Abstract
In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.
Highlights
During the last decade, Artificial Intelligence (AI) gained tremendous advances in different applications
The results show that the dust load and the composition of exhaust air significantly affect the NIR spectral patterns
Technology in order to develop more reliable and robust devices to monitor pollutants that nology in order to develop more reliable and robust devices to monitor pollutants that should shouldnot notbe beunderestimated underestimated[15,285]. Environmental pollutants such asheavy metals, pesticides, drugs, and biotoxins are extensively dangerous for all aspects of being organisms, including health, food, energy, etc
Summary
Artificial Intelligence (AI) gained tremendous advances in different applications. It has been used to solve complicated, nonlinear and dynamic problems. It is considered as an alternative approach to conventional procedures, or as a component of integrated systems to perform modeling, prediction, simulation and optimization at high speed [1]. AI technologies principally refer to artificial neural network (ANN), genetic algorithm (GA) and expert system (ES) chemometric methods. These technologies have been applied to agriculture, climate, finance, engineering, environment, education, medicine, nanotechnology and various disciplines [2,3,4]. According to Barr and Feigenbaum, AI is the part of computer science concerned with the design of intelligent computer systems, i.e., systems that exhibit the characteristics associated with intelligence in human behavior, understanding, language, learning, reasoning, solving problems and so on [5,6,7]
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