Aprendizado de máquina no controle de qualidade nas indústrias farmacêuticas e alimentícias

Aprendizado de máquina no controle de qualidade nas indústrias farmacêuticas e alimentícias

Author Lima, Felipe da Silva Autor UNIFESP Google Scholar
Advisor Lopes, Patricia Santos Autor UNIFESP Google Scholar
Abstract Quality Control is the department responsible for the activities such as sampling, specifications and test the final products and raw material, having a vital importance at the pharmaceutical and food industries, which deal directly with the health of the final customers. Machine learning is a subfield of studies in the Artificial Intelligence field, which works with agents/algorithms that can improve their behavior or performance by studying their own previous experiences. Recent studies aim at place together both concepts, presenting us new techniques and methodologies to improve the quality control in the pharmaceutical and food industries. It was searched articles at the Scopus and Web of Science databases, using the descriptors quality control, machine learning, pharmaceutical, food and bacterial resulting in 717 articles, which 16 have been fully included in this work. Of these articles, 10 focus on drugs and raw material analysis, and 6 focus on food analysis. The literature covers several analytical techniques associated with several learning algorithms, which the most seen are Raman Spectroscopy and FT-IR using the Support Vector Machine and Artificial Neural Network algorithms. Despite of showing good statistic data and low cost application, the authors agree that still need more in-depth studies to render these methods really useful and viable.

Quality Control is the department responsible for the activities such as sampling, specifications and test the final products and raw material, having a vital importance at the pharmaceutical and food industries, which deal directly with the health of the final customers. Machine learning is a subfield of studies in the Artificial Intelligence field, which works with agents/algorithms that can improve their behavior or performance by studying their own previous experiences. Recent studies aim at place together both concepts, presenting us new techniques and methodologies to improve the quality control in the pharmaceutical and food industries. It was searched articles at the Scopus and Web of Science databases, using the descriptors quality control, machine learning, pharmaceutical, food and bacterial resulting in 717 articles, which 16 have been fully included in this work. Of these articles, 10 focus on drugs and raw material analysis, and 6 focus on food analysis. The literature covers several analytical techniques associated with several learning algorithms, which the most seen are Raman Spectroscopy and FTIR using the Support Vector Machine and Artificial Neural Network algorithms. Despite of showing good statistic data and low cost application, the authors agree that still need more indepth studies to render these methods really useful and viable.
Keywords Controle de qualidade
Aprendizado de máquina
Medicamentos
Indústria
Alimentos
Revisão
Quality control
Machine learning
Industry
Pharmaceutical
Food
Review
Language Portuguese
Sponsor Não recebi financiamento
Date 2019
Publisher Universidade Federal de São Paulo
Extent 41 f
Access rights Closed access
Type Trabalho de conclusão de curso de graduação
URI https://repositorio.unifesp.br/handle/11600/60746

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