Estimation of cellulose crystallinity of sugarcane biomass using near infrared spectroscopy and multivariate analysis methods

A method for estimation of sugarcane (Saccharum spp.) biomass crystallinity using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as an alternative to the standard method using X-ray diffractometry (XRD) is proposed. Crystallinity was obtained using XRD from sugarcane bagasse. NIR spectra were obtained of the same material. PLS models were built using the NIR and crystallinity values. Cellulose crystallinity ranged from 50 to 81%. Two variable selection algorithms were applied to improve the predictive ability of models, i.e. (a) Ordered Predictors Selection (OPS) and (b) Genetic Algorithm. The best model, obtained with the OPS algorithm, presented values of correlation coefficient of prediction, root mean squared error of prediction and ratio of performance deviation equals to 0.92, 3.01 and 1.71, respectively. A scatter matrix among lignin, α-cellulose, hemicellulose, ash and crystallinity was built that showed that there was no correlation among these properties for the samples studied

Prediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)

O artigo não contém resumo em portuguêsThe building of multivariate calibration models using near-infrared spectroscopy (NIR) and partial least squares (PLS) to estimate the lignin content in different parts of sugarcane genotypes is presented. Laboratory analyses were performed to determine the lignin content using the Klason method. The independent variables were obtained from different materials: dry bagasse, bagasse-with-juice, leaf, and stalk. The NIR spectra in the range of 10 000–4000 cmÀ1 were obtained directly for each material. The models were built using PLS regression, and different algorithms for variable selection were tested and compared: iPLS, biPLS, genetic algorithm (GA), and the ordered predictors selection method (OPS). The best models were obtained by feature selection with the OPS algorithm. The values of the root mean square error prediction (RMSEP), correlation of prediction (RP), and ratio of performance to deviation (RPD) were, respectively, for dry bagasse equal to 0.85, 0.97, and 2.87; for bagasse-with-juice equal to 0.65, 0.94, and 2.77; for leaf equal to 0.58, 0.96, and 2.56; for the middle stalk equal to 0.61, 0.95, and 3.24; and for the top stalk equal to 0.58, 0.96, and 2.34. The OPS algorithm selected fewer variables, with greater predictive capacity. All the models are reliable, with high accuracy for predicting lignin in sugarcane, and significantly reduce the time to perform the analysis, the cost and the chemical reagent consumption, thus optimizing the entire process. In general, the future application of these models will have a positive impact on the biofuels industry, where there is a need for rapid decision-making regarding clone production and genetic breeding program

Direct conversion of glucose to 5-hydroxymethylfurfural using a mixture of niobic acid and niobium phosphate as a solid acid catalyst

The aim of this work was to optimize the acid-catalyzed conversion of glucose into 5-hydroxymethylfurfural (HMF) in an aqueous medium using niobic acid (NbO), niobium phosphate (NbP) and a mixture of both solid acid catalysts. A simplex-centroid mixture design was applied to optimize the mixture ratio between NbO and NbP. A central composite design was applied to process optimization. The studied variables were temperature (T), time (t) and substrate to catalyst weight ratio (RS/C). The mixture design revealed excellent glucose conversion (55%) and HMF selectivity (56%) when the weight ratio between NbO and NbP was equal to 1:1. The experimental results demonstrate that a mixture of both solid acids provides a better combination of Brønsted and Lewis acidity for effective glucose dehydration into HMF than each individual catalyst

Ganhos genéticos preditos por diferentes métodos de seleção em progênies de Eucalyptus urophylla Predicted genetic gains by various selection methods in Eucalyptus urophylla progenies

O objetivo deste trabalho foi avaliar parâmetros genéticos e comparar os ganhos preditos por meio de diferentes métodos de seleção em famílias de meios-irmãos de Eucalyptus urophylla. Foi utilizada seleção entre e dentro, seleção combinada e seleção com base em modelos mistos (REML/BLUP) para os caracteres diâmetro à altura do peito, altura total e volume total com casca. Foi utilizado o teste de progênie constituído de 100 famílias de meios-irmãos com 55 meses de idade, em espaçamento de 3x2 m, em delineamento de blocos ao acaso, com cinco repetições. As progênies apresentaram variabilidade genética significativa e elevada magnitude de herdabilidade para os caracteres estudados, o que evidencia alto controle genético e condições favoráveis para seleção. Todos os métodos avaliados foram eficientes para aplicação no melhoramento de eucalipto. No entanto, a seleção combinada e a seleção por modelos mistos (BLUP) proporcionam estimativas de ganhos significativamente maiores às obtidas com a seleção entre e dentro, e maior eficiência na escolha dos melhores indivíduos dentro da população.<br>The objective of this work was to evaluate genetic parameters and to compare predicted gains using different selection methods in half-sib families of Eucalyptus urophylla. Within and between selection, combined selection and selection based on mixed model equations (REML/BLUP) were used for the traits diameter at breast height, total height and total volume with bark. The progeny test used consisted of 100 55-month-old half-sib families distributed in a 3x2-m spacing, in randomized complete block design with five replicates. The progenies showed significant genetic variability and high heritability for the studied traits, which indicates high genetic control and favorable conditions for selection. All the methods tested were efficient in eucalyptus breeding. However, the combined selection and the selection based on mixed models (BLUP) provided gains significantly larger than those obtained with within and between selections, and were more efficient in the selection of the best individuals in the population

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora

Growing knowledge: an overview of Seed Plant diversity in Brazil

Abstract An updated inventory of Brazilian seed plants is presented and offers important insights into the country's biodiversity. This work started in 2010, with the publication of the Plants and Fungi Catalogue, and has been updated since by more than 430 specialists working online. Brazil is home to 32,086 native Angiosperms and 23 native Gymnosperms, showing an increase of 3% in its species richness in relation to 2010. The Amazon Rainforest is the richest Brazilian biome for Gymnosperms, while the Atlantic Rainforest is the richest one for Angiosperms. There was a considerable increment in the number of species and endemism rates for biomes, except for the Amazon that showed a decrease of 2.5% of recorded endemics. However, well over half of Brazillian seed plant species (57.4%) is endemic to this territory. The proportion of life-forms varies among different biomes: trees are more expressive in the Amazon and Atlantic Rainforest biomes while herbs predominate in the Pampa, and lianas are more expressive in the Amazon, Atlantic Rainforest, and Pantanal. This compilation serves not only to quantify Brazilian biodiversity, but also to highlight areas where there information is lacking and to provide a framework for the challenge faced in conserving Brazil's unique and diverse flora