• Laser & Optoelectronics Progress
  • Vol. 56, Issue 4, 041701 (2019)
Xiaofei Wang*, Xinyi Zhang, and Xinhe Xu
Author Affiliations
  • School of Instrumentation Science and Optoelectronic Engineering, Beijing Information Science and Technology University, Beijing 100192, China
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    DOI: 10.3788/LOP56.041701 Cite this Article
    Xiaofei Wang, Xinyi Zhang, Xinhe Xu. Comparison of Multi-Factor-Considered Blood Glucose Prediction Models by Near-Infrared Spectroscopy[J]. Laser & Optoelectronics Progress, 2019, 56(4): 041701 Copy Citation Text show less


    Taking the blood glucose concentration as an example, the accuracy of the blood glucose measurement system is improved by means of the simultaneous incorportation of the extraction data form dynamic spectra and the influencing factors of non-measured components into the prediction model. The blood glucose prediction model is established through the support vector machine algorithm. The modeling results show that the prediction value from the multi-factor-considered model is superior to that from the non-measurement-component-considered model. The correlation coefficient of the former is 0.9627, higher by 14.23% , the root mean square error is 0.13, reduced by 43.12%, and the number of samples with a relative error in the range of 10% is higher by 8.33%, compared with those of the latter.