Digitalization of Colorimetric Sensor Technologies for Food Safety.

Colorimetric sensors play a crucial role in promoting on-site testing, enabling the detection and/or quantification of various analytes based on changes in color. These sensors offer several advantages, such as simplicity, cost-effectiveness, and visual readouts, making them suitable for a wide range of applications, including food safety and monitoring. A critical component in portable colorimetric sensors involves their integration with color models for effective analysis and interpretation of output signals. The most commonly used models include CIELAB (Commission Internationale de l'Eclairage), RGB (Red, Green, Blue), and HSV (Hue, Saturation, Value). This review outlines the use of color models via digitalization in sensing applications within the food safety and monitoring field. Additionally, challenges, future directions, and considerations are discussed, highlighting a significant gap in integrating a comparative analysis toward determining the color model that results in the highest sensor performance. The aim of this review is to underline the potential of this integration in mitigating the global impact of food spoilage and contamination on health and the economy, proposing a multidisciplinary approach to harness the full capabilities of colorimetric sensors in ensuring food safety.

RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review.

Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be used for plant remote sensing; however, multispectral and hyperspectral cameras are technically complex and have a high cost. RGB imaging based on the analysis of color images of plants is definitely simpler and more accessible, but using this tool for remote sensing plant characteristics under changeable environmental conditions requires the development of methods to increase its informativity. Our review focused on using RGB imaging for remote sensing the characteristics of terrestrial plants. In this review, we considered different color models, methods of exclusion of background in color images of plant canopies, and various color indices and their relations to characteristics of plants, using regression models, texture analysis, and machine learning for the estimation of these characteristics based on color images, and some approaches to provide transformation of simple color images to hyperspectral and multispectral images. As a whole, our review shows that RGB imaging can be an effective tool for estimating plant characteristics; however, further development of methods to analyze color images of plants is necessary.

Using Computer Vision to Track Facial Color Changes and Predict Heart Rate.

The current technological advances have pushed the quantification of exercise intensity to new era of physical exercise sciences. Monitoring physical exercise is essential in the process of planning, applying, and controlling loads for performance optimization and health. A lot of research studies applied various statistical approaches to estimate various physiological indices, to our knowledge, no studies found to investigate the relationship of facial color changes and increased exercise intensity. The aim of this study was to develop a non-contact method based on computer vision to determine the heart rate and, ultimately, the exercise intensity. The method was based on analyzing facial color changes during exercise by using RGB, HSV, YCbCr, Lab, and YUV color models. Nine university students participated in the study (mean age = 26.88 ± 6.01 years, mean weight = 72.56 ± 14.27 kg, mean height = 172.88 ± 12.04 cm, six males and three females, and all white Caucasian). The data analyses were carried out separately for each participant (personalized model) as well as all the participants at a time (universal model). The multiple auto regressions, and a multiple polynomial regression model were designed to predict maximum heart rate percentage (maxHR%) from each color models. The results were analyzed and evaluated using Root Mean Square Error (RMSE), F-values, and R-square. The multiple polynomial regression using all participants exhibits the best accuracy with RMSE of 6.75 (R-square = 0.78). Exercise prescription and monitoring can benefit from the use of these methods, for example, to optimize the process of online monitoring, without having the need to use any other instrumentation.

The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse's Thoracolumbar Region Evaluated by Advanced Thermal Image Processing.

Appropriate matching of rider-horse sizes is becoming an increasingly important issue of riding horses' care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body's surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10-12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.

In Situ Control of Thermal Activation Conditions by Color for Serpentines with a High Iron Content.

Serpentine heat treatment at temperatures of 650-750 °C yields magnesium-silicate reagent with high chemical activity. Precise and express control of roasting conditions in laboratory kilns and industrial aggregates is needed to derive thermally activated serpentines on a large scale. Color change in serpentines with a high iron content during roasting might be used to indicate the changes in chemical activity in the technological process. This study gives a scientific basis for the express control of roasting of such serpentines by comparing the colors of the obtained material and the reference sample. Serpentines with different chemical activity were studied by X-ray diffraction, Mössbauer spectroscopy, and optical spectroscopy. The color parameters were determined using RGB (red, green, blue), CIELAB (International Commission on Illumination 1976 L*a*b), and HSB (hue, brightness, saturation) color models. The color of heat-treated samples was found to be affected by changes in the crystallochemical characteristics of iron included in the structure of the serpentine minerals. The color characteristics given by the CIELAB model were in good coherence with the acid-neutralizing ability and optical spectra of heat-treated serpentines. Thus, in contrast to the long-term analysis by these methods, the control by color palette provides an express assessment of the quality of the resulting product.

Performance Evaluation of Color Models in the Fusion of Functional and Anatomical Images.

Fusion of the functional image with an anatomical image provides additional diagnostic information. It is widely used in diagnosis, treatment planning, and follow-up of oncology. Functional image is a low-resolution pseudo color image representing the uptake of radioactive tracer that gives the important metabolic information. Whereas, anatomical image is a high-resolution gray scale image that gives structural details. Fused image should consist of all the anatomical details without any changes in the functional content. This is achieved through fusion in de-correlated color model and the choice of color model has greater impact on the fusion outcome. In the present work, suitability of different color models for functional and anatomical image fusion is studied. After converting the functional image into de-correlated color model, the achromatic component of functional image is fused with an anatomical image by using proposed nonsubsampled shearlet transform (NSST) based image fusion algorithm to get new achromatic component with all the anatomical details. This new achromatic and original chromatic channels of functional image are converted to RGB format to get fused functional and anatomical image. Fusion is performed in different color models. Different cases of SPECT-MRI images are used for this color model study. Based on visual and quantitative analysis of fused images, the best color model for the stated purpose is determined.

Usage patterns of blue flower color representation by Encyclopedia of Life content providers.

Encyclopedia of Life (EOL) is a resource for community-driven biodiversity data, focusing on species information and images. Research into blue flowers to compare color ('blueness') at different elevations revealed that data content providers describe flowers as blue for any color hue in the range from blue to magenta. We propose methods for standardizing color values and color searching within EOL by means of an expanded color vocabulary and improved access to image metadata, in order to improve the research capacity of this valuable resource.

Multi-leveled objects: color as a case study.

The paper presents color as a case study for the analysis of phenomena that pertain to several levels of reality and are typically framed by different sciences and disciplines. Color, in fact, is studied by physics, biology, phenomenology, and esthetics, among others. Our thesis is that color is a different entity for each level of reality, and that for this reason color generates different observables in the epistemologies of the different sciences. By analyzing color as a paradigmatic case of an entity naturally spreading over different levels of reality, the paper raises the question as to whether making explicit the usually implicit ontological assumptions embedded within the different observables exploited by the different sciences may eventually clarify some of the difficulties of developing a comprehensive theory of color.