摘要
采用偏稳健M回归方法有效地解决了人体血糖浓度近红外无创检测研究过程中由于样本奇异值影响模型稳健性的问题。该方法源于现有的迭代变权偏最小二乘法,计算快、易于实现,具有M估计的所有性质,且当权函数选择合适时,能降低奇异值的影响,建立具有稳健性的校正模型。采用该方法对近红外光谱实验数据进行了处理,并与传统的偏最小二乘(partialleast squares,PLS)建模方法进行了比较。结果表明,与PLS相比,该方法可建立稳健的校正模型提高预测精度,更适合复杂样品建模,对于人体血糖浓度近红外无创检测的进一步研究具有应用价值。
In the study of non-invasive measurement of human blood glucose concentration with near-infrared spectroscopy,the partial robust M-regression （PRM） is proposed in the present paper to solve the robustness of calibration model affected by outliers existing in the spectra data set.While keeping the good properties of M-estimators if an appropriate weighting scheme is chosen,PRM inherits the speed of computation and easy realization of the iterative reweighted partial least squares （IRPLS） algorithm,but is robust to all types of outliers.With the pretreatment of spectra based on PRM,the root mean square error of prediction （RMSEP） of calibration model was presented and compared with partial least squares （PLS）.Experimental results show that the robust calibration model PRM produces better prediction of glucose than the model of PLS when the components of the samples increase which is significant for non-invasive prediction of blood glucose levels.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2010年第8期2115-2119,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(60708026)
北京航空航天大学蓝天新星项目资助
关键词
偏稳健M回归
偏最小二乘
稳健性
近红外光谱
血糖浓度
Partial robust M-regression
Partial least-squares
Robustness
Near infrared spectroscopy
Human blood glucose
