为实现患者血液总胆固醇含量的无创检测, 检测了80例临床志愿者的手指脉动血液的动态光谱, 同时获取其血液内总胆固醇含量的临床化验结果。 对动态光谱加入谐波分量的数据进行了主成分分析, 提取数据中的重要有效成分。 对提取后的数据和总胆固醇实测值进行BP神经网络的建模并预测, 得到预测集相关系数为96.48%, 预测集最大相对误差为25.44%, 预测误差均方根为0.242 6 mmol·L-1。 由于在建模前对建模数据进行了主成分分析, 建模速度得到大幅度提高。 证明了动态光谱法结合主成分分析进行血液总胆固醇含量检测的可行性, 是无创血液成分分析研究的又一进展。

For non-invasive measurement of human blood cholesterol concentration, this experiment was carried out on 80 volunteers clinically. In vivo dynamic spectra of fingers were achieved and biochemical examinations of blood components contents including cholesterol were get as soon as possible. BP artificial neural network with inputs of dynamic spectra plus energy of harmonic waves processed by Principal Components Analysis(PCA) was used to establish the model of the total cholesterol values. The correlation between the predicted value and the true value of cholesterol is 96.48%. The maximum relative error is 25.44% and root-mean-square error of prediction is 0.242 6 mmol·L-1. The results show that PCA can make the process of computing faster and this study is another advance of dynamic spectra.