Neutrosophic statistical analysis food safety and handling management system according to ISO 22000:2018, for mass catering establishment

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Diego Armando Freire Muñoz
Bolívar Javier Llundo Michelena
Manuel Fernando Jaramillo Burgos

Abstract

Food safety and quality is a primordial aspect for any industry that works with food in a direct or indirect way, since it guarantees that the food used is free of any harmful substance and that it is safe for human consumption, collective food establishments must follow a series of practices and parameters that guarantee the safety of the food they serve to their customers, for this reason it is important that they follow public health rules and regulations regarding food safety and quality.  A descriptive and transversal type of research was developed from a neutrosophical approach. That is why, the objective of the present research is oriented towards: to develop a neutrosophical statistical analysis of the food safety and handling management system according to ISO 22000:2018, for collective food establishment. During the research, a set of theoretical, empirical and mathematical statistical methods were assumed, which propitiated to reveal the scientific level of the present study. After its application, important results were evidenced because it demonstrates the effectiveness of the management system, which contributes to the identification of hazards in the ingredients, the control of cross-contamination and the implementation of appropriate hygiene measures in the value chain of a food, study developed in restaurants in the city of Ambato.

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How to Cite
Neutrosophic statistical analysis food safety and handling management system according to ISO 22000:2018, for mass catering establishment. (2023). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 30, 275-282. https://fs.unm.edu/NCML2/index.php/112/article/view/474
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How to Cite

Neutrosophic statistical analysis food safety and handling management system according to ISO 22000:2018, for mass catering establishment. (2023). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 30, 275-282. https://fs.unm.edu/NCML2/index.php/112/article/view/474