Fine Texture Research Articles

Komunikasi singkat: Keberadaan alga hijau koin, di Pesisir Lhok Bubon, Samatiga, Aceh Barat

This study aims to report the diversity of the Halimeda genus on the West Aceh coast. Data was collected in the Lhok Bubon Coastal intertidal area, Samatiga, West Aceh Aceh Province. Macroalgae samples were collected using the cruising method along the beach with a depth of 1-2 m at low tide. The Halimeda genus found four species, namely Halimeda macroloba, Halimeda macrophysa, Halimeda incrassata, and Halimeda simulans. Halimeda macroloba was found to have a whitish-green talus. Halimeda macrophysa samples have irregular talus branches. Description Halimeda incrassata at the study site is light green, and the holdfast is attached to the sandy substrate to form large sediment clumps. Halimeda simulans were found to be linked to sand, which tends to have a finer texture. The results of this report provide information that Lhok Bubon Beach, West Aceh, has macroalgae potential that needs further study.

Carrageenan-induced conjugated oat protein isolate microgel particles as structure modulators in fat analogues and their digestion behaviors

HypothesisEngineering plant-based microgel particles (MPs) at a molecular scale is meaningful to prepare functional fat analogues. We hypothesize that oat protein isolate (OPI) and κ-carrageenan (CA) have synergy in MPs formation, using MPs with controllable structure, and further to fabricate fat analogues with adjustable characteristics is feasible. Their digestion fate will also be possibly modulated by interfacial coatings. ExperimentsOPI-based conjugated MPs with tunable rigidities by changing crosslinking densities were designed. The relationship between microgel structures, and emulsion gel properties was explored through spectroscopy, microstructure, rheology and tribology. The delivery to lycopene, as well as inhibiting digestion behaviors of fat analogues was evaluated in a simulated gastro-intestinal tract. FindingsThe rigidity of conjugated MPs could be tailored to optimize the performance of fat analogues. OPI-1 %CA MPs could stabilize emulsions up to 95 % oil fraction with fine texture. Tribological behaviors had a dependence on microgel elasticity and interfacial coatings, medium hard MP-stabilized emulsion was less disrupted without coalescence after oral processing. Digestion was delayed by denser and harder MPs by softening the interfacial particle layer or limiting lipase accessibility. Softer conjugated MPs possessed better flexibility and were broken down more easily leading to a higher rate of lipid digestion.

Progressive gap-filling in optical remote sensing imagery through a cascade of temporal and spatial reconstruction models

Filling gaps caused by thick cloud cover or sensor malfunctions has always posed a significant challenge in the preprocessing of optical remote sensing images. The concept of similar pixels, derived from spatial and temporal similarities in remote sensing scenes, has been widely embraced and extensively applied, leading to the development of various gap-filling models. However, in complex scenarios characterized by spatiotemporally heterogeneous surfaces and extensive missing areas, current models often produce noticeable noise-like artifacts, distorted spectral signatures, and unreliable spatial textures. To address this challenge, we propose a simple yet effective similar-pixel-based approach called progressive gap-filling through the cascading temporal and spatial framework (PGFCTS). Drawing inspiration from the negative correlation between spatial distances and the robustness of similar pixels, we employ a progressive gap-filling scheme to ensure that similar pixels are spatially close to the target pixel. This significantly enhances the effectiveness of similar pixels and improves the accuracy of the reconstruction model. Moreover, unlike traditional methods that integrate spatial and temporal information in parallel, our approach integrates these two sources in a cascading manner. Initially, a temporal model is used to obtain preliminary results with fine texture details, followed by a spatial model to enhance spectral fidelity. Through tests on two gap-filling missions and comparisons with seven classical methods, results demonstrate that PGFCTS effectively eliminates noise-like artifacts, faithfully restores spectral features, and accurately reproduces spatial details. Quantitative assessment reveals that PGFCTS consistently outperforms other methods, securing the best scores. Importantly, our method maintains its superiority over extended time intervals between the target and reference images. In summary, PGFCTS emerges as an effective solution for filling missing gaps and reproducing surface information, thereby enhancing the usability of optical images.

LEFB: A new low-light image contrast enhancement algorithm

Low-light images are challenging for both human observation and computer vision algorithms due to low visibility. To address this issue, various image enhancement techniques such as dehazing, histogram equalization, and neural network-based methods have been proposed. However, most existing methods often suffer from the problems of insufficient contrast and over-enhancement while enhancing the brightness, which not only affects the visual quality of images but also adversely impacts their subsequent analysis and processing. To tackle these problems, this paper proposes a low-light image enhancement method called LEFB. Specifically, the low-light image is first transformed into the LAB color space, and the L channel controlling brightness is enhanced using a local contrast enhancement algorithm. Then, the enhanced image is further enhanced using an exposure fusion-based contrast enhancement algorithm, and finally, a bilateral filtering function is applied to reduce image edge blurriness. Experimental evaluations are conducted on real datasets with four comparison algorithms. The results demonstrate that the proposed method has superior performance in enhancing low-light images, effectively addressing problems of insufficient contrast and over-enhancement, while preserving fine details and texture information, resulting in more natural and realistic enhanced images.

P‐69: Cause Analysis of Water Ripple Issue on Charging‐ratio Sensitive LCD Panels Based on TCON Timing Control Theory

Water Ripple is the fine rolling textures phenomenon that occurs on large size, high resolution and high refresh rate LCD panels. It is a tough issue in the field of high‐end TV. The root cause is that such panels are too sensitive to pixel charging‐ratio. To ameliorate the problem, this paper analyzed the causes of charging‐ratio fluctuation from the perspectives of cross clockdomain conversions, display transport protocols and SSC influence, based on the TCON timing control theory. Then a method was proposed to cover the issue. The results show that at least one or two levels of improvement can be achieved. This study can provide an idea for the solution of water ripple, which is important to actual products.

Contrast enhancement algorithm for infrared images based on atmospheric scattering model

With the rapid development of infrared technology, infrared cameras are widely used in the military, medical, and industrial fields. However, the thermal radiation information received by infrared cameras often undergoes degradation due to random factors such as scattering or reflection during transmission. Consequently, the final infrared image suffers from poor contrast and blurred vision. To address these problems, this paper proposes an infrared image contrast enhancement algorithm based on the atmospheric scattering model. Firstly, we modify the hypothesis of the dark channel prior theory, and the pseudo dark channel prior theory is deduced to meet the processing requirements of single-channel images. Secondly, we propose anti-target interference and filtering threshold method to accurately estimate the atmospheric illumination value and transmittance of the atmospheric scattering model. These methods effectively mitigate the influence of infrared targets on the atmospheric illumination value and eliminate the blocking artifact effect caused by pixel value jumps in the transmittance images. Furthermore, we employ adaptive histogram equalization with contrast limitation, highlighting the global contrast. This paper conducts experiments on the M3FD dataset and employs objective evaluations using Visible Edge (E), Figure Definition (FD), Peak Signal-to-Noise Ratio (PSNR), Information Entropy (IE), Structure Similarity Index Measure (SSIM), and Visual Information Fidelity (VIF). The results indicate that the proposed algorithm enhances the overall contrast of images while highlighting fine texture details, exhibiting good visual effects. Both subjective and objective evaluations outperform seven other contrast algorithms, demonstrating the effectiveness of the proposed method.

Improving Understanding of Visual Snow by Quantifying its Appearance and Effect on Vision.

Visual snow is the hallmark of the neurological condition visual snow syndrome (VSS) but the characteristics of the visual snow percept remain poorly defined. This study aimed to quantify its appearance, interobserver variability, and effect on measured visual performance and self-reported visual quality. Twenty-three participants with VSS estimated their visual snow dot size, separation, luminance, and flicker rate by matching to a simulation. To assess whether visual snow masks vision, we compared pattern discrimination thresholds for textures that were similar in spatial scale to visual snow as well as more coarse than visual snow, in participants with VSS, and with and without external noise simulating visual snow in 23 controls. Mean and 95% confidence intervals for visual snow appearance were: size (6.0, 5.8-6.3 arcseconds), separation (2.0, 1.7-2.3 arcmin), luminance (72.4, 58.1-86.8 cd/m2), and flicker rate (25.8, 18.9-32.8 frames per image at 120 hertz [Hz]). Participants with finer dot spacing estimates also reported greater visibility of their visual snow (τb = -0.41, 95% confidence interval [CI] = -0.62 to -0.13, P = 0.01). In controls, adding simulated fine-scale visual snow to textures increased thresholds for fine but not coarse textures (F(1, 22) = 4.98, P = 0.036, ηp2 = 0.19). In VSS, thresholds for fine and coarse textures were similar (t(22) = 0.54, P = 0.60), suggesting that inherent visual snow does not act like external noise in controls. Our quantitative estimates of visual snow constrain its likely neural origins, may aid differential diagnosis, and inform future investigations of how it affects vision. Methods to quantify visual snow are needed for evaluation of potential treatments.

Analysis of the Output Power 10Wp Polycrystalline Photovoltaic Panels on the Effect of Sand Dust and Brick Dust

Polycrystalline Photovoltaic Panels are a device that plays a role in converting photon energy from sunlight into electrical energy. Installing photovoltaic panels outdoors can result in dust buildup on their surfaces, potentially affecting the panel's output power because the light that can be converted into electrical energy is limited due to being covered by dust. An experiment was carried out by sprinkling 3 grams of sand and brick dust measuring less than 425 µm on the panel. Based on measurements and analysis, panels without treatment show the highest voltage, current and output power values of 12.69 V, 0.28 A and 3.55 W respectively. Meanwhile for panels with sand dust, the voltage values are 12, 19 V, current 0.244 A, and output power 2.97 W. Panels with brick dust recorded the lowest voltage values of 11.84 V, current 0.211 A, and output power 2.49 W. There was a decrease in the values of voltage, current, and output power on panels without treatment compared to panels exposed to dust. Panels with sand dust produce higher output power compared to panels exposed to brick dust, due to the different characteristics of sand dust which is coarser with larger particles compared to brick dust which has a finer texture and smaller particles. As a result, the larger sand dust particles cause less panel surface area covered by dust compared to brick dust, so that more sunlight can be converted into electrical energy.

Investigating Microbial Biosignatures in Aeolian Environments Using Micro-X-Ray: Simulation of PIXL Instrument Analyses at Jezero Crater Onboard the Perseverance Mars 2020 Rover.

Assessing the past habitability of Mars and searching for evidence of ancient life at Jezero crater via the Perseverance rover are the key objectives of NASA's Mars 2020 mission. Onboard the rover, PIXL (Planetary Instrument for X-ray Lithochemistry) is one of the best suited instruments to search for microbial biosignatures due to its ability to characterize chemical composition of fine scale textures in geological targets using a nondestructive technique. PIXL is also the first micro-X-ray fluorescence (XRF) spectrometer onboard a Mars rover. Here, we present guidelines for identifying and investigating a microbial biosignature in an aeolian environment using PIXL-analogous micro-XRF (μXRF) analyses. We collected samples from a modern wet aeolian environment at Padre Island, Texas, that contain buried microbial mats, and we analyzed them using μXRF techniques analogous to how PIXL is being operated on Mars. We show via μXRF technique and microscope images the geochemical and textural variations from the surface to ∼40 cm depth. Microbial mats are associated with heavy-mineral lags and show specific textural and geochemical characteristics that make them a distinct biosignature for this environment. Upon burial, they acquire a diffuse texture due to the expansion and contraction of gas-filled voids, and they present a geochemical signature rich in iron and titanium, which is due to the trapping of heavy minerals. We show that these intrinsic characteristics can be detected via μXRF analyses, and that they are distinct from buried abiotic facies such as cross-stratification and adhesion ripple laminations. We also designed and conducted an interactive survey using the Padre Island μXRF data to explore how different users chose to investigate a biosignature-bearing dataset via PIXL-like sampling strategies. We show that investigating biosignatures via PIXL-like analyses is heavily influenced by technical constraints (e.g., the XRF measurement characteristics) and by the variety of approaches chosen by different scientists. Lessons learned for accurately identifying and characterizing this biosignature in the context of rover-mission constraints include defining relative priorities among measurements, favoring a multidisciplinary approach to the decision-making process of XRF measurements selection, and considering abiotic results to support or discard a biosignature interpretation. Our results provide guidelines for PIXL analyses of potential biosignature on Mars.

DF3Net: Dual frequency feature fusion network with hierarchical transformer for image inpainting

In recent years, researchers have made significant strides in computer vision by leveraging transformers, achieving remarkable breakthroughs in low-level vision tasks. The inherent long-range dependency of transformers grants them potent remote modeling capabilities, surpassing those of Convolutional Neural Networks (CNNs) and enabling the extraction of global features and accurate semantic structures. However, it has been observed that single transformer frameworks lack sensitivity to high-frequency information in images, resulting in the generation of blurry reconstructed regions. To address this limitation, this paper proposes a novel two-branch Dual Frequency Feature Fusion Network (DF3Net) for image inpainting based on the hierarchical atrous transformer (HAT). Specifically, the head of the dual-frequency convolution (DFC) module decouples the feature maps into low and high-frequency components. The low-frequency factor goes through the proposed HAT branch, while the high-frequency component is input into the gated convolution module branch, effectively capturing both global structural information and local texture details. Finally, the DFC tail fuses the high and low-frequency features to output the reconstructed image. Moreover, the feature fusion between high and low-frequency branches is performed layer-wise within the network, enabling mutual learning between the two branches interactively, ensuring coherence in image semantic structure and texture details. Experimental evaluations on Places2, Paris StreetView, and CelebA-HQ with different mask ratios demonstrate that the proposed method outperforms state-of-the-art methods in enhancing the structural accuracy of image inpainting. It generates semantically reasonable images with fine texture details.

Study on the mechanical properties and deformation mechanism of copper with different volume fractions of gradient structure

In this study, ultrasonic rolling process (USRP) were performed on the rolled copper samples (with an initial grain size of 11.4 μm) to obtain the gradient structure (GS), which comprises nanocrystalline layer (NL), deformed layer (DL), and center layer (CL). The different GS layer (NL and DL) volume fractions of samples were obtained by performing different passes of USRP after rolling, and the effect of the GS layer volume fractions on the mechanical properties was further studied. The morphology and structure of (unrolled, rolled and rolled + USRP) samples were characterized by electron backscattering diffraction (EBSD) and transmission electron microscope (TEM). In-situ tensile testing was used to investigate the tensile deformation behavior of the GS sample. The results show that the rolled + USRP (12 passes) sample with the thickest GS layer (volume fraction of 42%) has the greatest strength (yield strength (YS) 262 MPa and ultimate tensile strength (UTS) 325 MPa). The fine grains, dislocations and textures jointly strengthen the GS sample by increasing the force required for dislocation slip, and their contribution to YS is 51.3%, 44.4% and 4.3%, respectively. In addition, the nanocrystalline grains of GS layer make microcracks evenly distributed and hard to gather, to efficiently hinder cracks propagation and result in good ductility of GS samples. This study provides an effective method to obtain GS materials with good comprehensive properties and a deeper understanding of their deformation behavior and strengthening mechanism.

Tactile texture recognition of multi-modal bionic finger based on multi-modal CBAM-CNN interpretable method

To solve the problem of lacking surface information while using a single-modal sensor to scan fine textures, a multi-modal bionic finger tactile sensor is used to perceive the information contained in textures, and a multi-modal tactile texture recognition method is proposed to address the issue of poor interpretability for tactile texture recognition models. First, the experimental platform of multi-modal bionic finger tactile texture information acquisition system is built, which can simultaneously collect 14 channels of tactile electrode signals, one channel of pressure signals and one channel of temperature signals. Second, the multi-modal tactile signals can be fused at the data level after signal processing, and then the wavelet transform method is carried out on the basis of data fusion. Third, a multi-modal CBAM-CNN network (Convolutional Block Attention Module, Convolutional Neural Network) is established to extract the multi-modal tactile features after the wavelet transform and the multi-modal tactile features are fused at feature level. To verify the performance of multi-modal CBAM-CNN model, 27(3*9) kinds of millimeter-level fine textures of 3 datasets are tested and the recognition accuracy of each dataset is 98.47%, 97.63%, 99.16%. And it can also be seen that the 14-channel modal data contributes the most to the performance of multi-modal CBAM-CNN model for various textures. The results of ablation experiments prove that the identification accuracy of the fused multi-modal CBAM-CNN is higher than the single-modal CBAM-CNN no matter in time domain or time–frequency domain. Moreover, the accuracy of the weighted fusion method based on correlation function is higher than horizontal fusion method in 14 channels of tactile electrode signals modal. Finally, to interpret the classification decision of the multi-modal CBAM-CNN network in each modal intuitively, the improved multi-modal CAM (Class Activation Map) algorithm is adopted to visualize the interest regions of the samples in detail, and the activated areas in the map make the identification result of CBAM-CNN in each modal more transparent and interpretable.

Multi-scale feature aggregation and fusion network with self-supervised multi-level perceptual loss for textures preserving low-dose CT denoising

Objective. The textures and detailed structures in computed tomography (CT) images are highly desirable for clinical diagnosis. This study aims to expand the current body of work on textures and details preserving convolutional neural networks for low-dose CT (LDCT) image denoising task. Approach. This study proposed a novel multi-scale feature aggregation and fusion network (MFAF-net) for LDCT image denoising. Specifically, we proposed a multi-scale residual feature aggregation module to characterize multi-scale structural information in CT images, which captures regional-specific inter-scale variations using learned weights. We further proposed a cross-level feature fusion module to integrate cross-level features, which adaptively weights the contributions of features from encoder to decoder by using a spatial pyramid attention mechanism. Moreover, we proposed a self-supervised multi-level perceptual loss module to generate multi-level auxiliary perceptual supervision for recovery of salient textures and structures of tissues and lesions in CT images, which takes advantage of abundant semantic information at various levels. We introduced parameters for the perceptual loss to adaptively weight the contributions of auxiliary features of different levels and we also introduced an automatic parameter tuning strategy for these parameters. Main results. Extensive experimental studies were performed to validate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can achieve better performance on both fine textures preservation and noise suppression for CT image denoising task compared with other competitive convolutional neural network (CNN) based methods. Significance. The proposed MFAF-net takes advantage of multi-scale receptive fields, cross-level features integration and self-supervised multi-level perceptual loss, enabling more effective recovering of fine textures and detailed structures of tissues and lesions in CT images.

Casein hydrolysate in naturally-fermented buckwheat sourdough: Effects on fermented and physicochemical characteristics, texture, and bacterial microbial composition

The effect of a casein hydrolysate (CH) on the fermentation and quality of a naturally-fermented buckwheat sourdough (NFBS) were investigated, through assessing the fermentation characteristics, carbohydrate and protein degradation, texture, and bacterial composition of NFBS. According to the assaying data, CH might both increase the amount of lactic acid bacteria by 2.62 % and shorten the fermentation period by at least 3 h, subsequently leading to enhanced degradation of carbohydrate and protein, accompanied by a softer texture. More importantly, CH increased the relative abundance of lactobacillus in NFBS, making it the dominant bacterial genus and inhibited the growth of spoilage bacteria. In addition, Spearman correlation analysis indicated that the pH value, lactic and acetic acid contents, carbohydrates, protease activity, and these textural indices like hardness, elasticity, and adhesion had a positive/negative correlation with the bacterial composition of NFBS (Spearman correlation coefficient: −0.93-0.95). CH was thus regarded to be helpful to NFBS processing and production mainly by shortening its fermentation time, improving its fermentation performance, causing a finer texture and microstructure, and changing bacterial composition.

3D Face Reconstruction with Feature Enhancement using Bi-FPN for Forensic Analysis

The representation of facial features in three-dimensional space plays a pivotal role in various applications such as facial recognition, virtual reality, and digital entertainment. However, achieving high-fidelity reconstructions from two-dimensional facial images remains a challenging task, particularly in preserving fine texture details. This research addresses this problem by proposing a novel approach that leverages a combination of advanced techniques, including Resnet, Flame model, Bi-FPN, and a differential render architecture. The primary objective of this study is to enhance texture details in reconstructed 3D facial images. The integration of Bi-FPN (Bi-directional Feature Pyramid Network) enhances feature extraction and fusion across multiple scales, facilitating the preservation of texture details across different regions of the face. The objective is to accurately represent facial features from 2D images in three-dimensional space. By combining these methods, the proposed framework achieves significant improvements in preserving fine texture details and overall facial structure. Experimental results demonstrate the effectiveness of the approach, suggesting its potential for various applications such as virtual try-on and facial animation.

A hyperspectral surgical microscope with super-resolution reconstruction for intraoperative image guidance.

Hyperspectral imaging (HSI) is an emerging imaging modality in medical applications, especially for intraoperative image guidance. A surgical microscope improves surgeons' visualization with fine details during surgery. The combination of HSI and surgical microscope can provide a powerful tool for surgical guidance. However, to acquire high-resolution hyperspectral images, the long integration time and large image file size can be a burden for intraoperative applications. Super-resolution reconstruction allows acquisition of low-resolution hyperspectral images and generates high-resolution HSI. In this work, we developed a hyperspectral surgical microscope and employed our unsupervised super-resolution neural network, which generated high-resolution hyperspectral images with fine textures and spectral characteristics of tissues. The proposed method can reduce the acquisition time and save storage space taken up by hyperspectral images without compromising image quality, which will facilitate the adaptation of hyperspectral imaging technology in intraoperative image guidance.

An Investigation into Green Influence on Materials and Mechanism of Water Cremation: Perspective Vision

Abstract: Water cremation is an ecologically sustainable burial option that uses 10% of the carbon footprint and 85% less energy than flame-based cremation. Water cremation does not emit as many toxic compounds, bacteria, and gases into the atmosphere as flames cremation. It employs fewer toxic chemicals than embalming fluids required for an earthen burial. Flame-based cremation may emit up to 150 pounds of carbon dioxide into the atmosphere, which has a substantial impact on the environment. As more individuals become aware of the negative consequences of traditional funeral practices, the popularity of green funerals grows. As more states adopt this affordable and environmentally responsible alternative, water cremation will only grow in popularity. It's an especially fitting way to remember exceptional loved ones who dedicated their lives to leading sustainable lifestyles. In a green cremation, the remains of the deceased individual are preserved. These are converted to ash and can be scattered, commemorated, or kept in an urn after being placed in a cremulator. Approximately 32% more ashes are produced by water cremation than by fire cremation. While water cremation produces a consistently white powder with a finer texture, conventional cremation produces a coarse, grayish-white hue. This might be useful if your family decides to disperse your loved one's ashes for memorial mementos or ash-scattering rituals among other family members.

High-Quality Synthesized Face Sketch Using Generative Reference Prior

Face sketch synthesis (FSS) is considered as an image-to-image translation problem, where a face sketch is generated from an input face photo. FSS plays a vital role in video/image surveillance-based law enforcement. In this paper, motivated by the recent success of generative adversarial networks (GAN), we consider conditional GAN (cGAN) to approach the problem of face sketch synthesis. However, despite the powerful cGAN model’s ability to generate fine textures, low-quality inputs characterized by the facial sketches drawn by artists cannot offer realistic and faithful details and have unknown degradation due to the drawing process, while high-quality references are inaccessible or even unexistent. In this context, we propose an approach based on Generative Reference Prior (GRP) to improve the synthesized face sketch perception. Our proposed model, that we call cGAN-GRP, leverages diverse and rich priors encapsulated in a pre-trained face GAN for generating high-quality facial sketch synthesis. Extensive experiments on publicly available face databases using facial sketch recognition rate and image quality assessment metrics as criteria demonstrate the effectiveness of our proposed model compared to several state-of-the-art methods.

Digital 3D Reconstruction of Ancient Chinese Great Wild Goose Pagoda by TLS Point Cloud Hierarchical Registration

Digital reconstruction of ancient buildings is very challenging due to its architectural complexity and structural delicacy. Therefore, how to apply new Earth Observation (EO) technology to digitally reconstruct the complete and real model of ancient buildings without damage has become an urgent issue. This article proposes a multi-scan point cloud hierarchical registration method for 3D reconstruction of the ancient Chinese Great Wild Goose Pagoda based on Terrestrial Laser Scanner (TLS) data. First, a multi-scan point cloud hierarchical registration method is elaborately designed for 3D reconstruction. Then, the outline feature of the ancient Chinese Great Wild Goose Pagoda is automatically extracted by point cloud slicing. Finally, fine modeling is carried out according to the outline feature extracted from the reconstructed point cloud, and after that block combination and texture mapping are conducted for 3D digitalization and documentation. The experiments indicate that the Root Mean Square Error (RMSE) of multi-scan hierarchical registration of the translation and rotation are less than 3 cm and 0.862 ∘ , respectively, and the complete and real model of the Great Wild Goose Pagoda is obtained by fine modeling and texture mapping, which provides an important reference for 3D digital protection of ancient buildings.

Preparation of 1,3‐Diglyceride Microcapsules with Low Glycemic Index

AbstractA large number of animal and clinical experiments show that 1,3‐diglyceride (1,3‐DAG) can significantly ameliorate obesity by improving blood lipid and glucose metabolism. After microencapsulation, it cannot only be used to prepare full‐nutrient food that meets the nutritional requirements of specific patients, but also improve the storage stability of oils. In this study, 1,3‐DAG microcapsules are prepared by using high‐amylose corn starch, resistant dextrin, and sodium caseinate as composite wall materials, and 1,3‐DAG with a purity of 80% as core material.Firstly, the water phase and oil phase are mixed evenly, and then the uniform emulsion is prepared by two steps of high‐speed shearing and high‐pressure homogenization. The prepared emulsion is desiccated by a spray dryer to prepare microcapsules. Taking the encapsulation rate as the judging criteria, five gradients of core and wall material are designed to obtain the optimal formula.The results show that when the amount of core material is 28% and the wall material is 70%, the encapsulation rate of 99.51% is the highest. The prepared 1,3‐DAG microcapsules have water content of 1.9% and solubility of 98.05%, the average particle size is 2.921 µm. The particles are uniform and stable, with fine color and texture, and a high oil content.