索算法的基本属性 它本身。

In the spectra of most biological samples, there are overlapping bands from different species. While standard multivariate calibration methods, most notably PLS, can extract individual analytes' concentrations with impressive accuracy, they cannot utilize the known spectrum of an analyte during the calibration process. We have recently developed a new calibration method called hybrid linear analysis (HLA) that incorporates the known spectrum into the calibration. Already we have seen that this method can generate equivalent or superior calibrations to PLS in predicting glucose in various media including blood serum. In our upcoming work, we will extend the application of this method to other analytes, as well as continue to explore the fundamental properties of the algorithm itself.