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Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer
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 b...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10465478/ https://www.ncbi.nlm.nih.gov/pubmed/37644026 http://dx.doi.org/10.1038/s41467-023-40925-3 |
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author | Kim, Un Jeong Lee, Suyeon Kim, Hyochul Roh, Yeongeun Han, Seungju Kim, Hojung Park, Yeonsang Kim, Seokin Chung, Myung Jin Son, Hyungbin Choo, Hyuck |
author_facet | Kim, Un Jeong Lee, Suyeon Kim, Hyochul Roh, Yeongeun Han, Seungju Kim, Hojung Park, Yeonsang Kim, Seokin Chung, Myung Jin Son, Hyungbin Choo, Hyuck |
author_sort | Kim, Un Jeong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10465478 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104654782023-08-31 Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer Kim, Un Jeong Lee, Suyeon Kim, Hyochul Roh, Yeongeun Han, Seungju Kim, Hojung Park, Yeonsang Kim, Seokin Chung, Myung Jin Son, Hyungbin Choo, Hyuck Nat Commun Article 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. Nature Publishing Group UK 2023-08-29 /pmc/articles/PMC10465478/ /pubmed/37644026 http://dx.doi.org/10.1038/s41467-023-40925-3 Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kim, Un Jeong Lee, Suyeon Kim, Hyochul Roh, Yeongeun Han, Seungju Kim, Hojung Park, Yeonsang Kim, Seokin Chung, Myung Jin Son, Hyungbin Choo, Hyuck Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer |
title | Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer |
title_full | Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer |
title_fullStr | Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer |
title_full_unstemmed | Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer |
title_short | Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer |
title_sort | drug classification with a spectral barcode obtained with a smartphone raman spectrometer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10465478/ https://www.ncbi.nlm.nih.gov/pubmed/37644026 http://dx.doi.org/10.1038/s41467-023-40925-3 |
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