EFFECT OF THE ESSENTIAL OILS OF "ROMERO" ROSMARINUS OFFICINALIS AND "PEREJIL" PETROSELINUM CRISPUM ON THE MICROBIOLOGICAL QUALITY OF "ALPACA" HAMBURGER VICUGNA PACOS WITH MACHINE LEARNING

The meat industry produces raw materials with healthy ingredients by modifying the diet and formulating meat products based on meat from other species. The objective of this research was to formulate a hamburger using alpaca (Vicugna pacos) and essential oils of rosemary (Rosmarinus officinalis) and parsley (Petroselinum crispum) in concentrations of 0.5% and 1.0%. For this, the essential oils were extracted, for each essential oil a yield was obtained, where 0.734% for the essential oil of rosemary and 0.634%, for the essential oil of parsley. Microbiological analyzes were carried out on the hamburgers to determine Escherichia coli, Salmonella sp. and Staphylococcus for seven days. Sensory attributes (color, smell, taste, and texture) were characterized by 30 untrained panelists (university students). After 7 days of follow-up, the hamburgers did not present the presence of microbiological parameters. Significant differences (p<0.001) were observed for concentrations and attributes. However, 0.5% parsley essential oil had higher odor, flavor, and texture scores compared to rosemary. It was concluded that our applied methodology allows to improve the useful life of the product, the antimicrobial effect and the acceptability, guaranteeing a good quality and nutritious product. Deep learning is one kind of machine learning with the overarching goal of making it easier to organize and use human knowledge across many domains, at scale. The field of microbiology benefits greatly from the application of mathematical principles to the maximization of variation functions. Multiple data types are used.