UV RESONANCE RAMAN FOR QUANTITATIVE ANALYSIS OF COLON TISSUES: CANCER

当从光谱数据预测分析物浓度时， 平均预测误差随着频谱的信噪比减小而减小 增大 然而，医疗情况通常会对 临床测量所需的时间。 的时间 约束，反过来，限制了信噪比，可以 办妥了一批 因此，当我们为分析物设计校准时， 预测，重要的是研究如何预测精度 随着采集时间的变化而变化。 我们的初步 使用我们的血清数据进行的调查表明， 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.