Garlic powder and wheat bran as fillers: Their effect on the physicochemical properties of edible biocomposites

Biocomposites with two different fillers, garlic and wheat bran, were studied. They were based on cassava starch and contained glycerol as a plasticizer and potassium sorbate as an antimicrobial agent and were characterized by scanning electron microscopy (SEM), differential scanning calorimetry (DSC) and infrared spectroscopy (IR). The mechanical performance at room and lower temperatures was also studied. SEM micrographies of fractured surfaces of the wheat bran composite films showed some ruptured particles of fiber while fibrils of garlic on the order of nanometers were observed when garlic composite films were studied. Mechanical tests, at room temperature, showed that the addition of wheat bran led to an increment in the storage modulus (E`) and hardening and a decrease in Tan delta, while the garlic composite showed a diminishing in the E` and hardening and did not produce significant changes in Tan delta values when compared with systems without fillers (matrix). In the range between -90 degrees C and 20 degrees C. all the materials studied presented two peaks in the Tan delta curve. In the case of the wheat bran composite, both relaxation peaks shifted slightly to higher temperatures, broadened and diminished their intensity when compared with those of the matrix; however garlic composite showed a similar behavior to the matrix. DSC thermograms of aqueous systems showed a slight shift of gelatinization temperature (T(gelatinization)) to higher values when the fillers were present. Thermograms of films showed that both, garlic and wheat bran composites, had a lower melting point than the matrix. IR data indicated that interaction between starch and fillers determined an increase in the availability of hydroxyl groups to be involved in a dynamic exchange with water. (C) 2010 Elsevier B.V. All rights reserved.

Separating the wheat from the chaff: unbiased filtering of background tandem mass spectra improves protein identification

Only a small fraction of spectra acquired in LC-MS/MS runs matches peptides from target proteins upon database searches. The remaining, operationally termed background, spectra originate from a variety of poorly controlled sources and affect the throughput and confidence of database searches. Here, we report an algorithm and its software implementation that rapidly removes background spectra, regardless of their precise origin. The method estimates the dissimilarity distance between screened MS/MS spectra and unannotated spectra from a partially redundant background library compiled from several control and blank runs. Filtering MS/MS queries enhanced the protein identification capacity when searches lacked spectrum to sequence matching specificity. In sequence-similarity searches it reduced by, on average, 30-fold the number of orphan hits, which were not explicitly related to background protein contaminants and required manual validation. Removing high quality background MS/MS spectra, while preserving in the data set the genuine spectra from target proteins, decreased the false positive rate of stringent database searches and improved the identification of low-abundance proteins.

Transformed generalized linear models

The estimation of data transformation is very useful to yield response variables satisfying closely a normal linear model, Generalized linear models enable the fitting of models to a wide range of data types. These models are based on exponential dispersion models. We propose a new class of transformed generalized linear models to extend the Box and Cox models and the generalized linear models. We use the generalized linear model framework to fit these models and discuss maximum likelihood estimation and inference. We give a simple formula to estimate the parameter that index the transformation of the response variable for a subclass of models. We also give a simple formula to estimate the rth moment of the original dependent variable. We explore the possibility of using these models to time series data to extend the generalized autoregressive moving average models discussed by Benjamin er al. [Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214-223]. The usefulness of these models is illustrated in a Simulation study and in applications to three real data sets. (C) 2009 Elsevier B.V. All rights reserved.

Transformed symmetric models

For the first time, we introduce a class of transformed symmetric models to extend the Box and Cox models to more general symmetric models. The new class of models includes all symmetric continuous distributions with a possible non-linear structure for the mean and enables the fitting of a wide range of models to several data types. The proposed methods offer more flexible alternatives to Box-Cox or other existing procedures. We derive a very simple iterative process for fitting these models by maximum likelihood, whereas a direct unconditional maximization would be more difficult. We give simple formulae to estimate the parameter that indexes the transformation of the response variable and the moments of the original dependent variable which generalize previous published results. We discuss inference on the model parameters. The usefulness of the new class of models is illustrated in one application to a real dataset.