Strain Measurement during Quasi-Static and Cyclic Loads in AL-6XN Material Using Digital Image Correlation Technique.

A customized digital image correlation (DIC) system was implemented to monitor the strain produced in a cold-rolled AL-6XN stainless steel plate, 3.0 mm thick, subjected to quasi-static and cyclic loading tests. A comparison of the DIC strain measurements was made against those provided by conventional extensometers. Furthermore, the DIC system was used to monitor the fatigue crack initiation in low-cycle fatigue tests. The true stress-strain behavior for the AL-6XN material was properly captured by the DIC measurements. For low-cycle fatigue tests (strain control), the strain mapping generated by DIC allowed for identifying zones with higher strain than the nominal strain amplitude applied (εa) since the first stages of the fatigue life (FL). These zones become potential fatigue crack initiation sites.

Poisson function and volume ratio of soft anisotropic materials under large deformations.

Accurate transverse deformation measurements are required for the estimation of the Poisson function and volume ratio. In this study, pure silicone and soft composite specimens were subjected to uniaxial tension, and the digital image correlation method was used to measure longitudinal and in- and out-of-plane transverse stretches. To minimize the effects of measurement errors on parameter estimation, the measured transverse stretches were defined in terms of the longitudinal stretch using a new formulation based on Poisson's ratios and two stretch-dependent parameters. From this formulation, Poisson functions and volume ratio for soft materials under large deformations were obtained. The results showed that pure silicone can be considered isotropic and nearly incompressible under large deformations, as expected. In contrast, Poisson's ratio of silicone reinforced with extensible fabric can exceed classical bounds, including negative value (auxetic behavior). The incompressibility assumption can be employed for describing the stress-stretch curve of pure silicone, while volume ratios are required for soft composites. Data of human skin, aortic wall, and annulus fibrosus from the literature were selected and analyzed. Except for the aortic wall, which can be considered nearly incompressible, the studied soft tissues must be regarded as compressible. All tissues presented anisotropic behavior.

Optimized Electrophoretic Deposition of Chitosan/Mesoporous Glass Nanoparticles with Gentamicin on Titanium Implants: Enhancing Hemocompatibility and Antibacterial Activity.

Titanium-based implants have long been studied and used for applications in bone tissue engineering, thanks to their outstanding mechanical properties and appropriate biocompatibility. However, many implants struggle with osseointegration and attachment and can be vulnerable to the development of infections. In this work, we have developed a composite coating via electrophoretic deposition, which is both bioactive and antibacterial. Mesoporous bioactive glass particles with gentamicin were electrophoretically deposited onto a titanium substrate. In order to validate the hypothesis that the quantity of particles in the coatings is sufficiently high and uniform in each deposition process, an easy-to-use image processing algorithm was designed to minimize human dependence and ensure reproducible results. The addition of loaded mesoporous particles did not affect the good adhesion of the coating to the substrate although roughness was clearly enhanced. After 7 days of immersion, the composite coatings were almost dissolved and released, but phosphate-related compounds started to nucleate at the surface. With a simple and low-cost technique like electrophoretic deposition, and optimized stir and suspension times, we were able to synthesize a hemocompatible coating that significantly improves the antibacterial activity when compared to the bare substrate for both Gram-positive and Gram-negative bacteria.

A digital image smartphone-based approach to Slovak Tokaj wine authentication chemometric assisted.

The authentication of Slovak wines in comparison to other similar wines from various geographical regions, namely Hungary, France, Austria, and Ukraine, was conducted using the OC-PLS, DD-SIMCA, and PLS-DM models, all of them operating in rigorous way. The study involved 63 samples, of which 41 originated from Slovakia, covering diverse wine types such as varietal wines, cuvée selections (different "putnový"), and essence. To capture digital images under controlled conditions, a custom-made cardboard box with white inner surfaces was devised and equipped with a smartphone. During the training phase, sensitivities of 96%, 100%, and 96% were attained for OC-PLS, DD-SIMCA, and PLS-DM, respectively. In the subsequent stages of validation and testing for DD-SIMCA and PLS-DM, the proposed methods displayed optimal efficiency, achieving both sensitivity and specificity rates of 100%. However, such results were not achieved in the case of OC-PLS, which exhibited efficiency levels of 90% in validation and 80% in testing.

Preparation of a Molecularly Imprinted Polymer on Polyethylene Terephthalate Platform Using Reversible Addition-Fragmentation Chain Transfer Polymerization for Tartrazine Analysis via Smartphone.

This work describes the preparation of a molecularly imprinted polymer (MIP) platform on polyethylene terephthalate (MIP-PET) via RAFT polymerization for analyzing tartrazine using a smartphone. The MIP-PET platform was characterized using Fourier transform infrared (FTIR) techniques, Raman Spectroscopy, X-ray photoelectron spectroscopy (XPS), and confocal microscopy. The optimal pH and adsorption time conditions were determined. The adsorption capacity of the MIP-PET plates with RAFT treatment (0.057 mg cm-2) was higher than that of the untreated plates (0.028 mg cm-2). The kinetic study revealed a pseudo-first-order model with intraparticle diffusion, while the isotherm study indicated a fit for the Freundlich model. Additionally, the MIP-PET demonstrated durability by maintaining its adsorption capacity over five cycles of reuse without significant loss. To quantify tartrazine, images were captured using a smartphone, and the RGB values were obtained using the ImageJ® free program. A partial least squares regression (PLS) was performed, obtaining a linear range of 0 to 7 mg L-1 of tartrazine. The accuracy of the method was 99.4% (4.97 ± 0.74 mg L-1) for 10 samples of 5 mg L-1. The concentration of tartrazine was determined in two local soft drinks (14.1 mg L-1 and 16.5 mg L-1), with results comparable to the UV-visible spectrophotometric method.

Fabrication of paper-based analytical devices using stencil-printed glass varnish barriers for colorimetric detection of salivary α-amylase.

BACKGROUND: Developing disposable paper-based devices has positively impacted analytical science, particularly in developing countries. Some benefits of those devices include their versatility, affordability, environmentally friendly, and the possibility of being integrated with portable electrochemical or colorimetric detectors. Paper-based analytical devices (PADs) comprising circular zones and microfluidic networks have been successfully employed in the analytical chemistry reign. However, the combination of the stencil-printing method and alternative binder has not been satisfactorily explored for fabricating colorimetric paper devices. RESULTS: We developed PADs exploring the stencil printing approach and glass varnish as the hydrophobic chemical agent. As a proof-of-concept, the colorimetric assay of salivary α-amylase (sAA) was performed in saliva samples. Through the scanning electron microscopy measurements, it was possible to indicate satisfactory definitions between native fibers and barrier, and that the measured values for the channel width revealed suitable fidelity (R2 = 0.99) with the nominal widths (ranging from 400 to 5000 µm). The proposed hydrophobic barrier exhibited excellent chemical resistance. The analytical applicability for detecting sAA revealed linear behavior in the range from 2 to 12 U mL-1 (R2 = 0.99), limit of detection of 0.75 U mL-1, reproducibility (RSD ≤2.4%), recovery experiments ranged from 89 to 108% and AGREE response (0.86). In addition, the colorimetric analysis of sAA in four different saliva samples demonstrated levels ranging from 202 to 2080 U mL-1, which enabled monitoring the absence and presence of periodontitis. SIGNIFICANCE: This report has presented the first use of a self-adhesive mask and glass varnish for creating circular zones and microfluidic architectures on paper without using thermic or UV curing treatments. Also, the proposed analytical methodology for detecting sAA exhibited suitable ecological impact considering the AGREE tool. We believe the proposed fabrication of paper devices emerges as a novel, simple, high-fidelity microfluidic channel and portable analytical approach for colorimetric sensing.

HeLa cell segmentation using digital image processing.

Computational cell segmentation is a vital area of research, particularly in the analysis of images of cancer cells. The use of cell lines, such as the widely utilized HeLa cell line, is crucial for studying cancer. While deep learning algorithms have been commonly employed for cell segmentation, their resource and data requirements can be impractical for many laboratories. In contrast, image processing algorithms provide a promising alternative due to their effectiveness and minimal resource demands. This article presents the development of an algorithm utilizing digital image processing to segment the nucleus and shape of HeLa cells. The research aims to segment the cell shape in the image center and accurately identify the nucleus. The study uses and processes 300 images obtained from Serial Block-Face Scanning Electron Microscopy (SBF-SEM). For cell segmentation, the morphological operation of erosion was used to separate the cells, and through distance calculation, the cell located at the center of the image was selected. Subsequently, the eroded shape was employed to restore the original cell shape. The nucleus segmentation uses parameters such as distances and sizes, along with the implementation of verification stages to ensure accurate detection. The accuracy of the algorithm is demonstrated by comparing it with another algorithm meeting the same conditions, using four segmentation similarity metrics. The evaluation results rank the proposed algorithm as the superior choice, highlighting significant outcomes. The algorithm developed represents a crucial initial step towards more accurate disease analysis. In addition, it enables the measurement of shapes and the identification of morphological alterations, damages, and changes in organelles within the cell, which can be vital for diagnostic purposes.

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.

Green and fast prediction of crude protein contents in bee pollen based on digital images combined with Random Forest algorithm.

Bee pollen is considered an excellent dietary supplement with functional characteristics, and it has been employed in food and cosmetics formulations and in biomedical applications. Therefore, understanding its chemical composition, particularly crude protein contents, is essential to ensure its quality and industrial application. For the quantification of crude protein in bee pollen, this study explored the potential of combining digital image analysis and Random Forest algorithm for the development of a rapid, cost-effective, and environmentally friendly analytical methodology. Digital images of bee pollen samples (n = 244) were captured using a smartphone camera with controlled lighting. RGB channels intensities and color histograms were extracted using open source softwares. Crude protein contents were determined using the Kjeldahl method (reference) and in combination with RGB channels and color histograms data from digital images, they were used to generate a predictive model through the application of the Random Forest algorithm. The developed model exhibited good performance and predictive capability for crude protein analysis in bee pollen (R2 = 80.93 %; RMSE = 1.49 %; MAE = 1.26 %). Thus, the developed analytical methodology can be considered environmentally friendly according to the AGREE metric, making it an excellent alternative to conventional analysis methods. It avoids the use of toxic reagents and solvents, demonstrates energy efficiency, utilizes low-cost instrumentation, and it is robust and precise. These characteristics indicate its potential for easy implementation in routine analysis of crude protein in bee pollen samples in quality control laboratories.

Raw dataset of compression tests on a vegetable oil-based polyurethane foam exposed to different ageing conditions.

The current dataset brings raw compression test information of a vegetable-based polyurethane foam (PUF) exposed to different temperatures over different periods of time. Such experimental dataset can provide researchers with important information in the application of numerical and data-driven simulations. Also, it saves money and time once the experimental part is already available. At total, 90 compression tests were done following the ASTM D1621-16 standard with pictures for digital image correlation (DIC) being simultaneously acquired. The 90 specimens were divided in nine different ageing conditions. The foam was considered transversely isotropic, thus, 10 specimens for each condition were divided in two groups, five specimens for direction 1 and five for direction 3, where direction 3 is the foam expansion direction. The 3D DIC results show longitudinal and transverse strains from virtual extensometers. The results are available in .TRA and .csv files for the tests and DIC outputs, respectively. Also, the dataset brings the pictures used for DIC in .TIF format. It also brings the dimensions of each specimen prior to the test in .txt format. These results provide information for the calculation of major mechanical properties that can be freely used in finite element models for different and creative ways to simulate the ageing process of a vegetable-based PUF.

Vectorial Image Representation for Image Classification.

This paper proposes the transformation S→C→, where S is a digital gray-level image and C→ is a vector expressed through the textural space. The proposed transformation is denominated Vectorial Image Representation on the Texture Space (VIR-TS), given that the digital image S is represented by the textural vector C→. This vector C→ contains all of the local texture characteristics in the image of interest, and the texture unit T→ entertains a vectorial character, since it is defined through the resolution of a homogeneous equation system. For the application of this transformation, a new classifier for multiple classes is proposed in the texture space, where the vector C→ is employed as a characteristics vector. To verify its efficiency, it was experimentally deployed for the recognition of digital images of tree barks, obtaining an effective performance. In these experiments, the parametric value λ employed to solve the homogeneous equation system does not affect the results of the image classification. The VIR-TS transform possesses potential applications in specific tasks, such as locating missing persons, and the analysis and classification of diagnostic and medical images.

A comprehensive study from the micro- to the nanometric scale: Evaluation of chilling injury in tomato fruit (Solanum lycopersicum).

Tomato fruit is susceptible to chilling injury (CI) during its postharvest handling at low temperature. The symptoms caused by this physiological disorder have been commonly evaluated by visual inspection at a macro-observation scale on fruit surface; however, the structure at deeper scales is also affected by CI. This work aimed to propose a descriptive model of the CI development in tomato tissue under the micro-scale, micro-nano-scale and nano-scale approaches using fractal analysis. For that, quality and fractal parameters were determined. In this sense, light microscopy, Environmental Scanning Electron Microscopy (ESEM) and Atomic Force Microscopy (AFM) were applied to analyse micro-, micro-nano- and nano-scales, respectively. Results showed that the morphology of tomato tissue at the micro-scale level was properly described by the multifractal behaviour. Also, generalised fractal dimension (Dq=0) and texture fractal dimension (FD) of CI-damaged pericarp and cuticle were higher (1.659, 1.601 and 1.746, respectively) in comparison to non-chilled samples (1.606, 1.578 and 1.644, respectively); however, FD was unsuitable to detect morphological changes at the nano-scale. On the other hand, lacunarity represented an appropriate fractal parameter to detect CI symptoms at the nano-scale due to differences observed between damaged and regular ripe tissue (0.044 and 0.025, respectively). The proposed multi-scale approach could improve the understanding of CI as a complex disorder to the development of novel techniques to avoid this postharvest issue at different observation scales.

Galería de imágenes de tumores malignos como herramienta didáctica en oncología / Gallery of malignancies images as didactic tool in oncology i: en

Introducción: Los tumores malignos son afecciones prevalentes que exigen un diagnóstico temprano para un tratamiento oportuno. La confección de una galería de imágenes como medio de enseñanza con la tecnología digital constituye una herramienta de aprendizaje de incuestionable valor en la especialidad de oncología. Objetivo: Confeccionar una galería de imágenes digitales de tumores malignos como medio de enseñanza alternativo para la docencia de residentes y profesionales de enfermería. Métodos: Se realizó una innovación tecnológica en el Hospital Oncológico Docente Provincial Conrado Benítez García de Santiago de Cuba, durante el bimestre enero-febrero del 2023. El objeto de estudio y el campo de acción fueron los recursos del aprendizaje y las imágenes digitalizadas sobre tumores malignos, respectivamente. Se combinaron los métodos de investigación teóricos (sistematización, vivencial y analítico sintético) y empíricos (análisis documental y encuesta en forma de cuestionario a informantes clave). Resultados: Como medio de enseñanza alternativo para la docencia médica y de enfermería, manualmente se confeccionó una galería digital con 164 imágenes de tumores malignos que incluían casi todas las localizaciones. Los expertos en informática, especialistas, residentes y enfermeras valoraron de muy adecuado el producto informático, basado en la cientificidad y la didáctica tecnológica para la formación académica integral. Conclusiones: La galería de imágenes digitales constituye una herramienta didáctica, motivacional, atractiva e interesante para el aprendizaje de las neoplasias malignas; es un recurso ilustrativo y utilitario para la docencia médica y del profesional de enfermería.

Introduction: Malignancies are prevalent affections that demand an early diagnosis for an opportune treatment. The making of an images gallery as teaching means with digital technology constitutes a learning tool of unquestionable value in the oncology specialty. Objective: To make a gallery of malignancies digital images as alternative teaching means for the residents and nursing professionals. Methods: A technological innovation was carried out in Conrado Benítez García Provincial Teaching Oncological Hospital in Santiago de Cuba, during the January-February two-month period, 2023. The study object and action field were the learning resources and the digital images on malignancies, respectively. The theoretical investigation methods (systematizing, experiential and analytic synthetic) and empiric (documental analysis and interviews in questionnaire form to key informants) were combined. Results: As alternative teaching means for medical and nursing teaching, a digital gallery was manually made with 164 malignancies images that included almost all the localizations. Computer science experts, specialists, residents and nurses valued as very appropriate the computer product, based on the scientificity and the technological didactics for the integral academic training. Conclusions: The gallery of digital images constitutes a didactic, motivational, attractive and interesting tool for learning of malignancies; it is an illustrative and utilitarian resource for medical and nursing professional teaching.

A New Generative Model for Textual Descriptions of Medical Images Using Transformers Enhanced with Convolutional Neural Networks.

The automatic generation of descriptions for medical images has sparked increasing interest in the healthcare field due to its potential to assist professionals in the interpretation and analysis of clinical exams. This study explores the development and evaluation of a generalist generative model for medical images. Gaps were identified in the literature, such as the lack of studies that explore the performance of specific models for medical description generation and the need for objective evaluation of the quality of generated descriptions. Additionally, there is a lack of model generalization to different image modalities and medical conditions. To address these issues, a methodological strategy was adopted, combining natural language processing and features extraction from medical images and feeding them into a generative model based on neural networks. The goal was to achieve model generalization across various image modalities and medical conditions. The results showed promising outcomes in the generation of descriptions, with an accuracy of 0.7628 and a BLEU-1 score of 0.5387. However, the quality of the generated descriptions may still be limited, exhibiting semantic errors or lacking relevant details. These limitations could be attributed to the availability and representativeness of the data, as well as the techniques used.

Classification of Alzheimer's disease stages from magnetic resonance images using deep learning.

Alzheimer's disease (AD) is a progressive type of dementia characterized by loss of memory and other cognitive abilities, including speech. Since AD is a progressive disease, detection in the early stages is essential for the appropriate care of the patient throughout its development, going from asymptomatic to a stage known as mild cognitive impairment (MCI), and then progressing to dementia and severe dementia; is worth mentioning that everyone suffers from cognitive impairment to some degree as we age, but the relevant task here is to identify which people are most likely to develop AD. Along with cognitive tests, evaluation of the brain morphology is the primary tool for AD diagnosis, where atrophy and loss of volume of the frontotemporal lobe are common features in patients who suffer from the disease. Regarding medical imaging techniques, magnetic resonance imaging (MRI) scans are one of the methods used by specialists to assess brain morphology. Recently, with the rise of deep learning (DL) and its successful implementation in medical imaging applications, it is of growing interest in the research community to develop computer-aided diagnosis systems that can help physicians to detect this disease, especially in the early stages where macroscopic changes are not so easily identified. This article presents a DL-based approach to classifying MRI scans in the different stages of AD, using a curated set of images from Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies databases. Our methodology involves image pre-processing using FreeSurfer, spatial data-augmentation operations, such as rotation, flip, and random zoom during training, and state-of-the-art 3D convolutional neural networks such as EfficientNet, DenseNet, and a custom siamese network, as well as the relatively new approach of vision transformer architecture. With this approach, the best detection percentage among all four architectures was around 89% for AD vs. Control, 80% for Late MCI vs. Control, 66% for MCI vs. Control, and 67% for Early MCI vs. Control.

Cost-effective smartphone-based method for low range chemical oxygen demand analysis.

Aiming the decentralization of monitoring policies and to facilitate the work of researchers, mainly in developing countries, the present method deals with the explanation of a simple and rapid protocol for chemical oxygen demand (COD) analysis through the use of digital smartphone devices coupled with a camera and a free app available for Android operating system that recognizes HSV (hue, saturation, value). The calibration of the method is done based on the theoretical values of potassium hydrogen phthalate for a proper and reliable build of the calibration curve by using the smartphone-based technique and the digested samples of COD. The coefficient of determination (R2) attained a value upper than 0.99, providing a high confidence levels, and the method achieved 97% of average accuracy in samples with COD values ranging from 0 to 150 mg L-1. Finally, the procedure here presented can be a great support for scientific laboratories and monitoring policies, once it can efficiently substitute expensive spectrophotometers and can improve and ensure the sustainable management of water sanitation, which is one of the sustainable goals proposed by the United Nations.•COD measurements, based on the use of a simple smartphone with a camera, can be a promising way for environmental analysis when spectrophotometers are not available, such as decentralized approaches.•The use of smartphone protocol is a novel initiative to fulfill sustainable development goal 6 on clean water and sanitation.•The smartphone is capable to read the difference of HSV values efficiently and can substitute the use of expensive spectrophotometers.

Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review.

Head and neck tumor differential diagnosis and prognosis have always been a challenge for oral pathologists due to their similarities and complexity. Artificial intelligence novel applications can function as an auxiliary tool for the objective interpretation of histomorphological digital slides. In this review, we present digital histopathological image analysis applications in oral squamous cell carcinoma. A literature search was performed in PubMed MEDLINE with the following keywords: "artificial intelligence" OR "deep learning" OR "machine learning" AND "oral squamous cell carcinoma". Artificial intelligence has proven to be a helpful tool in histopathological image analysis of tumors and other lesions, even though it is necessary to continue researching in this area, mainly for clinical validation.

An optimization method for stochastic reconstruction from empirical data - A limestone rock strain fields study-case using digital image correlation data.

Stochastic field reconstruction is a crucial technique to improve the accuracy of modern rock simulation. It allows explicit modelling of field conditions, often employed in uncertainty quantification analysis and upsampling and upscaling procedures. This paper presents a case-study of a framework for the stochastic reconstruction of rock's strain field using experimental data. The proposed framework is applied to a limestone outcrop in which the strain field is measured using Digital Image Correlation (DIC). Assuming that the strain fields of these rocks are well-represented by Gaussian random fields, we capitalize on the algorithms used for training Gaussian processes to estimate the correlation family and the parameters that best represent these fields. Although the spherical and exponential kernels often correspond to the best fit, our results depict that each field shall be analyzed separately and no general rule can be defined. We show that the method is versatile and can be employed in any measurable field reasonably represented by a Gaussian random field. Therefore, the present work aims to highlight the following topics:•A study-case of stochastic strain field reconstruction aims to contribute to uncertainty quantification of rock experimental procedures.•A stochastic minimization algorithm is presented to solve the maximum likelihood estimation to define the most suitable hyper-parameter: correlation length.•The calculated hyper-parameters of a set correlation functions are presented to best reproduce the strain fields of a rock sample.

A fully integrated 3D printed platform for sulfite determination in beverages via gas diffusion membrane extraction and digital video treatment.

In this study, we have described a miniaturized, simple, and low-cost device for sulfite determination in beverages by coupling Gas Diffusion Microextraction to paper-based analytical devices. The color change of an acid-base indicator - promoted by the generated gaseous SO2 - impregnated onto the paper surface was monitored in the function of time by video recording using a smartphone. The analytical information was related to the Hue, Saturation, Value (HSV) color space extracted from the video file. The complete analytical platform was built using a 3D printer, allowing the easy fabrication of a low-cost tailored device. Under optimized conditions, a linear relation from 5 to 90 mg L-1 was obtained using 30 µL of the reagent, 1 mL of sample, and 10 min of analysis. The relative standard deviation and the limit of detection were 2.2 % and 1.6 mg L-1, respectively. The method was successfully employed in several beverages, such as juices, soda, and coconut water.

Environmental application of a cost-effective smartphone-based method for COD analysis: Applicability in the electrochemical treatment of real wastewater.

This study aims to develop a cheap method for the evaluation of quality of water or the assessment of the treatment of water by chemical oxygen demand (COD) measurements throughout the use of the HSV color model in digital devices. A free application installed on a smartphone was used for analyzing the images in which the colors were acquired before to be quantified. The proposed method was also validated by the standard and spectrophotometric methods, demonstrating that no significant statistical differences were attained (average accuracy of 97 %). With these results, the utilization of this smartphone-based method for COD analysis was used/evaluated, for first time, by treating electrochemically a real water matrix with substantial organic and salts content using BDD and Pt/Ti anodes. Aiming to understand the performance of both anodes, bulk experiments were performed under real pH by applying current densities (j) of 15, 30, and 60 mA cm-2. COD abatement results (which were achieved with this novel smart water security solution) clearly showed that different organic matter removal efficiencies were achieved, depending on the electrocatalytic material used as well as the applied current density (42 %, 45 %, and 85 % for Ti/Pt while 93 %, 97 % and total degradation for BDD by applying 15, 30, and 60 mA cm-2, respectively). However, when the persulfate-mediated oxidation approach was used, with the addition of 2 or 4 g Na2SO4 L-1, COD removal efficiencies were enhanced, obtaining total degradation with 4 g Na2SO4 L-1 and by applying 15 mA cm-2. Finally, this smartphone imaging-based method provides a simple and rapid method for the evaluation of COD during the use of electrochemical remediation technology, developing and decentralizing analytics technologies for smart water solutions which play a key role in achieving the Sustainable Development Goal 6 (SDG6).