RamanLIGHT—a graphical user-friendly tool for pre-processing and unmixing hyperspectral Raman spectroscopy images

Abstract

Raman spectroscopy is a valuable tool for non-destructive vibrational analysis of chemical compounds in various samples. Through 2D scanning, it one can map the chemical surface distribution in a heterogeneous sample. These hyperspectral Raman images typically contain spectra of pure compounds that are hidden within thousands of sum spectra. Inspecting each spectrum to find the pure compounds in the dataset is impractical, and several algorithms have been described in the literature to help analyze such complex datasets. However, choosing the best approach(es) and optimizing the parameters is often difficult, and the necessary software was not yet combined in a single program. Therefore, we introduce RamanLIGHT, a fast and simple app to pre-process Raman mapping datasets and apply up to eight unsupervised unmixing algorithms to find endmember spectra of pure compounds. The user can select from six smoothing methods, four fluorescence baseline-removal methods, four normalization methods, and cosmic-ray and outlier removal to generate a uniform dataset prior to the unmixing. We included the most promising pre-processing methods, since there is no routine that perfectly fits all types of samples. Unmixed endmember spectra can be further used to visualize the distribution of compounds in a sample by creating abundance maps for each endmember separately, or a single labeled image containing all endmembers. It is also possible to create a mean spectrum for each endmember, which better describes the true compound spectrum. We tested RamanLIGHT on three samples: an aspirin-paracetamol-caffeine tablet, Alzheimer’s disease brain tissue and a phase-separated polymer coating. The datasets were pre-processed and unmixed within seconds to gain endmembers of known and unknown chemical compounds. The unmixing algorithms are sensitive to noisy spectra and strong fluorescence backgrounds, so it is important to apply pre-processing methods to a suitable degree. RamanLIGHT is freely available as a MATLAB and soon as standalone app.

Crystal-in-Amorphous Vanadate Catalysts for Universal Poison-Resistant Elimination of Nitric Oxide

“…However, when the temperature increases to 360 °C, the Raman signal of FeVO 4 increases, suggesting migration during heat treatment and release of active sites (Figure F and G). The Raman spectra obtained during in situ process were then processed by RamanLIGHT via vertex component analysis (Figure S13C and D). The Raman intensity of endmember 2 among the terminal V–O stretching region is much lower than endmember 1 in the A-FeVO catalyst, further suggesting the efficient adsorption of reactants over catalysts.…”

“…The in situ Raman spectra were analyzed via RamanLIGHT, which can be used to unmixed end-member spectra. 37 2.7. X-ray Absorption Spectra (XAS).…”

Porchlight: An Accessible and Interactive Aid in Preprocessing of Spectral Data

“…Finally, there is RamanLIGHT, which is domain specific in Raman hyperspectral imaging. 38 With the availability of such programs, one can argue, why not just use one package that does everything? In teaching chemometrics and spectral analysis, a student should learn the technique and the underlying mathematics, not an application.…”

“…PyChem is an open-source chemometrics package developed in Python 2 which includes preprocessing, but development of PyChem has ceased. Finally, there is RamanLIGHT, which is domain specific in Raman hyperspectral imaging . With the availability of such programs, one can argue, why not just use one package that does everything?…”

Unsupervised segmentation of biomedical hyperspectral image data: tackling high dimensionality with convolutional autoencoders

“…All of these have produced promising results on various HSI datsets in supervised or semi-supervised schemes [ 12 , 15 , 25 – 40 ]. However, in response to the usual lack of paired training data, effort has been placed towards developing fully unsupervised strategies (including for the separate task of spectral unmixing [ 41 ], and virtual staining [ 42 , 43 ]).…”