Measurement of glucose and other analytes in undiluted human serum with near-infrared transmission spectroscopy
Measurement of glucose and other analytes in undiluted human serum with near-infrared transmission spectroscopy
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DOI：
10.1016/s0003-2670(98)00318-3
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发表时间：
1998-10-05
影响因子：
6.2
通讯作者：
Small, GW
中科院分区：
文献类型：
--
作者：
Hazen, KH;Arnold, MA;Small, GW
近红外校准模型描述了总蛋白，白蛋白，球蛋白蛋白，甘油三酯，胆固醇，尿素，葡萄糖和乳酸的测量。242份未稀释的人血清样品的光谱在5000-4000 cm（-1）光谱范围内以2.5 mm光程长度一式三份收集。通过对原始和数字滤波光谱进行偏最小二乘（PLS）回归，为每个分析物生成校准模型。通过考虑光谱范围、模型因子数量和数字傅立叶滤波器高斯形滤波器响应函数的宽度/位置，对每种分析物单独优化模型。在我们的实验条件下，除了低于检测限的乳酸外，对每种分析物都进行了准确的测量。预测葡萄糖的标准误差（SEP）为23.3 mg/dl （1.29 mM）。在校正数据收集19个月后，通过评估50份人类血清样本的葡萄糖预测准确性，检验了葡萄糖校正模型的相关性和稳定性。此外，在所有预测完成之前，对这50个后续样本进行了盲处理，保留了他们的葡萄糖值。结果表明，在预测中有轻微的正偏差，对应于模型的轻微不稳定性。这种不稳定性很可能是由于光谱仪硬件的变化。然而，在这些盲测样本中，预测和实际葡萄糖水平之间的强烈相关性强烈表明，该校准模型是基于特定于葡萄糖的信息。(C) 1998 Elsevier Science B.V.版权所有
Near-infrared calibration models are described for the measurement of total protein, albumin protein, globulin protein, triglycerides, cholesterol, urea, glucose, and lactate. Spectra are collected in triplicate over the 5000-4000 cm(-1) spectral range with a 2.5 mm optical path length for 242 undiluted human serum samples. Calibration models are generated for each analyte by performing partial least-squares (PLS) regression on raw and digitally filtered spectra. Models are optimized individually for each analyte by considering spectral range, number of model factors and width/position of a Gaussian shaped filter response function for a digital Fourier filter. Accurate measurements are demonstrated for each analyte except lactate which is below the detection limit under our experimental conditions. Standard error of prediction (SEP) for glucose is 23.3 mg/dl (1.29 mM). Relevance and stability of the glucose calibration model are examined by evaluating the accuracy of glucose predictions from 50 human serum samples collected on a modified spectrometer nineteen months after the calibration data were collected. In addition, these 50 subsequent samples were treated in a blind manner by withholding their glucose values until all predictions were complete. Results indicate a slight positive bias in the predictions corresponding to a minor instability in the model. This instability is likely due to changes in the spectrometer hardware. Nevertheless, the strong correlation between predicted and actual glucose levels in these blind samples strongly suggests that this calibration model is based on information particular to glucose. (C) 1998 Elsevier Science B.V. All rights reserved.