Applying Discriminant and Cluster Analyses to Separate Allergenic from Non-allergenic Proteins

Abstract: As a result of increased healthcare requirements and the introduction of genetically modified foods, the problem of allergies is becoming a growing health problem. The concept of allergies has prompted the use of new methods such as genomics and proteomics to uncover the nature of allergies. In the present study, a selection of 1400 food proteins was analysed by PLS-DA (Partial Least Square-based Discriminant Analysis) after suitable transformation of structural parameters into uniform vectors. Then, the resulting strings of different length were converted into vectors with equal length by Auto and Cross-Covariance (ACC) analysis. Hierarchical and non-hierarchical (K-means) Cluster Analysis (CA) was also performed in order to reach a certain level of separation within a small training set of plant proteins (16 allergenic and 16 non-allergenic) using a new three-dimensional descriptor based on surface protein properties in combination with amino acid hydrophobicity scales. The novelty of the approach in protein differentiation into allergenic and non-allergenic classes is described in the article. The general goal of the present study was to show the effectiveness of a traditional chemometric method for classification (PLS–DA) and the options of Cluster Analysis (CA) to separate by multivariate statistical methods allergenic from non-allergenic proteins.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Applying Discriminant and Cluster Analyses to Separate Allergenic from Non-allergenic Proteins ; volume:17 ; number:1 ; year:2019 ; pages:401-407 ; extent:7 ; Open chemistry ; 17, Heft 1 (2019), 401-407 (gesamt 7)

Contributors, Places and Time

• Creator: Naneva, L. ; Nedyalkova, M. ; Madurga, S. ; Mas, F. ; Simeonov, V.

Further information

• DOI: 10.1515/chem-2019-0045
• URN: urn:nbn:de:101:1-2410161618037.105349669667
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:28 AM CEST