Search Results (131)

Background/Objectives: Enamel defects in molar-incisor hypomineralization (MIH) have a multifactorial etiology involving environmental, systemic, and genetic factors. These alterations represent an aesthetic and emotional challenge, especially in anterior teeth. Resin infiltration has emerged as a minimally invasive treatment for MIH opacities, though outcome predictability remains limited. This study aims to analyze the baseline characteristics of MIH enamel defects and identify specific patterns that may predict clinical outcomes. Methods: This was a single-arm, prospective, observational clinical study with a six-month follow-up, with a total of 101 MIH-affected teeth treated with Icon^® resin infiltration. Opacities were analyzed using CIELAB color parameters (Lab*), including luminance, lesion extent, affected tooth type, opacity location, and patient age. Treatment success was assessed using simple linear regression models with generalized estimating equations, which were based on different covariates. Clinical success was defined as the combined achievement of a significant reduction in ΔE, a decrease in L* indicating reduced opacity brightness, and a reduction in the relative surface area of the lesion at six months. Results: White opacities showed greater reduction after infiltration than yellow and brown ones (p < 0.029). Larger lesions exhibited greater improvement (p < 0.007). Canines and lateral incisors achieved better masking (p < 0.001), and incisal opacities had superior outcomes (p < 0.019). Additionally, younger patients experienced a greater reduction (p < 0.026). Conclusions: Resin infiltration enhances the esthetics of anterior teeth with MIH in pediatric patients. While no single predictive pattern was identified, white opacities achieved greater luminance reduction and better integration with sound enamel. Factors such as age, tooth type, opacity location, lesion extent, and color significantly influence treatment effectiveness and esthetic perception. Full article

Background/Objectives: Traditional medical image analysis methods often suffer from locality bias, limiting their ability to model long-range contextual relationships between spatially distributed anatomical structures. To overcome this challenge, this study proposes SPX-GNN (Superpixel Explainable Graph Neural Network). This novel method reformulates image analysis as a structural graph learning problem, capturing both local anomalies and global topological patterns in a holistic manner. Methods: The proposed framework decomposes images into semantically coherent superpixel regions, converting them into graph nodes that preserve topological relationships. Each node is enriched with a comprehensive feature vector encoding complementary diagnostic clues, including colour (CIELAB), texture (LBP and Haralick), shape (Hu moments), and spatial location. A Graph Neural Network is then employed to learn the relational dependencies between these enriched nodes. The method was rigorously evaluated using 5-fold stratified cross-validation on a public dataset comprising 4200 chest X-ray images. Results: SPX-GNN demonstrated exceptional performance in tuberculosis classification, achieving a mean accuracy of 99.82%, an F1-score of 99.45%, and a ROC-AUC of 100.00%. Furthermore, an integrated Explainable Artificial Intelligence module addresses the black box problem by generating semantic importance maps, which illuminate the decision mechanism and enhance clinical reliability. Conclusions: SPX-GNN offers a novel approach that successfully combines high diagnostic accuracy with methodological transparency. By providing a robust and interpretable workflow, this study presents a promising solution for medical imaging tasks where structural information is critical, paving the way for more reliable clinical decision support systems. Full article

This study explores the enhancement of Beech wood (Fagus sylvatica L.) surfaces through the laser engraving of motifs inspired by Romanian textile heritage, combining cultural preservation with modern surface design techniques. A digitization and computer-aided design (CAD)-based workflow was employed to accurately transfer traditional motifs onto wood substrates. Engraving was performed using a nitrogen laser at ten different power settings ranging from 10 W to 150 W, followed by color analysis of the engraved areas. The resulting surfaces were evaluated using the International Commission on Illumination (CIELab) system to identify optimal engraving conditions. Based on colorimetric analysis, three laser power settings were selected for final motif reproduction: 30 W, 45 W, and 105 W. The process enabled the accurate rendering of a traditional three-color motif, achieving both visual fidelity and aesthetic appeal. Results demonstrate that color-based laser engraving allows precise, durable, and culturally significant ornamentation of wooden surfaces. The conclusions highlight the potential of this technique to add artistic and commercial value to wood products while preserving and promoting cultural identity. Full article

Natural rubber quality assessment traditionally relies on subjective visual inspection, leading to inconsistent grading and processing inefficiencies. This study presents a colorimetric imaging system integrating 48-megapixel image acquisition with automated colorimetric analysis for objective rubber classification. Five rubber grades—white crepe, STR5, STR5L, RSS3, and RSS5—were analyzed using standardized 25 × 25 mm² specimens under controlled environmental conditions (25 ± 2 °C, 50 ± 5% relative humidity, 3200 K illumination). The image processing pipeline employed color space transformations from RGB through CIE1931 $XYZ$ to CIELAB coordinates, with yellowness index calculation following ASTM E313-20 standards. The classification algorithm achieved 100% accuracy across 100 validation specimens under controlled laboratory conditions, with a processing time of 1.01 ± 0.09 s per specimen. Statistical validation via one-way ANOVA confirmed measurement reliability (p > 0.05) with yellowness index values ranging from 8.52 ± 0.52 for white crepe to 72.15 ± 7.47 for RSS3. Image quality metrics demonstrated a signal-to-noise ratio exceeding 35 dB and a spatial uniformity coefficient of variation below 5%. The system provides 12-fold throughput improvement over manual inspection, offering objective quality assessment suitable for industrial implementation, though field validation under diverse conditions remains necessary. Full article

Greenhouse horticulture is an energy-intensive production system that requires innovative solutions to reduce energy demand without compromising crop yield or quality. Functional greenhouse covers are particularly promising, as they regulate solar radiation while integrating energy-harvesting technologies. In this study, six nanostructured glass coatings incorporating semiconductor-based quantum dots (QDs) and quantum wires (QWs) of Ge and TiN are developed using magnetron sputtering—an industrially scalable technique widely applied in smart window and energy-efficient glass manufacturing. The coatings’ optical properties are characterized in the laboratory, and their agronomic performance is evaluated in greenhouse trials with lamb’s lettuce (Valerianella locusta) and radish (Raphanus sativus). Plant growth, yield, and leaf color (CIELAB parameters) are analyzed in relation to spectral transmission and the daily light integral (DLI). Although uncoated horticultural glass achieves the highest yields, several Ge-QD coatings provide favorable compromises by selectively absorbing non-photosynthetically active radiation (non-PAR) while maintaining acceptable crop performance. These results demonstrate that nanostructured coatings can simultaneously sustain crop growth and enable solar energy conversion, offering a practical pathway toward energy-efficient and climate-smart greenhouse systems. Full article

Water loss during storage is a major cause of postharvest quality deterioration in cucumber, yet existing methods to monitor hydration are often destructive or require expensive instrumentation. We developed a low-cost, non-destructive approach for estimating fruit relative water content (RWC) using visible-light color imaging combined with an ensemble machine-learning model (Random Forest). A total of 1200 fruits were greenhouse-grown, harvested at market maturity, and equally divided between optimal and ambient storage temperature (10 and 25 °C, respectively). Digital images were acquired at harvest and at 7 d intervals during storage, and color parameters from four standard color systems (RGB, CMYK, CIELAB, HSV) were extracted separately for the neck, mid, and blossom regions as well as for the whole fruit. During storage, fruit RWC decreased from 100% (fully hydrated condition) to 15.3%, providing a broad dynamic range for assessing color–hydration relationships. Among the 16 color features evaluated, the mean cyan component (μC) of the CMYK space showed the strongest relationship with measured RWC (R² up to 0.70 for whole-fruit averages), reflecting the cyan region’s heightened sensitivity to dehydration-induced changes in pigments, cuticle properties and surface scattering. The Random Forest regression model trained on these features achieved a higher predictive accuracy (R² = 0.89). Predictive accuracy was also consistently higher when μC was calculated over the entire fruit surface rather than for individual anatomical regions, indicating that whole-fruit color information provides a more robust hydration signal than region-specific measurements. Our findings demonstrate that simple visible-range imaging coupled with ensemble learning can provide a cost-effective, non-invasive tool for monitoring postharvest hydration of cucumber fruit, with direct applications in quality control, shelf-life prediction and waste reduction across the fresh-produce supply chain. Full article

Bigels are promising delivery systems for bioactive compounds, combining the properties of hydrogels and oleogels. Pequi carotenoids, characterized by their natural yellow fluorescence, hold potential to replace the artificial dye tartrazine in foods while simultaneously enhancing their functional properties. This study developed food-grade bigels with varying oleogel-to-hydrogel ratios (40%, 60%, 80% OG) to assess the pigmentation capacity of pequi carotenoid extracts. Hydrogel contained agar and xanthan gum, while oleogel comprised beeswax, lecithin, sunflower oil, and 400 μg/100 g carotenoid extract. Bigel color was analyzed using the CIELAB system. Linear and multiple regression models were applied to assess the influence of crosslinking time (1 vs. 12 h), extraction solvent (acetone vs. [BMIM][BF4]), saponification, and oleogel ratio on color parameters. The color of the carotenoid-enriched bigels was mainly influenced by the extraction solvent and the oleogel ratio, while saponification and crosslinking time had only minor impacts. Although changes in L*, a*, and b* were observed across samples, ΔE* values generally reflected low perceptibility. Notably, more evident color differences were associated with variations in solvent type and oleogel ratio. These findings contribute to a better understanding of how formulation parameters influence the pigmentation behavior and support the development of natural, visually appealing functional foods. Full article

Accelerated artificial aging of zinc oxide (ZnO)-based acrylic artists’ paint, filled with calcium carbonate (CaCO₃) as an extender, was carried out for a total of 1963 h (~8 × 10⁷ lux·h), with assessments at specific intervals. The total color difference ΔE* was <2 (CIELab-76 system) over 1725 h of aging, while the human eye notices color change at ΔE* > 2. Oxidative degradation of organic components in the paint to form volatile products was revealed by attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, micro-Raman spectroscopy, and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). It appears that deep oxidation of organic intermediates and volatilization of organic matter may be responsible for the relatively small value of ΔE* color difference during aging of the samples. To elucidate the degradation pathways, principal component analysis (PCA) was applied to the spectral data, revealing: (1) the catalytic role of ZnO in accelerating photodegradation, (2) the Kolbe photoreaction, (3) the decomposition of the binder to form volatile degradation products, and (4) the relative photoinactivity of CaCO₃ compared with ZnO, showing slower degradation in areas with a higher CaCO₃ content compared with those dominated by ZnO. These results provide fundamental insights into formulation-specific degradation processes, offering practical guidance for the development of more durable artist paints and conservation strategies for acrylic artworks. Full article

Color grading of boiled shrimp is a critical factor influencing market price, yet the process is usually conducted visually by buyers such as middlemen and processing plants. This subjective practice raises concerns about accuracy, impartiality, and fairness, often resulting in disputes with farmers. To address this issue, this study proposes a standardized and automated grading approach based on image processing and artificial intelligence. The method requires only a photograph of boiled shrimp placed alongside a color grading ruler. The grading process involves two stages: segmentation of shrimp and ruler regions in the image, followed by color comparison. For segmentation, deep learning models based on Mask R-CNN with a Feature Pyramid Network backbone were employed. Four model configurations were tested, using ResNet and ResNeXt backbones with and without a Boundary Loss function. Results show that the ResNet + Boundary Loss model achieved the highest segmentation performance, with IoU scores of 91.2% for shrimp and 87.8% for the color ruler. In the grading step, color similarity was evaluated in the CIELAB color space by computing Euclidean distances in the L (lightness) and a (red–green) channels, which align closely with human perception of shrimp coloration. The system achieved grading accuracy comparable to human experts, with a mean absolute error of 1.2, demonstrating its potential to provide consistent, objective, and transparent shrimp quality assessment. Full article

The yellowing effect of camphorquinone (CQ) has led manufacturers to add alternative initiators into resin composites (RCs) to reduce the amount of CQ used. The aim of this study was to investigate the color change in commercial RCs with alternative photoinitiators besides CQ. Color change upon polymerization and aging in air and artificial saliva for up to 3 months was tested for seven commercial RCs (traditional and bulk-fill) with either CQ only or CQ and additional photoinitiators (CQ+). Color measurements were obtained with a spectrophotometer. Color change (ΔE) was calculated using the CIELab and CIEDE2000 formulae. ANOVA and a post hoc SNK test were conducted for statistical analysis. Upon polymerization, the ΔE of CQ+ was greater than that of CQ only, except in the case of dual-cure HyperFIL. The storage conditions did not affect the color change within 24 h for either air or artificial saliva, whereas they did have an influence on color stability when RCs were aged for 1 month and 3 months. The color changes in the RCs aged in artificial saliva were considered clinically acceptable for all RCs tested except HyperFIL. Additional photoinitiator systems tended to result in a greater color change upon polymerization but did not affect color change upon aging. During shade selection, especially when additional photoinitiators besides CQ are used, a guide reflecting the color after polymerization should be used. Full article

Shell color is a commercially valuable trait in eggs, and blue-green eggshells typically exhibit multiple color subtypes. To explore the relationship between the CIELab system and visual color classification and develop simplified discrimination indices, 2274 blue-green eggs across seven batches were selected. The L*, a*, and b* values of each egg were measured, and average visual classification (AveObs) was calculated from four numeric categories (Light = 1, Blue = 2, Green = 3, Olive = 4) separately assigned by four observers. After batch correction using ComBat, four algorithms—linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and neural network (NNET)—were compared. Correction substantially reduced the coefficients of variation of the L*, a*, and b* values. Correlations emerged: L* and b* (−0.722), a* and b* (0.451), and L* and a* (−0.088), while correlations of the L*, a*, and b* values with AveObs were −0.713, 0.218, and 0.771, respectively. The LDA model achieved superior comprehensive performance across all data scenarios, with the highest accuracy and efficiency as compared to the SVM, NNET, and RF models. Among the LDA functions, LD1 explained 78.53% of the variance, with L*, a*, and b* coefficients of −0.134, 0.063, and 0.349, respectively (ratio ≈ 1:0.47:2.60). Simplified formulas based on the L*, a*, and b* values were constructed and compared to the existing indices C* (=$\sqrt{{a*}^2+{b*}^2}$) and SCI (=L* − a* − b*). The correlation between L* − 2b* and AveObs was −0.803, similar to those for C* (0.797) and SCI (−0.782), while the correlation between L* − 4C* and AveObs was −0.810, significantly higher than that for SCI (p < 0.05). In conclusion, the LDA model demonstrated optimal performance in predicting color classification, and L* − 4C* is an ideal index for grading of blue-green eggs. Full article

Strawberry leaf extract (SLE) was used in dry fermented sausages, “Salchichón”, to enrich them with antioxidants. The effect of SLE on various characteristics was monitored during ripening and storage. SLE had a slight effect on microbiological characteristics; however, the pH after 3, 14, and 21 days was slightly lower (4.51–4.55) in the samples with higher SLE concentration (0.5% + 1% dextrose). Peroxide value (PV) and thiobarbituric acid reactive substances (TBARS) values of sausages with SLE and with ascorbic acid (reference antioxidant), at the end of ripening, were similar. SLE acted as a pro-oxidant when the sausage was stored in the light; however, it showed antioxidant activity in the dark and at 50 °C storage conditions. Higher extract concentration reduced redness a* value and increased yellowness b* value in the CIELab colour system. Addition of SLE to dry fermented sausages has no negative effect on the ripening process; however, storage conditions of the final product should be carefully controlled. Sensory analysis of the final product showed that SLE imparts a recognisable herbal odour; however, it did not reduce the overall product acceptability. It may be concluded that SLE may be a promising ingredient for increasing the nutritional quality of fermented sausages. Full article

Background/Objectives: Maxillofacial silicone prostheses’ long-term color stability remains a challenge. This study aimed to evaluate and compare the color stability of conventional and digital maxillofacial silicone elastomers mixed with nano-sized antimicrobial additives (ZnO nanoparticles and chlorhexidine salt-CHX) at various concentrations over a 10-week period. Methods: A total of nine groups (n = 10) of maxillofacial silicone elastomers were prepared. These included a control group (no additives), conventionally pigmented silicone, digitally pigmented silicone (Spectromatch system), and silicone mixed with ZnO or CHX at 1%, 3%, and 5% by weight. Specimens were fabricated in steel molds and cured at 100 °C for 1 h. Color measurements were performed at baseline and after 1, 4, 6, and 10 weeks using a Minolta Chroma Meter (CIELAB system, ΔE₀₀ formula). Data were analyzed using two-way ANOVA and Tukey HSD post hoc tests (α = 0.05). Results: Color changes (ΔE₀₀) ranged from 0.74 to 2.83 across all groups. The conventional pigmented silicone group showed the highest color difference (ΔE₀₀ = 2.83), while the lowest was observed in the ZnO 1% group (ΔE₀₀ = 0.74). Digital silicone and all antimicrobial-modified groups exhibited acceptable color stability (ΔE₀₀ < 3.1). Time significantly affected color difference, with the largest change occurring during the first four weeks (p < 0.05), followed by stabilization. Regression analysis confirmed high color stability over time for all groups except the conventional pigmented group. Conclusions: This is one of the first studies to directly compare digital and conventional pigmentation methods combined with nano-antimicrobials in maxillofacial silicones. Maxillofacial silicone elastomers mixed with up to 5% ZnO or CHX maintained acceptable color stability over 10 weeks. Digital pigmentation is similar to conventional methods. The incorporation of nano-antimicrobials offers significant microbial resistance and improved color retention. Full article

Monitoring pH and acidity during yoghurt fermentation is essential for product quality and process efficiency. Conventional measurement methods, however, are invasive and labour-intensive. This study developed artificial neural network (ANN) models to predict pH and titratable acidity during yoghurt fermentation using CIELAB colour parameters (L, a*, b*). Reconstituted milk powder with 12% total solids was prepared with varying protein levels (4.2–4.8%), inoculum concentrations (1–3%), and fermentation temperatures (36–44 °C). Data were collected every 10 min until pH 4.6 was reached. Forty models were trained for each output variable, using 90% of the data for training and 10% for validation. The first two phases of the fermentation process were clearly distinguishable, lasting between 4.5 and 7 h and exceeding 0.6% lactic acid in all treatments evaluated. The best pH model used two hidden layers with 28 neurons (R² = 0.969; RMSE = 0.007), while the optimal acidity model had four hidden layers with 32 neurons (R² = 0.868; RMSE = 0.002). The strong correlation between colour and physicochemical changes confirms the feasibility of this non-destructive approach. Integrating ANN models and colourimetry offers a practical solution for real-time monitoring, helping improve process control in industrial yoghurt production. Full article

Dialium guineense (velvet tamarind), Parkia biglobosa Jacq. (African locust bean) and Adanosonia digitata L. (baobab) are fruits from African plants whose nutritional potential remains poorly characterised. As such, their pulps and seeds were analysed for colour (CIELab system), moisture, ash, protein, fat, soluble and insoluble dietary fibre, free sugars (HPLC-RI), organic acids (HPLC-PDA), macro and microelements (XRF analyser) and amygdalin (HPLC-PDA). The colours of their pulps differed considerable (ΔE > 38 between the velvet tamarind and African locust bean) and the moisture content was lower in seeds (about 7%) compared to pulps (9–13%). Seeds were more concentrated in protein (20–28%) and fat (5–22%), whereas pulps were richer in sugar (1–12%). African locust bean pulp was the sweetest (39% total sugar), while baobab pulp contained the highest soluble fibre (>30%) and citric acid (3.2%), and velvet tamarind pulp was distinguished by its tartaric acid content (3.4%). Seeds of the African fruits presented higher Ca, P, S and Fe contents, whereas pulps had higher K content. No amygdalin (<6.34 mg per 100 g of dry weight) or toxic heavy metal contents were detected. The PCA segregated samples by pulp and seed and the PC1 explains the sugar and moisture of the pulps, while protein, fat and minerals are associated with the seeds. These data confirm that African fruit pulps and seeds have distinct functional profiles, are safe for food use and can be consumed, which is important for efforts to promote the conservation of these tropical plant species. Full article