Agrostat 2023

7-7 jul. 2023
Campus du Solbosch from l'Université libre de Bruxelles - Bruxelles (Bélgica)

https://agrostat2023.sciencesconf.org

The main themes of this conference will be : Sensometrics: centred on statistical methods for sensory analysis such as planning of sensory evaluations and analysis of data from panels of experts or consumer groups; study of the relation between instrumental measurements and sensory measurements; linking consumer preferences with sensory data, etc. Chemometrics: focuses on the extraction of information from data collected in analytical chemistry, physical measurements, followed by exploration and prediction in a supervised or unsupervised setting. It includes linear or non-linear procedures, data from multi-input tables, etc. Experimental designs: encompass methodologies that offer a general approach for joint optimisation of experimental planning and modelling of phenomena studied in experimental sciences. Process control: refers to methods of statistics or artificial intelligence used to develop and better control a process and to improve the quality of products; quantitative or qualitative modelling; experimental designs; validation of measurement methods; design of control charts; neural networks, fuzzy logic, etc. Big Data: encompasses massive database management; and using machine learning, deep learning, or artificial intelligence methods to understand consumer profiles. Predictive Microbiology and Risk Assessment: encompassing modelling for characterising and controlling the evolution of micro-organisms in food and their effects on human health; evaluation and management of shelf-life; integration of predictive microbiology in optimisation of food processes and novel technologies; advances in risk assessment methods and tools; modelling dose – response relationship; etc. Meta-analysis: including protocols and applications in agriculture, animal science, epidemiology, consumer science, food quality and safety.
Disciplina científica :  Statistics - Machine Learning - Applications - Computation - Methodology - Machine Learning - Theory

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