Smartphone-based colorimetric detection system for portable health tracking

Smartphone-based colorimetric detection platform using color correction algorithms to reduce external interference

Smartphone-based digital image colorimetry is a powerful, fast, low-cost approach to detecting target analytes. However, lighting conditions and camera parameters easily affect the detection results, significantly curtailing its applicability in multiple scenarios. In this study, an Android-based mobile application (SMP-CC) is developed, which offers a comprehensive package that includes image acquisition, color correction, and colorimetric analysis functions. Using a custom color card, a built-in algorithm in SMP-CC can minimize the color difference between the standard color block image captured by different smartphones under different lighting conditions and the standard value by an LS171 colorimeter less than 4.36. The algorithm significantly eliminates the impacts of external lighting conditions and differences in cell phone models. Furthermore, the feasibility of SMP-CC was verified by successful colorimetric detection of urine pH, glucose, and protein, demonstrating its potential in smartphone-based digital image colorimetry.

Recent advancements of smartphone-based sensing technology for diagnosis, food safety analysis, and environmental monitoring

The emergence of computationally powerful smartphones, relatively affordable high-resolution camera, drones, and robotic sensors have ushered in a new age of advanced sensible monitoring tools. The present review article investigates the burgeoning smartphone-based sensing paradigms, including surface plasmon resonance (SPR) biosensors, electrochemical biosensors, colorimetric biosensors, and other innovations for modern healthcare. Despite the significant advancements, there are still scarcity of commercially available smart biosensors and hence need to accelerate the rates of technology transfer, application, and user acceptability. The application/necessity of smartphone-based biosensors for Point of Care (POC) testing, such as prognosis, self-diagnosis, monitoring, and treatment selection, have brought remarkable innovations which eventually eliminate sample transportation, sample processing time, and result in rapid findings. Additionally, it articulates recent advances in various smartphone-based multiplexed bio sensors as affordable and portable sensing platforms for point-of-care devices, together with statistics for point-of-care health monitoring and their prospective commercial viability.

Advances in Microfluidic Paper-Based Analytical Devices (µPADs): Design, Fabrication, and Applications

Microfluidic Paper-based Analytical Devices (µPADs) have emerged as a new class of microfluidic systems, offering numerous advantages over traditional microfluidic chips. These advantages include simplicity, cost-effectiveness, stability, storability, disposability, and portability. As a result, various designs for different types of assays are developed and investigated. In recent years, µPADs are combined with conventional detection methods to enable rapid on-site detection, providing results comparable to expensive and sophisticated large-scale testing methods that require more time and skilled personnel. The application of µPAD techniques is extensive in environmental quality control/analysis, clinical diagnosis, and food safety testing, paving the way for on-site real-time diagnosis as a promising future development. This review focuses on the recent research advancements in the design, fabrication, material selection, and detection methods of µPADs. It provides a comprehensive understanding of their principles of operation, applications, and future development prospects.

A CRISPR/Cas12 trans-cleavage reporter enabling label-free colorimetric detection of SARS-CoV-2 and its variants

We present a label-free colorimetric CRISPR/Cas-based method enabling affordable molecular diagnostics for SARS-CoV-2. This technique utilizes 3,3'-diethylthiadicarbocyanine iodide (DISC2(5)) which exhibits a distinct color transition from purple to blue when it forms dimers by inserting into the duplex of the thymidine adenine (TA) repeat sequence. Loop-mediated isothermal amplification (LAMP) or recombinase polymerase amplification (RPA) was used to amplify target samples, which were subsequently subjected to the CRISPR/Cas12a system. The target amplicons would activate Cas12a to degrade nearby TA repeat sequences, preserving DISC2(5) in its free form to display purple as opposed to blue in the absence of the target. Based on this design approach, SARS-CoV-2 RNA was colorimetrically detected very sensitively down to 2 copies/μL, and delta and omicron variants of SARS-CoV-2 were also successfully identified. The practical diagnostic utility of this method was further validated by reliably identifying 179 clinical samples including 20 variant samples with 100% clinical sensitivity and specificity. This technique has the potential to become a promising CRISPR-based colorimetric platform for molecular diagnostics of a wide range of target pathogens.

Deep learning-augmented T-junction droplet generation

Droplet generation technology has become increasingly important in a wide range of applications, including biotechnology and chemical synthesis. T-junction channels are commonly used for droplet generation due to their integration capability of a larger number of droplet generators in a compact space. In this study, a finite element analysis (FEA) approach is employed to simulate droplet production and its dynamic regimes in a T-junction configuration and collect data for post-processing analysis. Next, image analysis was performed to calculate the droplet length and determine the droplet generation regime. Furthermore, machine learning (ML) and deep learning (DL) algorithms were applied to estimate outputs through examination of input parameters within the simulation range. At the end, a graphical user interface (GUI) was developed for estimation of the droplet characteristics based on inputs, enabling the users to preselect their designs with comparable microfluidic configurations within the studied range.

Advancing Point-of-Care Diagnosis: Digitalizing Combinatorial Biomarker Signals for Lupus Nephritis

To improve the efficiency and patient coverage of the current healthcare system, user-friendly novel homecare devices are urgently needed. In this work, we developed a smartphone-based analyzing and reporting system (SBARS) for biomarker detection in lupus nephritis (LN). This system offers a cost-effective alternative to traditional, expensive large equipment in signal detection and quantification. This innovative approach involves using a portable and affordable microscopic reader to capture biomarker signals. Through smartphone-based image processing techniques, the intensity of each biomarker signal is analyzed. This system exhibited comparable performance to a commercial Genepix scanner in the detection of two potential novel biomarkers of LN, VISG4 and TNFRSF1b. Importantly, this smartphone-based analyzing and reporting system allows for discriminating LN patients with active renal disease from healthy controls with the area-under-the-curve (AUC) value = 0.9 for TNFRSF1b and 1.0 for VSIG4, respectively, indicating high predictive accuracy.

Machine learning-assisted image label-free smartphone platform for rapid segmentation and robust multi-urinalysis

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.

Critical importance of RGB color space specificity for colorimetric bio/chemical sensing: A comprehensive study

The use of the RGB color model in colorimetric chemical sensing via imaging techniques is widely prevalent in the literature. However, the lack of specificity in the selection of RGB color space during capture and analysis presents a significant challenge in creating standardised methods for this field and possible discrepancies. In this study, we conducted a comprehensive comparison and contrast of a total of 68 RGB color spaces to evaluate their respective impacts on colorimetric bio/chemical sensing. We explore the impact of dynamic range, sensitivity, and limit of detection, and show that the lack of specificity in RGB color space selection can significantly impact colorimetric chemical sensing by 42-77%. We also explore the impact of underlying RGB comparisons and demonstrate a further 18.3% discrepancy between RGB color spaces. By emphasising the importance of proper RGB color space selection and handling, our findings contribute to a better understanding of this critical area and present valuable opportunities for future research. We further provide valuable insights for creating standardised methods in this field, which can be utilised to avoid discrepancies and ensure accurate and reliable analysis in colorimetric bio/chemical sensing.

Multi-Sensing Platform Based on 2D Monoelement Germanane

Covalently functionalized germanane is a novel type of fluorescent probe that can be employed in material science and analytical sensing. Here, a fluorometric sensing platform based on methyl-functionalized germanane (CH3 Ge) is developed for gas (humidity and ammonia) sensing, pH (1-9) sensing, and anti-counterfeiting. Luminescence (red-orange) is seen when a gas molecule intercalates into the interlayer space of CH3 Ge and the luminescence disappears upon deintercalation. This allows for direct detection of gas absorption via fluorometric measurements of the CH3 Ge. Structural and optical properties of CH3 Ge with intercalated gas molecules are investigated by density functional theory (DFT). To demonstrate real-time and on-the-spot testing, absorbed gas molecules are first precisely quantified by CH3 Ge using a smartphone camera with an installed color intensity processing application (APP). Further, CH3 Ge-paper-based sensor is integrated into real food packets (e.g., fish and milk) to monitor the shelf life of perishable foods. Finally, CH3 Ge-based rewritable paper is applied in water jet printing to illustrate the potential for secret communication with quick coloration and good reversibility by water evaporation.

Biobased Intelligent Food-Packaging Materials with Sustained-Release Antibacterial and Real-Time Monitoring Ability

It has been widely accepted that sustainable polymers derived from renewable resources are able to replace the short-turnover petroleum-based materials and reduce environmental impact in the future. However, their hydrophilic chemical structures rich with oxygen groups could lead to easy growth of bacteria, which greatly limit their applications in packaging materials. Here, we present an intelligent food-packaging material with sustained-release antibacterial and real-time monitoring ability based on totally biobased contents. In detail, sodium alginate with Artemisia argyi emission oil (encapsulated in gelatin-Arabic gum microcapsules) and citric acid-sourced pH-responsive carbon quantum dots (CQDs) are coated on bamboo cellulose papers. The obtained biobased composite material (almost 100% biocarbon content) with antibacterial ability is able to extend the shelf life of fresh shrimps and can be biodegraded. Moreover, owing to the introduction of CQDs, the composite can rapidly (within 1 s) detect slight pH variations (response pH ∼5, 10-9 mol/L of OH-) through an obvious color change (hue value from 305 to 355°). The developed strategy may open up new opportunities in the design of multifunctional biobased composites for intelligent applications.

A DFT Study of Phosphate Ion Adsorption on Graphene Nanodots: Implications for Sensing

The optical properties of graphene nanodots (GND) and their interaction with phosphate ions have been investigated to explore their potential for optical sensing applications. The absorption spectra of pristine GND and modified GND systems were analyzed using time-dependent density functional theory (TD-DFT) calculation investigations. The results revealed that the size of adsorbed phosphate ions on GND surfaces correlated with the energy gap of the GND systems, leading to significant modifications in their absorption spectra. The introduction of vacancies and metal dopants in GND systems resulted in variations in the absorption bands and shifts in their wavelengths. Moreover, the absorption spectra of GND systems were further altered upon the adsorption of phosphate ions. These findings provide valuable insights into the optical behavior of GND and highlight their potential for the development of sensitive and selective optical sensors for phosphate detection.

Smartphone-Enabled Colorimetry

A smartphone is used as a colorimeter. The performance characterization for colorimetry is presented using both the built-in camera and a clip-on dispersive grating. Certified colorimetric samples provided by Labsphere^® are considered as test samples. Color measurements directly performed utilizing the smartphone camera only are obtained using the RGB Detector app, downloaded from the Google Play Store. More precise measurements are achieved using the commercially available GoSpectro grating and related app. In both cases, to quantify the reliability and sensitivity of smartphone-based color measurements, the CIELab color difference ΔE between the certified and smartphone-measured colors is calculated and is reported in this paper. In addition, as an example of a practical application of interest for the textile industry, several samples of cloth fabrics with a palette of the most common colors are measured, and the comparison with the certified color values is presented.

An Update on the Use of Natural Pigments and Pigment Nanoparticle Adducts for Metal Detection Based on Colour Response

Natural pigments occur in plants as secondary metabolites and have been used as safe colourants in food. Studies have reported that their unstable colour intensity might be related to metal ion interaction, which leads to the formation of metal-pigment complexes. This underlines the need for further investigations on the use of natural pigments in metal detection using colorimetric methods, since metals are important elements and can be hazardous when present in large amounts. This review aimed to discuss the use of natural pigments (mainly betalains, anthocyanins, curcuminoids, carotenoids, and chlorophyll) as reagents for portable metal detection based on their limits of detection, to determine which pigment is best for certain metals. Colorimetric-related articles over the last decade were gathered, including those involving methodological modifications, sensor developments, and a general overview. When considering sensitivity and portability, the results revealed that betalains are best applied for copper, using a smartphone-assisted sensor; curcuminoids are best applied for lead, using a curcumin nanofiber; and anthocyanin is best applied for mercury, using anthocyanin hydrogel. This provides a new perspective on the use of colour instability for the detection of metals with modern sensor developments. In addition, a coloured sheet representing metal concentrations may be useful as a standard to support on-site detection with trials on masking agents to improve selectivity.

Deep learning-assisted ultra-accurate smartphone testing of paper-based colorimetric ELISA assays

Smartphone has long been considered as one excellent platform for disease screening and diagnosis, especially when combined with microfluidic paper-based analytical devices (μPADs) that feature low cost, ease of use, and pump-free operations. In this paper, we report a deep learning-assisted smartphone platform for ultra-accurate testing of paper-based microfluidic colorimetric enzyme-linked immunosorbent assay (c-ELISA). Different from existing smartphone-based μPAD platforms, whose sensing reliability is suffered from uncontrolled ambient lighting conditions, our platform is able to eliminate those random lighting influences for enhanced sensing accuracy. We first constructed a dataset that contains c-ELISA results (n = 2048) of rabbit IgG as the model target on μPADs under eight controlled lighting conditions. Those images are then used to train four different mainstream deep learning algorithms. By training with these images, the deep learning algorithms can well eliminate the influences of lighting conditions. Among them, the GoogLeNet algorithm gives the highest accuracy (>97%) in quantitative rabbit IgG concentration classification/prediction, which also provides 4% higher area under curve (AUC) value than that of the traditional curve fitting results analysis method. In addition, we fully automate the whole sensing process and achieve the "image in, answer out" to maximize the convenience of the smartphone. A simple and user-friendly smartphone application has been developed that controls the whole process. This newly developed platform further enhances the sensing performance of μPADs for use by laypersons in low-resource areas and can be facilely adapted to the real disease protein biomarkers detection by c-ELISA on μPADs.

Moving toward smart biomedical sensing

The development of novel biomedical sensors as highly promising devices/tools in early diagnosis and therapy monitoring of many diseases and disorders has recently witnessed unprecedented growth; more and faster than ever. Nonetheless, on the eve of Industry 5.0 and by learning from defects of current sensors in smart diagnostics of pandemics, there is still a long way to go to achieve the ideal biomedical sensors capable of meeting the growing needs and expectations for smart biomedical/diagnostic sensing through eHealth systems. Herein, an overview is provided to highlight the importance and necessity of an inevitable transition in the era of digital health/Healthcare 4.0 towards smart biomedical/diagnostic sensing and how to approach it via new digital technologies including Internet of Things (IoT), artificial intelligence, IoT gateways (smartphones, readers), etc. This review will bring together the different types of smartphone/reader-based biomedical sensors, which have been employing for a wide variety of optical/electrical/electrochemical biosensing applications and paving the way for future eHealth diagnostic devices by moving towards smart biomedical sensing. Here, alongside highlighting the characteristics/criteria that should be met by the developed sensors towards smart biomedical sensing, the challenging issues ahead are delineated along with a comprehensive outlook on this extremely necessary field.

3D Printing of pH Indicator Auxetic Hydrogel Skin Wound Dressing

The benefits of enclosing pH sensors into wound dressings include treatment monitoring of wounded skin and early detection of developing chronic conditions, especially for diabetic patients. A 3D printed re-entrant auxetic hydrogel wound dressing, doped with pH indicator phenol red dye, was developed and characterized. The re-entrant auxetic design allows wound dressing adhesion to complex body parts, such as joints on arms and legs. Tensile tests revealed a yield strength of 140 kPa and Young's modulus of 78 MPa. In addition, the 3D-printed hydrogel has a swelling capacity of up to 14%, limited weight loss to 3% in six days, and porosity of near 1.2%. A reasonable pH response resembling human skin pH (4-10) was obtained and characterized. The integration of color-changing pH indicators allows patients to monitor the wound's healing process using a smartphone. In addition to the above, the mechanical properties and their dependence on post-processing were studied. The results show that the resin composition and the use of post-treatments significantly affect the quality and durability of the wound dressings. Finally, a poly (acrylic acid) (PAA) and water-based adhesive was developed and used to demonstrate the performance of the auxetic wound dressing when attached to moving body joints.

Real-Time Biosensor Platform Based on Novel Sandwich Graphdiyne for Ultrasensitive Detection of Tumor Marker

Realization of a highly sensitive analysis and sensing platform is important for early-stage tumor diagnosis. In this work, a self-powered biosensor with a novel sandwich graphdiyne (SGDY) combined with an aptamer-specific recognition function was developed to sensitively and accurately detect tumor markers. Results indicated that the detection limits of microRNA (miRNA)-21 and miRNA-141 were 0.15 and 0.30 fM (S/N = 3) in the linear range of 0.05-10000 and 1-10000 fM, respectively. The newly designed platform has great promise for early-stage tumor diagnosis.

Nanozyme-based colorimetric biosensor with a systemic quantification algorithm for noninvasive glucose monitoring

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.

Smartphone-based digital image colorimetry for the determination of vancomycin in drugs

A simple smartphone-based digital image colorimetry is proposed for the determination of vancomycin in drugs. The analytical method relied on the reaction of vancomycin with copper(II) in ethanol–water medium with pH 4.3. The reaction resulted in the formation of a blue–grey complex, presenting an absorption maximum at 555 nm. A mobile application was used for smartphone-based analysis to decompose the individual channels of the colour model representations. The determination was performed using three smartphones followed by a comparison of the outcomes with spectrophotometric measurements. The most optimal analytical parameters were achieved for the H channel. The linear ranges obtained for the smartphone-based method proved to be comparable to the spectrophotometric range of 0.044–1.500 g dm−3 and were 0.049–1.500 g dm−3, 0.057–1.500 g dm−3, and 0.040–1.500 g dm−3 for Smartphones 1–3, respectively. Moreover, the determined coefficients of variance (CV, n = 9) and limits of detection (LOD) were 2.3% and 0.015 g dm−3, 6.2% and 0.017 g dm−3, and 2.5% and 0.012 g dm−3, respectively. Whereas for spectrophotometry, the obtained precision, CV was of 0.9% and a LOD of 0.013 g dm−3. The accuracy of the method was verified using model samples, generally the results were obtained with accuracy better than 10.9% (relative error). The method was applied to the determination of vancomycin in drugs. The results obtained by smartphone-based colorimetry did not differ from the expected values for more than 2.6%, were consistent with each other and with the results of spectrophotometric determinations.

Graphical abstract

An Electromechanical Lab-on-a-Chip Platform for Colorimetric Detection of Serum Creatinine

Chronic kidney disease (CKD) is a high-cost disease that affects approximately one in ten people globally, progresses rapidly, results in kidney failure or dialysis, and triggers other diseases. Although clinically used serum creatinine tests are used to evaluate kidney functions, these tests are not suitable for frequent and regular control at-home settings that obstruct the regular monitoring of kidney functions, improving CKD management with early intervention. This study introduced a new electromechanical lab-on-a-chip platform for point-of-care detection of serum creatinine levels using colorimetric enzyme-linked immunosorbent assay (ELISA). The platform was composed of a chip containing microreservoirs, a stirring bar coated with creatinine-specific antibodies, and a phone to detect color generated via ELISA protocols to evaluate creatinine levels. An electromechanical system was used to move the stirring bar to different microreservoirs and stir it inside them to capture and detect serum creatinine in the sample. The presented platform allowed automated analysis of creatinine in ∼50 min down to ∼1 and ∼2 mg/dL in phosphate-buffered saline (PBS) and fetal bovine serum (FBS), respectively. Phone camera measurements in hue, saturation, value (HSV) space showed sensitive analysis compared to a benchtop spectrophotometer that could allow low-cost analysis at point-of-care.