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Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer

Author

Listed:
  • Un Jeong Kim

    (Samsung Advanced Institute of Technology)

  • Suyeon Lee

    (Samsung Advanced Institute of Technology)

  • Hyochul Kim

    (Samsung Advanced Institute of Technology)

  • Yeongeun Roh

    (Samsung Advanced Institute of Technology)

  • Seungju Han

    (Samsung Advanced Institute of Technology)

  • Hojung Kim

    (Samsung Advanced Institute of Technology)

  • Yeonsang Park

    (Chungnam National University
    Institute of Quantum Systems)

  • Seokin Kim

    (Chung-Ang University)

  • Myung Jin Chung

    (Sungkyunkwan University
    Sungkyunkwan University
    Sungkyunkwan University School of Medicine
    Samsung Medical Center)

  • Hyungbin Son

    (Chung-Ang University)

  • Hyuck Choo

    (Samsung Advanced Institute of Technology)

Abstract

Measuring, recording and analyzing spectral information of materials as its unique finger print using a ubiquitous smartphone has been desired by scientists and consumers. We demonstrated it as drug classification by chemical components with smartphone Raman spectrometer. The Raman spectrometer is based on the CMOS image sensor of the smartphone with a periodic array of band pass filters, capturing 2D Raman spectral intensity map, newly defined as spectral barcode in this work. Here we show 11 major components of drugs are classified with high accuracy, 99.0%, with the aid of convolutional neural network (CNN). The beneficial of spectral barcodes is that even brand name of drug is distinguishable and major component of unknown drugs can be identified. Combining spectral barcode with information obtained by red, green and blue (RGB) imaging system or applying image recognition techniques, this inherent property based labeling system will facilitate fundamental research and business opportunities.

Suggested Citation

  • Un Jeong Kim & Suyeon Lee & Hyochul Kim & Yeongeun Roh & Seungju Han & Hojung Kim & Yeonsang Park & Seokin Kim & Myung Jin Chung & Hyungbin Son & Hyuck Choo, 2023. "Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40925-3
    DOI: 10.1038/s41467-023-40925-3
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    References listed on IDEAS

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    1. MohammadSadegh Faraji-Dana & Ehsan Arbabi & Amir Arbabi & Seyedeh Mahsa Kamali & Hyounghan Kwon & Andrei Faraon, 2018. "Compact folded metasurface spectrometer," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    2. Jie Bao & Moungi G. Bawendi, 2015. "A colloidal quantum dot spectrometer," Nature, Nature, vol. 523(7558), pages 67-70, July.
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