将O2-PLS方法应用于近红外血糖无损检测的基础实验中,将光谱矩阵和浓度矩阵中的目标因素和非目标因素分离,只取两矩阵中的目标因素建立校正模型。分别选用成分逐渐复杂的二成份葡萄糖溶液、四成份葡萄糖溶液和人体血浆样品作为研究对象,采用分组验证法对所建模型进行验证。结果显示,模型的预测相关系数均达到0.999,预测均方根误差(RMSEP)分别为1.446,1.93,.274 mg/dL(1 dL=0.1 L),预测精度均高于传统偏最小二乘回归(PLS)预测。而且相对于传统PLS模型,O2-PLS模型的预测精度受样品复杂程度的影响较小。结果表明,O2-PLS方法更适用于成分复杂的样品检测。

The O2-PLS method was applied to basic research on non-invasive measurement of human blood glucose. The O2-PLS method can respectively divide the spectral matrix and concentration matrix into aim factors and aimless factors,only using aim factors to build calibration model. Two-component glucose solution,four-component glucose solution and human plasma samples are taken as research objects,and test set validation method is used to evaluate prediction results. The results show that prediction correlation coefficients are all 0.999 and root mean square errors of prediction (RMSEP) were 1.446,1.93,.274 mg/dL respectively. The prediction accuracy of O2-PLS models are higher than that of PLS models. Moreover,the complexity of samples has less effect on the prediction accuracy of O2-PLS models compared with traditional partial least squares (PLS) models. The experimental results indicate that O2-PLS method is applicable to complex samples detection.