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Optimal choice of sample substrate and laser\ud wavelength for Raman spectroscopic analysis of\ud biological specimen

机译:样品基板和激光的最佳选择\ ud 拉曼光谱分析的波长 生物标本

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摘要

Raman spectroscopy is an optical technique based on the inelastic scattering of monochromatic light that can be used to identify the biomolecular composition of biological cells and tissues. It can be used as both an aid for understanding the etiology of disease and for accurate clinical diagnostics when combined with multivariate statistical algorithms. This method is non-destructive, potentially non-invasive and can be applied in vitro or in vivo directly or via a fiber optic probe. However, there exists a high degree of variability across experimental protocols, some of which result in large background signals that can often overpower the weak Raman signals being emitted. These protocols need to be standardised before the technique can provide reliable and reproducible experimental results in an everyday clinical environment. The objective of this study is to investigate the impact of different experimental parameters involved in the analysis of biological specimen. We investigate the Raman signals generated from healthy human cheek cells using different source laser wavelengths; 473 nm, 532 nm, 660 nm, 785 nm and 830 nm, and different sample substrates; Raman-grade calcium fluoride, IR polished calcium fluoride, magnesium fluoride, aluminium (100 nm and 1500 nm thin films on glass), glass, fused silica, potassium bromide, sodium chloride and zinc selenide, whilst maintaining all other experimental parameters constant throughout the study insofar as possible.
机译:拉曼光谱法是一种基于单色光非弹性散射的光学技术,可用于识别生物细胞和组织的生物分子组成。与多变量统计算法结合使用时,它既可以帮助理解疾病的病因,又可以进行准确的临床诊断。该方法是非破坏性的,可能是非侵入性的,并且可以直接或通过光纤探针在体外或体内应用。但是,整个实验方案之间存在高度的可变性,其中一些会导致较大的背景信号,而该背景信号通常会超过所发射的弱拉曼信号。在该技术可以在日常临床环境中提供可靠且可重复的实验结果之前,需要对这些协议进行标准化。这项研究的目的是调查涉及生物样本分析的不同实验参数的影响。我们研究了使用不同光源激光波长从健康人脸细胞产生的拉曼信号; 473 nm,532 nm,660 nm,785 nm和830 nm,以及不同的样品基板;拉曼级氟化钙,红外抛光氟化钙,氟化镁,铝(玻璃上的100 nm和1500 nm薄膜),玻璃,熔融石英,溴化钾,氯化钠和硒化锌,同时在整个过程中保持所有其他实验参数不变尽可能地学习。

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