多维标度线性回归技术应用于人体血清临床指标的FTIR光谱定量分析  被引量:2

Multidimensional Scaling Linear Regression Applied to FTIR Spectral Quantitative Analysis of Clinical Parameters of Human Blood Serum

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作  者:陈华舟[1] 宋奇庆[1] 石凯[1] 贾贞[1] 

机构地区:[1]桂林理工大学理学院,广西桂林541004

出  处:《光谱学与光谱分析》2015年第4期914-918,共5页Spectroscopy and Spectral Analysis

基  金:国家自然科学基金项目(11226219;61164020)资助

摘  要:人体血清临床指标是衡量人体健康和亚健康水平的重要因素之一,采用傅里叶红外(FTIR)光谱技术实现人体血清临床指标的多成分快速同时检测。提出利用多维标度法(MDS)对光谱变量进行降维,结合多元线性回归(MLR)技术,建立多维标度线性回归(MDS-MLR)模型,为血清四种临床生化指标(葡萄糖、低密度脂蛋白胆固醇、甘油三酯、尿素)的定量分析优选光谱信息波长点,优化定标预测模型,结合移动平均法(MA)进行光谱预处理,得到良好的建模效果。通过检验集样品进行验证,检验相关系数均在0.9以上。结果表明,MDS-MLR方法具有人体血清临床指标FTIR光谱分析的应用潜力。FTIR技术结合MDS-MLR定量分析方法可以实现对人体健康和亚健康水平的快速评定。In the present paper it was proposed to establish multidimensional scaling linear regression (MDS‐MLR) models by combining multidimensional scaling (MDS) with multiple linear regression (MLR) ,in which MDS owns the ability of dimen‐sionality reduction on spectral variables .Thus the informative spectral wavelengths can be selected for each of the 4 clinical pa‐rameters (i .e .glucose ,LDL cholesterol ,triglycerides and urea) by MDS‐MLR method ,and the effect of spectral colinearity can be restrained ,so that the calibration models can be optimized .Experiments showed that the MDS‐MLR models will allow to pro‐duce appreciated modeling results when combined with the preprocessing method of moving average (MA) .The optimized mod‐els were verified by the samples in validation set ,and the validating correlation coefficients for each clinical parameter were all upper than 0.9 .These results indicated that FTIR predicted values and the biochemical values of each clinical parameter were highly correlated ,which demonstrated that MDS‐MLR method has the potential of being applied to the FTIR spectroscopic anal‐ysis of human serum .FTIR rapid determination technology combined with the MDS‐MLR modeling method is expected to realize the rapid assessment of human health and sub‐health level .

关 键 词:傅里叶红外光谱 人体血清 临床指标 多维标度线性回归 

分 类 号:O433.4[机械工程—光学工程] O657.3[理学—光学]

 

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