Fiber-enriched breads are inferior to wheat flour (white) breads in terms of volume, taste, and textural characteristics. The aim of this study was to produce functional dietary fiber (DF)-enriched white bread (WB) with high consumer acceptance. Wheat fiber (WF) was added to wheat flour as an insoluble fiber source (5%); polydextrose and inulin were used as soluble fibers at three different concentrations (2, 4, and 6%) individually or in combination. Their effects on dough rheology (farinograph and extensograph properties) and bread properties (volume, baking loss, moisture, texture, color, sensory, and crumb-grain characteristics) were investigated. The addition of WF, polydextrose and inulin had significant effects on dough rheology and bread properties. Hardness, chewiness, cell density, and DF content of breads were generally increased, whereas volume yield, baking loss, moisture and porosity values decreased with increasing DF concentration. The bread with the highest DF content (PD6IN6) contained 7.1 times more DF than WB. According to the results of sensory analysis, except for the sample with 6% concentration of polydextrose and inulin, all the other breads had very high overall acceptance values. Although the breads produced in the study have a high fiber content, their important quality characteristics (moisture content, volume yield, cell density, color values and sensory properties) are similar to those of WB. As a result, the overall quality characteristics of bread made with different dietary fibers were maintained, and functional WB enriched with DF were produced with high consumer acceptance.
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Netw Neurosci
December 2024
Department of Psychology, Stanford University, Stanford, CA, USA.
The growing availability of large-scale neuroimaging datasets and user-friendly machine learning tools has led to a recent surge in studies that use fMRI data to predict psychological or behavioral variables. Many such studies classify fMRI data on the basis of static features, but fewer try to leverage brain dynamics for classification. Here, we pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data.
View Article and Find Full Text PDFACS Cent Sci
December 2024
School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China.
DNA computing leverages molecular reactions to achieve diverse information processing functions. Recently developed DNA origami registers, which could be integrated with DNA computing circuits, allow signal transmission between these circuits, enabling DNA circuits to perform complex tasks in a sequential manner, thereby enhancing the programming space and compatibility with various biomolecules of DNA computing. However, these registers support only single-write operations, and the signal transfer involves cumbersome and time-consuming register movements, limiting the speed of sequential computing.
View Article and Find Full Text PDFFront Artif Intell
December 2024
Faculty of Engineering, North-West University, Potchefstroom, South Africa.
Many industries utilize deep learning methods to increase efficiency and reduce costs. One of these methods, image segmentation, is used for object detection and recognition in localization and mapping. Segmentation models are trained using labeled datasets; however, manually creating datasets for every application, including deep-level mining, is time-consuming and typically expensive.
View Article and Find Full Text PDFAACE Clin Case Rep
September 2024
Department of Medicine, Suburban Hospital, Johns Hopkins Medicine, Bethesda, Maryland.
Background/objective: Calcium channel blockers, when taken in overdose quantities, can cause hyperglycemia requiring so-called hyperinsulinemic-euglycemic therapy. The objective of this report was to describe a patient with calcium channel blocker toxicity resulting from overdose of amlodipine.
Case Report: A 74-year-old man presented with a fall and loss of consciousness.
BMC Neurol
December 2024
Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA.
Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD.
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