Jae Kim*
The prevalence of gout in the United States has risen over the last twenty years and gout now affects 8.3 million Americans. Currently, gout is diagnosed either by extracting a fluid sample from the joint to look for formation of uric acid crystals or by drawing blood to measure the level of uric acid. Both of these methods are invasive, cumbersome, and time-consuming. The purpose of this study is to prevent gout attack with a novel approach called "noninvasive infrared uric acid monitoring." This approach utilizes a cost-effective and portable near-infrared (NIR) spectroscopybased device that can offer quick, noninvasive, and preventative way to monitor the patient's uric acid level. By using uric acid infrared spectroscopy characteristics, infrared wavelengths between 1400 to 1700nm are emitted on uric acid samples and detector senses non-absorbed Infrared light. The detected NIR signal gets amplified and filtered to maintain a high signal to noise ratio (SNR) over the wavelengths of interest. A linear regression algorithm is used to distinguish and predict the uric acid concentration among other biomarkers in the sample. Finally, a digital signal processing was used to process the data. Based on the Beer-Lambert's law, a linear relationship between the light absorbance and uric acid concentration is expected. Minimum detectable change in uric acid level was analyzed and a mathematical model was compared to the measured data. Another potential benefit of this approach is its versatility. By modifying infrared wavelength and the calibration system in the DSP chip, other biomarkers can be measured.
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