Smartphone-based colorimetric analysis for detection of saliva alcohol concentration

Microextraction with smartphone detection of thiocyanate in saliva of tobacco smokers using paper-based analytical method

This study presents a novel, cost-effective approach involving spectrophotometric and smartphone paper-based (SPB) methods and a distinctive salting-out air-assisted dispersive microextraction procedure to quantify thiocyanate in saliva samples. The method relies on the inhibitory effect of thiocyanate on quinoneimine dye formation during the Emerson reaction with sodium hypochlorite. Spectrophotometry quantifies the extracted dye by monitoring quinoneimine color intensity reduction at 525 nm. In the SPB method, extracted dye is applied to a paper strip, a smartphone captures the colored paper, and an application analyzes red, green, and blue components. All analyte determination and extraction variables were explored. Both methods exhibit good linearity (10-100 μg/L) with a coefficient of determination of 0.9991 and a limit of detection of 7.5 μg/L for the spectrophotometric method, and a coefficient of determination of 0.9988 and a limit of detection of 8.8 μg/L for the SPB method. The calculated values for the enrichment factor and extraction recovery of the developed extraction methodology were 46% and 93%, respectively. The methods detect thiocyanate in saliva samples, producing results comparable to a validated method.

On-site colorimetric food spoilage monitoring with smartphone embedded machine learning

Real-time and on-site food spoilage monitoring is still a challenging issue to prevent food poisoning. At the onset of food spoilage, microbial and enzymatic activities lead to the formation of volatile amines. Monitoring of these amines with conventional methods requires sophisticated, costly, labor-intensive, and time consuming analysis. Here, anthocyanins rich red cabbage extract (ARCE) based colorimetric sensing system was developed with the incorporation of embedded machine learning in a smartphone application for real-time food spoilage monitoring. FG-UV-CD100 films were first fabricated by crosslinking ARCE-doped fish gelatin (FG) with carbon dots (CDs) under UV light. The color change of FG-UV-CD100 films with varying ammonia vapor concentrations was captured in different light sources with smartphones of various brands, and a comprehensive dataset was created to train machine learning (ML) classifiers to be robust and adaptable to ambient conditions, resulting in 98.8% classification accuracy. Meanwhile, the ML classifier was embedded into our Android application, SmartFood++, enabling analysis in about 0.1 s without internet access, unlike its counterpart using cloud operation via internet. The proposed system was also tested on a real fish sample with 99.6% accuracy, demonstrating that it has a great advantage as a potent tool for on-site real-time monitoring of food spoilage by non-specialized personnel.

New spectrophotometric and smartphone-based colorimetric methods for determination of atenolol in pharmaceutical formulations

Novel spectrophotometric and smartphone-based colorimetric methods were developed and validated for the estimation of atenolol (ATE) in pharmaceutical formulations. The measurement procedure is based on the de-diazotization reaction, in which ATE is able to inhibit the diazotized sulfanilic acid from reacting with 8-hydroxy quinoline (8-HQ) in a basic medium. As a result, the formation of red-orange color azo-dye is hindered, and the color intensity is decreased proportionally to concentration of ATE. In spectrophotometric method the azo-dye color fate was monitored at 495 nm. While in smartphone-based colorimetric (SBC) method the captured image in the design processed by RGB App and transferred to the absorbance. The reactant concentrations were optimized using a central composite design (CCD) and response surface method. The methods exhibit good linearity in the 8.0 to 60.0 µg mL-1 range with no significant effect of interferences. The spectrophotometric method yields a linear equation with a slope of 0.0187 (R2 = 0.9993), a limit of detection (LOD) of 1.28 µg mL-1, and a limit of quantification (LOQ) of 4.28 µg mL-1. On the other hand, the smartphone-based colorimetric (SBC) method demonstrates a linear equation with a slope of 0.0127 (R2 = 0.9965), an LOD of 2.13 µg mL-1, and an LOQ of 7.09 µg mL-1. Analyzing ATE in pharmaceutical tablets was utilized to validate the applicability of the developed methods, and the results were statistically compared with those obtained by the HPLC method using the t-test and F-test.

Predictive Framework Development for User-Friendly On-Site Glucose Detection

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.

Development of an automated colorimeter controlled by Raspberry Pi4

A low-cost new instrument to carry out automated colorimetric analysis has been developed. The device consists of a carousel sampler, built by a 3D-printer, and a Raspberry Pi4-controlled signal measurement module based on the RGBC (red, green, blue and clear) responses of a TCS34725 color light-to-digital converter with IR filter. The device has been tested with calibration standards of different food dyes (Tartrazine, Red Allure AC and Brilliant Blue FCF) and three food samples containing one of each food dye. The new device provides R² > 0.995 and a LOD of 1.1, 1.4 and 0.1 μmol L^-1 for each food dye, respectively. The results are statistically comparable to those obtained with a conventional benchtop spectrophotometer. The proposed device achieves a reduction in sample and waste volume and in analysis time, minimizes the use of energy, and allows in situ measurements, being an automated method it is safer for operators in comparison to the reference method, yielding similar analytical results and following the principles of green analytical chemistry.

Mobile Data Collection of Cognitive-Behavioral Tasks in Substance Use Disorders: Where Are We Now?

INTRODUCTION

Over the last decades, our understanding of the cognitive, motivational, and neural processes involved in addictive behavior has increased enormously. A plethora of laboratory-based and cross-sectional studies has linked cognitive-behavioral measures to between-subject differences in drinking behavior. However, such laboratory-based studies inevitably suffer from small sample sizes and the inability to link temporal fluctuations in task measures to fluctuations in real-life substance use. To overcome these problems, several existing behavioral tasks have been transferred to smartphones to allow studying cognition in the field.

METHOD

In this narrative review, we first summarize studies that used existing behavioral tasks in the laboratory and self-reports of substance use with ecological momentary assessment (EMA) in the field. Next, we review studies on psychometric properties of smartphone-based behavioral tasks. Finally, we review studies that used both smartphone-based tasks and self-reports with EMA in the field.

RESULTS

Overall, studies were scarce and heterogenous both in tasks and in study outcomes. Nevertheless, existing findings are promising and point toward several methodological recommendations: concerning psychometrics, studies show that - although more systematic studies are necessary - task validity and reliability can be improved, for example, by analyzing several measurement sessions at once rather than analyzing sessions separately. Studies that use tasks in the field, moreover, show that power can be improved by choosing sampling schemes that combine time-based with event-based sampling, rather than relying on time-based sampling alone. Increasing sampling frequency can further increase power. However, as this also increases the burden to participants, more research is necessary to determine the ideal sampling frequency for each task.

CONCLUSION

Although more research is necessary to systematically study both the psychometrics of smartphone-based tasks and the frequency at which task measures fluctuate, existing studies are promising and reveal important methodological recommendations useful for researchers interested in implementing behavioral tasks in EMA studies.

A Chemometric-Assisted Colorimetric-Based Inexpensive Paper Biosensor for Glucose Detection

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.

An Automated Toolchain for Camera-Enabled Sensing of Drinking Water Chlorine Residual

Chlorine residual concentration is an important parameter to prevent pathogen growth in drinking water. Disposable color changing test strips that measure chlorine in tap water are commercially available to the public; however, the color changes are difficult to read by eye, and the data are not captured for water service providers. Here we present an automated toolchain designed to process digital images of free chlorine residual test strips taken with mobile phone cameras. The toolchain crops the image using image processing algorithms that isolate the areas relevant for analysis and automatically white balances the image to allow for use with different phones and lighting conditions. The average red, green, and blue (RGB) color values of the image are used to predict a free chlorine concentration that is classified into three concentration tiers (<0.2 mg/L, 0.2-0.5 mg/L, or >0.5 mg/L), which can be reported to water users and recorded for utility use. The proposed approach was applied to three different phone types under three different lighting conditions using a standard background. This approach can discriminate between concentrations above and below 0.5 mg/L with an accuracy of 90% and 94% for training and testing data sets, respectively. Furthermore, it can discriminate between concentrations of <0.2 mg/L, 0.2-0.5 mg/L, or >0.5 mg/L with weighted-averaged F1 scores of 79% and 88% for training and testing data sets, respectively. This tool sets the stage for tap water consumers and water utilities to gather frequent measurements and high-resolution temporal and spatial data on drinking water quality.

Development of a double monitoring system for the determination of Cr(VI) in different water matrices by HPLC-UV and digital image-based colorimetric detection method with the help of a metal sieve-linked double syringe system in complexation

This study reports a cheap, efficient, sensitive, and simple double monitoring analytical method for trace determination of Cr(VI), which is toxic and harmful even at very low concentrations. A metal sieve-linked double syringe (MSLDS) system was used to help the formation of chromium complex (Cr-diphenyl carbazide, DPC) subsequently determined by high-performance liquid chromatography-ultraviolet (HPLC-UV) and digital image-based colorimetry (DIC) systems. The metal complex was eluted through a Phenomenex-Aqua C18 with a mobile phase comprising of 50 mM ammonium formate solution (pH 4.0):acetonitrile (78:22, v/v) and detected by the UV detector at the wavelength of 581 nm. Under their optimum conditions, the HPLC-UV and DIC systems exhibited good linearity in ranges of 10-500 µg L^-1 and 100-1000 µg L^-1, respectively. The percent relative standard deviations (RSD%s) calculated for the lowest concentrations of both systems fell below 10%, and this confirmed good repeatability for replicate measurements. The accuracy of the proposed methods was evaluated by performing spike recovery experiments on wastewater, river water, and tap water samples. The calculated recovery results were in the range of 81.5-105.5% for HPLC-UV system and 93.8-111.1% for the DIC system. These results indicate that the proposed methods are suitable for routine Cr(VI) determination in terms of their rapidness, simplicity, good repeatability, and low cost.

Miniaturized Method for Chemical Oxygen Demand Determination Using the PhotoMetrix PRO Application

The analysis of chemical oxygen demand (COD) plays an important role in measuring water pollution, but it normally has a high ecological price. Advances in image acquisition and processing techniques enable the use of mobile devices for analytical purposes. Here, the PhotoMetrix PRO application was used for image acquisition and multivariate analysis. Statistical analysis showed no significant difference in the results compared to the standard method, with no adverse effect of the volume reduction. The cost of analysis and waste generation were reduced by one third, while the analysis time was reduced by one fifth. The miniaturized method was successfully employed in the analysis of several matrices and for the evaluation of advanced oxidation processes. The AGREE score was improved by 25% due to miniaturization. For these reasons, the miniaturized PhotoMetrix PRO method is a suitable option for COD analysis, being less hazardous to the environment due to reductions in the chemicals used and in waste generation.

Smartphone-Based Device for Colorimetric Detection of MicroRNA Biomarkers Using Nanoparticle-Based Assay

The detection of microRNAs (miRNAs) is emerging as a clinically important tool for the non-invasive detection of a wide variety of diseases ranging from cancers and cardiovascular illnesses to infectious diseases. Over the years, miRNA detection schemes have become accessible to clinicians, but they still require sophisticated and bulky laboratory equipment and trained personnel to operate. The exceptional computing ability and ease of use of modern smartphones coupled with fieldable optical detection technologies can provide a useful and portable alternative to these laboratory systems. Herein, we present the development of a smartphone-based device called Krometriks, which is capable of simple and rapid colorimetric detection of microRNA (miRNAs) using a nanoparticle-based assay. The device consists of a smartphone, a 3D printed accessory, and a custom-built dedicated mobile app. We illustrate the utility of Krometriks for the detection of an important miRNA disease biomarker, miR-21, using a nanoplasmonics-based assay developed by our group. We show that Krometriks can detect miRNA down to nanomolar concentrations with detection results comparable to a laboratory-based benchtop spectrophotometer. With slight changes to the accessory design, Krometriks can be made compatible with different types of smartphone models and specifications. Thus, the Krometriks device offers a practical colorimetric platform that has the potential to provide accessible and affordable miRNA diagnostics for point-of-care and field applications in low-resource settings.

Automatic flow delay through passive wax valves for paper-based analytical devices

Microfluidic paper-based analytical devices (μPADs) have been widely explored for point-of-care testing due to their simplicity, low cost, and portability. μPADs with multiple-step reactions usually require precise flow control, especially flow-delay. This paper reports the numerical, mathematical, and experimental studies of flow delay through wax valves surrounded by PDMS walls on paper microfluidics. The predried surfactant in the sample zone diffuses into the liquid sample which can therefore flow through the wax valves. The delay time is automatically regulated by the diffusion of the surfactant after sample loading. The numerical study suggested that both the elevated contact angle and the reduced porosity and pore size in the wax printed region could effectively prevent water but allow liquids with lower contact angles (e.g., surfactant solutions) to flow through. The PDMS walls fabricated using a low-cost liquid dispenser effectively prevented the leakage of surfactant solutions. By controlling the quantity, diffusion distance, and type of the surfactant predried on the chip, the system successfully achieved a delay time ranging from 1.6 to 20 minutes. A mathematical model involving the above parameters was developed based on Fick's second law to predict the delay time. Finally, the flow-delay systems were applied in sequential mixing and distance-based detection of either glucose or alcohol. Linear ranges of 1-100 mg dL^-1 and 1-40 mg dL^-1 were achieved for glucose and alcohol, respectively. The lower limit detection (LOD) of glucose and alcohol was 1 mg dL^-1. The LOD of glucose was only 1/11 of that detected using μPADs without flow control, indicating the advantage of controlling fluid flow. The systematic findings in this study provide critical guidelines for the development and applications of wax valves in automatic flow delay for point-of-care testing.

Development of a smartphone-based lateral-flow imaging system using machine-learning classifiers for detection of Salmonella spp

Salmonella spp. are a foodborne pathogen frequently found in raw meat, egg products, and milk. Salmonella is responsible for numerous outbreaks, becoming a frequent major public-health concern. Many studies have recently reported handheld and rapid devices for microbial detection. This study explored a smartphone-based lateral-flow assay analyzer which employed machine-learning algorithms to detect various concentrations of Salmonella spp. from the test line images. When cell numbers are low, a faint test line is difficult to detect, leading to misleading results. Hence, this study focused on the development of a smartphone-based lateral-flow assay (SLFA) to distinguish ambiguous concentrations of test line with higher confidence. A smartphone cradle was designed with an angled slot to maximize the intensity, and the optimal direction of the optimal incident light was found. Furthermore, the combination of color spaces and the machine-learning algorithms were applied to the SLFA for classifications. It was found that the combination of L*a*b and RGB color space with SVM and KNN classifiers achieved the high accuracy (95.56%). A blind test was conducted to evaluate the performance of devices; the results by machine-learning techniques reported less error than visual inspection. The smartphone-based lateral-flow assay provided accurate interpretation with a detection limit of 5 × 10⁴ CFU/mL commercially available lateral-flow assays.

Simple-to-use and portable device for free chlorine determination based on microwave-assisted synthesized carbon dots and smartphone images

A new portable and simple 3D printed device was designed for free chlorine determination in water samples. The analytical method was based on the quenching caused by free chlorine on the fluorescence emission of the carbon dots (CD) synthesized from citric acid and urea. The fluorescence was captured through the camera of a smartphone, which was coupled to the 3D printed device, and the images were processed using the RGB system by the ImageJ 1.51q software. The proposed method was selective and precise (RSD% 4.6, for n = 6), and the trueness of the results was evaluated by comparing the results obtained with those recovered by the spectrophotometric method 4500-Cl G (standard method), with good agreement between them. Moreover, the remarkable correlation between the CD signal and the free chlorine concentration resulted in a determination with low detection limits (limit of detection of 6 μg L^-1 and limit of quantification of 20 μg L^-1). Therefore, the new method and the related portable device could be considered a fast, economical and reliable alternative for the on-site determination of free chlorine in water samples.

A Portable Smartphone-based Platform with an Offline Image-processing Tool for the Rapid Paper-based Colorimetric Detection of Glucose in Artificial Saliva

In this study, a microfluidic paper-based analytical device (μPAD) was integrated with a smartphone app capable of offline (without internet access) image processing and analysis for the rapid colorimetric detection of glucose. A self-inking stamp was used to form hydrophobic channels on a piece of paper-towel due to its superior water absorption efficiency. As demonstrated, the developed sensor was employed for the colorimetric detection of glucose in artificial saliva in the linear scope of 0 - 1 mM with a calculated detection limit of 29.65 μM. The experimental results show that the quantitative analysis of glucose with the proposed smartphone platform could be completed in less than one minute. The app developed for the smartphone platform is capable of extracting the color-changing area with an embedded image processing tool which could address the problem of color uniformity in the detection zones of μPAD. The integrated platform has great potential to be used for non-invasive measurements of glucose in body fluids, like tears, sweat and saliva.

Color Space Transformation-Based Algorithm for Evaluation of Thermochromic Behavior of Cholesteric Liquid Crystals Using Polarized Light Microscopy

Cholesteryl ester liquid crystals exhibit thermochromic properties related to the existence of a twisted nematic phase. When used in applications such as thermal mapping, a color change is often monitored by video cameras. Thus, quantitative methods to evaluate thermochromic behavior (e.g., blue-start, red-start, red-end, color play and bandwidth) from video analysis are desirable. However, obtaining quantitative color measurements from digital images remains a significant technical challenge, especially for highly reflective samples such as liquid crystals (for which ultraviolet-visible (UV-vis) reflectance spectroscopy is typically used). We developed a method to determine thermochromic properties from videos of liquid crystal cooling under polarized light microscopy. We relate observed color transitions to quantifiable changes in the cumulative color difference in the International Commission on Illumination (CIE) L*a*b* color space and validate this method with UV-vis reflectance spectroscopy. The measured thermochromic behavior and associated measurement uncertainties (coefficient of variations) were comparable to UV-vis reflectance measurements.

Turbidimetric analysis of growth kinetics of bacteria in the laboratory environment using smartphone

For different microbiological and pathological studies, it is often required to monitor the growth of bacteria in a cultured medium in the laboratory environment. UV-VIS spectrophotometer is commonly used to estimate the growth of bacterial cell population by measuring the absorbance at 600 nm over a period of time. Colony-forming unit (CFU) is another approach, which has been routinely performed to estimate the live bacterial cells on semisolid agar plates. Herein, we demonstrate an alternative yet highly reliable sensing platform on a smartphone using which growth kinetics of different bacteria can be reliably monitored. The performance of the proposed smartphone sensor has been compared with the data obtained from OD600 and CFU analysis. A good correlation of bacterial growth rates enumerated based on the proposed smartphone sensor, bench-top spectrophotometer and CFU analysis have been observed under the experimental conditions.

Accurate device-independent colorimetric measurements using smartphones

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.

Magnetic Particles: Their Applications from Sample Preparations to Biosensing Platforms

The growing interest in magnetic materials as a universal tool has been shown by an increasing number of scientific publications regarding magnetic materials and its various applications. Substantial progress has been recently made on the synthesis of magnetic iron oxide particles in terms of size, chemical composition, and surface chemistry. In addition, surface layers of polymers, silica, biomolecules, etc., on magnetic particles, can be modified to obtain affinity to target molecules. The developed magnetic iron oxide particles have been significantly utilized for diagnostic applications, such as sample preparations and biosensing platforms, leading to the selectivity and sensitivity against target molecules and the ease of use in the sensing systems. For the process of sample preparations, the magnetic particles do assist in target isolation from biological environments, having non-specific molecules and undesired molecules. Moreover, the magnetic particles can be easily applied for various methods of biosensing devices, such as optical, electrochemical, and magnetic phenomena-based methods, and also any methods combined with microfluidic systems. Here we review the utilization of magnetic materials in the isolation/preconcentration of various molecules and cells, and their use in various techniques for diagnostic biosensors that may greatly contribute to future innovation in point-of-care and high-throughput automation systems.

State of the Art in Alcohol Sensing with 2D Materials

Since the discovery of graphene, the star among new materials, there has been a surge of attention focused on the monatomic and monomolecular sheets which can be obtained by exfoliation of layered compounds. Such materials are known as two-dimensional (2D) materials and offer enormous versatility and potential. The ultimate single atom, or molecule, thickness of the 2D materials sheets provides the highest surface to weight ratio of all the nanomaterials, which opens the door to the design of more sensitive and reliable chemical sensors. The variety of properties and the possibility of tuning the chemical and surface properties of the 2D materials increase their potential as selective sensors, targeting chemical species that were previously difficult to detect. The planar structure and the mechanical flexibility of the sheets allow new sensor designs and put 2D materials at the forefront of all the candidates for wearable applications. When developing sensors for alcohol, the response time is an essential factor for many industrial and forensic applications, particularly when it comes to hand-held devices. Here, we review recent developments in the applications of 2D materials in sensing alcohols along with a study on parameters that affect the sensing capabilities. The review also discusses the strategies used to develop the sensor along with their mechanisms of sensing and provides a critique of the current limitations of 2D materials-based alcohol sensors and an outlook for the future research required to overcome the challenges.

Design and application of a portable luminometer for bioluminescence detection

The silicon photomultiplier (SiPM) for low light detection has many advantages when compared to existing photon counting detectors, such as high sensitivity, low cost, robustness, and compact hardware. To facilitate the use of SiPM as a portable, field deployable device, an electrical circuit was designed consisting of an amplifier, comparator, and microcontroller. In addition, a 3D printing was used to create a portable cradle for housing the SiPM. To evaluate its detection ability, a laser experiment and bioluminescent experiments, including Pseudomonas fluorescens M3A detection, E. coli O157:H7 PhiV10nluc lysogen detection, and a luminescence-based detection of E. coli O157:H7 in ground meat using the engineered luminescent-based reporter phage PhiV10nluc, were conducted. In the same experimental setting, our previously developed smartphone-based luminometer called the bioluminescent-based analyte quantitation by smartphone and a conventional photomultiplier tube-based benchtop luminometer were used to compare detection levels and applicability for supporting luminescent phage-based pathogen detection. Results showed that the SiPM provides better performance in terms of time to detection and SNR and could be used as the light detection component of the PhiV10nluc phage-based detection format.

Development of a handheld dual-channel optical fiber fluorescence sensor based on a smartphone

In order to meet the needs of on-site, accurate, fast, and remote detection, we design a smartphone-based handheld dual-channel optical fiber fluorescence sensor (DOFFS), which is composed of a semiconductor laser for exciting fluorescence signals, a smartphone with a dual-bandpass filter for collecting fluorescence signals, a fiber coupler for transmitting light, and batteries for laser power supply. All the components are integrated into a 3D printed shell, on the side of which there are two fiber flanges used for fiber probe connection. The fluorescence signals of green and red quantum dots modified on the fiber probes can be captured by the smartphone camera and calculated by a self-developed Android application. The comparisons of single-channel and dual-channel fluorescence signals with pH show that the performance of the sensor is good. The proposed sensor not only can simultaneously detect dual-channel signals for fast detection needs, but it also is handheld with a small size of 79×57×154mm³ and inner power supply, and the fiber probes can be easily replaced, supporting remote and on-site applications. It is a potential tool for many occasions in many fields.

Simultaneous detection of salivary Δ9-tetrahydrocannabinol and alcohol using a Wearable Electrochemical Ring Sensor

Driving under the influence of cannabis and alcohol represents a major safety concern due to the synergistic or additive effect of these substances of abuse. Hence, rapid road-site testing of these substances is highly desired to reduce risks of fatal accidents. Here we describe a wearable electrochemical sensing device for the simultaneous direct, decentralized, detection of salivary THC and alcohol. The new ring-based sensing platform contains a voltammetric THC sensor and an amperometric alcohol biosensor on the ring cap, along with the wireless electronics embedded within the ring case. Rapid replacement of the disposable sensing-electrode ring cap following each saliva assay is accomplished by aligning spring-loaded pins, mounted on the electronic board (PCB), with the current collectors of the sensing electrodes. The printed dual-analyte sensor ring cover is based on a MWCNT/carbon electrode for the THC detection along with a Prussian-blue transducer, coated with alcohol oxidase/chitosan reagent layer, for the biosensing of alcohol. THC and alcohol can thus be detected simultaneously in the same diluted saliva sample within 3 min, with no cross talk and no interferences from the saliva matrix. The new wearable ring sensor platform should enable law enforcement personnel to screen drivers in a single traffic stop and offers considerable promise for addressing growing concerns of drug-impaired driving.

Mobile alcohol biosensors and pharmacotherapy development research

Recent advances in biosensor technology herald a major shift in the way alcohol use will be tracked in humans. Wearable biosensors can passively and continuously monitor wearers' alcohol consumption in real time. An important application of these biosensors is to improve the way medication for alcohol use disorder (AUD) is tested in clinical research. Both laboratory-based screening paradigms and clinical trials have methodological problems that impact their efficiency and predictive validity. Medication screening using laboratory-based methods is a resource-intensive assessment of a single episode of behavior in a non-representative setting. Clinical trials rely on participant self-report to document medication-induced changes in drinking behavior. This review describes how mobile biosensors can be leveraged to improve AUD medication development research. We first review the current state of alcohol biosensor technology with a focus on strengths and limitations of the devices. We describe how multiple biosensors can be combined to create a far more detailed record of drinking compared to single biosensor platforms. We then discuss each phase of the medication development pipeline in turn (i.e., phases 1-4) and describe how mobile biosensors can be incorporated in standard medication testing paradigms to improve efficiency and predictive validity. We conclude with discussion of challenges associated with using currently available biosensors for medication testing and recommendations for researchers wishing to incorporate alcohol biosensors into their own research.

Smartphone-based multispectral imaging and machine-learning based analysis for discrimination between seborrheic dermatitis and psoriasis on the scalp

For appropriate treatment, accurate discrimination between seborrheic dermatitis and psoriasis in a timely manner is crucial to avoid complications. However, when they occur on the scalp, differential diagnosis can be challenging using conventional dermascopes. Thus, we employed smartphone-based multispectral imaging and analysis to discriminate between them with high accuracy. A smartphone-based multispectral imaging system, suited for scalp disease diagnosis, was redesigned. We compared the outcomes obtained using machine learning-based and conventional spectral classification methods to achieve better discrimination. The results demonstrated that smartphone-based multispectral imaging and analysis has great potential for discriminating between these diseases.

Smartphone supported backlight illumination and image acquisition for microfluidic-based point-of-care testing

A smartphone-based image analysis system is advantageous for point-of-care testing applications. However, the processes of observation and image recording rely heavily on an external attachment that includes additional light sources. Moreover, microfluidic point-of-care devices are highly miniaturized, and can be clearly observed only under magnification. To address these issues, the present work proposes a novel imaging box for converting the built-in light source of a smartphone into uniform backlight illumination to avoid interference arising from reflections. A multi-piece orthoscopic lens is embedded in the imaging box to enable the imaging of micro-sized samples. As such, the colorimetric signal of a microchannel with a width as small as 25 µm can be faithfully recorded. Protein concentration quantification based on the bicinchoninic acid assay method was demonstrated with the proposed smartphone/imaging box system from an analysis of colorimetric signals. In addition, a microfluidic chip for conducting ABO blood typing was fabricated, and the microscopic imaging of induced blood coagulation can be clearly observed in a 3 µL sample using the proposed system. These results highlight the potential for adopting smartphone-based analysis systems in point-of-care testing applications.

Point-of-use detection of ascorbic acid using a spectrometric smartphone-based system

A rapid and portable analytical methodology has been developed for ascorbic acid (Vitamin C) quantification from aqueous samples using a spectrometric smartphone-based system for the first time. The method employs point-of-use approaches both for sample preparation and sample measurement, demonstrating the capability for mobile quality control of pharmaceutical and food products. Our approach utilizes an oxidation-reduction reaction between ascorbic acid and methylene blue, followed by a dispersive liquid-liquid microextraction (DLLME) to extract the aqueous-phase methylene blue into organic media. Then, a back-extraction procedure is employed to transfer the methylene blue to aqueous media, followed by analysis of the sample's absorption spectrum using the spectrometric smartphone-based system. The DLLME and back-extraction procedures are optimized by use of a two-step multivariate optimization strategy. Finally, vitamin C supplements and orange juice are used as real-world samples to assess the applicability of the smartphone-based method, which is successfully compared with the standard laboratory-based approach.

Development and application of a low-cost smartphone-based turbidimeter using scattered light

We demonstrate the development and application of a low-cost smartphone turbidimeter system to be used on water samples collected from natural resources. The proposed system depends on the spectroscopic measurements of both forward- and side-scattered light. A custom-designed cradle was fabricated using 3D printing, and plastic optical fibers were used to couple light from the smartphone's built-in flash and transmit the collected scattered light to the camera sensor. The performance parameters of the smartphone turbidimeter were investigated and compared to commercial systems, and the lowest limit of detection was found to be 5.58 NTU for forward-scattered detection. The results obtained in the proposed scattered-light-based spectroscopic turbidimeter and the practicality achieved by this extremely low-cost device will have a great impact on water science and technology.

Experimental Demonstration of Remote and Compact Imaging Spectrometer Based on Mobile Devices

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.

Single-Image-Referenced Colorimetric Water Quality Detection Using a Smartphone

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.

Mobile diagnostics: next-generation technologies forin vitrodiagnostics

The emergence of a wide range of applications of smartphones along with advances in ‘liquid biopsy’ has significantly propelled medical research particularly in the field ofin vitrodiagnostics (IVD).

Pencil-like imaging spectrometer for bio-samples sensing

Spectrally-resolved imaging techniques are becoming central to the investigation of bio-samples. In this paper, we demonstrate the use of a WIFI-camera as a detection module to assemble a pencil-like imaging spectrometer, which weighs only 140 g and has a size of 3.1 cm in diameter and 15.5 cm in length. The spectrometer is standalone, and works wirelessly. A smartphone or network computer can serve as the data receiver and processor. The wavelength resolution of the spectrometer is about 17 nm, providing repeatable measurements of spatial two-dimensional images at various wavelengths for various bio-samples, including bananas, meat, and human hands. The detected spectral range is 400 nm - 675 nm and a white LED array lamp is selected as the light source. Based on the detected spectra, we can monitor the impacts of chlorophyll, myoglobin, and hemoglobin on bananas, pork, and human hands, respectively. For human hand scanning, a 3D spectral image data cube, which exhibits excellent signal to background noise ratio, can be obtained within 16 sec. We envisage that the adaptation of imaging spectrometer devices to the widely-accepted smartphone technology will help to carry out on-site studies in various applications. Besides, our pencil-like imaging spectrometer is cost-effective (<$300) and easy to assemble. This portable imaging spectrometer can facilitate the collection of large amounts of spectral image data. With the help of machine learning, we can realize object recognition based on spectral classification in the future.

Recent Progress in Optical Biosensors Based on Smartphone Platforms

With a rapid improvement of smartphone hardware and software, especially complementary metal oxide semiconductor (CMOS) cameras, many optical biosensors based on smartphone platforms have been presented, which have pushed the development of the point-of-care testing (POCT). Imaging-based and spectrometry-based detection techniques have been widely explored via different approaches. Combined with the smartphone, imaging-based and spectrometry-based methods are currently used to investigate a wide range of molecular properties in chemical and biological science for biosensing and diagnostics. Imaging techniques based on smartphone-based microscopes are utilized to capture microscale analysts, while spectrometry-based techniques are used to probe reactions or changes of molecules. Here, we critically review the most recent progress in imaging-based and spectrometry-based smartphone-integrated platforms that have been developed for chemical experiments and biological diagnosis. We focus on the analytical performance and the complexity for implementation of the platforms.

Histogram analysis for smartphone-based rapid hematocrit determination

A novel and rapid analysis technique using histogram has been proposed for the colorimetric quantification of blood hematocrits. A smartphone-based "Histogram" app for the detection of hematocrits has been developed integrating the smartphone embedded camera with a microfluidic chip via a custom-made optical platform. The developed histogram analysis shows its effectiveness in the automatic detection of sample channel including auto-calibration and can analyze the single-channel as well as multi-channel images. Furthermore, the analyzing method is advantageous to the quantification of blood-hematocrit both in the equal and varying optical conditions. The rapid determination of blood hematocrits carries enormous information regarding physiological disorders, and the use of such reproducible, cost-effective, and standard techniques may effectively help with the diagnosis and prevention of a number of human diseases.

Smartphone-based colorimetric detection via machine learning

We report the application of machine learning to smartphone-based colorimetric detection of pH values. The strip images were used as the training set for Least Squares-Support Vector Machine (LS-SVM) classifier algorithms that were able to successfully classify the distinct pH values. The difference in the obtained image formats was found not to significantly affect the performance of the proposed machine learning approach. Moreover, the influence of the illumination conditions on the perceived color of pH strips was investigated and further experiments were conducted to study the effect of color change on the learning model. Non-integer pH levels are identified as their nearest integer pH values, whereas the test results for integer pH levels using JPEG, RAW and RAW-corrected image formats captured under different lighting conditions lead to perfect classification accuracy, sensitivity and specificity, which proves that colorimetric detection using machine learning based systems is able to adapt to various experimental conditions and is a great candidate for smartphone-based sensing in paper-based colorimetric assays.

Advances in developing rapid, reliable and portable detection systems for alcohol

Development of portable, reliable, sensitive, simple, and inexpensive detection system for alcohol has been an instinctive demand not only in traditional brewing, pharmaceutical, food and clinical industries but also in rapidly growing alcohol based fuel industries. Highly sensitive, selective, and reliable alcohol detections are currently amenable typically through the sophisticated instrument based analyses confined mostly to the state-of-art analytical laboratory facilities. With the growing demand of rapid and reliable alcohol detection systems, an all-round attempt has been made over the past decade encompassing various disciplines from basic and engineering sciences. Of late, the research for developing small-scale portable alcohol detection system has been accelerated with the advent of emerging miniaturization techniques, advanced materials and sensing platforms such as lab-on-chip, lab-on-CD, lab-on-paper etc. With these new inter-disciplinary approaches along with the support from the parallel knowledge growth on rapid detection systems being pursued for various targets, the progress on translating the proof-of-concepts to commercially viable and environment friendly portable alcohol detection systems is gaining pace. Here, we summarize the progress made over the years on the alcohol detection systems, with a focus on recent advancement towards developing portable, simple and efficient alcohol sensors.

Protein, enzyme and carbohydrate quantification using smartphone through colorimetric digitization technique

In this paper the utilization of smartphone as a detection platform for colorimetric quantification of biological macromolecules has been demonstrated. Using V-channel of HSV color space, the quantification of BSA protein, catalase enzyme and carbohydrate (using D-glucose) have been successfully investigated. A custom designed android application has been developed for estimating the total concentration of biological macromolecules. The results have been compared with that of a standard spectrophotometer which is generally used for colorimetric quantification in laboratory settings by measuring its absorbance at a specific wavelength. The results obtained with the designed sensor is found to be similar when compared with the spectrophotometer data. The designed sensor is low cost, robust and we envision that it could promote diverse fields of bio-analytical investigations. Schematic illustration of the smartphone sensing mechanism for colorimetric analysis of biomolecular samples.

Smartphone-based low light detection for bioluminescence application

We report a smartphone-based device and associated imaging-processing algorithm to maximize the sensitivity of standard smartphone cameras, that can detect the presence of single-digit pW of radiant flux intensity. The proposed hardware and software, called bioluminescent-based analyte quantitation by smartphone (BAQS), provides an opportunity for onsite analysis and quantitation of luminescent signals from biological and non-biological sensing elements which emit photons in response to an analyte. A simple cradle that houses the smartphone, sample tube, and collection lens supports the measuring platform, while noise reduction by ensemble averaging simultaneously lowers the background and enhances the signal from emitted photons. Five different types of smartphones, both Android and iOS devices, were tested, and the top two candidates were used to evaluate luminescence from the bioluminescent reporter Pseudomonas fluorescens M3A. The best results were achieved by OnePlus One (android), which was able to detect luminescence from ~10⁶ CFU/mL of the bio-reporter, which corresponds to ~10⁷ photons/s with 180 seconds of integration time.

Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis

We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis.