IMPROVEMENT OF NIR RAMAN SYSTEM FOR BLOOD ANALYSIS

在大多数生物样品的光谱中， 来自不同物种的条带。 标准多元校正 方法，最值得注意的是PLS，可以提取单个分析物 浓度与令人印象深刻的准确性，他们不能利用已知的 在校准过程中分析物的光谱。 我们有 最近开发了一种新的校准方法，称为混合线性 分析（HLA），其将已知光谱结合到 校准。 我们已经看到，这种方法可以生成 在预测血糖方面，与PLS相当或上级校准 包括血清在内的各种介质。 在今后的工作中，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 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.