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摘要:Artificial scent screening systems(known as electronic noses, E-nose) have been researched extensively. A portable, automatic, and accurate, real-time E-nose requires both robust cross-reactive sensing and fingerprint pattern recognition. Few E-noses have been commercialized because they suffer from either sensing or pattern recognition issues. Here, we combined cross-reactive colorimetric barcode combinatorics and deep convolutional neural networks(DCNN) to form a meat freshness monitoring system that concurrently provides scent fingerprint and fingerprint recognition. The barcodes-compris-ing of 20 different types of porous nanocomposites of chitosan, dye and cellulose acetate-form scent fingerprints that are identifiable by DCNN. A fully supervised DCNN trained using 3475 labeled barcode images predicted meat freshness with an overall accuracy of 98.5%. Incorporating DCNN into a smartphone application formed a simple platform for rapid barcode scanning and food freshness identifica-tion in real time. Our system is fast, accurate and non-destructive, enabling consumers and all stakeholders in the food supply chain to monitor food freshness.
会议名称:

第九届亚洲毒理学大会暨中国毒理学会第八次中青年科技论坛

会议时间:

2021-10-20

会议地点:

中国浙江杭州

  • 专辑:

    工程科技Ⅰ辑; 信息科技

  • 专题:

    轻工业手工业; 自动化技术

  • DOI:

    10.26914/c.cnkihy.2021.052512

  • 分类号:

    TS207.3;TP183

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