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Permittivity extraction of glucose solutions through artificial neural networks and non-invasive microwave glucose sensing

机译:通过人工神经网络和非侵入性微波葡萄糖感应血糖溶液的介质提取

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摘要

An accurate low-cost method is presented for measuring the complex permittivity of glucose/water solutions. Moreover, a compact non-invasive RF/microwave sensor is presented for glucose sensing with the reasoning behind design parameters as well as simulation and measurement results. The complex permittivity values of aqueous solutions of glucose were measured with an in-house manufactured open-ended coaxial probe and the values were extracted from the measured complex reflection coefficients (S11) utilizing artificial neural networks. The obtained results were validated against a commercial probe. The values were fitted to the Debye relaxation model for ease of evaluation for a desired glucose concentration at a desired frequency. The proposed permittivity model in this paper is valid for glucose concentrations of up to 16 g/dl in the 0.3-15 GHz range. The model is useful for simulating and validating non-invasive RF glucose sensors. (C) 2018 Elsevier B.V. All rights reserved.
机译:提出了一种用于测量葡萄糖/水溶液的复杂介电常数的准确的低成本方法。 此外,呈现紧凑的无侵入式RF /微波传感器,用于葡萄糖感测,在设计参数之后的推理以及模拟和测量结果。 用内部制造的开放式同轴探针测量葡萄糖水溶液的复杂介电常数值,并且利用人工神经网络从测量的复合反射系数(S11)中提取值。 获得的结果针对商业探针验证。 为了易于评估所需的葡萄糖浓度,将该值安装在Debye弛豫模型中,以便在所需频率下进行所需的葡萄糖浓度。 本文所提出的介电常数模型对于在0.3-15GHz范围内的葡萄糖浓度高达16g / d1。 该模型可用于模拟和验证非侵入性RF葡萄糖传感器。 (c)2018年elestvier b.v.保留所有权利。

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