Gardner Benjamin, Matousek Pavel, Stone Nicholas
School of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK.
Analyst. 2021 Feb 21;146(4):1260-1267. doi: 10.1039/d0an01940b. Epub 2020 Dec 18.
The first near infrared window in biological tissue (λ∼ 700-950 nm) is of great interest for its potential to safely deliver light based diagnosis and therapeutic interventions, especially in the burgeoning field of nano-theranostics. In this context, Raman spectroscopy is increasingly being used to provide rapid non-invasive chemical molecular analysis, including bulk tissue analysis by exploiting the near infrared window, with transmission Raman spectroscopy (TRS). The disadvantage of this approach, is that when probing depths of several centimetres self-attenuation artefacts are typically exhibited, whereby TRS spectra can suffer from relative changes in the "spectral features" due to differential absorption of Raman photons by the various constituents of biological tissues. Simply put, for a homogenous substance with increasing thickness, spectral variances occur due to the optical properties of the material and not through changes in the chemical environment. This can lead to misinterpretation of data, or features of interest become obscured due to the unwanted variance. Here we demonstrate a method to correct TRS data for this effect, which estimates the pathlengths derived from peak attenuation and uses expected optical properties to transform the data. In a validation experiment, the method reduced total Raman spectral intensity variances >5 fold, and improved specific peak ratio distortions 35×. This is an important development for TRS, Spatially Offset Raman Spectroscopy (SORS) and related techniques operating at depth in the near IR window; applicable to samples where there is large sample thickness and inter- and intra-sample thickness is variable i.e. clinical specimens from surgical procedures such as breast cancer. This solution is expected to yield lower detection limits and larger depths in future applications such as non-invasive breast cancer diagnosis in vivo.
生物组织中的第一个近红外窗口（λ∼700 - 950 nm）因其具有安全进行基于光的诊断和治疗干预的潜力而备受关注，特别是在新兴的纳米诊疗领域。在此背景下，拉曼光谱越来越多地被用于提供快速的非侵入性化学分子分析，包括利用近红外窗口通过透射拉曼光谱（TRS）对大块组织进行分析。这种方法的缺点是，当探测深度达到几厘米时，通常会出现自衰减伪像，即TRS光谱可能会因生物组织的各种成分对拉曼光子的不同吸收而出现“光谱特征”的相对变化。简单来说，对于一种均匀物质，随着厚度增加，光谱变化是由于材料的光学性质而非化学环境的变化引起的。这可能导致数据误解，或者由于不必要的变化而使感兴趣的特征变得模糊。在这里，我们展示了一种针对这种效应校正TRS数据的方法，该方法通过估计从峰值衰减得出的光程长度，并利用预期的光学性质来转换数据。在一个验证实验中，该方法将总拉曼光谱强度变化降低了5倍以上，并将特定峰比失真改善了35倍。这对于TRS、空间偏移拉曼光谱（SORS）以及在近红外窗口深度操作的相关技术来说是一项重要进展；适用于样品厚度大且样品间和样品内厚度可变的样本，即来自外科手术（如乳腺癌手术）的临床标本。预计该解决方案在未来应用（如体内非侵入性乳腺癌诊断）中会产生更低的检测限和更大的探测深度。
