探讨一种基于近红外反射光谱的人体血清白蛋白、 球蛋白和总蛋白三种生化指标的无创检测方法。 采集58例志愿者舌尖处近红外反射光谱, 考虑这些光谱数据与血清蛋白浓度间因个体差异等存在非线性映射关系, 在计算归一化光谱反射率及分析样本蛋白含量统计分布上, 采用支持向量机分别建立三种蛋白成分近红外光谱定量回归模型, 并与传统的偏最小二乘法进行比较。 实验结果表明, 支持向量机校正模型的预测效果较好且明显优于偏最小二乘法校正模型, 对白蛋白、 球蛋白和总蛋白的预测相关系数分别达到0.894, 0.931和0.863, 预测的均方误差为2.19, 1.93和4.38。 因此, 支持向量机可有效抵抗活体检测定量分析中存在的非线性因素, 提高模型的鲁棒性。 同时也表明舌的近红外光谱信息能够较客观的反映人体理化指标的变化, 用于血清蛋白含量的快速无创检测具有较高的可行性。

In the present paper, a kind of noninvasive determination for human serum protein concentration of albumin, globulin and total protein was explored based on the technology of near-infrared reflectance spectra. Reflectance spectra on the tongue tip of 58 volunteers were collected. Because these is an nonlinear mapping relationship induced by the individual differences between these spectra data and serum protein concentration, SVM was used to establish quantitative regression models of 3 kinds of protein concentration respectively after the normalized spectral reflectance was calculated and the protein content statistics distribution of the sample set was analyzed. In addition, results of SVM were compared with that of PLS. The results show that the predictive effect for calibrated model of SVM is obviously better than that of PLS. Using SVM model to predict the prediction set, the correlation coefficients of ALB, GLB and TP are respectively 0.894, 0.931 and 0.863, and root mean square errors are 2.19, 1.93 and 4.38. So SVM can resist the impact of nonlinear factors among in-vivo determinations, and enhance the robustness of the models. Meanwhile it was also showed that the near infrared spectral information for tongue can relatively objectively reflect changes in human physiological and biochemical indexes, and this technology for noninvasive determination of serum protein concentration is highly feasible.<作者简国家自然科学基金项目（30973964）, 天津市应用基础及前沿技术研究计划项目（11JCZDJC17100）, 天津市科技计划项目: 科技型中小企业创新基金项目（10ZXCXSY10400）资助介>李家星, 女, 1979年生, 天津大学精密仪器与光电子工程学院在读博士