Recent developments in computer vision-based analytical chemistry: A tutorial review

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

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

Feasibility of using colorimetric devices for whole and ground coffee roasting degrees prediction

BACKGROUND

Coffee roasting is one of the crucial steps in obtaining a high-quality product as it forms the product's color and flavor characteristics. Roast control is made by visual inspection or traditional instruments such as the Agtron spectrophotometer, which can have high implementation costs. Therefore, the present study evaluated colorimetric approaches (a bench colorimeter, smartphone digital images, and a colorimetric sensor) to predict the Agtron roasting degrees of whole and ground coffee. Two calibration approaches were assessed, that is, multiple linear regression and least-squares support vector machine. For that, 70 samples of whole and ground roasted coffees comprising the Agtron roasting range were prepared.

RESULTS

The results showed that all three colorimetric acquisition types were efficient for the model building, but the bench colorimeter and the smartphone digital images generally performed with good determination coefficients and low errors as measured by external validation. For the whole bean coffee, the best model presented a determination coefficient (R2) of 0.99 and a root-mean-squared error (RMSE) of 1.91%, while R2 of 0.99 and RMSE of 0.87% was obtained for ground coffee, both using the colorimeter.

CONCLUSION

The obtained models presented good prediction capability, as assessed by external validation and randomization tests. The obtained findings point to an alternative for coffee roasting monitoring that can lead to higher digitalization and local control of the process, even for smaller producers, due to its lower costs. © 2024 Society of Chemical Industry.

A smartphone image-based method for the colorimetric characterization of historical pigments in mural paintings

In this article, the detailed description of a smartphone-based analytical method to turn a smartphone into a colorimeter is described and applied to mural painting replicas. The method consists of a camera characterization process, to estimate CIELAB values from RGB camera responses, correcting for potential experimental interferences, like the effect of the lighting/capture geometry or software for color modification and enhancement. Next, the estimated CIELAB descriptors were used to build a chemometric Support Vector Classifier to objectively distinguish among the colors of the different pigments. The method was validated in terms of color reproduction and classification performance.•A smartphone-based method is described to capture mural painting color.•A color gamut-specific camera characterization procedure is developed and the performance of the smartphone-based colorimeter is compared with a spectrophotometer.•A classification method based on the estimated CIELAB descriptors is validated in terms of classification performance.

Multiphase electroextraction of malachite green from surface water and its determination using digital imaging and chemometric tools

This study introduces a novel method for the quantification of malachite green (MG), a pervasive cationic dye, in surface water by synergizing multiphase electroextraction (MPEE) with digital image analysis (DIA) and partial least square discriminant analysis. Aimed at addressing the limitations of conventional DIA methods in terms of quantitation limits and selectivity, this study achieves a significant breakthrough in the preconcentration of MG using magnesium silicate as a novel sorbent. Demonstrating exceptional processing efficiency, the method allows for the analysis of 10 samples within 20 min, exhibiting remarkable sensitivity and specificity (over 0.95 and 0.90, respectively) across 156 samples in both training and test sets. Notably, the method detects MG at low concentrations (0.2 µg L-1) in complex matrices, highlighting its potential for broader application in environmental monitoring. This approach not only underscores the method's cost-effectiveness and simplicity but also its precision, making it a valuable tool for the preliminary testing of MG in surface waters. This study underscores the synergy among MPEE, DIA, and chemometric tools, presenting a cost-efficient and reliable alternative for the sensitive detection of water contaminants.

Evaluation of semi-quantitative colorimetric assays based on loop-mediated isothermal amplification indicators by using image analysis

Colorimetric assays are some of the most convenient detection methods, creating discoloration in solutions that is visible to the naked eye. However, colorimetric reactions have some limitations regarding the variability in the color perception of individuals caused by factors such as color blindness, experience, and gender. Semi-quantitative chromatic analysis has been used as an alternative method to differentiate between two colors and accurately interpret the results from a numerical value, with high confidence. Therefore, we developed and determined the optimal model between Red-Green-Blue (RGB) and Commission Internationale de l'Eclairage (CIE) Lab color spaces to establish a semi-quantitative colorimetric assay via image analysis by the ImageJ program for loop-mediated isothermal amplification (LAMP), using the dyes malachite green and phenol red. The semi-quantitative colorimetric assays using the color distance values of the CIELab color space (ΔEab) were more suitable than those using the RGB color space (ΔERGB) for chromatic differentiation between positive and negative reactions in both indicator dyes, demonstrating the feasibility of this assay to be applied in the detection of a wide range of pathogens and infectious diseases.

Region of interest selection in heterogeneous digital image: Wine age prediction by comprehensive two-dimensional gas chromatography

This study integrates genetic algorithm (GA) with partial least squares regression (PLSR) and various variable selection methods to identify impactful regions of interest (ROI) in heterogeneous 2D chromatogram images for predicting wine age. As wine quality and aroma evolve over time, transitioning from youthful fruitiness to mature, complex flavors, which leads to alterations in the composition of essential aroma-contributing compounds. Chromatograms are segmented into subimages, and the GA-PLSR algorithm optimizes combinations based on grayscale, red-green-blue (RGB), and hue-saturation-value (HSV) histograms. The selected subimage histograms are further refined through interval selection, highlighting the compounds with the most significant influence on wine aging. Experimental validation involving 38 wine samples demonstrates the effectiveness of this approach. Cross-validation reduces the PLS model error from 2.8 to 2.4 years within a 10 × 10 subset, and during prediction, the error decreases from 2.5 to 2.3 years. The study presents a novel approach utilizing the selection of ROI for efficient processing of 2D chromatograms focusing on predicting wine age.

Dispersive surfactant micelle-mediated extraction combined with a smartphone-based portable colorimeter: a cost-effective and simple approach for cobalt determination

In this research, we present a cost-effective, environmentally friendly methodology for the precise determination of trace levels of cobalt in various environmental matrices, based on a new surfactant micelle-mediated extraction combined with digital image analysis. Specifically, cetyltrimethylammonium bromide (CTAB) serves as the key surfactant. Prior to extraction, the conversion of Co2+ ions into hydrophobic species is achieved through the utilization of 1-nitroso 2-naphthol as a chelating agent. The procedure involves injecting a tetrahydrofuran (THF) solution of CTAB into water samples containing the target analytes and some added KI, resulting in the formation of a turbid solution due to CTAB dispersion within the medium. Following centrifugation, the resulting precipitate is re-dissolved in 1 mL of dimethylformamide and subjected to analysis using a self-constructed colorimeter, which is based on a mobile device. In the colorimeter, digital image analysis is conducted using the RGB color space, with the G channel value serving as the analytical signal. Our investigation encompasses the exploration and optimization of several critical parameters influencing the extraction and complex formation processes. Under optimal conditions, a linear range spanning 10-2.00 × 102 μg L-1 is achieved, exhibiting a correlation coefficient of 0.994. The detection limit (DL) is determined to be 4.1 μg L-1. The relative standard deviations for the determination of Co2+ at concentrations of 40 and 100 μg L-1 are found to be 7.0 and 6.6, respectively, for five replicates. Further assessments include an evaluation of the impact of common cations and anions on the proposed method, which subsequently qualifies it for the efficient preconcentration and quantification of cobalt in diverse environmental matrices.

Estimation of planar angles from non-orthogonal imaging

Photogrammetry-based methods are traditionally used to estimate the geometrical parameters using optical images. These methods employ specific equipment, computationally sophisticated and expensive algorithms, and utilize projective geometry to reconstruct real-life scenes up to a scale. In this work, we used a computationally less-expensive method for sparse reconstruction to estimate the planar angles using two-view geometry and linear algorithms from non-orthogonal images acquired by a smartphone camera. First, intrinsic camera parameters were determined. Next, scale-invariant feature transform was used to identify the correspondence points from each pair of images. Epipolar constraint was applied on all these points to determine the essential matrix using the eight-point algorithm. Thereafter, extrinsic camera parameters were estimated from the essential matrix and combined with the intrinsic matrix to get the camera projection matrix. Finally, linear triangulation was used to get the sparse point cloud representing the scene. Planar angles were estimated by backprojecting the chosen image points and applying simple vector algebra on the obtained 3D points. The method was successful in estimating the planar angles in less than 10 s on non-curved edges with an average error of 3% by using only ten images. Given the simplicity of methods used, this technique can be integrated into a smartphone for on-site measurements as well as large deformations.

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

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

Computer vision as a new paradigm for monitoring of solution and solid phase peptide synthesis

We report a strategy for the camera-enabled non-contact colourimetric reaction monitoring and optimisation of amide bond formation, mediated by coupling reagents. For amide bond formation in solution phase, investigation of reactions mediated by HATU, PyAOP, and DIC/Oxyma evidenced correlations between colour parameters extracted from video data and conversion to amide product measured by off-line HPLC analysis of concentration. These correlations, supported by mutual information analysis, were further investigated using video recordings of solid phase peptide synthesis (SPPS), co-analysed by off-line HPLC to track remaining unreacted substrate in solution. An optimisation method of coupling time in SPPS was derived from ΔE (a measurement of colour contrast), giving comparable isolated peptide yield and purity at 65-95% reduced overall reaction time. The same colour data enabled data-rich monitoring of reaction rate attenuation, consisted with computationally-derived measures of amino acid steric bulk. These findings provide a foundation for exploring the use of camera technology and computer vision towards automated and online mechanistic profiling of SPPS.

Toward Citizen Science-Based Ocean Acidification Observations Using Smartphone Devices

pH is a key parameter in many chemical, biological, and biogeochemical processes, making it a fundamental aspect of environmental monitoring. Rapid and accurate seawater pH measurements are essential for effective ocean observation and acidification investigations, resulting in the need for novel solutions that allow robust, precise, and affordable pH monitoring. In this study, a versatile smartphone-based environmental analyzer (vSEA) was used for the rapid measurement of seawater pH in a field study. The feasibility of the use of the vSEA algorithm for pH quantification was explored and verified. When used in conjunction with a three-dimensional (3D)-printed light-proof shell, the quality of captured images is guaranteed. The quantitative accuracy of vSEA pH measurements reached 0.018 units with an uncertainty of <0.01, meeting the requirements of the Global Ocean Acidification Observing Network (GOA-ON) for "weather" goals (permitting a maximum pH uncertainty of 0.02). The vSEA-pH system was successfully applied for on-site pH measurements in coastal seawater and coral systems. The performance of the vSEA-pH system was validated using different real-world samples, and t-test results showed that the vSEA-pH system was consistent with pH measurements obtained using a state-of-the-art benchtop spectrophotometer (t = 1.986, p = 0.7949). The vSEA-pH system is applicable to different types of smartphone devices, making it possible for vSEA-pH to be widely promoted for public citizen use. The vSEA-pH system offers a simple, accurate, and applicable method for the on-site measurement of seawater pH, assisting the large-scale monitoring of ocean acidification by allowing the contribution of citizen science-based data collection.

Exploring Strategies to Mitigate the Lightness Effect on the Prediction of Soybean Oil Content in Blends of Olive and Avocado Oil Using Smartphone Digital Image Colorimetry

Extra virgin olive oil (EVOO) and avocado oil (AVO) are recognized for their unique sensory characteristics and bioactive compounds. Declared blends with other vegetable oils are legal, but undeclared mixing is a common type of fraud that can affect product quality and commercialization. In this sense, this study explored strategies to mitigate the influence of lighting in order to make digital image colorimetry (DIC) using a smartphone more robust and reliable for predicting the soybean oil content in EVOO and AVO blends. Calibration models were obtained by multiple linear regression using the images' RGB values. Corrections based on illuminance and white reference were evaluated to mitigate the lightness effect and improve the method's robustness and generalization capability. Lastly, the prediction of the built model from data obtained using a distinct smartphone was assessed. The results showed models with good predictive capacities, R2 > 0.9. Generally, models solely based on GB values showed better predictive performances. The illuminance corrections and blank subtraction improved the predictions of EVOO and AVO samples, respectively, for image acquisition from distinct smartphones and lighting conditions as evaluated by external validation. It was concluded that adequate data preprocessing enables DIC using a smartphone to be a reliable method for analyzing oil blends, minimizing the effects of variability in lighting and imaging conditions and making it a potential technique for oil quality assurance.

Teaching old presumptive tests new digital tricks with computer vision for forensic applications

Presumptive (or 'spot') tests have served forensic scientists, law enforcement, and legal practitioners for over a hundred years. Yet, the intended design of such tests, enabling quick identification of drugs by-eye, also hides their full potential. Here, we report the development and application of time-resolved imaging methods of reactions attending spot tests for amphetamines, barbiturates, and benzodiazepines. Analysis of the reaction videos helps distinguish drugs within the same structural class that, by-eye, are judged to give the same qualitative spot test result. It is envisaged that application of these results will bridge the existing suite of field and lab-based confirmatory forensic tests, and support a broader range of colorimetric sensing technologies.

Miniaturized on-chip electromembrane extraction with QR code-based red-green-blue analysis using a customized Android application for copper determination in environmental and food samples

A miniaturized on-chip electromembrane extraction device with QR code-based red-green-blue analysis was designed to determine copper in water, food, and soil. The acceptor droplet consisted of ascorbic acid as the reducing agent and bathocuproine as the chromogenic reagent. The formation of a yellowish-orange complex was a sign of copper in the sample. Then, the qualitative and quantitative analysis of the dried acceptor droplet was done by the customized Android app that was developed based on image analysis concepts. In this application, principal component analysis was performed on the data for the first time to reduce the three dimensions, red, green, and blue, to one dimension. The effective extraction parameters were optimized. The limit of detection and limit of quantification were 0.1 µg mL^-1. Intra- and inter-assay relative standard deviations ranged between 2.0 and 2.3 % and 3.1-3.7 %, respectively. The calibration range was studied between 0.1 and 25 µg mL^-1 (R² = 0.9814).

PVD for Decorative Applications: A Review

Physical Vapor Deposition (PVD) is a widely utilized process in various industrial applications, serving as a protective and hard coating. However, its presence in fields like fashion has only recently emerged, as electroplating processes had previously dominated this reality. The future looks toward the replacement of the most hazardous and toxic electrochemical processes, especially those involving Cr(VI) and cyanide galvanic baths, which have been restricted by the European Union. Unfortunately, a complete substitution with PVD coatings is not feasible. Currently, the combination of both techniques is employed to achieve new aesthetic features, including a broader color range and diverse textures, rendering de facto PVD of primary interest for the decorative field and the fashion industry. This review aims to outline the guidelines for decorative industries regarding PVD processes and emphasize the recent advancements, quality control procedures, and limitations.

Decentralized environmental applications of a smartphone-based method for chemical oxygen demand and color analysis

This study is focused on a proposal of a smartphone imaging-based quantification for providing a simple and rapid method for the analysis of chemical oxygen demand (COD) and color throughout the use of the HSV and/or RGB model in digital devices. For COD, calibration curves were done based on the theoretical values of potassium biphthalate for a proper comparison between the spectrophotometer and the smartphone techniques. The smartphone camera and application attain an average accuracy higher than the analysis in the spectrophotometer (98.3 and 96.2%, respectively). In the color analysis, it was demonstrated that only the UV-vis bands measurement is not feasible to perform the real abatement of the dye in the water because the limiting concentration that allows obtaining a linear relationship in this equipment related to the dye concentration is about 10 mg L^-1. Above this value, the spectrophotometer can not reach the real difference of color in the solution. Meanwhile, the smartphone method by using the camera reaches linearity until 50 mg L^-1. From an environmental point of view, smartphones have been used for monitoring several organic and inorganic pollutants, however, no attempts have been published related to their use to evaluate the color and COD during wastewater treatment. Therefore, this investigation also aims to assess the utilization of these methods, for the first time, when high-colored water polluted by methylene blue (MB) was electrochemically treated by using a boron-dopped diamond (BDD) as the anode, with different current densities (j = 30, 45, 60, and 90 mA cm^-2). COD and color abatement results clearly showed that different organic matter/color removal efficiencies were achieved, depending on the j used. All the results are aligned with the studies already available in the literature, with the total removal of color in 120 min of electrolysis with 60 and 90 mA cm^-2, and almost 80% of COD abatement with the higher j. Moreover, samples of real effluent from beauty salons were compared, with standard deviation varying from only 3 to 40 mg O₂ L^-1, which is acceptable for COD values close to 2000. Finally, the methods here presented can be a great benefit for public water monitoring policies, since it is cheap and has a decentralized characteristic, given that smartphones are very common and portable devices.

Droplet-based luminescent sensor supported onto hydrophobic cellulose substrate for assessing fish freshness following smartphone readout

In this work, two sensitive droplet-based luminescent assays with smartphone readout for the determination of trimethylamine nitrogen (TMA-N) and total volatile basic nitrogen (TVB-N) are reported. Both assays exploit the luminescence quenching of copper nanoclusters (CuNCs) produced when exposed to volatile nitrogen bases. In addition, hydrophobic-based cellulose substrates demonstrated their suitability as holders for both in-drop volatile enrichment and subsequent smartphone-based digitization of the enriched colloidal solution of CuNCs. Under optimal conditions, enrichment factors of 181 and 153 were obtained with the reported assays for TMA-N and TVB-N, respectively, leading to methodological LODs of 0.11 mg/100 g and 0.27 mg/100 g for TMA-N and TVB-N, respectively. The repeatability, expressed as RSD, was 5.2% and 5.6% for TMA-N and TVB-N, respectively (N = 8). The reported luminescent assays were successfully applied to the analysis of fish samples, showing statistically comparable results to those obtained with the reference methods of analysis.

Computer vision for non-contact monitoring of catalyst degradation and product formation kinetics

We report a computer vision strategy for the extraction and colorimetric analysis of catalyst degradation and product-formation kinetics from video footage. The degradation of palladium(ii) pre-catalyst systems to form 'Pd black' is investigated as a widely relevant case study for catalysis and materials chemistries. Beyond the study of catalysts in isolation, investigation of Pd-catalyzed Miyaura borylation reactions revealed informative correlations between colour parameters (most notably ΔE, a colour-agnostic measure of contrast change) and the concentration of product measured by off-line analysis (NMR and LC-MS). The breakdown of such correlations helped inform conditions under which reaction vessels were compromised by air ingress. These findings present opportunities to expand the toolbox of non-invasive analytical techniques, operationally cheaper and simpler to implement than common spectroscopic methods. The approach introduces the capability of analyzing the macroscopic 'bulk' for the study of reaction kinetics in complex mixtures, in complement to the more common study of microscopic and molecular specifics.

Simple colorimetric copper(II) sensor - Spectral characterization and possible applications

New o-hydroxyazocompound L bearing pyrrole residue was obtained in the simple synthetic protocol. The structure of L was confirmed and analyzed by X-ray diffraction. It was found that new chemosensor can be successfully used as copper(II) selective spectrophotometric regent in solution and can be also applied for the preparation of sensing materials generating selective color signal upon interaction with copper(II). Selective colorimetric response towards copper(II) is manifested by a distinct color change from yellow to pink. Proposed systems were effectively used for copper(II) determination at concentration level 10^-8 M in model and real samples of water.

Synthesis and Application of a New Polymer with Imprinted Ions for the Preconcentration of Uranium in Natural Water Samples and Determination by Digital Imaging

This work proposes the synthesis of a new polymer with imprinted ions (IIP) for the pre-concentration of uranium in natural waters using digital imaging as a detection technique. The polymer was synthesized using 2-(5-bromo-2-pyridylazo)-5-diethylaminophenol (Br-PADAP) for complex formation, ethylene glycol dimethacrylate (EGDMA) as a crosslinking reagent, methacrylic acid (AMA) as functional monomer, and 2,2'-azobisisobutyronitrile as a radical initiator. The IIP was characterized by Fourier transform infrared spectroscopy and scanning electron microscopy (FTIR). Uranium determination was performed using digital imaging (ID), and some experimental conditions (sample pH, eluent concentration, and sampling flow rate) were optimized using a two-level full factorial design and Doelhert response surface methodology. Thus, using the optimized conditions, the system allowed the determination of uranium with detection and quantification limits of 2.55 and 8.51 µg L^-1, respectively, and a pre-concentration factor of 8.2. All parameters were determined using a 25 mL sample volume. The precision expressed as relative deviation (RSD%) was 3.5% for a solution with a concentration of 50 µg L^-1. Given this, the proposed method was used for the determination of uranium in four samples of natural waters collected in the city of Caetité, Bahia, Brazil. The concentrations obtained ranged from 35 to 75.4 μg L^-1. The accuracy was evaluated by the addition/recovery test, and the values found ranged between 91 and 109%.

Novel Hyperspectral Analysis of Thin-Layer Chromatographic Plates-An Application to Fingerprinting of 70 Polish Grasses

The advantages of hyperspectral imaging in videodensitometry are presented and discussed with the example of extracts from 70 Polish grasses. An inexpensive microscope camera was modified to cover the infrared spectrum range, and then 11 combinations of illumination (254 nm, 366 nm, white light), together with various filters (no filter, IRCut, UV, cobalt glass, IR pass), were used to register RGB HDR images of the same plate. It was revealed that the resulting 33 channels of information could be compressed into 5-6 principal components and then visualized separately as grayscale images. We also propose a new approach called principal component artificial coloring of images (PCACI). It allows easy classification of chromatographic spots by presenting three PC components as RGB channels, providing vivid spots with artificial colors and visualizing six principal components on two color images. The infrared region brings additional information to the registered data, orthogonal to the other channels and not redundant with photos in the visible region. This is the first published attempt to use a hyperspectral camera in TLC and it can be clearly concluded that such an approach deserves routine use and further attention.

Using a biphasic system and digital imaging analysis with chemometric tools for simultaneous determination of Cu2+ and furfural in cachaça

The present study reports, for the first time, the development and application of a highly efficient method based on digital imaging analysis for the simultaneous determination of Cu²⁺ and furfural in cachaça samples using a two-phase system and chemometrics tools. Furfural reacts with aniline in an acidic medium to form a Schiff base, which exhibits a pink color. On the other hand, Cu²⁺ reacts with cuprizone in a basic medium to form a blue complex. The two reactions were performed on a porcelain plate, and a smartphone was used to capture the colorimetric images. Partial least squares (PLS) regression was used to construct the prediction models for Cu²⁺ and furfural contents in cachaça samples. After finding the best PLS models, the ordered predictor selection (OPS) analysis was performed in order to select the most predictive variables. The method developed was found to be effective in estimating the amounts of Cu²⁺ and furfural in cachaça samples, with a mean absolute error of 0.2 mg L^-1 for the Cu²⁺ model, and 0.3 mg per 100 mL of anhydrous alcohol for the furfural model. The method proposed in this study is simple and straightforward; it does not require complex technical knowledge and can be used by the producers themselves in the cachaça manufacturing process.

Digital image analysis for biothreat detection via rapid centrifugal microfluidic orthogonal flow immunocapture

We report clear proof-of-principle for centrifugally-driven, multiplexed, paper-based orthogonal flow sandwich-style immunocapture (cOFI) and colorimetric detection of Zaire Ebola virus-like particles. Capture antibodies are immobilized onto nanoporous nitrocellulose membranes that are then laminated into polymeric microfluidic discs to yield ready-to-use analytical devices. Fluid flow is controlled solely by rotational speed, obviating the need for complex pneumatic pumping systems, and providing more precise flow control than with the capillary-driven flow used in traditional lateral flow immunoassays (LFIs). Samples containing the antigen of interest and gold nanoparticle-labeled detection antibodies are pumped centrifugally through the embedded, prefunctionalized membrane where they are subsequently captured to generate a positive, colorimetric signal. When compared to the equivalent LFI counterparts, this cOFI approach generated immunochromatographic colorimetric responses that are objectively darker (saturation), more intense (grayscale), and less variable regarding total area of the color response. We also describe an image analysis approach that enables access to rich color data and area statistics without the need for a commercial 'strip reader' or custom-written image analysis algorithms. Instead, our analytical method exploits inexpensive equipment (e.g., smart phone, flatbed scanner, etc.) and freely available software (Fiji distribution of ImageJ) to permit characterization of immunochromatographic responses that includes multiple color metrics, offering insights beyond typical grayscale analysis. The findings reported here stand as clear proof-of-principle for the feasibility of disc-based, centrifugally driven orthogonal flow through a membrane with immunocapture (cOFI) and colorimetric readout of a sandwich-type immunoassay in less than 15 minutes. Once fully developed, this cOFI platform could render a faster, more accurate diagnosis, while processing multiple samples simul-taneously.

Use of Digital Images as a Low-Cost System to Estimate Surface Optical Parameters in the Ocean

Ocean color is the result of absorption and scattering, as light interacts with the water and the optically active constituents. The measurement of ocean color changes enables monitoring of these constituents (dissolved or particulate materials). The main objective of this research is to use digital images to estimate the light attenuation coefficient (Kd), the Secchi disk depth (ZSD), and the chlorophyll a (Chla) concentration and to optically classify plots of seawater using the criteria proposed by Jerlov and Forel using digital images captured at the ocean surface. The database used in this study was obtained from seven oceanographic cruises performed in oceanic and coastal areas. Three approaches were developed for each parameter: a general approach that can be applied under any optical condition, one for oceanic conditions, and another for coastal conditions. The results of the coastal approach showed higher correlations between the modeled and validation data, with rp values of 0.80 for Kd, 0.90 for ZSD, 0.85 for Chl_a, 0.73 for Jerlov, and 0.95 for Forel-Ule. The oceanic approach failed to detect significant changes in a digital photograph. The most precise results were obtained when images were captured at 45° (n = 22; Fr cal=11.02>Fr crit=5.99). Therefore, to ensure precise results, the angle of photography is key. This methodology can be used in citizen science programs to estimate Z_SD, K_d, and the Jerlov scale.

Red cabbage extract-mediated colorimetric sensor for swift, sensitive and economic detection of urease-positive bacteria by naked eye and Smartphone platform

The bacterial pathogens have caused various serious infectious diseases in the human body, and even some threats to human life by leading to deaths. Enterobacteriaceae species especially urease positive ones, Proteus mirabilis (P. mirabilis) and Klebsiella pneumoniae (K. pneumoniae), show resistance to antibiotics and cause respiratory and urinary tract infections. We have developed natural indicator-incorporated colorimetric urease tests with a naked eye and smartphone readout to rapidly, sensitively and economically detect P. mirabilis and K. pneumoniae. We utilized anthocyanin found as a predominant component in red cabbage (Brassica oleracea) extract as a natural pH indicator instead of toxic and synthetic indicators. As a mechanistic explanation for the detection of P. mirabilis and K. pneumoniae, urease enzymes secreted from the P. mirabilis and K. pneumoniae hydrolyze urea to produce ammonia (NH₃), which increases the pH value of the reaction environment and leads to deprotonation from anthocyanins. The changes in the molecular structure and electronic structure of anthocyanins are responsible for revealing many different colors. We demonstrated how some reaction parameters including the concentration of the bacteria (colony-forming unit, CFU), the concentration of anthocyanin in the tests, initial color and pH values (pHs) of the tests influence their detection performance. We further developed a 3D-printed smartphone platform with smartphone based digital image processing software to improve the detection limit and shorten the detection time. We claim that natural indicator-incorporated rapid urease tests providing colorimetric readout evaluated by the human eye and smartphone imaging processing has great potential in practical use and they can be implemented in clinics.

Computer Vision for Kinetic Analysis of Lab- and Process-Scale Mixing Phenomena

A software platform for the computer vision-enabled analysis of mixing phenomena of relevance to process scale-up is described. By bringing new and known time-resolved mixing metrics under one platform, hitherto unavailable comparisons of pixel-derived mixing metrics are exemplified across non-chemical and chemical processes. The analytical methods described are applicable using any camera and across an appreciable range of reactor scales, from development through to process scale-up. A case study in nucleophilic aromatic substitution run on a 5 L scale in a stirred tank reactor shows how camera and offline concentration analyses can be correlated. In some cases, it can be shown that camera data hold the power to predict reaction progress.

PiRamid: A compact Raspberry Pi imaging box to automate small-scale time-lapse digital analysis, suitable for laboratory and field use

Digital imaging permits the quantitation of many experiments, such as microbiological growth assays, but laboratory digital imaging systems can be expensive and too specialised. The Raspberry Pi camera platform makes automated, controlled imaging affordable with accessible customisation. When combined with open source software and open-source 3D printed hardware, the control over image quality and capture of this platform permits the rapid development of novel instrumentation. Here we present "PiRamid", a compact, portable, and inexpensive enclosure for autonomous imaging both in the laboratory and in the field. The modular three-piece 3D printed design makes it easy to incorporate different camera systems or lighting configurations (e.g., single wavelength LED for fluorescence). The enclosed design allows complete control of illumination unlike a conventional digital camera or smartphone, on a tripod or handheld, under ambient lighting. The stackable design permits rapid sample addition or camera focus adjustment, with a corresponding change in magnification and resolution. The entire unit is small enough to fit within a microbiological incubator, and cheap enough (∼£100) to scale out for larger parallel experiments. Simply, Python scripts fully automate illumination and image capture for small-scale experiments with an ∼110×85 mm area at 70-90 µm resolution. We demonstrate the versatility of PiRamid by capturing time-resolved, quantitative image data for a wide range of assays. Bacterial growth kinetics was captured for conventional microbiology (agar Petri dishes), 3D printed custom microbiology labware and microfluidic microbiology. To illustrate application beyond microbiology, we demonstrate time-lapse imaging of crystal growth and degradation of salad leaves. Minor modifications permit epi-illumination by addition of a LED ring to the camera module. We conclude that PiRamid permits inexpensive digital capture and quantitation of a wide range of experiments by time-lapse imaging to simplify both laboratory and field imaging.

Development of a versatile smartphone-based environmental analyzer (vSEA) and its application in on-site nutrient detection

The citizen-science-based environmental survey can benefit from the smartphone technology used in chemical and biological sensing of a wide range of analytes. Quantification by smartphone-based colorimetric assays is being increasingly reported, however, most of the quantification uses empirical formula or complex exhaustive methods. In this study, a versatile and robust algorithm is proposed to overcome these limitations. A model is established to simulate and analyze the conversion process from the camera's spectral information into RGB (Red, Green, Blue) color information. Moreover, the feasibility of the algorithm for the quantification of different analytes is also explored. Based on this algorithm, a versatile smartphone-based environmental analyzer (vSEA) is built and its reliability, versatility, and analytical performance are comprehensively optimized. The good linearity (R² ≥ 0.9954) and precision (relative standard deviations < 5.3%) indicates that the vSEA is accurate enough to quantify the nutrients in most natural waters. Furthermore, the vSEA is used for the field measurement of five important nutrients, and the results show no significant difference compared to conventional methods. The vSEA offers a simpler and easier method for the on-site measurement of nutrients in natural water bodies, which can aid in the emergency monitoring of aqueous ecosystems and the performance of citizen-science-based research.

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

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

A fluorescence digital image-based method using carbon quantum dots to evaluate the compliance of a biocidal agent

In this work, a simple, low-cost and easy-to-handle analytical procedure based on carbon quantum dots (CQDs) is proposed to check commercially available formulated microbicides that are used to mitigate the transmission of viruses, such as SARS-COV-2, or bacterial diseases. For this purpose, CQDs were synthesized via pyrolysis using citric acid and ethylenediamine as precursors to produce an intense fluorescence that is used to measure the concentration of hypochlorite, an important biocidal agent present in sanitizing mats, by quenching mechanisms. The characterization of the CQDs was performed using IR spectrophotometry, UV-Vis spectrophotometry, spectrofluorometry, thermogravimetric analysis, scanning electron microscopy, dynamic light scattering, X-ray diffraction, energy-dispersive spectroscopy, and zeta potential measurements. For analytical purposes, fluorescence was measured in a UV chamber irradiated using an LED with the maximum emission at 350 nm. A smartphone was coupled to the UV chamber to measure the fluorescence quenching due to the presence of hypochlorite, and further the digital images were decomposed by RGB data using free software. Tests of pH, CQD concentration and stability of the fluorescence emitted were performed. The stability study of the fluorescence emitted by the CQD solution showed a relative standard deviation lower than 5.0%. The fluorescence digital image-based (FDIB) method resulted in a linear range from 17.44 μmol L^-1 to 90.0 μmol L^-1 with an LOD of 3.30 μmol L^-1 for the determination of hypochlorite using a microplate made of PLA (polylactic acid) customized using a 3D printer. Furthermore, the hypochlorite concentration was tested in situ for its compliance in a sanitizing mat, in a real use situation (daily, a group of four people, each one kept their feet on the mat for 30 s). After 2.5 h, the monitored concentration of hypochlorite was 0.04953% (w/v) or 7.63 mmol L^-1, and therefore, it was inefficient to act as a sanitizing agent. Thus, for the first time in the literature, an FDIB method with CQDs is used to verify in situ microbicide practices with a fast and low-cost analytical procedure.

A new and accessible instrumentation to determine urea in UHT milk using digital image analysis

This research demonstrates the development, optimization and application of a new low-cost detection system, based on digital image analysis, for the detection of urea in ultra-high-temperature (UHT) milk samples. The apparatus built in the laboratory, allows the capture of images through a simple system built by polyvinyl chloride (PVC) tubes, a digital microscope and a peristaltic mini-pump, after the colorimetric reaction between urea and diacetylmonoxime (butane-2,3-dionammonoxime). The red, green and blue (RGB) and hue, saturation and value (HSV) color systems were studied, with the saturation channel of the HSV color system selected as the analytical signal. Subsequently, the experimental chemical conditions were evaluated through multivariate experimental designs and the optimal conditions were defined. The proposed method was validated, and the detection and quantification limits presented by the method were 0.35 mg L^-1 and 0.52 mg L^-1, respectively; precision, ranged between 1.6  and 2.8 %. The results were compared with those obtained using the mid-infrared technique and no statistically significant differences were observed at a 95 % confidence level. The proposed method was applied to eight UHT milk samples that presented urea content ranging from 187 to 386 mg L^-1. The mean values obtained are in agreement with values presented in other studies for the determination of urea in milk. The results indicated that the system described and validated here is promising and can be applied to assess the authenticity and quality of milk.

Towards citizen science. On-site detection of nitrite and ammonium using a smartphone and social media software

Citizen scientists-based water quality surveys are becoming popular because of their wide applications in environmental monitoring and public education. At present, many similar studies are reported on collecting samples for later laboratory analysis. For environmentally toxic analytes such as ammonium and nitrite, on-site detection is a promising choice. However, this approach is limited by the availability of suitable methods and instruments. Here, a simple on-site detection method for ammonium and nitrite is reported. The chemistry of this method is based on the classic Griess reaction and modified indophenol blue reaction. Digital image colorimetry is carried out using a smartphone with a custom-made WeChat mini-program or free built-in applications (APPs). Using a simple and low-cost analytical kit, the detection limit of 0.27 μmol/L and 0.84 μmol/L is achieved for nitrite and ammonium, respectively, which are comparable to those achieved with a benchtop spectrophotometer. Relative standard deviations (n = 7) for low and high concentrations of nitrite are 3.6% and 4.3% and for ammonium are 5.6% and 2.6%, respectively. Identical results with a relative error of less than 10% are obtained using different smartphones (n = 3), color extracting software (n = 6), and with multiple individual users (n = 5). These results show the robustness and applicability of the proposed method. The on-site application is carried out in an in-campus wastewater treatment plant and at a local river. A total of 40 samples are analyzed and the analytical results are compared with that obtained by a standard method and a spectrophotometer, followed by a paired t-test at a 95% confidence level. This proposed on-site analytical kit has the advantages of simplicity and portability and has the potential to be popular and useful for citizen science-based environmental monitoring.

Waterproof Cellulose-Based Substrates for In-Drop Plasmonic Colorimetric Sensing of Volatiles: Application to Acid-Labile Sulfide Determination in Waters

The present work reports on the assessment of widely available waterproof cellulose-based substrates for the development of sensitive in-drop plasmonic sensing approaches. The applicability of three inexpensive substrates, namely, Whatman 1PS, polyethylene-coated filter paper, and tracing paper, as holders for microvolumes of colloidal solutions was evaluated. Waterproof cellulose-based substrates demonstrated to be highly convenient platforms for analytical purposes, as they enabled in situ generation of volatiles and syringeless drop exposure unlike conventional single-drop microextraction approaches and can behave as sample compartments for smartphone-based colorimetric sensing in an integrated way. Remarkably, large drop volumes (≥20 μL) of colloidal solutions can be employed for enrichment processes when using Whatman 1PS as holder. In addition, the stability and potential applicability of spherical, rod-shaped, and core-shell metallic NPs onto waterproof cellulose-based substrates was evaluated. In particular, Au@AgNPs showed potential for the colorimetric detection of in situ generated H₂S, I₂, and Br₂, whereas AuNRs hold promise for I₂, Br₂, and Hg⁰ colorimetric sensing. As a proof of concept, a smartphone-based colorimetric assay for determination of acid-labile sulfide in environmental water samples was developed with the proposed approach taking advantage of the ability of Au@AgNPs for H₂S sensing. The assay showed a limit of detection of 0.46 μM and a repeatability of 4.4% (N = 8), yielding satisfactory recoveries (91-107%) when applied to the analysis of environmental waters.

A Green Analytical Methodology for Detecting Adulteration in Automotive Urea-SCR Products Using Microfluidic-Paper Analytical Devices

The application of urea-based selective catalytic reduction products (i.e., Urea-SCR) provides a reduction of NOx and, therefore, minimizes pollution emissions from vehicles fueled by diesel. Such products can be easily found in the market; however, they are often susceptible to adulteration, mainly in terms of the urea content and dilution with non-mineralized water. In this study, we propose a simple, low-cost, disposable, and straightforward paper-based microfluidic device for the quality-control of Urea-SCR products for the first time by quantifying urea and water hardness simultaneously via colorimetric reactions using a small volume of sample. 4-(dimethylamino)benzaldehyde and Eriochrome T were used as colorimetric indicators for urea and water hardness determination, respectively. Each reagent (1.5 µL) was combined with 6 µL of sample for analysis, contributing to an expressive reduction of waste generation. Digital images of the µPAD were obtained, and linear relations between color intensity and urea and Ca2+ and Mg2+ concentrations in the range of 0.2 to 1.0% and 0.1 to 3.5 mmol L−1 were obtained with a correlation coefficient higher than 0.99. Recovery experiments were employed to evaluate the accuracy of the methodology, revealing suitable values between 91.5 and 115%. Brazilian Urea-SCR samples were acquired from different distributors and submitted to the proposed procedure to evaluate its applicability. The application of microfluidic paper-based devices with colorimetric reactions enables the quality control of Urea-SCR products with high accuracy, portability, low consumption of reagents, and no generation of toxic residues; thereby contributing to the green analytical chemistry field.

Switchable-hydrophilicity solvent-based liquid-phase microextraction in an on-line system: Cobalt determination in food and water samples

An on-line system employing switchable-hydrophilicity solvent-based liquid-phase microextraction (SHS-LPME) is described in this work. The method is based on the preconcentration of the species formed between cobalt and the reagent 1-nitroso-2-naphthol (NN), with subsequent detection by digital image colorimetry. The system's operation begins with the on-line mixture of sample, switchable solvent, and an alkaline agent in a reaction coil. Then the mixture is transported to an extraction chamber. The introduction of a proton donor leads to the passage of the solvent to its hydrophobic form, which allows phase separation. The rich phase is then directed to a glass tube, where detection is performed. Octanoic acid, sodium carbonate, and sulfuric acid were used as the extraction solvent, the alkaline agent, and the proton donor, respectively. Under optimized conditions, the method presented a detection limit of 0.8 μg L^-1 and an enrichment factor of 41. The precision obtained was 4.8% (20 μg L^-1). The accuracy of the method was tested by the analysis of Tomato Leaves certified reference material (NIST 1573a). The method was applied to the determination of cobalt in food, dietary supplements, and water samples. The method is presented as a green alternative and very accessible to the determination of cobalt in the analyzed samples.

Development of a Screening Method for Sulfamethoxazole in Environmental Water by Digital Colorimetry Using a Mobile Device

Antibiotic resistance is a major health concern of the 21st century. The misuse of antibiotics over the years has led to their increasing presence in the environment, particularly in water resources, which can exacerbate the transmission of resistance genes and facilitate the emergence of resistant microorganisms. The objective of the present work is to develop a chemosensor for screening of sulfonamides in environmental waters, targeting sulfamethoxazole as the model analyte. The methodology was based on the retention of sulfamethoxazole in disks containing polystyrene divinylbenzene sulfonated sorbent particles and reaction with p-dimethylaminocinnamaldehyde, followed by colorimetric detection using a computer-vision algorithm. Several color spaces (RGB, HSV and CIELAB) were evaluated, with the coordinate a_star, from the CIELAB color space, providing the highest sensitivity. Moreover, in order to avoid possible errors due to variations in illumination, a color palette is included in the picture of the analytical disk, and a correction using the a_star value from one of the color patches is proposed. The methodology presented recoveries of 82–101% at 0.1 µg and 0.5 µg of sulfamethoxazole (25 mL), providing a detection limit of 0.08 µg and a quantification limit of 0.26 µg. As a proof of concept, application to in-field analysis was successfully implemented.

Phosphate determination in environmental, biological and industrial samples using a smartphone as a capture device

A lab-made device was built to analyse phosphate in four different matrices using a smartphone as a capturing device.