The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks
Open accessAbstract
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.
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Cites
Dermatologist-level classification of skin cancer with deep neural networks
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Rakow, N. A., & Suslick, K. S. (2000) · Nature · doi:10.1038/35021028 · Cited by 1,490
The Optoelectronic Nose: Colorimetric and Fluorometric Sensor Arrays
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Humans Can Discriminate More than 1 Trillion Olfactory Stimuli
Bushdid, C., Magnasco, M. O., Vosshall, L. B., & Keller, A. (2014) · Science · doi:10.1126/science.1249168 · Cited by 938
Nanocellulose: a promising nanomaterial for advanced electrochemical energy storage
Chen, W., Yu, H., Lee, S.-Y., Wei, T., Li, J., & Fan, Z. (2018) · Chemical Society Reviews · doi:10.1039/c7cs00790f · Cited by 766
Identifying facial phenotypes of genetic disorders using deep learning
Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., Basel-Salmon, L., Krawitz, P. M., Kamphausen, S. B., Zenker, M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0279-0 · Cited by 739
Chitosan nanoparticles preparation and applications
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Self-citationJia, R., Tian, W., Bai, H., Zhang, J., Wang, S., & Zhang, J. (2019) · Nature Communications · doi:10.1038/s41467-019-08675-3 · Cited by 494
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Xu, C., Solomon, S. A., & Gao, W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00760-z · Cited by 259
Self-citationWu, Z., Rong, L., Yang, J., Wei, Y., Tao, K., Zhou, Y., Yang, B., Xie, X., & Wu, J. (2021) · Small · doi:10.1002/smll.202104997 · Cited by 97
Self-citationExplainable Deep-Learning-Assisted Sweat Assessment via a Programmable Colorimetric Chip
Liu, Z., Li, J., Li, J., Yang, T., Zhang, Z., Wu, H., Xu, H., Meng, J., & Li, F. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.2c03927 · Cited by 59
Metadata
- Authors
- Guo, L., Wang, T., Wu, Z., Wang, J., Wang, M., Cui, Z., Ji, S., Cai, J., Xu, C., & Chen, X.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2020-10
- Volume
- 32
- Issue
- 45
- Pages
- e2004805-e2004805
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W3090417167
- Cited by
- 292
- References
- 39
- FWCI
- 8.92
- Type
- journal article
- Language
- en
- Open access
- green
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- Guo, L., Wang, T., Wu, Z., Wang, J., Wang, M., Cui, Z., Ji, S., Cai, J., Xu, C., & Chen, X. (2020). Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks. Advanced Materials. https://doi.org/10.1002/adma.202004805
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