Species identification of ruminant milk by genotyping of the κ-casein gene

The development of molecular genetic and bioinformatic systems for identifying the species of milk and the raw material composition of dairy products is of great scientific and practical importance with the purpose of introducing developments in the system for controlling the turnover of falsified products. The aim of the research is to develop a method of PCR-RFLP analysis for species identification of milk and dairy products from agricultural ruminant animals by the kappa-casein gene (CSN3) with the possibility of qualitative and relative quantitative assessment of species-specific DNA of the tested biomaterial. The objects of research were samples of raw milk and milk powder, pasteurized cream, and hard and semi-hard cheeses. The developed method of species identification of milk and dairy products includes sample preparation of the studied samples, nucleic acid extraction, combined PCR-RFLP technique, detection of obtained results by the method of horizontal electrophoresis in agarose gel and their analysis, including using the developed mathematical algorithms and software. The synergistic effect established in combined operation of 2 restriction enzymes ensured their application in a mix with increased performance in an ergonomic way in the context of DNA authentication of cow, goat, and sheep milk and dairy products based on them. The specificity and sensitivity of the proposed method is potentially suitable for implementing the development of a system to control the turnover of falsified and counterfeit goods.

Automated summary

The development of molecular genetic and bioinformatic systems for identifying the species of milk and dairy products is important for controlling turnover of falsified products. The study aims to develop a method using PCR-RFLP analysis to identify species from agricultural ruminant animals. The method includes sample preparation, nucleic acid extraction, PCR-RFLP technique, gel electrophoresis, and result analysis using software and algorithms.