Prospec??o de reservat?rios de oleorresina de Copaifera L. por meio de an?lises n?o destrutivas
Ano de defesa: | 2021 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , , , , |
Tipo de documento: | Tese |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal Rural do Rio de Janeiro
|
Programa de Pós-Graduação: |
Programa de P?s-Gradua??o em Ci?ncias Ambientais e Florestais
|
Departamento: |
Instituto de Florestas
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | https://tede.ufrrj.br/jspui/handle/jspui/5753 |
Resumo: | Copa?ba oleoresin (Copaifera L.) is a potential raw material for several industry segments, due to its multiple properties. However, identifying the location of the reservoirs of this substance in tree trunks is an obstacle to the predictability of its continued supply, affecting the sustainable commercialization of the product. In an environment of high heterogeneity among arboreal individuals, it becomes a constant challenge in the search for non-invasive methods for prospecting for oleoresin. In this work, by means of literature review and experimentation, we aimed to: i) review general aspects concerning the genus Copaifera L. and researches that reinforce its potential, the demand for technologies for non-timber forest products, the main technologies available, besides the main aspects perceived as challenges for this enterprise; ii) analyze the potential of impulse tomography (IT) for the prospection of oleoresin reservoirs in the trunk of Copaifera sp. trees; iii) verify the relationship between dendrometric, meteorological and phenological variables (presence/absence of leaves) with the speed propagation of mechanical waves (VPOM) and with the average VPOM (VmPOM); iv) evaluate different configurations of artificial neural networks (ANN) and indicate the most appropriate model for predicting the oleoresin volume of Copaifera sp., based on dendrometric, acoustic and seasonal variables. The tomographies were performed in cross sections, in 35 trees, at diameter height at breast height (DBH or 0%) and, among these, in 18 at heights of 25%, 50%, 75% and 100% (1st bifurcation), obtaining the variables: VmPOM, minimum VPOM, maximum VPOM, prospecting height in percentage (Hp%), total tree height, diameter in Hp% and percentage of area of the section affected by low velocities. At this site, we also investigated the interference of different seasonal periods on tomographic results, through acoustic, dendrometric and meteorological (minimum and maximum temperature, relative humidity), in addition to the leaf phenology condition, in two groups of trees: a) group A = 14 trees ? seasonal transition to rainy period (2018); b) group B = 14 trees ? seasonal dry period (2019). In addition, different artificial neural networks (ANN) configurations were tested, aiming at the prediction of oleoresin volume, in which the general Multilayer Perceptron (MLP) supervised learning architecture was employed. For all analyses, descriptive, experimental and multivariate statistical tests were used. It is possible to prospect reservoirs with a significant amount of oleoresin using IT, but mainly to indicate the exclusion of trees, necessarily without reservoirs or other discontinuities. IT is sensitive to capture changes in the trunk of trees as a function of seasonal periods. The indication of a high-accurate ANN (training correlation = 0,994 and validation = 0,996) brings oleoresin management closer to other interesting technologies for its planning and management, such as applications to improve the RNA-user interface, so as to optimize the inventory stage and, mainly, the cost-benefit analysis associated with a management area. |
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Latorraca, Jo?o Vicente de Figueiredo284.741.551-34Latorraca, Jo?o Vicente de FigueiredoSanquetta, Carlos RobertoSilva Filho, Dem?stenes Ferreira daVidaurre, Graziela BaptistaTommasiello Filho, Mario093.578.187-07http://lattes.cnpq.br/7491861433785820Martins, Bianca Cerqueira2022-06-09T20:59:36Z2021-12-03MARTINS, Bianca Cerqueira. Prospec??o de reservat?rios de oleorresina de Copaifera L. por meio de an?lises n?o destrutivas. 2021. 182 F. Tese (Doutorado em Ci?ncias Ambientais e Florestais) - Instituto de Florestas, Departamento de Produtos Florestais, Universidade Federal Rural do Rio de Janeiro, Serop?dica, 2021.https://tede.ufrrj.br/jspui/handle/jspui/5753Copa?ba oleoresin (Copaifera L.) is a potential raw material for several industry segments, due to its multiple properties. However, identifying the location of the reservoirs of this substance in tree trunks is an obstacle to the predictability of its continued supply, affecting the sustainable commercialization of the product. In an environment of high heterogeneity among arboreal individuals, it becomes a constant challenge in the search for non-invasive methods for prospecting for oleoresin. In this work, by means of literature review and experimentation, we aimed to: i) review general aspects concerning the genus Copaifera L. and researches that reinforce its potential, the demand for technologies for non-timber forest products, the main technologies available, besides the main aspects perceived as challenges for this enterprise; ii) analyze the potential of impulse tomography (IT) for the prospection of oleoresin reservoirs in the trunk of Copaifera sp. trees; iii) verify the relationship between dendrometric, meteorological and phenological variables (presence/absence of leaves) with the speed propagation of mechanical waves (VPOM) and with the average VPOM (VmPOM); iv) evaluate different configurations of artificial neural networks (ANN) and indicate the most appropriate model for predicting the oleoresin volume of Copaifera sp., based on dendrometric, acoustic and seasonal variables. The tomographies were performed in cross sections, in 35 trees, at diameter height at breast height (DBH or 0%) and, among these, in 18 at heights of 25%, 50%, 75% and 100% (1st bifurcation), obtaining the variables: VmPOM, minimum VPOM, maximum VPOM, prospecting height in percentage (Hp%), total tree height, diameter in Hp% and percentage of area of the section affected by low velocities. At this site, we also investigated the interference of different seasonal periods on tomographic results, through acoustic, dendrometric and meteorological (minimum and maximum temperature, relative humidity), in addition to the leaf phenology condition, in two groups of trees: a) group A = 14 trees ? seasonal transition to rainy period (2018); b) group B = 14 trees ? seasonal dry period (2019). In addition, different artificial neural networks (ANN) configurations were tested, aiming at the prediction of oleoresin volume, in which the general Multilayer Perceptron (MLP) supervised learning architecture was employed. For all analyses, descriptive, experimental and multivariate statistical tests were used. It is possible to prospect reservoirs with a significant amount of oleoresin using IT, but mainly to indicate the exclusion of trees, necessarily without reservoirs or other discontinuities. IT is sensitive to capture changes in the trunk of trees as a function of seasonal periods. The indication of a high-accurate ANN (training correlation = 0,994 and validation = 0,996) brings oleoresin management closer to other interesting technologies for its planning and management, such as applications to improve the RNA-user interface, so as to optimize the inventory stage and, mainly, the cost-benefit analysis associated with a management area.O oleorresina de copa?ba (Copaifera L.) ? uma mat?ria-prima potencial para diversos segmentos da ind?stria, devido ?s suas m?ltiplas propriedades. Por?m, a identifica??o da localiza??o dos reservat?rios desta subst?ncia nos troncos das ?rvores ? um obst?culo ? previsibilidade de seu abastecimento continuado, afetando a comercializa??o sustent?vel do produto. Em um ambiente de elevada heterogeneidade entre os indiv?duos arb?reos, torna-se um desafio constante na busca de m?todos n?o invasivos para a prospec??o do oleorresina. Neste trabalho, por meio de revis?o bibliogr?fica e experimenta??o, buscou-se: i) revisar aspectos gerais ? respeito do g?nero Copaifera L. e de pesquisas que refor?am o seu potencial, da demanda por tecnologias para produtos florestais n?o madeireiros, das principais tecnologias dispon?veis, al?m dos principais aspectos entendidos como desafios para este empreendimento; ii) analisar o potencial da tomografia de impulso (TI) para a prospec??o de reservat?rios de oleorresina no tronco de ?rvores de Copaifera sp.; iii) verificar a rela??o entre vari?veis dendrom?tricas, meteorol?gicas e fenol?gica (presen?a/aus?ncia de folhas) com a velocidade de propaga??o de ondas mec?nicas (VPOM) e com as VPOM m?dias (VmPOM); iv) avaliar diferentes configura??es de Redes neurais artificiais (RNA) e indicar o modelo mais apropriado para a predi??o do volume oleorresina de Copaifera sp., com base em vari?veis dendrom?tricas, ac?sticas e sazonais. As tomografias foram realizadas em se??es transversais, em 35 ?rvores, na altura do di?metro ? altura do peito (DAP ou 0%) e, entre essas, em 18 nas alturas a 25%, 50%, 75% e 100% (1? bifurca??o), sendo obtidas as vari?veis: VmPOM, VPOM m?nima, VPOM m?xima, altura de prospec??o em porcentagem (Hp%), altura total da ?rvore, di?metro em Hp% e porcentagem de ?rea da se??o afetada por velocidades baixas. Investigou-se a interfer?ncia de diferentes per?odos sazonais nos resultados tomogr?ficos, por meio de vari?veis ac?sticas, dendrom?tricas, meteorol?gicas (temperatura m?nima e m?xima, umidade relativa do ar), al?m da condi??o da fenologia foliar, em dois grupos de ?rvores: a) grupo A = 14 ?rvores ? per?odo sazonal transi??o para chuvoso (2018); b) grupo B = 14 ?rvores ? per?odo sazonal seco (2019). Al?m disso, foram testadas diferentes configura??es de redes neurais artificiais (RNA), visando a predi??o do volume de oleorresina, nas quais empregou-se a arquitetura geral de aprendizado supervisionado Multilayer Perceptron (MLP). Para todas as an?lises, utilizou-se tetes estat?sticos descritivos, experimentais e estat?stica multivariada. ? poss?vel prospectar reservat?rios com uma quantidade significativa de oleorresina utilizando-se TI, mas, principalmente, indicar a exclus?o de ?rvores, necessariamente, sem reservat?rio ou outras descontinuidades. A TI ? sens?vel para captar mudan?as no tronco das ?rvores, em fun??o de per?odos sazonais. A indica??o de uma RNA de alta precis?o (correla??o treinamento = 0.994 e valida??o = 0.996) aproxima o manejo de oleorresina de outras tecnologias interessantes para seu planejamento e gest?o, como aplicativos para melhorar a interface RNA-usu?rio, de modo a otimizar a etapa de invent?rio e, principalmente, a an?lise do custo-benef?cio associado ? uma ?rea de manejo.Submitted by Jorge Silva (jorgelmsilva@ufrrj.br) on 2022-06-09T20:59:36Z No. of bitstreams: 1 2021 - Bianca Cerqueira Martins (parcial).pdf: 332333 bytes, checksum: 36d132b9ddb9329d7ff170f192934c7b (MD5)Made available in DSpace on 2022-06-09T20:59:36Z (GMT). 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dc.title.por.fl_str_mv |
Prospec??o de reservat?rios de oleorresina de Copaifera L. por meio de an?lises n?o destrutivas |
dc.title.alternative.eng.fl_str_mv |
Prospection of oleoresin reservoirs from Copaifera L. by non-destructive analysis |
title |
Prospec??o de reservat?rios de oleorresina de Copaifera L. por meio de an?lises n?o destrutivas |
spellingShingle |
Prospec??o de reservat?rios de oleorresina de Copaifera L. por meio de an?lises n?o destrutivas Martins, Bianca Cerqueira Biotecnologia Deep learning Tecnologia de produtos florestais Tomografia de impulso Biotechnology Deep learning Forest products technology Ci?ncias Ambientais |
title_short |
Prospec??o de reservat?rios de oleorresina de Copaifera L. por meio de an?lises n?o destrutivas |
title_full |
Prospec??o de reservat?rios de oleorresina de Copaifera L. por meio de an?lises n?o destrutivas |
title_fullStr |
Prospec??o de reservat?rios de oleorresina de Copaifera L. por meio de an?lises n?o destrutivas |
title_full_unstemmed |
Prospec??o de reservat?rios de oleorresina de Copaifera L. por meio de an?lises n?o destrutivas |
title_sort |
Prospec??o de reservat?rios de oleorresina de Copaifera L. por meio de an?lises n?o destrutivas |
author |
Martins, Bianca Cerqueira |
author_facet |
Martins, Bianca Cerqueira |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Latorraca, Jo?o Vicente de Figueiredo |
dc.contributor.advisor1ID.fl_str_mv |
284.741.551-34 |
dc.contributor.referee1.fl_str_mv |
Latorraca, Jo?o Vicente de Figueiredo |
dc.contributor.referee2.fl_str_mv |
Sanquetta, Carlos Roberto |
dc.contributor.referee3.fl_str_mv |
Silva Filho, Dem?stenes Ferreira da |
dc.contributor.referee4.fl_str_mv |
Vidaurre, Graziela Baptista |
dc.contributor.referee5.fl_str_mv |
Tommasiello Filho, Mario |
dc.contributor.authorID.fl_str_mv |
093.578.187-07 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/7491861433785820 |
dc.contributor.author.fl_str_mv |
Martins, Bianca Cerqueira |
contributor_str_mv |
Latorraca, Jo?o Vicente de Figueiredo Latorraca, Jo?o Vicente de Figueiredo Sanquetta, Carlos Roberto Silva Filho, Dem?stenes Ferreira da Vidaurre, Graziela Baptista Tommasiello Filho, Mario |
dc.subject.por.fl_str_mv |
Biotecnologia Deep learning Tecnologia de produtos florestais Tomografia de impulso |
topic |
Biotecnologia Deep learning Tecnologia de produtos florestais Tomografia de impulso Biotechnology Deep learning Forest products technology Ci?ncias Ambientais |
dc.subject.eng.fl_str_mv |
Biotechnology Deep learning Forest products technology |
dc.subject.cnpq.fl_str_mv |
Ci?ncias Ambientais |
description |
Copa?ba oleoresin (Copaifera L.) is a potential raw material for several industry segments, due to its multiple properties. However, identifying the location of the reservoirs of this substance in tree trunks is an obstacle to the predictability of its continued supply, affecting the sustainable commercialization of the product. In an environment of high heterogeneity among arboreal individuals, it becomes a constant challenge in the search for non-invasive methods for prospecting for oleoresin. In this work, by means of literature review and experimentation, we aimed to: i) review general aspects concerning the genus Copaifera L. and researches that reinforce its potential, the demand for technologies for non-timber forest products, the main technologies available, besides the main aspects perceived as challenges for this enterprise; ii) analyze the potential of impulse tomography (IT) for the prospection of oleoresin reservoirs in the trunk of Copaifera sp. trees; iii) verify the relationship between dendrometric, meteorological and phenological variables (presence/absence of leaves) with the speed propagation of mechanical waves (VPOM) and with the average VPOM (VmPOM); iv) evaluate different configurations of artificial neural networks (ANN) and indicate the most appropriate model for predicting the oleoresin volume of Copaifera sp., based on dendrometric, acoustic and seasonal variables. The tomographies were performed in cross sections, in 35 trees, at diameter height at breast height (DBH or 0%) and, among these, in 18 at heights of 25%, 50%, 75% and 100% (1st bifurcation), obtaining the variables: VmPOM, minimum VPOM, maximum VPOM, prospecting height in percentage (Hp%), total tree height, diameter in Hp% and percentage of area of the section affected by low velocities. At this site, we also investigated the interference of different seasonal periods on tomographic results, through acoustic, dendrometric and meteorological (minimum and maximum temperature, relative humidity), in addition to the leaf phenology condition, in two groups of trees: a) group A = 14 trees ? seasonal transition to rainy period (2018); b) group B = 14 trees ? seasonal dry period (2019). In addition, different artificial neural networks (ANN) configurations were tested, aiming at the prediction of oleoresin volume, in which the general Multilayer Perceptron (MLP) supervised learning architecture was employed. For all analyses, descriptive, experimental and multivariate statistical tests were used. It is possible to prospect reservoirs with a significant amount of oleoresin using IT, but mainly to indicate the exclusion of trees, necessarily without reservoirs or other discontinuities. IT is sensitive to capture changes in the trunk of trees as a function of seasonal periods. The indication of a high-accurate ANN (training correlation = 0,994 and validation = 0,996) brings oleoresin management closer to other interesting technologies for its planning and management, such as applications to improve the RNA-user interface, so as to optimize the inventory stage and, mainly, the cost-benefit analysis associated with a management area. |
publishDate |
2021 |
dc.date.issued.fl_str_mv |
2021-12-03 |
dc.date.accessioned.fl_str_mv |
2022-06-09T20:59:36Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
MARTINS, Bianca Cerqueira. Prospec??o de reservat?rios de oleorresina de Copaifera L. por meio de an?lises n?o destrutivas. 2021. 182 F. Tese (Doutorado em Ci?ncias Ambientais e Florestais) - Instituto de Florestas, Departamento de Produtos Florestais, Universidade Federal Rural do Rio de Janeiro, Serop?dica, 2021. |
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https://tede.ufrrj.br/jspui/handle/jspui/5753 |
identifier_str_mv |
MARTINS, Bianca Cerqueira. Prospec??o de reservat?rios de oleorresina de Copaifera L. por meio de an?lises n?o destrutivas. 2021. 182 F. Tese (Doutorado em Ci?ncias Ambientais e Florestais) - Instituto de Florestas, Departamento de Produtos Florestais, Universidade Federal Rural do Rio de Janeiro, Serop?dica, 2021. |
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Universidade Federal Rural do Rio de Janeiro |
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Universidade Federal Rural do Rio de Janeiro |
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