Influence of temperature on the precision of noninvasive glucose sensing by near-infrared spectroscopy

2008 ◽  
Author(s):  
Rong Liu ◽  
Wenliang Chen ◽  
Yun Chen ◽  
Kexin Xu
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Glucose Sensing ◽  
Influence Of Temperature ◽  
Influence Of Temperature On

Net analyte signal with floating reference theory in non-invasive blood glucose sensing by near-infrared spectroscopy

2012 ◽  
Vol 10 (8) ◽  
pp. 083002-83005 ◽  
Author(s):  
Wanjie Zhang Wanjie Zhang ◽  
Rong Liu Rong Liu ◽  
Wen Zhang Wen Zhang ◽  
Jiaxiang Zheng Jiaxiang Zheng ◽  
Kexin Xu Kexin Xu
Keyword(s):  
Infrared Spectroscopy ◽  
Blood Glucose ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Glucose Sensing ◽  
Non Invasive ◽  
Net Analyte Signal ◽  
Analyte Signal ◽  
Reference Theory

Noninvasive Blood Glucose Sensing with Near-Infrared Spectroscopy Based on Interstitial Fluid

2013 ◽  
Vol 33 (11) ◽  
pp. 1117001 ◽  
Author(s):  
吴春阳 Wu Chunyang ◽  
卢启鹏 Lu Qipeng ◽  
丁海泉 Ding Haiquan ◽  
高洪智 Gao Hongzhi
Keyword(s):  
Infrared Spectroscopy ◽  
Blood Glucose ◽  
Near Infrared Spectroscopy ◽  
Interstitial Fluid ◽  
Near Infrared ◽  
Glucose Sensing

Near-Infrared Spectroscopy for Noninvasive Glucose Sensing

2009 ◽  
pp. 357-390 ◽  
Author(s):  
Mark A. Arnold ◽  
Jonathon T. Olesberg ◽  
Gary W. Small
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Glucose Sensing

scholarly journals Monte Carlo simulation-based thinning and calculating method for noninvasive blood glucose sensing by near-infrared spectroscopy

2017 ◽  
Vol 10 (02) ◽  
pp. 1650041
Author(s):  
Congcong Ma ◽  
Jia Qin ◽  
Qi Zhang ◽  
Junsheng Lu ◽  
Kexin Xu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Infrared Spectroscopy ◽  
Blood Glucose ◽  
Light Source ◽  
Near Infrared Spectroscopy ◽  
Measurement System ◽  
Near Infrared ◽  
Glucose Sensing ◽  
Positioning Error ◽  
Calculating Method

Previous results show that the floating reference theory (FRT) is an effective tool to reduce the influence of interference factors on noninvasive blood glucose sensing by near-infrared spectroscopy (NIRS). It is the key to measure the floating reference point (FRP) precisely for the application of FRT. Monte Carlo (MC) simulation has been introduced to quantitatively investigate the effects of positioning errors and light source drifts on measuring FRP. In this article, thinning and calculating method (TCM) is proposed to quantify the positioning error. Meanwhile, the normalization process (NP) is developed to significantly reduce the error induced by light source drift. The results according to TCM show that 7[Formula: see text][Formula: see text]m deviations in positioning can generate about 10.63% relative error in FRP. It is more noticeable that 1% fluctuation in light source intensity may lead to 12.21% relative errors. Gratifyingly, the proposed NP model can effectively reduce the error caused by light source drift. Therefore, the measurement system for FRPs must meet that the positioning error is less than 7[Formula: see text][Formula: see text]m, and the light source drift is kept within 1%. Furthermore, an improvement for measurement system is proposed in order to take advantage of the NP model.

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