Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks

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  • Lingling Guo
    International Joint Research Laboratory for Biointerface and Biodetection State Key Lab of Food Science and Technology, and School of Food Science and Technology Jiangnan University 1800 Lihu Road Wuxi Jiangsu Province 214122 P. R. China
  • Ting Wang
    Innovative Center for Flexible Devices (iFLEX) Max Planck–NTU Joint Lab for Artificial Senses School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore
  • Zhonghua Wu
    School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore
  • Jianwu Wang
    Innovative Center for Flexible Devices (iFLEX) Max Planck–NTU Joint Lab for Artificial Senses School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore
  • Ming Wang
    Innovative Center for Flexible Devices (iFLEX) Max Planck–NTU Joint Lab for Artificial Senses School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore
  • Zequn Cui
    Innovative Center for Flexible Devices (iFLEX) Max Planck–NTU Joint Lab for Artificial Senses School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore
  • Shaobo Ji
    Innovative Center for Flexible Devices (iFLEX) Max Planck–NTU Joint Lab for Artificial Senses School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore
  • Jianfei Cai
    Department of Data Science & AI Monash University Clayton Victoria 3168 Australia
  • Chuanlai Xu
    International Joint Research Laboratory for Biointerface and Biodetection State Key Lab of Food Science and Technology, and School of Food Science and Technology Jiangnan University 1800 Lihu Road Wuxi Jiangsu Province 214122 P. R. China
  • Xiaodong Chen
    Innovative Center for Flexible Devices (iFLEX) Max Planck–NTU Joint Lab for Artificial Senses School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore

書誌事項

公開日
2020-10
権利情報
  • http://onlinelibrary.wiley.com/termsAndConditions#vor
DOI
  • 10.1002/adma.202004805
公開者
Wiley

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<jats:title>Abstract</jats:title><jats:p>Artificial scent screening systems (known as electronic noses, E‐noses) 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, cross‐reactive colorimetric barcode combinatorics and deep convolutional neural networks (DCNNs) are combined to form a system for monitoring meat freshness that concurrently provides scent fingerprint and fingerprint recognition. The barcodes—comprising 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 predicts meat freshness with an overall accuracy of 98.5%. Incorporating DCNN into a smartphone application forms a simple platform for rapid barcode scanning and identification of food freshness in real time. The system is fast, accurate, and non‐destructive, enabling consumers and all stakeholders in the food supply chain to monitor food freshness.</jats:p>

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