Sensory response and physical characteristics of gluten-free and gum-free bread with chia flour

Abstract The aim of this study was to evaluate the physical quality and sensory acceptance of gluten-free breads with different percentages of chia flour (Salvia Hispanica L .). The chia flour was used to substitute rice flour and soy flour in order to replace the gum required in this type of bread. Four formulations were developed; a standard made with gum, and three formulations with 2.5%, 5.0% and 7.5% of chia flour. Analyses of specific volume, cooking losses and the rise in dough of the breads were performed. Sensory analysis included tests for affective acceptability and purchase intent. The results showed that the bread with 2.5% chia flour had specific volume and cooking losses similar to the standard. In terms of the rise in dough, the standard showed the highest values, followed by the bread made with 5.0% chia flour. The substitution of soy and rice flour with 2.5% of chia flour produced bread with sensory characteristics similar to the standard in all of the analyzed attributes; it also received higher purchase intent. Using chia flour at a concentration of 2.5%, compared to rice flour and soy flour, proved that it was possible to replace gum in the bread formulation.

Pipeline for macro- and microarray analyses

The pipeline for macro- and microarray analyses (PMmA) is a set of scripts with a web interface developed to analyze DNA array data generated by array image quantification software. PMmA is designed for use with single- or double-color array data and to work as a pipeline in five classes (data format, normalization, data analysis, clustering, and array maps). It can also be used as a plugin in the BioArray Software Environment, an open-source database for array analysis, or used in a local version of the web service. All scripts in PMmA were developed in the PERL programming language and statistical analysis functions were implemented in the R statistical language. Consequently, our package is a platform-independent software. Our algorithms can correctly select almost 90% of the differentially expressed genes, showing a superior performance compared to other methods of analysis. The pipeline software has been applied to 1536 expressed sequence tags macroarray public data of sugarcane exposed to cold for 3 to 48 h. PMmA identified thirty cold-responsive genes previously unidentified in this public dataset. Fourteen genes were up-regulated, two had a variable expression and the other fourteen were down-regulated in the treatments. These new findings certainly were a consequence of using a superior statistical analysis approach, since the original study did not take into account the dependence of data variability on the average signal intensity of each gene. The web interface, supplementary information, and the package source code are available, free, to non-commercial users at http://ipe.cbmeg.unicamp.br/pub/PMmA.