Literature DB >> 33547854

A graphical user interface (NWUSA) for Raman spectral processing, analysis and feature recognition.

Dongliang Song1, Yishen Chen1, Jie Li1, Haifeng Wang1, Tian Ning1, Shuang Wang1.   

Abstract

It is a practical necessity for non-professional users to interpret biologically derived Raman spectral information for obtaining accurate and reliable analytical results. An integrated Raman spectral analysis software (NWUSA) was developed for spectral processing, analysis, and feature recognition. It provides a user-friendly graphical interface to perform the following preprocessing tasks: spectral range selection, cosmic ray removal, polynomial fitting based background subtraction, Savitzky-Golay smoothing, area-under-curve normalization, mean-centered procedure, as well as multivariate analysis algorithms including principal component analysis (PCA), linear discriminant analysis, partial least squares-discriminant analysis, support vector machine (SVM), and PCA-SVM. A spectral dataset obtained from two different samples was utilized to evaluate the performance of the developed software, which demonstrated that the analysis software can quickly and accurately achieve functional requirements in spectral data processing and feature recognition. Besides, the open-source software can not only be customized with more novel functional modules to suit the specific needs, but also benefit many Raman based investigations, especially for clinical usages.
© 2021 Wiley-VCH GmbH.

Keywords:  NWUSA; Raman spectroscopy; analysis software; classification recognition; graphical user interface; spectral processing

Mesh:

Year:  2021        PMID: 33547854     DOI: 10.1002/jbio.202000456

Source DB:  PubMed          Journal:  J Biophotonics        ISSN: 1864-063X            Impact factor:   3.207


  1 in total

1.  Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming.

Authors:  Charles Farber; Dmitry Kurouski
Journal:  Front Plant Sci       Date:  2022-04-26       Impact factor: 6.627

  1 in total

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