Institute of Nuclear Physics Polish Academy of Sciences, PL-31342 Krakow, Poland.
Institute for Medicine and Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Cells. 2021 Apr 20;10(4):953. doi: 10.3390/cells10040953.
Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler's smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing.
傅里叶变换红外光谱（FT-IR）广泛应用于生物材料化学成分的分析，具有揭示疾病分子基础新方面的潜力，包括不同类型的癌症。FT-IR 在癌症研究中的潜力在于其监测细胞生化状态的能力，这些细胞经历恶性转化，进一步使用适当的数学方法检查区分正常和癌变的光谱特征。可以使用化学计量学工具（如偏最小二乘判别分析（PLS-DA）分类和偏最小二乘回归（PLSR））进行此类检查，适当应用预处理方法及其正确顺序对于成功至关重要。在这里，我们比较了几种常用的红外生物光谱学中的最新方法（去噪、基线校正和归一化），并添加了以前未在红外生物光谱分类问题中使用的方法：Mie 消光扩展乘法信号校正、Eiler 平滑和概率商归一化。我们比较了所有这些方法及其对从五个不同前列腺正常和癌细胞系采集的实验 FT-IR 光谱的数据结构、分类和回归能力的影响。此外，我们还测试了添加光谱噪声的影响。总的来说，我们得出的结论是，在分析的数据集的情况下，基线校正对 PLS-DA 和 PLSR 的数据结构和性能影响最大；因此，应该特别注意这一数据预处理步骤。
