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Article

Multivariate Chemometrics as a Strategy to Predict the Allergenic Nature of Food Proteins

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Department of Inorganic Chemistry, Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria
2
Department of Analytical Chemistry, Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(10), 1616; https://doi.org/10.3390/sym12101616
Received: 3 September 2020 / Revised: 16 September 2020 / Accepted: 21 September 2020 / Published: 29 September 2020
(This article belongs to the Special Issue Chemometrics in Assessing Molecular Structures and Properties)
The purpose of the present study is to develop a simple method for the classification of food proteins with respect to their allerginicity. The methods applied to solve the problem are well-known multivariate statistical approaches (hierarchical and non-hierarchical cluster analysis, two-way clustering, principal components and factor analysis) being a substantial part of modern exploratory data analysis (chemometrics). The methods were applied to a data set consisting of 18 food proteins (allergenic and non-allergenic). The results obtained convincingly showed that a successful separation of the two types of food proteins could be easily achieved with the selection of simple and accessible physicochemical and structural descriptors. The results from the present study could be of significant importance for distinguishing allergenic from non-allergenic food proteins without engaging complicated software methods and resources. The present study corresponds entirely to the concept of the journal and of the Special issue for searching of advanced chemometric strategies in solving structural problems of biomolecules. View Full-Text
Keywords: food proteins; allergenicity; multivariate statistics; structural and physicochemical descriptors; classification food proteins; allergenicity; multivariate statistics; structural and physicochemical descriptors; classification
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MDPI and ACS Style

Nedyalkova, M.; Simeonov, V. Multivariate Chemometrics as a Strategy to Predict the Allergenic Nature of Food Proteins. Symmetry 2020, 12, 1616. https://doi.org/10.3390/sym12101616

AMA Style

Nedyalkova M, Simeonov V. Multivariate Chemometrics as a Strategy to Predict the Allergenic Nature of Food Proteins. Symmetry. 2020; 12(10):1616. https://doi.org/10.3390/sym12101616

Chicago/Turabian Style

Nedyalkova, Miroslava, and Vasil Simeonov. 2020. "Multivariate Chemometrics as a Strategy to Predict the Allergenic Nature of Food Proteins" Symmetry 12, no. 10: 1616. https://doi.org/10.3390/sym12101616

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