Color Extraction Research Articles

Application of Different Aqueous Ultrasound-assisted Extraction Process Parameters on the Quality of Pandan (Pandanus amaryllifolius Roxb) Leaves Extract

Pandan of Pandanaceae family, known for its aromatic foliage, vibrant green color, and rich in bioactive compounds, requires green extraction technology to stabilise compounds. Hence, this study aims to optimise process parameters of aqueous ultrasound-assisted extraction (UAE) on the color, yield, and antioxidant content of pandan extract using the Response Surface Method (RSM), and compare the volatile compounds present in optimised ultrasonicated pandan extract with macerated extract (control). The analysis includes DPPH and FRAP assays, total phenolic content (TPC), color, and yield. Optimally validated parameters (R2 ≥ 0.90, p-value lack-of-fit ≥ 0.05) were: 80°C extraction temperature, 90 min ultrasonic time, 2.60 % of solid in 20 mL distilled water yielded promising antioxidant activities: 277.64 mg/g AAE, 19.90 mg/g GAE, and 1.48 g/g AAE for DPPH, TPC, and FRAP assays, respectively). UAE technique has significantly (p<0.05) higher yield of extract (86.24%) and DPPH scavenging activity (277.64 mg/g AAE) compared with control. The 3-methyl-2(5H)-furanone is the most abundant volatile compound found in both extracts. The cavitation in ultrasonication generates currents, increasing pandan's mass transfer rate into the solvent medium. The optimised ultrasonication condition contributes to the increased quality of the pandan extract.

The Effects of a Saccharomyces cerevisiae Strain Overexpressing the Endopolygalacturonase PGU1 Gene on the Aminoacidic, Volatile, and Phenolic Compositions of Cabernet Sauvignon Wines

The addition of pectinase enzymes during the maceration stage of grape skins in order to improve the extraction yields and color of red wines is a common practice in many wineries. The objective of this work was to study in depth the changes that occurred in the aminoacidic, volatile, and phenolic compositions of Cabernet Sauvignon wines fermented with a Saccharomyces cerevisiae strain genetically modified with the gene encoding for endopolygalacturonase (PGU1) in transcriptional fusion with the promoter of the phosphoglycerate kinase (PGK1) gene, both from S. cerevisiae origin. A higher yield extraction of wine was obtained in wines fermented with the modified strain (PW), increasing by around 6.1% compared to the control wine (CW). Moreover, there was a 40% decrease in the malic acid content in the PW, thus suggesting that this modified yeast could be investigated as a malic acid-reducing agent. There were slight differences in other aroma volatile compounds studied as well as in the phenolic content. However, there was a considerable increase in the amino acid content in the PW.

Optimizing dyeing parameters for sustainable wool dyeing using quinoa plant components with antibacterial properties

This study focuses on the sustainable utilization of Quinoa plant components, particularly its leaves, as a waste agricultural material for natural dyeing applications. Two distinct Quinoa genotypes, Titicaca and Giza, were selected for their natural dyeing properties. UV-VIS absorption spectra of Quinoa colorant extracts provided insights into their chemical composition, revealing distinctive peaks indicative of betalains (400–450 nm), chlorophyll (600–650 nm), carotenoids (400–500 nm), and aromatic amino acids (250–280 nm). Wool samples dyed with different plant parts such as flowers, leaves, and stalks exhibited distinct yellow hue, with leaves demonstrating the highest color strength. Metal mordants such as iron (II) sulphate, copper sulphate, and tin (II) chloride influenced color outcomes, highlighting their role in tailoring final appearance. Fastness properties of dyed wool samples were evaluated, with leaves showing moderate staining resistance and good light fastness. Additionally, antibacterial properties of leaves of Giza Quinoa variety were assessed, showing promising activity against Staphylococcus aureus. The antibacterial properties of fabrics dyed with the leaf extract by mordanting method with different metal salt mordants, particularly Fe, Cr, and Cu, exhibited significant enhancement. These results highlight the multifaceted benefits of Quinoa plant components in sustainable natural dyeing and textile applications, emphasizing their potential for eco-friendly practices in the textile industry.

Furniture design based on image color extraction algorithm

With the increasing demand for personalized and customized home products, how to realize the innovative design of furniture and improve the design efficiency has become a research hotspot for related professionals. Aiming at these problems, the study extracts the main color of furniture images by optimizing the K-mean clustering algorithm, uses the simulated annealing algorithm to color-match the furniture, and reconstructs the image by edge detection to design a furniture design method based on image color extraction. The results revealed that in the foreground part, the correct rate of color match based on the design method was 95.7%, and in the background part, the correct rate of color match based on the design method was 94.81 %, which proved its effectiveness. The average feature point extraction time and the average feature point matching time of the design-based algorithm were 5.45 ms and 9.83 ms, respectively, which proved its high computational efficiency. In furniture color edge detection and overall color match, the image obtained based on the design method was significantly clearer, and the overall coherence, saturation and brightness were closer to the input image. In addition to raising the standard of furniture design, the study's design methodology increases design efficiency and offers solid technical support for the area.

Asymmetric lesion detection with geometric patterns and CNN-SVM classification

In dermoscopic images, which allow visualization of surface skin structures not visible to the naked eye, lesion shape offers vital insights into skin diseases. In clinically practiced methods, asymmetric lesion shape is one of the criteria for diagnosing Melanoma. Initially, we labeled data for a non-annotated dataset with symmetrical information based on clinical assessments. Subsequently, we propose a supporting technique—a supervised learning image processing algorithm—to analyze the geometrical pattern of lesion shape, aiding non-experts in understanding the criteria of an asymmetric lesion. We then utilize a pre-trained convolutional neural network (CNN) to extract shape, color, and texture features from dermoscopic images for training a multiclass support vector machine (SVM) classifier, outperforming state-of-the-art methods from the literature. In the geometry-based experiment, we achieved a 99.00 % detection rate for dermatological asymmetric lesions. In the CNN-based experiment, the best performance is found 94 % Kappa Score, 95 % Macro F1-score, and 97 % weighted F1-score for classifying lesion shapes (Asymmetric, Half-Symmetric, and Symmetric).

Application of Attention-Enhanced 1D-CNN Algorithm in Hyperspectral Image and Spectral Fusion Detection of Moisture Content in Orah Mandarin (Citrus reticulata Blanco)

A method fusing spectral and image information with a one-dimensional convolutional neural network(1D-CNN) for the detection of moisture content in Orah mandarin (Citrus reticulata Blanco) was proposed. The 1D-CNN model integrated with three different attention modules (SEAM, ECAM, CBAM) and machine learning models were applied to individual spectrum and fused information by passing the traditional feature extraction stage. Additionally, the dimensionality reduction of hyperspectral images and extraction of one-dimensional color and textural features from the reduced images were performed, thus avoiding the large parameter volumes and efficiency decline inherent in the direct modeling of two-dimensional images. The results indicated that the 1D-CNN model with integrated attention modules exhibited clear advantages over machine learning models in handling multi-source information. The optimal machine learning model was determined to be the random forest (RF) model under the fusion information, with a correlation coefficient (R) of 0.8770 and a root mean square error (RMSE) of 0.0188 on the prediction set. The CBAM-1D-CNN model under the fusion information exhibited the best performance, with an R of 0.9172 and an RMSE of 0.0149 on the prediction set. The 1D-CNN models utilizing fusion information exhibited superior performance compared to single spectrum, and 1D-CNN with the fused information based on SEAM, ECAM, and CBAM respectively improved Rp by 4.54%, 0.18%, and 10.19% compared to the spectrum, with the RMSEP decreased by 11.70%, 14.06%, and 31.02%, respectively. The proposed approach of 1D-CNN integrated attention can obtain excellent regression results by only using one-dimensional data and without feature pre-extracting, reducing the complexity of the models, simplifying the calculation process, and rendering it a promising practical application.

Scene‐content‐sensitive real‐time adaptive foveated rendering

AbstractIn recent years, techniques for accelerating rendering by exploiting the limitations of the human visual system have become increasingly prevalent. The foveated rendering method significantly reduces the computational requirements during rendering by reducing image quality in peripheral regions. In this paper, we propose a scene‐content‐sensitive real‐time adaptive foveated rendering method. First, we pre‐render the three‐dimensional (3D) scene at a low resolution. Then, we utilize the low‐resolution pre‐rendered image as input to extract edge, local contrast, and color features. Subsequently, we generate a screen‐space region division map based on the gaze point position. Next, we calculate the visual importance of each 16 × 16 pixel tile based on edge, local contrast, color, and screen‐space region. We then map the visual importance to the shading rate to generate a shading rate control map for the current frame. Finally, we complete the rendering of the current frame based on variable rate shading technology. Experimental results demonstrate that our method effectively enhances the visual quality of images near the foveal region while generating high quality foveal region images. Furthermore, our method can significantly improve performance compared to per‐pixel shading method and existing scene‐content‐based foveated rendering methods.

Evaluation of non-anatomical characteristics for wood identification of six Korean oak species

Summary This study aimed to investigate and compare the non-anatomical characteristics of six Korean oak wood species (Quercus variabilis, Q. serrata, Q. mongolica, Q. dentata, Q. aliena and Q. acutissima) to provide identification keys and quality indices for the effective utilization of these species. Non-anatomical characteristics, including heartwood fluorescence, fluorescence and color of water and ethanol extracts, froth test, chrome azurol S test and burning splinter test, were evaluated according to the International Association of Wood Anatomists (IAWA) lists. None of the six Korean oak species displayed heartwood fluorescence or fluorescence in the ethanol extract. Only Q. serrata exhibited bright blue fluorescence in the aqueous extract. Each species showed distinct colors in both water and ethanol extracts, and the water extract of all species was darker than that of the ethanol extract. A weak positive reaction in the froth test was observed only for Q. serrata. Furthermore, Q. variabilis and Q. acutissima showed positive reactions to both heartwood and sapwood in the chrome azurol S test. The burning splinter test revealed that only Q. acutissima was transformed into charcoal. Thus, water extract fluorescence and color, ethanol extract color, froth test, chrome azurol S test, and burning splinter test can be used to identify the six Korean oak species.

Extraction of colours from plants as natural food colorants

Extraction of colours from plants as natural food colorants

Inhibition of bleaching of stored red hot pepper through appropriate postharvest technologies and practices

The colour qualities of hot red pepper are the major issue in pepper value chain. Due to poorly coordinated scientific evidence, information on the factors causing colour loss and the inhibition mechanism is not well known. Therefore, this review paper aimed to summarize the inhibition mechanism of stored red hot pepper bleaching through appropriate postharvest technologies and practices. The information in this paper was gathered from a variety of sources, including journal articles, books, book chapters, workshop proceedings, FAO reports, and AOAC official methods of analysis. According to these studies, carotenoids, surface colour, and extractable colour (ASTA value) are the primary colourants that define hot red pepper. The findings demonstrate that low-temperature drying methods, such as open sun drying, are best for preserving the red hot pepper powder’s colour quality, while higher temperatures cause the colour to darken. Blanching, the use of desiccants (CaCl2), and chemical dipping are pretreatments that preserve the best colour quality by hastening the drying time. Similarly, storage of red hot pepper powder at lower temperatures (5oC) resulted in less colour degradation. In other words, materials used for packaging that have a high barrier to light, moisture, and air, such as laminated aluminium, amber or black polyethylene, and high-density polyethylene, maintained a higher level of colour quality. Through their influence on drying and processing times, breeding technologies, varieties, and maturity level also impact colour quality. In conclusion, the colour quality of red hot pepper is highly influenced by environmental, biological, and processing methods. It is, therefore, critical to use appropriate drying and pretreatment techniques, storage time, well-managed storage temperature, appropriate processing methods and packing materials, and improved agronomic practices for the sustainable management of colour fading and adulteration that can occur throughout the value chain.

Dominant Color Detection For Online Fashion Retrievals

This paper introduces a novel approach aimed at efficiently extracting dominant colors from online fashion images. The method addresses challenges related to detecting overlapping objects and computationally expensive methods by combining K-means clustering and graph-cut techniques into a framework. This framework incorporates an adaptive weighting strategy to enhance color extraction accuracy. Additionally, it introduces a two-phase fashion apparel detection method called YOLOv4, which utilizes U-Net architecture for clothing segmentation to precisely separate clothing items from the background or other elements. Experimental results show that K-means with YOLOv4 outperforms K-means with the U-Net model. These findings suggest that the U-Net architecture and YOLOv4 models can be effective methods for complex image segmentation tasks in online fashion retrieval and image processing, particularly in the rapidly evolving e-commerce environment.

Integrating Image Analysis and Machine Learning for Moisture Prediction and Appearance Quality Evaluation: A Case Study of Kiwifruit Drying Pretreatment.

The appearance of dried fruit clearly influences the consumer's perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance.

Cosine-enhanced tuna swarm optimized exponential entropy segmentation method for sand grain microscopic images

The feature extraction of sand grain size, color and texture is a necessary step to identify clastic components. The sand grain features are complex and various, which brings difficulties to geological identification. Aiming at these images, a cosine-enhanced tuna swarm optimized exponential entropy segmentation method is proposed, which can effectively preserve the texture features of various sand grains. Firstly, for the tuna swarm optimization (TSO) algorithm, three improvement strategies are proposed: cosine spiral movement, cosine parabolic movement and Gauss-Cauchy mutation, which improve the TSO's global and local search. This algorithm is called cosine-enhanced TSO (CETSO). Benchmark function experiments showed that the convergence accuracy and stability of CETSO are greatly improved, and the convergence speed is also slightly increased. Secondly, CETSO optimized the exponential entropy to automatically determine the segmentation thresholds, and the feasibility of the method is verified by taking the information content of the segmented image as the standard. Finally, segmentation experiments were carried out on the Yarlung Zangbo River sand microscopic image dataset, and the results show that the method has high segmentation accuracy and stability for images with high contrast, rich texture, or significant differences in the size of sand debris. Compared with TSO, the CETSO optimized exponential entropy segmented images achieved an improvement of 21% and 93% in the evaluation of the average and standard deviation of the peak signal-to-noise ratio on thirty experiments. And this method has a fast processing speed, and it only takes about 0.85s to divide an image on average, which meets the needs of engineering applications.

Research on bearing remaining useful life anti-noise prediction based on fusion of color-grayscale time-frequency features

In contemporary industrial processes, vibration signals collected from bearings often contain significant noise, challenging the efficacy of conventional predictive models in extracting critical degradation features and accurately predicting the remaining useful life (RUL) of bearings. Addressing these challenges, this paper introduces a novel method for predicting bearing RUL under noisy conditions, leveraging a dual-branch multi-scale convolutional attention network (DMCSA) integrated with a dense residual feature fusion network (DRF). Initially, the method applies continuous wavelet transform (CWT) to vibration signals to extract color time-frequency image data, followed by grayscale processing to construct a comprehensive color-grayscale time-frequency image dataset, thereby augmenting the model’s input features. Enhanced channel and spatial attention mechanisms, combined with multi-scale convolutions, facilitate superior feature extraction and selection. The model’s resilience to noise is fortified by incorporating noise into the training dataset. Subsequently, selected color-gray time-frequency features undergo fusion and relearning through the DRF framework at the model’s backend. The crayfish optimization algorithm (COA) is deployed for the astute determination of the model’s critical hyperparameters. The proposed DMCSA-DRF model is then applied to predict the health indicator (MSCA-DRF-HI) of the test dataset, culminating in the accurate prediction of the bearings’ RUL. Validation experiments demonstrate that our method surpasses comparative models in prediction accuracy under diverse noise interferences, signifying a substantial advancement in predictive performance.

Effect of packaging materials and storage duration on the functional quality of red hot peppers (Capsicum annum L.) pods

The preservation of the functional quality of red pepper requires appropriate packaging materials, storage duration, and condition. This study evaluated the effect of the packaging materials and the storage duration on the functional quality of the red hot pepper pod for ten months at ambient storage temperature (25±2 °C) in four packaging materials: Jute sacks, Fertilizer Liner Sheet (FLS), Fertilizer Woven Polypropylene + Liner Sheet (FWPP + LS), and PICS bags (Purdue Improved Crop Storage) using two-factor factorial designs. The samples were analyzed at two-month intervals for water activity (aw, extractable color, total phenolic compounds, pungency index and oleoresin content. Except for aw, other functional qualities of red hot pepper degrade with advanced storage in all packaging materials. At the end of storage, the maximum values of aw (0.76) and the minimum values of aw (0.38) were recorded in jute sacks and PICS bags. Extractable color (ASTA units) decreased from 225.42 to 133.11, 228.71–139.09, 274.29–171, and 288.91–218.98 in jute bags, FLS, FWPP + LS, and PICS bags, respectively. Phenolic compounds (GAE mg/g dry samples) degraded from 2.32 to 1.02, 2.33–1.37, 2.4–1.35, and 2.59–1.85 in jute bags, FLS, FWPP + LS and PICS bags, respectively. The pungency index (Abs/g dry samples) of the samples changed from 4.55 to 2.56, 5.49–4.00, 5.00–3.49, and 4.74–2.72 in jute bags, FLS, FWPP + LS, and PICS bags, respectively. The oleoresin content (%) decreased from 12.88 to 7.18, 12.92–7.78, 13.19–8.45 and 13.58–9.88 in jute sacks, FLS, FWPP + LS and PICS bags, respectively. The study revealed that the PICS bags retained the highest functional qualities compared to other packaging materials.

Extraction yield and properties of biogenic silica from palm oil fuel ash: effect of re-ashing pretreatment and solvent concentration

Abstract Pretreatments and extraction conditions can affect the yield and characteristics of biogenic silica (biosilica) from palm oil fuel ash (POFA). This study aimed to examine how reashing pretreatment and varying NaOH concentrations influence the yield and properties of biosilica extracted from POFA by a simple sol-gel method. Re-ashing pretreatment was conducted using a furnace at a temperature of 600°C for 2h. All samples were leached using a citric acid solution. Extraction process was carried out using NaOH solutions with concentrations of 6%, 8%, and 10%). The results showed that re-ashing pretreatment and concentrations of NaOH significantly influenced (p<0.05) the extraction yield, color, crystallinity, and specific surface area of POFA-derived biosilica. Re-ashing pretreatment in combination with a 10% NaOH solution produced the highest extraction yield of biosilica (17.25% w/w) with a bright white color (L=91.83; WI=91.32), SiO2 concentration of 94.68%, crystallinity of 50.10%, and specific BET surface area of 390.80m2/g. XRD patterns confirmed the amorphous nature of POFA-derived biosilica. FTIR spectra revealed the presence of silica functional groups in all samples. This study demonstrated the important roles of re-ashing pretreatment and NaOH concentration in producing POFA-derived biosilica with better yield and properties that could broaden its potential applications.

ColorNetVis: An Interactive Color Network Analysis System for Exploring the Color Composition of Traditional Chinese Painting.

In the field of digital humanities, color research aims to discover explanations for painting history and color usage habits. However, researchers analyzing color relationships is challenging and time-consuming, as it requires color extraction and a detailed review of many painting images for reference and comparison of color relationships. In our work, we propose ColorNetVis, an interactive color network analysis tool that enables researchers to explore color relationships through color networks. The core of ColorNetVis is a bipartite network model that establishes a bipartite relationship between colors and Chinese painting within a scope based on color difference measurement. It constructs a one-mode color network through projection algorithms and similarity calculation methods to discover the relationship between colors. We propose a coordinated set of views to demonstrate the combination of determined color networks with painting types and real-world attributes. We use color space view, color attribute distribution view, and single color query components to assist researchers in conducting detailed color analysis and validation. Through case studies, researcher reviews, and user studies, we demonstrate that ColorNetVis can effectively help researchers discover knowledge of color relationships and potential color research directions.

Exploring the Tar Component Characteristics of Coalfield Fire Residual Coal Under Different Constant Temperature Oxidation

ABSTRACT Due to incomplete combustion of coal, a large amount of tar will be produced in coal fire area, which will cause serious pollution to the environment. In this paper, we dissolve the tar components in the residual coal by solvent extraction technique, analyze the organic components in the residual coal tar at different temperatures by GC-MS, study the characteristics of the residual coal tar components at different temperatures, and analyze the influence of the residual coal tar on the combustion and rekindling characteristics of the fire zone of the coalfield. The results show that: the components of coal combustion residual tar are mainly alkane organics which are easy to be oxidized and volatilized, and the content is more than 55%. At 300°C, the color of residual tar extract is light yellow or yellowish brown, and becomes colorless with the increase of temperature. At 300–500°C, the lower the temperature in the fire zone, the more residual tar in the coal, and the greater the risk of reignition in the extinguishing fire zone. The types of organic compounds in the tar residue from combustion have obvious characteristics at different temperature stages. Organic species have obvious characteristics at different temperature stages. By identifying the temperature stage of the coalfield fire area development, the organic structure characteristics and change trend of the residual coal tar can be deduced, and mastering the organic structure characteristics of the residual tar in the coalfield fire area can also reflect the temperature stage of coal fire area to a certain extent.

Remote sensing image dehazing using generative adversarial network with texture and color space enhancement

Remote sensing is gradually playing an important role in the detection of ground information. However, the quality of remote-sensing images has always suffered from unexpected natural conditions, such as intense haze phenomenon. Recently, convolutional neural networks (CNNs) have been applied to deal with dehazing problems, and some important findings have been obtained. Unfortunately, the performance of these classical CNN-based methods still needs further enhancement owing to their limited feature extraction capability. As a critical branch of CNNs, the generative adversarial network (GAN), composed of a generator and discriminator, has become a hot research topic and is considered a feasible approach to solving the dehazing problems. In this study, a novel dehazed generative adversarial network (GAN) is proposed to reconstruct the clean images from the hazy ones. For the generator network of the proposed GAN, the color and luminance feature extraction module and the high-frequency feature extraction module aim to extract multi-scale features and color space characteristics, which help the network to acquire texture, color, and luminance information. Meanwhile, a color loss function based on hue saturation value (HSV) is also proposed to enhance the performance in color recovery. For the discriminator network, a parallel structure is designed to enhance the extraction of texture and background information. Synthetic and real hazy images are used to check the performance of the proposed method. The experimental results demonstrate that the performance can significantly improve the image quality with a significant increment in peak-signal-to-noise ratio (PSNR). Compared with other popular methods, the dehazing results of the proposed method closely resemble haze-free images.

Cocos Nucifera Dyeing on Cotton Fabric as an Eventual Replacement

A traditional technique for colouring textiles, yarns, or fibres is called "natural dyeing," and it involves employing dyes made from plants, insects, and minerals. The usage of this age-old technique predates synthetic dyes by thousands of years, as documented by numerous cultures across the globe. In this procedure, colour compounds are extracted from natural sources and applied to textiles using a variety of methods, such as resist dyeing, direct application, or immersion dyeing. Natural dyes are typically more environmentally friendly and biodegradable than synthetic ones, which frequently include harsh chemicals and may have adverse effects on the environment. The relationship between natural colouring and traditional workmanship as well as sustainability is one of its most alluring features. It encourages a greater respect for nature and the use of renewable resources. A vast spectrum of colours can also be produced by natural dyes, however they might be more delicate and complex than their synthetic equivalents. Although natural dyeing has numerous benefits, there are drawbacks as well, including color fastness, consistency, and the availability of dye supplies. However, artists and textile aficionados are still exploring and expanding the potential of natural dyeing techniques in modern contexts through careful experimentation, sharing of knowledge, and creativity. The goal of the current endeavour was to employ the extract that was previously described in a sustainable way. Thus, an attempt is made to colour cotton material at different temperatures utilising alum as a colourant extract and a mordant. To extract colour, the coconut husks were soaked and then cooked for half an hour. The colourant extract and mordants were used to dye the fabric for an hour at a moderate temperature. Tests were conducted on the coloured cloth samples to determine their resistance to light, rubbing, and cleaning. Beneficial results were found. The study's findings so suggest that eco-friendly cotton fabric dyeing has a promising future.