Title: Comparison analysis between PLS and NN in noninvasive blood glucose concentration prediction
Abstract: A series pair data of NIR spectral and measured BGL are collected for an OGTT experiment from a healthy volunteer. The collected data are then calibrated by using partial least squares (PLS) regression and feed-forward back-propagation neural network (NN). A comparative analysis between both calibration models is analysed. From the PLS and NN calibration models, root mean square error prediction of 0.5282 mmol/L and 0.2952 mmol/L, respectively, were achieved. The correlation factor of 0.9247 and 0.9863 were obtained from PLS and NN calibration models respectively.
Publication Year: 2009
Publication Date: 2009-12-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 13
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