Accurate fish-freshness prediction label based on red cabbage anthocyanins. (August 2022)

• Record Type: Journal Article
• Authors: Fang, Shuliang; Guan, Zhihao; Su, Cheng; Zhang, Wenshuo; Zhu, Jian; Zheng, Yuewei; Li, Houbin; Zhao, Pingping; Liu, Xinghai
• Is Part Of: Food control. Volume 138(2022)
• Publication Date: 2022-08
• Subjects: Red cabbage anthocyanin -- Color indicator label -- Back propagation neural network -- Freshness detection -- Screen printing; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2022.109018
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: Biosafe colorimetric labels that can accurately evaluate food freshness have been widely investigated in recent years. Here, red cabbage anthocyanin labels and back propagation (BP) neural network are combined to form a system for monitoring fish freshness. Anthocyanins extracted from red cabbage were used as color response pigments and carboxymethyl chitosan/oxidized sodium alginate (CMCS/OSA) as the solid matrix. They were dispersed in silica sol to obtain colorimetric labels using the screen-printing approach. The label is recognized by the mobile phone to obtain freshness information, rather than the traditional method with the color card. The labels underwent color gradation during the storage period which was driven by response of anthocyanins to changes in pH. Computers are more sensitive to changes in color than the human eye. The labels are divided into three categories according to the freshness of the fish. BP neural network trained with labeled red cabbage anthocyanin label images predicted fish freshness with an overall accuracy of 92.6%. Integrating a BP neural network into a smartphone application forms a simple system for fast label scanning and real-time identification of fish freshness. The system can be used for food quality control throughout the supply chain. Graphical abstract: The anthocyanin extracted from purple cabbage was used to prepare the label identified by the smartphone to evaluate the freshness of fish. Image 1 Highlights: Anthocyanins extracted from purple cabbage were used as pH-sensitive substances. Fish freshness indicator labels were prepared by screen printing with hydrogel ink. The label displayed a noticeable color response to ammonia vapor and trimethylamine vapor. The software was developed with pattern recognition and BP neural networks to replace human eye recognition. The developed software was successfully applied to identification label color to indicate the freshness of fish.