Beijing Key Laboratory of Lignocellulosic Chemistry, Beijing Forestry University, Beijing, 100083, China.
Department of Wood Science and Engineering, Chonnam National University, Gwangju 500757, South Korea.
Sci Rep. 2017 Jan 5;7:39891. doi: 10.1038/srep39891.
The spectral contaminants are inevitable during micro-Raman measurements. A key challenge is how to remove them from the original imaging data, since they can distort further results of data analysis. Here, we propose a method named "automatic pre-processing method for Raman imaging data set (APRI)", which includes the adaptive iteratively reweighted penalized least-squares (airPLS) algorithm and the principal component analysis (PCA). It eliminates the baseline drifts and cosmic spikes by using the spectral features themselves. The utility of APRI is illustrated by removing the spectral contaminants from a Raman imaging data set of a wood sample. In addition, APRI is computationally efficient, conceptually simple and potential to be extended to other methods of spectroscopy, such as infrared (IR), nuclear magnetic resonance (NMR), X-Ray Diffraction (XRD). With the help of our approach, a typical spectral analysis can be performed by a non-specialist user to obtain useful information from a spectroscopic imaging data set.
光谱污染物在微拉曼测量中是不可避免的。一个关键的挑战是如何从原始成像数据中去除它们，因为它们会扭曲数据分析的进一步结果。在这里，我们提出了一种名为“拉曼成像数据集的自动预处理方法 (APRI)”的方法，它包括自适应迭代重加权惩罚最小二乘 (airPLS) 算法和主成分分析 (PCA)。它利用光谱特征本身消除基线漂移和宇宙尖峰。通过从木材样品的拉曼成像数据集去除光谱污染物来说明 APRI 的实用性。此外，APRI 在计算上效率高、概念简单，并且有可能扩展到其他光谱方法，如红外 (IR)、核磁共振 (NMR)、X 射线衍射 (XRD)。在我们的方法的帮助下，非专业用户可以进行典型的光谱分析，从光谱成像数据集中获取有用的信息。
