Production of certified reference material Escherichia coli in milk-based product

Abstract

Reference materials are an important tool in ensuring the validity of the test results efficiently and economically. National Institute for Food Control has implemented several studies, characterization of microbiology chemistry for food and feeding stuffs certified reference materials (CRM) in order to support laboratories in control and maintaining test results quality. CRM for Escherichia coli (E. coli) in food is our study’s first interest, because E. coli indicates unfavorable hygienic conditions and fecal contamination in foods. Milk and dairy products are used for any stage of human life from infancy to elder. CRM for E. coli in milk-based products were characterized by using a network of competency laboratories leading to certified values in accordance with ISO 17034:2016 requirements. These certified reference materials produced are accredited conforming to ISO 17034:2016 by the America Association of Laboratories Association.

Generalized Method of Mathematical Prototyping of Energy Processes for Digital Twins Development

“…Methods of identification theory [10,11], methods of machine learning [12][13][14][15][16][17][18][19][20], including deep learning based on neural networks [14], and symbolic regression [15][16][17][18][19][20][21] are often used to obtain mathematical models of renewable energy systems (RES). However, all these modeling methods belong to the class of simulation models that do not consider actual physical and chemical processes and therefore do not guarantee their correctness in the entire range of operating conditions.…”

“…However, all these modeling methods belong to the class of simulation models that do not consider actual physical and chemical processes and therefore do not guarantee their correctness in the entire range of operating conditions. To obtain correct (not contradicting the general physical laws) models of systems, it is necessary to set some restrictions arising from the general physical laws and the system functioning mechanism [10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

“…The features of building a system model described above make it possible to develop a unified approach to build models of systems of various physical and chemical nature [39,40], based on mechanics [29,30], including continuum mechanics [28,31], electrodynamics [28,32], the theory of electric and magnetic circuits [32], on modern nonequilibrium thermodynamics [33][34][35][36][37][38] and incorporating methods of identification theory, machine learning methods, including deep learning based on neural networks, and on symbolic regression [15][16][17][18][19][20][21]. Such models take measured parameters as input and return controlled parameters that have practical value as output [1][2][3][4][5][6][7][8][9].…”