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

Un Jeong Kim, Suyeon Lee, Hyochul Kim, Yeongeun Roh, Seungju Han, Hojung Kim, Yeonsang Park, Seokin Kim, Myung Jin Chung, Hyungbin Son and Hyuck Choo ()
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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
Seokin Kim: Chung-Ang University
Myung Jin Chung: Sungkyunkwan University
Hyungbin Son: Chung-Ang University
Hyuck Choo: Samsung Advanced Institute of Technology

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-40925-3

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