ABSTRACTIntroductionThe Kyoto Classification of Gastritis enables endoscopic assessment of Helicobacter pylori (H. pylori) infection status and gastric cancer risk. Initially developed for image‐enhanced endoscopy, texture and color enhancement imaging (TXI) allows us to easily distinguish differences in mucosal structure and color. The aim of this study was to evaluate the use of TXI in the visibility of endoscopic findings of gastritis.MethodsThis was a retrospective analysis using prospectively collected endoscopic data from a prospective, single‐center study, in which 220 patients undergoing esophagogastroduodenoscopy were enrolled. Endoscopic images were obtained using both white light imaging (WLI) and TXI (TXI‐1 and TXI‐2). Ten endoscopists (five experts and five trainees) independently evaluated 52 matched image sets, scoring visibility on a 5‐point scale. Inter‐rater reliability was assessed using intraclass correlation coefficients. Objective color analysis using the CIE L*a*b* color space and ΔE* values was also performed for map‐like redness.ResultsTXI‐1 improved visibility for diffuse redness, spotty redness, map‐like redness, patchy redness, atrophic border, red streak, and the regular arrangement of collecting venules. Visibility of intestinal metaplasia was also enhanced by TXI‐1, although to a lesser extent than other findings. TXI‐1 demonstrated “moderate” to “substantial” inter‐rater reliability. Objective colorimetric analysis confirmed significantly greater ΔE* values with TXI‐1 versus WLI for map‐like redness.ConclusionTXI‐1 enhances the visibility of key endoscopic features of H. pylori–associated gastritis. TXI‐1 may serve as a useful tool for endoscopic assessment of H. pylori–associated gastritis.Trial RegistrationThe study protocol was registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN000045323).

Effects and mechanisms of colour protectants combined with ultrasound treatment on colour enhancement in hot-air-dried green peppers (Piper nigrum L.).

ABSTRACT Endoscopy has become a cornerstone in diagnosing gastrointestinal diseases, notably for gastric intestinal metaplasia (GIM)—a condition that significantly elevates the risk of gastric cancer. Variability in medical expertise and patient‐specific complexities render sole reliance on clinicians for GIM diagnosis a process that is both time‐intensive and challenging. There is an urgent clinical need for advanced deep learning methods to assist in clinical decision‐making, particularly in the identification and quantification of GIM areas. Linked Color Imaging (LCI) represents a pioneering advancement in endoscopic technology, offering distinctive color enhancement capabilities. However, the scarcity of GIM datasets acquired using LCI has hindered extensive exploration of tailored deep learning applications. This study directly addresses these dual challenges through two strategic approaches. Firstly, we meticulously assembled a comprehensive and unique dataset—designated as ZD‐LCI‐GIM—comprising 1020 GIM images from 249 patients and 864 non‐GIM images from 303 patients. Each image was acquired via LCI gastroscopy and meticulously annotated by seasoned gastroenterologists—measures that ensure high‐quality data for analysis. Secondly, we introduce VM‐UNetV2—a deep learning model specifically designed for medical image segmentation tasks. This model achieves impressive performance on the ZD‐LCI‐GIM dataset, with a mean Intersection over Union (IoU) of 64.13%, accuracy of 88.79%, F1 score (Dice Similarity Coefficient, DSC) of 78.14%, specificity of 90.96%, and sensitivity of 82.06% for GIM segmentation. Moreover, the model exhibits exceptional efficiency, processing 34.61 frames per second (FPS) when run on an NVIDIA RTX 4090 GPU. In addition, our algorithm demonstrates superior accuracy and competitive efficiency in comprehensive experiments on the public datasets Endoscene, CVC‐ClinicDB, Kvasir, CVC‐ColonDB, and ETIS‐LaribPolypDB. The code for this work is available at https://github.com/nobodyplayer1/VM‐UNetV2 .

ABSTRACTObjectivesColonoscopy is a reliable technique for the detection, diagnosis, and treatment of adenomas and early cancer. Image‐enhanced endoscopy (IEE) is important for detecting colorectal lesions. Texture and color‐enhancement imaging (TXI) has recently emerged as a novel modality for IEE. Thus, TXI operates in two modes: mode 1 (TXI1) enhances the structure, color, and brightness, whereas mode 2 (TXI2) does not. We have previously reported the detection of colorectal adenomas using TXI. We aimed to determine the detection yields of TXI1 and TXI2 using the data from our previous study.MethodsWe retrospectively analyzed the colonoscopy data from three institutions between August 2020 and January 2021. The patients were classified into two groups: TXI1 and TXI2. The mean number of adenomas detected per procedure (MAP), adenoma detection rate (ADR), and flat adenoma detection rate (FDR) were compared between groups.ResultsThe evaluations (95% confidence intervals) for the TXI1 versus TXI2 groups were as follows: MAP, 1.5 (1.3–1.7) versus 1.5 (1.3–1.7); ADR, 56.8% (47.3–65.9) versus 59.7% (50.3–68.6); and FDR, 68.6% (59.5–76.9) versus 63.9% (54.6–72.5), with no statistically significant differences between the groups.ConclusionThe detection rates of colorectal lesions were comparable between the TXI1 and TXI2 groups.

This article explores the optimization of color and lighting effects in animation design and proposes a neural network-based strategy. Traditional methods often suffer from inefficiency and subpar results in color matching and light-shadow processing. To address this, a deep learning model was developed, integrating color space conversion, color transfer, and detail enhancement techniques to optimize animation visuals. During the experiments, 50 color enhancement tests were conducted on the same base image to compare the proposed system with traditional approaches in terms of speed and quality. Results show that the new system significantly improves processing speed while delivering superior image clarity, richer colors, and more realistic lighting effects. Practical case comparisons further confirm the effectiveness of the strategy in real-world applications. In conclusion, this study introduces an innovative technical approach for animation design, enhancing both efficiency and visual quality in color and lighting optimization.

AI-powered programmable wetting-delamination μPAD for point-of-care food safety detection.

Hydrochromic photonic paper is a promising substitution for traditional paper due to its eco-friendly coloration technique and potential compatibility with inkjet printing. However, it is highly desired yet very challenging to fabricate photonic paper with robust mechanical stability in a scalable manner for commercial popularity. Inspired by nacre, a scalable photonic paper is fabricated with a highly aligned "brick-and-mortar" architecture via roll-to-roll-assisted blade-coating. This hierarchical architecture is constructed by high-content (≈50 wt.%), low-aspect-ratio (≈33.2) α-zirconium phosphate nanoplates bonded to poly(acrylamide-co-diacetone acrylamide) via interfacial hydrogen-bonds. The resultant photonic paper features a near-equal "brick-mortar" ratio, effectively balancing the conflict between high strength (≈24.2MPa), toughness (≈13.1MJm- 3), and structural color. Therefore, it demonstrates high-contrast multicolor output and spatiotemporal color evolution through hygroscopic salt ink self-sustaining layer spacing. Importantly, the robust mechanical stability supports over 100 cycles of high-resolution rewritable printing using commercial printers, along with proof-of-concept demonstrations of Braille printing and erasure. Moreover, the hierarchical nanoplates endow the photonic paper with excellent flame retardancy, meeting higher storage safety requirements. This work presents an enlightening biomimetic design to integrate scalable assembly, structural color, and mechanical enhancement in photonic paper via low-aspect ratio nanoplates, offering a promising platform for the sustainable paper industry.

Abstract Background Despite normal-appearing mucosa at white light endoscopy (WLE), up to 30% ulcerative colitis (UC) patients experience persistent histological activity, with increased risk of adverse outcomes (1). Virtual electronic chromoendoscopy (VCE) can enhance visualisation of subvisual alterations, potentially bridging this gap (2). The MONET study aims to develop a novel endoscopic scoring system based on multimodal VCE features to distinguish between quiescent and active colitis in UC. Methods We developed a scoring system incorporating three VCE modalities of the EVIS-X1 CV 1500 (Olympus, Japan) platform: Narrow-Band Imaging (NBI), Texture and Colour Enhancement Imaging (TXI) and Red Dichromatic Imaging (RDI). Eight international IBD-experienced endoscopists participated in a two-round modified Delphi process: an in-person consensus meeting followed by an anonymised online survey (Qualtrics) to select mucosal and vascular patterns representative of different UC activity grades. Consensus was defined as ≥ 75% participant agreement. Twenty-one high-quality videos encompassing varying grades of inflammation were prospectively recorded using WLE, NBI, TXI, and RDI according to a standardised protocol from the ongoing multicenter international MONET study (NCT06709209). Videos were independently scored according to selected features. Interobserver agreement was evaluated using Gwet’s AC1/AC2 coefficient. Results Overall, 12 mucosal and 12 vascular features were identified (Table 1, Figure 1). Among mucosal features, crypt architecture (AC1 0.76), microerosions (AC1 0.76) and erosions (AC1 0.80) demonstrated substantial agreement. For vascular features, crowded and intramucosal bleeding showed substantial agreement (AC1 0.78 and 0.73, respectively), while honeycombing showed moderate agreement (AC1 0.50). NBI-specific features, including honeycombing, branching vessels, and intramucosal bleeding, demonstrated substantial agreement (AC1 0.63, 0.73, and 0.65, respectively). Using TXI, crypt architecture (AC1 0.7), microerosions (AC1 0.72), and erosions (AC1 0.78) yielded substantial agreement. Deep vessels visibility, assessed through RDI, yielded substantial agreement (AC1 0.77). The agreement for the remaining features was almost perfect. The anonymised Delphi voting survey confirmed consensus for all 24 identified elements into the final MONET score. Conclusion The novel VCE-based MONET score demonstrated high interobserver agreement, supporting its robustness and clinical relevance. It holds potential to bridge the gap between endoscopic assessment and histologic activity, particularly in mildly active UC. Multicenter prospective validation is underway to support broader adoption and future integration into AI-assisted endoscopic evaluation.

Optimized spatial automatic color enhancement technique: A novel approach for color restoration in retinopathy of prematurity (ROP) retinal images

Abstract Background Accurate endoscopic assessment of mucosal inflammation is critical in managing ulcerative colitis (UC). Mayo endoscopic score (MES) and Ulcerative Colitis Endoscopic Index of Severity (UCEIS) have significant interobserver variability. The role of Texture and Colour Enhancement Imaging (TXI) in assessing inflammation is uncertain. We aimed to assess the reliability of TXI in UC. Methods A multicentre international survey-based study was designed with 20 sets of high-definition white light (HDWLE) images or videos matched with TXI. Each set had varying severities of inflammation as determined by Nancy and Picasso histopathology scores. The MES and UCEIS were rated along with confidence level (high vs low). To assess interobserver variability, we used percentage agreement and Kappa score with 95% confidence intervals stratified by imaging modality and scoring system. A mixed logistic regression model was constructed to assess accuracy. Results 30 gastroenterologists from 4 centres were included. 19 (63.3%) were experts and 10 (33.3%) had prior TXI experience. Interobserver variability was moderate overall with percentage agreement between 66-70% and Kappa scores between 0.51-0.58. There was no difference in interobserver variability between HDWLE and TXI. Agreement was slightly higher for UCEIS than MES (Table 1). 75% reported high confidence prediction with confidence consistent across histological severity. Participants rated severity accurately 75% of the time using MES with accuracy increasing with increase in histological severity. Results for UCEIS were similar with accuracy of 74%. There was no evidence of a difference in accuracy between the two imaging modalities irrespective of the index used for scoring. Conclusion Interobserver variability remains a challenge in accurately assessing inflammation in UC despite advances in technology and validated scoring systems. Our study suggests that TXI achieves moderate interobserver agreement with accuracy of 75% and is not superior to HDWLE. Conflict of interest: Dr. Subhaharan, Deloshaan: No conflict of interest Kakkadasam Ramaswamy, Pradeep: None Yang, Dong-Hoon: No conflict of interest Jones, Mark: No conflict of interest Pillai, Sooraj: No conflict of interest John, Sneha: No conflict of interest

The leather industry is undergoing a transformative shift towards sustainable practices due to increasing environmental concerns associated with synthetic dyes. This research explores the potential of natural dyes derived from tea extracts as an eco-friendly alternative for leather dyeing. In this study, the aqueous extracts of dried commercial tea leave (Camellia Sinensis) (10%, 12%, and 15% w/v) with and without mordant were used for dyeing of chrome tanned goat leathers and its color intensity parameters were examined using Milton Roy Colormate HDS spectrophotometer. In addition, the quality of dyed leather was evaluated by dye exhaustion test, fastness test, tensile strength test, scanning electronic microscopy (SEM) imaging, and thermogravimetric analysis. The optimized application of dye extract was 12% (without mordant) showed better color intensities (L* = 56.33 ± 0.57, DL* = -13.74 ± 0.48), highest dye exhaustion (84.68 ± 0.06%), tensile strength (18.75 ± 0.25), and residual mass in TGA, better color fastness (wet-4/5, dry-4/5, perspiration-3/4, and light fastness-4/5 in Grey Scale rating), and best fiber orientation in SEM image. In addition, another optimized was (with mordant) 15% dye extract with 1% Copper (II) Sulphate produced the highest color intensity (L* = 51.21± 1.06, DL* = -18.86±0.72). These findings offer the potential of tea extracts as a viable solution for natural leather dyeing, offering both environmental and aesthetic benefits. Bangladesh J. Sci. Ind. Res. 60(4), 251-262, 2025

The pursuit of whiter teeth has led to the innovation of various colour enhancement products, including toothpaste formulations containing blue covarine. This systematic review and meta-analysis assesses their efficacy and safety. To evaluate the efficacy of blue covarine-containing toothpaste in colour enhancement and its safety. We conducted a comprehensive search of databases such as PubMed, Scholar, Web of Science, and Cochrane Reviews, from inception until march 25, 2023. Eligible studies involved human participants using toothpaste with blue covarine and reported on its efficacy and safety in tooth colour perception altering. the JBI critical appraisal tools were used to assess the risk of bias. The meta-analysis synthesised data from several studies that measured tooth colour improvement and perception of tooth colour. Results showed significant improvement in tooth colour and perceived brightness in individuals using blue covarine toothpaste compared to placebo or other methods. Subgroup analyses revealed consistent results across varying study designs, sample sizes, and follow-up durations. However, limitations such as study heterogeneity and small sample sizes were noted. Toothpaste containing blue covarine is effective for tooth colour perception altering and can be recommended as a non-invasive option by dental professionals, taking into account individual baseline tooth colour and oral health. While promising, the long-term safety and effects of such toothpaste warrant further research. Dental professionals should personalise treatment recommendations and prioritise patient education to optimise outcomes. Future research should focus on long-term impacts, different formulations, and larger diverse samples to fully elucidate the safety and efficacy profiles.

Controlling the quality of prints is a vital process in the current printing industries so as to maintain the quality of the mass production in terms of consistency, accuracy, and aesthetic features. The typical machine-vision systems find it difficult to detect the invisible defects like streaks, blotches, misalignment and color differences because of shortcomings of hand-made extraction of features. The proposed research is an intelligent print quality control system based on the Convolutional Neural Network (CNN) models to detect and classify defects automatically. The method starts with the generation of a set of complete dataset which consists of high-resolution images of controlled light and camera conditions. All images are annotated and labelled based on certain types of defects in order to enable supervised learning. The preprocessing of the data, such as normalization, resizing, augmentation, color enhancement, etc. are used to enhance model robustness and generalization. Various CNNs, including VGG, ResNet and MobileNet, are studied using the method of transfer learning to maximize the accuracy and performance. The CNN model created to be customized comprises of other layers as well covering localization of defect and region based detection. The real-time image capture, CNN inference and decision-making modules are incorporated into the system architecture, and executed at the edge computing devices or on the GPU, to offer them fast processing. The classification accuracy and real-time processing results suggest that the experimental results are better than the traditional vision-based systems.

Choline is vital for liver function and productivity in laying hens, serving as a methyl donor, lipotropic agent, and precursor for key biomolecules like phosphatidylcholine and acetylcholine. Methionine also donates methyl groups and supports protein and feather development. This study investigated choline propionate, a novel organic choline source, versus conventional choline chloride in laying hen diets. A total of 576 Hy-Line Brown hens (50 wk old) were allocated to two trials (288 birds each) with four treatment groups (6 replicates of 12 hens). Trial 1 evaluated choline propionate (120 ppm, 160 ppm) and choline chloride (160 ppm) in a choline-deficient diet. Trial 2 assessed methionine-sparing effects with a 15% methionine and cysteine reduction. Experimental data was analyzed using analysis of variance (ANOVA) procedure of SAS, by the comparison of Least Square Means (LSM) using either Tukey’s parametric test for egg production performance, egg quality parameters, serum biochemical markers, lipids, and fatty acid profiles or Kruskal-Wallis non-parametric test for liver histopathological and liver lesion scoring. In Trial 1, both choline sources improved egg production and quality. Egg production rate with choline propionate supplementation achieved more than 88% compared to 32% in the control group. Overall, significant egg weight increase of more than 3% was recorded for the 120 and 160 ppm choline propionate groups compared to the control. Egg shell strength improved by more than 30% with the choline propionate supplementation compared to the control. Choline propionate at 160 ppm showed significant enhancement of yolk color by 2% (P < 0.0001) when compared to the control group. A notable 25% lower serum aminotransferase (AST) levels was observed in the 160 ppm choline propionate group, suggesting a liver protection function compared to the control group. In Trial 2, methionine reduction had no significant effect on performance, with egg production improved numerically in choline-treated groups, suggesting mild methionine-sparing activity. Hens supplemented with choline propionate showed no visible signs of liver steatosis or discoloration, suggestive of improved hepatic condition while controls showed signs of fatty liver traits. Choline propionate demonstrated functional benefits in laying hens under the current study conditions, supporting its relevance as an alternative, supplementary choline source.

The application of digital image processing to medicinal and aromatic leaves has become increasingly important in industries such as pharmaceuticals, cosmetics, and food, as leaf quality directly influences their usability and market value. This study investigates various image enhancement techniques applied to the DIMPSAR and MAP 177 Medicinal Leaf Datasets to improve visual quality, facilitating better feature extraction and analysis. Methods such as contrast enhancement, edge enhancement, sharpening, noise reduction, morphological operations, High Dynamic Range (HDR) enhancement, denoising and restoration, and color enhancement are systematically evaluated based on performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Root MSE (RMSE). The results indicate that Linear Contrast Stretching (LCS) and median filtering are the most effective techniques, offering significant contrast and noise reduction improvements while preserving essential structural leaf details. Additionally, techniques such as white balance and unsharp masking enhance image consistency and sharpness, whereas Histogram Equalization (HE) and tone mapping introduce distortions that degrade image quality, limiting their applicability. Lastly, edge detection and morphological operations, primarily used for structure extraction, were found to amplify noise and distortions rather than improve image clarity.

ABSTRACTBackground and AimThis study aimed to objectively evaluate colorectal lesion visibility using white‐light imaging (WLI), narrow‐band imaging (NBI), and texture and color enhancement imaging (TXI) with an eye tracking system.MethodsTwelve endoscopists assessed 120 images of 30 colorectal lesions (29 adenomas, one sessile serrated lesion) captured using WLI, NBI, TXI mode‐1, and TXI mode‐2. Eye movements were recorded to analyze the lesion detection ability and time taken. The miss rate (calculated as the ratio of missed lesions from 360 images) and detection time (defined as the time from displaying the image to the start of gaze on lesions) were compared among the four modalities.ResultsThe miss rates of WLI, NBI, TXI mode‐1, and TXI mode‐2 were 16.4%, 6.4%, 11.7%, and 16.7%, respectively. The miss rate of NBI was significantly lower than those of WLI and TXI mode‐2 (p < 0.001). The detection times (median [interquartile range]) of WLI, NBI, TXI mode‐1, and TXI mode‐2 were 0.71 (0.43–1.37) s, 0.54 (0.34–0.89) s, 0.63 (0.44–1.13) s, and 0.64 (0.37–1.28) s, respectively. NBI had the shortest detection time among the four modalities (p < 0.001 compared with WLI and TXI mode‐2; p = 0.005 compared with TXI mode‐1).ConclusionsObjective assessment using the eye tracking system revealed that NBI provided the best colorectal lesion visibility, with the lowest miss rate and shortest detection time.

Increased incidence of infections caused by carbapenem-resistant Acinetobacter baumannii (CRAB) in intensive care units has necessitated the development of rapid diagnostics. Resistance is primarily caused by OXA-type carbapenemases, of which OXA-23 is the most widespread. This study describes the development of recombinant monoclonal antibodies targeting OXA-23 and their application in a paper-based assay for rapid CRAB detection. Six scFv binders were identified from an immunized phage-displayed mouse antibody library against the OXA-23 protein, of which scFv 11 and 13 exhibited high sensitivity. These clones retained their binding activity in the presence of detergent-based cell lysis buffer and demonstrated specific detection of OXA-23 with no cross-reactivity to related carbapenemases. A paper-based visual detection assay was designed using mono-biotinylated OXA scFv 11 and streptavidin-conjugated gold nanoparticles as detector molecules, with a provision for silver-based color enhancement for signal amplification. The assay enabled the detection of OXA-23-producing A. baumannii at a limit of 1 × 10⁴ CFU/ml within 30min, from culture lysis to result interpretation. This detection limit aligns with the bacterial loads typically observed in ventilator-associated pneumonia, highlighting the suitability of the assay for clinically relevant detection. The assay showed 100% sensitivity and 100% specificity when evaluated against well-characterized clinical isolates. It offers operational ease, and clinically relevant sensitivity and specificity comparable to existing immunochromatographic platforms. The silver-based color enhancement step further improves visual identification, thereby allowing the unambiguous detection of low bacterial loads without the need for complex instrumentation. The assay is expected to facilitate early resistance detection in CRAB and improve clinical decision-making.

Different image-enhanced endoscopy (IEE) methods have shown promise in improving colorectal lesion detection, but direct comparisons remain limited. This network meta-analysis compared the detection and diagnostic performance of various IEE modalities for colorectal lesions. We searched PubMed, Embase, and Cochrane Library for randomized controlled trials (RCTs) up to May 31, 2025. Primary outcomes included adenoma (adenoma detection rate [ADR]) and advanced ADR; secondary outcomes included polyp (polyp detection rate), sessile serrated lesion detection rate, and mean number of colonic lesions detected per patient. Surface Under the Cumulative Ranking Curve (SUCRA) and pairwise risk ratios (RRs) with 95% confidence intervals (CI) were used. Lesion characterization was evaluated by the summary receiver operating characteristic area under the curve (AUC). In total, 54 RCTs with 28,663 participants were included. Regarding ADR, texture and color enhancement imaging (TXI) ranked highest (SUCRA: 88.4; RR: 1.48, 95% CI: 1.09-2.02), followed by linked color imaging (LCI) when compared with high-definition white light imaging (HD-WLI). For advanced adenoma detection, both TXI (RR: 1.42) and LCI (RR: 1.23) showed superiority over HD-WLI. New-generation narrow band imaging (NBI) ranked highest for the number of adenomas detected (SUCRA: 82.3%), and LCI led in polyps (SUCRA: 88.4%). For lesion characterization, new-generation NBI (AUC: 0.880) and blue light imaging (AUC: 0.812) effectively distinguished adenomas from non-neoplastic lesions. However, this finding is based on a limited number of RCTs and should be interpreted cautiously. Our findings indicate TXI and LCI may provide improved detection performance compared with conventional imaging. Blue light imaging and new-generation NBI seem to enhance lesion characterization. Further comparative studies are needed to guide optimal IEE modality selection for colorectal neoplasia.

Abstract Thin‐film interference is widely leveraged in classical optics as a minimalistic yet powerful lever for controlling optical fields, underpinning technologies from anti‑reflective coatings to photovoltaics. However, extending this concept to diamond membranes and other thin film solid‐state quantum emitter host materials has remained unexplored, partly due to the challenges in fabricating ultrathin membranes. Here, a wedge‐shaped diamond membrane is engineered to demonstrate thickness‐dependent interference phenomena. This can not only be used as a broadband reflector or wavelength‐specific absorber, but it can also significantly modulate both excitation and emission intensities of silicon vacancy (SiV) centers, leading to a photoluminescence enhancement up to 96‐fold. This route circumvents the need for high‑Q cavities or intricate nanolithography, and the sample gradient offers a visualization of broadband resonances and anti‑resonances across the visible to near‑infrared spectrum. These findings offer an alternative method for controlling effective brightness enhancement or suppression of diamond color centers‐based quantum photonics. Beyond quantum emission enhancement, diamond membranes could serve as a passive layer such as a broadband reflector or a wavelength‐specific absorber, and find applications in heterostructures, photovoltaics, display technologies, and even in semiconductor thermal management.

Object of the study is semi-smoked sausage from a plant-based meat substitute, enriched with blood of slaughtered animals in various proportions. The problem to be solved is that the blood of farm animals, being a valuable source of protein, is thrown away by the tons as waste, and is aggravated by the emerging serious problems of environmental pollution. But in many countries, most of the blood proteins are used as binders, natural color enhancers, emulsifiers, fat substitutes and agents for salting meat. The article presents the results of the study of organoleptic evaluation, mineral and physicochemical composition of semi-smoked sausage from plant-based meat substitute enriched with blood of slaughtered animals. Soybeans and chickpeas were selected as plant raw materials in a ratio of 70:30. According to the results of the organoleptic evaluation, it was found that the addition of blood of slaughtered animals into the recipe of investigational product in the amount of 2%, improves the taste and color of the finished product. In the course of the study it was proved that when 2% dry blood of slaughtered animals was added to a semi-smoked sausage from plant-based meat substitute, the amount of protein increased by 2.26% compared with the control sample. Also, the increased fat content in the investigational samples of semi-smoked sausage showed that the blood of slaughtered animals can be used as a fat substitute. Based on studies of the mineral composition of semi-smoked sausage from plant-based meat substitutes, it has been proven that adding 2–6% blood of slaughtered animal increases the content of mineral substances compared to the control sample. Studies have shown that adding 2%, 4%, 6% blood of slaughtered animals to a semi-smoked sausage from plant-based meat substitute increases the content of essential and non-essential amino acids compared to the control sample. It was found that in experimental samples with the addition of 2%, 4%, 6% blood of slaughtered animals, the content of saturated and polyunsaturated fatty acids increases compared with the control sample. Thereby enriching and increasing the biological and nutritional value of the finished product