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Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. An Author Correction to this article was published on 11 September 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, 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. Miniaturization of optical spectrometers has been an active area of research because the demand for portable scientific and industrial characterization tools remains high 1 , 2 , 3 , 4 , 5. Furthermore, smartphones are ubiquitous devices that provide numerous applications and services. Recently, many efforts have focused on converting smartphone cameras into optical spectrometers for mobile food inspection 6 , 7 beauty care 8 , health care 9 , and other applications 10 , 11 , 12 , 13 , In these cases, the image sensor of the smartphone detects optical signals from the object of interest—such as reflectance, fluorescence, and Raman emissions. Most research on smartphone-based spectrometers uses gratings as a dispersion component, assembled in an external optics module 6 , 7 , 8 , 9 , 10 , 11 , 12 , Gratings is an excellent optical component in spectrometer to disperse optical signals with high spectral resolution, but is not easy to minimize its form factor to fit into smartphone. To overcome this issue, mini spectrometers by replacing conventional grating with such as photonic crystals 14 , 15 , metasurfaces 16 , 17 , 18 , quantum dots 19 and silicone nanowires 20 integrated on charge coupled detector CCD or CMOS image sensors have been investigated. Thus, experimental results in the literature 14 , 15 , 16 , 17 , 18 , 19 , 20 have substantial limitations—especially in terms of capturing weak and high spectral resolution required for Raman signatures. Due to the increasing online pharmacies and supply chain, counterfeit drugs have become threatening even to public health safety. This issue becomes more critical since increasing the online pharmacies and supply chain can provide blind spots for counterfeit or substandard drugs to be distributed into the public health market Food and Drug Administration database. The identification accuracy is insufficient due to similar appearance, absence in the database, or other technical issues. In this sense, Raman spectrum can provide valuable information on drugs, and there have been some researches in the literature on classifying drugs by Raman spectroscopy with the aid of machine learning 22 , 23 , 24 , 25 , Classifying pharmaceutical ingredients, and detection of newly emerging psychoactive substance and illicit drugs were demonstrated by partial least squares-discriminate analysis PLS-DA 22 , principal component analysis PCA 23 and CNN 24 , respectively. Detection of illicit drugs 25 or psychoactive drugs 26 were demonstrated even in human urine and finger marks to prevent patients from overdose or misuse of it by support vector machines SVM and PLS-DA, respectively. We demonstrated smartphone based Raman spectrometer which are enough for drug classification. The Raman spectrometer is composed of 2D periodic array of band pass filters on the image sensor of a Samsung Galaxy Note 9, with a compact external Raman module. As a demonstration, we experimentally investigated 54 commonly used drugs for diabetes, hyperlipidemia, hypertension, painkillers, and nutritional supplements; which frequently come in almost identical shapes, sizes, and colors. Since each spectral barcode of drug contains unique Raman signatures of the material, we conducted the identification of spectral barcodes of drugs with a convolutional neural network CNN embedded in the smartphone. In addition, identification accuracy can be further enhanced by information fusion with spectral barcode and conventional RGB images taken by the smartphone camera. Another advantage of spectral barcode-based classification is that we can identify chemical component of unknown drugs once other drugs with the same chemical component are in the database. Integrating with AI capability in the smartphone spectrometer allows users to analyze the spectrum at various places and situations. This will enhance its portability and usability of smartphone spectrometer in numerous disciplines including drug classification. Our proposed concept of a CNN powered spectral barcode will facilitate many research and business opportunities for smartphone spectrometers. Figure 1 shows schematics of the smartphone Raman spectrometer and spectral barcode; which is the 2D Raman intensity map acquired with the smartphone Raman spectrometer, and an artificial intelligence algorithm embedded in the smart phone for classification. The miniaturized external Raman module is attached to the rear-wide camera of the Samsung Galaxy Note 9, and its detailed optical components and configurations is shown in Supplementary Fig. The Raman emission, which is excited by positioning the specimen at the focal point, i. The rest CHs are blocked by metal as position indicators exhibited as black squares in Fig. The spectral width and transmission rate of the band pass filters range from 1—1. The details of the filter structure and fabrication can be found in Methods. In Supplementary Table 1 , the smartphone Raman spectrometer of this work is compared with miniaturized spectrometers which are controllable by android smartphones, or embedded in the smartphone 12 , 29 , The compared details are shown in the caption of Supplementary Table 1. As the role of the external module in this work is just to excite and collect Raman signals from the specimen without additional connecting electronic board to the smartphone, the smartphone Raman spectrometer becomes more compact and versatile with minimized external module. We embedded an artificial intelligence algorithm in the smartphone for classification. From the image, a unique spectral barcode of the specimen is generated, which contains the Raman information of the sample. The Methods explains the detailed process to convert a raw image—acquired with the smartphone spectrometer—to a spectral barcode, a unique spectral identifier. Analogous to conventional barcodes, our work introduces a new concept of symbology to map spectral information into a spectral barcode: a set of multiple wavelengths, physical positions, and continuously variable transmitted Raman intensities at given wavelengths after normalization. Our spectral barcodes can express bits of information since CHs deliver different wavelength information and one pixel of the image sensor encodes 10 bits. This is comparable with conventional 2D barcodes, which contain ca. The capacity of the encoding information of the spectral barcode can be enhanced by increasing the number of CHs or adapting sensor with higher dynamic range. Supplementary Fig. Whereas they have a virtually indistinguishable appearance, one can easily distinguish their Raman spectra—obtained with a commercial spectrometer as well as corresponding Raman spectral barcodes obtained with our smartphone spectrometer. When comparing the Raman spectra obtained with the two measurements, blue squares indicate the Raman peaks or major spectral components of each drug and the corresponding locations in the Raman spectral barcode. Although the spectrum obtained with the smartphone Raman spectrometer exhibited a lower spectral resolution, it matched well with that of the commercial Raman spectrometer. The FWHM at these corresponding bands of band pass filters ranged between 1 and 1. We demonstrated drug classification with a smartphone Raman spectrometer because this tool can provide important information in healthcare; for example, when distinguishing counterfeit from legal drugs, or choosing the correct drug pill among similar looking drug pills to prevent misuse. To overcome the issues of previous works as explained in the introduction, Raman spectroscopy provides molecular fingerprints and is suitable for identifying drugs by their chemical compositions and functions. We chose the most widely prescribed drugs for three common diseases hypertension, diabetes, and hyperlipidemia and three over-the-counter medicines vitamin B6, vitamin C, and acetaminophen for drug classification. Medical professionals prescribe amlodipine, losartan, and valsartan for hypertension; glimepiride and metformin for diabetes; and atorvastatin, rosuvastatin, and simvastatin for hyperlipidemia. Figure 2 shows representative spectral barcodes of 11 major components found in hypertension, diabetes, hyperlipidemia, and the other over-the-counter drugs. Spectral barcodes result from sharp Raman bands and broad fluorescence, which produce different patterns. Most of the spectral barcodes are readily distinguishable; but in some cases, drugs with different major components for example, amlodipine, losartan, and simvastatin need a classification algorithm to distinguish. Representative spectral barcodes of amlodipine, losartan, and valsartan for hypertension; glimepride and metformin for diabetes, atorvastatin, rosuvastatin, and simvastatin for hyperlipidemia; and vitamin B6, vitamin C, and Tylenol for over-the-counter drugs. Each panel also shows the specific brand names that correspond to the spectral barcodes for each major component. Figure 3 shows the schematics of data processing for drug classification based on spectral barcodes. When combined with CNN, Raman spectroscopy becomes a powerful tool for predicting the major components of drugs and even their brand identities. We used 54 drugs 1—54 in Supplementary Fig. The details to obtain Raman spectral images are explained in the Methods. The average value with standard deviation of normalized Raman intensity at each wavelength of the spectral barcodes is plotted. We used RGB images as additional information to improve drug classification accuracy by their brand name. Among various classification algorithms such as Bayesian network, support vector machine SVM , etc. Alex neural network AlexNet and visual geometry group neural network VGGNet , and implemented a shortcut add skipping convolution This CNN is made up of one conventional residual block of ResNet, consisting of a convolution layer with batch normalization, add, and rectified linear unit ReLu ; and two fully connected layers produced after flattening one with batch normalization and ReLu, and the other with batch normalization and softmax. ReLu is a common activation function in deep learning algorithms and returns a max 0, input , which provides a threshold in various parameters generated during the execution of the algorithm. The Methods describes details of the CNN architecture, training method, and database. To identify the brand name of each drug, we applied another CNN—simplified ResNet—followed by classification of the major component. The architecture of the CNN for identifying the brand name was similar to that of the CNN for classifying the major component, except the size of the fully connected layers since the size is related to the dimensions of the final result; the brand name of the drug. Encoding the spectral barcode from the 2D Raman image of a drug as well as classification by major component or brand name with convolutional neural network CNN. One conventional residual block of residual neural network ResNet convolution layer with batch normalization, add, and rectified linear unit ReLu. Two fully connected layers followed by flattening one with batch normalization and ReLu, and one with batch normalization and softmax as an activation function. We combined a CNN for the red, green and blue RGB images of the drugs taken by the smartphone camera as a tool to enhance the accuracy of drug classification. The smartphone shows the results as an auxiliary classification tool. Figure 4 shows the confusion matrix for classifying the major chemical components of the drugs. The confusion matrix is for evaluating the performance in classification problems, comparing the actual class, and predicting the class with a classification algorithm. Diagonal and off-diagonal terms represent the correct and incorrect cases, respectively. The overall accuracy for 54 drugs major component was Additionally, we confirmed the expandability and effectiveness of the CNN for spectral barcodes by identifying four drugs as listed A1, A2, A3, A4 in Supplementary Fig. Even though these drugs are excluded in CNN training procedure both training and validation set , the trained CNN accurately predicted the major components from the spectral barcodes once the spectral barcode of the same major components were in the database. Regarding Dymit, Glucophase, and Metofol, other 11 drugs with the same major components metformin were in the database. Regarding Glimel, eight drugs with glimepiride were in the database. The prediction accuracy for the major component of three drugs from metformin and one drug from glimepiride was obtained from and trials, respectively. Only one failure from metformin was confirmed, which corresponds to Diagonal and off diagonal terms represent correct and wrong classification of drugs. Color scale bar for relative number at each cases out of total trials is shown in the right corner. Classifications on various applications by CNN have been done using full spectrum of objects under interest obtained by benchtop or portable spectrometers using high signal-to-noise ratio SNR CCD and conventional grating 22 , 26 with high spectral resolution. Thus, the developed spectrometer on CMOS image sensor exhibits SNR and Q factor enough to classify drugs by Raman spectral barcode, and is suitable for lower power consumption. It might occasionally be necessary to identify the names as well as brands of drugs that are in the same drug group because brand-specific additives or coatings can affect the behavior in the body, such as speed of absorption or allergic reaction. The squares of the same color indicate the Raman peaks which are from the same major chemical component, metformin. Higher fluorescence appears for Glu-M SR than Diabex as the overall intensity was high in the spectral barcodes. The accuracy in terms of classifying brand names remained still large: The accuracy of the CNN for differentiating one major component from the others was high, and thus misclassifying cases were most common among drugs with the same major components. Three spectral barcodes from the same major component of drug category metformin. The Raman spectrum extracted from each barcode are below the spectral barcode, along with the reference Raman spectrum from a commercial spectrometer, indicated by a black solid line and red symbols connected by lines, respectively. The insets show the RGB images of each drug. The gray-shaded tapered areas indicate fluorescence or background, induced by additives or binders. The appearance of the drugs such as color and shape provides additional information for identification as RGB images taken by the smartphone camera exhibit various shapes and colors Supplementary Fig. We achieved classification by subsampling i. By additionally applying the CNN of RGB images as an auxiliary classification tool, the accuracy of identifying the exact brand name was slightly increased up to We designed the final CNN for predicting the brand name to use the product of the outputs from both CNNs as a combined method, treating them with equal importance. One could further optimize the prediction accuracy by adjusting the output ratio between two types of CNNs. In this work, we introduced the concept of the spectral barcode, obtained with a smartphone Raman spectrometer. Even with relatively lower spectral resolution and SNR due to the inherent properties of band pass filter arrays and CMOS image sensor compared with commercially available spectrometers installed with grating and CCD, the smartphone Raman spectrometer exhibits still high enough Q factor as portable spectrometer with high efficiency in terms of power consumption. Only external excitation and collection optics are needed to excite and collect Raman signals from the specimen without additional connecting electronic board to the smartphone. This makes the smartphone spectrometer more compact with minimized external module and versatile. Integrating with AI capability in the smartphone spectrometer makes the developed spectrometer more powerful. We demonstrated drug classification by spectral barcodes containing weak Raman signals with In the measurement aspect for prediction accuracy enhancement, detection of major components under thick coating may be possible, for example, by introducing spatially offset Raman spectroscopy SORS. Moreover, systematic understanding how drug companies mix major components in collaboration with medical society is necessary to develop more powerful drug classification CNN. In the future, by reducing the size of channel to one-pixel level and increasing the density of CH arrays, simultaneous measurement of spectral and morphological information of the object under interest can be achieved, which is called hyperspectral imaging, by using smartphone camera. This will extensively increase the portability and usability opening up new field in smartphone business. Vitamin B6 plidoxine Tab. The name, appearance and pharmaceutical companies of each drug can be found in Supplementary Fig. Images for training only, monitoring the training to validate and testing the classification accuracy belong to mutually exclusive group. The drug samples need to be placed at the focal point of the excitation laser. Depending on the position of drug against the objective lens The aperture size is 0. Furthermore, excitation light can be scattered by the etched marks depending on the contacting position of drugs. These can slightly influence the background intensity of the Raman signal. Thus, drugs are placed randomly on the hole of the objective lens to obtain Raman images for training testing CNN. RGB images were acquired in the normal direction with constraints that the drugs were placed on black paper under typical room light. Preprocessing of the RGB images consisted of denoising, extracting contours, erasing the background, resizing the images, and color normalization. The RGB images for training were augmented to reduce the dependence on the position, angle, and size of the drug in the images. In this study, the relatively small number of images was sufficient, even though tens of thousands or hundreds of thousands of images are generally used in the artificial intelligence field. That is why there is little chance of other signals in spectral barcodes, and condition of RGB image measurements is limited. Each band pass consists of one vertical pair of DBR separated with a Si cavity layer, performing as a Fabry—Perot filter. Each filter has a narrow band in the range of 1. Laser diodes were purchased from Thorlabs, Inc. Commercially available rechargeable batteries were used as an external power supply. The outer module case works as dark room to block ambient light and pinholes are designed to exclude unwanted scattered light or residual excitation light source. The laser power and the frequency were stable for hours by supplying the external electricity. Also, heat sink was carefully designed in the attachable Raman module to maintain the output wavelength. The laser can be powered by the external power supply such as commercially available rechargeable battery. The rechargeable battery can be as small as to be installed in the attachable Raman module as shown in Fig. The position of the rear camera and the size of the image sensor can be varied depending on the model of the smartphone. By only modifying the position of outlet of the Raman signal to the image sensor, the optics set-up of the attachable external module can be applicable to other models of the smartphone. It is transformed into a spectral barcode after a series of processing, denoising, averaging these 4 sets of CHs, and normalizing. The spectra were carefully measured to compare intensities in absolute values. One feature of the ResNet is a shortcut, adding input-to-output after convolution and batch normalization. After flattening, two fully connected layers are followed by batch normalization; one with ReLu, and one with softmax as an activation function. The final output has 11 dimensions, the number of major components. To identify the brand name of the drug, a classification algorithm in series is designed. There is one CNN for classifying the major component and nine CNNs for identifying the brand name, because there is no need to identify the brand name for vitamins B6 and C. The structure of the CNN for classifying the brand name of the drug is similar to that for the major component. The only difference is the size of the fully connected layer. After flattening, three fully connected layers are achieved by batch normalization, the last layer with softmax, and the other layers with ReLu as an activation function. The result of the accuracy is The overfitting is avoided by monitoring the training loss and validation loss simultaneously. As over fitted, validation loss starts to saturate or even increase while training loss keeps decreasing. Therefore, training and validation losses are monitored during training process, as epoch increases. Also overfitting can occur with complicated algorithm structure, and thus the number of hidden layers and parameters needs to be optimized. Furthermore, batch normalization is added after convolution layer and fully-connected layer to prevent gradient vanishing problem which stops updating the parameters in CNN. Android application was produced using program language C , and installed in galaxy not 9 from Samsung. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Source data and database containing minimum set of spectral barcodes and RGB images of each drugs used in this work have been deposited in the repository The software with a readme. Cai, F. Pencil-like imaging spectrometer for bio-samples sensing. Express 8 , Baik, K. Pharmaceutical tablet classification using a portable spectrometer with combinations of visible and near-infrared spectra. In: Int. Ubiquitous Futur. Yu, X. Development of a handheld spectrometer based on a linear variable filter and a complementary metal-oxide-semiconductor detector for measuring the internal quality of fruit. Infrared Spectrosc. Yang, Z. Miniaturization of optical spectrometers. Science , Article Google Scholar. Kang, J. Direct observation of glucose fingerprint using in vivo Raman spectroscopy. Liang, P. Rapid and reagentless detection of microbial contamination within meat utilizing a smartphone-based biosensor. Yu, L. Disposable lateral flow-through strip for smartphone-camera to quantitatively detect alkaline phosphatase activity in milk. Chao, E. Smartphone-based applications for skin monitoring and melanoma detection. Dermatologic Clin. Edwards, P. Smartphone based optical spectrometer for diffusive reflectance spectroscopic measurement of hemoglobin. Ayas, S. Counting molecules with a mobile phone camera using plasmonic enhancement. ACS Photonics 1 , 17—26 Barnett, P. Miniature spatial heterodyne Raman spectrometer with a cell phone camera detector. Chandler, L. A smart handheld Raman spectrometer with cloud and AI deep learning. SPIE , —1 Google Scholar. Zeng, F. Analyst , — Correia, J. Single-chip CMOS optical microspectrometer. Senors Actuators B: Phys. Pervez, N. Photonic crystal spectrometer. Horie, Y. Visible wavelength color filters using dielectric subwavelength gratings for backside-illuminated CMOS image sensor technologies. Nano Lett. Faraji-Dana, M. 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Detection and identification of drug traces in latent fingermarks using Raman spectroscopy. Wang, S. Concept of a high-resolution miniature spectrometer using an integrated filter array. Park, Y. Singh, A. A review of supervised machine learning algorithms. He, K. Deep residual learning for image recognition. In: Proc. Simonyan, K. Very deep convolutional networks for large-scale image recognition. Kim, U. Download references. Institute of Quantum Systems, Daejeon, , Korea. You can also search for this author in PubMed Google Scholar. K and. H devised and carried out the CNN. All authors reviewed the manuscript. Correspondence to Hyuck Choo. Nature Communications thanks the anonymous, reviewer s for their contribution to the peer review of this work. A peer review file is available. Reprints and permissions. Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer. Nat Commun 14 , Download citation. Received : 19 February Accepted : 14 August Published : 29 August Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature. Download PDF. Subjects Electrical and electronic engineering Imaging techniques Raman spectroscopy. This article has been updated. 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. Detection and identification of drug traces in latent fingermarks using Raman spectroscopy Article Open access 24 February Deep learning-enabled mobile application for efficient and robust herb image recognition Article Open access 21 April Nanopore analysis of salvianolic acids in herbal medicines Article Open access 05 March Introduction Miniaturization of optical spectrometers has been an active area of research because the demand for portable scientific and industrial characterization tools remains high 1 , 2 , 3 , 4 , 5. Results Smartphone Raman spectrometer and spectral barcode Figure 1 shows schematics of the smartphone Raman spectrometer and spectral barcode; which is the 2D Raman intensity map acquired with the smartphone Raman spectrometer, and an artificial intelligence algorithm embedded in the smart phone for classification. Full size image. Discussion In this work, we introduced the concept of the spectral barcode, obtained with a smartphone Raman spectrometer. Database — Raman images per drug and — RGB images of both sides per drug were taken, respectively. Attachable Raman module Laser diodes were purchased from Thorlabs, Inc. Android application Android application was produced using program language C , and installed in galaxy not 9 from Samsung. Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Data availability Source data and database containing minimum set of spectral barcodes and RGB images of each drugs used in this work have been deposited in the repository Code availability The software with a readme. References Cai, F. Article Google Scholar Kang, J. Article Google Scholar Liang, P. Google Scholar Zeng, F. View author publications. Ethics declarations Competing interests The authors declare no competing interests. Peer review Peer review information Nature Communications thanks the anonymous, reviewer s for their contribution to the peer review of this work. Supplementary information. Suppementary information. Peer review file. Description of Additional Supplementary Files Document. Supplementary Movie 1. Reporting Summary. About this article. Cite this article Kim, U. Copy to clipboard. This article is cited by Quantitative, high-sensitivity measurement of liquid analytes using a smartphone compass Mark Ferris Gary Zabow Nature Communications Search Search articles by subject, keyword or author. Show results from All journals This journal. Advanced search. Close banner Close. Email address Sign up. Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing.

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