无损血糖监测是一种方便且无痛的血糖监测方法。 目前, 大部分的血糖检测方法都是有损的。 提出了一种基于近红外反射光谱的无损血糖检测方法。 近红外反射光谱是一种安全、 简单并且有效的方法, 被应用于很多领域。 采用口服葡糖糖耐量试验来采集数据, 用偏最小二乘回归方法来建模。 使用市售血糖仪采指尖血作为参考值, 同时用光谱仪提取手掌光谱, 共取得42组样本。 血糖浓度范围在5~12 mmol·L-1。 采用留一法交叉验证, 获得所有数据的交叉验证的均方根误差为1.16 mmol·L-1。 通过归一化和无关变量消除的预处理方法来减少噪声并消除一些额外因素, 优化的均方根误差为0.79 mmol·L-1。 基于个人的数据进行建模, 得到了远小于整体数据的结果: 0.41 mmol·L-1。 该方法在个人血糖检测的市场化方面有广阔的应用前景。

Noninvasive glucose detection is highly required for more convenient and less pain glycaemic monitoring. Most of currently used methods are invasive. In this paper, a near-infrared reflectance spectroscopy (NIRS) is proposed to detect blood glucose to protect patient absent of pain. NIRS is a safe, simple and efficient technology applied in many fields. Experiments, based on Oral Glucose Tolerance Test (OGTT), were conducted to collect data modeling with partial least squares (PLS) regression. 42 samples of fingertip blood and palm were measured by commercially available blood glucose meter and NIRS separately at the same time. The glucose concentration range is between 5 and 12 mmol·L-1. With leave-one-out cross-validation, we obtained a result of root mean square error of cross-validation (RMSECV) of 1.16 mmol·L-1 for all the data. With the pre-processing methods of normalization and un-informative variables elimination reducing noise and eliminating some additional effects, we get a better result of 0.79 mmol·L-1. A RMSECV of 0.41 mmol·L-1 for individual modeling is much less than the total modeling. It has a broad application prospect in individual customization.