Malik Bilal, Benaissa Mohammed
Sheffield University, Sheffield, UK.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:6169-71. doi: 10.1109/EMBC.2012.6347402.
This paper proposes the use of locally weighted partial least square regression (LW-PLSR) as an alternative multivariate calibration method for the prediction of glucose concentration from NIR spectra. The efficiency of the proposed model is validated in experiments carried out in a non-controlled environment or sample conditions using mixtures composed of glucose, urea and triacetin. The collected data spans the spectral region from 2100 nm to 2400 nm with spectra resolution of 1 nm. The results show that the standard error of prediction (SEP) decreases to 23.85 mg/dL when using LW-PLSR in comparison to the SEP values of 49.40 mg/dL, and 27.56 mg/dL using Principal Component Regression (PCR) and Partial Least Square (PLS) regression respectively.
本文提出使用局部加权偏最小二乘回归(LW-PLSR)作为一种替代的多元校准方法,用于从近红外光谱预测葡萄糖浓度。所提出模型的有效性在非受控环境或样品条件下使用由葡萄糖、尿素和三醋精组成的混合物进行的实验中得到验证。收集的数据涵盖2100纳米至2400纳米的光谱区域,光谱分辨率为1纳米。结果表明,与分别使用主成分回归(PCR)和偏最小二乘(PLS)回归时预测标准误差(SEP)值为49.40毫克/分升和27.56毫克/分升相比,使用LW-PLSR时预测标准误差降至23.85毫克/分升。