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Extended Near-Infrared Optoacoustic Spectrometry for Sensing Physiological Concentrations of Glucose

机译:扩展近红外光声光谱法检测葡萄糖的生理浓度

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摘要

Glucose sensing is pursued extensively in biomedical research and clinical practice for assessment of the carbohydrate and fat metabolism as well as in the context of an array of disorders, including diabetes, morbid obesity, and cancer. Currently used methods for real-time glucose measurements are invasive and require access to body fluids, with novel tools and methods for non-invasive sensing of the glucose levels highly desired. In this study, we introduce a near-infrared (NIR) optoacoustic spectrometer for sensing physiological concentrations of glucose within aqueous media and describe the glucose spectra within 850–1,900 nm and various concentration ranges. We apply the ratiometric and dictionary learning methods with a training set of data and validate their utility for glucose concentration measurements with optoacoustics in the probe dataset. We demonstrate the superior signal-to-noise ratio (factor of ~3.9) achieved with dictionary learning over the ratiometric approach across the wide glucose concentration range. Our data show a linear relationship between the optoacoustic signal intensity and physiological glucose concentration, in line with the results of optical spectroscopy. Thus, the feasibility of detecting physiological glucose concentrations using NIR optoacoustic spectroscopy is demonstrated, enabling the sensing glucose with ±10 mg/dl precision.
机译:在生物医学研究和临床实践中,广泛地追求葡萄糖感测,以评估碳水化合物和脂肪的代谢以及在包括糖尿病,病态肥胖症和癌症在内的一系列疾病的背景下。当前使用的用于实时葡萄糖测量的方法是侵入性的并且需要进入体液,并且非常需要用于非侵入性感测葡萄糖水平的新颖工具和方法。在这项研究中,我们介绍了一种近红外(NIR)声光光谱仪,用于感测水介质中葡萄糖的生理浓度,并描述了850–1,900 nm和不同浓度范围内的葡萄糖光谱。我们将比例和字典学习方法与一组训练数据一起应用,并验证其在探针数据集中通过光声测量葡萄糖浓度的效用。我们证明了在宽葡萄糖浓度范围内,通过比率学习方法通​​过字典学习可实现出色的信噪比(约3.9因子)。我们的数据显示了光声信号强度与生理葡萄糖浓度之间的线性关系,这与光谱学的结果一致。因此,证明了使用近红外光谱技术检测生理性葡萄糖浓度的可行性,使传感葡萄糖的精度达到±10µmg / dl。

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