Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

Abstract

High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

Types of cell death and apoptotic stages in Chinese Hamster Ovary cells distinguished by Raman spectroscopy

“…63 evenly spaced spectra were collected; each spectrum was estimated to on average analyze 10 cells. A suite of automated procedures, implemented using MATLAB (MathWorks, Natick, MA), including baseline correction, cosmic ray spike removal, noise reduction, and PCA, was used for data processing and analysis (Schulze & Turner, ). Spectra were normalized to the phenylalanine peak at 1002 cm −1 to control for cell loading.…”

Model‐based pre‐processing in Raman spectroscopy of biological samples

“…Even though pre‐processing techniques of Raman spectra have been extensively studied in recent years, in applied Raman spectroscopy, there is still a need for user‐friendly ‘all‐in‐one’ approaches that facilitate both spectral interpretability and good quantitative correction of the Raman spectra . EMSC is a pre‐processing framework satisfying these requirements, but a thorough quantitative evaluation of EMSC and feasible extensions comparing with standard pre‐processing approaches has yet to be published.…”

Denoising applied to spectroscopies – part I: concept and limits

“…partially empty matrix (63). SVD is widely used in several domains including acoustics (64), geophysics (65), air quality (66), electrocardiograms (67), image compression (68), video surveillance (69), MRI (70), data mining (71) and even on Facebook (72). Nevertheless, SVD is still not so commonly used in spectroscopies like NMR (73)(74)(75)(76) and Raman spectroscopy (77)(78)(79)(80), despite its use could significantly reduce experimental time and be of particular interest for the scientific community.…”