• 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
  • show less
    DOI: 10.3788/LOP56.041701 Cite this Article Set citation alerts
    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
    Experiment scheme for measuring blood glucose
    Fig. 1. Experiment scheme for measuring blood glucose
    RBF modeling results with non-measurement-component considered. (a) Predicted values and true values; (b) relative errors
    Fig. 2. RBF modeling results with non-measurement-component considered. (a) Predicted values and true values; (b) relative errors
    Linear kernel function modeling results with non-measurement-component considered. (a) Predicted values and true values; (b) relative errors
    Fig. 3. Linear kernel function modeling results with non-measurement-component considered. (a) Predicted values and true values; (b) relative errors
    Modeling results without non-measurement-component considered. (a) Predicted values and true values; (b) relative errors
    Fig. 4. Modeling results without non-measurement-component considered. (a) Predicted values and true values; (b) relative errors
    Relative errors of SVM model
    Fig. 5. Relative errors of SVM model
    ModelingTraining setPrediction set
    RRMSECRRMSEP
    With non-measurement-component considered0.99930.020.96270.13
    Without non-measurement-component considered0.93440.170.86550.23
    Table 1. Model parameters
    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
    Download Citation