以血糖浓度为例,采用将动态光谱提取数据和非测量组分影响因素一同纳入预测模型的方式来提高血糖测量系统中的精度。通过支持向量算法建立血糖预测的模型,建模结果表明,考虑多因素模型的预测值优于未考虑非测量组分模型中的预测值。与后者相比,前者的相关系数达到0.9627,提高了14.23%;均方根误差为0.13,减少了43.12%;相对误差在10%范围内的样本数量增加8.33%。

Taking the blood glucose concentration as an example, the accuracy of the blood glucose measurement system is improved by means of the simultaneous incorportation of the extraction data form dynamic spectra and the influencing factors of non-measured components into the prediction model. The blood glucose prediction model is established through the support vector machine algorithm. The modeling results show that the prediction value from the multi-factor-considered model is superior to that from the non-measurement-component-considered model. The correlation coefficient of the former is 0.9627, higher by 14.23% , the root mean square error is 0.13, reduced by 43.12%, and the number of samples with a relative error in the range of 10% is higher by 8.33%, compared with those of the latter.