Method for Removing Spectral Contaminants to Improve Analysis of Raman Imaging Data

Abstract

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.

Confocal Raman spectroscopy: Evaluation of a non-invasive technique for the detection of topically applied ketorolac tromethamine in vitro and in vivo

“…Therefore, in order to extract Raman signal from the raw acquired spectrum, it was necessary to pre-process the acquired spectrum (Hosseini et al, 2002). Furthermore, cosmic ray spikes, randomly generated due to cosmic radiation, which affected different wavenumbers each time, needed to be removed (Hosseini et al, 2002;Zhang et al, 2017). For the latter, there are two approaches described in the literature (Zhang et al, 2017;Li and Dai, 2011).…”

“…Furthermore, cosmic ray spikes, randomly generated due to cosmic radiation, which affected different wavenumbers each time, needed to be removed (Hosseini et al, 2002;Zhang et al, 2017). For the latter, there are two approaches described in the literature (Zhang et al, 2017;Li and Dai, 2011). In this study, we went for the easiest method, by replacing the intensities of the cosmic peaks with the average from the neighboring frames (left and right from the ray) as suggested by Zhang et al (2017).…”

“…For the latter, there are two approaches described in the literature (Zhang et al, 2017;Li and Dai, 2011). In this study, we went for the easiest method, by replacing the intensities of the cosmic peaks with the average from the neighboring frames (left and right from the ray) as suggested by Zhang et al (2017).…”

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

“…The spectrum may be complicated by instrumental noise, complex chemical composition of products, environmental factors and other sources of variability [19]. As a consequence, spectral and image preprocessing and correction are necessary to improve the quality of the data before data analysis [29]. Moreover, the chemometrics is crucial for information extraction and better interpretation of the acquired data.…”

Potential of Raman spectroscopy for the analysis of plasma/serum in the liquid state: recent advances

“…Labspec, includes real-time spikes correction, enabling visual inspection of the spectra, and/or manual spike removal when very few spikes are present. Some algorithms have been developed to perform automated spike removal in the case of a large dataset, for which manual correction is not possible [72][73][74][75][76]. The most commonly used smoothing algorithm for de-noising the spectra without losing much information is that of Savitzky-Golay [77].…”