摘要：
Foods and beverages with excessive tannins acid (TA) content taste astringent and bitter. The overconsumption of TA could result in nutritional and digestive problems. In this study, the cellulose nanocrystals (CNC)/fish swim bladder gelatin (FG) composite sponge was prepared with glutaraldehyde as a crosslinking agent. The TA adsorption performance of the sponge was discussed. The freeze-dried CNC/FG composite sponge had a porous network structure. CNC was combined into the FG matrix as a reinforcing phase. The mechanical strength, thermal stability, and swelling properties of the composite sponge were improved with the addition of an appropriate amount of CNC. Although CNC decreased the porosity of composite sponge, the increase in active adsorption sites resulted in an overall positive effect on its TA adsorption properties. Under the optimal adsorption conditions, the TA removal rate of 1.0 % CNC composites reached 80.4 %. Furthermore, the sponge retained a TA removal rate of 54 % after five cycles of adsorption and desorption using 50 % ethanol. The results demonstrated that CNC/FG composite sponge has application potential in the field of adsorption materials for TA.
摘要：
The current art image classification methods have low recall and accuracy rate issues . To improve the classification performance of art images, a new adaptive classification method is designed employing multi-scale convolutional neural networks (CNNs). Firstly, the multi-scale Retinex algorithm with color recovery is used to complete the enhancement processing of art images. Then the extreme pixel ratio is utilized to evaluate the image quality and obtain the art image that can be analyzed. Afterward, edge detection technology is implemented to extract the key features in the image and use them as initial values of the item to be trained in the classification model. Finally, a multi-scale convolutional neural network (CNN) is constructed by using extended convolutions, and the characteristics of each level network are set. The decision fusion method based on maximum output probability is employed to calculate different subclassifies' probabilities and determine the final category of an input image to realize the art image adaptive classification. The experimental results show that the proposed method can effectively improve the recall rate and precision rate of art images and obtain reliable image classification results.
