Extraction Method Research Articles

WuMKG: a Chinese painting and calligraphy multimodal knowledge graph

Chinese Painting and Calligraphy (ChP&C) holds significant cultural value, representing integral aspects of both Chinese culture and global art. A considerable amount of ChP&C works are dispersed worldwide. With the emergence of digital humanities, a vast collection of cultural artifacts data is now available online. However, the online databases of these artifacts remain decentralized and diverse, posing significant challenges to their effective organization and utilization. Addressing this, our paper focuses on the Wu Men School of Painting and proposes a framework for constructing a multimodal knowledge graph for the ChP&C domain. We construct the domain ontology by analyzing the ChP&C knowledge schema. Then, we acquire knowledge from diverse data sources, including textual and visual information. To enhance data collection around collecting historical context and subject matter, we propose methods for seal extraction and subject extraction specific to ChP&C. We validate the effectiveness of these methods on the constructed dataset. Finally, we construct the Wu Men Multimodal Knowledge Graph (WuMKG) and implement applications such as cross-modal retrieval, knowledge-based question-answering and visualization.

Physical and Morphological Characterisation of Typha Australis Fibres

Typha australis is an invasive plant from Senegal also found in wetlands. In order to limit its proliferation, typha australis is used in the energy sector but also in construction. This study contributes to the valorisation of typha australis in the field of biocomposites. For this purpose, typha fibres were used. The fibres were extracted manually using a plastic comb, then subdivided into three zones, namely the bottom, middle and top, and dried. The resulting fibres were characterised physically by measuring density, water content and absorption coefficient, and morphologically by determining the morphology but also of the section of the fibres. many respects, the physical property results concerning density show an average of the three areas of 1.53 g/cm3 with a water content between 6 and 10% and an absorption coefficient showing two main absorption phases. As for the tomography results, we directly notice the presence of fibre bundles and not of individual fibres due to the manual extraction method used. Nevertheless, the results obtained suggest a behaviour comparable to that of the most commonly used natural fibres, namely flax.

Extraction of phenolic compounds with antioxidant activity from olive pomace using natural deep eutectic solvents: modelling and optimization by response surface methodology

This work explored the use of natural deep eutectic solvents (NADES) for extracting phenolic compounds with antioxidant activity from Uruguayan olive pomace (OP), a by-product of olive oil mills. Among nine NADES tested, lactic acid-glucose (La-Gc) was the most effective solvent for phenolic compound extraction. Further investigation focused on optimizing the extraction process using La-Gc. Response surface methodology was employed to analyze the impact of extraction temperature, water content in NADES and solid–liquid ratio on total phenols content (TPC) and antioxidant activity (FRAP and DPPH assays). Temperature and solid–liquid ratio greatly influenced TPC and antioxidant activity, while water content in NADES only showed significant influence on antioxidant activity. The optimum extraction conditions that maximized TPC and antioxidant activity were 80 ∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}^{\\circ }\ ext {C}$$\\end{document}, 68% (w/w) of water in NADES and solid–liquid ratio of 0.014 g/mL, showing a TPC of 15.56 mg GAE/g db, FRAP of 178.14 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu$$\\end{document}mol FSE/g db and DPPH of 72.75 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu$$\\end{document}mol TRE/g db, with hydroxytyrosol content of 1.24 mg/g db. These values were significantly higher than those of extracts obtained from conventional solvents under identical extraction conditions. These results highlight the suitability of the present extraction method using La-Gc as solvent to obtain phenolic compounds with antioxidant activity from OP.

Differential Extraction and Preliminary Identification of Polyphenols from Ugni candollei (White Murta) Berries

Ugni candollei, commonly known as white murta, is a native Chilean berry with a polyphenol composition that has been underexplored. This study aimed to establish a comprehensive profile of white murta polyphenols using ultra-performance liquid chromatography electrospray ionization Orbitrap mass spectrometry (UPLC-ESI-ORBITRAP MS). Additionally, it compared the efficacy of conventional extraction methods with emerging techniques such as deep eutectic solvent (DES) extraction and hot pressurized water extraction (HPWE). The analysis tentatively identified 107 phenolic compounds (84 of them reported for the first time for this cultivar), including 25 phenolic acids, 37 anthocyanins, and 45 flavonoids. Among the prominent and previously unreported polyphenols are ellagic acid acetyl-xyloside, 3-p-coumaroylquinic acid, cyanidin 3-O-(6′-caffeoyl-glucoside, and phloretin 2′-O-xylosyl-glucoside. The study found HPWE to be a promising alternative to traditional extraction of hydroxybenzoic acids, while DES extraction was less effective across all categories. The findings reveal that white murta possesses diverse phenolic compounds, potentially linked to various biological activities.

Large language model based framework for automated extraction of genetic interactions from unstructured data.

Extracting biological interactions from published literature helps us understand complex biological systems, accelerate research, and support decision-making in drug or treatment development. Despite efforts to automate the extraction of biological relations using text mining tools and machine learning pipelines, manual curation continues to serve as the gold standard. However, the rapidly increasing volume of literature pertaining to biological relations poses challenges in its manual curation and refinement. These challenges are further compounded because only a small fraction of the published literature is relevant to biological relation extraction, and the embedded sentences of relevant sections have complex structures, which can lead to incorrect inference of relationships. To overcome these challenges, we propose GIX, an automated and robust Gene Interaction Extraction framework, based on pre-trained Large Language models fine-tuned through extensive evaluations on various gene/protein interaction corpora including LLL and RegulonDB. GIX identifies relevant publications with minimal keywords, optimises sentence selection to reduce computational overhead, simplifies sentence structure while preserving meaning, and provides a confidence factor indicating the reliability of extracted relations. GIX's Stage-2 relation extraction method performed well on benchmark protein/gene interaction datasets, assessed using 10-fold cross-validation, surpassing state-of-the-art approaches. We demonstrated that the proposed method, although fully automated, performs as well as manual relation extraction, with enhanced robustness. We also observed GIX's capability to augment existing datasets with new sentences, incorporating newly discovered biological terms and processes. Further, we demonstrated GIX's real-world applicability in inferring E. coli gene circuits.

Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

Accurate state-of-health (SOH) estimation is critical for reliable and safe operation of lithium-ion batteries. However, reliable and stable battery SOH estimation remains challenging due to diverse battery types and operating conditions. In this paper, we propose a physics-informed neural network (PINN) for accurate and stable estimation of battery SOH. Specifically, we model the attributes that affect the battery degradation from the perspective of empirical degradation and state space equations, and utilize neural networks to capture battery degradation dynamics. A general feature extraction method is designed to extract statistical features from a short period of data before the battery is fully charged, enabling our method applicable to different battery types and charge/discharge protocols. Additionally, we generate a comprehensive dataset consisting of 55 lithium-nickel-cobalt-manganese-oxide (NCM) batteries. Combined with three other datasets from different manufacturers, we use a total of 387 batteries with 310,705 samples to validate our method. The mean absolute percentage error (MAPE) is 0.87%. Our proposed PINN has demonstrated remarkable performance in regular experiments, small sample experiments, and transfer experiments when compared to alternative neural networks. This study highlights the promise of physics-informed machine learning for battery degradation modeling and SOH estimation.

Green Extraction of Polyphenols from Elaeagnus angustifolia L. Using Natural Deep Eutectic Solvents and Evaluation of Bioactivity

In the study, natural deep eutectic solvents (NADESs) were used as alternatives to traditional chemical solvents for the extraction of polyphenols from Elaeagnus angustifolia L. Nine NADESs were tested for the first time and compared with ethanol and water (traditional solvents) regarding the extraction of phenolic compounds from E. angustifolia L. These solvents were particularly effective at extracting polyphenols, whose low water solubility usually requires high amounts of organic solvents. The solvent based on choline chloride and malonic acid provided optimal results and was selected for further optimization. The effects of material-to-liquid ratio, ultrasound time, and ultrasound temperature on the extraction efficiency were studied through single-factor experiments. These parameters were optimized by Box–Behnken design using response surface methodology. The optimal conditions identified were 49.86 g/mL of material-to-liquid ratio, 31.10 min of ultrasound time, and 62.35 °C of ultrasound temperature, resulting in a high yield of 140.30 ± 0.19 mg/g. The results indicated that the NADES extraction technique provided a higher yield than the conventional extraction process. The antioxidant activity of the extract of polyphenols from E. angustifolia L. was determined, and UPLC–IMS–QTOF–MS was used to analyze the phenolic compounds in it. The results revealed that the scavenging ability of 1,1-diphenyl-2-picryl-hydrazil and 2,2′-azinobis-(3-ethylbenzthiazoline-6-sulphonate) extracted by NADES was higher than that of polyphenols extracted by water and ethanol. Furthermore, a total of 24 phenolic compounds were identified in the extract. To the best of our knowledge, this is the first study in which a green and efficient NADES extraction method has been used to extract bioactive polyphenols from E. angustifolia L., which could provide potential value in pharmaceuticals, cosmetics, and food additives.

Synergistic corrosion inhibition of rubber seed extract with KI on cold rolled steel in sulfuric acid solution

BackgroundPlant-based inhibitor has emerged an important topic owing to the combing merits of eco-friendly, low cost, rich natural resources as well as easy preparation. Rubber seed is the agricultural waste, and is mainly composed of oil and fatty acids those contain many polar oxygen atoms, and so it can be extracted to prepare the potential corrosion inhibitor. MethodsRubber seed extract (RSE) is prepared using reflux extraction method with the extracting solvent of ethanol aqueous solution. The synergistic inhibition effect of RSE and potassium iodide (KI) on the corrosion of cold rolled steel (CRS) in 0.50 M H2SO4 solution is firstly studied by weight loss, potentiodynamic polarization (PDP) curves and electrochemical impedance spectroscopy (EIS) methods. CRS surface was thoroughly characterized using metallographic microscope, scanning electron microscope (SEM), atomic force microscope (AFM), X-ray photoelectron spectroscopy (XPS) and contact angle measurements. FTIR, UV–vis and liquid chromatography mass spectrometry (LC-MS) compositional analysis of RSE were used to analyze the efficient components in RSE. Significant findingsThe results indicate that individual RSE or KI cannot function as a successful inhibitor to prevent CRS corrosion, but the mixture of RSE/KI performs better at inhibiting, with a maximal inhibition efficacy of up to 96.4% of 200 mg L−1 RSE/100 mg L−1 KI. The synergistic coefficient of the mixture is larger than 1, indicating that a true synergism. RSE/KI can drastically retard both cathodic and anodic processes, and interfacial capacitance falls as the charge transfer resistance of CRS/solution rises. It exhibits remarkable inhibition properties when the capacitor arc grows. There is only a small corrosion response in the inhibited media, as evidenced by the variations in electrical conductivity and ultraviolet spectrum before and after immersion. The hydrophilicity and surface roughness of CRS surface is dropped as a result of the introduction of RSE/KI. A series of metallurgical microscopy, AFM, SEM or contact angle tests indicate a synergistic inhibition effect is more pronounced for RSE/KI. XPS results clearly demonstrate that elements including Fe, S, N, and I are bonded to the surface of CRS. The primary ingredients of oleic acid, polyphenols, phenylpropanoids can also produce a strong synergism with KI.

Ultrasound and pulsed electric field treatment effect on the thermal properties, oxidative stability and fatty acid profile of oils extracted from berry seeds

AbstractThe seeds of berry fruits which are considered as a by-product may be valorized by recovering the oil they content. Nevertheless, the conventional extraction methods are time, energy and organic solvents consuming. In order to make extraction more environmental-friendly, alternative methods, like ultrasound-assisted and pulsed electric field-assisted processes, are being developed. The procedure of extraction may however influence the quality of obtained oil. The following study aims to define the effects of ultrasound and pulsed electric field application in the extraction process on the thermal properties, i.e., oxidative stability, melting and cyclic heating/cooling profiles, thermal decomposition characteristics. Additionally, fatty acid profile assessment was included in the study. Seeds of blackberries, blackcurrants, chokeberries, raspberries and redcurrants were used to extract oil. Based on the results, it can be summarized that extraction method influenced the resistance of oils to oxidation. Ultrasound-assisted process resulted in oils with the highest oxidation induction times. Melting profile was slightly influenced by extraction method, with peak temperatures indicating the presence of low-melting and middle-melting triacylglycerol fractions. Differential scanning calorimetry with heat/cool/heat procedure let determine crystallization peak temperatures around − 60 °C, which could be associated with the specific triacylglycerol profile of berry seed oils. The courses of thermogravimetric analysis curves were comparable for all the tested samples. The fatty acid profile study revealed that all the studied thermal properties were affected by the unique fatty acid percentage share, with a great predominance of polyunsaturated fatty acids. Obtained results allowed to conclude that extraction method influenced oxidative stability, thermal properties and fatty acid profile only to some extent. The most promising extraction method among analyzed seems to be ultrasound-assisted extraction as it provided oil with high oxidative stability, typical thermal properties and unchanged fatty acid profile, without being harmful to natural environment due to possible reduction in solvent and time consumption.

Oral Administration Properties Evaluation of Three Milk-derived Extracellular Vesicles Based on Ultracentrifugation Extraction Methods.

Milk-derived extracellular vesicles (M-EVs) are low-cost, can be prepared in large quantities, and can cross the gastrointestinal barrier for oral administration. However, the composition of milk is complex, and M-EVs obtained by different extraction methods may affect their oral delivery. Based on this, we propose a new method for extracting M-EVs based on cryogenic freezing treatment (Cryo-M-EVs) and compare this method with the previously reported acetic acid treatment (Acid-M-EVs) method and the conventional ultracentrifugation method (Ulltr-M-EVs). The new method simplifies the pretreatment step and achieves 25-fold and 2-fold higher yields than Acid-M-EVs and Ulltr-M-EVs. And it was interesting to note that Cryo-M-EVs and Acid-M-EVs had higher cellular uptake efficiency, and Cryo-M-EVs presented the best transepithelial transport effect. After oral administration of the three M-EVs extracted by three methods in mice, Cryo-M-EVs effectively successfully crossed the gastrointestinal barrier and achieved hepatic accumulation, whereas Acid-M-EVs and Ultr-M-EVs mostly resided in the intestine. The M-EVs obtained by the three extraction methods showed a favorable safety profile at the cellular as well as animal level. Therefore, when M-EVs obtained by different extraction methods are used for oral drug delivery, we can utilize their accumulation properties at different sites to better deal with different diseases. This article is protected by copyright. All rights reserved.

SYNTHETIC DATA GENERATION FOR ANN MODELING OF THE HYDRODYNAMIC PROCESSES OF IN-SITU LEACHING

The work presents an approach to enhance the forecasting capabilities of In-Situ Leaching processes during both the production stage and early prognosis. ISL, a crucial method for resource extraction, demands rapid on-site forecasting to guide the deployment of new technological blocks. Traditional modeling techniques, though effective, are hindered by their computational demands and network throughput requirements, particularly when dealing with substantial datasets or remote computing needs. The integration of AI technologies, specifically neural networks, offers a promising opportunity for expedited calculations by leveraging the power of forward propagation through pretrained neural models. However, a critical challenge lies in transforming conventional numerical datasets into a format suitable for neural modeling. Furthermore, the scarcity of training data during the production phase, where vital parameters are concealed underground, poses an additional challenge in training AI models for In-Situ Leaching processes. This research addresses these challenges by proposing a methodology for generating training data tailored to the most resource-intensive Computational Fluid Dynamics problems encountered during modeling. Traditional numerical modeling techniques are harnessed to construct training datasets comprising input and corresponding expected output data, with a particular focus on varying well network patterns. Subsequent efforts are directed at the conversion of the acquired data into a format compatible with neural networks. The data is normalized to align with the data ranges stipulated by the activation functions employed within the neural network architecture. This preprocessing step ensures that the neural model can effectively learn from the generated data, facilitating accurate forecasting of In-Situ Leaching processes. An advantage of proposed technique lies in provision of large, reliable datasets to train neural network to predict hydrodynamic properties based on technological regimes currently active or expected on ISL site. A major implication of this approach lies in applicability of pre-trained AI technologies to forecast future or determine current hydrodynamic regime in the stratum circumventing cost deterministic simulations currently deployed at mining sites. Hence, innovative approach outlined in this paper holds promise for optimizing forecasting, allowing for quicker and more efficient decision-making in resource extraction operations while getting around the computational barriers associated with traditional methods.

Urinary miRNAs: Technical Updates.

Due to its non-invasive nature and easy accessibility, urine serves as a convenient bi-ological fluid for research purposes. Furthermore, urine samples are uncomplicated to preserve and relatively inexpensive. MicroRNA (miRNAs), small molecules that regulate gene expression post-transcriptionally, play vital roles in numerous cellular processes, including apoptosis, cell differentiation, development, and proliferation. Their dysregulated expression in urine has been proposed as a potential bi-omarker for various human diseases, including bladder cancer. To draw reliable conclusions about the roles of urinary miRNAs in human diseases, it is essential to have dependable and reproducible methods for miRNA extraction and profiling. In this review, we address the technical challenges associated with studying urinary miRNAs and provide an update on the current technologies used for urinary miRNA isolation, quality control assessment, and miRNA profiling, highlighting both their advantages and limitations.

Do Pores Exist?—Foundational Issues in Pore Structural Characterisation

This work reviews a range of fundamental theoretical considerations in pore structural characterisation. The pore concept is essential for providing a better understanding of physical processes arising within porous media than purely phenomenological approaches. The notion of a pore structure is found to be independently valid and invariant during theory change concerning said physical processes, even for structural models obtained via indirect methods. While imaging methods provide a more direct characterisation of porous solids, there is often a surfeit of information beyond that which can be wielded with current computing power to predict processes sufficiently accurately. Unfortunately, the pore network model extraction methods cannot decide in advance the level of simplification necessary to obtain the optimum minimal idealisation for a given physical process. Pore network models can be obtained with differing geometrical and topological properties, but similar mass transfer rates, for reasons that are often not clear. In contrast, the ‘pore-sifting’ strategy aims to explicitly identify the key feature of the void space that controls a mass transport process of interest.

Characteristics of lipid metabolism after treatment of colon cancer mice with American ginseng vesicles.

Lipid molecules are present in tumours and play an important role in the anti-inflammatory response as well as in antiviral protection. Changes in the type and location of lipids in the intestine following exposure to environmental stressors play an important role in several disorders, including ulcerative colitis (UC), inflammatory bowel disease (IBD), and colorectal cancer. The aim of this work is to provide a new theoretical basis for tumour initiation and development by accurately measuring the spatial distribution of lipids and metabolites in intestinal tissue. Spatial metabolomics allows the detection of samples with minimal sample volume by label-free imaging of complex samples in their original state. The distribution of lipid molecules in tumours has not been reported, although the distribution of lipid molecules in intestinal tissue has been reported in the literature. The range of lipid profiles in colon cancer mouse tumour tissue was compiled using a spatial metabolomics: lipid extraction method. The changes in lipid distribution in two regions after oral administration of American Ginseng (Panax quinquefolius L.) vesicles were also compared. Tumour tissue samples were extracted with 80% methanol-20% formic acid in water. The resulting spatial metabolic profile allowed the identification of seven lipid classes in mouse tumours. The distribution of fibre tissue cells was 23.2% higher than tumour tissue cells, with the exception of the fatty acid (FA) species.

Fault feature extraction method based on maximum envelope spectrum PFGI2 and empirical wavelet transform

Fast kurtogram (FK) is an efficient method for processing non-stationary signals, widely recognized by scholars as a rapid and effective approach for fault diagnosis. However, it has limitations in distinguishing between periodic pulse and random interference pulses due to the drawbacks in its frequency band segmentation methods and the inherent shortcomings of the kurtosis index itself. To address this, this paper proposes a fault feature extraction method based on the maximum envelope spectrum power function-based Gini indices (PFGI2) and empirical wavelet transform. This method, inspired by the concept of FK, constructs a series of band-pass filters following the principles of empirical wavelet decomposition. It applies envelope spectrum analysis to a series of sub-bands and calculates the PFGI2 value for each, to identify the optimal sub-band. The effectiveness of the proposed method is validated through simulations of vibration signals and experimental data.

A Feature Extraction Method for Prognostic Health Assessment of Gas Compressor Valves

Abstract This paper presents features derived from the pressure-volume (PV) diagram that is useful in estimating different valve faults in reciprocating compressors with a strong potential of remaining useful life prediction. The PV diagram is expected to deviate depending on valve wear conditions. The valve degradation types explored in this work are leakage, seat wear, and spring fatigue in the suction and discharge assemblies of a Dresser-Rand ESH-1 compressor commonly used in the petrochemical industry. The proposed method estimates the polytropic exponent on the compression and expansion phase as well as the discharge and suction valve loss power and uses them as features for a quadratic discriminant analysis. The features are created through in-cylinder pressure, suction pressure, discharge pressure, and crank angle measurements collected on a single-stage, dual-acting compressor operating on air with wear precisely machined and seeded into the poppets of the inlet and outlet valves. A very high classification accuracy is achieved in distinguishing the wear types, severity, and location with strong prognostic trends.

Bioaccumulation and translocation of radionuclides heavy metals in Cynodon dactylon: A phytoremediation approach in Al-Dora refinery

Oil extraction methods such as fracking and drilling can disperse radioactive elements into the environment, posing potential hazards. Certain plants like Bermuda grass can combat this through rhizodegradation, a process that breaks down pollutants into harmless substances. The efficiency of this process varies by location and is influenced by factors such as soil compatibility and the presence of beneficial microbes. A study showed that Bermuda grass roots absorbed high levels of radioactive elements, especially Uranium in the soil. The distribution and concentration of these isotopes in Bermuda grass varied across different sites. For instance, Cesium-137 showed higher activity in leaves than in soil. These findings highlight the varied accumulation patterns of elements in Bermuda grass and the influence of natural radionuclide distribution on the environment. Approaches like soil amendments, microbial assistance, and genetically modified Bermuda grass can enhance rhizoremediation efficiency. Strategic planning and species selection are key to overcoming rhizoremediation challenges and enhancing its effectiveness. In addition, soil samples from the AL-Dora Refinery showed a high degree of contamination by heavy metals, with the emission from burning fuel and crude oil being the main sources. This indicates the high contamination of soil by these heavy metals. Overall, the text discusses the potential of Bermuda grass in mitigating the environmental impacts of oil extraction through rhizoremediation, and the challenges and strategies in enhancing its effectiveness. It also highlights the issue of soil contamination by heavy metals due to oil refinery emissions. This research examines the levels of heavy metal contamination in the soil of the Al-Dora refinery area, situated in central Iraq. The contamination factor (Cf) was employed to ascertain the contamination status, revealing a significant to extremely high degree of contamination by various heavy metals. The study further investigates the bioaccumulation and translocation of these heavy metals from the soil to the roots and leaves of Cynodon dactylon plants. Two methodologies were utilized to assess the role of Cynodon dactylon in soil phytoremediation: a comparison of heavy metal concentrations in plant parts with global standards, and the application of the Bioconcentration factor (BCF). The findings suggest a high potential for Cynodon dactylon to be employed in the clean-up and phytoremediation process of the contaminated soil. This presents a promising strategy towards environmental remediation in areas contaminated with heavy metals.

POSSIBILITIES OF OBTAINING AND VALORIZING DIETARY FIBERS IN THE CONTEXT OF THE CIRCULAR BIOECONOMY

This article aimed to review the recent literature on the characterization of dietary fibers and their role in the human body, focusing on the methods of extraction of dietary fibers from agro-food waste as well as their use in various areas of the food industry. Dietary fibers are biologically active substances with beneficial effects on human health. Soluble dietary fiber is involved in reducing cholesterol levels and blood sugar levels, while insoluble dietary fiber helps regulate intestinal transit and maintain colon health. It would be useful to identify the extraction procedures and characterization of dietary fibers from agro-food waste. Fruit waste contains relevant amounts of bioactive compounds, such as: phenolic acids, flavonoids, lignins, carotenoids, etc. In addition, fruit waste contains significant amounts of dietary fiber with nutraceutical important activities, such as modulating the intestinal microbiota, lowering cholesterol, triglycerides and glycemic load in the blood. Hence, obtaining dietary fiber from agro-industrial waste can solve a number of economic and environmental problems that aim reducing waste, increasing the sustainability and profitability of companies in the Republic of Moldova. Dietary fiber can be used in various types of food, such as products of bakery and confectionery, meat, dairy products, and pasta. They can be used as stabilizers with an impact on food texture or ingredients with low caloric value, partially replacing caloric constituents such as fats, starch or sugars. Another important property is the prebiotic effect of dietary fibers. This property is determined by the fact that dietary fibers are indigestible or poorly digestible and are fermented selectively by intestinal microbiota, conferring health benefits to the host.

Processing and Analysis of Sinapis Alba (Yellow Mustard) Seeds Oil Obtained by Cold Pressed Method

Yellow mustard oil is made from the seeds of the yellow mustard plant, scientifically known as Sinapis Alba. The seeds are cold-pressed to extract the oil, ensuring that the natural flavors and nutrients are preserved. This traditional method of extraction, known as cold pressed method, allows the oil to retain its natural properties and distinct flavor.

Eco-friendly dyeing of cotton with natural colorants using natural mordants obtained from aloe vera

Due to sustainable and green processing requirements from buyers, the researchers are working on natural dyes to dye textile materials. In this study, cotton fabric was successfully dyed with a natural colorant which was extracted from black tea, and aloe vera was used as a natural mordant to enhance the fixation of natural dyes. The aqueous extraction method was used to extract colorant from black tea powder in a water bath at the temperature of 85–95 °C for 60 min. Three mordanting techniques were applied for cotton dyeing by changing parameters such as dye concentration (2, 4, 8 % o.w.f) and mordant concentration (20, 40, and 60 g/L). Pretreated cotton fabric was dyed on a high-temperature (HT) dyeing machine at 80 °C for 40 min at pH 5.5. The dyed cotton samples were analyzed for color strength (K/S), colorfastness, and antibacterial assessment (qualitative) using standard test protocols. The results showed that the maximum color strength value was achieved with 8 % dye and 60 g/L mordant using pre mordanting method. The dyed fabrics revealed excellent colorfastness to washing, whereas the color fastness to light, perspiration, and crocking was satisfactory. The antibacterial property of the dyed fabric was also satisfactory which is confirmed by no colonial growth on or below the samples. In a nutshell, this study presented an ecofriendly approach using black tea leaves and aloe vera contributing to green and sustainable production.