Bayes Modelling and Simulation for Milk Quality Testing

M. Thangamani

doi:https://doi.org/10.59232/MS-V1I1P105

Research Article | Open Access | Download Full Text

Volume 1 | Issue 1 | Year 2023 | Article Id: MS-V1I1P105 DOI: https://doi.org/10.59232/MS-V1I1P105

Bayes Modelling and Simulation for Milk Quality Testing

M. Thangamani

Received	Revised	Accepted	Published
03 Jul 2023	21 Aug 2023	28 Sep 2023	03 Oct 2023

Citation

M. Thangamani. “Bayes Modelling and Simulation for Milk Quality Testing.” DS Journal of Modeling and Simulation, vol. 1, no. 1, pp. 41-48, 2023.

Abstract

Milk is a nutrient-dense meal that is high in protein, which is essential for growth. Milk is an aqueous liquid composed of dissolved carbohydrates, protein aggregates and minerals, in which milk fat globules are suspended. As it is created to be a source of nutrition for a neonate, every component is advantageous to the developing child. Because it is a crucial component of our diet, and these adulterants have serious consequences on health. To overcome these issues, proposed a bayes modelling and simulation for milk quality testing. The main purpose of this work is to determine whether the Korean milk quality index (KMQI) is real or fake by calculating the chemical and water content in the milk. Initially, preprocessing techniques provide a solution based on the milk quality index. Finally, the naive bayes algorithm calculates whether the milk contains water or chemical or is pure milk. The Naive Bayes algorithm accurately predicted milk, even in instances where data was absent. It is recommended as a dependable instrument for converting conventional and digital milk management.

Keywords

["Korean Milk Quality Index", "Milk quality index", "Somatic Cell Count", "Standard Plate Count", "Preliminary Culture Count", "Laboratory Pasteurized Count."]

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