SIGNAL TO NOISE RATIO ANALYSIS FOR PREDICTION OF BLOOD ANALYTES

当从光谱数据预测分析物浓度时， 平均预测误差随谱的信噪比减小 增加。然而，医疗情况通常会对 临床测量可以花费的时间。时间 约束反过来又限制了可以 已实现。因此，当我们为分析物设计校准时 预测，重要的是要研究如何提高预测的准确性 作为收集时间的函数进行更改。我们的预赛 使用我们的血清数据进行的调查表明，我们 可以将未知样品的测量时间减少到 30或60秒，同时保持临床预测水平 精确度。

When analyte concentrations are predicted from spectral data, the mean prediction error decreases as the signal-to-noise of the spectrum increases. However, medical situations often place an upper limit on the amount of time that a clinical measurement can take. The time constraint, in turn, limits the signal-to-noise ratio that can be achieved. Therefore, when we design calibrations for analyte prediction, it is important to study how the prediction accuracy changes as a function of collection time. Our preliminary investigations, using our data on blood serum, have indicated that we can decrease the measurement time for unknown samples to as little as 30 or 60 seconds while preserving a clinical level of prediction accuracy.