[Objectives]This study was conducted to detect the contents of heavy metal lead and chromium in food packaging bags.[Methods]The contents of heavy metal lead and chromium in food packaging bags were determined by micr...[Objectives]This study was conducted to detect the contents of heavy metal lead and chromium in food packaging bags.[Methods]The contents of heavy metal lead and chromium in food packaging bags were determined by microwave digestion-flame atomic absorption spectrophotometer.With concentrated nitric acid and 30%hydrogen peroxide solution as the digestion system,food packaging bags of different materials,plastic packaging bags and paper packaging bags,were ultrasonically digested and then determined for the contents of heavy metal lead and chromium by flame atomic absorption spectrophotometry.[Results]The determination results showed that the linear correlation coefficient of lead was 0.9967,and the linear correlation coefficient of chromium was 0.9977.The method has the characteristics of simplicity,high analysis speed and high sensitivity.[Conclusions]This study provides a theoretical basis for the safety of food packaging bags.展开更多
As a fine-grained classification problem, food image classification faces many difficulties in the specific implementation. Different countries and regions have different eating habits. In particular, Asian food image...As a fine-grained classification problem, food image classification faces many difficulties in the specific implementation. Different countries and regions have different eating habits. In particular, Asian food images have a complicated structure, and the related classification methods are still very scarce. There is an urgent need to develop a feature extraction and fusion scheme based on the characteristics of Asian food images. To solve the above problems, we proposed an image classification model SLGC (SURF-Local and Global Color) that combines image segmentation and feature fusion. By studying the unique structure of Asian foods, the color features of the images are merged into the representation vectors in the local and global dimensions, respectively, thereby further enhancing the effect of feature extraction. The experimental results show that the SLGC model can express the intrinsic characteristics of Asian food images more comprehensively and improve classification accuracy.展开更多
基金Supported by Special Scientific Research Project of Shaanxi Provincial Department of Education(16JK1275)。
文摘[Objectives]This study was conducted to detect the contents of heavy metal lead and chromium in food packaging bags.[Methods]The contents of heavy metal lead and chromium in food packaging bags were determined by microwave digestion-flame atomic absorption spectrophotometer.With concentrated nitric acid and 30%hydrogen peroxide solution as the digestion system,food packaging bags of different materials,plastic packaging bags and paper packaging bags,were ultrasonically digested and then determined for the contents of heavy metal lead and chromium by flame atomic absorption spectrophotometry.[Results]The determination results showed that the linear correlation coefficient of lead was 0.9967,and the linear correlation coefficient of chromium was 0.9977.The method has the characteristics of simplicity,high analysis speed and high sensitivity.[Conclusions]This study provides a theoretical basis for the safety of food packaging bags.
文摘As a fine-grained classification problem, food image classification faces many difficulties in the specific implementation. Different countries and regions have different eating habits. In particular, Asian food images have a complicated structure, and the related classification methods are still very scarce. There is an urgent need to develop a feature extraction and fusion scheme based on the characteristics of Asian food images. To solve the above problems, we proposed an image classification model SLGC (SURF-Local and Global Color) that combines image segmentation and feature fusion. By studying the unique structure of Asian foods, the color features of the images are merged into the representation vectors in the local and global dimensions, respectively, thereby further enhancing the effect of feature extraction. The experimental results show that the SLGC model can express the intrinsic characteristics of Asian food images more comprehensively and improve classification accuracy.
