In this article, we discuss the use of advanced statistical techniques (functional data analysis) in millimeter-wave (mm-wave) spectroscopy for biomedical applications. We employ a W-band transmit–receive unit with a reference channel to acquire spectral data. The choice of the W-band is based on a tradeoff between penetration through the skin providing an upper bound for the frequencies and spectral content across the band. The data obtained are processed using functional principal component logit regression (FPCLoR), which enables to obtain a predictive model for sustained hyperglycemia, typically associated with diabetes. The predictions are based on the transmission data from noninvasive mm-wave spectrometer at W-band. We show that there exists a frequency range most suitable for identification, classification, and prediction of sustained hyperglycemia when evaluating the functional parameter of the functional logit model (β). This allows for the optimization of the spectroscopic instrument in the aim to obtain a compact and potential low-cost noninvasive instrument for hyperglycemia assessment. Furthermore, we also demonstrate that the statistical tools alleviate the problem of calibration, which is a serious obstacle in similar measurements at terahertz and IR frequencies.

中文翻译：

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使用毫米波光谱法对小鼠进行非侵入性高血糖状态检测的高级统计技术

在本文中，我们将讨论在生物医学应用中使用毫米波 (mm-wave) 光谱学的先进统计技术（功能数据分析）。我们采用带有参考通道的 W 波段发射-接收单元来获取光谱数据。W 波段的选择基于穿透皮肤之间的权衡，为整个波段的频率和频谱内容提供上限。获得的数据使用功能主成分 logit 回归 (FPCLoR) 进行处理，从而能够获得持续高血糖（通常与糖尿病相关）的预测模型。这些预测基于 W 波段无创毫米波光谱仪的传输数据。我们表明存在一个最适合识别、分类、在评估功能对数模型 (β) 的功能参数时预测持续高血糖。这允许优化光谱仪器，以获得用于高血糖评估的紧凑且潜在的低成本无创仪器。此外，我们还证明了统计工具减轻了校准问题，这是在太赫兹和红外频率下进行类似测量的严重障碍。