Development of a multiplex real time pcr assay for detection and quantification of equine meat in processed bovine meat products
Is this your thesis?
This record came from a bulk archive import. If it’s yours, link it to your profile.
Abstract (EN)
Meat is one of the most important foodstuffs in human nutrition, especially in terms of its highly rich content of essential amino acids, vitamins, minerals and fat. With the gradual increase of the world population, the demand for foodstuffs and therefore meat and meat products is also steadily increasing. As one of the countries with the fastest growing population in the world, Turkey, too, the consumption of red meat is of utmost importance in order to ensure a healthy and balanced diet. Today, the most common problem in meat products is adulteration and originality. In various regions of the world including Turkey , Ireland and Mexico, a high rate of mislabeling cases of meat products have been reported between 2000-2015. For customers, it is difficult to perceive the meat of different animal species when they are mixed in products such as sausage, salami, and meatballs. The fact that meat is mixed with other meats and presented for consumption is one of the issues that worries the public about food adultaration. Identification of the meat source in meat products and verification of the label are important in terms of economic reasons, religion and health related factors as well as the frouds. In this study, a method has been developed which allows both species identification and quantitative analysis by using Real-Time Polymerase Chain Reaction (Real Time PCR) method using specific primers for horse and donkey mitochondrial cytochrome b (cytb) gene. As a reference, triple meat mixtures containing horse and donkey meat were prepared in known quantities in bovine meat and DNA isolations were performed. Single, double and triple reactions were prepared with horse and donkey primers in which DNA dilution series were used as template, and the calibration curves with high linear correlation value and reaction efficiency between 0.05% and 50% were obtained. Furthermore, forty processed meat samples which already analyzed for horse and donkey meat by a validated method were re-analyzed by the quantification method developed. Fifteen out of forty samples were positive for horse and donkey meat. The quantitative method was able to determine the horse and donkey meat at the level of 1- 50% in a processed meat. The results have once again shown the importance of inspections and reliable methods of analysis in our country. As a result, an easy, fast, reliable and quantitative Real-Time PCR method has been developed for the detection of possible horse and donkey meats. In addition, with value of 0.3% and 1% of LoD and LoQ, respectively, it is possible to obtain correct results with this method as to whether it is a real adulteration or a false positive result due to the used method.
Author
Sevda Gökbora
How to Cite
Sevda Gökbora (Master Thesis). Development of a multiplex real time pcr assay for detection and quantification of equine meat in processed bovine meat products, 2018, Akdeniz University.
Keywords
License
Tüm Hakları Saklıdır
This work is shared under the specified license terms.
More theses from Akdeniz University
- The purpose of present study was to determine the levels of situational anxiety caused by the pressure of the competitive situations exposed to by child athletes and to objectively evaluate the parameters of Heart Rate Variability (HRV) and state anxiety accompanying the change in emotional state.(2022)
- Numerical investigation of the notch effect in interference fit connections(2023)
- Proje tabanlı öğrenimin İngilizce hazırlık sınıfı öğrencilerinin konuşma yeterlilikleri ve iletişim kurma istekleri üzerine etkisi(2025)
- The formation of Medieval Islamic economic thought within the scope of East-West interaction(2024)
- A cost comparison of rubble mound breakwater with breakwater covered by antifer block(2018)
- Enderunlu Fazıl – Hûbân-nâme (Edition critique)(2024)
