Incorporating high level of potato flour into wheat flour enhances nutritional values of bread but induces a series of problems that lead to the decline of the bread quality. To overcome the barrier, wheat gluten and carboxymethylcellulose (CMC) were added into potato–wheat composite flour to improve dough machinability and bread quality. The rheological properties, thermo-mechanical properties and microstructures of dough were investigated. The results showed that the interaction between gluten and CMC mitigated the discontinuity of gluten matrix and gluten protein aggregation caused by the addition of potato flour, which yielded a more branched and compact gluten network. The compact three-dimensional viscoelastic structure induced improvements of gas retention capacity and dough stability, making it mimic the machinability properties of wheat flour dough. Bread qualities were apparently improved with the combined use of 4% gluten and 6% CMC, of which specific volume increased by 42.86%, and simultaneously, hardness reduced by 75.93%. 相似文献
A method constructinq C~1 Piecewise quintic polynomial over a triangular grid to interpo-late function values and partial derivatives at vertices is presented in this paper.The set of precise poly-nomials of this method is discussed. 相似文献
LaNiO3 was synthesized by sol-gel method in which lanthanum nitrate and nickel nitrate were used as start materials and citric acid was used as complex for gel formation.The precursor was dried and subsequently heated at elevated temperature to form the desired product.XRD analysis shows that pure LaNiO3 was synthesized.Electrical conductivity and electrochemical performance of the material were tested.The electrical conductivity decreases from 34.5that there are current peaks in the curve, which is the evidence of the electrochemical activity of LaNiO3. 相似文献
Promoting the spatial resolution of hyperspectral sensors is expected to improve computer vision tasks. However, due to the physical limitations of imaging sensors, the hyperspectral image is often of low spatial resolution. In this paper, we propose a new hyperspectral image super-resolution method from a low-resolution (LR) hyperspectral image and a high resolution (HR) multispectral image of the same scene. The reconstruction of HR hyperspectral image is formulated as a joint estimation of the hyperspectral dictionary and the sparse codes based on the spatial-spectral sparsity of the hyperspectral image. The hyperspectral dictionary is learned from the LR hyperspectral image. The sparse codes with respect to the learned dictionary are estimated from LR hyperspectral image and the corresponding HR multispectral image. To improve the accuracy, both spectral dictionary learning and sparse coefficients estimation exploit the spatial correlation of the HR hyperspectral image. Experiments show that the proposed method outperforms several state-of-art hyperspectral image super-resolution methods in objective quality metrics and visual performance.