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

dc.contributor.advisorLopes, Patricia Santos [UNIFESP]
dc.contributor.advisorLatteshttp://lattes.cnpq.br/7939687315116927pt_BR
dc.contributor.authorLima, Felipe da Silva [UNIFESP]
dc.date.accessioned2021-03-25T17:09:56Z
dc.date.available2021-03-25T17:09:56Z
dc.date.issued2019
dc.description.abstractQuality 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.pt_BR
dc.description.abstractQuality 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.en
dc.description.sponsorshipNão recebi financiamentopt_BR
dc.format.extent41 fpt_BR
dc.identifier.urihttps://repositorio.unifesp.br/handle/11600/60746
dc.languageporpt_BR
dc.publisherUniversidade Federal de São Paulopt_BR
dc.rightsAcesso restritopt_BR
dc.subjectControle de qualidadept_BR
dc.subjectAprendizado de máquinapt_BR
dc.subjectMedicamentospt_BR
dc.subjectIndústriapt_BR
dc.subjectAlimentospt_BR
dc.subjectRevisãopt_BR
dc.subjectQuality controlen
dc.subjectMachine learningen
dc.subjectIndustryen
dc.subjectPharmaceuticalen
dc.subjectFooden
dc.subjectReviewen
dc.titleAprendizado de máquina no controle de qualidade nas indústrias farmacêuticas e alimentíciaspt_BR
dc.typeTrabalho de conclusão de curso de graduaçãopt_BR
unifesp.campusInstituto de Ciências Ambientais, Químicas e Farmacêuticas (ICAQF)pt_BR
unifesp.departamentoBiblioteca do Instituto de Ciências Ambientais, Químicas e Farmacêuticas (ICAQF)pt_BR
unifesp.graduacaoFarmáciapt_BR
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