RGB Values Research Articles

A computer vision enhanced smart phone platform for microfluidic urine glucometry.

Glucose is an important biomarker for diagnosing and prognosing various diseases, including diabetes and hypoglycemia, which can have severe side effects, symptoms, and even lead to death in patients. As a result, there is a need for quick and economical glucose level measurements to help identify those at potential risk. With the increase in smartphone users, portable smartphone glucose sensors are becoming popular. In this paper, we present a disposable microfluidic glucose sensor that accurately and rapidly quantifies glucose levels in human urine using a combination of colorimetric analysis and computer vision. This glucose sensor implements a disposable microfluidic device based on medical-grade tapes and glucose analysis strips on a glass slide integrated with a custom-made polydimethylsiloxane (PDMS) micropump that accelerates capillary flow, making it economical, convenient, rapid, and equipment-free. After absorbing the target solution, the disposable device is slid into the 3D-printed main chassis and illuminated exclusively with Light Emitting Diode (LED) illumination, which is pivotal to color-sensitive experiments. After collecting images, the images are imported into the algorithm to measure the glucose levels using computer vision and average RGB values measurements. This article illustrates the impressive accuracy and consistency of the glucose sensor in quantifying glucose in sucrose water. This is evidenced by the close agreement between the computer vision method used by the sensor and the traditional method of measuring in the biology field, as well as the small variation observed between different sensor performances. The exponential regression curve used in the study further confirms the strong relationship between glucose concentrations and average RGB values, with an R-square value of 0.997 indicating a high degree of correlation between these variables. The article also emphasizes the potential transferability of the solution described to other types of assays and smartphone-based sensors.

A simple and label-free fluorescent DNA sensor for visual detection of aptamer-based berberine.

The intrinsic fluorescence of berberine is very weak, which can be enhanced by its interaction with specific aptamers. A simple and sensitive DNA sensor for visual detection of berberine has here been established. When using this sensor, there was a good linear relationship between the change in fluorescence intensity of berberine and the concentration of berberine in the range of 16-2000 nM, with a detection limit of 5.1 nM. The change in fluorescence intensity was caused by the addition of aptamers. A detection limit of 170.1 nM was acquired by reading the RGB values of fluorescent images with a smartphone for the quantification of berberine. Common antibiotics did not interfere with the measurement of the berberine concentration. The molecular ion peaks of the complexes formed by the aptamer and berberine could be clearly observed by electrospray ionization mass spectrometry. The UV-vis absorption spectra, circular dichroism spectra, and fluorescence spectra indicated a strong interaction between berberine and the aptamer. The dissociation constant (Kd) between berberine and the aptamer was 1.91 μM. This sensor was both simple and sensitive, requiring only a 21-base oligonucleotide. It realized a visual quantitative analysis with a smartphone. This method could also be used for similar fluorescence visualization determination of aptamer-based drug molecules.

RED-GREEN-BLUE COLOR ANALYSIS OF NASAL MUCOSA OF MIDDLE MEATUS IN THE DIAGNOSIS OF CHRONIC RHINOSINUSITIS WITHOUT POLYPOSIS (CRSSNP)

INTRODUCTION: Chronic Rhinosinusitis (CRS) can be defined broadly as a chronic inflammatory condition of the nose and paranasal sinus mucosa. There is need of a simple; easy to perform objective test that can make diagnosis of CRSsNP even in OPD settings. This prompted us to investigate use of smart phones as an objective method in cases of CRSsNP. OBJECTIVE- This study was done to analyse the color changes of middle meatus area by doing RGB analysis with Smartphone for objective diagnosis of CRSsNP. It was also seen whether severity of CRSsNP could be measured objectively by comparing RGB analysis to SNOT-22 score METHODS- Prospective, Observational, Analytical and hospital-based study of 75 patients presenting with symptoms of CRSsNP in the OPD setting RESULT- A total of 75 and 50 patients were included in case and control groups respectively. On further analysis, there was statistically significant difference in the color changes in the form of RGB values in CRSsNP and control group only at uncinate process (middle part) with R value (p=0.007), R value at middle turbinate (anterior end) (p <0.001) and B value at middle turbinate (anterior end) (p <0.001). CONCLUSION- This study concluded that R values at middle part of uncinate process and anterior end of middle turbinate along with B value at anterior end of middle turbinate may provide the diagnosis of CRSsNP objectively using the smart phone. KEYWORDS- Chronic rhinosinusitis, chronic rhinosinusitis without nasal polyposis, sinonasal outcome test, red-green-blue.

Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves

This study aims to clarify the influence of photographic environments under different light sources on image-based SPAD value prediction. The input variables for the SPAD value prediction using Random Forests, XGBoost, and LightGBM were RGB values, HSL values, HSV values, light color temperature (LCT), and illuminance (ILL). Model performance was assessed using Pearson’s correlation coefficient (COR), Nash–Sutcliffe efficiency (NSE), and root mean squared error (RMSE). Especially, SPAD value prediction with Random Forests resulted in high accuracy in a stable light environment; CORRGB+ILL+LCT and CORHSL+ILL+LCT were 0.929 and 0.922, respectively. Image-based SPAD value prediction was effective under halogen light with a similar color temperature at dusk; CORRGB+ILL and CORHSL+ILL were 0.895 and 0.876, respectively. The HSL value under LED could be used to predict the SPAD value with high accuracy in all performance measures. The results supported the applicability of SPAD value prediction using Random Forests under a wide range of lighting conditions, such as dusk, by training a model based on data collected under different illuminance conditions in various light sources. Further studies are required to examine this method under outdoor conditions in spatiotemporally dynamic light environments.

An ICT-based highly fluorescent isoquinoline scaffold for selective Hg(II) detection in real-water samples: Development of a smart, low-cost RGB-Arduino electronic platform

Selective detection and quantification of Hg2+ ions is crucial to minimize health and environmental risks. Fluorescent organic small-molecule probes have been expeditiously utilized owing to their unique set of improved properties. However, isoquinoline core has not been extensively explored as a fluorescence platform partly due to synthetic challenges. Herein, a serendipitously discovered synthetic route to access a small yet highly functionalized novel isoquinoline-based probe, IQ is reported. The synthesis is achieved through the in-situ generation of ammonia, followed by intermolecular [5C + 1 N] aza-annulation reaction with a ketendithioacetal-based precursor, P-IQ. IQ displayed excellent recognition ability towards Hg2+ ions in H2O:ACN (99:1, v/v) via ICT-off fluorescent quenching behavior. Comparative FT-IR, 1H/13C NMR, mass spectral studies, and DFT analyses were carried out to validate the suggested mechanisms. Reversible studies confirm the secondary recognition effect of in-situ generated (IQ + Hg2+) complex on cysteine. The binding constant and LOD were estimated to be 3.7 × 104 M−1 and 0.86 µM, respectively. Further, IQ was utilized to evaluate the mercury ion content in real water samples demonstrating its effectiveness in water quality monitoring. The practical utility of IQ was further explored by developing TLC strips, Whatman filter-paper strips, and a low-cost, portable Arduino-based platform. Arduino microcontroller is interfaced with an RGB sensor to detect color changes and quantify mercury concentration w.r.t. RGB values.

Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm.

In the present era of climate change, underutilized crops such as rice beans and adzuki beans are gaining prominence to ensure food security due to their inherent potential to withstand extreme conditions and high nutritional value. These legumes are bestowed with higher nutritional attributes such as protein, fiber, vitamins, and minerals than other major legumes of the Vigna family. With the typical nutrient evaluation methods being expensive and time-consuming, non-invasive techniques such as near infrared reflectance spectroscopy (NIRS) combined with chemometrics have emerged as a better alternative. The present study aims to develop a combined NIRS prediction model for rice bean and adzuki bean flour samples to estimate total starch, protein, fat, sugars, phytate, dietary fiber, anthocyanin, minerals, and RGB value. We chose 20 morphometrically diverse accessions in each crop, of which fifteen were selected as the training set and five for validation of the NIRS prediction model. Each trait required a unique combination of derivatives, gaps, smoothening, and scatter correction techniques. The best-fit models were selected based on high RSQ and RPD values. High RSQ values of >0.9 were achieved for most of the studied parameters, indicating high-accuracy models except for minerals, fat, and phenol, which obtained RSQ <0.6 for the validation set. The generated models would facilitate the rapid nutritional exploitation of underutilized pulses such as adzuki and rice beans, showcasing their considerable potential to be functional foods for health promotion.

Portable smartphone-assisted highly sensitive detection of mercury ions based on gold nanoparticle-modified NH2-UiO-66 metal-organic framework.

A novel portable smartphone-assisted colorimetric method was reported for the determination of Hg2+ with good analytical performance. AZr(IV)-based metal-organic framework functionalized with amino groups(NH2-UiO-66) has been adopted as asupporting platform to anchor gold nanoparticles (AuNPs), avoiding the migration and aggregation of AuNPs. With the addition of Hg2+, the formation of gold amalgam proved possible to enhance peroxidase-like activity of the composite (AuNPs/NH2-UiO-66), accelerating the oxidization of zymolyte 3,3',5,5'-tetramethylbenzidine (TMB). In the meantime, the color of the reaction solution turned a vivid blue, and the red, green, and blue (RGB) values of the solution color changed accordingly. On account of this strategy, the quantitative detection of Hg2+ could be achieved. After the optimization of the experiment conditions, the average color intensity (Ic) resulting from RGB values was linear related to the concentration of Hg2+ from 10 to 100nM, accompanied with a detection limit (LOD) down to 5.4nM calculated by 3σ/S. The successful application of the designed method has been promoted to detect Hg2+ in some water samples, displaying a great potential in practical application. Furthermore, the use of a smartphone made our proposed method simple and accurate, and thus puts forward a possible way for in situ and real-time monitoring.

Prototype of a device adaptable to walk-sticks with a system to communicate the visible spectrum to blind people.

This project aims to design and build a device adaptable to canes for blind people that allows them to know the color of everyday objects through a tactile response. The prototype of the device identifies the colors by means of the TCS3200 color sensor that, when detecting the light reflected by the objects, converts it into an electrical signal to later process it with the Arduino Nano microcontroller and thus generate a tactile response using a servomotor. For this purpose, mechanical and electronic designs of the color recognition and representation systems using the Feelipa color code were developed, and a functional prototype of the adaptive device that identifies primary colors was developed. Algorithms for the classification of secondary colors from their RGB values continue to be developed, and the mechanical designs are being improved to take into account aesthetic and ergonomic aspects that facilitate their use

Customized Integrating-Sphere System for Absolute Color Measurement of Silk Cocoon with Corrugated Microstructure.

Silk fiber, recognized as a versatile bioresource, holds wide-ranging significance in agriculture and the textile industry. During the breeding of silkworms to yield new varieties, optical sensing techniques have been employed to distinguish the colors of silk cocoons, aiming to assess their improved suitability across diverse industries. Despite visual comparison retaining its primary role in differentiating colors among a range of silk fibers, the presence of uneven surface texture leads to color distortion and inconsistent color perception at varying viewing angles. As a result, these distorted and inconsistent visual assessments contribute to unnecessary fiber wastage within the textile industry. To solve these issues, we have devised an optical system employing an integrating sphere to deliver consistent and uniform illumination from all orientations. Utilizing a ColorChecker, we calibrated the RGB values of silk cocoon images taken within the integrating sphere setup. This process accurately extracts the authentic RGB values of the silk cocoons. Our study not only helps in unraveling the intricate color of silk cocoons but also presents a unique approach applicable to various specimens with uneven surface textures.

Nanoplasmonic biosensors for multicolor visual analysis of acetylcholinesterase activity and drug inhibitor screening in point-of-care testing

The monitoring of acetylcholinesterase (AChE) activity and the screening of its inhibitors are significance of the diagnosis and drug therapy of nervous diseases. A metal ions-mediated signal amplification strategy was developed for the highly sensitive and multicolor assay of AChE activity and visually screening its drug inhibitors. After the specific reaction between AChE and acetylthiocholine (ATCh), the hydrolysis product thiocholine (TCh) can directly and decompose the α-FeOOH nanorods (NRs) to release amounts of Fe2+, which was regarded as Fenton reagent to efficiently catalyze H2O2 to produce ·OH. Then, the as-formed ·OH can further largely shorten the gold nanobipyramids (Au NBPs), generating a series of palpable color variations. The linear range for AChE activity was 0.01–500.0 U/L with the limit of detection as low as 0.0074 U/L. The vivid visual effects could be easily distinguished for the multicolor assay of AChE activity by naked eye in visible light. To achieve the point-of-care testing, Au NBPs were further assembled on polymeric electrospun nanofibrous films (ENFs) surface as test strips for the easy-to-use test of AChE activity by RGB values with a smartphone. Fascinatingly, this proposed strategy can be used for the visual screening AChE inhibitors or non-inhibitors. Comparing with the clinical drugs (rivastigmine tartrate, and donepezil), some natural alkaloids such as evodiamine, caffeine, camptothecin, and berberine hydrochloride were selected as inhibitor modes to confirm the drug screening capability of this method. This proposed strategy may have great potential in the other disease-related enzymatic biomarkers assay and the rapid screening of drug therapy.

SPECTROPHOTO AND DIGITAL COLORIMETRIC DETERMINATION INDICATOR OF BETA-ADRENOREACTIVITY OF ERYTHROCYTE MEMBRANES OF PATIENTS WITH BRONCHIAL ASTHMA

The change in the adrenoreactive properties of membrane and cellular structures under the influence of catecholamines makes it possible to consider the beta-adrenergic reactivity of the erythrocyte membrane (-ARM) as a systemic indicator of the organism's adrenoreactivity. To assess adrenoreactivity, a technique is used based on a quantitative assessment of the degree of inhibition of hypoosmotic hemolysis of erythrocytes in vitro in the presence of the beta-blocker propranolol. The article presents data on the comparison of spectrophoto- and colorimetric determination of erythrocyte membrane beta-adrenoreactivity based on the registration of erythrocyte hemolysis with digital signal processing. A smartphone was used to register the images of the analyzed samples. Photography was carried out in a special box under standard lighting conditions. The chromaticity of the obtained images was evaluated using the Photoshop program within the RGB color model intended for computer color rendering. The obtained RGB values were used for quantitative determinations. The study was performed on 20 blood samples of patients suffering from bronchial asthma. The discrepancy between the results of determining -ARM using the colorimetric and spetrophotometric methods was no more than 5.6%, which indicates the possibility of replacing the spectrophotometer with image registration in digital format using software.

The application of barcode readable assay and linear regression RGB analysis using a customized smartphone app in on-chip electromembrane extraction for simultaneous determination of heavy metal ions

This paper presents an on-chip electromembrane extraction (OC-EME) followed by RGB analysis with a barcode-readable customized app for the determination of three heavy metal ions; Cr(VI), Ni(ΙΙ), and Cu(ΙΙ) in water samples. First, the OC-EME procedure was performed on a single transparent platform including three divided channels for extraction of analytes and subsequently complex formation with chromogenic reagents. The RGB values of colorful complexes were then analyzed by Photoshop software and green value intensities of all three colors showed the best concentration dependence signal. Effective parameters such as the composition of supported liquid membrane (SLM), the voltage of extraction, the composition of donor and acceptor solutions, sample volume, and flow rate were investigated and optimized. Also, a smartphone app was developed for RGB analysis on a barcode-readable program to detect and quantify the extracted heavy metal(s). A fast, easy, and accurate quantification analysis was offered using principal component analysis (PCA) and a trained linear regression model. Under optimized conditions, good calibration fittings were observed within the ranges of 0.05–1.5 mg l−1 for Cr(VI), 1.0–70.0 mg l−1 for Ni(II), and 0.5–30.0 mg l−1 for Cu(II) via the customized app. Intra- and inter-day RSDs% of the method were obtained less than 6.9 % and 7.8 %, respectively. Finally, the proposed procedure was successfully applied for the analysis of target ions in water samples with relative recoveries between 85.0 % and 116.4 %.

Simple dual-readout immunosensor based on phosphate-triggered and potassium permanganate for visual detection of fenitrothion

Multicolor-based visual immunosensor is a promising tool for rapid analysis without the use of bulky instruments. Herein, an anti-fenitrothion nanobody-alkaline phosphatase fusion protein (VHHjd8-ALP) was employed to develop a multicolor visual immunosensor (MVIS) and a ratiometric fluorescence MVIS (RFMVIS, respectively). After one-step competitive immunoassay, the VHHjd8-ALP bound to microplate catalyzed phenyl phosphate disodium salt (ArP) into phenol. Under high alkaline condition (pH 12), the phenol reduced KMnO4 to intermediate (K2MnO4) and further to MnO2 in alkaline condition (pH 12), accompanied by a visible color transition of purple-green-yellow, which can be used for semiquantitative visual analysis or qualitative detection by measuring RGB value. RFMVIS was proposed on the basis of MVIS to further improve sensitivity. The CdTe quantum dot and fluorescein were used as signal probes to develop the fluorescent immunosensor. The CdTe dots with red emission (644 nm) was quenched by oxidation of KMnO4, whereas the fluorescein with green emission (520 nm) remained constant, accompanied by a fluorescent color transition of green-yellow-red. By measuring the ratio of the fluorescence intensity (I644/I520), the ratiometric fluorescence immunosensor was developed for qualitative analysis. The two visual immunosensors were sensitive and simple, and they showed good accuracy and practicability in the recovery test, thus are ideal tools for rapid screening.

Polydopamine on Copper-Doped Cerium Dioxide Nanosheets as Peroxidase Mimics for the Intelligent Detection of Cholesterol.

The catalytic ability of nanozymes has become an enzymology hotspot in the field of application. Most nanozymes were characterized to simultaneously have oxidase-like and peroxidase-like activities, but the practical application often focuses on certain activity; other complex activities may cause interference. The peroxidase-like activity (POD-like activity) of nanozymes have been widely used in the colorimetric detection of H2O2 or substances producing H2O2 as an intermediate, such as the detection of small biological molecules with the oxidative reaction of a chromogenic reagent in the presence of POD-like nanozymes. In this work, we used polydopamine (PDA) as the surface coating of Cu-CeO2 nanosheets (PDA@ Cu-CeO2), which enhanced peroxidase-like activity while inhibiting their oxidase-like activity, providing a feasible method for the sensitive determination of cholesterol by integrating visual colorimetric detection and a smartphone application as a readout. The absorbance intensity and RGB values displayed a linear range on cholesterol from 0.05 to 1.2 mM with the LOD (limit of detection) of 42.7 and 99.4 μM. In addition, the method is expected to apply in detecting cholesterol in human serum with acceptable accuracy.

Exploring cross-cultural and gender differences in facial expressions: a skin tone analysis using RGB Values

Facial expressions serve as crucial indicators of an individual's psychological state, playing a pivotal role in face-to-face communication. This research focuses on advancing collaboration between machines and humans by undertaking a thorough investigation into facial expressions. Specifically, we delve into the analysis of emotional variations related to changes in skin tone across different genders and cultural backgrounds (Black and white). The research methodology is structured across three phases. In Phase I, image data is acquired and meticulously processed from the Chicago face dataset, resulting in 12,402 augmented images across five classes (Normal case, Benign case, Adenocarcinoma, Squamous-cell-carcinoma, Large-cell-carcinoma). Phase II involves the identification of Regions of Interest (ROI) and the extraction of RGB values as features from these ROIs. Various methods, including those proposed by Kovac, Swift, and Saleh, are employed for precise skin identification. The final phase, Phase III, centers on the in-depth analysis of emotions and presents the research findings. Statistical techniques, such as Descriptive statistics, independent sample T-tests for gender and cross-cultural comparisons, and two-way ANOVA, are applied to RED, BLUE, and GREEN pixel values as response variables, with gender and emotions as explanatory variables. The rejection of null hypotheses prompts a Post Hoc test to discern significant pairs of means. The results indicate that both cross-cultural backgrounds and gender significantly influence pixel colors, underscoring the impact of different localities on pixel coloration. Across various expressions, our results exhibit a minimal 0.05% error rate in all classifications. Notably, the study reveals that green pixel color does not exhibit a significant difference between Anger and Neutral emotions, suggesting a near-identical appearance for green pixels in these emotional states. These findings contribute to a nuanced understanding of the intricate relationship between facial expressions, gender, and cultural backgrounds, providing valuable insights for future research in human–machine interaction and emotion recognition.

An RGB Pseudo-Colorization Method for Filtering of Multi-Source Graphical Data

Artificial colorization (pseudo-colorization) is a commonly used method to improve the readability of images obtained from sources (sensors) that do not reflect the original color of the object of observation (e.g., X-ray). It is designed to draw the observer’s attention to the important details of the analyzed image (e.g., disease changes in medical imaging). Analogous needs occur in the process of assessing the emission security (EMSEC) of imaging devices used to process classified information, which is made on the basis of the analysis of images reproduced from compromising emanations related to the operation of these devices. The presence of many graphic elements in an image may reduce the level of perception of the information contained in it. Such images may be very noisy or contain overlapping graphic symbols, the source of which is devices processing graphic information operating in close proximity to each other. The use of various types of measures enabling data filtration at various stages of their processing, e.g., the use of a directional antenna, frequency filtering, point filtering or contextual contrast modification, does not always prove effective. The solution to the filtration problem is the pseudo-colorization of the image. However, the image colorization used based on the typical “Hot”, “Radar” or “Cold” color palettes does not meet the requirements for filtering graphic data from many sources. It is necessary to use a filter that will allow the sharp cut-off of graphic data at the border between the background and the graphic symbol. For the pseudo-colorization process itself, the exponential function as a function of transforming the amplitudes of image pixels from the gray color space to the RGB color space is sufficient. However, the smooth transition of the function shape from zero values to values greater than zero results in a low efficiency of filtering graphic data from noise. In this article, a method of filtering an image based on the pseudo-colorization of its content, i.e., reproduction of a compromising emanation signal level in the RGB value of image pixel color components, was proposed. A quadratic function was proposed as the transformation function. The higher effectiveness of the method based on the use of a square function (compared to the exponential function) was shown by conducting tests on many images, some of which are presented in this article. The proposed solution is a universal approach and can be used in various fields related to image analysis and the need for their filtration. Its universality is related to the possibility of changing function parameters affecting its position on the value axis from 0 to 255, its width, its minimum and its maximum value for each RGB channel.

A simple dual responsive chemosensor for selective sensing of Cs+ for environmental monitoring and mimicking molecular logic gates

Detection of toxic metals is of vital importance to safeguard both public health and the ecosystem. Herein, we investigate the newly designed and synthesised isoxazole-based azo dye, (E)-cyclopentyl(5-((5-(4-fluorophenyl) isoxazole-3-yl) diazenyl)-2-hydroxyphenyl) methanone (FPAZ), as a dual chromogenic and fluorogenic sensor. FPAZ demonstrates high selectivity, reusability and ultra-sensitivity towards Cs+ ions manifested through naked eye detection in aqueous medium by employing simple and economic optical spectroscopy techniques. The color change from colourless to dark yellow and enhancement of fluorescence intensity reveal about FPAZ-Cs+ complexation by UV–Vis and fluorescence spectroscopy respectively. The complexation is also supported by DFT calculations. The LOD is estimated to be 0.476 µM, which by far, is the lowest LOD obtained for Cs+ detection. Further, FPAZ is fabricated with various flexible materials (paper, cotton, non-woven fabric) which provide information about on-site Cs+ ion contamination by means of change in relative RGB values using a handy smart-phone camera. Besides this, the logic gate as IMPLICATION and INHIBIT is designed employing Cs+ and Cl- ions as inputs and absorbance maxima as output. Overall, the developed chemosensor is simple, quick, and more promising than previously reported systems, as it does not need any chemical modification, expensive instruments, or expertise.

Addressing the sample volume dependency of the colorimetric glucose measurement on microfluidic paper-based and thread/paper-based analytical devices using a novel low-cost analytical viewpoint

The colorimetric method is widely exploited for glucose measurement on microfluidic paper-based and microfluidic thread/paper-based analytical devices, μPADs and μTPADs, respectively. However, two significant challenges still hinder the real-world applications: the variation of the generated color based on sample volume variation, and the consistent dependency on an imaging camera - usually a smartphone camera - for color readout. The latter also suffers from multiple limitations, such as the variable ambient light conditions and imaging variation among different smartphone brands and models. This manuscript suggests a new device demonstrating a novel viewpoint to the colorimetric method to address both aforementioned challenges. The presented device, Volume-Independent Autonomous box (VIA box), continuously monitors the transient color of the µPAD upon the introduction of the sample till color stabilization, in contrast to similar studies where merely the final stabilized color is read. The interpretation of the transient color profile with respect to time results in the measurement of the glucose level independent of sample volume. In addition, the VIA box monitors µPAD’s color using an ordinary RGB sensor instead of an imaging camera in similar studies. Since merely RGB values are recorded in any measurement instance, the computational cost is extremely low compared to the relevant literature where image processing techniques are used. VIA box exploits a simple low-end microcontroller to continuously monitor the transient color, interpret the color profile, and show the glucose level. The samples used in this manuscript are made of artificial sweat samples, including the known glucose range in real sweat samples.

Exploiting digital images and videos for urea determination in milk based on enzymatic hydrolysis monitoring

As an endogenous species and a milk adulterant, urea determination is important for quality control in dairy industry. This work proposes a novel, simple, and cost-effective method to determine urea by the analysis of digital images and videos. Urea hydrolysis under urease catalysis releases ammonia and the increase in pH makes feasible the photometric measurements based on the dissociation of the phenol red acid-base indicator. Digital images were acquired by a smartphone camera under controlled conditions and intensities of the reflected radiation were converted to the RGB color system using a free app (PhotoMetrix®). Digital videos were exploited for procedure optimization and evaluation of matrix effects from RGB values obtained with ImageJ® software. A linear response was achieved within 2.0–25.0 mg L-1 urea (R2 =0.996), with a coefficient of variation (n = 10) of 1.6%, and limits of detection (95% confidence level) and quantification of 0.5 and 1.5 mg L-1, respectively. Recoveries from 95% to 112% were estimated from samples spiked with urea and results for commercial milk samples achieved by the standard additions method agreed with the reference values at the 95% confidence level. The procedure is applicable to whole, semi-skim, and skim milk either processed using pasteurization or under ultra-heat treatment.

Data‐Driven Fine Element Tuning of Halide Double Perovskite for Enhanced Photoluminescence

AbstractElement tuning of targeted materials and obtaining the optimal synthesis recipe are major goals for many material scientists. However, this is often limited by conventional trial‐and‐error procedures, which are time‐consuming and labor‐intensive. In this work, fine element tuning of halide double perovskite Cs2NaxAg1‐xInyBi1‐yCl6 is conducted by performing a data‐driven investigation combining high‐throughput experiments with machine learning (ML). A positive correlation between the more accessible R value in emission RGB values (the intensities of the red/green/blue primary colors) and photoluminescence intensity is revealed, and over a thousand R values of the Cs2NaxAg1‐xInyBi1‐yCl6 crystals synthesized with different additives and element compositions are collected. More importantly, the volume ratios of Na+/Ag+ (VNa: VAg) and Bi3+/In3+ (VBi: VIn) with the corresponding R values are correlated through ML, and the synergistic regulation of the two ion pairs is revealed. A possible correlation between R and XRD is also proposed. Finally, different emission intensities of LED beads coated with Cs2NaxAg1‐xInyBi1‐yCl6 synthesized using parameters obtained from ML are demonstrated, and an emission enhancement of ≈50 times is observed between the brightest and dimmest LEDs. This work illustrates that data‐driven investigation helps guide material synthesis and will significantly reduce the workload for developing novel materials, especially for complex compositions.