OCTAVVS: A Graphical Toolbox for High-Throughput Preprocessing and Analysis of Vibrational Spectroscopy Imaging Data

Abstract

Modern vibrational spectroscopy techniques enable the rapid collection of thousands of spectra in a single hyperspectral image, allowing researchers to study spatially heterogeneous samples at micrometer resolution. A number of algorithms have been developed to correct for effects such as atmospheric absorption, light scattering by cellular structures and varying baseline levels. After preprocessing, spectra are commonly decomposed and clustered to reveal informative patterns and subtle spectral changes. Several of these steps are slow, labor-intensive and require programming skills to make use of published algorithms and code. We here present a free and platform-independent graphical toolbox that allows rapid preprocessing of large sets of spectroscopic images, including atmospheric correction and a new algorithm for resonant Mie scattering with improved speed. The software also includes modules for decomposition into constituent spectra using the popular Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) algorithm, augmented by region-of-interest selection, as well as clustering and cluster annotation.

Effect of pre-analytical variables on Raman and FTIR spectral content of lymphocytes

“…All pre-processing of FTIR spectra was carried out in OCTAVVS (Open Chemometrics Toolkit for Analysis and Visualisation of Vibrational Spectroscopy Data) version 0.1.17, in Python version 3.9.13. 44…”

Optimised Pre-Processing of Raman Spectra for Colorectal Cancer Detection Using High-Performance Computing

“…30 EMSC provides a model-based approach for the correction of unwanted additive and multiplicative effects within spectra. It is used for various applications in Raman spectroscopy, from tracking metabolic products via EMSC coefficients, 31 as a pre-processing step, 32–34 and recently enabling the removal of water from human blood serum. 35…”

“…30 EMSC provides a model-based approach for the correction of unwanted additive and multiplicative effects within spectra. It is used for various applications in Raman spectroscopy, from tracking metabolic products via EMSC coefficients, 31 as a preprocessing step, [32][33][34] and recently enabling the removal of water from human blood serum. 35 Extended multiplicative scatter correction works by modelling the additive and multiplicative effects in a sample which are not indicative of the chemical fingerprint of the sample but external factors.…”

Application of Advanced Non-Linear Spectral Decomposition and Regression Methods for Spectroscopic Analysis of Targeted and Non-Targeted Irradiation Effects in an In-Vitro Model

“…Spectral images were acquired using a 6.25 μm × 6.25 μm pixel size and a spectral resolution of 4 cm −1 over the range 720–4000 cm −1 with 128 scans per pixel. All data pre-processing steps were implemented in Python (v 3.9.12) using the OCTAVVS library for pre-processing [ 45 ]. Firstly, individual spectra were extracted from the images with outliers removed using Rosner’s test applied to the PC scores of spectra within a given class.…”

“…For this exercise a total of 31 reference spectra were utilised, which included spectra of nucleic acids (DNA, RNA), protein (actin, keratin, ubiquitin, histone), glycoprotein (apolipiprotein-E3, apoliprotein-E4), lipid (phosphatidylcholine, phosphatidylinositol, phosphatidylethanolamine, phosphatidylserine, ceramide), carbohydrate (glycogen), nucleoside (ATP), cytokines (IL1, IL6, IL8), antioxidants (catalase, cysteine, glutathione (in both oxidised and reduced forms), vitamin C, vitamin E, tryptophan, β-carotene) and various signalling molecules of radiobiological importance (cytochrome-C, TGF-β1, TGF-β2, TNF-α, protein-kinase K). For all spectra processing was conducted in OCTAVVS before CLS regression [ 45 ]. All reference molecules were purchased from Sigma-Aldrich and their spectra were acquired as described elsewhere [ 10 ].…”