Seleção de variáveis em dados de espectroscopia no infravermelho para controle de qualidade

Nos últimos anos, a espectroscopia no infravermelho (IR) ganhou grande aceitação em diversas áreas de pesquisa por ser uma técnica rápida, simples e não destrutiva que permite a quantificação de diversos componentes químicos em amostras. Apesar de a IR resultar em valores de absorbância que auxiliam na caracterização da amostra, tal técnica acaba por gerar bancos de dados compostos por centenas, ou até milhares, de variáveis altamente correlacionadas e ruidosas, comprometendo o resultado de diversas técnicas de análise multivariada. Dentro deste cenário, esta Tese apresenta novas metodologias para seleção de variáveis, também chamada de seleção de comprimentos de onda quando aplicados em dados de IR, com o intuito de auxiliar o reconhecimento de padrões para o controle de qualidade em diversas áreas. Tais metodologias são apresentadas em três artigos onde as proposições visam à solução de problemas específicos: no primeiro artigo, amostras de erva mate são categorizadas de acordo com seu país de origem através de uma nova metodologia para seleção de variáveis Para tanto, um problema de Programação Quadrática, combinado com a Informação Mútua entre as variáveis, é utilizado para reduzir a redundância entre as variáveis retidas e maximizar sua relação com o local de origem da amostra; por sua vez, o segundo artigo adequa as proposições do primeiro artigo para um problema de predição, onde o objetivo é determinar a concentração de cocaína e adulterantes em amostras de cocaína laboratoriais e apreendidas; por fim, o terceiro artigo utiliza a estatística do teste de Kolmogorov-Smirnov para duas amostras em uma abordagem de seleção de intervalos de comprimentos de onda com o intuito de identificar falsificações em medicamentos para disfunção erétil. A aplicação dos métodos em bancos de dados com distintas características e a validação dos resultados corrobora a adequabilidade das proposições desta tese.Over the last few years infrared (IR) spectroscopy gained wide acceptance in many research fields as a quick, simple and non-destructive technique allowing the quantification of many chemical compounds. Although IR provide many absorbance values that helps the sample characterization, this technique also generate databases comprised by hundreds, or even thousands, of highly noisy and correlated wavenumbers, jeopardizing the results of many multivariate analysis techniques. Under such scenario, this thesis presents new variables selection methodologies (also called wavenumber selection when applied in IR data) aimed to recognize patterns for quality control in many areas. Such methodologies are presented in three papers where the propositions are tailored for the solution of specific problems: on the first paper, yerba mate samples are categorized according to their country of origin through a novel variable selection methodology. Thereunto a quadratic programming problem, combined with the Mutual Information among variables, is utilized to reduce the redundancy among variables and increase their relationship with the samples’ place of origin; the second paper adequate the first paper propositions for a prediction method which aims to determine cocaine and adulterants concentration in laboratorial and seized cocaine samples; lastly, the third paper uses the two-samples Kolmogorov-Smirnov statistic in an wavenumber interval selection method aimed for the identification of counterfeit erectile dysfunction medicines. The application of the methods in databases with distinct characteristics and the results validation corroborates the suitability of this thesis propositions

Biopolymers from Natural Resources

This work covers all aspects related to the obtainment, production, design, and processing of biopolymers obtained from natural resources. Moreover, it studies characteristics related to the improvement of their performance to increase their potential application at an industrial level, in line with the concept of a global circular economy. Thus, this work firstly classifies biopolymers obtained from natural resources (e.g., biobased building blocks and biopolymers extracted directly from plants and biomass), and then summarizes several cutting-edge research works focused on enhancing the performance of biopolymers from natural resources to extend their application in the industrial sector, and contribute to the transition to more sustainable plastics

Redução de dimensionalidade para dados espectrais colineares

Na análise de dados, a identificação das variáveis relevantes para uma determinada tarefa de aprendizagem da máquina pode ajudar a construir modelos mais precisos, robustos e explicáveis. Embora avanços recentes em redes neurais, como autoencoders e redes neurais profundas, tenham proporcionado abordagens que implicitamente realizam a redução de dimensionalidade, tais modelos usualmente requerem grandes tamanhos de amostra e podem não ser explicáveis, podendo ter aplicabilidade restrita em diversos tipos de bancos de dados, como os de espectroscopia. Bancos de dados espectroscópicos têm como característica um elevado número de variáveis que tendem a ser colineares e geralmente se apoiam em menor número de amostras do que variáveis, o que pode deteriorar o desempenho de diversas técnicas multivariadas aplicadas a tais dados. Desta forma, esta tese propõe métodos de seleção de variáveis aplicados a dados espectroscópicos com o objetivo de realizar agrupamento, classificação e regressão em conjuntos de dados abrangendo diferentes áreas. Esta tese é composta de quatro artigos, três de pesquisa aplicada, e uma comunicação. No primeiro artigo, um índice de importância de variáveis (IIV) é proposto para selecionar os comprimentos de onda mais relevantes para o agrupamento de amostras de acordo com suas similaridades. O IIV proposto é baseado na combinação do escalonamento multidimensional (para redução de dimensionalidade) e análise de Procrustes para derivar uma matriz de projeção. No segundo artigo, com o objetivo de selecionar variáveis para um problema de regressão, outro VII é derivado com base nos pesos da matriz de projeção obtida a partir de uma redução de dimensão através da regressão inversa por fatias localizadas (LSIR). No terceiro artigo, uma comunicação relacionada a um artigo publicado recentemente, foram apontadas falhas de projeto em um experimento com o objetivo de classificar espectros Raman de plasma sanguíneo de pacientes positivos para COVID e controles. Esta comunicação também estabeleceu baselines não enviesados para o quarto artigo, no qual o algoritmo de Máxima Relevância Mínima Redundância (mRMR) para seleção de variáveis é melhorado a fim de levar em conta as dependências lineares no conjunto de variáveis selecionadas. O aprimoramento proposto, denominado PCA-mRMR, é aplicado ao mesmo conjunto de dados do terceiro artigo com propósito de classificação. Em todos os três artigos de pesquisa, os métodos propostos foram comparados com abordagens de seleção de variáveis já existentes e seu desempenho foi avaliado.In data analysis, identifying the most relevant features for a given machine learning task can help build more accurate, robust, and explainable models. Although recent advances in neural networks, such as autoencoders and deep neural nets, have provided approaches that implicitly perform dimension reduction, they usually require large sample sizes and may not be explainable. One of such cases is the analysis of spectroscopic data, which is characterised by colinear features (variables or wavelengths) and usually have less samples than features, thus suffering for the curse of dimensionality. Considering this setting, this thesis presents propositions for features election methods applied to spectroscopic data with the goal to perform clustering, classification, and regression in datasets spanning different areas. This thesis is comprised of four articles, three applied research ones, and one communication. In the first article, a feature importance index (FII) is proposed to select the most relevant wavelengths for clustering. This FII is based on the combination of multidimensional scaling (for dimension reduction) and Procrustes analysis to derive a projection matrix. In the second article, with the goal of selecting features for a regression problem, another FII is derived based on the weights of the projection matrix from a Localized Sliced Inverse Regression dimension reduction. In the third article, a communication related to a recent published article, design flaws were pointed out in an experiment aiming to classify Raman spectra of blood plasma of COVID positive patients and controls. This article also established unbiased baselines for the fourth article. In the fourth article, the Maximum Relevancy Minimum Redundancy (mRMR) algorithm for feature selection is improved in order to account for linear dependencies in the selected features. The proposed improved, named PCA-mRMR, is applied to the same dataset of article three, being a classification task. In all three research articles, the proposed methods were compared against existing baseline approaches and their performance were assessed

Genetic and Phenotypic Variation in Tree Crops Biodiversity

Recently, there has been a dramatic increase in the use of DNA-derived data and innovative phenotyping to obtain insights into the causative genes underlying traits of agronomical interest or to characterize tree genetic resources. The latter, in particular, could represent an important source of genetic diversity that can be readily used to enhance the adaptability to limiting environmental factors and resistance to biotic stresses or to promote novel genotypes with improved agronomic traits. On the whole, the studies collected in this book report on tree crop biodiversity characterization that could provide the essential building blocks to ensure future improvements in production and quality, as well as for innovations in tree crop development and utilization

Effect of canopy position and non-detructive determination of rind biochemical properties of citrus fruit during postharvest non-chilling cold storage.

Doctor of Philosophy in Horticultural Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.No abstract provided.This thesis is a compilation of manuscripts where each individual chapter is an independent article/manuscript introduced disjointedly

Theme Issue Honoring Professor Robert Verpoorte's 75th Birthday: Past, Current and Future of Natural Products Research

This theme issue is to celebrate Professor Robert Verpoorte’s 75th birthday. Prof. Verpoorte has been working in Leiden University over 40 years. There is no need to dwell upon the contributions of Dr. Verpoorte to plant-derived natural products research during his whole life. Dr. Verpoorte was a highly productive scientist throughout his academic career, with over 800 scientific publications in the form of research papers, books, and book chapters. His research interests are very diverse, cover- ing numerous topics related to plant-based natural products such as plant cell biotech- nology, biosynthesis, metabolomics, genetic engineering, and green technology, as well as the isolation of new biologically active compounds. He has left indelible footprints in all these fields, and he is widely recognised as a pioneer in the work of the biosynthesis of indole alkaloids, NMR-based metabolomics, and green technology in natural products production. As close friends and colleagues who have been in nearly daily contact with him over the last 20 years viewing all of these remarkable scientific contributions, we felt compelled to recognize this by the publication of a Special Issue of this journal dedicated to him.Thus, this Special Issue has now finally been released with the help of many of his colleagues and former students as a token of our gratitude to his impressive work.The Special Issue covers five main natural products topics: (1) chemical profiling and metabolomics, (2) separation/isolation and identification of plant specialized metabolites, (3) pharmacognosy of natural products to identify bioactive molecules from natural prod- ucts, (4) novel formulation of natural products, and (5) overview of natural products as a source of bioactive molecules

Dipterocarps protected by Jering local wisdom in Jering Menduyung Nature Recreational Park, Bangka Island, Indonesia

Apart of the oil palm plantation expansion, the Jering Menduyung Nature Recreational Park has relatively diverse plants. The 3,538 ha park is located at the north west of Bangka Island, Indonesia. The minimum species-area curve was 0.82 ha which is just below Dalil conservation forest that is 1.2 ha, but it is much higher than measurements of several secondary forests in the Island that are 0.2 ha. The plot is inhabited by more than 50 plant species. Of 22 tree species, there are 40 individual poles with the average diameter of 15.3 cm, and 64 individual trees with the average diameter of 48.9 cm. The density of Dipterocarpus grandiflorus (Blanco) Blanco or kruing, is 20.7 individual/ha with the diameter ranges of 12.1 – 212.7 cm or with the average diameter of 69.0 cm. The relatively intact park is supported by the local wisdom of Jering tribe, one of indigenous tribes in the island. People has regulated in cutting trees especially in the cape. The conservation agency designates the park as one of the kruing propagules sources in the province. The growing oil palm plantation and the less adoption of local wisdom among the youth is a challenge to forest conservation in the province where tin mining activities have been the economic driver for decades. More socialization from the conservation agency and the involvement of university students in raising environmental awareness is important to be done