Graphical analysis of correlations between levels of consumer acceptance, longevity and fragrance silage, through artificial neural networks and analysis of main components

Authors

DOI:

https://doi.org/10.33448/rsd-v9i6.3192

Keywords:

Perfumes; Fragrances; Artificial neural networks; Performance indicators; PCA.

Abstract

The fragrance and flavoring industry (F&F) generates millions of dollars worldwide and is responsible for the olfactory characteristics of personal hygiene products, perfumes, cosmetics, house holding and any and all products that contain an artificially produced aroma. The research and development of new fragrances are concentrated among the 5 largest fragrance houses in the world and for that reason there is a great concern to maintain the secrecy between the creative processes of these companies. This fierce competition market limits the capacity for innovation in the creation of new products to what is usually successful in the market and the acceptance statistics of those who have been proposed among competitors. These companies are increasingly restricted to innovating within a previously launched universe, producing flankers - versions of products already established in the market. Thus, this article aims to graphically analyze data from a virtual perfumery library, modeled using a multi-layered neural network and resilient backpropagation neural network, validated through principal component analysis. Graphical analysis provides an interpretation of the correlations between levels of consumer acceptance for a perfume and the performance indicators for that fragrance. This study reiterates the existence of correlations between the user's consumption profile and the properties of the fragrances, supporting future studies of exclusive formulation of individually customized compositions for groups or individuals, demonstrating potential use in perfume engineering.

Author Biographies

Vanessa Gomes Matos, Universidade Federal da Bahia

Engenheira Química (Universidade Salvador). Mestranda em Engenharia Química na área de Modelagem, Simulação e Controle de Processos Químicos Industriais (Universidade Federal da Bahia). Docente na UNEF (Unidade de Ensino Superior de Feira de Santana). Analista de Processos ( OL Papéis). Integrou grupo de pesquisa no Centro de Estudos em Captura de CO2 (CECAP) da Universidade Salvador, através do Programa de Institucional de Bolsas de Iniciação Científica - PIBIC, em parceria com a Fundação de Amparo à Pesquisa do Estado da Bahia - FAPESB, desenvolvendo o projeto "Avaliação da Capacidade de Adsorção de CO2 a partir de Zeólitas 13X Modificadas por Aminas". Possui experiência na área de projetos industriais, tendo estagiado no setor de Engenharia e Projetos da Elekeiroz S.A, na Unidade de Camaçari - BA. 

Regina Ferreira Vianna, Universidade Federal da Bahia

Professora Titular da Universidade Federal da Bahia (UFBA), PhD pela The University of Leeds, UK (1995), Mestre em Engenharia Química pela UNICAMP, SP, Brasil (1991), Especialista em Processos de Separação, UFBA (1985), Especialista em Engenharia de Processamento Petroquímico, UFBA (1984) e graduada em Engenharia Química, UFBA (1984). Foi Vice-Diretora da Escola Politécnica da UFBA (2014-2018), coordenou o Colegiado do Curso de Engenharia Química (2008-2010), implantou, coordenou e atuou como Assessora da Diretoria Geral no escritório regional da Agência Nacional do Petróleo, Gás Natural e Biocombustíveis na Bahia (2000-2007) e Engenheira de Processos na Nitrofertil (1994-1998). Atualmente é professora no Departamento de Engenharia Química da UFBA e pesquisadora no Programa de Pós-Graduação em Engenharia Química da Universidade Federal da Bahia (PPEQ/UFBA). Desenvolve pesquisas nas áreas de Modelagem e Simulação, controle e otimização de processos, tanto através de métodos convencionais quanto de não convencionais.

Diego de Jesus Leite, Universidade Estadual de Feira de Santana

É graduando no curso de Engenharia de Computação na Universidade Estadual de Feira de Santana. Tem experiência na área de Gestão da Tecnologia da Informação. Possui um certificação Microsoft Student to Business (2011) em Infraestrutura de servidores.

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Published

24/04/2020

How to Cite

MATOS, V. G.; VIANNA, R. F.; LEITE, D. de J. Graphical analysis of correlations between levels of consumer acceptance, longevity and fragrance silage, through artificial neural networks and analysis of main components. Research, Society and Development, [S. l.], v. 9, n. 6, p. e194963192, 2020. DOI: 10.33448/rsd-v9i6.3192. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/3192. Acesso em: 17 apr. 2024.

Issue

Vol. 9 No. 6

Section

Engineerings

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