Research of Outlier Samples Elimination Methods for Near-Infrared Spectral Analysis of Blood Glucose_Journal of Biomedical Engineering

Authors：

LINYongzhong ,  LILina , LINTianliang

Department of Detection and Control Engineering, College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China;

Corresponding author：

LILina, Email: lilina@hqu.edu.cn

Keywords：

near-infrared; spectral analysis; blood glucose; outlier sample; Monte-Carlo cross validation

DOI：

10.7507/1001-5515.20150234

Video：

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Abstract Full text Figures/Tables Video References Cited by

For the near-infrared (NIR) spectral analysis of the concentration of blood glucose, the calibration accuracy can be affected because of the existing of outlier samples. In this research, a Monte-Carlo cross validation (MCCV) method is constructed for eliminating outlier samples. The human blood plasma experiment in vitro and the human body experiment in vivo were introduced to evaluate the MCCV method for its application effect in NIR spectral analysis of blood glucose. And the uninformative sample elimination method based on modified uninformative variable elimination (MUVE-USE) was employed in this study for the comparison with MCCV. The results indicated that, like the MUVE-USE method, the outlier samples elimination method based on MCCV could be used to eliminate the outlier samples which came from gross errors (such as bad sample) or system errors (such as baseline drift). In addition, the outlier samples from the random errors of uncertain causes which affect model accuracy can be eliminated simultaneously by MCCV. The elimination of multiple outlier samples is beneficial to the improvement of prediction accuracy of calibration model.

Citation： LINYongzhong, LILina, LINTianliang. Research of Outlier Samples Elimination Methods for Near-Infrared Spectral Analysis of Blood Glucose. Journal of Biomedical Engineering, 2015, 32(6): 1323-1328, 1334. doi: 10.7507/1001-5515.20150234 Copy

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1. SO C F, ZENG Y, CHOI K S, et al. Modified sequential floating selection for blood glucose monitoring using near infrared spectral data[J]. Journal of Applied Spectroscopy, 2013, 80(2):284-288.
2. 肖君, 王龙, 骆清铭, 等.基于近红外三波长的血糖检测系统研制[J].光电子·激光, 2007, 18(9):1135-1138.
3. 陈星旦, 高静, 丁海泉.论无创血糖监测的红外光谱方法(特邀)[J].中国光学, 2012, 5(4):317-326.
4. ALLEGRINI F, OLIVIERI A C. An integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration[J]. Talanta, 2013, 115:755-760.
5. 史波林, 赵镭, 刘文, 等.苹果内部品质近红外光谱检测的异常样本分析[J].农业机械学报, 2010, 41(2):132-137.
6. 闵顺耕, 李宁, 张明祥.近红外光谱分析中异常值的判别与定量模型优化[J].光谱学与光谱分析, 2004, 24(10):1205-1209.
7. 刘智超, 蔡文生, 邵学广.蒙特卡洛交叉验证用于近红外光谱奇异样本的识别[J].中国科学:B辑:化学, 2008, 38(4):316-323.
8. 陈斌, 邹贤勇, 朱文静. PCA结合马氏距离法剔除近红外异常样品[J].江苏大学学报(自然科学版), 2008, 29(4):277-279.
9. MOUMEN T E. Model-wise and point-wise random sample consensus for robust regression and outlier detection[J]. Neural Networks, 2014, 59:23-35.
10. JOAN F, ENRIC C. Outlier detection for the generalized rank annihilation method in HPLC-DAD analysis[J]. Talanta, 2011, 83:1147-1157.
11. PENG Jiang-tao, PENG Si-long, HU Yong. Partial least squares and random sample consensus in outlier detection[J]. Anal Chim Acta, 2012, 719:24-29.
12. JUN K, TETSUO I, SHIGEO M. Elimination of the uninformative calibration sample subset in the modified UVE-PLS method[J]. Anal Sci, 2001, 17:319-322.
13. WALCZAK B, MASSART D L. Robust principal components regression as a detection tool for outliers[J]. Chemometrics and Intelligent Laboratory Systems, 1995, 27(1):41-54.
14. PELL R J. Multiple outlier detection for multivariate calibration using robust statistical techniques[J]. Chemometrics and Intelligent Laboratory Systems, 2000, 52(1):87-104.
15. WIEGAND P, PELL R, COMAS E. Simultaneous variable selection and outlier detection using a robust genetic algorithm[J]. Chemometrics and Intelligent Laboratory Systems, 2009, 98(2):108-114.
16. 张新荣.基于鲁棒尺度的统计建模数据中异常点去除算法的研究及应用[J].计算机应用研究, 2010, 27(9):3319-3321.
17. 杨锦瑜, 李博岩, 梁逸曾, 等.基于主灵敏度矢量回归评价中药色谱指纹图谱[J].计算机与应用化学, 2005, 22(4):38-42.
18. LIU Zhi-chao, CAI Wen-sheng, SHAO Xu-eguang. Outlier detection in near-infrared spectroscopic analysis by using Monte Carlo cross-validation[J]. Science in China Series B-chemistry, 2008, 51(8):751-759.

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