Incorporating phenolic acids into polysaccharide films improves their physical properties, in turn improving their potential commercial applicability as a preservation material for different foods. This study aimed to develop films from curdlan and tea polyphenols, and determine the effect of their contents on the water vapor permeability (WVP) and mechanical properties (tensile strength and elongation at break) of the films. Different ratios of tea polyphenols were incorporated into the curdlan-based films to improve their properties. The results obtained showed that the tensile strength and elongation at break of films were likely to be significantly decreased by adding tea polyphenols, especially at a content of 0.6%, which resulted in a 50% decrease. Meanwhile, the WVP and moisture content of the films was also decreased. However, a low WVP can prevent moisture loss from food. Other film properties, such as antioxidant efficiency, were also investigated. The results showed that the antioxidant potential of the film can be improved by tea polyphenols. The composite films were also applied to the preservation of chilled meat, which resulted in the shelf life being extended by about 3–5 days. Some properties, such as water resistance and DPPH (1,1-diphenyl-2-picrylhydrazyl) free radical scavenging capacity of the composite film, were improved.

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Multi-subject fMRI data analysis is an interesting and challenging problem in human brain decoding studies. The inherent anatomical and functional variability across subjects make it necessary to do both anatomical and functional alignment before classification analysis. Besides, when it comes to big data, time complexity becomes a problem that cannot be ignored. This paper proposes Gradient Hyperalignment (Gradient-HA) as a gradient-based functional alignment method that is suitable for multi-subject fMRI datasets with large amounts of samples and voxels. The advantage of Gradient-HA is that it can solve independence and high dimension problems by using Independent Component Analysis (ICA) and Stochastic Gradient Ascent (SGA). Validation using multiclassification tasks on big data demonstrates that Gradient-HA method has less time complexity and better or comparable performance compared with other state-of-the-art functional alignment methods.

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