2022
DOI: 10.1016/j.saa.2022.121276
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Toxins’ classification through Raman spectroscopy with principal component analysis

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Cited by 9 publications
(8 citation statements)
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“…Each sample was measured at a minimum of 3 points to ensure spectral reproducibility. The obtained spectra were processed by applying the following: (1) concave rubber band correction, (2) Min-Max normalization and (3) smoothing (number of smoothing points = 17) in the OPUS 8.2.28 program (Bruker Optik GmbH, Karlsruhe, Germany) [ 49 ].…”
Section: Methodsmentioning
confidence: 99%
“…Each sample was measured at a minimum of 3 points to ensure spectral reproducibility. The obtained spectra were processed by applying the following: (1) concave rubber band correction, (2) Min-Max normalization and (3) smoothing (number of smoothing points = 17) in the OPUS 8.2.28 program (Bruker Optik GmbH, Karlsruhe, Germany) [ 49 ].…”
Section: Methodsmentioning
confidence: 99%
“…Previously, using Raman spectroscopy and PCA we had classified protein toxins including those from snake venoms in accordance with their structural features; moreover, using this approach it was possible to distinguish the disulfide isomers of the peptide toxin. 12 In the present work, we have carried out Raman spectroscopic studies on a number of dry venoms from various venomous snakes supplemented with data analysis by PCA. The resulting PCA score plot (Fig.…”
Section: Discussionmentioning
confidence: 99%
“…The reduction in the dimension of spectral data and their presentation in a visual form using principal component analysis (PCA) 11 and clustering methods allow quick classification of samples of protein nature. 12 In this work, we used Raman spectroscopy to classify the venoms of several species of snakes from the families Elapidae and Viperidae, based on the spectra recorded from micrograms of dry venoms.…”
Section: Introductionmentioning
confidence: 99%
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“…In order to further improve the calculation efficiency, the dimensions of the Raman spectral data were reduced by principal component analysis. 23 Each sample in the original Raman spectrum contains 1751 data points. After the dimension reduction by principal component analysis, just 74 extracted principal components can represent 100% of the information in the original data.…”
Section: Njc Papermentioning
confidence: 99%