Ariel University Center of Samaria, Department of Electrical and Electronics Engineering, Ariel 44837, Israel.
J Biomed Opt. 2011 Feb;16(2):027001. doi: 10.1117/1.3540408.
Saccharide interferences such as Dextran, Galactose, etc. have a great potential to interfere with near infrared (NIR) glucose analysis since they have a similar spectroscopic fingerprint and are present physiologically at large relative concentrations. These can lead to grossly inappropriate interpretation of patient glucose levels and resultant treatment in critical care and hospital settings. This study describes a methodology to reduce this effect on glucose analysis using an NIR Fourier transform spectroscopy method combined with a multivariate calibration technique (PLS) using preprocessing by orthogonal signal correction (OSC). A mathematical approach based on the use of a single calibration based bias and slope correction was applied in addition to a standard OSC was investigated. This approach is combined with a factorial interferent calibration design to accommodate for interference effects. We named this approach as a slope and bias OSC (sbOSC). sbOSC differs from OSC in the way it handles the prediction. In sbOSC, statistics on slope and bias obtained from a set of calibration samples are then used as a validation parameter in the prediction set. Healthy human volunteer blood with different glucose (80 to 200 mg/dL) and hematocrit (24 to 48 vol.%) levels containing high expected levels of inteferents have been measured with a transmittance near-infrared Fourier transform spectrometer operates in the broadband spectral range of 1.25-2.5 μm (4000-8000 cm(-1)). The effect of six interferents compounds used in intensive care and operating rooms, namely Dextran, Fructose, Galactose, Maltose, Mannitol, and Xylose, were tested on blood glucose. A maximum interference effect (MIE) parameter was used to rank the significance for the individual interferent type on measurement error relative to the total NIR whole blood glucose measurement error. For comparison, a YSI (Yellow Springs Instrument) laboratory reference glucose analyzer and NIR data were collected at the same time as paired samples. MIE results obtained by sbOSC were compared with several standard spectral preprocessing approaches and show a substantial reduced effect of saccharide interferences. NIR glucose measurement results are substantially improved when comparing standard error of prediction from validation samples; and resulting MIE values are small.
糖干扰物,如葡聚糖、半乳糖等,由于具有相似的光谱指纹,且在生理上以相对较大的浓度存在,因此对近红外(NIR)葡萄糖分析具有很大的干扰潜力。这些可能导致对患者血糖水平的严重不当解释,并导致在重症监护和医院环境中的治疗结果。本研究描述了一种使用 NIR 傅里叶变换光谱法结合偏最小二乘法(PLS)并通过正交信号校正(OSC)预处理来减少这种对葡萄糖分析影响的方法。还研究了一种基于使用单个校准偏置和斜率校正的数学方法,以及一种标准 OSC。该方法与因子干扰校准设计相结合,以适应干扰影响。我们将这种方法命名为斜率和偏置 OSC(sbOSC)。sbOSC 与 OSC 的不同之处在于它处理预测的方式。在 sbOSC 中,从一组校准样本中获得的斜率和偏置的统计信息随后用作预测集的验证参数。使用透射近红外傅里叶变换光谱仪测量了具有不同葡萄糖(80 至 200mg/dL)和红细胞压积(24 至 48 体积%)水平的健康人类志愿者血液,该光谱仪在 1.25-2.5μm(4000-8000cm(-1)) 的宽带光谱范围内运行。在血液葡萄糖测量中测试了在重症监护室和手术室中使用的六种干扰化合物,即葡聚糖、果糖、半乳糖、麦芽糖、甘露醇和木糖。使用最大干扰效应(MIE)参数来对单个干扰物类型对测量误差相对于整个 NIR 全血葡萄糖测量误差的重要性进行排序。为了比较,同时使用 YSI(Yellow Springs Instrument)实验室参考葡萄糖分析仪和 NIR 数据收集配对样本。通过 sbOSC 获得的 MIE 结果与几种标准光谱预处理方法进行了比较,结果表明糖干扰的影响大大降低。与验证样本的预测标准误差相比,NIR 葡萄糖测量结果得到了显著改善;并且产生的 MIE 值很小。