Search Results (30)

Smartphone colorimetry has emerged as a low-cost and accessible approach for participatory environmental monitoring. In this feasibility study, mangrove soil samples collected at two depths (approximately 0 and 30 cm) and three distances from the shoreline (−10, 0, and 10 m) were analyzed using smartphone colorimetry. The redness (a*) and yellowness (b*) tended to decrease from the seaward side toward the landward side. The lightness (L*) showed a strong agreement with measurements obtained from a standard spectrophotometer, whereas systematic deviations were observed for chromatic coordinates, with underestimation of a* and overestimation of b* by the smartphone measurements. Soil colors were further examined alongside mineral composition determined by X-ray fluorescence (XRF) and organic matter characteristics obtained from thermogravimetric analysis (TGA). No systematic relationships were identified between color parameters and mineral composition or organic matter weight loss, highlighting the complex and multi-factorial nature of mangrove soil color. Although wetting generally reduced L* and b* values, the responses to increasing water content were not monotonic. These findings indicate that smartphone colorimetry is effective for capturing relative variations in soil lightness under controlled conditions, while emphasizing the need for calibration and cautious interpretation. The accessibility of smartphone-based measurements also suggests potential in public engagement. Full article

A digital colorimetric method (ACETimage), which utilizes aldol condensation, crotonization, and resinification, was developed and validated to quantify acetaldehyde in the head fraction of Pisco distillation. The optimal conditions for the reaction were as follows: the head Pisco samples were placed in headspace vials, 20% w/w NaOH was added, and the mixture was boiled in water for 2 min. The Color Grab app was used to capture and analyze images of the reactions, with a screen brightness intensity of 0.5, within a maximum post-reaction time of 10 min. The Euclidean distance (ED-RGB) was the color parameter most sensitive to changes, showing a linear correlation with the square of acetaldehyde concentration, with R² values ranging from 0.9926 to 0.9976. The limit of detection (LOD) and limit of quantification (LOQ) for the ACETimage method were determined to be 30 and 95.3 mg/L, respectively. A significant correlation was observed between the acetaldehyde content measured using ACETimage and gas chromatography (Spearman’s r = 0.9373). Bland–Altman analysis indicated that the differences between the two methods were within the 95% limits of agreement. ACETimage offers a rapid, cost-effective, and user-friendly solution for monitoring acetaldehyde levels during Pisco distillation, enabling easy implementation in production environments, both artisanal and industrial, with minimal sample preparation and limited personnel training. Full article

Packaging materials made from polypropylene (PP) can be used to protect cultural heritage objects from damage ensuring their long-life preservation. This research work concerns the assessment of recycled polypropylene hollow chamber sheets as potential packaging materials for archival collections and cultural heritage objects. It was carried out through a multidisciplinary diagnostic methodology combining mechanical methods, non-destructive imaging techniques in visible light (VIS), and ultraviolet-induced visible luminescence (UVL), as well as handheld digital microscopy, colorimetry, glossimetry, and SEM microanalysis. The results showed that the condition and mechanical performance of the specimens are affected by the ageing process. Full article

In this study, we developed a rapid method for determining lead(II) by integrating solid-phase extraction with digital image colorimetry to reduce the time and labor required for the analysis of lead(II) in water samples. The solid-phase extraction column was packed with cellulose as a bio-based adsorbent, which facilitated adsorption and enrichment of lead(II) during sample loading. The elution step, which is time-consuming and solvent-intensive, was eliminated from the procedure. An aqueous solution of sodium rhodizonate was added to react with lead(II), forming a red–brown complex. The color intensity was quantified using a smartphone-based digital image colorimetry. The method showed good linearity in the range of 0.01–0.8 mg L⁻¹ with R² > 0.99. In tap, river, and spring water, the recovery was 93.5% to 97.5% with a relative standard deviation of 1.7–4.8%. Five complementary greenness assessment tools confirmed the environmental friendliness of the method. This rapid pretreatment and detection technique can be applied to analyzing lead(II) in aqueous samples. Full article

To overcome the complexity of yarn color measurement using spectrophotometry with yarn winding techniques and to enhance consistency with human visual perception, a yarn color measurement method based on digital photography is proposed. This study employs a photographic colorimetry system to capture digital images of single yarns. The yarn and background are segmented using the K-means clustering algorithm, and the centerline of the yarn is extracted using a skeletonization algorithm. Spectral reconstruction and colorimetric principles are then applied to calculate the color values of pixels along the centerline. Considering the nonlinear characteristics of human brightness perception, the final yarn color is obtained through a nonlinear texture-adaptive weighted computation. The method is validated through psychophysical experiments using six yarns of different colors and compared with spectrophotometry and five other photographic measurement methods. Results indicate that among the seven yarn color measurement methods, including spectrophotometry, the proposed method—based on centerline extraction and nonlinear texture-adaptive weighting—yields results that more closely align with actual visual perception. Furthermore, among the six photographic measurement methods, the proposed method produces most similar to those obtained using spectrophotometry. This study demonstrates the inconsistency between spectrophotometric measurements and human visual perception of yarn color and provides methodological support for developing visually consistent color measurement methods for textured textiles. Full article

The classification of agricultural products is of great importance for quality control, optimized marketing, efficient logistics, research progress, consumer satisfaction, and sustainability. Dragon fruit has many varieties that need to be identified quickly and accurately for packaging and marketing. Considering the increasing demand for dragon fruit, it is obvious that an automated classification system has significant commercial and scientific value by increasing sorting efficiency and reducing manual labor costs. This study aimed to classify four commonly produced dragon fruit varieties according to their color, mechanical, and physical properties using machine learning models. Data were collected from 224 dragon fruits (53 American beauty, 57 Dark star, 65 Vietnamese white, and 49 Pepino dulce variety). Classification was performed using measurable physical and mechanical properties obtained through digital image processing, colorimetry, electronic weighing, and stress–strain testing. These methods provided objective and reproducible data collection for the models. Three models—Random Forest, Gradient Boosting, and Support Vector Classification—were implemented and their performances were evaluated using accuracy, precision, recall, Matthews’s correlation coefficient, Cohen’s Kappa, and F1-Score. The Random Forest model showed the highest performance in all metrics, achieving 98.66% accuracy, while the Support Vector Classification model had the lowest success. The superior performance of the Random Forest model can be attributed to its ability to handle complex, nonlinear relationships among multiple variables while preventing overfitting through ensemble learning. However, potential challenges in dragon fruit classification include variations due to environmental factors, genetic variation, and hybridization. Future research can focus on incorporating biochemical or genetic markers and improving real-time classification for industrial applications. Full article

Soil color serves as a critical indicator of its properties and conditions. It is shaped by a complex interplay of mineral and organic matter content, moisture levels, and other environmental variables. Additionally, human activities such as land-use changes and intensive agricultural practices can profoundly alter soil color. Soil color, driven by the presence of organic matter, plays a crucial role in understanding soil fertility. Its strong correlation with soil organic carbon content makes it a valuable parameter for assessing soil quality in agricultural practices. A variety of techniques have been developed to measure soil color, ranging from traditional Munsell color matching to modern color meters. Digital image colorimetry enables rapid on-site assessments of soil color, but environmental conditions such as soil water content and lighting conditions should be considered. Spectroscopic methods, particularly diffuse reflectance spectroscopy, pave the way for a more reliable and accurate composition analysis. Advances in remote sensing and computational methods are combined to explore the intricate relationships between soil color and environmental factors. Such an integrated approach not only enhances scalability but also leads to more insights and actionable strategies for environmental management and sustainable agriculture. Full article

Self-cleaning cementitious materials, particularly with TiO₂-based photocatalytic coatings, offer significant benefits by reducing surface deterioration and maintenance requirements, even in harsh urban environments. Despite the growing interest in self-cleaning cementitious materials, an international standard test method to calculate their efficiency has not yet been established for this specific type of substrate. The objective of this study was to evaluate and compare three different techniques for assessing the photocatalytic efficiency (PE) of cementitious materials coated with TiO₂: (i) spectrophotometric colorimetry (SPC); (ii) digital image processing-based colorimetry (DIP); and (iii) UV-Vis spectrophotometry (UV-Vis). Rhodamine B (RhB) was used as a model pollutant, and the photocatalytic efficiency was monitored under UV-Vis light. The results showed that each method has distinct advantages and specific challenges. SPC proved to be a practical and efficient approach, similarly to DIP, which was also accessible, providing reliable and accurate measurements. UV-Vis stood out for its precision but required careful application on cement-based substrates due to their unique porosity and adsorption characteristics. These results underscore the complementary potential of these techniques and highlight the importance of developing standardized protocols that integrate their strengths to facilitate the wider adoption of self-cleaning materials. Full article

We report a workflow and a software description for digital image colorimetry aimed at obtaining a quantitative dose–response curve and the minimal inhibitory concentration in the Resazurin Microtiter Assay (REMA) test of the activity of antimycobacterial drugs. The principle of this analysis is based on the newly established correspondence between the intensity of the ${\mathrm{a}}^{*}$ channel of the CIE L*a*b* colour space and the concentration of resorufin produced in the course of this test. The whole procedure can be carried out using free software. It has sufficiently mild requirements for the quality of colour images, which can be taken by a typical smartphone camera. Thus, the approach does not impose additional costs on the medical examination points and is widely accessible. Its efficiency is verified by applying it to the case of two representatives of substituted 2-(quinolin-4-yl) imidazolines. The direct comparison with the data on the indicator’s fluorescence obtained using a commercial microplate reader argues that the proposed approach provides results of the same range of accuracy on the quantitative level. As a result, it would be possible to apply the strategy not only for new low-cost studies but also for expanding databases on drug candidates by quantitatively reprocessing existing data, which were earlier documented by images of microplates but analysed only qualitatively. Full article

A simple and miniaturized protocol was developed for chloride extraction from Brazilian pre-salt crude oil for further salt determination by colorimetry. In this protocol, the colorimetric analysis of chloride using digital images was carried out in an aqueous phase obtained after a simple and miniaturized extraction carefully developed for this purpose. A portable device composed of a homemade 3D-printed chamber with a USB camera was used. The PhotoMetrix app converted the images into RGB histograms, and a partial least squares (PLS) model was obtained from chemometric treatment. The sample preparation was performed by extraction after defining the best conditions for the main parameters (e.g., extraction time, temperature, type and volume of solvent, and sample mass). The PLS model was evaluated considering the coefficient of determination (R²) and the root mean square errors (RMSEs)—calibration (RMSEC), cross-validation (RMSECV), and prediction (RMSEP). Under the optimized conditions, an extraction efficiency higher than 84% was achieved, and the limit of quantification was 1.6 mg g⁻¹. The chloride content obtained in the pre-salt crude oils ranged from 3 to 15 mg g⁻¹, and no differences (ANOVA, 95%) were observed between the results and the reference values by direct solid sampling elemental analysis (DSS-EA) or the ASTM D 6470 standard method. The easy-to-use colorimetric analysis combined with the extraction method’s simplicity offered a high-throughput, low-cost, and environmentally friendly method, with the possibility of portability. Additionally, the decrease in energy consumption and waste generation, increasing the sample throughput and operators’ safety, makes the proposed method a greener approach. Furthermore, the cost savings make this a suitable option for routine quality control, which can be attractive in the crude oil industry. Full article

Road markings (RMs) typically consist of a paint layer and a retroreflective layer. They play a crucial role in road safety by offering visibility and guidance to drivers. Over their lifetime, dirt particles, oils, and greases are adsorbed on the RM surface, reducing their visibility and service life. A self-cleaning ability has been widely studied in several substrates. However, for RMs, this represents a breakthrough and a sustainable advance, while having the potential to increase their service life and enhance road safety. In this context, nanotechnology can be a strong ally through the application of semiconductor materials, such as TiO₂, to develop the self-cleaning ability. In addition to this novelty in RMs, quantifying this ability in terms of pollutant removal efficiency is also a challenge. In this sense, artificial intelligence (AI) and colorimetry can be combined to achieve improved results. The aims of the work herein reported were to assess the self-cleaning capability in an RM paint through the mass incorporation of semiconductors, evaluate their photocatalytic efficiency using traditional (spectrophotometric) and modern (AI-enhanced) colorimetry techniques, and compare the results obtained using both techniques. To this end, a water-based acrylic RM paint was modified through the mass incorporation of 0.5%, 1%, 2%, and 3% of nano-TiO₂, and a pollutant model widely used, Rhodamine B, was applied onto their surface. The samples were irradiated with a light source that simulates sunlight for 0, 3, 6, 12, 24, and 48 h. Visual analysis and spectrophotometric and artificial intelligence-enhanced colorimetry techniques were used and compared to evaluate the pollutant removal. The results confirm that RM paints with 2% and 3% nano-TiO₂ incorporated have a significantly higher pollutant removal ability and that both colorimetric techniques used are suitable for this assessment. Full article

Alpha-fetoprotein (AFP) is a key marker for early cancer detection and assessment. However, the current detection methods struggle to balance accuracy with the need for decentralized medical treatment. To address this issue, a new AFP analysis platform utilizing digital image colorimetry has been developed. Functionalized gold nanoparticles act as colorimetric agents, changing from purple-red to light gray-blue when exposed to different AFP concentrations. A smartphone app captures these color changes and calculates the AFP concentration in the sample. To improve detection accuracy, a hardware device ensures uniform illumination. Testing has confirmed that this system can quantitatively analyze AFP using colorimetry. The limit of detection reached 0.083 ng/mL, and the average accuracy reached 90.81%. This innovative method enhances AFP testing by offering portability, precision, and low cost, making it particularly suitable for resource-limited areas. Full article

(1) Background: This paper illustrates an innovative methodological approach chosen to study and map the colors of the medieval wall painting of Santa Maria Antiqua in the Roman Forum, one of the pilot sites of the EHEM project (Enhancement of Heritage Experiences: The Middle Ages). Digital Layered Models of Architecture and Mural Paintings over Time). (2) Methods: Two methods were employed to gather information about colors and mapping. Specifically, colorimetry was utilized for spot measurements, and hypercolorimetric multispectral imaging (HMI) was employed to map the same colors sampled through colorimetry. (3) Results: Chromatic data for all colors in the wall paintings were obtained in the CIELAB color space. Additionally, chromatic similarity maps were generated using the innovative HMI system, a multispectral imaging technique capable of obtaining color data information through advanced calibration software named SpectraPick^® (Version 1.1). This comprehensive approach facilitates a thorough understanding of color characteristics and distribution. (4) Conclusions: The color measurements and mapping represent significant advancements in the interpretation of medieval wall paintings, which are often fragmentary and stratigraphically complex. This research sheds new light on the colors used and enhances our understanding of the original appearance of the iconographic patterns. Furthermore, it enables the reconstruction of colors that closely resemble the originals. Full article

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. Full article

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. Full article