The Application of Multivariate Statistical Methods in Ecotoxicology and Environmental Biochemistry

• Published in: Proceedings of the International Conference on Applied Innovations in IT 2022-03, Vol.10 (1), p.99-104
• Main Authors: Halina Falfushynska, Oleg Lushchak, Eduard Siemens
• Format: Article
• Language: English
• Subjects: linear discriminant analysis; multivariate statistical analysis; principal component analysis
• ISSN: 2199-8876
• DOI: 10.25673/76937

Pesticide pollution of surface- and groundwater are a subject of national importance indeed. However, far too little attention has been paid to find out suitable protocols and algorithms for ecotoxicological data analysis and generalisation. The aim of the present study was to implement Multivariate statistical analysis techniques for prediction of toxicity level of widely-used organophosphate pesticides to living organisms and find out the most appropriate statistical technique out of implemented to integrate biological data. The generalization of the results of biochemical and physiological measurements in zebrafish, Daphnia and Drosophila exposed to widely-used pesticides namely chlorpyrifos, roundup, and malathion have been done using principal component analysis, linear discriminant analysis and classification and regression tree analysis. All of three applied multivariate statistical techniques claimed chlorpyrifos to be the most toxic pesticides out of tested based on responses of living organisms. The importance of battery of biomarkers for risk assessment when compare to individual indices was proved using classification and regression tree analysis and discriminant analysis and daphnia’s protein carbonyls level and zebrafish’s lactate dehydrogenase activity pertain to the most sensitive indices for group distinguishing. We propose to combine the most widely used in life sciences Principal Component Analysis with classification and regression tree analysis and discriminant analysis to better highlight the important biological entities and reveal insightful patterns in the data.