This study characterised pineapple pomace (PP) and evaluated its application in extrusion to enhance fibre content of the final product. The pomace had low fat (0.61%) and high dietary fibre (45.22%), showing its potential for fibre enrichment of nutritionally poor products, as some extruded snacks. Results also showed low microbiological counts, water activity, and pH indicating good microbiological quality and low risk of physicochemical deterioration. During extrusion, pomace (0%, 10.5% and 21%), moisture (14%, 15% and 16%) and temperature (140 and 160°C) were evaluated. The PP addition decreased expansion and luminosity; while increasing redness of the extrudates compared to the control (0% pomace/14% moisture/140°C). When hardness, yellowness, water absorption, and bulk density were compared to the control, there was no effect (p>0.05) of 10.5% PP addition on the extrudates, indicating that, at this level, PP could be added without affecting the properties of the final extruded product.

The bulk-fill composites investigated exhibited higher LT, independent of different filler content and characteristics. Although an increase in composite thickness reduced LT, the BFS of bulk-fill composites at deeper layers was not compromised.

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Some techniques of multivariate statistical analysis can only be conducted on a complete data matrix, but the process of data collection often misses some elements. Imputation is a technique by which the missing elements are replaced by plausible values, so that a valid analysis can be performed on the completed data set. A multiple imputation method is proposed based on a modification to the singular value decomposition (SVD) method for single imputation, developed by Krzanowski. The method was evaluated on a genotype × environment (G × E) interaction matrix obtained from a randomized blocks experiment on Eucalyptus grandis grown in multienvironments. Values of E. grandis heights in the G × E complete interaction matrix were deleted randomly at three different rates (5%, 10%, 30%) and were then imputed by the proposed methodology. The results were assessed by means of a general measure of performance (T acc ), and showed a small bias when compared to the original data. However, bias values were greater than the variability of imputations relative to their mean, indicating a smaller accuracy of the proposed method in relation to its precision. The proposed methodology uses the maximum amount of available information, does not have any restrictions regarding the pattern or mechanism of the missing values, and is free of assumptions on the data distribution or structure. Key words: missing data, nonparametric, eigenvalue, eigenvector, genotype-environment IMPUTAÇÃO MÚLTIPLA LIVRE DE DISTRIBUIÇÃO EM MATRIZ DE INTERAÇÃO POR MEIO DE DECOMPOSIÇÃO POR VALOR SINGULARRESUMO: Algumas técnicas de análise estatística multivariada só podem ser realizadas com uma matriz de dados completa, porém o processo de coleta dos dados freqüentemente leva a uma matriz com dados ausentes. A imputação é uma técnica, na qual os dados ausentes são preenchidos com valores plausíveis, para uma posterior análise na matriz completa. Neste trabalho, nós propomos um método de imputação múltipla, baseado no método da decomposição por valores singulares (DVS) para imputação simples, desenvolvido por Krzanowski, e avaliado numa matriz de interação genótipos × ambientes (G × E), proveniente de um ensaio com o delineamento aleatorizado em blocos em multiambientes com genótipos de Eucalyptus grandis. Valores da altura de E. grandis da matriz completa de interação G × E foram retirados aleatoriamente em três diferentes proporções (5%, 10%, 30%), os quais foram imputados valores dados pelo método proposto. Os resultados obtidos por meio da medida geral de exatidão ou acurácia (T acc ) mostraram um viés pequeno, em relação aos valores originais. No entanto, seus valores foram maiores do que a variabilidade dos valores imputados em relação à sua média, indicando uma exatidão ou acurácia menor do método proposto em relação à sua precisão. A metodologia proposta utiliza o maior número de informação disponível, não possui qualquer restrição quanto ao padrão e mecanismo de ausência de dados e é livre de suposição sobre a distribuição ou estrutura dos...

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Anthropogenic forest disturbance and land use change (LUC) in the Amazon region is the main source of greenhouse gas emissions to the atmosphere in Brazil, due to the carbon (C) and nitrogen (N) emitted from vegetation clearance. Land use conversion associated with management practices plays a key role in the distribution and origin of C in different soil organic matter (SOM) fractions. Here, we show how changing land use systems have influenced soil C and N stocks, SOM physical fractions, and the origin of SOM in the Santarém region of the eastern Brazilian Amazon. Soil C and N stocks were calculated for the surface layer of 0-30 cm. Anthropogenic disturbances to the standing forest, such as selective logging and wildfires, led to significant declines in soil C and N stocks. However, in the long-term, the conversion of the Amazon forest to pasture did not have a noticeable effect on soil C and N stocks, presumably because of additional inputs from pasture grasses. However, the conversion to cropland did lead to reductions in soil C and N content. According to the physical fractionation of SOM, LUC altered SOM quality, but silt and clay remained the combined fraction that contributed the most to soil C storage. Our results emphasize the importance of implementing more sustainable forest management systems, whilst also calling further attention to the need for fire monitoring systems, helping to ensure the resilience of C and N stocks and sequestration in forest soils; thereby contributing towards urgently needed ongoing efforts to mitigate climate change.

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Fusarium mycotoxins deoxynivalenol (DON), nivalenol (NIV) and zearalenone (ZEN) were investigated in wheat from the 2009 and 2010 crop years. Samples (n = 745) from commercial fields were collected in four wheat producing regions (WPR) which differed in weather conditions. Analyses were performed using HPLC-DAD. Contamination with ZEN, DON and NIV occurred in 56, 86 and 50%, respectively. Also, mean concentrations were different: DON = 1046 µg kg(-1), NIV < 100 µg kg(-1) and ZEN = 82 µg kg(-1). Co-occurrence of ZEN, DON and NIV was observed in 74% of the samples from 2009 and in 12% from 2010. Wet/cold region WPR I had the highest mycotoxin concentration. Wet/moderately hot region WPR II had the lowest mycotoxin levels. Furthermore, the mean concentration of each mycotoxin was higher in samples from 2009 as compared with those from 2010. Precipitation during flowering or harvest periods may explain these results.