Université François-Rabelais de Tours, Faculté de Pharmacie, EA 6295 Nanomédicaments et Nanosondes, 31 avenue Monge, 37200 Tours, France.
Analyst. 2017 Apr 10;142(8):1285-1298. doi: 10.1039/c6an01888b.
Infrared spectroscopy is a reliable, rapid and cost effective characterisation technique, delivering a molecular finger print of the sample. It is expected that its sensitivity would enable detection of small chemical variations in biological samples associated with disease. ATR-IR is particularly suitable for liquid sample analysis and, although air drying is commonly performed before data collection, just a drop of human serum is enough for screening and early diagnosis. However, the dynamic range of constituent biochemical concentrations in the serum composition remains a limiting factor to the reliability of the technique. Using glucose as a model spike in human serum, it has been demonstrated in the present study that fractionating the serum prior to spectroscopic analysis can considerably improve the precision and accuracy of quantitative models based on the partial least squares regression algorithm. By depleting the abundant high molecular weight proteins, which otherwise dominate the spectral signatures collected, the ability to monitor changes in the concentrations of the low molecular weight constituents is enhanced. The Root Mean Square Error for the Validation set (RMSEV) has been improved by a factor of 5 following human serum processing with an average relative error in the predictive values below 1% being achieved. Moreover, the approach is easily transferable to different bodily fluids, which would support the development of more efficient and suitable clinical protocols for exploration of vibrational spectroscopy based ex vivo diagnostic tools.
红外光谱是一种可靠、快速且具有成本效益的特性描述技术,可提供样品的分子指纹图谱。预计其灵敏度将能够检测到与疾病相关的生物样本中微小的化学变化。ATR-IR 特别适用于液体样品分析,尽管在收集数据之前通常会进行空气干燥,但只需一滴人血清就足以进行筛查和早期诊断。然而,血清成分中组成生化浓度的动态范围仍然是该技术可靠性的限制因素。本研究以葡萄糖作为人血清中的模型峰,证明在进行光谱分析之前对血清进行分级可以极大地提高基于偏最小二乘回归算法的定量模型的精度和准确性。通过耗尽丰富的高分子量蛋白质,否则这些蛋白质会主导收集的光谱特征,从而增强了监测低分子量成分浓度变化的能力。人血清处理后,验证集的均方根误差(RMSEV)提高了 5 倍,预测值的平均相对误差低于 1%。此外,该方法易于转移到不同的体液,这将支持开发更有效和合适的临床方案,以探索基于振动光谱的体外诊断工具。