Kim Un Jeong, Lee Suyeon, Kim Hyochul, Roh Yeongeun, Han Seungju, Kim Hojung, Park Yeonsang, Kim Seokin, Chung Myung Jin, Son Hyungbin, Choo Hyuck
Metaphotonics TU, Samsung Advanced Institute of Technology, Suwon, Gyeonggi-do, 16419, Republic of Korea.
Machine Learning TU, Samsung Advanced Institute of Technology, Suwon, Gyeonggi-do, 16419, Republic of Korea.
Nat Commun. 2023 Aug 29;14(1):5262. doi: 10.1038/s41467-023-40925-3.
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.
科学家和消费者一直希望能使用随处可见的智能手机来测量、记录和分析材料的光谱信息,将其作为独特的指纹识别。我们通过智能手机拉曼光谱仪展示了基于化学成分的药物分类。该拉曼光谱仪基于配备带通滤波器周期性阵列的智能手机CMOS图像传感器,可捕捉二维拉曼光谱强度图,在本研究中被新定义为光谱条形码。在此,我们展示借助卷积神经网络(CNN),11种主要药物成分的分类准确率高达99.0%。光谱条形码的优势在于,即使是药品品牌也可区分,未知药物的主要成分也能被识别。将光谱条形码与通过红、绿、蓝(RGB)成像系统获取的信息相结合,或应用图像识别技术,这种基于固有特性的标记系统将推动基础研究并创造商业机会。