Chromogenic Chemosensor for Simultaneous Detection of PO43¯ and CO32¯ Anions in Organo-Aqueous Solutions: Application in Arduino Based Electronic Color Sensor Device and Logic Gate.

Three new chromogenic receptors have been synthesized with the primary objective of facilitating the selective recognition of PO43¯and CO32¯ ions in an organo-aqueous medium. R1 and R2 exhibit an extraordinary detection limit aligning with both EPA and WHO guidelines. R1 shows LOD of 0.135 ppm for PO43¯ and 0.175 ppm for CO32¯, while R2 sets forth a LOD of 0.427 ppm for PO43¯ and 0.729 ppm for CO32¯. The binding mechanism involves intramolecular charge transfer (ICT) band are substantiated by comprehensive studies that include UV-Vis titration, 1H-NMR titration, DFT studies and electrochemical studies. Chemosensors were employed in the formulation of logic gate, the fabrication of a paper strip test kit and its application in RGB color sensor device.

Rapid assessment of soil accessible Cr(â ¥) in the field by a portable RGB color sensor.

Field rapid determination of soil accessible Cr(Ⅵ) is of great significance for on-site assessment and decision-making about the health risks of contaminated sites. When the thickness of solutions with various concentrations of Cr(Ⅵ) is constant, there would be a quantitative relationship between the chromogenic difference of Cr(Ⅵ) solutions and the concentration of Cr(Ⅵ). The chromogenic difference could be described by Red (R), Green (G), Blue (B) values. Based on the chromogenic reaction between 1,5-diphenylcarbazide and Cr(Ⅵ), this study first established the calibration curve between the chromogenic difference and the concentration of Cr(Ⅵ) in standard solution with or without 0.01 M CaCl2, using an RGB color sensor. This is the subsequent determination basis of the method for rapidly assessing accessible Cr(Ⅵ) in the field (M-RGB). Then, the concentration of accessible Cr(Ⅵ) of contaminated soil with "hand-shaking + standing" field extraction method was compared with "end-over-end shaking" laboratory extraction method. Finally, the accessible Cr(Ⅵ) of contaminated soil extractants was determined via M-RGB integrating the field extraction method. Results indicated there was a highly significant linear relationship between colorimetric difference value (∆E) and Cr(Ⅵ) concentration in the range of 0.1-3 mg/L (R2 > 0.99, P < 0.01), based on the Euclidean formula for calculating ∆E. The "hand-shaking + standing" field extraction method was effective in obtaining accessible Cr(Ⅵ) extractants with or without 0.01 M CaCl2, with the high extraction efficiency within 100±1%. The concentrations of accessible Cr(Ⅵ) in various polluted soils determined by M-RGB were consistent with that determined by the ultraviolet-visible spectrophotometry, with the relative error within ±5%, and the relative standard deviation ≤ 20%. The spiked recovery experiments showed that the recovery of M-RGB was between 95% and 105%, which means M-RGB could realize the trace analysis for accessible Cr(Ⅵ) in the field.

An intelligent intestinal bleeding diagnosis and treatment capsule system based on color recognition.

To our best knowledge, there are no non-invasive and painless means for the diagnosis and treatment of intestinal bleeding as of now, especially the segment of intestine that cannot be reached by endoscopy. We proposed an intelligent intestinal bleeding diagnosis and treatment capsule (IBDTC) system for the first time to diagnose and treat intestinal bleeding with low power consumption, estimated to be about 2.16mW. A hue-saturation-light (HSL) color space method was applied to diagnose bleeding according to H (hue) values of the film dyed by blood. A MEMS-based micro-igniter works as the critical component of the micro-thruster that houses the propellant (74.6% potassium nitrate, 11.9% sulfur, 13.5% charcoal) and the detonating agent (dinitrodiazophenol), to help release drug. Bleeding detection and ignition tests were performed to justify its feasibility and reliability. Results demonstrated that the bleeding diagnosis module of the IBDTC can effectively detect bleeding and the micro-igniter can successfully ignite the propellant. Owing to its simplicity and intelligence, the IBDTC system will pave a way for future accurate treatment of small intestinal bleeding with no injury, no pain, no complicated supporting equipment, no need for in vitro operation and positioning.

Development of a "turn off-on" whole-cell biosensor for sulforaphane detection based on the ultrasensitive activator HrpRS.

Sulforaphane (SFN), a defense secondary metabolite, can be used to predict the health status of plants and also has pharmacological effects, including anticancer, antioxidant, and anti-inflammatory properties. The detection of SFN is therefore of great significance for the prevention and treatment of diseases. In this study, a "turn off" whole-cell biosensor that can rapidly and robustly respond to the presence of SFN was constructed based on the orthogonal genetic components (hrpR, hrpS, and PhrpL ) of Pseudomonas syringae (PS). The final optimized biosensor, p114(30R-30S), was able to inhibit 91.7% of the fluorescence intensity in the presence of 100-µM SFN. Subsequently, a HrpRS-regulated OFF-ON genetic switch was designed by reconstituting a reverse σ70 promoter on the σ54 -PhrpL promoter sequence; this was coupled with dual-color reporter genes to construct a "turn off-on" whole-cell SFN biosensor. The PhrpLB variant increased the expression of green fluorescence a factor of 11.9 and reduced the expression of red fluorescence by 85.8% compared with the system in the absence of SFN. Thus, a robust switching of signal output from "turn off" to "turn on" was realized. In addition, the biosensor showed good linearity in the SFN concentration ranges of 0.1-10 µM (R2  = 0.99429) and 10-100 µM (R2  = 0.99465) and a detection limit of ~0.1 µM.

Development and application of DPPH impregnated paper based color sensor disc to detect vegetable oils addition in cow ghee.

Ghee is a premium product in Southeast Asia and is prone to adulteration with vegetable oils/ fats. The main aim of the study was to develop an easy-to-use paper-based sensor to detect this adulteration. Hence, a protocol involving hexane and acetonitrile for the extraction of synthetic antioxidants from adulterated ghee and its rapid detection using DPPH was standardized. Paper-based discs impregnated with 4 mM DPPH were developed. The developed paper-based disc sensors worked well and their response time was indirectly proportional to the antioxidant concentration (0.0025-0.02%). Using the developed disc sensors, the palm oil, and sunflower oil added to cow ghee @2.5% or more, and 1% or more, respectively could be detected. The shelf life of the developed sensors was 30 and 90 days at 30 °C and 4-6 °C, respectively. In stored cow ghee samples, the response time of the sensors increased as the storage period of ghee samples increased. The cutoff limit to declare the sample of cow ghee as unadulterated was fixed to 60 min. Based on the response time of the sensor, the level of detection of vegetable oils in stored cow ghee was found to be 2.5%.

Smart prototype for an electronic color sensor device for visual simultaneous detection of macrofuran based on a coated paper strip.

Nowadays, in the clinical, pharmaceutical, and environmental sectors, the development of facile and sensitive analytical methods and/or innovative devices for the follow-up and detection of antibiotics and pharmaceutical formulations, in general, are urgently needed and still challenging. This work declared three vital applications for broad-spectrum nitrofurantoin (macrofuran) antibiotic detection and quantification: A colorimetric method, a coated paper strip-based nano-lanthanum complex prototype and fabrication of smart electronic color sensor device-based coated paper strips. The colorimetric method showed a significant response upon increasing the concentration of the nitrofurantoin in a range between (1.0-100.0 ng/mL) via a visual color change from orange-yellow to red colors degree with detection and quantification limits of 0.175 and 0.53 ng/mL, respectively, whereas the nano-lanthanum complex coated paper strip prototype showed qualitative on-site sensing for nitrofurantoin via naked eye color changes which can be detected anywhere. Moreover, a smart prototype for detecting macrofuran in the means of paper color change in the RGB color component extraction algorithm and the grayscale projection value processing algorithm was fabricated. The change in RGB color on the coated paper strip was detected using an electronic color sensor device. The developed colorimetric method, coated paper strip, and the electronic color sensor device prototype exhibited fast, simple, costless, and selective towards macrofuran over the competing analyzed. As well as, showed good applicability in the different real samples spiked with different concentrations of macrofuran.

A Real-Time Detection Device for the Rapid Quantification of Skin Casual Sebum Using the Oil Red O Staining Method.

The human skin sebum suggests that it (along with other epidermal surface lipids) plays a role in skin barrier formation, the moderation of cutaneous inflammation, and antimicrobial defense. Various methods have been developed for collecting and measuring skin sebum. We tested methods of detection using "color intensity", by staining the skin casual sebum. This process was conducted in three steps; first, the selection of materials for sebum collection; second, staining the collected sebum; third, the development of a device that can measure the level of stained sebum. A plastic film was used to effectively collect sebum that increased with the replacement time of the sebum. In addition, the collected sebum was stained with Oil Red O (ORO) and checked with RGB; as a result, the R2 value was higher than 0.9. It was also confirmed that the correlation value was higher than 0.9 in the comparison result with Sebumeter®, which is a common standard technology. Finally, it was confirmed that the R2 value was higher than 0.9 in the detection value using the sensor. In conclusion, we have proven the proof of concept (PoC) for this method, and we would like to introduce an effective sebum measurement method that differs from the existing method.

A protocol for digitizing colors: the case of measuring color variables for forested wetland soils.

A procedure is presented and discussed that highlights the use of the Nix Pro Color Sensor ("Nix") in digitizing soil colors with applications for forested wetland soils. Informed by our soil color investigations using both the Munsell Soil Color Chart (MSCC) and the Nix in forested wetlands of the northern Virginia area, we crafted a standard operating procedure (SOP), adaptable to various locations and/or soil types, that guides users-regardless of knowledge of soil ecology or familiarity with the Nix-to successfully assess and monitor soil colors at various depths. Our SOP outlines steps for digitally collecting, storing, and sharing soil color data. Through the implementation of this procedure, soil color monitoring can enter the digital age, removing barriers of entry to soil color determination and enhancing individuals' interest in monitoring and understanding of the importance of soil color as an environmental and ecological indicator. With continued refinement and adaptation to intended use, the SOP herein presented has the potential to aid wetland/watershed assessment by providing data on soil colors that can be tracked over time while also encouraging public engagement in environmental monitoring of soils.

Blue as an Underrated Alternative to Green: Photoplethysmographic Heartbeat Intervals Estimation under Two Temperature Conditions.

Since photoplethysmography (PPG) sensors are usually placed on open skin areas, temperature interference can be an issue. Currently, green light is the most widely used in the reflectance PPG for its relatively low artifact susceptibility. However, it has been known that hemoglobin absorption peaks at the blue part of the spectrum. Despite this fact, blue light has received little attention in the PPG field. Blue wavelengths are commonly used in phototherapy. Combining blue light-based treatments with simultaneous blue PPG acquisition could be potentially used in patients monitoring and studying the biological effects of light. Previous studies examining the PPG in blue light compared to other wavelengths employed photodetectors with inherently lower sensitivity to blue, thereby biasing the results. The present study assessed the accuracy of heartbeat intervals (HBIs) estimation from blue and green PPG signals, acquired under baseline and cold temperature conditions. Our PPG system is based on TCS3472 Color Sensor with equal sensitivity to both parts of the light spectrum to ensure unbiased comparison. The accuracy of the HBIs estimates, calculated with five characteristic points (PPG systolic peak, maximum of the first PPG derivative, maximum of the second PPG derivative, minimum of the second PPG derivative, and intersecting tangents) on both PPG signal types, was evaluated based on the electrocardiographic values. The statistical analyses demonstrated that in all cases, the HBIs estimation accuracy of blue PPG was nearly equivalent to the G PPG irrespective of the characteristic point and measurement condition. Therefore, blue PPG can be used for cardiovascular parameter acquisition. This paper is an extension of work originally presented at the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace.

The article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with a conveyor belt, robot and an industrial color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form of type A standard uncertainty and type B standard uncertainty, in order to obtain combined standard uncertainties results. Based on the acquired data, control charts of RGB color components for identified products are created. Influence of product speed on the measuring process identification and process stability is monitored. In case of identification uncertainty i.e., measured values are outside the limits of control charts, the K-nearest neighbor machine learning algorithm is used. This algorithm, based on the Euclidean distances to the classified value, estimates its most accurate iteration. This results into the comprehensive system for identification of product moving on conveyor belt, where based on the data collection and statistical analysis using machine learning, industry usage reliability is demonstrated.

Design and Implementation of Universal Cyber-Physical Model for Testing Logistic Control Algorithms of Production Line's Digital Twin by Using Color Sensor.

This paper deals with the design and implementation of a universal cyber-physical model capable of simulating any production process in order to optimize its logistics systems. The basic idea is the direct possibility of testing and debugging advanced logistics algorithms using a digital twin outside the production line. Since the digital twin requires a physical connection to a real line for its operation, this connection is substituted by a modular cyber-physical system (CPS), which replicates the same physical inputs and outputs as a real production line. Especially in fully functional production facilities, there is a trend towards optimizing logistics systems in order to increase efficiency and reduce idle time. Virtualization techniques in the form of a digital twin are standardly used for this purpose. The possibility of an initial test of the physical implementation of proposed optimization changes before they are fully implemented into operation is a pragmatic question that still resonates on the production side. Such concerns are justified because the proposed changes in the optimization of production logistics based on simulations from a digital twin tend to be initially costly and affect the existing functional production infrastructure. Therefore, we created a universal CPS based on requirements from our cooperating manufacturing companies. The model fully physically reproduces the real conditions of simulated production and verifies in advance the quality of proposed optimization changes virtually by the digital twin. Optimization costs are also significantly reduced, as it is not necessary to verify the optimization impact directly in production, but only in the physical model. To demonstrate the versatility of deployment, we chose a configuration simulating a robotic assembly workplace and its logistics.

Tackling Heterogeneous Color Registration: Binning Color Sensors.

Intelligent systems for interior lighting strive to balance economical, ecological, and health-related needs. For this purpose, they rely on sensors to assess and respond to the current room conditions. With an augmented demand for more dedicated control, the number of sensors used in parallel increases considerably. In this context, the present work focuses on optical sensors with three spectral channels used to capture color-related information of the illumination conditions such as their chromaticities and correlated color temperatures. One major drawback of these devices, in particular with regard to intelligent lighting control, is that even same-type color sensors show production related differences in their color registration. Standard methods for color correction are either impractical for large-scale production or they result in large colorimetric errors. Therefore, this article shows the feasibility of a novel sensor binning approach using the sensor responses to a single white light source for cluster assignment. A cluster specific color correction is shown to significantly reduce the registered color differences for a selection of test stimuli to values in the range of 0.003-0.008 Δu'v', which enables the wide use of such sensors in practice and, at the same time, requires minimal additional effort in sensor commissioning.

Use of Multiple Bacteriophage-Based Structural Color Sensors to Improve Accuracy for Discrimination of Geographical Origins of Agricultural Products.

A single M13 bacteriophage color sensor was previously utilized for discriminating the geographical origins of agricultural products (garlic, onion, and perilla). The resulting discrimination accuracy was acceptable, ranging from 88.6% to 94.0%. To improve the accuracy further, the use of three separate M13 bacteriophage color sensors containing different amino acid residues providing unique individual color changes (Wild sensor: glutamic acid (E)-glycine (G)-aspartic acid (D), WHW sensor: tryptophan (W)-histidine (H)-tryptophan (W), 4E sensor: four repeating glutamic acids (E)) was proposed. This study was driven by the possibility of enhancing sample discrimination by combining mutually characteristic and complimentary RGB signals obtained from each color sensor, which resulted from dissimilar interactions of sample odors with the employed color sensors. When each color sensor was used individually, the discrimination accuracy based on support vector machine (SVM) ranged from 91.8-94.0%, 88.6-90.3%, and 89.8-92.1% for garlic, onion, and perilla samples, respectively. Accuracy improved to 98.0%, 97.5%, and 97.1%, respectively, by integrating all of the RGB signals acquired from the three color sensors. Therefore, the proposed strategy was effective for improving sample discriminability. To further examine the dissimilar responses of each color sensor to odor molecules, typical odor components in the samples (allyl disulfide, allyl methyl disulfide, and perillaldehyde) were measured using each color sensor, and differences in RGB signals were analyzed.

Predicting forested wetland soil carbon using quantitative color sensor measurements in the region of northern Virginia, USA.

Forested wetland soils within the Piedmont and Coastal Plain physiographic provinces of Northern Virginia (NOVA) were investigated to determine the utility of a handheld colorimeter, the Nix Pro Color Sensor ("Nix"), for predicting carbon contents (TC) and stocks (TC stocks) from on-site color measurements. Both the color variables recorded with each Nix scan ("Nix color variables"; n = 15) and carbon contents significantly differed between sites, with redder soils (higher a and h) at Piedmont sites, and higher TC at sites with darker soils (lower values of L, or lightness; p < 0.05). Nix-carbon correlation analysis revealed strong relationships between L (lightness), X (a virtual spectral variable), R (additive red), and KK (black) and log-transformed TC (Ln[TC]; |r| = 0.70; p < 0.01 for all). Simple linear regressions were conducted to identify how well these four final Nix variables could predict soil carbon. Using all color measurements, about 50% of Ln(TC) variability could be explained by L, X, R, or KK (p < 0.01), yet with higher predictive power obtained for Coastal Plain soils (0.55 < R2 < 0.65; p < 0.01). Regression model strength was maximized between Ln(TC) and the four final Nix variables using simple linear regressions when color measurements observed at a specific depth were first averaged (0.66 < R2 < 0.70; p < 0.01). While further study is warranted to investigate Nix applicability within various soil settings, these results demonstrate potential for the Nix and its soil color measurements to assist with rapid field-based assessments of soil carbon in forested wetlands.

Nix Pro Color Sensor provides comparable color measurements to HunterLab colorimeter for fresh beef.

The HunterLab MiniScan (HunterLab) colorimeter is used in meat quality research worldwide for measuring meat color; however, the Nix Pro Color Sensor (Nix) could be a less expensive alternative that is easier to operate. Therefore, the objective of this study was to compare the two colorimeters to objectively evaluate fresh beef color. Longissimus thoracis muscle from one side of A maturity beef carcasses (n = 200) was evaluated using both the HunterLab (3 technical replicate scans) and Nix (3, 5, 7, and 9 technical replicate scans) colorimeters. The correlation between the HunterLab and Nix for L* (lightness), a* (redness), and b* (yellowness) values ranged between r = 0.80 to 0.85 and the Bland Altman Limits of Agreement analysis indicated good agreement between the Nix and HunterLab colorimeters for all the color parameters. These results indicated that the Nix colorimeter could be a viable alternative for HunterLab colorimeters.

Discrimination of phthalate species using a simple phage-based colorimetric sensor in conjunction with hierarchical support vector machine.

Here, the analytical potential of an M13 bacteriophage-based color sensor for discrimination of 4 phthalates with similar molecular structures (bis-(2-ethylhexyl)-phthalate (BEHP), dibutyl phthalate (DBP), diethyl phthalate (DEP), and benzyl-butyl-phthalate (BBP)) was investigated. The pattern and magnitude of the RGB color changes were different depending on the functional groups present in the phthalate structures. For example, BEHP possessing a long alkyl chain resulted in a minute color change, while the variation of color was substantially large when BBP containing an additional benzene ring was measured. Since a tryptophan-histidine-tryptophan residue possessing indole and imidazole was present on the self-assembled phages, the π-π interaction of benzene in BBP with the sensor surface produced a considerably greater color change. To evaluate the multi-modally varying color signals due to diverse interactions of the phthalates with the sensor and to discriminate them, support vector machine (SVM), which can construct a boundary hyperplane among complexly scattered sample groups, was used. In addition, hierarchical SVM (H-SVM) was adopted to deal with multi-class discrimination. The use of H-SVM improved the discrimination accuracy up to 90.1%, compared to 87.1% using SVM. The demonstrated color sensor is versatile and can be potentially adopted as an on-site screening tool. Strategies to improve the accuracy further for real applications are also discussed.

Monitoring of Cell Concentration during Saccharomyces cerevisiae Culture by a Color Sensor: Optimization of Feature Sensor Using ACO.

The odor information produced in Saccharomyces cerevisiae culture is one of the important characteristics of yeast growth status. This work innovatively presents the quantitative monitoring of cell concentration during the yeast culture process using a homemade color sensor. First, a color sensor array, which could visually represent the odor changes produced during the yeast culture process, was developed using eleven porphyrins and one pH indicator. Second, odor information of the culture substrate was obtained during the process using the homemade color sensor. Next, color components, which came from different color sensitive spots, were extracted first and then optimized using the ant colony optimization (ACO) algorithm. Finally, the back propagation neural network (BPNN) model was developed using the optimized feature color components for quantitative monitoring of cell concentration. Results demonstrated that BPNN models, which were developed using two color components from FTPPFeCl (component B) and MTPPTE (component B), can obtain better results on the basis of both the comprehensive consideration of the model performance and the economic benefit. In the validation set, the average of determination coefficient R P 2 was 0.8837 and the variance was 0.0725, while the average of root mean square error of prediction (RMSEP) was 1.0033 and the variance was 0.1452. The overall results sufficiently demonstrate that the optimized sensor array can satisfy the monitoring accuracy and stability of the cell concentration in the process of yeast culture.

Color Measurement and Analysis of Fruit with a Battery-Less NFC Sensor.

This paper presents a color-based classification system for grading the ripeness of fruit using a battery-less Near Field Communication (NFC) tag. The tag consists of a color sensor connected to a low-power microcontroller that is connected to an NFC chip. The tag is powered by the energy harvested from the magnetic field generated by a commercial smartphone used as a reader. The raw RGB color data measured by the colorimeter is converted to HSV (hue, saturation, value) color space. The hue angle and saturation are used as features for classification. Different classification algorithms are compared for classifying the ripeness of different fruits in order to show the robustness of the system. The low cost of NFC chips means that tags with sensing capability can be manufactured economically. In addition, nowadays, most commercial smartphones have NFC capability and thus a specific reader is not necessary. The measurement of different samples obtained on different days is used to train the classification algorithms. The results of training the classifiers have been saved to the cloud. A mobile application has been developed for the prediction based on a table-based method, where the boundary decision is downloaded from a cloud service for each product. High accuracy, between 80 and 93%, is obtained depending on the kind of fruit and the algorithm used.

Integrated High Resolution Digital Color Light Sensor in 130 nm CMOS Technology.

This article presents a color light detection system integrated in 130 nm CMOS technology. The sensors and corresponding electronics detect light in a CIE XYZ color luminosity space using on-chip integrated sensors without any additional process steps, high-resolution analog-to-digital converter, and dedicated DSP algorithm. The sensor consists of a set of laterally arranged integrated photodiodes that are partly covered by metal, where color separation between the photodiodes is achieved by lateral carrier diffusion together with wavelength-dependent absorption. A high resolution, hybrid, ∑∆ ADC converts each photo diode's current into a 22-bit digital result, canceling the dark current of the photo diodes. The digital results are further processed by the DSP, which calculates normalized XYZ or RGB color and intensity parameters using linear transformations of the three photo diode responses by multiplication of the data with a transformation matrix, where the coefficients are extracted by training in combination with a pseudo-inverse operation and the least-mean square approximation. The sensor system detects the color light parameters with 22-bit accuracy, consumes less than 60 µA on average at 10 readings per second, and occupies approx. 0.8 mm(2) of silicon area (including three photodiodes and the analog part of the ADC). The DSP is currently implemented on FPGA.

Prediction of compost organic matter via color sensor.

Composted materials serve as an effective soil nutrient amendment. Organic matter in compost plays an important role in quantifying composted materials overall quality and nutrient content. Measuring organic matter content traditionally takes considerable time, resources, and various laboratory equipment (e.g., oven, muffle furnace, crucibles, precision balance). Much like the quantitative color indices (e.g., sRGB R, sRGB G, sRGB B, CIEL*a* b*) derived from the low-cost NixPro2 color sensor have proven adept at predicting soil organic matter in-situ, the NixPro2 color sensor has the potential to be effective for predicting organic matter in composted materials without the need for traditional laboratory methods. In this study, a total of 200 compost samples (13 different compost types) were measured for organic matter content via traditional loss-on-ignition (LOI) and via the NixPro2 color sensor. The NixPro2 color sensor showed promising results with an LOI-prediction model utilizing the CIEL*a* b* color model through the application of the Generalized Additive Model (GAM) algorithm yielding an excellent prediction accuracy (validation R2 = 0.87, validation RMSE = 4.66 %). Moreover, the PCA scoreplot differentiated the three lowest organic matter compost types from the remaining 10 compost types. These results have valuable practical significance for the compost industry by predicting compost organic matter in real time without the need for laborious, time-consuming methods.