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The growth in demand for fish products as a result of globalization of trade caused a risks and threats of selling poor-quality and falsified fish products. This has become a great problem both for supervising agencies and for consumers.Many countries have regulations on food labelling and safety. For example, in the Russian Federation, Republic of Belarus and Republic of Kazakhstan has been passed the Technical Regulation of the Customs Union TR CU022/2011 “Food products in part of their labeling” that aims to prevent misinformation of consumers to ensuring realization of consumer rights to reliable information about food products, and Technical Regulation TR EAEU040/2016 “On safety of fish and fish products” requires indication of the zoological name of the species of the aquatic biological resource or the object of aquaculture.Fish species identification is traditionally carried out based on external morphological traits. However, it becomes impossible to identify species by ichthyological traits upon fish cutting, if the head and fins are removed, and the body is cut on pieces (especially, in case of fillets) and even more so upon technological processing. In this case, objective analytical methods of species identification are used, which are based on ELISA or PCR. However, DNA‑based methods have several advantages compared to ELISA methods and complement traditional morphological identification methods. This paper gives a wide overview of the most recent and used methods of fish species identification based on DNA analysis such as single-strand conformation polymorphism (SSCP) analysis, species-specific PCR, real-time PCR, polymerase chain reaction-restriction fragment length polymorphism analysis (PCR-RFLP), DNA barcoding, Sanger sequencing and next-generation sequencing (NGS).

The present article represents an analysis of trends in development of test-systems for identification of meat. These test systems are commonly used in food production and research laboratories. The relevance of development of methods for identification of meat kinds is related not only to the food restrictions, which are practiced in some religions and related to consumption of certain types of meat, but also with the hygienic aspects of food production. Also, this research is inspired also by the acute issue of food products adulteration and the replacement of one type of meat with another one. The article considers the trends in the develop  ment of microanalysis method that use immunochromatic research, i. e. methods based on molecular biology. Also this article considers the devices that do not use chromatographic methods of analysis. Examples of the development of test systems based on various methods of analysis for the identification of meat are given below. Attention is focused on the prospects of combining these methods, including colorimetric methods for identification of meat. It is also specified that the emergence of new dyes and new enzyme systems, suitable for use in enzyme-immunoassay, can enhance the sensitivity of these test systems. It is also noted that the development of technologies associated with sorbents can contribute to a better separation of the test substrates and this way to increase the sensitivity of the test in case of small amounts of test substrate. It is also noted that the use of various types of iso  thermal amplification can reduce the analysis time necessary for meat identification. Various schemes of devices for microanalysis are given; their advantages and disadvantages are listed. An example of proteomes application for meat identification is given. It is shown that this method can also be applied in the heat treatment of meat. The prospects for the development of such devices are analyzed. It is concluded that the development of systems for microanalysis in the form of quick tests is quite relevant and promis  ing. It is indicated that theoretically in the future such analytical systems, due to the use of microfluidic technologies, will be able to combine several methods. The authors proposed to use machine-aided cognition methods to analyze data obtained from similar test systems in order to increase their sensitivity.

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