Red Green Blue Values Research Articles

Physiological characteristics of ornamental caladiums (Caladium x hortulanum Birdsey, Araceae Juss.) through leaf colour diversity

This work aims to analyze the physiological characteristics of caladium ornamental plants with different leaf colors. Ten leaf colors, i.e., dark maroon, maroon, red, pink, white, pinkish young green, yellow, lime green, pinkish dark green, and dark green, were arranged in a completely randomized design and subjected to colorimetric red–green–blue (RGB) and portable photosynthesis measurement. Caladium leaf color, defined by the colorimetric RGB value, significantly impacted leaf physiological properties. Heatmap analysis apparently clustered caladiums into dark-colored group and light-colored ones. Dark-colored caladiums, as indicated by lower RGB values, exhibited higher photosynthesis rate, stomatal conductance, and water use efficiency (WUE). In contrast, light-colored caladium show a higher RGB, leaf temperature and light use efficiency (LUE) than the dark ones. By considering their leaf color-based physiological adaptability, this work recommends moderate sunlight and adequate water conditions for growing light-colored caladiums, while dark caladiums potentially tolerate excess light and water scarcity. Further research, including quantitative pigment analysis, is necessary to verify this hypothesis.

Biomolecule-regulation of fluorescent probe signaling: Homogeneous rapid portable protease sensing in serum

BackgroundAs is well documented, prostate cancer (PCa) being the second most prevalent cancer in men worldwide, emphasizing the importance of early diagnosis for prognosis. However, conventional prostate-specific antigen (PSA) testing lacks sufficient diagnostic efficiency due to its relatively low sensitivity and limited detection range. Mounting evidence suggests that matrix metalloproteinase 9 (MMP-9) expression increases with the aggressive behavior of PCa, highlighting the significance of detecting the serum level of MMP-9 in patients. Developing a non-immune rapid, portable MMP-9 detection strategy and investigating its representativeness of PCa serum markers hold considerable implications. ResultsHerein, our study developed a simple, homogeneous dual fluorescence and smartphone-assisted red-green-blue (RGB) visualization peptide sensor of MMP-9, utilizing cadmium telluride quantum dots (CdTe QDs) and calcein as signal reporters. The essence of our approach revolves around the proteolytic ability of MMP-9, exploiting the selective recognition of molecule-Cu2+ complexes with different molecular weights by CdTe QDs and calcein. Under optimized conditions, the limits of detection (LODs) for MMP-9 were 0.5 pg/mL and 6 pg/mL using fluorescence and RGB values readouts, respectively. Indeed, this strategy exhibited robust specificity and anti-interference ability. MMP-9 was quantified in 42 clinical serum samples via dual-fluorescence analysis, with 12 samples being visually identified with a smartphone. According to receiver operating characteristic curve (ROC) analysis, its sensitivity and specificity were 90 % and 100 %, respectively, with an area under curve (AUC) value of 0.903. Significance and noveltyOf note, the results of the aforementioned analysis were highly consistent with the serum level of PSA, clinical color Doppler flow imaging (CDFI), and histopathological results. Therefore, this simple, rapid, homogeneous fluorescence and visualization strategy can reliably measure MMP-9 levels and exhibit promising potential in point-of-care testing (POCT) applications for PCa patients.

Investigation of the effect of laser technology on the color of polyester fabrics

Purpose The purpose of this study is to determine the effect of laser treatment on disperse dye-uptake and fastness values of polyester fabrics. Furthermore, it was aimed to evaluate colors directly over the photos of fabric samples instead of color measuring with spectrophotometer which is thought to be useful in terms of online digital color assessment.Design/methodology/approach In this study, 100% polyester (150 denier) single jersey knitted fabrics (weight: 145 g/m2, course density: 15 loops/cm, wale density: 24 loops/cm) were used in the trials. The effect of laser treatments before and after dyeing on color was investigated. Laser treatments were applied to fabrics at different resolutions (20, 25 and 30 dpi) and pixel times (60, 80 and 100 µs) before dyeing. The power of the laser beam was 210 W and the wavelength was 10.6 µm. In order to determine the effect of laser treatment on polyester; FTIR analysis, SEM-EDX analysis and bursting strength tests were applied to untreated and treated fabric samples.Findings It was found that treatments with laser have a significant effect on disperse dye-uptake of polyester fibers, and for this reason laser-treated fabrics were dyed in darker shade. Furthermore, it was determined that the samples treated at 30 dpi started to melt and the fabric was damaged considerably, but the fabrics treated at 20 and 25 dpi were not affected at all. Another result obtained regarding the use of laser technology in polyester fabrics is that if some areas of fabrics are not treated with laser and some other areas are treated with laser at 20 dpi 60 µs and 25 dpi 60 µs, it will be possible to obtain patterns containing three different shades of the same color on the fabric.Originality/valueWhen the literature is examined, it is seen that there are various studies on the dyeability and patterning of polyester fabrics with disperse dyes by laser technology. As it is known, today color measurement is done digitally using a spectrophotometer. However, when we look at a photograph on computer screens, the colors we see are defined by RGB (red-green-blue) values, while in the spectrophotometer they are defined by L*a*b* (L*: lightness-darkness, a*: redness-greenness, b*: yellowness-blueness) values. Especially when it is desired to produce various design products by creating patterns with laser technology, it would be more useful to show the color directly to the customer on the computer screen and to be able to speak over the same values on the color. For this reason, in this study, the color measurement of the fabric samples was not made with a spectrophotometer, instead, the RGB values obtained from the photographs of the samples were converted into L*a*b* values with MATLAB and interpreted, that is, a digital color evaluation was made on the photographs. Therefore, it is believed that this study will contribute to the literature.

KLASIFIKASI KEMATANGAN BUAH ALPUKAT MENTEGA BERDASARKAN FITUR WARNA MENGGUNAKAN SUPPORT VECTOR MACHINE

The ripeness level of the avocado greatly affects the shelf life of the fruit and also determines the taste of the fruit. Determining the proper ripeness of a fruit will play an important role in increasing the nutritional value and ripeness of avocado affects the quality of avocado oil. Classification of fruit maturity manually has many limitations because it is influenced by human subjectivity so that the application of digital image processing needs to be used.This study aims to classify avocado fruit ripeness which is divided into three categories, namely Unripe, Ripe, and Rotten. This research is intended to facilitate the classification of ripeness in fruit through its color. This study used avocado fruit as an experimental material which was carried out using the SVM (Support Vector Machine) method. The colors of the various avocados and in different positions and conditions of light contrast are used as data to classify the types of avocados. Data collection was taken from the selected Keagle Dataset of 150 data. The data is grouped into two data sets, namely dataset-1 (120 training data and 30 test data) and dataset-2 (90 training data and 60 test data). The image will be converted to grayscale to facilitate the classification process and look for HSV (Hue, Saturation, Value) and RGB (Red, Green, Blue) values to classify the fruit. The results from dataset-1 has an accuracy rate of 86%, 90% precision, and 86% Recall, while dataset-2 has an accuracy rate of 85%, 84% precision, and 85% Recall.

Efficient smartphone-based measurement of phosphorus in water

Agricultural runoff is one of the main sources of excess phosphorus (P) in different water bodies, subsequently leading to eutrophication and harmful algal blooms. To effectively monitor P levels in water, there is a need for simple measurement tools and extensive public involvement to enable regular and widespread sampling. Several smartphone-based P measurement methods have been reported, which extract red-green-blue (RGB) values from colorimetric reactions to build statistical regression models for P quantification. However, these methods typically require meticulous light conditions, involve initial equipment investment, and have undergone limited testing for large-scale applications. To overcome these limitations, this study developed a smartphone-based, equipment-free and facile P colorimetric analysis method. Following the standard procedure of the ascorbic acid approach, colorimetric reactions were captured by a smartphone camera, and RGB values were extracted using Python code for modeling. Different indoor light conditions, phone types, containers, and types of water samples were examined, resulting in a collection of 1922 images. The best regression model, employing random forest with RGB values and container types as inputs, achieved an R2 of 0.97 and an RMSE of 0.051 for P concentrations ranging from 0.01 to 1.0 mg P/L. Additionally, the optimal classification model could estimate the level of P below 0.1 mg P/L with an accuracy of 95.2 (or 77.4 % for <0.05 mg P/L). The strong performance of the developed models, which are also available freely online, suggests an easy and effective tool for more frequent P measurement and greater public involvement.

Cell phone microscopy enabled low-cost manufacturable colorimetric urine glucose test.

Glucose serves as a pivotal biomarker crucial for the monitoring and diagnosis of a spectrum of medical conditions, encompassing hypoglycemia, hyperglycemia, and diabetes, all of which may precipitate severe clinical manifestations in individuals. As a result, there is a growing demand within the medical domain for the development of rapid, cost-effective, and user-friendly diagnostic tools. In this research article, we introduce an innovative glucose sensor that relies on microfluidic devices meticulously crafted from disposable, medical-grade tapes. These devices incorporate glucose urine analysis strips securely affixed to microscope glass slides. The microfluidic channels are intricately created through laser cutting, representing a departure from traditional cleanroom techniques. This approach streamlines production processes, enhances cost-efficiency, and obviates the need for specialized equipment. Subsequent to the absorption of the target solution, the disposable device is enclosed within a 3D-printed housing. Image capture is seamlessly facilitated through the use of a smartphone camera for subsequent colorimetric analysis. Our study adeptly demonstrates the glucose sensor's capability to accurately quantify glucose concentrations within sucrose solutions. This is achieved by employing an exponential regression model, elucidating the intricate relationship between glucose concentrations and average RGB (Red-Green-Blue) values. Furthermore, our comprehensive analysis reveals minimal variation in sensor performance across different instances. Significantly, this study underscores the potential adaptability and versatility of our solution for a wide array of assay types and smartphone-based sensor systems, making it particularly promising for deployment in resource-constrained settings and undeveloped countries. The robust correlation established between glucose concentrations and average RGB values, substantiated by an impressive R-square value of 0.98709, underscores the effectiveness and reliability of our pioneering approach within the medical field.

Optical Particle Visualization Technique Using Red–Green–Blue and Core Storage Shed Flow Field Analysis

This study uses a flow visualization method to analyze the flow field of a shed-type coal storage shed, comparing and verifying the findings through numerical calculation. Initially, a coal warehouse-scale model is created for flow visualization. Laser-based cross-sectional analysis yields essential flow data, from which red–green–blue values are extracted, and the flow object with the highest G value is selected. Subsequently, as the video frame changes, the moving object is tracked, and the direction is derived. The velocity vector of the moving object within the designated area is derived. Finally, we compare the results of the flow visualization experiment with the simulation outcome. Notably, the error rate in regions characterized by high flow velocity is found to be low, and a high implementation rate is observed in areas with many floating objects to track. Conversely, implementation accuracy is lower in low-velocity fields. Both methods result in a recirculation zone at the top of the inlet, and a flow stagnation region occurs on the upper part of the central wall.

Adult thermoregulatory behaviour does not provide, by itself, an adaptive explanation for the reflectance–climate relationship (Bogert's pattern) inIberianbutterflies

AbstractThe association of darker, less reflective insect wings with cooler environments (Bogert's rule) is thought to be related to adult thermoregulation, but the adaptive explanation and the implications for sensitivity to climate warming are yet to be tested. We re‐evaluate the pattern for butterflies using finer resolution data than in previous approaches, both geographically and morphologically, and test its correlation with recent evidence of impacts of warming on butterflies.We compared reflectance–climate relationships at different grid sizes, selected the best subset of reflectance measurements and tested the contribution of the species basking mode, the phylogenetic structure of the data and the correlation between reflectance and published abundance or altitudinal shifts. We used standardised RGB (Red, Green, Blue) values from 222 species from the Iberian Peninsula, and regional mean temperature and precipitation data from the study area (10 and 50‐km resolutions) and Europe (50‐km resolution).Correlations between reflectance and temperature increased at finer geographical and morphological resolutions. However, the butterfly basking mode did not improve the statistical explanation of the pattern. Reflectance shows a strong phylogenetic structure, while variance partitioning indicated a poor pure contribution of the climate variables in the reflectance–climate correlation.Overall, mean temperature and precipitation were only modest predictors of butterfly reflectance. No correlation between reflectance and recent abundance or altitudinal shifts was found using the hypothesised best estimates of reflectance. Although significant correlations between butterfly shading and altitudinal shifts were found for two of the reflectance measurements, this is interpreted as weak, probably artifactual evidence on the predictive power of this relationship.The strong phylogenetic pattern of the reflectance and the low fraction of the reflectance measures analysed suggest that tests for alternative explanations are still needed to shed light on the meaning of the colour–environment relationships in butterflies, which probably are of a complex nature. From an adaptive point of view, unravelling the basis of Bogert's pattern in butterflies requires a closer, habitat‐level approach and alternative variables to adult thermoregulatory behaviour to be tested.

Multispectral Image Generation from RGB Based on WSL Color Representation: Wavelength, Saturation, and Lightness

Image processing techniques are based nearly exclusively on RGB (red–green–blue) representation, which is significantly influenced by technological issues. The RGB triplet represents a mixture of the wavelength, saturation, and lightness values of light. It leads to unexpected chromaticity artifacts in processing. Therefore, processing based on the wavelength, saturation, and lightness should be more resistant to the introduction of color artifacts. The proposed process of converting RGB values to corresponding wavelengths is not straightforward. In this contribution, a novel simple and accurate method for extracting the wavelength, saturation, and lightness of a color represented by an RGB triplet is described. The conversion relies on the known RGB values of the rainbow spectrum and accommodates variations in color saturation.

Rapid and sensitive smartphone non-enzymatic colorimetric assay for the detection of glucose in food based on peroxidase-like activity of Fe3O4@Au nanoparticles

A low-cost and reliable analytical method based on the combination of a newly designed Fe3O4@Au as peroxidase mimetics, supported on smartphone analysis software package was proposed for the determination of glucose content in food samples. The nanocomposite was prepared by self-assembling technique, and the characterization was carried out using transmission electron microscopy (TEM), Fourier transforms infrared, and X-ray diffractometer. Record the color change of the solution with a smartphone camera and optimize the operation parameters and reaction conditions. A smartphone with a free self-developed app was accustomed live the RGB (red–greenblue) values of color intensity within the Fe3O4@Au system and were processed with Image J software before computationally convert them glucose concentrations. At the optimization experiment, reaction temperature of 60 °C, reaction time of 50 min and the amount of addition of Fe3O4@Au 0.0125 g was the optimal combination of detecting glucose smartphone color detection system. Hereon, the accuracy of the proposed method was evaluated by comparison between smartphone colorimetry and UV–vis spectrophotometer, a linear calibration in the range of 0.25 ∼ 15 mmol/L glucose was obtained with minimum detection limit of 1.83 and 2.25 μmol/L, respectively. The proposed method was applied effectively to the detection of glucose in actual samples. The results were in accordance with the conventional UV–vis spectrophotometer method.

Оценка степени пигментации трабекулы у больных первичной открытоугольной глаукомой с помощью колориметрической шкалы

Purpose. Development of a photocolorimetric scale to assess the extent of trabecula pigmentation along its circumference in patients with primary open-angle glaucoma. Material and methods.Study of 36 patients (62 eyes) with primary open-angle glaucoma was performed. Average age: 61.5 ± 9.8 years (48–75 years). Men – 24 (40 eyes), women – 12 (22 eyes). All patients underwent a comprehensive ophthalmic examination, as well as photoregistration of the iridocorneal angle. Using the Pixelmator Pro (Pixelmator Team, USA) and the photographic images, we performed colorimetric estimation of the degree of the iridocorneal angle pigmentation. RGB (Red, Green, Blue) values for weak, moderate, pronounced, and sharply pronounced trabecular pigmentation were determined. Using color model HSL (Hue, Saturation, Lightness) derived percentages (range) corresponding to a certain degree of trabecular pigmentation. Results.Basing on the results, a colorimetric scale was developed for quantifying the trabecular pigmentation degree in patients with primary open-angle glaucoma. The results obtained by us will serve in the future for more accurate objectification of the definition of pigmentation degree, which is certainly of practical importance in the clinical practice of ophthalmologists. Conclusion. The application of HSL-color models allows to estimate with high reliability the differences between levels of trabecular pigmentation in patients with glaucoma. Using a photocolorimetric scale allows to estimate objectively the efficacy of laser operations aimed at reducing the degree of trabecular pigmentation of the iridocorneal angle. Keywords: primary open-angle glaucoma, gonioscopy, photocolorimetric assessment of the pigmentation degree, colorimetric scale

An Efficient Regression Method To Predict Soil pH Using RGB Values

The fertility of a soil is governed by potential of Hydrogen (pH) value of the soil. This research paper presents a novel approach for predicting the pH value of a soil by using RGB (Red, Green, Blue) values of an image. The study uti- lizes machine learning techniques to develop a model that can accurately pre- dict the soil pH based on the colour information captured in an image of the soil. The model was trained with a dataset containing RGB and correspond- ing pH value as the attributes and tested using a variety of images. Results show that the proposed model is able to predict soil pH with minimal error, demonstrating the potential for using image analysis as a practical and effi- cient method for soil pH determination in agriculture and soil science. With the available dataset, various regression approaches have been implemented to predict the soil pH value, and eventually the experimental results shows that the polynomial regression is the most effective method as the data is not linear for analysing this dataset.

Functionalized Silver Nanoparticles for Rapid Detection of Mn2+ Employing a Smartphone Platform

AbstractThis study describes the development of a simple, reliable, and low‐cost method to rapidly detect Mn2+ based on co‐functionalized silver nanoparticles (AgNPs) and the smartphone platform to be applied as an on‐site device in the field. The co‐functionalized AgNPs was investigated with other cations (Al3+, Ba2+, Ca2+, Cd2+, Co2+, Cr3+, Cu2+, Fe2+, Fe3+, K+, Li+, Mg2+, Na+, Ni2+, Pb2+ and Zn2+) at pH 3–10. The results showed that this functionalized AgNPs was selectively binded to Mn2+ at pH 10, which caused the color of the solution changing from yellow to orange‐red and can be measured by the smartphone application called PhotoMetrix. This application was used to measure color intensities based on RGB (red, green, blue) values and converted into Mn2+ concentration by using multivariate calibration curves within 10 minutes. The smartphone detection showed a high selectivity of AgNPs with Mn2+ and provided a positive coefficient correlation between the intensity of channel R and the concentration of Mn2+ with a linear range of 4.0×10−5–2.0×10−4 M (R2=0.9814) and a limit of detection of 2.7×10−5 M. Furthermore, the quantification of Mn2+ in real water samples can be successfully applied by this method. The obtained results were in close agreement with those obtained with inductively coupled plasma optical emission spectroscopy (ICP‐OES). Therefore, the development of a colorimetric system based on a smartphone system device was found to be feasible and reliable for on‐site Mn2+ detection in the real water samples.

A photoelectrochemical sensor for detection of 17β-estradiol using high-response type II heterojunction: Implementation of visualization strategy

It’s imperative to detect low levels of 17β-estradiol (E2) and facilitate its analysis. Here, a photoelectrochemical (PEC) sensor using Ag2S-sensitized BiOBr photoanode material was designed for high-sensitivity analysis of E2. The PEC sensing mechanism was discussed, and the dramatic enhancement of photocurrent was attributed to the superior photoelectric conversion performance of the BiOBr/Ag2S type II heterojunction. With a wide linear range of 0.001–100 nM and a detection limit of 0.18 pM, the constructed PEC sensor exhibited desirable detection performance. Meanwhile, the sensor represented remarkable specificity and stability which was successfully applied to estimate E2 contents in lake water, human serum and milk samples. Furthermore, to cast off the restriction of the workstation, Prussian blue was modified as a cathode in another region of the PEC sensing electrode in advance, and finally, a visualization device for E2 detection was structured. The visual detection could be performed with the naked eye or quantified with the help of average RGB (red-green-blue) values on the basis of the change in color intensity. This study provided a promising approach for the development of practical PEC sensors.

Smartphone-based colorimetric sensor array using gold nanoparticles for rapid distinguishment of multiple pesticides in real samples

A simple, sensitive method for pesticide distinguishment based on a colorimetric sensor array using diverse gold nanoparticles (AuNPs) at room temperature is presented in this study. Acetylcholinesterase (AChE) hydrolysis ability was influenced by different pesticides and produced different concentrations of thiocholine by hydrolyzing acetylthiocholine iodide (ATCh). Thiocholine could be easily linked to the AuNPs through an Au − S covalent bond, and AuNPs underwent aggregation, resulting in a visible color change due to alteration of surface plasmon resonance properties. Based on these results, we successfully distinguished eight pesticides (glyphosate, thiram, imidacloprid, tribenuron methyl, nicosulfuron, thifensulfuron methyl, dichlorprop, and fenoprop) utilizing five different AuNPs by colorimetric assay. The limit of detection (LOD) of this visual method for all pesticides was less than 1.5 × 10-7 M, which was more sensitive than the U.S. Environmental Protection Agency regulations specify (1.18 ∼ 3.91 × 10-6 M). This method was further improved by combining a portable smartphone device with a color picking application using (color name AR) and RGB (red, green, blue) values. The method was successfully applied to pesticide residue distinguishment in real samples by linear discriminant analysis (LDA).

Application study of RGB color extraction in water toxicity detection

In this work, a colorimetric biosensor based on the rapid color change of Escherichia coli DH 5α (E. coli) and p-benzoquinone (BQ) was introduced to evaluate water toxicity. Here, dark brown quinhydroquinone (QHQ) was obtained from the reaction between E. coli and BQ. The inhibition ratios were calculated by RGB (red, green, blue) values of samples or oxidation current of hydroquinone (HQ). Here, RGB technology was used for biotoxicity assessment for the first time. The results suggested that the biosensor-based RGB has an obvious dose-dependent response. The IC20 values of Hg2+ and Cu2+ obtained based on RGB values were 1.0 and 1.5 mg L-1, respectively. This biosensor can be used to detect quickly toxicity of the Cu2+ and Hg2+ in water without complicated operation steps. Therefore, the biosensor has potential application value in the field of water toxicity detection.

Kinetics Study of Chicken Breast Meat (&lt;i&gt;Pectoralis major&lt;/i&gt;) Color Changes Measured Using the TCS 3200 Color Sensor during Storage at Room Temperature

Chicken meat has a high nutritional content that makes its freshness rapidly deteriorates. A color change characterized the degradation. Color changes could influence the consumer perception toward food quality. Human perception and evaluation of color are often subjective. Sensors can provide better detection accuracy toward this phenomenon than the human senses. This study aims to determine the change of color attribute of chicken breast meat kinetically and classify meat quality based on color changes during meat storage using Principal Component Analysis (PCA). The experiment was performed with equipment consisting of a Raspberry Pi, Arduino, and a TCS 3200 color sensor. The meat sample was stored in a dark-colored container along with the sensor for 24 hours storage at room temperature. The measurement was done every hour in three replications. Color data from sensor readings in the frequency form was then converted into RGB (Red, Green, Blue) values and finally to L*, a*, b* values during the experiment. The data obtained was sent to the database for kinetic analysis and quality classification using PCA. It was found that the change of color attribute of Chroma (C), Hue Angle (Ho), Color Difference with True Red (DE), and Color Difference (AE) followed zero-order or first-order kinetics reactions. While from the PCA resulted, two chicken meat quality classes, PC 1, explained 85.4%, and PC 2 explained 12.5%.

A portable and smartphone-based plasmonic system for on-site measurement of airborne redox-active compounds by light-initiated redox reaction

The determination of airborne redox-active compounds (ARC) is essential for monitoring adverse environmental changes and understanding the potential impact on human health. With the exception of relying on the common total organic carbon (TOC) analyzer and dithiothreitol (DTT) assay, there is currently no easy-to-use method for quantitative assessment. Herein, a compatible and easy-to-use colorimetric sensing strategy was developed for on-site ARC quantification. This approach integrated a plasmonic sensing system, where the ARC-induced gold nanoparticles (AuNPs) reduction, localized surface plasmon resonance (LSPR), and the light-mediated photochemistry collaborated to achieve a limit of detection (LOD) at 0.05 μg∙mm−2 on the filters. Distinguished from the abovementioned methods, the ARC content can be directly determined by in-situ monitoring the colorimetric reaction on the sampling quartz filters (QF) without additional sample pre-treatment. Apart from utilizing the standard benchtop photodetector (e.g., UV–VIS spectrophotometer), the colorimetric images of AuNP@QF samples could also be imaged by a smartphone camera in a sealed box and analyzed through a smartphone-based application to read the RGB (Red, Green, Blue) values for ARC quantification. The good correlation between the results using the spectrophotometer and smartphone validate the applicability of the AuNP sensor. The smartphone-based method was then deployed to test real-world aerosols collected from Zurich, Bern and Rigi and the results of ARC exhibited a positive correlation with that of PM10. With the advantages of low-cost, easy-operation and no need for pre-treatment, this smartphone-based plasmonic system holds great potential for rapid and portable ARC detection and corresponding air quality assessment.

Smartphone-assisted miniature device based on nitrogen and sulfur co-doped carbon dots for point-of-care testing of tetracycline

A miniature device was design for the point-of-care testing (POCT) of tetracycline (TC) including a ratio fluorescence test strip, a sample slot, a UV lamp and a smartphone. The nitrogen and sulfur co-doped carbon dots (N, S-CDs) and Eu3+ were dropped onto the filter paper to construct the ratio fluorescence test strips for the specific detection of TC. Under the excitation at 390 nm, the fluorescence emission of N, S-CDs at 530 nm decreases through inner filter effect (IEF) after addition of Eu3+. When the further addition of TC, the emission of N, S-CDs at 530 nm kept unchanged while the emission of Eu3+ at 616 nm was obviously enhanced for the antenna effect (AE) between Eu3+ and TC. The ratio changes of the two-fluorescence emission realized the quantitative detection of TC. In addition, the test strips with different concentrations of TC showed different fluorescence color from green to red under a 365 nm UV lamp. The miniature device was designed as a fluorescence photo reader with the merits of the powerful functions of smartphones and the portability of test strips. The smartphone camera takes a fluorescent color image of the test strips and the photos are recognized by a color recognizer on the smartphone to obtain RGB (red–greenblue) values which reflect the concentrations of the analytes. Therefore, we established a fast, sensitive and efficient POCT of TC. In particular, the proposed nanomaterial-based POCT platform will open a new route towards the development of ratio fluorescence probe for TC analysis for environment samples.

Kağıt Tabanlı Kolorimetrik Yöntemle Askorbik Asit (Vitamin C) Tayini

Ascorbic acid (AA) is the most important water-soluble organic biomolecule with antioxidant properties, known as vitamin C in many foods and beverages. The number of different methods developed for the determination of antioxidant properties of foods is increasing. Paper-based microfluidic analytical devices (µPADs) have been applied in the development of fast, simple, and cost-effective field analyzes in recent years. These developed analyzes are used as an alternative to laboratory-equipped qualitative and quantitative analyzes. In this study, a paper-based colorimetric method was applied for the determination of ascorbic acid. Geometric shapes with hydrophobic barrier were created on Whatman filter papers with the help of a wax pen. Using the principle of reduction of the AgNO3 solution in the presence of antioxidants in these shapes, the images of the color transformations were recorded with the help of a smartphone camera. ImageJ software was used in the image processing to determine the concentrations of standard AA solutions. Linear calibration curves for AA concentrations in the 0,2 to 1,0 mM range were obtained in grayscale and RGB (Red-Green-Blue) values. The coefficient of determination of the calibration curves (R2), respectively; 0,9389 for grayscale, 0,9728, 0,9628, and 0,8859 for Red, Green, and Blue channels. High-Performance Liquid Chromatography (HPLC) was used as a standard method to compare with the paper-based method. Freshly squeezed orange juice was used for the determination of ascorbic acid in the real food sample. The ascorbic acid value determined in orange juices by HPLC and colorimetric methods were statistically compared.