Tutorial: multivariate classification for vibrational spectroscopy in biological samples

Camilo L. M. Morais

doi:10.1038/s41596-020-0322-8

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Tutorial: multivariate classification for vibrational spectroscopy in biological samples

2020-06-17

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DOI：10.1038/s41596-020-0322-8

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Abstract

En 中文

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. A tutorial for multivariate classification analysis of vibrational spectroscopy data (Fourier-transform infrared, Raman and near-IR) is presented. Guidelines are provided for data preprocessing, data selection, feature extraction, classification and model validation.

Keywords:

NEAR-INFRARED SPECTROSCOPY

LEAST-SQUARES REGRESSION

QUADRATIC DISCRIMINANT-ANALYSIS

SUPPORT VECTOR MACHINES

RAMAN-SPECTROSCOPY

MIDINFRARED SPECTROSCOPY

COMPUTATIONAL ANALYSIS

FTIR-SPECTROSCOPY

MATLAB TOOLBOX

CHEMOMETRICS

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