RGB color sensor for colorimetric determinations: Evaluation and quantitative analysis of colored liquid samples

PySpectro: A modular 3D printed, machine learning assisted optical device for recognition and quantification of samples

Over the past decade, science and technology have achieved great advancements driven by the synergy between materials and manufacturing processes; coupled with the growth of informatics, which offers powerful tools to process and interpret data, new analytical devices have been developed. This work describes a modular 3D printed instrument that utilizes the AS7262 light sensor coupled with a LED to perform absorbance and reflectance measurements. The mode of operation can be switched by conveniently attaching different 3D printed parts. An Arduino Nano is used for operating the electronics, and a python-based software is employed for data handling. The device, beside spectra acquisition, allows rapid identification and quantification of samples through a database and machine learning (ML) algorithms. A recursive methodology for regression specifically designed allowed sample quantification in a range spanning around 2.5 orders of magnitude with errors generally below 10%. PySpectro was used on homogeneous solution and on PADs (Paper-based Analytical Devices) for food dyes and phosphomolybdic assay for phosphate. The device may find applications in any colorimetric detection also outside the laboratory environment and can be a time-saving tool for fast preliminary determinations or educational purposes.

Diagnosis of Indian Big Four and monocled Cobra snakebites in envenomed plasma using smartphone-based digital imaging colourimetry method

BACKGROUND

Venomous or dry bites can result from snake envenomation. Therefore, developing a detection test for venomous snakebites in envenomed patients can prevent from unnecessary antivenom therapy for dry bites, thereby, saving them from adverse effects and cost of antivenom therapy.

METHODOLOGY

This study demonstrates a method for the diagnosis of medically significant 'Big Four' Indian snake venoms (Naja naja, Bungarus caeruleus, Daboia russelii, Echis carinatus) in the plasma of experimentally envenomed animals (envenomed under laboratory conditions). Rabbit polyclonal antibodies (PAbs) were produced by generating modified bespoke peptides identified by computational analysis from the antigenic sites of the main toxins found in the proteome of India's 'Big Four' venomous snakes. The polyclonal antibody formulation (FPAb) prepared by mixing the five representative PAbs in the ratio of 1:1:1:1:1 demonstrated synergistic immune recognition of the 'Big Four' snakes and Naja kaouthia venoms. The recognition for these venoms under in vitro and in vivo conditions by FPAb was significantly higher (p<0.05) than commercial polyvalent antivenom produced against native venom toxins. The FPAb was tested to detect the venoms in subcutaneously envenomed rat plasmas until 240 minutes post-injection. Fourier-transform infrared spectroscopy, zeta potential, transmission electron microscopy, and atomic force microscopy characterised gold nanoparticles (AuNP) conjugated with FPAb. The FPAb-conjugated AuNP demonstrated aggregation upon interaction with venom toxins, changing the colour from red through burgundy to blue, monitored using a smartphone. From the digital image colourimetry analysis of the images, calibration curves for venoms were obtained, and each venom in the envenomed plasma at different time intervals was quantified using these curves.

CONCLUSION

A method for detection of venomous snakebites has been reported. The formulation of polyclonal antibodies generated against toxins of 'Big Four' venomous snakes of India immune-recognise venoms of 'Big Four' venomous snakes of India and N. kaouthia venoms under both in vitro and in vivo conditions. The antibody formulation conjugated to AuNP detected the venoms in envenomed plasma. This method of detection has potential to be useful for snakebite management in clinical settings.

"All-U-Want" Strand Displacement Amplification: A Versatile Signal Amplification Method for Nucleic Acid Biosensing

Strand displacement amplification (SDA) is an isothermal DNA amplification technique. Herein, we developed a novel SDA system, designated All-U-Want SDA (AUW-SDA), which was used as a signal amplification strategy for the construction of nucleic acid detection biosensors. AUW-SDA is capable of target turnover and can be utilized for detection of nucleic acid sequences without available 3'-ends. Of particular significance is the ability of AUW-SDA to generate a substantial number of programmable sequences in accordance with the specifications of the sensor signal output methods, irrespective of the sequence of the target nucleic acid. We used the N gene of SARS-CoV-2 as a model target to develop a sensing platform with dual signal outputs. The colorimetric signals were generated by the G-quadruplex/hemin DNAzyme, in which the G-rich sequences were produced by AUW-SDA with a C-rich primer. On the other hand, by altering the sequence within the replaceable region of the primer, an activator sequence was obtained from AUW-SDA, which could trigger the activity of CRISPR/Cas12a, cleaving the probes modified with a fluorophore and quencher at each end and subsequently yielding the fluorescent signals. After the DNA sequences and reaction conditions were optimized, the limit of detection (LOD) values of the fluorescent and colorimetric assays were estimated to be 0.672 fM and 13.3 fM, respectively. The biosensors were utilized for biological sample detection. The reliability of the proposed method was validated against RT-qPCR results. In addition, a portable scanner-assisted high-throughput RGB analysis (PSHRA) method was developed. This method was applied to our biosensor for multilocus detection of SARS-CoV-2. The results obtained were satisfactory, indicating the potential of this approach for field testing or point-of-care (POC) diagnostics.

Colorimetric Analysis of Transmitted Light Through Plasmonic Paper for Next-Generation Point-of-Care (PoC) Devices

This study identifies the optimal conditions for enhancing the performance of the Color Picker System, a device designed for colorimetric sensing using plasmonic paper. A simulation study was conducted toanalyze the transmittance spectra of plasmonic paper embedded in different mixtures, resulting in a comprehensive color chart that includes the chromatic response as well as the RGB values of transmitted light. The filtering properties of the plasmonic paper were evaluated through colorimetric analysis, combining the transmittance characteristics with the spectrum of different light sources. Optimizing the correlation between these filtering properties and the light source enhances both sensitivity and precision. Arrays of nanoparticles with high absorbance, combined with Cold LED light sources, emerge as ideal components for the device set-up. Among the light sources tested, the White LED uniquely generates a red signal while producing the most significant variations in the green channel. In contrast, the Cold LED and Xenon Arc lamp produce the strongest colorimetric signals in the blue channel. This study provides a deep understanding of the filtering properties of plasmonic paper, opening a new way for the implementation of nanoparticle arrays in colorimetric sensing.

CRISPR-associated Plasmonic Colorimeter Method (Ca-PCM): A real-time RGB detection system for gold nanoparticles-based nucleic acid biosensors

BACKGROUND

The detection of genetic sequences represents the gold standard procedure for species discrimination, genetic characterisation of tumours, and identification of pathogens. The development of new molecular detection methods, accessible and cost effective, is of great relevance. Biosensors based on plasmonic nanoparticles, such as gold nanoparticles (AuNPs), provide a powerful and versatile platform for highly sensitive, economic, user-friendly and label-free sensing. However, the readout techniques typically employed with such sensors lack temporal and kinetic information, which hampers the ability to perform quantitative detection.

RESULTS

In this study, a novel methodology designated the 'CRISPR-associated Plasmonic Colorimeter Method' (Ca-PCM), has been developed. This method combines RNA target recognition by CRISPR LwaCas13a, AuNPs' aggregation, and real-time colorimetric Red-Green-Blue (RGB) analysis. The system registers the AuNP's plasmonic signatures in real-time using a RGB colour sensor with 3-channel silicon photodiodes having blue, green and red sensitivities. The acquired signals are automatically analysed by an algorithm designed to distinguish between positive and negative samples and to correlate the temporal spectral patterns of aggregation with dose-dependent molecular detection of the RNA target. In addition, the combination of Ca-PCM with a previous isothermal amplification allows the target efficient detection in real clinical applications.

SIGNIFICANCE

We have shown that the combination of RGB analysis and continuous temporal measurements is a novel and promising method to characterise the behaviour of gold nanoparticle-based biosensors and to achieve dose-dependent target detection. In addition, the simplicity and cost-effectiveness of this new approach expand the possibilities of other plasmonic-based biosensors and their applicability in low-resources clinical environments.

Optimization of Cocoa Pods Maturity Classification Using Stacking and Voting with Ensemble Learning Methods in RGB and LAB Spaces

Determining the maturity of cocoa pods early is not just about guaranteeing harvest quality and optimizing yield. It is also about efficient resource management. Rapid identification of the stage of maturity helps avoid losses linked to a premature or late harvest, improving productivity. Early determination of cocoa pod maturity ensures both the quality and quantity of the harvest, as immature or overripe pods cannot produce premium cocoa beans. Our innovative research harnesses artificial intelligence and computer vision technologies to revolutionize the cocoa industry, offering precise and advanced tools for accurately assessing cocoa pod maturity. Providing an objective and rapid assessment enables farmers to make informed decisions about the optimal time to harvest, helping to maximize the yield of their plantations. Furthermore, by automating this process, these technologies reduce the margins for human error and improve the management of agricultural resources. With this in mind, our study proposes to exploit a computer vision method based on the GLCM (gray level co-occurrence matrix) algorithm to extract the characteristics of images in the RGB (red, green, blue) and LAB (luminance, axis between red and green, axis between yellow and blue) color spaces. This approach allows for in-depth image analysis, which is essential for capturing the nuances of cocoa pod maturity. Next, we apply classification algorithms to identify the best performers. These algorithms are then combined via stacking and voting techniques, allowing our model to be optimized by taking advantage of the strengths of each method, thus guaranteeing more robust and precise results. The results demonstrated that the combination of algorithms produced superior performance, especially in the LAB color space, where voting scored 98.49% and stacking 98.71%. In comparison, in the RGB color space, voting scored 96.59% and stacking 97.06%. These results surpass those generally reported in the literature, showing the increased effectiveness of combined approaches in improving the accuracy of classification models. This highlights the importance of exploring ensemble techniques to maximize performance in complex contexts such as cocoa pod maturity classification.

Evaluation of the performance of 3D printed (spectro)photometers based on multi-channel color sensors for colorimetric determinations

Portable and affordable (spectro)photometers based on digital color sensors are gaining significance in analytical chemistry due to their compact design, cost-effectiveness and integration capabilities. Despite their inherent straight-forward operation, various digital light sensors with distinct performance and optical characteristics are available on the market. For instance, AS7341 and AS7262 are multi-channel color sensors that covers eight and six spectra regions, respectively, while TCS34725 operates using three channels (red, green, and blue). In this study, the analytical performance of 3D printed, reflectance-based (spectro)photometers employing different digital color sensors was evaluated. Spectra resolution, signal gain and LED current were systematically evaluated to ensure the best performance for each sensor. The sensors' analytical performance was obtained by quantifying different dyes in aqueous solutions and sunset yellow in beverages as a proof-of-concept. All three sensors presented sub-millimolar detection with excellent linear coefficient (r2 > 0,99) and sensitivity comparable to a conventional spectrophotometer. In the quantification of sunset yellow (λmax = 480 nm) in the presence of tartrazine (λmax = 430 nm), the AS sensors showed enhanced sensitivity and linearity, minimizing the spectral overlapping. Their versatility and cost-effectiveness make them ideal for real-time, portable applications. Furthermore, their compatibility with microcontrollers and capability for wireless data transmission enhance their utility across diverse analytical environments.

Modified perylene diimide for femto molar level detection of glucose: smartphone-assisted colorimetric glucose detection kits

In this report, a functionalized hydroxyphenyl benzothiazole (HBT) derivative has been synthesized and anchored onto the perylene diimide (PDI) core at the -bay position (PHI). PHI has been explored for the generation of radical anions (PH1˙-) and dianions (PH12-) in 20% HEPES buffer-DMSO solution using H2S as a sacrificial electron donor. The PH1˙- has a half-life (t1/2) of 1.5 h and 3 h in oxygenated and hypoxic conditions, respectively. The formation of radical anions has been confirmed by optical (absorbance and fluorescence) methods, cyclic voltammetry (CV), differential pulse voltammetry (DPV), and femtosecond transient absorbance spectroscopy along with current-voltage (I-V) and NOBF4 studies. The PH1˙- showed peroxidase-like activity for the reduction of H2O2 as low as 170 fmol L-1 (fM) giving a colour change from sea green to pink. The biochemical assay which consists of PH1˙-+ GOx has been further utilized as a glucose sensor. Upon addition of glucose (0-8 nM) in the biochemical assay, the in-situ produced H2O2 (after oxidation of glucose with GOx) oxidized PH1˙- to PH1 giving a sea green to pink colorimetric read out along with a decrease in the absorption intensities at 720, 815, 880 and 950 nm and the emergence of absorption intensity at 541 nm. The lowest limit of detection is 85 fM. We also explored this biochemical assay for the detection of 860 fM of glucose in a 10% blood serum. Similarly, fluorometric, CV and DPV studies were carried out for the detection of glucose using this biochemical assay. The smartphone-assisted RGB colour analyser showed large variations in the red colour and this RGB based colour differentiation can be used for the detection of 1 nM of glucose.

A colour sensor integrated into a microcontroller platform as a reliable tool for measuring pH changes in biochemistry applications

Colorimetry is a widely used technique for optical detection in point-of-care testing and on-site detection. Although some studies employ a multiplex approach to analyse coloured solutions, many still analyse one sample at a time. We have prepared a simple and affordable colorimetric assay based on a TCS34725 colour sensor (ams-OSRAM) integrated into an M5Stack module and an RGB LED module both inserted into a 3D printed frame. We found that the colorimetric assay can be easily transferred to a colour sensing platform, and the signal range obtained using the prepared colorimeter is more than 200 times larger than that obtained using digital image colorimetry (DIC) for the same samples containing cholinesterase or urease as a model enzyme providing a change in pH of the processed solution. The assay appears to be ready for practical use.

Rapid and highly sensitive colorimetric LAMP assay and integrated device for visual detection of monkeypox virus

BACKGROUND

The monkeypox virus (MPXV) is a linear double-stranded DNA virus with a large genome that causes tens of thousands of infections and hundreds of deaths in at least 40 countries and regions worldwide. Therefore, timely and accurate diagnostic testing could be an important measure to prevent the ongoing spread of MPXV and widespread epidemics.

RESULTS

Here, we designed multiple sets of primers for the target region of MPXV for loop-mediated isothermal amplification (LAMP) detection and identified the optimal primer set. Then, the specificity in fluorescent LAMP detection was verified using the plasmids containing the target gene, pseudovirus and other DNA/RNA viruses. We also evaluated the sensitivity of the colorimetric LAMP detection system using the plasmid and pseudovirus samples, respectively. Besides, we used monkeypox pseudovirus to simulate real samples for detection. Subsequent to the establishment and introduction of a magnetic beads (MBs)-based nucleic acid extraction technique, an integrated device was developed, characterized by rapidity, high sensitivity, and remarkable specificity. This portable system demonstrated a visual detection limit of 137 copies/mL, achieving sample-to-answer detection within 1 h.

SIGNIFICANCE

The device has the advantages of integration, simplicity, miniaturization, and visualization, which help promote the realization of accurate, rapid, portable, and low-cost testing. Meanwhile, this platform could facilitate efficient, cost-effective and easy-operable point-of-care testing (POCT) in diverse resource-limited settings in addition to the laboratory.

Identification of taurine biomarker in human biofluids using plasmonic patterns of silver nanostructure

Taurine is now widely used as a new biomarker for cardiovascular and neurodegenerative diseases. This study discusses the importance of accurately determining taurine biomarker levels in various tissues and fluids for the early diagnosis of important pathologies and diseases. Current methods for taurine analysis face challenges such as low sensitivity, lack of selectivity, and complex procedures. Therefore, an efficient analytical method/technique is urgently needed by clinicians. A new paper-based photochemical method using triangular silver nanoparticles (TA-AgNPs) as optical nanoprobes was developed to detect taurine in human blood plasma and urine samples. This method involves a chemical reaction between taurine and TA-AgNPs, leading to a color change at pH 4.8, which is detected using a paper-based colorimetry (PCD) assay. The reaction is further confirmed by UV-visible spectrophotometry as the interaction between taurine and TA-AgNPs causes a significant change in the absorption spectrum, enabling the rapid and reliable measurement of this important biomarker with a detection limit of less than 0.2 μM to 20 mM. The method has been successfully applied to bioanalyzing taurine in human body fluids. Additionally, it requires optimized single-drop paper/parafilm-based colorimetric devices (OD-PCDs) for in situ and on-demand taurine analysis. This study represents the first use of TA-AgNPs for the specific and sensitive detection of taurine in real samples. The sensor design allows for the direct quantification of biomarkers in biological samples without the need for derivatization procedures or sample preparation. The simplicity and portability of OD-PCDs make them promising for tracking and monitoring. This method is expected to contribute to improving environmental health and occupational safety and represents a significant advancement in colorimetric analysis for the sensitive and selective detection of taurine, potentially providing a platform for the identification of taurine and other biomarkers.

A prediction model based on digital breast pathology image information

BACKGROUND

The workload of breast cancer pathological diagnosis is very heavy. The purpose of this study is to establish a nomogram model based on pathological images to predict the benign and malignant nature of breast diseases and to validate its predictive performance.

METHODS

In retrospect, a total of 2,723 H&E-stained pathological images were collected from 1,474 patients at Qingdao Central Hospital between 2019 and 2022. The dataset consisted of 509 benign tumor images (adenosis and fibroadenoma) and 2,214 malignant tumor images (infiltrating ductal carcinoma). The images were divided into a training set (1,907) and a validation set (816). Python3.7 was used to extract the values of the R channel, G channel, B channel, and one-dimensional information entropy from all images. Multivariable logistic regression was used to select variables and establish the breast tissue pathological image prediction model.

RESULTS

The R channel value, B channel value, and one-dimensional information entropy of the images were identified as independent predictive factors for the classification of benign and malignant pathological images (P < 0.05). The area under the curve (AUC) of the nomogram model in the training set was 0.889 (95% CI: 0.869, 0.909), and the AUC in the validation set was 0.838 (95% CI: 0.7980.877). The calibration curve results showed that the calibration curve of this nomogram model was close to the ideal curve. The decision curve results indicated that the predictive model curve had a high value for auxiliary diagnosis.

CONCLUSION

The nomogram model for the prediction of benign and malignant breast diseases based on pathological images demonstrates good predictive performance. This model can assist in the diagnosis of breast tissue pathological images.

Thermally programmable time delay switches for multi-step assays in paper-based microfluidics

Paper-based microfluidic devices offer advantages such as low cost and disposability for point-of-care diagnostic applications. However, actuation of fluids on paper can be a challenge in multi-step and complex assays. In this work, a thermally programmable time-delay switch (TPTDS) is presented which operates by causing delays in the fluid path of a microfluidics paper-based analytical device (μPAD) by utilizing screen-printed wax micro-bridges. The time-delay is achieved through an electrical power feedback loop which indirectly adjusts the temperature of each individual micro-bridge, melting the wax into the paper. The melted wax manipulates the fluid flow depending on its penetration depth into the paper channel, which is a function of the applied temperature. To demonstrate functionality of the proposed method, the TPTDS is employed to automate and perform the nitrate assay which requires sequential delivery of reagents. Colorimetric detection is used to quantify the results by utilizing an electronic color sensor.

Innovative approaches to suppress non-specific adsorption in molecularly imprinted polymers for sensing applications

Molecularly imprinted polymers (MIPs) are synthetic antibodies developed to bind selectively with specific molecules. They function through a particular recognition process involving their cavities and functional groups. Nevertheless, functional groups located outside these cavities are the main cause of non-specific molecule binding, thus reducing the effectiveness of MIPs in sensing applications. This work focused on enhancing the selectivity and performance of MIPs through electrostatic modification with surfactants. The study investigates the use of two surfactants, namely sodium dodecyl sulfate (SDS) and cetyl trimethyl ammonium bromide (CTAB), to eliminate non-specific adsorption in MIPs. The binding isotherms of the target molecule sulfamethoxazole (SMX) on MIPs and non-imprinted polymers (NIPs) were analyzed, showing higher adsorption capacity of MIPs due to the specific cavities. The modification with SDS or CTAB effectively eliminated non-specific adsorption in MIPs. The kinetic adsorption behavior further demonstrated the efficacy of MIP+--SDS/CTAB in the selective adsorption of SMX. Calibration curves showcase the methodology's analytical capabilities, achieving low limit of detection for SMX 6 ng mL-1 using MIP +-SDS. The stability study confirmed that the developed MIP +/--SDS/CTAB remains stable even at high temperatures, demonstrating its suitability for on-site applications. The methodology was successfully applied to detect SMX in milk and water samples, achieving promising recoveries. Overall, the electrostatic modification of MIPs with surfactants emerges as a valuable strategy for enhancing selectivity and performance in target molecule recognition and detection.

Rapid assessment of soil accessible Cr(Ⅵ) 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.

Using an inexpensive RGB color sensor for field quantitative assessment of soil accessible Cu(Ⅱ)

Field rapid determination of soil accessible Cu(Ⅱ) was important for environmental safety and human health risk assessment. In this study, an inexpensive red, green, and blue (RGB) color sensor was used for quantitative color difference analysis of the colored solution for soil accessible Cu(Ⅱ) with bis-cyclohexanone oxalydihydrazone as color reagent to develop a new method for analyzing soil accessible under field conditions. First, the calibration curve for RGB color sensor method was established in the standard solutions of Cu(II). Then the "hand shaking + standing" field extraction method for accessible Cu(Ⅱ) was developed. Finally, the method was applied in contaminated soils in the laboratory and in the field, and set the values determined by atomic absorption spectroscopy (AAS) as the standard ones. Results indicated that in the range of 0.1-5 mg L-1 Cu(II), the RGB Euclidean chromogenic difference values were directly linear correlated with the concentration of Cu(II) (R2 > 0.999). The interference of Fe(Ⅲ) and Mn(Ⅱ) could be eliminated by adding citric acid. The "hand shaking + standing" field extraction method could effectively extract the accessible Cu(Ⅱ) from soil with the high extraction rates. The concentrations of accessible Cu(II) in various polluted soils determined by RGB color sensor method were consistent with that determined by AAS, with the relative error within ±5%, the relative standard deviation ≤ 20%. The recovery of Cu(II) in RGB color sensor method was between 97% and 105%, which could meet the requirements of trace analysis of accessible Cu(Ⅱ) in the field. The high accuracy and precision of RGB color sensor method was reconfirmed in the rapid field quantitative assessment of soil accessible Cu(Ⅱ). Due to that the RGB color sensor was low cost, rechargeable, portable, mobile, ambient light resistant, the method would have a great potential for the determination of accessible Cu(Ⅱ) in contaminated soils.

Machine learning-assisted image label-free smartphone platform for rapid segmentation and robust multi-urinalysis

This study presents a groundbreaking approach for the early detection of chronic kidney disease (CKD) and other urological disorders through an image-label-free, multi-dipstick identification method, eliminating the need for complex machinery, label libraries, or preset coordinates. Our research successfully identified reaction pads on 187 multi-dipsticks, each with 11 pads, leveraging machine learning algorithms trained on human urine data. This technique aims to surpass traditional colourimetric methods and concentration-colour curve fitting, offering more robust and precise community screening and home monitoring capabilities. The developed algorithms enhance the generalizability of machine learning models by extracting primary colours and correcting urine colours on each reaction pad. This method's cost-effectiveness and portability are significant, as it requires no additional equipment beyond a standard smartphone. The system's performance rivals professional medical equipment without auxiliary lighting or flash under regular indoor light conditions, effectively managing false positives and negatives across various categories with remarkable accuracy. In a controlled experimental setting, we found that random forest algorithms, based on a Bagging strategy and applied in the HSV colour space, showed optimal results in smartphone-assisted urinalysis. This study also introduces a novel urine colour correction method, significantly improving machine learning model performance. Additionally, ISO parameters were identified as crucial factors influencing the accuracy of smartphone-based urinalysis in the absence of additional lighting or optical configurations, highlighting the potential of this technology in low-resource settings.

Low-Cost Optical Sensors for Soil Composition Monitoring

Studying soil composition is vital for agricultural and edaphology disciplines. Presently, colorimetry serves as a prevalent method for the on-site visual examination of soil characteristics. However, this technique necessitates the laboratory-based analysis of extracted soil fragments by skilled personnel, leading to substantial time and resource consumption. Contrastingly, sensor techniques effectively gather environmental data, though they mostly lack in situ studies. Despite this, sensors offer substantial on-site data generation potential in a non-invasive manner and can be included in wireless sensor networks. Therefore, the aim of the paper is to develop a low-cost red, green, and blue (RGB)-based sensor system capable of detecting changes in the composition of the soil. The proposed sensor system was found to be effective when the sample materials, including salt, sand, and nitro phosphate, were determined under eight different RGB lights. Statistical analyses showed that each material could be classified with significant differences based on specific light variations. The results from a discriminant analysis documented the 100% prediction accuracy of the system. In order to use the minimum number of colors, all the possible color combinations were evaluated. Consequently, a combination of six colors for salt and nitro phosphate successfully classified the materials, whereas all the eight colors were found to be effective for classifying sand samples. The proposed low-cost RGB sensor system provides an economically viable and easily accessible solution for soil classification.

Development of a portable optical device with a multi-channel spectrometer sensor for quantification of glycerol in wine: a maker approach for on-site analysis

In this work we present a novel and environmentally friendly approach for quantifying glycerol in wine samples using a portable optical device based on the maker concept and do-it-yourself (DIY) principles. This method offers significant advantages, including cost-effectiveness, reduced sample and reagent consumption, and the potential for integrating IoT (Internet of Things) technology. The chemical strategy involves the oxidation of glycerol using periodate, followed by the formation of the 3,5-diacetyl-1,4-dihydrolutidine (DDL) compound through a reaction with acetylacetone. The utilization of a cost-effective AS7341 color sensor as a detector enables accurate and sensitive detection of glycerol levels in wine samples. The optimized procedure demonstrates adequate analytical performance for glycerol determination in wine samples, encompassing a wide linear range (0.5 mg L-1 to 40.0 mg L-1), high correlation coefficient (r = 0.998), and low limits of detection (0.050 mg L-1). The method exhibits excellent precision, with the coefficient of variation estimated to be 0.1% for 10 independent measurements of a 20 mg L-1 solution. These features render it suitable not only for routine glycerol analysis in the wine industry, but also for addressing challenges related to wine adulteration and counterfeiting.

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%.

Smartphone-controlled biosensor for viral respiratory infectious diseases: Screening and response

Outbreaks of emerging viral respiratory infectious diseases (VRIDs) including coronavirus disease 2019 (COVID-19) seriously endanger people's health. However, the traditional nucleic acid detection required professionals and larger instruments and antigen-antibody detection suffered a long window period of target generation. To facilitate the VRIDs detection in time for common populations, a smartphone-controlled biosensor, which integrated sample preparation (electromembrane extraction), biomarker detection (red-green-blue model) and remote response technology (a built-in APP), was developed in this work. With the intelligent biosensor, VRIDs could be recognized in the early stage by using endogenous hydrogen sulfide as the biomarker. Importantly, it only took 15 min to accomplish the whole process of screening and response to VRIDs. Moreover, the experimental data showed that this smartphone-controlled biosensor was suitable for ordinary residents and could successfully differentiate non-communicable respiratory diseases from VRIDs. To the best of our knowledge, this is the first time that a smartphone-controlled biosensor for screening and response to VRIDs was reported. We believe that the present biosensor will help ordinary residents jointly deal with the challenges brought by COVID-19 or other VRIDs in the future.

Development of an automated colorimeter controlled by Raspberry Pi4

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

A Portable Measurement Device Based on Phenanthroline Complex for Iron Determination in Water

In this work, a newly developed self-contained, portable, and compact iron measurement system (IMS) based on spectroscopy absorption for determination of Fe²⁺ in water is presented. One of the main goals of the IMS is to operate the device in the field as opposed to instruments commonly used exclusively in the laboratory. In addition, the system has been tuned to quantify iron concentrations in accordance with the values proposed by the regulations for human consumption. The instrument uses the phenanthroline standard method for iron determination in water samples. This device is equipped with an optical sensing system consisting of a light-emitting diode paired with a photodiode to measure absorption radiation through ferroin complex medium. To assess the sensor response, four series of Fe²⁺ standard samples were prepared with different iron concentrations in various water matrices. Furthermore, a new solid reagent prepared in-house was investigated, which is intended as a "ready-to-use" sample pre-treatment that optimizes work in the field. The IMS showed better analytical performance compared with the state-of-the-art instrument. The sensitivity of the instrument was found to be 2.5 µg Fe²⁺/L for the measurement range established by the regulations. The linear response of the photodiode was determined for concentrations between 25 and 1000 µg Fe²⁺/L, making this device suitable for assessing iron in water bodies.

Promising instrument-free detections of various analytes using smartphones with Spotxel® Reader

In consideration of the problems related to food safety, environmental pollution, and the spread of infected diseases nowadays, we urgently need testing methods that can be easily performed by common people. Smartphone-based detections are promising for general applications. However, some of these analytical strategies require a combination of accessories and instruments, such as portable electrochemical workstations, mini multi-mode microplate readers, and complex temperature control devices, etc., which are small but still expensive. Herein, we comprehensively introduce a free app (Spotxel^® Reader) that can provide accurate data analysis for microplate or parallel-format test sensors without an instrument. By simulating the optical signal of the test samples through a smartphone, the sensing results can be obtained for free. We discuss the detection strategies involved in the reported smartphone-based analyses using Spotxel^® Reader. Prospects for the development of this free app for future detection applications are presented. This review aims to popularize free analysis software, so that ordinary people may realize convenient tests.

Design and Engineering of a Palm-Sized Optical Immunosensing Device for the Detection of a Kidney Dysfunction Biomarker

Creatinine is one of the most common and specific biomarkers for renal diseases, usually found in the serum and urine of humans. Its level is extremely important and critical to know, not only in the case of renal diseases, but also for various other pathological conditions. Hence, detecting creatinine in clinically relevant ranges in a simplistic and personalized manner is interesting and important. In this direction, an optical sensing device has been developed for the simple, point-of-care detection of creatinine. The developed biosensor was able to detect creatinine quantitatively based on optical signals measured through a change in color. The sensor has been integrated with a smartphone to develop a palm-sized device for creatinine analysis in personalized settings. The sensor has been developed following facile chemical modification steps to anchor the creatinine-selective antibody to generate a sensing probe. The fabricated sensor has been thoroughly characterized by FTIR, AFM, and controlled optical analyses. The quantitative analysis is mediated through the reaction between picric acid and creatinine which was detected by the antibody-functionalized sensor probe. The differences in color intensity and creatinine concentrations show an excellent dose-dependent correlation in two different dynamic ranges from 5 to 20 μM and 35 to 400 μM, with a detection limit of 15.37 (±0.79) nM. Several interfering molecules, such as albumin, glucose, ascorbic acid, citric acid, glycine, uric acid, Na⁺, K⁺, and Cl^-, were tested using the biosensor, in which no cross-reactivity was observed. The utility of the developed system to quantify creatinine in spiked serum samples was validated and the obtained percentage recoveries were found within the range of 89.71-97.30%. The fabricated biosensor was found to be highly reproducible and stable, and it retains its original signal for up to 28 days.

RGB-Detector: A Smart, Low-Cost Device for Reading RGB Indexes of Microfluidic Paper-Based Analytical Devices

A user-friendly, low-cost detector able to read the RGB indexes of microfluidic paper-based analytical devices (µPADs) was developed. The RGB-detector was built with 3D printing using PLA+ and reused Li-ion batteries. It is Arduino-based, which provides an easy interface between the sensor TCS3200, which reads the quadratic wave of the times corresponding to the RGB numbers, the Arduino itself, whose software translates the times into RGB values, and the touchscreen display, NX3224T028, which shows the results. This detector permits multi-sample analysis since it has a sample holder that can keep up to six µPADs simultaneously and rotate after the display's request. This work shows how the readings of the RGB indexes by the proposed RGB-detector implement the measurements' reproducibility. As a proof-of-concept, the RGB-detector application to a green array of µPADs for pH measurement coupled with chemometric analysis allowed us to achieve good results in terms of precision and agreement with the pH values measured by a classical pH-meter.