研究目的
To propose and demonstrate a 'guide star' assisted method for noninvasive glucose measurement combining merits of photoacoustic (PA) and near-infrared spectroscopy (NIRS).
研究成果
The proposed 'guide star' assisted method for noninvasive glucose measurement combines the merits of PA and NIRS, offering a promising solution to enhance sensitivity without increasing system complexity. The method demonstrated feasibility for measuring glucose in physiological range, with longer optical path lengths showing better sensitivity and accuracy. The approach is undergoing a patent procedure and has potential for future diabetes care and other PA sensing applications.
研究不足
The study acknowledges that longer optical path length tends to provide higher sensitivity, but this improvement is restricted in a certain range due to the decrease in PA signal amplitude as optical path length increases. Individual calibration for this method is an essential procedure to find out the optimized tissue thickness and set the baseline.
1:Experimental Design and Method Selection:
The study proposed a novel 'guide star' assisted photoacoustic (GSPA) method for noninvasive glucose measurement. A virtual photodiode was employed to amplify the PA signal difference caused by glucose concentration variations indirectly.
2:Sample Selection and Data Sources:
In vitro experiments were conducted on aqueous glucose solution and human blood serum (HBS) with concentrations varying in human physiological range (50~350 mg/dL).
3:List of Experimental Equipment and Materials:
A tunable laser system (OPOTEK, Opolette 355), quartz cuvettes with different optical path lengths (1 mm and 2 mm), a single-element piezoelectric ultrasound transducer, and a black tape (3M, Heavy Duty Vinyl Electrical Tape 22) as the 'guide star' were used.
4:Experimental Procedures and Operational Workflow:
The laser beam was focused to an area with diameter of 1 mm, passed through the sample, and the received signal was amplified and digitized for analysis.
5:Data Analysis Methods:
The performance was evaluated in terms of coefficient of determination (R2), root mean square error (RMSE) of prediction, Bland-Altman plot, and Clarke’s Error Grid analysis.
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