Graphical analysis of correlations between levels of consumer acceptance, longevity and fragrance silage, through artificial neural networks and analysis of main components
Keywords:Perfumes; Fragrances; Artificial neural networks; Performance indicators; PCA.
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.
Aggarwal, C. C., Al-Garawi, F., & Yu, P. S. (2001). Intelligent Crawling on the World Wide Web with Arbitrary Predicates.
Branca, A., Simonian, P., Ferrante, M., Novas, E., & Negri, R. M. (2003). Electronic nose based discrimination of a perfumery compound in a fragrance. Sensors and Actuators B: Chemical, 92(1–2), 222–227. https://doi.org/10.1016/S0925-4005(03)00270-3
Carles, J. (1968). A method of creation and perfumery- Part 1. Soap , Perfumery & Cosmetics.
Cleto, P., Ferreira, R., Gomes, R., & Rodrigues, M. T. (2010). Reconhecimento de Acordes Musicais: Uma Abordagem Via Perceptron Multicamadas. Mecánica Computacional, 29(93), 9169–9175. Retrieved from https://cimec.org.ar/ojs/index.php/mc/article/view/3659
Devecchi, R. (2015). O marketing olfativo no varejo ! São Paulo. Retrieved from http://www.raiingredients.com.br/extra1.pdf
Dias, S. M., & da Silva, R. R. (1996). Perfumes- Uma Química Inesquecível. Química Nova Na Escola.
Haykin, S. (1990). Neural Networks and Learning Machines. Hamilton, Ontario, Canada.
Hongyu, K., Sandanielo, V. L. M., & Oliveira Junior, G. J. (2015). Principal Component Analysis: theory, interpretations and applications. Engineering and Science, 5(1), 83–90. https://doi.org/10.18607/ES20165053
Ludwig Junior, O., & Costa, E. M. M. (2007). Redes Neurais - Fundamentos e Aplicações com Programas em C (1st ed.). Rio de Janeiro: Editora Ciência Moderna.
Mata, V. G., Gomes, P. B., & Rodrigues, A. E. (2005a). Engineering perfumes. AIChE Journal, 51(10), 2834–2852. https://doi.org/10.1002/aic.10530
Mata, V. G., Gomes, P. B., & Rodrigues, E. (2005b). Effect of Nonidealities in Perfume Mixtures Using the Perfumery Ternary Diagrams (PTD) Concept. Industrial & Engineering Chemistry Research, 4435–4441. https://doi.org/10.1021/ie048760w
Menczer, F., Pant, G., & Srinivasan, P. (2003). Topical web crawlers: Evaluating adaptive algorithms. Retrieved from https://www.researchgate.net/publication/228755990_ Topical_web_crawlers_Evaluating_adaptive_algorithms
Morais, E. C. (2010). Reconhecimento de Padrões e Redes Neurais Artificiais em Predição de Estruturas Secundárias de Proteínas. Universidade Federal do Rio de Janeiro. Retrieved from https://www.cos.ufrj.br/uploadfile/1277729485.pdf
Reis, T. (2013). Algoritmo Rastreador Web Especialista Nuclear.
Soares, A., Dorlivete, P., Shitsuka, M., Parreira, F. J., & Shitsuka, R. (2018). METODOLOGIA DA PESQUISA CIENTÍFICA. Santa Maria, RS. Retrieved from https://repositorio.ufsm.br/bitstream/handle/1/15824/Lic_Computacao_Metodologia-Pesquisa-Cientifica.pdf?sequence=1
Sobrinho, J. L. V. (2019). Rastreador Web Não Supervisionado para Aquisição, Enriquecimento e Predição de Dados de Usuários de Redes Sociais por Intermédio de Métodos de Inteligência Computacional.
Teixeira, M. A., Barrault, L., Rodr, O., Carvalho, C. C., & Rodrigues, E. (2014). Perfumery Radar 2 . 0 : A Step toward Fragrance Design and Classi fi cation. Industrial & Engineering Chemistry Research.
Teixeira, M. A., Rodríguez, O., Gomes, P., Mata, V., & Rodrigues, A. E. (2013). Perfume engineering : design, performance and classification. Elsevier Science.
Teixeira, M. A., Rodríguez, O., Gomes, P., Mata, V., Rodrigues, A. E., Teixeira, M. A., … Rodrigues, A. E. (2013). Chapter 3 – Performance of Perfumes. In Perfume Engineering (pp. 61–94). https://doi.org/10.1016/B978-0-08-099399-7.00003-1
Teixeira, M. A., Rodríguez, O., & Rodrigues, A. E. (2010). Perfumery radar: A predictive tool for perfume family classification. Industrial and Engineering Chemistry Research, 49(22), 11764–11777. https://doi.org/10.1021/ie101161v
Vera G. Mata, *, Paula B. Gomes, and, & Rodrigues, A. E. (2005). Effect of Nonidealities in Perfume Mixtures Using the Perfumery Ternary Diagrams (PTD) Concept. https://doi.org/10.1021/IE048760W
Verzbickas, A., Mocelin, E. F., Neto, M. B. de S., & Siega, R. T. (2013). RELATÓRIO WEB CRAWLERS.
How to Cite
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.