TY - JOUR
T1 - Portable food-freshness prediction platform based on colorimetric barcode combinatorics and deep convolutional neural networks
AU - Guo, Lingling
AU - Wang, Ting
AU - Wu, Zhonghua
AU - Wang, Jianwu
AU - Wang, Ming
AU - Cui, Zequn
AU - Ji, Shaobo
AU - Cai, Jianfei
AU - Xu, Chuanlai
AU - Chen, Xiaodong
PY - 2020/11/12
Y1 - 2020/11/12
N2 - 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.
AB - 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.
KW - colorimetric barcode combinatorics
KW - deep convolutional neural networks
KW - food freshness
UR - http://www.scopus.com/inward/record.url?scp=85091759681&partnerID=8YFLogxK
U2 - 10.1002/adma.202004805
DO - 10.1002/adma.202004805
M3 - Article
C2 - 33006183
AN - SCOPUS:85091759681
VL - 32
JO - Advanced Materials
JF - Advanced Materials
SN - 0935-9648
IS - 45
M1 - 2004805
ER -