Color Extraction Research Articles

Green Application of Ultrasonic Waves for Extraction of Yellow Colorant from Haar Singhar and its Colouring Behaviour in Cotton Dyeing

Natural dyes have grown in popularity due to their eco-friendliness. They can be used as a substitute for synthetic dyes to minimize environmental pollution. This research investigates the natural colouring behaviour of haar singhar flower (HSF) extract in cotton dyeing. Colorant was extracted under various conditions utilizing various extraction mediums such as aqueous, basic, and acidic. On cotton, it was discovered that applying 55 mL of aqueous extract containing 1.5 g/100 mL sodium chloride for 40 minutes at 70 °C yielded the best colour output. A new hue with good colour fastness was developed utilizing chemical and bio-mordants. The existence of nyctanthin as a colouring agent in haar singhar flowers was discovered through FTIR analysis of the extract. The CIE Lab system revealed that using 2 g/100 mL of pistachio shell as a bio-mordant resulted in good quality reddish yellow hues. It was discovered that ultrasonic radiations have a high potential for isolating colourants and dyeing cotton fabric under decreased conditions of temperature, time and volume. The application of biomordants has made the procedure greener, more efficient, and safer.

Preparation of Bioactive De-Chlorophyll Rhein-Rich Senna alata Extract.

Senna alata leaves display various biological activities as a result of their rhein and phenolic composition. The objective of this study was to develop bioactive de-chlorophyll rhein-rich S. alata extracts. The rhein content was quantified using a validated high-performance liquid chromatography-diode array detection (HPLC-DAD) method. The best process parameters for maximizing rhein were established using ultrasound-assisted extraction (UAE). The optimal conditions for the parameters were determined using the Box-Behnken design (BBD); 95% v/v ethanol was used as the extraction solvent at 59.52 °C for 18.4 min with a solvent-to-solid ratio of 25.48:1 (mL/g) to obtain the predicted value of rhein at 10.44 mg/g extract. However, the color of the rhein-rich extract remained dark brown. For the removal of chlorophyll, liquid-liquid extraction with vegetable oils and adsorption with bleaching agents were employed. The bleaching agents were significantly more effective at removing chlorophyll and had less of an effect on the reduction in rhein content than vegetable oils. The presence of rhein and phenolics in the de-chlorophyll extracts might be responsible for their antioxidant, anti-inflammatory, and antibacterial activities. These findings indicate that rhein-rich extract and its de-chlorophyll extracts possess sufficient biological activities for the further development of cosmeceuticals and pharmaceuticals.

Classification of Longan Types Using The Back-Propagation Neural Network Algorithm Based on Leaf Morphology With Shape Characteristics

In Indonesia, longan fruit is abundantly accessible. The Longan fruit comes in a number of kinds, and they vary in terms of their morphology, including the characteristics of their leaves. The varieties of longan fruit are to be categorized in this study based on the shape of the leaves. The author uses the RGB color extraction function, the Grey Level Co-occurrence Matrix (GLCM), and the Shape feature to get data for each cultivar. The accuracy value is then processed using the Back-Propagation Neural Network (BPNN) technique to determine the accuracy value that will be used as a determinant of the categorization of the Longan leaf image. The eccentricity and metric parameters are key components of the method. The BPNN algorithm demonstrated its usefulness for categorizing various kinds of longan fruit leaves during testing by obtaining an accuracy of 70%.

Research on Artificial Intelligence in New Year Prints: The Application of the Generated Pop Art Style Images on Cultural and Creative Products

Chinese New Year prints constitute a significant component of the country’s cultural heritage and folk art. Yangliuqing New Year prints are the most important and widely circulated of all the different kinds of New Year prints. Due to a variety of factors including societal change, industrial structure change, and economic development, New Year prints, which were deeply rooted in agricultural society, have been adversely impacted, and have even reached the brink of disappearance. With the protection and effort from the government and researchers, New Year prints can finally be preserved. However, the underlying problems remain, such as receiving little attention, a singular product form, and being unable to keep up with the times, especially among the younger generation. In this paper, the researchers first processed Yangliuqing New Year prints through the GANs model. Then, the image is segmented by binarization and color extraction of images from the Pop art dataset by the K-Means algorithm, followed by colorizing the binarized and segmented image. Finally, usable high-quality Pop art style Yangliuqing New Year prints are generated. The generated images are used in the development of cultural and creative products. Questionnaires were then distributed based on the empirical research scale. The results of this study are as follows: 1. The method proposed in this study can generate high-quality Pop art style New Year prints. 2 Using Pop art style New Year print images in the design of cultural and creative products is popular among the younger generation, and they possess a great propensity to purchase. This study solves the problems encountered by the current cultural heritage of New Year prints, and broadens the artistic expression forms and product categories, and provides research ideas for the cultural heritage of the same type that is facing similar problems. In the future, researchers will continue to explore the incorporation of AI technology in New Year prints to stimulate the vitality of traditional cultural heritage.

A Method for Style Transfer from Artistic Images Based on Depth Extraction Generative Adversarial Network

Depth extraction generative adversarial network (DE-GAN) is designed for artistic work style transfer. Traditional style transfer models focus on extracting texture features and color features from style images through an autoencoding network by mixing texture features and color features using high-dimensional coding. In the aesthetics of artworks, the color, texture, shape, and spatial features of the artistic object together constitute the artistic style of the work. In this paper, we propose a multi-feature extractor to extract color features, texture features, depth features, and shape masks from style images with U-net, multi-factor extractor, fast Fourier transform, and MiDas depth estimation network. At the same time, a self-encoder structure is used as the content extraction network core to generate a network that shares style parameters with the feature extraction network and finally realizes the generation of artwork images in three-dimensional artistic styles. The experimental analysis shows that compared with other advanced methods, DE-GAN-generated images have higher subjective image quality, and the generated style pictures are more consistent with the aesthetic characteristics of real works of art. The quantitative data analysis shows that images generated using the DE-GAN method have better performance in terms of structural features, image distortion, image clarity, and texture details.

Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video

Wireless Capsule Endoscopy (WCE) allows direct visual inspecting of the full digestive system of the patient without invasion and pain, at the price of a long examination by physicians of a large number of photographs. This research presents a new approach to color extraction to differentiate bleeding frames from normal ones and locate more bleeding areas. We have a dual-system suggestion. We use entire color information on the WCE pictures and the pixel-represented clustering approach to get the clustered centers that characterize WCE pictures as words. Then we evaluate the status of a WCE framework using the nearby SVM and K methods (KNN). The classification performance is 95.75% accurate for the AUC 0.9771% and validates the exciting performance for bleeding classification provided by the suggested approach. Second, we present a two-step approach for extracting saliency maps to emphasize bleeding locations with a distinct color channel mixer to build a first-stage salience map. The second stage salience map was taken with optical contrast.We locate bleeding spots following a suitable fusion approach and threshold. Quantitative and qualitative studies demonstrate that our approaches can correctly distinguish bleeding sites from neighborhoods.

Palm Oil Maturity Classification Using K-Nearest Neighbors Based on RGB and L*a*b Color Extraction

This study aims to classify the maturity level of oil palm using the K-Nearest Neighbors (KNN) method based on the extraction of RGB and L*a*b color features. This classification determines the optimal oil production based on the color of the palm. However, this process is often carried out by humans, so sorting is not optimal because it is done manually, and it takes a long time. Thus, we propose automatic detection using machine learning based approach. In this study, the image processing methods are used to classify the level of oil palm maturity. The process starts with taking 150 photos with a smartphone. Each of them is then grouped into 3 classes, namely ripe (50), intermediate (50), and unripe (50). The oil palm image is pre-processed using the segmentation method (removing background), resizing, and cropping. Then, color features using RGB and L*a*b are extracted. Each of these extracted color features becomes input in the training and testing process of the KNN algorithm and is validated using k-fold cross-validation, with the results of accuracy being 95.4% and 97.4%. The combination of KNN and L*a*b color extraction approaches has better accuracy than KNN and RGB in terms of model accuracy (by 2–4.8 percentage points).

Cold mash na obtenção de mosto cervejeiro com baixo teor de carboidratos: um estudo de brassagem

Abstract Cold mash is an innovative and promising brewing technique for obtaining free-alcohol and low-alcohol beers, with a sensory profile similar to traditional beers. While traditional mashing is carried out at high temperatures, between 50 and 75 oC, this technique uses low temperatures, around 10 oC, but with a longer time. This allows more intense extraction of flavors and color from the malt while keeping the concentration of fermentable sugars low, as the enzymatic starch hydrolysis does not occur at its maximum efficiency. A study was performed on the mashing stage of the brewing process using the cold mash technique, with subsequent physicochemical characterization, to evaluate possible changes in these parameters. To obtain beer worts with low concentrations of fermentable sugars, modifications were made to mashing temperature and time, and a two-level factorial design was applied to evaluate the influence of these variables, demonstrating that the main factor mashing time was of great importance in the response. The extract content, related to the concentration of carbohydrates, presented results around 6 oBx, while the traditional wort has between 11 and 12 oBx, and the protein content was 0.12 to 0.13%, being below than reported in the literature. For pH, the results were around 6.0, which is above the recommended for brewer's wort, demonstrating that the mashing technique employed promoted changes in this parameter, possibly due to the limited action of the phosphatase enzyme, responsible for lowering the pH through the production of phosphoric acid.

Managing extraction of colour, phenolics and aromas in Pinot noir wine production: Alternative use of grape marc

Managing extraction of colour, phenolics and aromas in Pinot noir wine production: Alternative use of grape marc

A New Cosmetic Science: Design and Evaluation of Herbal Lipstick

Cosmetic Science is a crucial multidisciplinary component in many scientific disciples and spans the natural sciences, humanities, and social sciences. The 35-year-old predecessor, Cosmetic Science, is totally revised in New Cosmetic Science. In addition to talking about cosmetics’ safety, this article also describes the “usefulness of cosmetics,” a subject that is quickly gaining importance and comprising body cosmetics, dental care, cosmetics, makeup cosmetics, perfumes, and skin care cosmetics. The presentation of oral care and body cosmetics includes information about each product’s performance, types, primary ingredients, suggested uses, and manufacturing processes. Cosmetics have both physical and psychological effects. Lipstick formulas are used in cosmetics to improve the appearance of lips. Lipstick is a cosmetic item that gives the lips color, texture, and protection while also having pigments, oils, and waxes. Several different types of lipstick are only worn by women. The natural and safe component is found in organic lipstick. Additionally, they contain natural nutrients that support healthy lips. Using natural color extracts from various natural sources might lessen the negative effect.

A New Cosmetic Science: Design and Evaluation of Herbal Lipstick

Cosmetic Science is a crucial multidisciplinary component in many scientific disciples and spans the natural sciences, humanities, and social sciences. The 35-year-old predecessor, Cosmetic Science, is totally revised in New Cosmetic Science. In addition to talking about cosmetics’ safety, this article also describes the “usefulness of cosmetics,” a subject that is quickly gaining importance and comprising body cosmetics, dental care, cosmetics, makeup cosmetics, perfumes, and skin care cosmetics. The presentation of oral care and body cosmetics includes information about each product’s performance, types, primary ingredients, suggested uses, and manufacturing processes. Cosmetics have both physical and psychological effects. Lipstick formulas are used in cosmetics to improve the appearance of lips. Lipstick is a cosmetic item that gives the lips color, texture, and protection while also having pigments, oils, and waxes. Several different types of lipstick are only worn by women. The natural and safe component is found in organic lipstick. Additionally, they contain natural nutrients that support healthy lips. Using natural color extracts from various natural sources might lessen the negative effect.

A New Cosmetic Science: Design and Evaluation of Herbal Lipstick

Cosmetic Science is a crucial multidisciplinary component in many scientific disciples and spans the natural sciences, humanities, and social sciences. The 35-year-old predecessor, Cosmetic Science, is totally revised in New Cosmetic Science. In addition to talking about cosmetics’ safety, this article also describes the “usefulness of cosmetics,” a subject that is quickly gaining importance and comprising body cosmetics, dental care, cosmetics, makeup cosmetics, perfumes, and skin care cosmetics. The presentation of oral care and body cosmetics includes information about each product’s performance, types, primary ingredients, suggested uses, and manufacturing processes. Cosmetics have both physical and psychological effects. Lipstick formulas are used in cosmetics to improve the appearance of lips. Lipstick is a cosmetic item that gives the lips color, texture, and protection while also having pigments, oils, and waxes. Several different types of lipstick are only worn by women. The natural and safe component is found in organic lipstick. Additionally, they contain natural nutrients that support healthy lips. Using natural color extracts from various natural sources might lessen the negative effect.

HPTLC-based fingerprinting: An alternative approach for fructooligosaccharides metabolism profiling

Fructans are categorized as fructose-based metabolites with no more than one glucose in their structure. Agave species possess a mixture of linear and ramified fructans with different degrees of polymerization. Among them, fructooligosaccharides are fructans with low degree of polymerization which might be approachable by high performance thin layer chromatography (HPTLC). Thus, this study used two emblematic Agave species collected at different ages as models to explore the feasibility of HPTLC-based fingerprinting to characterize fructooligosaccharides (FOS) production, accumulation, and behavior through time. To do so, high performance anion exchange was also used as analytical reference to determine the goodness and robustness of HPTLC data. The multivariate data analysis showed separation of samples dictated by species and age effects detected by both techniques. Moreover, linear correlations between the increase of the age in agave and their carbohydrate fraction was established in both species by both techniques. Oligosaccharides found to be correlated to species and age factors, these suggest changes in specific carbohydrate metabolism enzymes. Thus, HPTLC was proven as a complementary or stand-alone fingerprinting platform for fructooligosaccharides characterization in biological mixtures. However, the type of derivatizing reagent and the extraction color channel determined the goodness of the model used to scrutinize agavin fructooligosaccharides (aFOS).

A Computer Vision System for Bitumen Content Estimation in Flotation Froth with Degraded Images*

Oil sand is one of the main oil resources in Canada. In oil sands operation, primary separation cell, where bitumen is separated from the sand using water-based gravity separation, generates froth, middling and tailing. Tailing contains solids, water, and residual bitumen. The presence of bitumen in the tailing leads to loss of bitumen and increases environmental footprints. In order to remove residual bitumen, tailing is sent to a flotation cell. Determining the bitumen content in the froth of the flotation cell is essential for ensuring the efficiency of the process operation and improving environmental performance. Thus, samples of the flotation froth are often collected over a relatively long period of time and then sent to a laboratory for analysis. While laboratory analysis can provide a more accurate measurement of cumulative bitumen content over the sample collection period, it is costly and time-consuming. The long processing time in the laboratory will hinder real-time applications. In recent years, computer vision technology has been widely used to monitor and control flotation processes. This article proposes a computer vision-based solution for estimating cumulative bitumen content in flotation froth that usually contains a relatively small amount of bitumen. In spite of the frequent recording of images of the froth, the corresponding bitumen content label (laboratory analysis result) is not commonly available. The froth is collected batch by batch, and there is only one laboratory analysis of bitumen content in each batch, which reflects the cumulative bitumen content collected over the entire batch. Furthermore, images of the froth are often degraded by noise and varying lighting conditions. To solve the problem, a Kalman filter-based algorithm is proposed to restore the contaminated images. Gray-level co-occurrence matrix (GLCM) is then used to extract color and textural features from the restored images. Using these features, a linear regression model is built for predicting cumulative bitumen content over each batch. Validation results in a laboratory-scale flotation process demonstrate the proposed algorithm is promising in estimating bitumen content.

Quality Characteristics and Antioxidant Activities of Commercial Leaf Tea Extract

Purpose: In this study, the physicochemical properties and antioxidant activities of 23 types of tea were analyzed. Through this study, we tried to provide the basic data for tea research.Methods: Leaf tea extracts were prepared using hot water at 70℃, and their physicochemical properties, such as color intensity and pH, were analyzed. The total flavonoid content, 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging ability, and α-glucosidase inhibitory activity were measured as antioxidant activity.Results: The color of most leaf tea extracts was brown, with black tea having the deepest color. Black tea had the highest total flavonoid content at 593 mg QE/g. Eoseongcho tea had the highest DPPH scavenging ability at 97.56%. The α-glucosidase inhibitory activities of black tea and Chinese bush-clover tea were strong, while those of mistletoe, plantain, and rosemary leaf teas were exceptionally low.Conclusion: Black tea which has excellent total flavonoid content, DPPH scavenging ability, and α-glucosidase inhibitory activity, is expected to be most effective functional tea when consumed after meals among the multi consumption commercial leaf teas market.

AR Make-up Filter for Social Media using the HSV Color Extraction

Choosing the appropriate cosmetics is an arduous task. Because cosmetics are tested directly on the skin to ensure each person’s preferences are met. The consumer repeatedly tries a sample and then discards it until he discovers one that meets his tastes. The cosmetics business and consumers are affected by this move. Companies can utilize Augmented Reality (AR) technology as an alternative to mass-producing cosmetic samples. The difficulty of deploying augmented reality is the difficulty of putting cosmetics into camera video streams. Each individual bears the burden of skin color and its effect on light. HSV Color Extraction was the method employed for this study. The application of augmented reality intends to enable consumers to test cosmetics with their chosen color and assist businesses in competing in the industry by promoting items and engaging customers. This work makes it easier to choose cosmetics using augmented reality and social media. AR simulates the usage of the desired color cosmetics, whereas social media allows users to obtain feedback on their color preferences. The outcomes of this study indicate that augmented reality (AR) apps can display filters in bright, dim, and even wholly dark lighting conditions. This research contributes originality that cosmetic firms can utilize to market their products on social media.

Image Analysis and Processing for Generating Camouflages from Digital Earth Photographs

Camouflage is present both on the civilian market and in military. It is used by hunters and military enthusiasts, but also by clothing designers and game developers. They are forced to design their own or choose between already developed camouflages, both military and civilian, so they cannot adapt it to their needs in a fast and easy way. Currently, there are not many software solutions that allow for easy generation of digital camouflages and support the user in selecting the colors for final camouflage result. The approach presented in the paper proposes to solve these problems by analyzing the colors from digital Earth images of the target areas and using the developed image processing algorithm for generating digital camouflages. Based on the proposed approach and its designed algorithm, the application was created to allow the user to easily generate the digital camouflages. The paper also presents the results of analysis of camouflage quality, comparing the camouflages generated with the developed algorithms and their application and the selected market generators together with the selected military digital camouflages. By using the proposed algorithm to generate the camouflage and implementing the centroid algorithm for color extraction, it was possible to create better quality camouflages compared to those created by existing software solutions. This was supported by an analysis of the camouflage quality in chosen terrain variants, in which developed application achieved the best results.

Valorization of Pelargonium graveolens L’Hér. Hydrodistillation Solid Waste as Natural Dye for Wool Fabrics

ABSTRACT The huge amount of solid waste produced during the hydrodistillation of Pelargonium graveolens L’Hér. is a serious environmental hazard. The aim of this research is to reduce this environmental pollution and to valorize this waste by using its extract as a natural dye. Extracts were used for dyeing wool samples. The dyed fabrics showed shades ranging between brown and beige. By using the Minitab 19 Software, a definitive screening design was used to optimize the dye extraction process. The studied extraction process parameters were pH, temperature, and extraction time. The main studied responses were the color strength and the polyphenol, flavonoid, and condensed tannin contents (mg.g−1). Based on the photochemical study and the UV–vis absorption spectra, it was found that the polyphenol content varies between 4.144 and 7.567 mg.g−1. For the tannin content, the highest value was equal to 120.967 mg.g−1. To optimize the colorant extraction process, the K/S was also measured by the spectrocolorimeter. Process optimum conditions were of 11, 100°C, and 65 min, respectively, for pH, temperature, and extraction time. The optimum K/S value was equal to 115.15. Therefore, the solid waste hydrodistillation of Pelargonium graveolens can be an interesting choice for the natural dyeing of textile fabrics.

Kmeans Clustering Segmentation on Water Microbial Image with Color and Texture Feature Extraction

Image segmentation is one of the analytical processes for digital image recognition, where this process divides the digital image into several unique regions based on homogeneous pixels. The process of homogeneous grouping images is based on several colour, texture and shape features. Colour in digital image processing is very important because colour has many information humans can easily understand. Colour has various features, combining colour intensity and grey (grayscale) and binary (black and white) values. However, the colour feature extraction process has many weaknesses. If the object used has a very small si[1]ze and range area, the use of colour features needs to be combined with extraction, and the segmentation process can be maximized. This study uses colour and texture features in the extraction process. It uses bacterial objects (microbes) from water, with limited image quality and objects that tend to be difficult to identify. The colour space feature extraction process is combined with a Gabor filter so that the segmentation process produces high-quality accuracy. Good. The Gabor filter used in this study is combined with the L*a*b space vector to increase accuracy in the segmentation process. The results showed that the use of texture features resulted in an increase in accuracy of 17.5% by testing the cluster value of 1.2.

Maneuvering target oriented digital camouflage image generation method

Since the digital camouflage generation method based on fixed single background image cannot fully meet the hidden need of maneuvering targets. In this paper, a method of generating digital camouflage image based on several background images is proposed. Firstly, according to the active range of the maneuvering target, several background images are collected. Secondly, in order to resolve the problem that the clustering algorithm is sensitive to the initial clustering center and easy to fall into local optimum, K-harmonic means (KHM) clustering algorithm is introduced and initial clustering center is determined based on color histogram. Again, KHM clustering is used to extract color features from several background images, the first clustering is extracted from a single background image, and the second clustering is extracted with several color features sets extracted from the first clustering of background images as input. Finally, the regular hexagon is used as the basic unit of the digital camouflage image to construct the spot template, and the digital camouflage image is generated by the lowest horizontal line algorithm. Example verification and camouflage effect detection show that the digital camouflage image generated by this method can effectively realize the concealment of maneuvering targets and has good camouflage effect.