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Xu Q, Yan R, Gui X, Song R, Wang X. Machine learning-assisted image label-free smartphone platform for rapid segmentation and robust multi-urinalysis. Anal Bioanal Chem 2024; 416:1443-1455. [PMID: 38228897 DOI: 10.1007/s00216-024-05147-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/31/2023] [Accepted: 01/11/2024] [Indexed: 01/18/2024]
Abstract
This study presents a groundbreaking approach for the early detection of chronic kidney disease (CKD) and other urological disorders through an image-label-free, multi-dipstick identification method, eliminating the need for complex machinery, label libraries, or preset coordinates. Our research successfully identified reaction pads on 187 multi-dipsticks, each with 11 pads, leveraging machine learning algorithms trained on human urine data. This technique aims to surpass traditional colourimetric methods and concentration-colour curve fitting, offering more robust and precise community screening and home monitoring capabilities. The developed algorithms enhance the generalizability of machine learning models by extracting primary colours and correcting urine colours on each reaction pad. This method's cost-effectiveness and portability are significant, as it requires no additional equipment beyond a standard smartphone. The system's performance rivals professional medical equipment without auxiliary lighting or flash under regular indoor light conditions, effectively managing false positives and negatives across various categories with remarkable accuracy. In a controlled experimental setting, we found that random forest algorithms, based on a Bagging strategy and applied in the HSV colour space, showed optimal results in smartphone-assisted urinalysis. This study also introduces a novel urine colour correction method, significantly improving machine learning model performance. Additionally, ISO parameters were identified as crucial factors influencing the accuracy of smartphone-based urinalysis in the absence of additional lighting or optical configurations, highlighting the potential of this technology in low-resource settings.
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Affiliation(s)
- Qianfeng Xu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Rongguo Yan
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
| | - Xinrui Gui
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Ruoyu Song
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiaoli Wang
- Sanya Central Hospital (Hainan Third People's Hospital), Sanya, China.
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2
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Kishnani V, Gupta A. Predictive Framework Development for User-Friendly On-Site Glucose Detection. ACS APPLIED BIO MATERIALS 2023; 6:4336-4344. [PMID: 37683114 DOI: 10.1021/acsabm.3c00532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
This study explores a smartphone-based spot detection framework for glucose in a rapid, simple, and affordable paper-based analytical device (PAD), which employs machine-learning algorithms to estimate various glucose concentrations. Herein, two different detection mixtures were chosen with chitosan (C) and without chitosan (WC) for the color change analysis. Being a biopolymer, chitosan improves the analytical performance of PADs when used with a chromogenic agent. Moreover, the influence of the illumination conditions and camera optics on the professed color of glucose strips was observed by choosing various illumination conditions and different smartphones. Hence, this study focuses on developing a framework for smartphone-based simple and user-friendly spot-based glucose detection (with a concentration range of 10-40 mM) at any illumination conditions and in any direction of illumination. Additionally, the combination of color spaces and machine-learning algorithms was applied for the signal enhancement. It was observed that the machine learning classifiers, cubic support vector machine (SVM) and narrow neutral network show higher accuracy for the WC samples, which are 92.7 and 92.3%, respectively. The samples with chitosan show higher accuracy for the linear discriminant and quadratic SVM classifiers, which are 94.1 and 93.9%, respectively. Simultaneously, cubic SVM shows ∼93% accuracy for both cases. In order to assess the performance of the devices, a blind test was also conducted. This study demonstrates the potential of the developed system for initial disease screening at the user end. By incorporating machine learning techniques, the platform can provide reliable and accurate results, thus paving the way for estimating the accuracy of the results for improved initial healthcare screening and diagnosis of any disease.
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Affiliation(s)
- Vinay Kishnani
- Department of Mechanical Engineering, Indian Institute of Technology, Jodhpur 342030, Rajasthan, India
| | - Ankur Gupta
- Department of Mechanical Engineering, Indian Institute of Technology, Jodhpur 342030, Rajasthan, India
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3
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Fairooz T, McNamee SE, Finlay D, Ng KY, McLaughlin J. A novel patches-selection method for the classification of point-of-care biosensing lateral flow assays with cardiac biomarkers. Biosens Bioelectron 2023; 223:115016. [PMID: 36586151 DOI: 10.1016/j.bios.2022.115016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/27/2022] [Accepted: 12/13/2022] [Indexed: 12/27/2022]
Abstract
Cardiovascular Disease (CVD) is amongst the leading cause of death globally, which calls for rapid detection and treatment. Biosensing devices are used for the diagnosis of cardiovascular disease at the point-of-care (POC), with lateral flow assays (LFAs) being particularly useful. However, due to their low sensitivity, most LFAs have been shown to have difficulties detecting low analytic concentrations. Breakthroughs in artificial intelligence (AI) and image processing reduced this detection constraint and improved disease diagnosis. This paper presents a novel patches-selection approach for generating LFA images from the test line and control line of LFA images, analyzing the image features, and utilizing them to reliably predict and classify LFA images by deploying classification algorithms, specifically Convolutional Neural Networks (CNNs). The generated images were supplied as input data to the CNN model, a strong model for extracting crucial information from images, to classify the target images and provide risk stratification levels to medical professionals. With this approach, the classification model produced about 98% accuracy, and as per the literature review, this approach has not been investigated previously. These promising results show the proposed method may be useful for identifying a wide variety of diseases and conditions, including cardiovascular problems.
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Affiliation(s)
- Towfeeq Fairooz
- School of Engineering, Ulster University, Belfast, United Kingdom.
| | - Sara E McNamee
- School of Engineering, Ulster University, Belfast, United Kingdom.
| | - Dewar Finlay
- School of Engineering, Ulster University, Belfast, United Kingdom.
| | - Kok Yew Ng
- School of Engineering, Ulster University, Belfast, United Kingdom.
| | - James McLaughlin
- School of Engineering, Ulster University, Belfast, United Kingdom.
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4
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Elsenety MM, Mohamed MBI, Sultan ME, Elsayed BA. Facile and highly precise pH-value estimation using common pH paper based on machine learning techniques and supported mobile devices. Sci Rep 2022; 12:22584. [PMID: 36585481 PMCID: PMC9803664 DOI: 10.1038/s41598-022-27054-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022] Open
Abstract
Numerous scientific, health care, and industrial applications are showing increasing interest in developing optical pH sensors with low-cost, high precision that cover a wide pH range. Although serious efforts, the development of high accuracy and cost-effectiveness, remains challenging. In this perspective, we present the implementation of the machine learning technique on the common pH paper for precise pH-value estimation. Further, we develop a simple, flexible, and free precise mobile application based on a machine learning algorithm to predict the accurate pH value of a solution using an available commercial pH paper. The common light conditions were studied under different light intensities of 350, 200, and 20 Lux. The models were trained using 2689 experimental values without a special instrument control. The pH range of 1: 14 is covered by an interval of ~ 0.1 pH value. The results show a significant relationship between pH values and both the red color and green color, in contrast to the poor correlation by the blue color. The K Neighbors Regressor model improves linearity and shows a significant coefficient of determination of 0.995 combined with the lowest errors. The free, publicly accessible online and mobile application was developed and enables the highly precise estimation of the pH value as a function of the RGB color code of typical pH paper. Our findings could replace higher expensive pH instruments using handheld pH detection, and an intelligent smartphone system for everyone, even the chef in the kitchen, without the need for additional costly and time-consuming experimental work.
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Affiliation(s)
- Mohamed M. Elsenety
- grid.411303.40000 0001 2155 6022Department of Chemistry, Faculty of Science, Al-Azhar University, Nasr City, Cairo 11884 Egypt
| | - Mahmoud Basseem I. Mohamed
- grid.411303.40000 0001 2155 6022Department of Chemistry, Faculty of Science, Al-Azhar University, Nasr City, Cairo 11884 Egypt
| | - Mohamed E. Sultan
- grid.411303.40000 0001 2155 6022Department of Chemistry, Faculty of Science, Al-Azhar University, Nasr City, Cairo 11884 Egypt
| | - Badr A. Elsayed
- grid.411303.40000 0001 2155 6022Department of Chemistry, Faculty of Science, Al-Azhar University, Nasr City, Cairo 11884 Egypt
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5
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Shi SZ, Sheng T, Wang YY, Zhang KK, Liu S, Gao HW. Color-deconvolution-based feature image extraction and application in water quality analysis. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:4912-4921. [PMID: 36441632 DOI: 10.1039/d2ay01600a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
We propose a feature color extraction method that improves the accuracy of water quality analysis using a digital image and eliminates the effect of interfering ions and chromogenic agents on the color after a color reaction. The proposed method is based on color deconvolution (CD) combined with machine learning for substance measurement in water. After an ordinary camera acquires the solution image after color reaction, the CD algorithm is applied to extract the feature image, calculate the first-order, second-order, and third-order color moments corresponding to RGB channels, and construct a gradient boosting regression tree prediction model based on color moment features to detect substances in water. In predicting ammonia, nitrite, and orthophosphate concentrations, the mean square error values were 0.01029, 0.00063, and 0.1361, and the mean absolute error values were 0.08103, 0.02231, and 0.32886, respectively. There was no significant difference in the results of the comparative spectrophotometric method on the actual water samples. The spiked recoveries of the samples ranged from 94% to 120%, confirming that the method can effectively measure the content of substances in water.
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Affiliation(s)
- Sheng-Zhe Shi
- College of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, P. R. China.
| | - Tao Sheng
- College of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, P. R. China.
| | - Yan-Yan Wang
- College of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, P. R. China.
| | - Kai-Kai Zhang
- College of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, P. R. China.
| | - Sheng Liu
- College of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, P. R. China.
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior, Huaibei 235000, P. R. China
| | - Hong-Wen Gao
- College of Environmental Science and Engineering, Tongji University, Shanghai 200092, P. R. China
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6
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Kishnani V, Kumari S, Gupta A. A Chemometric-Assisted Colorimetric-Based Inexpensive Paper Biosensor for Glucose Detection. BIOSENSORS 2022; 12:bios12111008. [PMID: 36421125 PMCID: PMC9688802 DOI: 10.3390/bios12111008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/31/2022] [Accepted: 11/08/2022] [Indexed: 05/20/2023]
Abstract
This article reports a simple and inexpensive leak-proof paper pad with an initial selection of a paper substrate on the grounds of surface morphology and fluid absorption time. Herein, a drying method is used for glucose detection on a paper pad through colorimetric analysis, and the spot detection of glucose is analyzed by optimizing the HRP concentration and volume to obtain accurate results. The rapid colorimetric method for the detection of glucose on the paper pad was developed with a limit of detection (LOD) of 2.92 mmol L-1. Furthermore, the effects of the detection conditions were investigated and discussed comprehensively with the help of chemometric methods. Paper pads were developed for glucose detection with a range of 0.5-20 mM (apropos to the normal glucose level in the human body) and 0.1-0.5 M (to test the excessive intake of glucose). The developed concept has huge potential in the healthcare sector, and its extension could be envisioned to develop the reported paper pad as a point-of-care testing device for the initial screening of a variety of diseases.
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7
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Doǧan V, Isık T, Kılıç V, Horzum N. A field-deployable water quality monitoring with machine learning-based smartphone colorimetry. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:3458-3466. [PMID: 36000587 DOI: 10.1039/d2ay00785a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Water quality monitoring is an increasing global concern as the pollution of water sources causes adverse effects on economic growth and human health. Traditional approaches to the detection of pollutants are time-consuming and labor-intensive due to the requirement of sophisticated equipment or laboratory settings. Therefore, portable devices featuring rapid response and easy operation are indispensable in water quality monitoring. Herein, smartphone-based colorimetric pollutant quantification is demonstrated in a machine learning (ML) framework. As a proof of concept, the presence of seven ions in water was analyzed using colorimetric strips. The color variation on the strip indicators was captured under eight lighting conditions with five smartphones, providing robustness against the illumination variation and camera optics for ML classifiers. Color and texture features were extracted from the images to train the classifiers. Among the twenty-three classifiers, K-Nearest Neighbors exhibits the best classification performance, leading to the integration with our custom-designed Android application called Hydro Sens. The proposed approach was also tested with real samples taken from local water sources. The results prove that incorporating color strips with ML with a smartphone application can be used for water quality monitoring, which offers promising alternatives for sophisticated equipment that is especially applicable in resource-limited settings.
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Affiliation(s)
- Vakkas Doǧan
- Department of Electrical and Electronics Engineering, Izmir Katip Celebi University, 35620 Turkey.
| | - Tuǧba Isık
- Department of Mineral Analysis and Technologies, General Directorate of Mineral Research and Exploration (MTA), Ankara, Turkey
| | - Volkan Kılıç
- Department of Electrical and Electronics Engineering, Izmir Katip Celebi University, 35620 Turkey.
| | - Nesrin Horzum
- Department of Engineering Sciences, Izmir Katip Celebi University, 35620 Izmir, Turkey
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8
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Jeon HJ, Kim HS, Chung E, Lee DY. Nanozyme-based colorimetric biosensor with a systemic quantification algorithm for noninvasive glucose monitoring. Theranostics 2022; 12:6308-6338. [PMID: 36168630 PMCID: PMC9475463 DOI: 10.7150/thno.72152] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 08/20/2022] [Indexed: 11/10/2022] Open
Abstract
Diabetes mellitus accompanies an abnormally high glucose level in the bloodstream. Early diagnosis and proper glycemic management of blood glucose are essential to prevent further progression and complications. Biosensor-based colorimetric detection has progressed and shown potential in portable and inexpensive daily assessment of glucose levels because of its simplicity, low-cost, and convenient operation without sophisticated instrumentation. Colorimetric glucose biosensors commonly use natural enzymes that recognize glucose and chromophores that detect enzymatic reaction products. However, many natural enzymes have inherent defects, limiting their extensive application. Recently, nanozyme-based colorimetric detection has drawn attention due to its merits including high sensitivity, stability under strict reaction conditions, flexible structural design with low-cost materials, and adjustable catalytic activities. This review discusses various nanozyme materials, colorimetric analytic methods and mechanisms, recent machine learning based analytic methods, quantification systems, applications and future directions for monitoring and managing diabetes.
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Affiliation(s)
- Hee-Jae Jeon
- Weldon School of Biomedical Engineering, Purdue University, Indiana 47906, USA
- Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Hyung Shik Kim
- Department of Bioengineering, College of Engineering, and BK FOUR Biopharmaceutical Innovation Leader for Education and Research Group, Hanyang University, Seoul 04763, Republic of Korea
| | - Euiheon Chung
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea
- AI Graduate School, GIST, Gwangju 61005, Republic of Korea
- Research Center for Photon Science Technology, GIST, Gwangju 61005, Republic of Korea
| | - Dong Yun Lee
- Department of Bioengineering, College of Engineering, and BK FOUR Biopharmaceutical Innovation Leader for Education and Research Group, Hanyang University, Seoul 04763, Republic of Korea
- Institute of Nano Science and Technology (INST), Hanyang University, Seoul 04763, Republic of Korea
- Institute for Bioengineering and Biopharmaceutical Research (IBBR), Hanyang University, Seoul 04763, Republic of Korea
- Elixir Pharmatech Inc., Seoul 07463, Republic of Korea
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9
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Sajed S, Kolahdouz M, Sadeghi MA. Prediction of Arsenic Concentration in Water Samples Using Digital Imaging Colorimetry and Multi‐Variable Regression. ChemistrySelect 2022. [DOI: 10.1002/slct.202201376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Samira Sajed
- School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran
| | - Mohammadreza Kolahdouz
- School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran
| | - Mohammad Amin Sadeghi
- School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran
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10
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AuYoung B, Gutha Ravichandran A, Patel D, Dave N, Shah A, Wronko-Stevens B, Bettencourt F, Rajan R, Menon N. A Low-Cost Paper-Based Device for the Colorimetric Quantification of Bilirubin in Serum Using Smartphone Technology. Front Chem 2022; 10:869086. [PMID: 35873049 PMCID: PMC9301313 DOI: 10.3389/fchem.2022.869086] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 06/10/2022] [Indexed: 01/17/2023] Open
Abstract
Total bilirubin values have been used as a potential marker to pre-screen and diagnose various liver-based diseases such as jaundice, bile obstruction, liver cancer, etc. A device known as KromaHealth Kit, composed of paper and an acrylic backbone, is developed to quantify total bilirubin in human serum using image processing and machine learning technology. The biochemical assays are deposited on absorbent paper pads that act as reaction zones when serum is added. A dedicated smartphone app captures images of the colorimetric changes on the pad and converts them into quantitative values of bilirubin. The range of bilirubin concentration that can be quantified using the device ranges from 0.5 mg/dl to 7.0 mg/dl. The precision, limit of detection, interference analysis, linearity, stability, and comparison with a predicate are studied in this paper in accordance with clinical and laboratory standards institute. The results indicate that the KromaHealth Kit can be used as an inexpensive alternative to conventional bilirubin testing in clinical settings. With its level of precision, ease-of-use, long shelf-life, and short turnaround time, it will prove to be invaluable in limited-resource settings.
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Affiliation(s)
- Brittany AuYoung
- Division of Product Development, Group K Diagnostics, Philadelphia, PA, United States
| | | | - Divykumar Patel
- Manufacturing Department, Group K Diagnostics, Philadelphia, PA, United States
| | - Nisarg Dave
- Division of Product Development, Group K Diagnostics, Philadelphia, PA, United States
| | - Achal Shah
- Division of Product Development, Group K Diagnostics, Philadelphia, PA, United States
| | | | - Franklin Bettencourt
- Division of Product Development, Group K Diagnostics, Philadelphia, PA, United States
| | - Reshma Rajan
- Division of Product Development, Group K Diagnostics, Philadelphia, PA, United States
- *Correspondence: Reshma Rajan, ; Nidhi Menon,
| | - Nidhi Menon
- Division of Product Development, Group K Diagnostics, Philadelphia, PA, United States
- *Correspondence: Reshma Rajan, ; Nidhi Menon,
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11
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Siribunbandal P, Kim YH, Osotchan T, Zhu Z, Jaisutti R. Quantitative Colorimetric Detection of Dissolved Ammonia Using Polydiacetylene Sensors Enabled by Machine Learning Classifiers. ACS OMEGA 2022; 7:18714-18721. [PMID: 35694520 PMCID: PMC9178764 DOI: 10.1021/acsomega.2c01419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 05/12/2022] [Indexed: 06/15/2023]
Abstract
Easy-to-use and on-site detection of dissolved ammonia are essential for managing aquatic ecosystems and aquaculture products since low levels of ammonia can cause serious health risks and harm aquatic life. This work demonstrates quantitative naked eye detection of dissolved ammonia based on polydiacetylene (PDA) sensors with machine learning classifiers. PDA vesicles were assembled from diacetylene monomers through a facile green chemical synthesis which exhibited a blue-to-red color transition upon exposure to dissolved ammonia and was detectable by the naked eye. The quantitative color change was studied by UV-vis spectroscopy, and it was found that the absorption peak at 640 nm gradually decreased, and the absorption peak at 540 nm increased with increasing ammonia concentration. The fabricated PDA sensor exhibited a detection limit of ammonia below 10 ppm with a response time of 20 min. Also, the PDA sensor could be stably operated for up to 60 days by storing in a refrigerator. Furthermore, the quantitative on-site monitoring of dissolved ammonia was investigated using colorimetric images with machine learning classifiers. Using a support vector machine for the machine learning model, the classification of ammonia concentration was possible with a high accuracy of 100 and 95.1% using color RGB images captured by a scanner and a smartphone, respectively. These results indicate that using the developed PDA sensor, a simple naked eye detection for dissolved ammonia is possible with higher accuracy and on-site detection enabled by the smartphone and machine learning processes.
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Affiliation(s)
- Papaorn Siribunbandal
- Department
of Physics, Faculty of Science and Technology, Thammasat University, Pathumthani 12121, Thailand
- Research
Unit in Innovative Sensors and Nanoelectronic Devices, Thammasat University, Pathumthani 12121, Thailand
| | - Yong-Hoon Kim
- School
of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon 16419, Korea
| | - Tanakorn Osotchan
- Department
of Physics, Faculty of Science, Mahidol
University, Bangkok 10400, Thailand
| | - Zhigang Zhu
- School
of Health Science and Engineering, University
of Shanghai for Science and Technology, Shanghai 200093, China
| | - Rawat Jaisutti
- Department
of Physics, Faculty of Science and Technology, Thammasat University, Pathumthani 12121, Thailand
- Research
Unit in Innovative Sensors and Nanoelectronic Devices, Thammasat University, Pathumthani 12121, Thailand
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12
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Zhang Y, Tseng TM, Schlichtmann U. ColoriSens: An open-source and low-cost portable color sensor board for microfluidic integration with wireless communication and fluorescence detection. HARDWAREX 2022; 11:e00312. [PMID: 35572858 PMCID: PMC9095929 DOI: 10.1016/j.ohx.2022.e00312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/25/2022] [Accepted: 04/26/2022] [Indexed: 06/15/2023]
Abstract
Microfluidic colorimetric biosensors have shown promising potential for detecting metal cations, anions, organic dyes, drugs, pesticides. As for today, most colorimetric sensors are read by a smartphone or professional optical imaging system, and there is still a lack of an affordable and reliable colorimetric detector for the microfluidic chip. Integrating those reading and detection capabilities into a microfluidic system is essential for point-of-care (POC) detection and can enable more complex microfluidic operations, such as lab-on-a-chip experiments or programmable microfluidics. We developed an open-source colorimetric detection sensor board that can be integrated into the existing microfluidic system. This sensor board has a built-in UV source that enables fluorescence detection. With built-in USB and Wi-Fi connectivity and a set of simple APIs, microfluidic systems can communicate directly with this sensor board, even wirelessly. The sensor was designed for low-cost. With a total build cost of less than 12 EUR per unit, it is ideal for low-cost systems and DIY microfluidic users. Along with the sensor board, we also designed a companion microfluidic chip carrier cartridge which can be modified depending on the chip's dimension. To demonstrate the sensor, we also developed a cross-platform open-source client application to demonstrate the communication APIs and the functionality of the sensor board.
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13
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Ramos Carvalho Â, Candice Genz Bazana L, Meneghello Fuentefria A, Flôres Ferrão M. Digital images coupled to PLS regression for pH prediction in sterile culture medium. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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14
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Resmi PE, Sachin Kumar S, Alageswari D, Suneesh PV, Ramachandran T, Nair BG, Satheesh Babu TG. Development of a paper-based analytical device for the colourimetric detection of alanine transaminase and the application of deep learning for image analysis. Anal Chim Acta 2021; 1188:339158. [PMID: 34794561 DOI: 10.1016/j.aca.2021.339158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/21/2021] [Accepted: 10/06/2021] [Indexed: 10/20/2022]
Abstract
A paper-based colourimetric assay for the detection of alanine transaminase has been developed. In the presence of alanine transaminase, 2,4-dinitrophenyl hydrazine changes to pyruvate hydrazone leading to a colour change from pale yellow to dark yellow. Reaction conditions were optimized using absorption spectroscopic studies. Hydrophobic patterns on the Whatman chromatographic paper were created by wax printing, and the reagents were drop cast at the reagent zone. On the paper device, the intensity of the yellow colour increases with ALT concentration in the range of 20-140 U/L in human serum. For the quantification of ALT, coloured images were captured using a digital camera and were processed with Image J software. The machine learning approach was also explored for the ALT analysis by training with colour images of the paper device and testing using a cross-validation procedure. The results obtained with real clinical samples on the paper device showed good accuracy of less than 5% relative error with the clinical lab results. Furthermore, the paper device shows high selectivity to ALT in the presence of various interfering species in blood serum with a sensitivity of 0.261 a.u/(U/L), a detection limit of 4.12 U/L, and precise results with an RSD of less than 7%. For the testing of whole blood, a plasma separation membrane was integrated with the patterned paper.
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Affiliation(s)
- P E Resmi
- Amrita Biosensor Research Lab, Amrita School of Engineering Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India; Department of Sciences, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India
| | - S Sachin Kumar
- Centre for Computational Engineering and Networking, Amrita School of Engineering Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India
| | - D Alageswari
- Amrita Biosensor Research Lab, Amrita School of Engineering Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India; Department of Sciences, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India
| | - P V Suneesh
- Amrita Biosensor Research Lab, Amrita School of Engineering Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India; Department of Sciences, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India
| | - T Ramachandran
- Department of Sciences, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India
| | - Bipin G Nair
- Amrita School of Biotechnology, Amritapuri, Kollam, 690525, India
| | - T G Satheesh Babu
- Amrita Biosensor Research Lab, Amrita School of Engineering Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India; Department of Sciences, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India.
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15
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Khanal B, Pokhrel P, Khanal B, Giri B. Machine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices. ACS OMEGA 2021; 6:33837-33845. [PMID: 34926930 PMCID: PMC8675014 DOI: 10.1021/acsomega.1c05086] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/19/2021] [Indexed: 06/14/2023]
Abstract
Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary, resulting in less accurate results. Recently, machine-learning (ML)-assisted models have been used in image analysis. We evaluated a combination of four ML models-logistic regression, support vector machine (SVM), random forest, and artificial neural network (ANN)-as well as three image color spaces, RGB, HSV, and LAB, for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras and users for food color and enzyme inhibition assays to create training and test datasets. The prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML models and color space combinations. All models better predicted coarse-level classifications than fine-grained concentration classes. ML models using the sample color along with a reference color increased the models' ability to predict the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration class prediction accuracy obtained for food color was 0.966 when using the ANN model and LAB color space. The accuracy for enzyme inhibition assay was 0.908 when using the SVM model and LAB color space. Appropriate models and color space combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.
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Affiliation(s)
- Bidur Khanal
- Center
for Analytical Sciences, Kathmandu Institute
of Applied Sciences, Kathmandu 44600, Nepal
- Nepal
Applied Mathematics and Informatics Institute for Research, Kathmandu 44600, Nepal
| | - Pravin Pokhrel
- Center
for Analytical Sciences, Kathmandu Institute
of Applied Sciences, Kathmandu 44600, Nepal
| | - Bishesh Khanal
- Nepal
Applied Mathematics and Informatics Institute for Research, Kathmandu 44600, Nepal
| | - Basant Giri
- Center
for Analytical Sciences, Kathmandu Institute
of Applied Sciences, Kathmandu 44600, Nepal
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16
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Askarian B, Ho P, Chong JW. Detecting Cataract Using Smartphones. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2021; 9:3800110. [PMID: 34786216 PMCID: PMC8580365 DOI: 10.1109/jtehm.2021.3074597] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 02/27/2021] [Accepted: 03/15/2021] [Indexed: 12/04/2022]
Abstract
Objective: Cataract, which is the clouding of the crystalline lens, is the most prevalent eye disease accounting for 51% of all eye diseases in the U.S. Cataract is a progressive disease, and its early detection is critical for preventing blindness. In this paper, an efficient approach to identify cataract disease by adopting luminance features using a smartphone is proposed. Methods: Initially, eye images captured by a smartphone were cropped to extract the lens, and the images were preprocessed to remove irrelevant background and noise by utilizing median filter and watershed transformation. Then, a novel luminance transformation from pixel brightness algorithm was introduced to extract lens image features. The luminance and texture features of different types of cataract disease images could be obtained accurately in this stage. Finally, by adopting support vector machines (SVM) as the classification method, cataract eyes were identified. Results: From all the images that we fed into our system, our method could diagnose diseased eyes with 96.6% accuracy, 93.4% specificity, and 93.75% sensitivity. Conclusion: The proposed method provides an affordable, rapid, easy-to-use, and versatile method for detecting cataracts by using smartphones without the use of bulky and expensive imaging devices. This methodcan be used for bedside telemedicine applications or in remote areas that have medical shortages. Previous smartphone-based cataract detection methods include texture feature analysis with 95 % accuracy, Gray Level Co-occurrence Matrix (GLCM) method with 89% accuracy, red reflex measurement method, and RGB color feature extraction method using cascade classifier with 90% accuracy. The accuracy of cataract detection in these studies is subject to changes in smartphone models and/or environmental conditions. However, our novel luminance-based method copes with different smartphone camera sensors and chroma variations, while operating independently from sensors’ color characteristics and changes in distances and camera angle. Clinical and Translational Impact—This study is an early/pre-clinical research proposing a novel luminance-based method of detecting cataract using smartphones for remote/at-home monitoring and telemedicine application.
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Affiliation(s)
- Behnam Askarian
- Department of Electrical and Computer EngineeringTexas Tech University Lubbock TX 79409 USA
| | - Peter Ho
- Lubbock Eye Clinic Lubbock TX 79410 USA
| | - Jo Woon Chong
- Department of Electrical and Computer EngineeringTexas Tech University Lubbock TX 79409 USA
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17
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Schackart KE, Yoon JY. Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors. SENSORS (BASEL, SWITZERLAND) 2021; 21:5519. [PMID: 34450960 PMCID: PMC8401027 DOI: 10.3390/s21165519] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/09/2021] [Accepted: 08/13/2021] [Indexed: 01/06/2023]
Abstract
Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor's signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.
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Affiliation(s)
- Kenneth E. Schackart
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, USA;
| | - Jeong-Yeol Yoon
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, USA;
- Department of Biomedical Engineering, The University of Arizona, Tucson, AZ 85721, USA
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18
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Noviana E, Ozer T, Carrell CS, Link JS, McMahon C, Jang I, Henry CS. Microfluidic Paper-Based Analytical Devices: From Design to Applications. Chem Rev 2021; 121:11835-11885. [DOI: 10.1021/acs.chemrev.0c01335] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Eka Noviana
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta, Indonesia 55281
| | - Tugba Ozer
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
- Department of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Istanbul, Turkey 34220
| | - Cody S. Carrell
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Jeremy S. Link
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Catherine McMahon
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Ilhoon Jang
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
- Institute of Nano Science and Technology, Hanyang University, Seoul, South Korea 04763
| | - Charles S. Henry
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
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19
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Alawsi T, Mattia GP, Al-Bawi Z, Beraldi R. Smartphone-based colorimetric sensor application for measuring biochemical material concentration. SENSING AND BIO-SENSING RESEARCH 2021. [DOI: 10.1016/j.sbsr.2021.100404] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
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20
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Kap Ö, Kılıç V, Hardy JG, Horzum N. Smartphone-based colorimetric detection systems for glucose monitoring in the diagnosis and management of diabetes. Analyst 2021; 146:2784-2806. [PMID: 33949379 DOI: 10.1039/d0an02031a] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Diabetes is a group of metabolic conditions resulting in high blood sugar levels over prolonged periods that affects hundreds of millions of patients worldwide. Measuring glucose concentration enables patient-specific insulin therapy, and is essential to reduce the severity of the disease, potential complications, and related mortalities. Recent advances and developments in smartphone-based colorimetric glucose detection systems are discussed in this review. The importance of glucose monitoring, data collection, transfer, and analysis, via non-invasive/invasive methods is highlighted. The review also presents various approaches using 3D-printed materials, screen-printed electrodes, polymer templates, designs allowing multiple glucose analysis, bioanalytes and/or nanostructures for glucose detection. The positive effects of advances in improving the performance of smartphone-based platforms are introduced along with future directions and trends in the application of emerging technologies in smartphone-based diagnostics.
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Affiliation(s)
- Özlem Kap
- Department of Engineering Sciences, İzmir Katip Çelebi University, 35620 Turkey.
| | - Volkan Kılıç
- Department of Electrical and Electronics Engineering, İzmir Katip Çelebi University, 35620 Turkey
| | - John G Hardy
- Department of Chemistry, Lancaster University, Lancaster, Lancashire LA1 4YB, UK and Materials Science Institute, Lancaster University, Lancaster, Lancashire LA1 4YB, UK
| | - Nesrin Horzum
- Department of Engineering Sciences, İzmir Katip Çelebi University, 35620 Turkey.
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21
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Almeida PLD, Lima LMA, Almeida LFD. A 3D-printed robotic system for fully automated multiparameter analysis of drinkable water samples. Anal Chim Acta 2021; 1169:338491. [PMID: 34088373 DOI: 10.1016/j.aca.2021.338491] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 04/02/2021] [Accepted: 04/04/2021] [Indexed: 10/21/2022]
Abstract
This work describes a 3D-printed robotic system named RSAWA (robotic system for automatic water analysis) for fully automated water analysis. RSAWA consists of a robotic arm coupled to a syringe pump, temperature and conductivity sensors, a low-cost webcam as colorimetric detector, and a 96-well microplate placed on a 3D-printed platform. The robotic system is controlled by software and it performs all analytical procedures. RSAWA was applied to measure conductivity (CDT), pH, total alkalinity (TA), total hardness (TH), chloride (Cl-), nitrite (NO2-), total dissolved phosphorus (TP), and total iron (TI) in drinkable water samples. A simple circuit was designed for conductivity determinations, while colorimetric pH determinations were carried out using Hue values extracted from digital images and a pH universal indicator. HSV histograms were used to calculate Pearson's correlation coefficients, allowing the construction of accurate titration curves. In addition to achieving sample throughputs of 112 h-1 for TA and TH determinations and 92 h-1 for Cl- determinations, RSAWA produced 99.5% less waste than the corresponding reference methods during titrations. Colorimetric measurements were performed through RGB vector norms calculated from digital images were used as analytical signals. Limits of quantification (μg L-1) were 6.83, 13.0 and 1.5 mg L-1 for NO2-, TP, and TI determinations, respectively. Sample throughputs (samples h-1) were 83 for NO2- and TP and 72 for TI with a 98.5% reduction in waste generation. Thus, RSAWA is a low-cost, feasible, and environmentally friendly alternative to quickly and accurately determine several chemical and physicochemical parameters in aqueous samples.
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Affiliation(s)
- Pedro Lemos de Almeida
- Instituto Federal de Educação, Ciência e Tecnologia Do Sertão de Pernambuco, Campus Salgueiro, CEP, 56000-000, Salgueiro, Pernambuco, Brazil; Universidade Federal da Paraíba, CCEN, Departamento de Química, CEP, 58051-970, João Pessoa, Paraíba, Brazil
| | - Lidiane Macedo Alves Lima
- Universidade Federal Rural de Pernambuco, Departamento de Química, CEP, 52171-900, Recife, Pernambuco, Brazil
| | - Luciano Farias de Almeida
- Universidade Federal da Paraíba, CCEN, Departamento de Química, CEP, 58051-970, João Pessoa, Paraíba, Brazil.
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22
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Gómez A, Bueno D, Gutiérrez JM. Electronic Eye Based on RGB Analysis for the Identification of Tequilas. BIOSENSORS-BASEL 2021; 11:bios11030068. [PMID: 33801493 PMCID: PMC8000478 DOI: 10.3390/bios11030068] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 02/21/2021] [Accepted: 02/25/2021] [Indexed: 11/16/2022]
Abstract
The present work reports the development of a biologically inspired analytical system known as Electronic Eye (EE), capable of qualitatively discriminating different tequila categories. The reported system is a low-cost and portable instrumentation based on a Raspberry Pi single-board computer and an 8 Megapixel CMOS image sensor, which allow the collection of images of Silver, Aged, and Extra-aged tequila samples. Image processing is performed mimicking the trichromatic theory of color vision using an analysis of Red, Green, and Blue components (RGB) for each image's pixel. Consequently, RGB absorbances of images were evaluated and preprocessed, employing Principal Component Analysis (PCA) to visualize data clustering. The resulting PCA scores were modeled with a Linear Discriminant Analysis (LDA) that accomplished the qualitative classification of tequilas. A Leave-One-Out Cross-Validation (LOOCV) procedure was performed to evaluate classifiers' performance. The proposed system allowed the identification of real tequila samples achieving an overall classification rate of 90.02%, average sensitivity, and specificity of 0.90 and 0.96, respectively, while Cohen's kappa coefficient was 0.87. In this case, the EE has demonstrated a favorable capability to correctly discriminated and classified the different tequila samples according to their categories.
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23
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Rodrigues JF, Florea L, de Oliveira MCF, Diamond D, Oliveira ON. Big data and machine learning for materials science. DISCOVER MATERIALS 2021; 1:12. [PMID: 33899049 PMCID: PMC8054236 DOI: 10.1007/s43939-021-00012-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/01/2021] [Indexed: 05/11/2023]
Abstract
Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.
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Affiliation(s)
- Jose F. Rodrigues
- Institute of Mathematical Sciences and Computing, University of São Paulo (USP), São Carlos, SP Brazil
| | - Larisa Florea
- SFI Research Centre for Advanced Materials and BioEngineering Research Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | - Maria C. F. de Oliveira
- Institute of Mathematical Sciences and Computing, University of São Paulo (USP), São Carlos, SP Brazil
| | - Dermot Diamond
- Insight Centre for Data Analytics, National Centre for Sensor Research, Dublin City University, Dublin 9, Dublin, Ireland
| | - Osvaldo N. Oliveira
- São Carlos Institute of Physics, University of São Paulo (USP), São Carlos, SP Brazil
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24
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Dabbagh SR, Rabbi F, Doğan Z, Yetisen AK, Tasoglu S. Machine learning-enabled multiplexed microfluidic sensors. BIOMICROFLUIDICS 2020; 14:061506. [PMID: 33343782 PMCID: PMC7733540 DOI: 10.1063/5.0025462] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 12/01/2020] [Indexed: 05/02/2023]
Abstract
High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.
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Affiliation(s)
| | - Fazle Rabbi
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey
| | | | - Ali Kemal Yetisen
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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25
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Hoque Tania M, Kaiser MS, Abu-Hassan K, Hossain MA. Pathological test type and chemical detection using deep neural networks: a case study using ELISA and LFA assays. JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT 2020. [DOI: 10.1108/jeim-01-2020-0038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Purpose
The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals have created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology and so on.
Design/methodology/approach
The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training deep learning (DL) models on thousands of images of these tests using transfer learning, this paper (1) classifies the type of the assay and (2) classifies the colourimetric results.
Findings
This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time.
Originality/value
To the best of the authors’ knowledge, this is the first attempt to provide colourimetric assay type classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities, it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.
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26
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Rajendran VK, Bakthavathsalam P, Bergquist PL, Sunna A. Smartphone technology facilitates point-of-care nucleic acid diagnosis: a beginner's guide. Crit Rev Clin Lab Sci 2020; 58:77-100. [PMID: 32609551 DOI: 10.1080/10408363.2020.1781779] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The reliable detection of nucleic acids at low concentrations in clinical samples like blood, urine and saliva, and in food can be achieved by nucleic acid amplification methods. Several portable and hand-held devices have been developed to translate these laboratory-based methods to point-of-care (POC) settings. POC diagnostic devices could potentially play an important role in environmental monitoring, health, and food safety. Use of a smartphone for nucleic acid testing has shown promising progress in endpoint as well as real-time analysis of various disease conditions. The emergence of smartphone-based POC devices together with paper-based sensors, microfluidic chips and digital droplet assays are used currently in many situations to provide quantitative detection of nucleic acid targets. State-of-the-art portable devices are commercially available and rapidly emerging smartphone-based POC devices that allow the performance of laboratory-quality colorimetric, fluorescent and electrochemical detection are described in this review. We present a comprehensive review of smartphone-based POC sensing applications, specifically on microbial diagnostics, assess their performance and propose recommendations for the future.
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Affiliation(s)
| | - Padmavathy Bakthavathsalam
- School of Chemistry and Australian Centre for Nanomedicine, University of New South Wales, Sydney, Australia
| | - Peter L Bergquist
- Department of Molecular Sciences, Macquarie University, Sydney, Australia.,Department of Molecular Medicine & Pathology, University of Auckland, Auckland, New Zealand.,Biomolecular Discovery Research Centre, Macquarie University, Sydney, Australia
| | - Anwar Sunna
- Department of Molecular Sciences, Macquarie University, Sydney, Australia.,Biomolecular Discovery Research Centre, Macquarie University, Sydney, Australia
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27
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A portable sol-gel urea colorimetric method for the determination of urea in feedstuffs. Food Chem 2020; 319:126545. [DOI: 10.1016/j.foodchem.2020.126545] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 08/17/2019] [Accepted: 03/01/2020] [Indexed: 11/22/2022]
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28
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Accurate device-independent colorimetric measurements using smartphones. PLoS One 2020; 15:e0230561. [PMID: 32214340 PMCID: PMC7098568 DOI: 10.1371/journal.pone.0230561] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 03/04/2020] [Indexed: 11/19/2022] Open
Abstract
Smartphones provide an ideal platform for colorimetric measurements due to their low cost, portability and image quality. As with any imaging-based colorimetry system, ambient light and device variations introduce error which must be dealt with. We propose a novel processing method consisting of a one-time calibration stage to account for inter-phone variations, and an innovative use of ambient light subtraction with image pairs to account for variation in ambient light. Data collection is kept very simple, making it particularly useful for use in the field, since nothing additional is required in the images. Ambient subtraction is first demonstrated for a range of colors and phones (Samsung S8 and LG Nexus 5X), and the Subtracted Signal to Noise Ratio (SSNR) is defined as a metric for assessing whether an image pair is appropriate at the time of image capture. The experimentally determined SSNR threshold below which to suggest retaking the images is 3.4. The classification accuracy for results using the proposed calibration pipeline is then compared to the simplest image metadata-based alternative and is found to be greatly superior. Finally, a custom colorcard is shown to improve the accuracy of device-independent results for known smaller ranges of colors over a standard colorcard, making this a possible application-specific modification to the overall processing pipeline.
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29
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Low-cost Point-of-Care Biosensors Using Common Electronic Components as Transducers. BIOCHIP JOURNAL 2020. [DOI: 10.1007/s13206-020-4104-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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30
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Solanki S, Pandey CM, Gupta RK, Malhotra BD. Emerging Trends in Microfluidics Based Devices. Biotechnol J 2020; 15:e1900279. [PMID: 32045505 DOI: 10.1002/biot.201900279] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 01/28/2020] [Indexed: 01/03/2023]
Abstract
One of the major challenges for scientists and engineers today is to develop technologies for the improvement of human health in both developed and developing countries. However, the need for cost-effective, high-performance diagnostic techniques is very crucial for providing accessible, affordable, and high-quality healthcare devices. In this context, microfluidic-based devices (MFDs) offer powerful platforms for automation and integration of complex tasks onto a single chip. The distinct advantage of MFDs lies in precise control of the sample quantities and flow rate of samples and reagents that enable quantification and detection of analytes with high resolution and sensitivity. With these excellent properties, microfluidics (MFs) have been used for various applications in healthcare, along with other biological and medical areas. This review focuses on the emerging demands of MFs in different fields such as biomedical diagnostics, environmental analysis, food and agriculture research, etc., in the last three or so years. It also aims to reveal new opportunities in these areas and future prospects of commercial MFDs.
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Affiliation(s)
- Shipra Solanki
- Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Delhi, 110042, India.,Department of Applied Chemistry, Delhi Technological University, Shahbad Daulatpur, Delhi, 110042, India
| | - Chandra M Pandey
- Department of Applied Chemistry, Delhi Technological University, Shahbad Daulatpur, Delhi, 110042, India
| | - Rajinder K Gupta
- Department of Applied Chemistry, Delhi Technological University, Shahbad Daulatpur, Delhi, 110042, India
| | - Bansi D Malhotra
- Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Delhi, 110042, India
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31
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Katoh A, Maejima K, Hiruta Y, Citterio D. All-printed semiquantitative paper-based analytical devices relying on QR code array readout. Analyst 2020; 145:6071-6078. [DOI: 10.1039/d0an00955e] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Colorimetric assay enabling user independent semiquantitative result readout through an array of sequentially unmasked QR codes.
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Affiliation(s)
- Aya Katoh
- Department of Applied Chemistry
- Keio University
- Yokohama 223-8522
- Japan
| | - Kento Maejima
- Department of Applied Chemistry
- Keio University
- Yokohama 223-8522
- Japan
| | - Yuki Hiruta
- Department of Applied Chemistry
- Keio University
- Yokohama 223-8522
- Japan
| | - Daniel Citterio
- Department of Applied Chemistry
- Keio University
- Yokohama 223-8522
- Japan
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32
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Edachana RP, Kumaresan A, Balasubramanian V, Thiagarajan R, Nair BG, Thekkedath Gopalakrishnan SB. Paper-based device for the colorimetric assay of bilirubin based on in-situ formation of gold nanoparticles. Mikrochim Acta 2019; 187:60. [DOI: 10.1007/s00604-019-4051-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 11/27/2019] [Indexed: 11/29/2022]
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33
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Field analysis free chlorine in water samples by a smartphone-based colorimetric device with improved sensitivity and accuracy. Microchem J 2019. [DOI: 10.1016/j.microc.2019.104200] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Biswas S, Chakraborty J, Agarwal A, Kumbhakar P. Gold nanostructures for the sensing of pH using a smartphone. RSC Adv 2019; 9:34144-34151. [PMID: 35529967 PMCID: PMC9073677 DOI: 10.1039/c9ra07101f] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 10/07/2019] [Indexed: 11/21/2022] Open
Abstract
Recently, metal nanostructures have been found to be capable of recognizing small changes in their surrounding environment, which can be utilized as significant sensing tools. In this study, we demonstrated colorimetric sensing of pH by gold nanostructures (GNs) using a simple smartphone. An indigenously developed Android app based on the CIELab 1931 analysis, which could run in a smartphone, was used for the precise determination of the pH value of liquid media. The pH value of an unknown solution obtained from the developed Android app was also compared with that obtained from the conventional ratiometric technique and a commercial pH meter. In another endeavor, it was found that the synthesized GNs demonstrated a high energy transfer efficiency from a donor (namely, the rhodamine 6G, (Rh 6G)) dye. This property of the GNs can be utilized further in the future for studying different bimolecular activities within the human body. It was found that the photoluminescence (PL) of Rh 6G was quenched when it was kept in the vicinity of the synthesized GNs, which was explained in terms of the Förster energy transfer mechanism. Thus, the present study will open up a plethora of opportunities for researchers to employ the nanostructures of gold and other metals in developing low-cost and Internet of Things (IoT)-based sensing devices using only a smart phone.
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Affiliation(s)
- Subrata Biswas
- Nanoscience Laboratory, Dept. of Physics, National Institute of Technology Durgapur 713209 West Bengal India
| | - Jayjeet Chakraborty
- Department of Computer Science and Engineering (CSE), National Institute of Technology Durgapur 713209 West Bengal India
| | - Avinash Agarwal
- Department of Computer Science and Engineering (CSE), National Institute of Technology Durgapur 713209 West Bengal India
| | - Pathik Kumbhakar
- Nanoscience Laboratory, Dept. of Physics, National Institute of Technology Durgapur 713209 West Bengal India
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Rabha D, Sarmah A, Nath P. Design of a 3D printed smartphone microscopic system with enhanced imaging ability for biomedical applications. J Microsc 2019; 276:13-20. [PMID: 31498428 DOI: 10.1111/jmi.12829] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 08/31/2019] [Accepted: 09/04/2019] [Indexed: 01/01/2023]
Abstract
Portable, low-cost smartphone platform microscopic systems have emerged as a potential tool for imaging of various micron and submicron scale particles in recent years (Ozcan; Pirnstill and Coté; Breslauer et al.; Zhu et al.). In most of the reported works, it involves either the use of sophisticated optical set-ups along with a high-end computational tool for postprocessing of the captured images, or it requires a high-end configured smartphone to obtain enhanced imaging of the sample. Present work reports the working of a low-cost, field-portable 520× optical microscope using a smartphone. The proposed smartphone microscopic system has been designed by attaching a 3D printed compact optical set-up to the rear camera of a regular smartphone. By using cloud-based services, an image processing algorithm has been developed which can be accessed anytime through a mobile broadband network. Using this facility, the quality of the captured images can be further enhanced, thus obviating the need for dedicated computational tools for postprocessing of the images. With the designed microscopic system, an optical resolution ∼2 µm has been obtained. Upon postprocessing, the resolution of the captured images can be improved further. It is envisioned that with properly designed optical set-up in 3D printer and by developing an image processing application in the cloud, it is possible to obtain a low-cost, user-friendly, field-portable optical microscope on a regular smartphone that performs at par with that of a laboratory-grade microscope. LAY DESCRIPTION: With the ever-improving features both in hardware and software part, smartphone becomes ubiquitous in the modern civilised society with approximately 8.1 billion cell phone users across the world, and ∼40% of them can be considered as smartphones. This technology is undoubtedly the leading technology of the 21st century. Very recently, various researchers across the globe have utilised different sensing components embedded in the smartphone to convert it into a field-portable low-cost and user-friendly tool which can be used for different sensing and imaging purposes. By using simple optical components such as lens, pinhole, diffuser etc. and the camera of the smartphone, various groups have converted the phone into a microscopic imaging system. Again, by removing the camera lenses of the phone, holography images of microscopic particles by directly casting its shadows on the CMOS sensor on the phone has been demonstrated. The holographic images have subsequently been processed using the dedicated computational tool, and the original photos of the samples can be obtained. All the reported smartphone-based microscopic systems either suffer from relatively low field-of-view (FOV), resolution or it needs a high computational platform. Present work, demonstrate an alternative approach by which a reasonably good resolution (<2 µm) along with high optical magnification (520×) and a large FOV (150 µm) has been obtained on a regular smartphone. For postprocessing of the captured images an image processing algorithm has been developed in the cloud and the same can be accessed by the smartphone application, obviating the need of dedicated computational tool and a high-end configured smartphone for the proposed microscope. For the development of the proposed microscopic system, a simple optical set-up has been fabricated in a 3D printer. The set-up houses all the required optical components and the sample specimen with the 3D-printed XY stage, and it can be attached easily to the rear camera of the smartphone. Using the proposed microscopic system, enhanced imaging of USAF target and red blood cells have been successfully demonstrated. With the readily available optical components and a regular smartphone, the net cost involvement is significantly low (less than $250, including the smartphone). We envisioned that the designed system could be utilised for point-of-care diagnosis in resource-poor settings where access to the laboratory facilities is very limited.
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Affiliation(s)
- D Rabha
- Applied Photonics and Nanophotonics Laboratory, Department of Physics, Tezpur University, Sonitpur, Assam, India
| | - A Sarmah
- Department of Pathology, Tezpur Medical College and Hospital, Sonitpur, Assam, India
| | - P Nath
- Applied Photonics and Nanophotonics Laboratory, Department of Physics, Tezpur University, Sonitpur, Assam, India
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Rajendran VK, Bakthavathsalam P, Bergquist PL, Sunna A. A portable nucleic acid detection system using natural convection combined with a smartphone. Biosens Bioelectron 2019; 134:68-75. [DOI: 10.1016/j.bios.2019.03.050] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 03/23/2019] [Accepted: 03/25/2019] [Indexed: 01/24/2023]
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Liu J, Geng Z, Fan Z, Liu J, Chen H. Point-of-care testing based on smartphone: The current state-of-the-art (2017–2018). Biosens Bioelectron 2019; 132:17-37. [DOI: 10.1016/j.bios.2019.01.068] [Citation(s) in RCA: 175] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 01/18/2019] [Accepted: 01/27/2019] [Indexed: 12/20/2022]
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Shang J, Yu L, Sun Y, Chen X, Kang Q, Shen D. On site determination of free chlorine in water samples by a smartphone-based colorimetric device with improved sensitivity and reliability. NEW J CHEM 2019. [DOI: 10.1039/c9nj03954f] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Absorbance in a long-path portable colorimetric device was measured by a ratiometric fluorescent strategy in a smartphone platform.
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Affiliation(s)
- Jian Shang
- College of Chemistry
- Chemical Engineering and Materials Science
- Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong
- Shandong Normal University
- Jinan 250014
| | - Lei Yu
- College of Chemistry
- Chemical Engineering and Materials Science
- Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong
- Shandong Normal University
- Jinan 250014
| | - Yan Sun
- College of Chemistry
- Chemical Engineering and Materials Science
- Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong
- Shandong Normal University
- Jinan 250014
| | - Xiaolan Chen
- College of Chemistry
- Chemical Engineering and Materials Science
- Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong
- Shandong Normal University
- Jinan 250014
| | - Qi Kang
- College of Chemistry
- Chemical Engineering and Materials Science
- Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong
- Shandong Normal University
- Jinan 250014
| | - Dazhong Shen
- College of Chemistry
- Chemical Engineering and Materials Science
- Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong
- Shandong Normal University
- Jinan 250014
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Experimental Demonstration of Remote and Compact Imaging Spectrometer Based on Mobile Devices. SENSORS 2018; 18:s18071989. [PMID: 29933615 PMCID: PMC6068658 DOI: 10.3390/s18071989] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Revised: 06/03/2018] [Accepted: 06/20/2018] [Indexed: 12/30/2022]
Abstract
Imaging spectrometers show great potential for environmental and biomedical sensing applications. Selfie sticks, which are tools used to take photographs or videos, have gained global popularity in recent years. Few people have connected these two objects, and few people have researched the application of imaging spectrometers to perform scientific monitoring in point-of-use scenarios. In this paper, we develop a compact imaging spectrometer (35 g in weight, 18 mm in diameter, and 72 mm in length) that can be equipped on a motorized selfie stick to perform remote sensing. We applied this system to perform environmental and facial remote sensing via motorized scanning. The absorption of chlorophyll and hemoglobin can be found in the reflectance spectra, indicating that our system can be used in urban greening monitoring and point-of-care testing. In addition, this compact imaging spectrometer was also easily attached to an underwater dome port and a quad-rotor unmanned aerial vehicle to perform underwater and airborne spectral detection. Our system offers a route toward mobile imaging spectrometers used in daily life.
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Kılıç V, Alankus G, Horzum N, Mutlu AY, Bayram A, Solmaz ME. Single-Image-Referenced Colorimetric Water Quality Detection Using a Smartphone. ACS OMEGA 2018; 3:5531-5536. [PMID: 31458756 PMCID: PMC6641965 DOI: 10.1021/acsomega.8b00625] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 05/10/2018] [Indexed: 05/25/2023]
Abstract
In this paper, we present a smartphone platform for colorimetric water quality detection based on the use of built-in camera for capturing a single-use reference image. A custom-developed app processes this image for training and creates a reference model to be used later in real experimental conditions to calculate the concentration of the unknown solution. This platform has been tested on four different water quality colorimetric assays with various concentration levels, and results show that the presented platform provides approximately 100% accuracy for colorimetric assays with noticeable color difference. This portable, cost-effective, and user-friendly platform is promising for application in water quality monitoring.
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Affiliation(s)
- Volkan Kılıç
- Department
of Electrical and Electronics Engineering, Department of Engineering
Sciences, and Department of Material Science and Engineering, Izmir Katip Celebi University, Balatcik Campus, Cigli, Izmir 35620, Turkey
| | - Gazihan Alankus
- Department
of Mechatronics Engineering, Izmir University
of Economics, Balcova, 35330 Izmir, Turkey
| | - Nesrin Horzum
- Department
of Electrical and Electronics Engineering, Department of Engineering
Sciences, and Department of Material Science and Engineering, Izmir Katip Celebi University, Balatcik Campus, Cigli, Izmir 35620, Turkey
| | - Ali Y. Mutlu
- Department
of Electrical and Electronics Engineering, Department of Engineering
Sciences, and Department of Material Science and Engineering, Izmir Katip Celebi University, Balatcik Campus, Cigli, Izmir 35620, Turkey
| | - Abdullah Bayram
- Department
of Electrical and Electronics Engineering, Department of Engineering
Sciences, and Department of Material Science and Engineering, Izmir Katip Celebi University, Balatcik Campus, Cigli, Izmir 35620, Turkey
| | - Mehmet E. Solmaz
- Department
of Electrical and Electronics Engineering, Department of Engineering
Sciences, and Department of Material Science and Engineering, Izmir Katip Celebi University, Balatcik Campus, Cigli, Izmir 35620, Turkey
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