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Art and design, as a professional field closely linked with market demand, is imperative to reform the teaching mode. In this paper, the concept of constructivism is incorporated into the professional education of art design, and a new teaching framework is established through the guidance of teaching concepts. Secondly, the RGB color model is applied in the teaching of color design, and the beauty of the color scheme is calculated through the Berkhof equation in color harmony so as to improve the color-matching ability of students’ art design. Big data analysis is used to evaluate the students’ design works comprehensively. Finally, taking College H as an example, the effectiveness of computer-aided teaching in graphic arts design is analyzed, and the impact of the method on the employment of graduates is verified. The results show that in the experimental group using computer-assisted teaching and the control group of traditional teaching mode, the average grade difference is 17.91 points, Sig=0.003. The difference is significant, which indicates that computer-assisted teaching significantly improves the students’ art design professional ability. This innovative model has a significant impact on the enhancement of art design majors in colleges and universities and provides a practical path for innovative artistic design teaching.

This study addressed the need for a more accessible and efficient method of analyzing phenolic content in vegetable oils. The research aimed to develop a method that could be widely adopted by both researchers and industry professionals, ultimately revolutionizing the way phenolic content in vegetable oils is analyzed. This study developed a method of determining the total phenolic content (TPC) in vegetable oils using smartphone image analysis in the RGB color model. The method employed a gallic acid calibration solution and demonstrated exceptional determination coefficients for the RGB colors. The R—red color was selected as the basis for the analyses, and the method was statistically equivalent to standard UV/Vis spectrophotometry. The highest TPC was determined in hemp and olive oils, while the lowest was found in rice bran, grapeseed, and macadamia nut oils. This study concluded that smartphone image analysis, mainly using the R component of the RGB color model, was a superior alternative to traditional spectrophotometric methods for determining the TPC in vegetable oils. This innovative approach could revolutionize phenolic content analysis by providing researchers and industry professionals with a cost-effective, safe, and efficient tool. The estimated limit of detection (LOD) of 1.254 mg L−1 and limit of quantification (LOQ) of 3.801 mg L−1 further confirmed the reliability and comparability of the method. With these findings, it was expected that the method would be widely adopted in the future.

Food residues are a promising resource for obtaining natural pigments, which may replace artificial dyes in the industry. However, their use still presents challenges due to the lack of suitable sources and the low stability of these natural compounds when exposed to environmental variations. In this scenario, the present study aims to identify different food residues (such as peels, stalks, and leaves) as potential candidates for obtaining natural colorants through eco-friendly extractions, identify the colorimetric profile of natural pigments using the RGB color model, and develop alternatives using nanotechnology (e.g., liposomes, micelles, and polymeric nanoparticles) to increase their stability. The results showed that extractive solution and residue concentration influenced the RGB color profile of the pigments. Furthermore, the external leaves of Brassica oleracea L. var. capitata f. rubra, the peels of Cucurbita maxima, Cucurbita maxima x Cucurbita moschata, and Beta vulgaris L. proved to be excellent resources for obtaining natural pigments. Finally, the use of nanotechnology proved to be a viable alternative for increasing the stability of natural colorants over storage time.

The demand to improve image display in TFT-LCD, implementation of design for image processing is important. In order to meet the specific requirements of low-end Thin Film Transistor-Liquid-Crystal-Display (TFT-LCD) image display. This paper adopts a novel algorithm to conduct subsequent processing of the medical image after SCALER scaling, including contrast adjustment, gamma correction and dithering. Dithering algorithm is the focus of our research. After the study of some classical video image processing algorithms, and considering the real-time requirements, an intelligent algorithm is implemented for hardware implementation and improvement. For each part, MATLAB language is firstly used for advanced simulation to verify its feasibility, and then Right-To-Left (RTL) hardware language description is carried out. The characteristics extraction from images is performed implementing RGB standard and grayscale images. The pixel intensity is analyzed for each RGB component and the variance is calculated. When a panel displays a variation of 6% related with their reference values, the panel is rejected. The results obtained from classification shows a 95.24% accuracy rate in the detection of defects. The results of the two simulations show that the design achieves the expected goal, and the processing time is shorter.

Industries that use fruits as raw materials must, at some point in the process, classify them to discard the unsuitable ones and thus ensure the quality of the final product. To produce mango nectar, it is necessary to ensure that the mango is mature enough to start the extraction of the nectar; however, sorting thousands of mangoes may require many people, who can easily lose attention and reduce the accuracy of the result. Such kind of decision can be supported by current Artificial Intelligence techniques. The theoretical details of the processing are presented, as well as the programming code of the neural network using SCILAB as a computer language; the code includes the color extraction from mango images. SCILAB programming is simple, efficient and does not require computers with large processing capacity. The classification was validated with 30 images (TIF format) of Manila variety mango; the mangoes were placed on a blue background to easily separate the background from the object of interest. Four and six mangoes were used to train the neural network. This application of neural networks is part of an undergraduate course on artificial intelligence, which shows the potential of these techniques for solving real and concrete problems.

Introduction. The priority task of the food industry is to provide population with functional products since the health of nation and its people largely depends on the diet. New formulations and technologies for meat products broaden the range of functional foods. Flaxseed oil is an excellent source of functional ingredients as it is rich in polyunsaturated fatty acids and tocopherols. The present research featured horsemeat as a promising raw material of high nutritional and biological value. Horsemeat is a traditional food source for many nations. The research objective was to develop a new technology for horsemeat in sauce and to select the optimal thermal processing method. Study objects and methods. The authors tested several methods of heat treatment and used a standard nine-point scale to assess the sensory properties of the finished product. The experiment involved standard physicochemical and organoleptic research methods. The color characteristics were described using digital image processing. Results and discussion. The study delivered a new formulation of sauce with flaxseed oil, which improved the quality of the fat component of the finished product. The new sauce proved to be rich in polyunsaturated fatty acids and possessed high sensory and technological properties. A comparative analysis of the heat treatment methods included traditional frying and stewing, cooking in a steam convection oven, and a sous-vide technology. The sous vide technology appeared to have the best structural-mechanical, physicochemical, and sensory properties. Software processing of digital images made it possible to compare the color of raw, semifinished, and cooked meat samples. The traditional cooking methods of frying and stewing showed the most pronounced changes in the color, while the sous-vide technology demonstrated a smooth color change. As for the quality of the finished product, it proved to satisfy 40% of daily intake for polyunsaturated fatty acids and 20% for tocopherol, which makes the product functional. Conclusion. The new technology made it possible to expand the range of functional meat products. The new digital image processing program helped to register changes in shape and color of meat samples after various heat treatment methods.

Soil color is commonly used as an indicator to classify soil and identify its properties. However, color-based soil assessments are susceptible to variations in light conditions and the subjectivity of visual evaluations. This study proposes a novel method of calibrating digital images of soil, regardless of lighting conditions, to ensure accurate identification. Two different color space models, RGB and CIELAB, were assessed in terms of their potential utility in calibrating changes to soil color in digital images. The latter system was determined to be suitable, as a result of its ability to accurately reflect illuminance and color temperature. Linear regression equations relating soil color and light conditions were developed based on digital images of four different types of soil samples, each photographed under 15 different light conditions. The proposed method can be applied to calibrate variations in the soil color obtained by digital images, thus allowing for more standardized, objective, and accurate classification and evaluation of soil based on its color.

Light sensitivity is important for marine benthic invertebrates, and it plays a vital role in the oysters settling. Generally, the emerging of eyespot is a signal of oyster larvae settling, while like most of the other coastal species, the oysters are threatened by artificial light pollution. Crassostrea gigas and Crassostrea sikamea are two oyster species naturally distributed in China, and their hybrids are potential material for oyster cross-breeding. Therefore, we investigated the phototaxis of hybrid eyespot larvae and eyeless larvae under different light intensities and wavelengths to uncover how light affects their behaviors. The results indicated that hybrid oyster larvae had positive phototaxis to specific light intensity and wavelength. We further concluded that 5 lx was the positive phototaxis light intensity for the eyeless hybrid larvae, and that the acceptable light intensity range of the eyespot hybrid larvae expanded to 5–10 lx, but no higher than 15 lx; besides, the hybrid larvae behaved negatively to the light over 25 lx. The present study also suggested the positive effects of green light on larvae gathering and the induction of red light on eyespot larvae settling. In conclusion, our study may contribute to the understanding of phototaxis of hybrid oyster larvae, as well as the further perspective of light pollution on benthic communities and coastal system restoration.

Growing interest in the use of microalgae as a sustainable feedstock to support a green, circular, bio-economy has led to intensive research and development initiatives aimed at increasing algal biomass production covering a wide range of scales. At the heart of this lies a common need for rapid and accurate methods to measure algal biomass concentrations. Surrogate analytical techniques based on chlorophyll content use solvent extraction methods for chlorophyll quantification, but these methods are destructive, time consuming and require careful disposal of the resultant solvent waste. Alternative non-destructive methods based on chlorophyll fluorescence require expensive equipment and are less suitable for multiple sampling of small cultures which need to be maintained under axenic growth conditions. A simple, inexpensive and non-destructive method to estimate chlorophyll concentration of microalgal cultures in situ from digital photographs using the RGB color model is presented. Green pixel intensity and chlorophyll a, b and total chlorophyll concentration, measured by conventional means, follow a strong linear relationship (R2 = 0.985–0.988). In addition, the resulting standard curve was robust enough to accurately estimate chlorophyll concentration despite changes in sample volume, pH and low concentrations of bacterial contamination. In contrast, use of the same standard curve during nitrogen deprivation (causing the accumulation of neutral lipids) or in the presence of high quantities of bacterial contamination led to significant errors in chlorophyll estimation. The low requirement for equipment (i.e., a simple digital camera, available on smartphones) and widely available standard software for measuring pixel intensity make this method suitable for both laboratory and field-based work, particularly in situations where sample, qualified personnel and/or equipment is limited. By following the methods described here it should be possible to produce a standard curve for chlorophyll analysis in a wide range of testing conditions including different microalga cultures, culture vessel and photographic set up in any particular laboratory.

ED light, a green energy-saving light source, can cause color cast. For this reason, LED light is seldom favored by designers. The purpose of the paper is to provide shoppers who are observing product colors in an LED-lighted setting with an innovative color identification model. Based on designers’ product color comparison, the paper employs high-reliability mechanic visual perception in combination with grey relational grade. Grey relational grade is applied to eliminate electrical fault pertaining to mechanic visual perception, whereby appropriate LED parameters and color cast inclination can be obtained. The paper first mimics retail store display windows. The color temperature and illuminance of LED light sources are adjustable. Two degrees of illuminance, including high illuminance (1500 lux) and low illuminance (500 lux), and two light source color temperatures, including yellow light (2700 K) and white light (4000 K), were assigned for study. Four colors, including red, yellow, blue and green of the natural color system, were selected as product colors. The mechanic visual perception sensor was used to identify the object (product) color, which is then converted into an RGB color model to serve as research data of color cast measurement, and the grey relational grade was applied to obtain the most appropriate LED light parameters and the color cast of the four colors. The data analysis reveals that green shows the least color cast when it is lighted by a yellow LED light source with low illuminance, yellow and blue have the least color cast when it is lighted by a white LED light source with high illuminance and red displays the least color cast when it is lighted by a white LED light source with low-illuminance. The analysis also indicates each color’s cast inclination in blackness, chromaticness and hue. As a result, LED light that is more acceptable to designers is suggested for display windows, thus reducing problems with product color cast.

Problems of identification of the color parameters of moving raster image objects in pattern recognition in modern security systems, which include real-time video-surveillance systems, are declared in this paper. The description of algorithms of identification of the color parameters in frames coming from real-time video streams is given. The practical application of the developed algorithms in program modules of corresponding video systems is demonstrated.