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Application of chemometric methods to the analysis of multimodal chemical images of biological tissues
Author(s) -
Carmen Bedia,
Àngels Sierra,
Romà Tauler
Publication year - 2020
Publication title -
analytical and bioanalytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.86
H-Index - 166
eISSN - 1618-2650
pISSN - 1618-2642
DOI - 10.1007/s00216-020-02595-8
Subject(s) - chemical imaging , mass spectrometry imaging , principal component analysis , chemistry , rgb color model , chemometrics , mass spectrometry , analytical chemistry (journal) , resolution (logic) , sample preparation , biological system , artificial intelligence , hyperspectral imaging , computer science , chromatography , biology
Current histology techniques, such as tissue staining or histochemistry protocols, provide very limited chemical information about the tissues. Chemical imaging technologies such as infrared, Raman, and mass spectrometry imaging, are powerful analytical techniques with a huge potential in describing the chemical composition of sample surfaces. In this work, three images of the same tissue slice using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, infrared microspectroscopy, and an RGB picture from a conventional hematoxylin/eosin (H/E) staining are simultaneously analyzed. These fused images were analyzed by multivariate curve resolution-alternating least squares (MCR-ALS), which provided, for each component, its distribution within the tissue surface, its IR spectrum fingerprint, its characteristic mass values, and the contribution of the RGB channels of the H/E staining. Compared with the individual analysis of each of the images alone, the fusion of the three images showed the relationship between the different types of chemical/biological information and enabled a better interpretation of the tissue under study. In addition, the least-squares projection of the MCR-ALS resolved spectra of components at low spatial resolution onto the IR and RBG images at high spatial resolution, provided a better delimitation of the sample constituents on the image, giving a more precise description of their distribution on the investigated tissue. The application of this procedure can be of interest in different research areas in which a good description of the spatial distribution of the chemical constituents of the samples is needed, such as in biomedicine, food, or environmental research.

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