Machine Learning Applied to Milk Sample Classification

Mía Leon, Diego Ossa, José Antonio Taquía Gutiérrez

Research output: Chapter in Book/Report/Conference proceedingPaper (Conference contribution)peer-review

Abstract

The document presents the results of the evaluation of the milk sample classification process through the modeling of
machine learning techniques, with random forest being the most accurate according to its accuracy percentage of
96%. The paper presents the results of the evaluation of the milk sample classification process through the modeling
of machine learning techniques. This research aimed to discriminate the presence or absence of adulterants, which
allows the obtaining of dairy products suitable for human consumption. Also, accelerate and specify the inspection
process of these samples. The relevance of the present study can be understood from the product under analysis:
milk. This is mass consumption, especially in children. Therefore, it is considered relevant to demonstrate efficiently
that quality products are provided to the population and this document is a contribution to the credibility of the integrity
of dairy products.
Original languageAmerican English
Title of host publicationMachine Learning Applied to Milk Sample Classification
Place of PublicationAustralia
PublisherIEOM Society International
Chapter1
Number of pages9
ISBN (Electronic) 979-8-3507-0542-3
StatePublished - 15 Mar 2023

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