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Tutorial: multivariate classification for vibrational spectroscopy in biological samples

Nature protocols, 2020-07, Vol.15 (7), p.2143-2162 [Peer Reviewed Journal]

COPYRIGHT 2020 Nature Publishing Group ;Springer Nature Limited 2020. ;ISSN: 1754-2189 ;EISSN: 1750-2799 ;DOI: 10.1038/s41596-020-0322-8 ;PMID: 32555465

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Title:
Tutorial: multivariate classification for vibrational spectroscopy in biological samples
Author: Morais, Camilo L M ; Lima, Kássio M G ; Singh, Maneesh ; Martin, Francis L
Subjects: Algorithms ; Analysis ; Animals ; Automatic classification ; Biological analysis ; Biological materials ; Biological properties ; Biological samples ; Cells ; Classification ; Data mining ; Deep learning ; Discriminant analysis ; Feature extraction ; Forensic science ; Fourier transform infrared spectroscopy ; Fourier transforms ; Humans ; Infrared analysis ; Infrared spectroscopy ; Learning algorithms ; Machine learning ; Medical research ; Medicine, Experimental ; Methods ; Microbiological studies ; Molecules ; Multivariate Analysis ; Near infrared radiation ; Near infrared spectroscopy ; Preprocessing ; Raman spectroscopy ; Sample preparation ; Spectrochemical analysis ; Spectroscopic analysis ; Spectroscopy ; Spectroscopy, Fourier Transform Infrared ; Spectrum analysis ; Spectrum Analysis - methods ; Spectrum Analysis, Raman ; Statistics as Topic - methods ; Vibration ; Vibrational spectra
Is Part Of: Nature protocols, 2020-07, Vol.15 (7), p.2143-2162
Description: Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental.
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 1754-2189
EISSN: 1750-2799
DOI: 10.1038/s41596-020-0322-8
PMID: 32555465
Source: MEDLINE
ProQuest Central

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Tutorial: multivariate classification for vibrational spectroscopy in biological samples

COPYRIGHT 2020 Nature Publishing Group ;Springer Nature Limited 2020. ;ISSN: 1754-2189 ;EISSN: 1750-2799 ;DOI: 10.1038/s41596-020-0322-8 ;PMID: 32555465

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