Search Results (52)

Sweat testing is central to cystic fibrosis diagnosis, but conventional analysis depends on clinical instrumentation, creating a need for portable point-of-care alternatives. As Part 1 of this two-part study, we systematically optimized Na⁺- and Cl⁻-selective colorimetric optodes and their measurement protocols for potential integration into a wearable device for in situ sweat analysis. Fifteen chromoionophore-based sensor compositions were screened over the physiologically relevant range of 5–100 mmol/L. Candidate optodes were selected based on stability in NaCl solutions and artificial sweat, hysteresis below 0.1 log units, and equilibration times under 15 min. Their analytical performance was evaluated by spectrophotometry and digital color analysis using smartphones and research-grade cameras, with a robustness parameter used to quantify signal reliability under different imaging conditions. A simple smartphone setup provided more robust performance than the tested laboratory imaging configurations. Incorporating light-scattering TiO₂ particles into the PVC matrix produced opaque films that significantly reduced interference from colored samples without compromising sensitivity or response kinetics. Validation in artificial sweat yielded recoveries above 93% across pH 5.5–8.0. These results establish optimized sensor compositions and measurement conditions for colorimetric Na⁺ and Cl⁻ determination in sweat and provide the analytical basis for wearable-device development in Part 2. Full article

Smartphone-based augmented reality (AR) may help dentists visualize an implant axis during treatment or training. Before a virtual overlay can be displayed, however, the application must first recognize the real target reliably. This in vitro study tested how five lighting conditions affected recognition of a two-colored, 3D-printed polylactic acid (PLA) dental model by a Unity/Vuforia smartphone application. The application ran on a Samsung Galaxy S22. A GoPro HERO10 recorded the smartphone display at 240 frames per second, and the videos were evaluated frame by frame. Red, green, blue, daylight-like light-emitting diode (LED) and halogen illumination were tested at a central and an eccentric model position, with ten repetitions per condition and position. The model was not recognized under red or blue illumination within the predefined observation interval. Recognition was successful in all repetitions under green, daylight-like and halogen illumination. Recognition speed differed among these successful conditions, and the eccentric position delayed recognition under daylight-like and halogen illumination. A supplementary region-of-interest analysis also showed lighting-dependent differences in image brightness and contrast. These findings indicate that lighting should be standardized and documented when smartphone-based dental AR systems are developed and tested. Full article

This study explores the valorization of olive pomace through the green recovery of bioactive phenolic compounds for application in active packaging for olive oil preservation, alongside the development of a low-cost analytical strategy aligned with white analytical chemistry principles. Ultrasound-assisted extraction using 50% (v/v) aqueous ethanol significantly improved polyphenol recovery, reducing extraction time to 2 min while increasing efficiency compared to conventional maceration. Total phenolic content was determined using the Folin–Ciocalteu method and measured with both a UV–Vis spectrophotometer and a portable 3D-printed smartphone-based device, which showed excellent agreement with the reference method and comparable analytical performance. Optimized extracts were incorporated into starch–glycerol films, enhancing UV-barrier properties and enabling controlled release of phenolics. When applied to olive oil packaging, the films reduced color degradation under accelerated aging, indicating improved photo-oxidative stability. Composting tests suggested the biodegradation capability of the developed materials under the evaluated conditions. Overall, the study demonstrates an integrated sustainable approach combining waste valorization, active packaging development, and accessible analytical innovation. Full article

In recent years, the authenticity of sliced mutton has become a growing concern due to the incorporation of non-mutton ingredients and the increasing use of processed and reconstituted meat products. In this study, a low-cost and non-destructive authentication method integrating smartphone-based image acquisition with machine learning and deep learning techniques was developed for the identification of real, processed, and reconstituted sliced mutton. A total of 600 images were collected under standardized conditions, from which color features in RGB, HSV, and Lab color spaces and texture features derived from the gray-level co-occurrence matrix (GLCM) were extracted. Statistical analyses, including the Kruskal–Wallis test, Dunn’s post hoc test, and principal component analysis, demonstrated significant inter-class differences and confirmed the discriminative capability of the extracted features. Four machine learning models (KNN, LDA, RF, and SVM) and three transfer learning-based convolutional neural networks (VGG16, ResNet50, and InceptionV3) were subsequently developed and evaluated. Among the machine learning models, SVM achieved the best classification performance, while VGG16 demonstrated the highest deep learning performance with an accuracy of 96.42 ± 0.51%. Misclassification analysis indicated that processed sliced mutton represented the primary source of classification ambiguity because of its overlapping visual characteristics with both real and reconstituted products. Furthermore, Grad-CAM visualization revealed that the CNN model focused predominantly on texture and structural regions closely associated with muscle and fat distribution, providing interpretability for the classification results. These findings suggest that smartphone-acquired RGB images combined with machine learning and deep learning methods may serve as a promising, low-cost screening approach for preliminary sliced mutton authentication in market surveillance and supply chain inspection. Full article

Smartphone-based proximal sensing has emerged as a promising low-cost approach for pasture monitoring. A critical component of this methodology is accurate leaf segmentation, as it directly affects the reliability of subsequent image-based analyses. Despite advances in computer vision, the role of preprocessing strategies in segmentation performance remains insufficiently explored, particularly under resource-constrained conditions. This study presents a systematic comparative evaluation of three preprocessing pipelines based on HSV and CIELab color spaces for the segmentation of Urochloa grass leaves (Urochloa hybrid Mavuno and Urochloa decumbens) using smartphone imagery acquired field conditions. The pipelines were assessed using a multi-criteria framework, including the Fisher Discriminant Ratio (FDR), Intersection over Union (IoU), Overlap Error (OE), Structural Similarity Index (SSIM), and Edge Preservation Index (EPI), complemented by discordance map analysis. The results demonstrate that preprocessing design significantly influences segmentation stability, boundary preservation, and robustness to illumination variability. Pipelines based on HSV channels showed high sensitivity to shadows and non-uniform lighting, leading to reduced segmentation consistency. In contrast, the CIELab-based pipeline relying on the a* channel achieved superior performance, with higher discriminative capacity, improved edge preservation, and lower computational cost. These findings highlight that carefully designed classical preprocessing strategies remain highly effective for low-cost, real-time applications, even in the absence of computationally intensive models. This work establishes a robust segmentation foundation for future integration with advanced analytical methods, including machine learning approaches, and supports the development of scalable smartphone-based tools for pasture monitoring. Full article

Capillary refill time (CRT) is widely used in pediatric and emergency medicine as an indicator of peripheral circulation. CRT is defined as the time required for the skin to return to its original color after external compression is applied and then released. In current clinical practice, however, CRT assessment remains qualitative and relies heavily on the magnitude and consistency of compression applied by the measurer, as well as on subjective visual color perception, which together result in limited measurement reliability. To improve measurement reliability, several quantitative CRT measurement devices have been developed. Nevertheless, these devices are dedicated specifically to CRT measurement, which limits their versatility and complicates clinical implementation. In this study, we developed a simple and quantitative CRT measurement method using a smartphone. Based on skin color changes captured by the rear camera, we proposed a method to assess the adequacy of the applied compression force and implemented an application to calculate CRT. In addition, we investigated an algorithm to reduce the influence of pulse waves observed in the post-release waveform, enabling more stable CRT estimation. Furthermore, a dedicated smartphone case was designed to immobilize the finger during measurement, thereby improving measurement reliability. The feasibility of the proposed method was evaluated by examining agreement with a previously developed CRT measurement device and by assessing intraexaminer reliability, confirming its effectiveness. Full article

The maturity of cannabis flowers at harvest critically influences cannabinoid yield and product quality. However, conventional assessment methods rely on subjective visual inspection of trichomes and stigmas, making them inherently inconsistent. This research presents an automated framework integrating computer vision and deep learning to objectively evaluate cannabis flower maturity. High-resolution macro images were acquired using low-cost smartphone-based systems under greenhouse and laboratory conditions. A two-stage pipeline was implemented: a fine-tuned Faster R-CNN model detected trichomes (Precision: 0.815; Recall: 0.802), while a YOLOv8 classifier categorized them into clear, milky, or amber classes (Accuracy: 98.6%). In parallel, a YOLOv8 segmentation model delineated stigmas (AP50: 52.2%) to compute color ratios as maturity indicators. Features were aggregated at the flower level and correlated with HPLC-measured cannabinoid concentrations. A dataset of over 14,000 images was collected across multiple imaging sessions to support training, evaluation, and correlation experiments. Results demonstrated that stigma coloration—detectable with low-end devices—provides a robust visual indicator of peak chemical maturity, with the green-to-orange transition aligning with maximum cannabinoid concentration. This work offers a scalable, cost-effective solution for real-time maturity assessment in cannabis cultivation, contributing to improved harvest timing and quality control. Full article

Estimating nitrogen (N) and the corresponding crude protein (CP) content in forage crops is essential for optimizing fertilization and livestock nutrition. However, standard methods such as the Dumas and Kjeldahl techniques are destructive, costly, and impractical for field use in certain regions of developing countries. This study evaluated four non-destructive approaches—morphometric measurements, Pantone^® color scales, smartphone-based RGB analysis (ColorDetector app), and SPAD chlorophyll readings—for predicting N and CP in Megathyrsus maximus (Mombasa grass). A total of 120 samples were collected under three nitrogen fertilization levels and assessed using linear mixed-effects models with cross-validation. Morphometric variables showed poor performance (R² < 0.01), indicating low correlation with nutrient content. Pantone-based RGB models provided slightly better predictions (R² ≈ 0.30) but were limited by subjectivity and discrete data. SPAD-based models demonstrated moderate predictive accuracy (R² ≈ 0.53; RMSE ≈ 0.46%). The highest accuracy was achieved with smartphone-derived RGB data, where full RGB models reached R² = 0.60 and RMSE = 0.45%. Based on these results, a practical green color scale was developed from RGB values to support real-time, in-field nitrogen and crude protein assessment. This study highlights smartphone imaging as a scalable, low-cost, and accurate tool for non-destructive estimation of nitrogen and crude protein in tropical forages, offering an accessible alternative to laboratory methods for producers and field technicians. Full article

In this work, a 3D microfluidic paper-based analytical device (3D-µPAD) was developed for the smartphone-based colorimetric determination of phosphate in environmental samples. The assay relied on the formation of a blue-colored product (molybdenum blue) in the detection area of the 3D-µPAD upon reduction of the heteropolyacid H₃PMo₁₂O₄₀ formed in the presence of phosphate. A number of experimental parameters were optimized, including geometric aspects of 3D-µPADs, digitization and image processing conditions, the amount of chemicals deposited in specific areas of the 3D-µPAD, and the reaction time. In addition, the stability of the device was evaluated at three different storage temperatures. Under optimal conditions, the working range was found to be from 4 to 25 mg P/L (12–77 mg PO₄⁻³/L). The limits of detection (LOD) and quantification (LOQ) were 0.015 mg P/L and 0.05 mg P/L, respectively. The repeatability and intermediate precision of a 5 mg P/L standard were 4.8% and 7.1%, respectively. The proposed colorimetric assay has been successfully applied to phosphorous determination in various waters, soils, and sediments, obtaining recoveries in the range of 94 to 107%. The ready-to-use 3D-µPAD showed a greener profile than the standard method for phosphate determination, being affordable, easy-to-use, and suitable for citizen science applications. Full article

This study presents a comprehensive evaluation of recent YOLO architectures, YOLOv8s, YOLOv9s, YOLOv10s, YOLO11s, and YOLO12s, for the detection of red, yellow, and purple raspberry fruits under field conditions. Images were collected using an smartphone camera under varying illumination, weather, and occlusion conditions. Each model was trained and evaluated using standard object detection metrics (Precision, Recall, mAP₅₀, mAP_50:95, F₁-score), while inference performance was benchmarked on both high-performance (NVIDIA RTX 5080) and embedded (NVIDIA Jetson Orin NX) platforms. All models achieved high and consistent detection accuracy across fruits of different colors, confirming the robustness of the YOLO algorithm design. Compact variants provided the best trade-off between accuracy and computational cost, whereas deeper architectures yielded marginal improvements at higher Latency. TensorRT optimization on the Jetson device further enhanced real-time inference, particularly for embedded deployment. The results indicate that modern YOLO architectures have reached a level of architectural maturity, where advances are driven by optimization and specialization rather than structural redesign. These findings underline the strong potential of YOLO-based detectors as core components of intelligent, edge-deployable systems for precision agriculture and automated fruit detection. Full article

This work investigates smartphone-based image processing for monitoring reservoir water quality, driven by the necessity for accessible and economical environmental evaluation techniques. The objective is to examine the correlation between water color and essential water quality parameters, including turbidity, total phosphorus, and chlorophyll-a, utilizing basic, readily accessible technology. The concept entails taking water photos from three principal reservoirs in Taiwan—Shimen, Liyutan, and Hushan—utilizing a smartphone camera, succeeded by sophisticated image processing algorithms, encompassing RGB color space analysis and ripple filtering. The findings indicate strong correlations between the G/R ratio obtained from the photos and conventional water quality parameters, particularly turbidity and chlorophyll-a. The correlation analysis yielded R² = 0.72 (p < 0.01) for turbidity and R² = 0.68 (p < 0.05) for chlorophyll-a, confirming the statistical significance of the results. Full article

Hybrid hydrophobic coatings (HHCs), which combine organic and inorganic materials, have demonstrated superior weathering resistance compared to conventional organic coatings in conserving stone heritage structures. Among the inorganic components of HHCs, nanosilica is especially promising because of its ability to form durable, weathering-resistant and hydrophobic silane-based structures. This overview examined recent studies, advances, and emerging trends about nanosilica-based HHCs from 2020 to 2024 using the “Boolean strategy” and search terms “stone”, “heritage”, “hydrophobic”, and “coating”, capturing 5244 articles. After screening for titles containing “nanosilica” (470 items remained), excluding works related to “consolidants” and “cement” (171 items remained), and requiring quantitative data on formulations, methods, and performance of nanosilica-based HHCs in stone heritage structures, 16 relevant works were identified. China and Italy dominated research works on nanosilica-based HHC development, which was applied to stone heritage structures composed of carbonate materials (e.g., limestone, dolomite, and Palazzolo carbonates) and silica-rich materials (e.g., Qingshi stone, Hedishi stone, and red sandstone). Key evaluation metrics reported by multiple authors to evaluate HHC efficacy included water contact angle (WCA), total color difference (TCD), and solution pH. Moreover, ultraviolet light (UV) durability, thermomechanical stability, biocidal efficiency, and graffiti protection were achieved when nanosilica was combined with other nanomaterials. Integrating emerging technologies, such as artificial intelligence (AI), internet-of-things (IoT), and smartphones with colorimeter apps could improve accessibility, real-time monitoring and reliability of HHC testing, while adherence to standardized testing protocols would further enhance comparability and practical application across studies. Overall, this overview provides valuable insights into nanosilica-based HHCs for researchers and restorers/conservators of stone heritage structures. Full article

Background/Objectives: Cardiovascular disease remains a leading cause of death worldwide, with chest pain often serving as an initial reason for emergency visits. However, the severity of chest pain does not necessarily correlate with the severity of myocardial infarction. Facial expressions are an essential medium to convey the intensity of pain, particularly in patients experiencing speech difficulties. Automating the recognition of facial pain expression may therefore provide an auxiliary tool for monitoring chest pain without replacing clinical diagnosis. Methods: Using streaming technology, the system captures real-time facial expressions and classifies pain levels using a deep learning framework. The PSPI scores were incorporated with the YOLO models to ensure precise classification. Through extensive fine-tuning, we compare the performance of YOLO-series models, evaluating both computational efficiency and diagnostic accuracy rather than focusing solely on accuracy or processing time. Results: The custom YOLOv4 model demonstrated superior performance in pain level recognition, achieving a precision of 97% and the fastest training time. The system integrates a web-based interface with color-coded pain indicators, which can be deployed on smartphones and laptops for flexible use in healthcare settings. Conclusions: This study demonstrates the potential of automating pain assessment based on facial expressions to assist healthcare professionals in observing patient discomfort. Importantly, the approach does not infer the underlying cause of myocardial infarction. Future work will incorporate clinical metadata and a lightweight edge computing model to enable real-time pain monitoring in diverse care environments, which may support patient monitoring and assist in clinical observation. Full article

Smartphone-based strain monitoring of structural components is an emerging approach to structural health monitoring. However, the existing techniques suffer from limited accuracy and poor cross-device adaptability. This study aims to optimize the smartphone-based Micro Image Strain Sensing (MISS) method by replacing the traditional Connected Component Labeling (CCL) algorithm with the arc-support line segments (ASLS) algorithm, thereby significantly enhancing the stability and adaptability of circle detection in micro-images captured by diverse smartphones. Additionally, this study evaluates the impact of lighting conditions and lens distortion on the optimized MISS method. The experimental results demonstrate that the ASLS algorithm outperforms CCL in terms of recognition accuracy (maximum error of 0.94%) and cross-device adaptability, exhibiting greater robustness against color temperature and focal length variations. Under fluctuating lighting conditions, the strain measurement noise remains within ±0.5 με and with a maximum error of 7.0 με compared to LVDT measurements, indicating the strong adaptability of the optimized MISS method to external light changes. Barrel distortion in microscopic images induces a maximum pixel error of 5.66%, yet the final optimized MISS method achieves highly accurate strain measurements. The optimized MISS method significantly improves measurement stability and engineering applicability, enabling effective large-scale implementation for strain monitoring of civil infrastructure. Full article

Antimicrobial resistance (AMR) is a significant global threat and is driven by the overuse of antibiotics in both clinical and agricultural settings. This issue is further complicated by the lack of rapid surveillance tools to detect resistant bacteria in clinical, environmental, and food systems. Of particular concern is the rise in resistance to carbapenems, a critical class of beta-lactam antibiotics. Rapid detection methods are necessary for prevention and surveillance effort. This study utilized a gold nanoparticle-based plasmonic biosensor to detect three CR genes: bla_KPC-3, bla_NDM-1, and bla_OXA-1. Optical signals were analyzed using both a spectrophotometer and a smartphone app that quantified visual color changes using RGB values. This app, combined with a simple boiling method for DNA extraction and a portable thermal cycler, was used to evaluate the biosensor’s potential for POC use. Advantages of the portable bacterial detection device include real time monitoring for immediate decision-making in critical situations, field and on-site testing in resource-limited settings without needing to transport samples to a centralized lab, minimal training required, automatic data analysis, storage and sharing, and reduced operational cost. Bacteria were inoculated into sterile water, river water, and turkey rinse water samples to determine the biosensor’s success in detecting target genes from sample matrices. Magnetic nanoparticles were used to capture and concentrate bacteria to avoid time-consuming cultivation and separation steps. The biosensor successfully detected the target CR genes in all tested samples using three gene-specific DNA probes. Target genes were detected with a limit of detection of 2.5 ng/L or less, corresponding to ~10³ CFU/mL of bacteria. Full article