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Cruz das Almas, Brazil

Rambo M.K.D.,University of Tocantins | Ferreira M.M.C.,University of Campinas | Amorim E.P.,Embrapa Cassava and Fruits
Chemometrics and Intelligent Laboratory Systems | Year: 2016

The physical-chemical composition of multiple biomasses can be predicted from one single calibration model instead of compositional prediction conducted by individual models. In this work, multi-product models, involving banana, coffee and coconut samples were built by partial least square regression (PLS) for ten different chemical constituents (total lignin, klason lignin, acid insoluble lignin, acid soluble lignin, extractives, moisture, ash, glucose, xylose and total sugars). The developed PLS models show satisfactory results, with relative error (RE%) less than 20.00, except for ash and xylose models; ratio performance deviation (RPD) values above 4.4 and range error ratio (RER) values above 4.00. This means that all models are qualified for screening calibration. Principal component analysis (PCA) was useful to demonstrate the possibility and the rationale for combining three biomass residues into one calibration model. The results have shown the potential of NIR in combination with chemometrics to quantify the chemical composition of feedstocks. © 2016 Elsevier B.V.. Source

Vilamiu R.G.D.,Embrapa Agricultural Informatics | Ternes S.,Embrapa Agricultural Informatics | Braga G.A.,University of Campinas | Laranjeira F.F.,Embrapa Cassava and Fruits
AIP Conference Proceedings | Year: 2012

The objective of this work was to present a compartmental deterministic mathematical model for representing the dynamics of HLB disease in a citrus orchard, including delay in the disease's incubation phase in the plants, and a delay period on the nymphal stage of Diaphorina citri, the most important HLB insect vector in Brazil. Numerical simulations were performed to assess the possible impacts of human detection efficiency of symptomatic plants, as well as the influence of a long incubation period of HLB in the plant. © 2012 American Institute of Physics. Source

Rambo M.K.D.,University of Campinas | Amorim E.P.,Embrapa Cassava and Fruits | Ferreira M.M.C.,University of Campinas
Analytica Chimica Acta | Year: 2013

Banana (stalk, leaf, rhizome, rachis and stem) and coffee (leaf and husks) residues are promising feedstock for fuel and chemical production. In this work we show the potential of near-infrared spectroscopy (NIR) and multivariate analysis to replace reference methods in the characterization of some constituents of coffee and banana residues. The evaluated parameters were Klason lignin (KL), acid soluble lignin (ASL), total lignin (TL), extractives, moisture, ash and acid insoluble residue (AIR) contents of 104 banana residues (B) and102 coffee (C) residues from Brazil. PLS models were built for banana (B), coffee (C) and pooled samples (B+C). The precision of NIR methodology was better (p<0.05) than the reference method for almost all the parameters, being worse for moisture. With the exception of ash (B and C) and ASL (C) content, which was predicted poorly (R2<0.80), the models for all the analytes exhibited R2>0.80. The range error ratios varied from 4.5 to 16.0. Based on the results of external validation, the statistical tests and figures of merit, NIR spectroscopy proved to be useful for chemical prediction of banana and coffee residues and can be used as a faster and more economical alternative to the standard methodologies. © 2013 Elsevier B.V. Source

de Oliveira E.J.,Embrapa Cassava and Fruits | Dias N.L.P.,Federal University of Reconcavo da Bahia | Dantas J.L.L.,Embrapa Cassava and Fruits
Euphytica | Year: 2012

This study was conducted to define a list of sufficient minimum descriptors to distinguish between papaya genotypes quickly and precisely. To this end, 30 quantitative and 21 multi-category descriptors related to plant characteristics, such as leaves, flowers, fruit and seeds were evaluated in 27 genotypes of papaya, including crops, local varieties and improved lines. The quantitative descriptors were subjected to principal components analyses using the Singh and direct selection methods, whereas a correlation analysis was conducted for the qualitative descriptors. Eighteen and fifteen quantitative descriptors were discarded by the Singh and direct selection methods, respectively. However, considering the simultaneous analyses of these methodologies, 60% of the descriptors were selected to maximize the total variation of the genotypes. Six of the multi-category descriptors were monomorphic, and two were highly correlated with other characteristics and were discarded. The minimum descriptors that were selected had high discrimination potentials when they were analyzed together. Thus, for the purposes of the protection of varieties and the classification of the genotypes of papaya, there were found to be 18 quantitative and 13 multi-category minimum descriptors that contributed significantly to the total variation and possessed low correlation with each other. The elimination of descriptors did not entail a loss of information. Those descriptors that contributed most significantly to the first three principal components were the stem diameters, fruit lengths and widths, inflorescence peduncle lengths, thickness of fruit skin, leaf widths, dry and fresh seed weights and ratio between fruit lengths and widths and between total soluble solids and total titratable acidities. © 2011 Springer Science+Business Media B.V. Source

de Oliveira E.J.,Embrapa Cassava and Fruits | de Resende M.D.V.,Embrapa Forestry Research | da Silva Santos V.,Embrapa Cassava and Fruits | Ferreira C.F.,Embrapa Cassava and Fruits | And 4 more authors.
Euphytica | Year: 2012

The main objective of this study was to estimate the selection accuracy and to predict the genetic gain in cassava breeding using genomic selection methodologies. We evaluated 358 cassava genotypes for the following traits: shoot weight (SW), fresh root yield (FRY), starch fraction amylose content (AC), dry matter content (DMC), and starch yield (S-Y). Genotyping was performed using 390 single nucleotide polymorphisms (SNPs), which were used as covariates in the random regression-best linear unbiased prediction model for genomic selection. The heritability values detected by markers for the SW, FRY, AC, DMC, and S-Y traits were 0.25, 0.25, 0.03, 0. 20, and 0.26, respectively. Because the low heritability detected for AC, this trait was eliminated from further analysis. Using only the most informative SNPs (118, 92, 56, and 97 SNPs for SW, FRY, DMC, and S-Y, respectively) we observed higher selection accuracy which were 0.83, 0.76, 0.67, and 0. 77, respectively to SW, FRY, DMC, and S-Y. With these levels of accuracy and considering a selection cycle reduced by half the time, the theoretical gains with genomic selection compared to phenotypic selection for DMC, FRY, and SW would be 39. 42 %, 56.90 %, and 73.96 %, respectively. These results indicate that in the cassava, genomic selection can substantially speed up selection cycles, thereby increasing gains per unit time. Although there are high expectations for incorporating this strategy into breeding programs, we still need to validate the model for other traits and evaluate whether the selection accuracy can be improved using more SNPs. © 2012 Springer Science+Business Media B.V. Source

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