Citation: YUAN Jing-Ze, LU Qi-Peng, WANG Jing-Li, DING Hai-Quan, GAO Hong-Zhi, WU Chun-Yang, LI Wan-Xia. Support Vector Regression for Non-invasive Detection of Human Hemoglobin. Chinese Journal of Analytical Chemistry, 2017, 45(9): 1291-1296. doi: 10.11895/j.issn.0253-3820.170090 [复制]
Support Vector Regression for Non-invasive Detection of Human Hemoglobin
采用线性渐变滤光片（Linear variable filter，LVF），优化设计高性能、便携式的人体血液成分近红外检测设备，研究了支持向量回归（Support vector regression，SVR）模型对人体血红蛋白（Hemoglobin，Hb）的预测能力及稳定性，以实现贫血疾病的无创诊断。无创采集100位志愿者食指前端光谱信息并划分定标集、验证集1和2。应用网格搜索方法优选惩罚参数与核函数参数c=5.28，g=0.33，用以建立稳健的SVR模型。随后，分别对验证集1和2中Hb水平进行定量分析。实验结果表明：预测标准偏差（RMSEP）分别为10.20 g/L和10.85 g/L，相对预测标准偏差（R-RMSEP）为6.85%和7.48%，测量精度较高且SVR模型对不同样品的适应性较强，基本满足临床检测要求。基于SVR算法自行设计的LVF型近红外光谱检测设备在贫血症的无创诊断中有着良好的应用前景。
To facilitate noninvasive diagnosis of anemia, high-performance and portable near infrared (NIR) spectrometer for human blood constituents was designed and fabricated based on linear variable filter (LVF). Meanwhile, the performance of support vector regression (SVR) model for quantitative analysis of human hemoglobin (Hb) was investigated. Spectral data were collected noninvasively from 100 volunteers by self-designed LVF-NIR spectrometer, then divided into calibration set, validation set 1 and 2. To establish a robust SVR model, grid search method was applied to optimize the penalty parameter and kernel function parameter c=5.28, g=0.33. Then, Hb levels in the validation 1 and 2 sets were quantitatively analyzed. The results showed that the root mean square error of prediction (RMSEP) were 10.20 g/L and 10.85 g/L, respectively, and the relative RMSEP (R-RMSEP) were 6.85% and 7.48%, respectively. The results indicated that the SVR model had high prediction accuracy to Hb level and adaptability to different samples, and could satisfy the requirements of clinical measurement. Based on the SVR algorithm, the self-designed LVF-NIR spectrometer has a wide application prospect in the field of non-invasive anemia diagnosis.
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