Extraction Techniques Research Articles

Advances in Grape Seed Oil Extraction Techniques and Their Applications in Food Products: A Comprehensive Review and Bibliometric Analysis

Global wine production has grown, resulting in an increase in waste within the industry. This has raised concerns among producers and scientists worldwide, prompting them to seek solutions for its management. The aim is to explore the latest advancements in using grape seed oil as a byproduct and its applications within the food industry. To achieve this, a bibliometric analysis was conducted using the Scopus database covering the period from 1990 to 2023. Additionally, a comprehensive literature review was conducted on extraction techniques, compositions, properties, and innovative applications in food. A bibliometric analysis revealed that interest in grape seed oil has grown over the past fifteen years. The majority of research on this grape byproduct is concentrated in Asian countries. Grape seed oil is a rich source of lipophilic compounds, including fatty acids, phytosterols, and vitamin E, which provide antioxidant and antimicrobial properties. The literature indicates that only oil obtained through pressing is used in food products, such as meat products, dairy drinks, and chocolates, either directly or in emulsions. These findings suggest that further research and innovation are needed to explore how this waste can be used in new food sources, particularly in countries with high wine production.

Trends in extraction techniques for the determination of organic micropollutants in liver tissues of vertebrates

Trends in extraction techniques for the determination of organic micropollutants in liver tissues of vertebrates

Maximizing the Hesperidin Extraction Using Supercritical Carbon Dioxide and Ethanol: Theoretical Prediction and Experimental Results

Agroindustrial waste can be valorized towards the obtaining of several products such as pigments, proteins, fibers, and polyphenolic compounds with antioxidant capacity. Orange peel waste is a promising source of polyphenolic compounds such as hesperidin. However, conventional extraction techniques present some environmental limitations such as high solvent consumption and high wastewater generation. Supercritical fluid extraction (SFE) has emerged as a green extraction technique due to the low use of solvent. The aim of this study was to maximize the hesperidin extraction based on theoretical predictions of the operating conditions from empirical thermodynamic models using SFE with carbon dioxide (CO2) as a solvent. The theoretical conditions were validated experimentally on a semi-pilot scale. The extracts were evaluated in terms of hesperidin content, total polyphenol content, and antioxidant capacity. Thermodynamic prediction of the operating conditions showed that the ethanol used as a co-solvent promotes hesperidin extraction. The optimum operating conditions were 25 °C, 80 bar, and a volumetric co-solvent concentration of 10%. The validation of the operating conditions resulted in a final hesperidin concentration of 11.5 ± 0.03 g/kg of orange peel waste. The experimental results were 30.26-times higher using 10% vol of ethanol than the extraction of hesperidin with pure ethanol as a co-solvent. The total polyphenol content and antioxidant capacity resulted in 831.92 ± 40.01 mg Galic acid/100 g orange peel waste, 15.41± 0.91 EC50/mL, and 5.31 ± 0.67 µMolTrox/100 g orange peel. Finally, the prediction of operating conditions from empirical thermodynamic models such as the Peng–Robinson equation of state with some modifications (Stryjek Vera) for solid–gas equilibrium solubility calculations, allows for maximizing the content of the polyphenolic compounds using SFE.

Microwave-Assisted Enzymatic Green Solvent Technology for the Extraction and Characterization of Sesame Seed Oil

Traditional methods of extracting oil with organic solvents raise issues related to health, safety, and the environment. Microwave-assisted enzymatic green solvent extraction (MAEGSE) technology is environment friendly because it involves less hazardous chemical synthesis, utilizes renewable feedstock, and decreases the chemical load and emissions produced by organic solvents. The solvents methanol, ethanol, and various cell wall degrading enzymes such as hemicellulose, viscozyme L, pectinase, xylanase, and cellulase were used in this study. The MAEGSE process of sesame oil was conducted, and the results indicated that, unlike the controlled group, no enzymes were used during the process. Among all the combinations a higher oil extraction efficiency was obtained with combination of pectinase and ethanol (37.27%) followed by the xylanase with ethanol (36.75%) from sesame seed without compromising the quality of oil. Among the enzymes tested, pectinase resulted in the highest oil extraction efficiency, followed by xylanase. Use of pectinase improved the oil extraction efficiency in both ethanol and methanol mediums. The outcomes suggested that the application of pectinase enzymes plays a significant role in increasing the oil yield in comparison with the control without compromising quality of oil. Oil extracted without the use of enzyme (control) had a lower free fatty acid (FFA) concentration than the oils extracted with MAEGSE. Even though the specific gravity (SG) and refractive index (RI) of extracted oils were not significantly different in the solvents, the saponification value (SV), peroxide value (PV), iodine value (IV), and free fatty acid (FFA) were found to have minor differences among the extraction techniques.

Robustly learning the Hamiltonian dynamics of a superconducting quantum processor.

Precise means of characterizing analog quantum simulators are key to developing quantum simulators capable of beyond-classical computations. Here, we precisely estimate the free Hamiltonian parameters of a superconducting-qubit analog quantum simulator from measured time-series data on up to 14 qubits. To achieve this, we develop a scalable Hamiltonian learning algorithm that is robust against state-preparation and measurement (SPAM) errors and yields tomographic information about those SPAM errors. The key subroutines are a novel super-resolution technique for frequency extraction from matrix time-series, tensorESPRIT, and constrained manifold optimization. Our learning results verify the Hamiltonian dynamics on a Sycamore processor up to sub-MHz accuracy, and allow us to construct a spatial implementation error map for a grid of 27 qubits. Our results constitute an accurate implementation of a dynamical quantum simulation that is precisely characterized using a new diagnostic toolkit for understanding, calibrating, and improving analog quantum processors.

KnowVID-19: A Knowledge-Based System to Extract Targeted COVID-19 Information from Online Medical Repositories

We present KnowVID-19, a knowledge-based system that assists medical researchers and scientists in extracting targeted information quickly and efficiently from online medical literature repositories, such as PubMed, PubMed Central, and other biomedical sources. The system utilizes various open-source machine learning tools, such as GROBID, S2ORC, and BioC to streamline the processes of data extraction and data mining. Central to the functionality of KnowVID-19 is its keyword-based text classification process, which plays a pivotal role in organizing and categorizing the extracted information. By employing machine learning techniques for keyword extraction—specifically RAKE, YAKE, and KeyBERT—KnowVID-19 systematically categorizes publication data into distinct topics and subtopics. This topic structuring enhances the system’s ability to match user queries with relevant research, improving both the accuracy and efficiency of the search results. In addition, KnowVID-19 leverages the NetworkX Python library to construct networks of the most relevant terms within publications. These networks are then visualized using Cytoscape software, providing a graphical representation of the relationships between key terms. This network visualization allows researchers to easily track emerging trends and developments related to COVID-19, long COVID, and associated topics, facilitating more informed and user-centered exploration of the scientific literature. KnowVID-19 also provides an interactive web application with an intuitive, user-centered interface. This platform supports seamless keyword searching and filtering, as well as a visual network of term associations to help users quickly identify emerging research trends. The responsive design and network visualization enables efficient navigation and access to targeted COVID-19 literature, enhancing both the user experience and the accuracy of data-driven insights.

Research on a Joint Extraction Method of Track Circuit Entities and Relations Integrating Global Pointer and Tensor Learning

To address the issue of efficiently reusing the massive amount of unstructured knowledge generated during the handling of track circuit equipment faults and to automate the construction of knowledge graphs in the railway maintenance domain, it is crucial to leverage knowledge extraction techniques to efficiently extract relational triplets from fault maintenance text data. Given the current lag in joint extraction technology within the railway domain and the inefficiency in resource utilization, this paper proposes a joint extraction model for track circuit entities and relations, integrating Global Pointer and tensor learning. Taking into account the associative characteristics of semantic relations, the nesting of domain-specific terms in the railway sector, and semantic diversity, this research views the relation extraction task as a tensor learning process and the entity recognition task as a span-based Global Pointer search process. First, a multi-layer dilate gated convolutional neural network with residual connections is used to extract key features and fuse the weighted information from the 12 different semantic layers of the RoBERTa-wwm-ext model, fully exploiting the performance of each encoding layer. Next, the Tucker decomposition method is utilized to capture the semantic correlations between relations, and an Efficient Global Pointer is employed to globally predict the start and end positions of subject and object entities, incorporating relative position information through rotary position embedding (RoPE). Finally, comparative experiments with existing mainstream joint extraction models were conducted, and the proposed model’s excellent performance was validated on the English public datasets NYT and WebNLG, the Chinese public dataset DuIE, and a private track circuit dataset. The F1 scores on the NYT, WebNLG, and DuIE public datasets reached 92.1%, 92.7%, and 78.2%, respectively.

A Wushu Leg Gesture Recognition Algorithm Based on Random Forest and Bone Feature Extraction (RF-SFE)

Addressing the challenges of complex movement trajectories and rapid action changes in martial arts performances, this study introduces a novel posture recognition algorithm based on Random Forest and Skeletal Feature Extraction (RF-SFE) for martial arts leg movements. Unlike traditional posture recognition methods that struggle with accuracy, RF-SFE aims to provide intelligent analysis of training postures to assist practitioners in efficient training. The algorithm initially employs advanced skeletal feature extraction techniques to identify and articulate the relative positions and movements specific to martial arts. These extracted spatial features enhance the flexibility in modeling the unique dynamics of martial arts. Subsequently, Random Forest classification is utilized to categorize different leg movements, leveraging its strength in handling high-dimensional data and providing robust classification. Comparative experiments on diverse martial arts posture datasets demonstrate a significant improvement in recognition rates over baseline methods. This validates the effectiveness of the RF-SFE method in recognizing martial arts postures, offering scientific guidance for practitioners’ training regimens.

Response Surface Methodology-Aided Optimization of Bioactive Compound Extraction from Apple Peels Through Pulsed Electric Field Pretreatment and Ultrasonication

Apple by-products (i.e., peels) are often thrown away, yet they are highly nutritious and provide numerous advantages as they contain a variety of nutrients such as vitamins, minerals, and antioxidants. Apple peels also comprise a high level of antioxidants, particularly polyphenols and flavonoids. This research aimed to determine the most efficacious extraction techniques and parameters to accomplish maximum bioactive compounds recovery from apple peels. Several extractions were conducted, including stirring, ultrasonication, and pulsed electric field-assisted extractions. Response surface methodology and several factors such as temperature, extraction duration, and solvent composition were considered to have a major impact on the isolation of bioactive compounds. The findings indicated that the most practical and efficient approach was to combine the pulsed electric field process with ultrasonication and stirring at 80 °C for 30 min, while 75% aqueous ethanol comprised the optimal solvent concentration, demonstrating the critical role of the solvent in optimizing extraction efficiency. The optimal conditions were obtained through response surface methodology with a statistical significance of p < 0.05. The extract exhibited a total polyphenolic content (TPC) of 17.23 mg gallic acid equivalents (GAE) per g of dry weight (dw), an ascorbic acid content (AAC) of 3.99 mg/g dw, and antioxidant activity of 130.87 μmol ascorbic acid equivalents (AAE)/g dw, as determined by FRAP and 95.38 μmol AAE/g dw from the DPPH assay. The measured antioxidant activity highlighted the significant potential of apple peels as a cost-effective source of exceptionally potent extracts.

Utilizing Data Mining Techniques in Geophysical and Biological Analysis for Assessing Environmental Risks

This study aimed to assess environmental risks using data extraction techniques. It focused on geophysical and biological factors and addressed the urgent need for effective risk management strategies to reduce soil erosion, water pollution, and air quality deterioration. A comprehensive dataset was created through the systematic collection of geophysical and biological data including temperature, soil composition, and biological abundance index. It used equipment such as satellite sensors and mountain transmitting stations. Various statistical tools used include decision trees and random forest algorithms. They were used to analyze data and identify important environmental risk factors. The results showed some interesting insights, revealing that the Neural Network has an accuracy of 95.5%, and the Random Forest algorithm predicts risk with an accuracy of 92.0%. It analyzed the classification of environmental hazard zones and identified high-risk zones, such as Zone A, which contains 10,000 people affected by erosion and Zone D, 20,000 people who were at risk from soil contamination. The study concludes that social media can significantly improve understanding and management of environmental risks. Additionally, it provides a useful framework for decision-makers and stakeholders to promote sustainable environmental practices.

Exploring the Extraction and Application of Anthocyanins in Food Systems

The continued use of synthetic additives in food production and preservation has raised concerns among consumers, given their potential negative health effects. Researchers and food industry experts have been exploring plants with potent functional compounds as an alternative source of natural compounds such as anthocyanins, which can be utilized to produce safe food additives. Given their various biological activities, great potential exists for anthocyanins to be used as additives to preserve food or produce functional foods with health benefits. However, to fully realize the economic and health benefits of anthocyanins, a deep understanding of the different plant sources of anthocyanins, as well as their composition, extraction techniques, and function in various products, is warranted. Therefore, this paper critically reviews the different plant sources of anthocyanins and their application in the food and nutraceutical industries, highlighting emerging extraction techniques, health benefits, and challenges in the use of anthocyanins in the food industry. This review also offers insights that could be used in future research and help establish novel and sustainable methods for manufacturing anthocyanin-based value-added products and ingredients.

Lipidomic Profiling of Common Wheat Flours from 1891-2010.

Wheat lipids are a minor constituent of wheat, with an important influence on its processing properties. While breeding aimed to improve the protein composition of wheat flour, its influence on the lipid composition remains unknown. We therefore analyzed the lipidome of 60 different common wheat (Triticum aestivum) flours representing cultivars registered and grown in Germany from 1891 to 2010. Four different extraction techniques were tested before the application of a semiquantitative, untargeted UHPLC-MS/MS method. The measurements included 16 different lipid classes and 102 different lipid species. Based on the lipid profile, discrimination between old (registered between 1891 to 1950) and modern (1951 to 2010) cultivars was possible. While the lipid class composition remained constant, differences were due to variations within the class of triacylglycerols, with modern cultivars containing less unsaturated fatty acids than the older ones. Our results imply that improving the lipid class composition of common wheat is a promising target for further breeding.

Equivalence Ratio-Driven Flame Response of an Industrial Premixed Burner: Experiments and Modeling

Abstract This paper presents an analysis of the unsteady heat release rate response of premixed flames to equivalence ratio perturbations for an industrial premixed swirl-based burner. During this investigation, perfectly and technically premixed flames were acoustically forced via fuel/air mixture flow and air flow modulations, respectively, at the same operating conditions. From the resulting flame transfer functions (FTFs), measured using the multimicrophone method, the equivalence ratio driven FTF was isolated and extracted by removing the velocity driven component, i.e., the measured FTF from the perfectly premixed flame, from the technically premixed FTF with two novel extraction techniques. The results are compared with FTFs obtained directly in a previous experimental campaign where the fuel flow was acoustically forced, the resulting equivalence ratio fluctuations measured via an infrared absorption technique, and the heat release rate response to the forcing was quantified using chemiluminescence measurements. The results from both measurement approaches agreed well highlighting the validity of the techniques. Further, to understand the governing features of the equivalence ratio driven FTF, a physics-based analytical model following the G-equation approach was developed. The contributions from flame surface area, flame speed, and heat of reaction oscillations were modeled to describe the heat release rate dynamics. A limited number of physical parameters in the analytical model were anchored on one test condition, optimized and restricted to values, which were all physically reasonable, and were subsequently used for model predictions at other operating conditions. The FTF model predictions compared well with experimental data across a range of different operating conditions. Finally, the relative contributions from flame surface area, flame speed, and heat of reaction oscillations on the features of the FTFs were identified and explored.

A multimodal deep learning-based algorithm for specific fetal heart rate events detection.

This study aims to develop a multimodal deep learning-based algorithm for detecting specific fetal heart rate (FHR) events, to enhance automatic monitoring and intelligent assessment of fetal well-being. We analyzed FHR and uterine contraction signals by combining various feature extraction techniques, including morphological features, heart rate variability features, and nonlinear domain features, with deep learning algorithms. This approach enabled us to classify four specific FHR events (bradycardia, tachycardia, acceleration, and deceleration) as well as four distinct deceleration patterns (early, late, variable, and prolonged deceleration). We proposed a multi-model deep neural network and a pre-fusion deep learning model to accurately classify the multimodal parameters derived from Cardiotocography signals. These accuracy metrics were calculated based on expert-labeled data. The algorithm achieved a classification accuracy of 96.2 % for acceleration, 94.4 % for deceleration, 90.9 % for tachycardia, and 85.8 % for bradycardia. Additionally, it achieved 67.0 % accuracy in classifying the four distinct deceleration patterns, with 80.9 % accuracy for late deceleration and 98.9 % for prolonged deceleration. The proposed multimodal deep learning algorithm serves as a reliable decision support tool for clinicians, significantly improving the detection and assessment of specific FHR events, which are crucial for fetal health monitoring.

An effective Key Frame Extraction technique based on Feature Fusion and Fuzzy-C means clustering with Artificial Hummingbird

Key frame extraction is very important in video summarization and content-based video analysis to address the problem of data redundancy in a video. Key frame extraction enables quick navigation and expert video arrangement in many applications. The visually impaired can benefit from the use of key frame extraction for rapid object recognition and tracking. Most key frame extraction techniques consider only a single visual feature instead of multiple features or full pictorial information of the video. This study proposes a key frame extraction method from a video that (i) first removes insignificant frames by pre-processing, (ii) second, four visual and structural feature differences among the consecutive frames are extracted and aggregated to identify informative frames, (iii) third, to cluster the obtained frames, a hybrid FCM-AHA method is proposed by combining Fuzzy C-means(FCM) with artificial hummingbird optimization algorithm (AHA) to circumvent the local minima trapping problem of FCM, and finally, from each cluster, the two frames having greatest Euclidean distance from all the other frames within a cluster is selected as key frames to remove redundant frames. Experimental results on the Open video and YouTube datasets show that the suggested method outperforms state-of-the-art methods both in terms of subjective qualitative analysis and objective quantitative evaluation, e.g., Precision, Recall, and F-score. Further, results are also taken on real video to demonstrate its applicability in real-life applications.

Machine Learning Applied to Lithium‐Ion Battery State Estimation for Electric Vehicles: Method Theoretical, Technological Status, and Future Development

ABSTRACTLithium‐ion batteries (LIBs) are extensively utilized in electric vehicles due to their high energy density and cost‐effectiveness. LIBs exhibit dynamic and nonlinear characteristics, which raise significant safety concerns for electric vehicles. Accurate and real‐time battery state estimation can enhance safety performance and prolong battery lifespan. With the rapid advancement of big data, machine learning (ML) holds substantial promise for state estimation. This paper systematically reviews several common ML algorithms, detailing the basic principles of each and illustrating their structures with flowcharts. We compare the advantages and disadvantages of various methods. Subsequently, we discuss feature extraction techniques employed in recent studies for estimating state of charge (SOC), state of health (SOH), state of power (SOP), and remaining useful life (RUL), as well as the application of these ML methods in state estimation. Finally, we discuss the challenges associated with using ML methods for state estimation and outline future development trends.

AI-Driven Cardiomegaly Detection Using Cutting-Edge Chest X–ray imagery

This project aims to develop an innovative approach for detecting cardiomegaly, a condition characterized by an enlarged heart, using machine learning techniques, specifically Support Vector Machines (SVM), and state-of-the-art chest X-ray imagery. Cardiomegaly serves as a significant indicator of various cardiac diseases, and early detection through accurate imaging analysis can lead to timely intervention and improved patient outcomes. The proposed machine learning solution utilizes SVM algorithms trained on a diverse dataset of chest X-ray images to automatically identify signs of cardiomegaly with high precision and reliability. By harnessing the power of SVM, this system offers a non-invasive and cost-effective method for screening large populations for cardiac abnormalities, facilitating early diagnosis and appropriate medical management. Key features of the developed solution include robust feature extraction techniques, real-time analysis, and integration with existing healthcare infrastructure for seamless implementation in clinical settings. Moreover, the system prioritizes patient privacy and data security by adhering to relevant regulations and standards governing medical imaging and information management. Overall, this project represents a significant advancement in the field of cardiac health monitoring, providing healthcare professionals with a valuable tool for early detection and management of cardiomegaly, ultimately improving patient care and reducing the burden of cardio vascular disease

Analytical Determination of Squalene in Extra Virgin Olive Oil and Olive Processing By-Products, and Its Valorization as an Ingredient in Functional Food—A Critical Review

Squalene is a bioactive compound with significant health benefits, predominantly found in extra virgin olive oil (EVOO) and its processing by-products. This critical review explores the analytical determination of squalene in EVOO and various by-products from olive oil production, highlighting its potential as a valuable ingredient in functional foods. An overview of existing analytical methods is provided, focusing on different approaches to sample preparation before analytical determination, evaluating their effectiveness in quantifying squalene concentrations. Studies not primarily centered on analytical methodologies or squalene quantification were excluded. A critical gap identified is the absence of an official method for squalene determination, which hinders comparability and standardization across studies, underscoring the importance of developing a reliable, standardized method to ensure accurate quantification. The valorization of squalene involves advocating for its extraction from olive oil processing by-products to enhance sustainability in the olive oil industry. By recovering squalene, the industry can not only reduce waste but also enhance functional food products with this health-promoting compound. Additionally, there is a need for economically sustainable and environmentally friendly extraction techniques that can be scaled up for industrial application, thus contributing to a circular economy within the olive oil sector.

Gramian angular field-based state-of-health estimation of lithium-ion batteries using two-dimensional convolutional neural network and bidirectional long short-term memory

Accurate assessment of the state of health (SOH) of Lithium-ion batteries (LIBs) is crucial for ensuring the stability and reliability of associated equipment. However, in practical applications, current feature extraction techniques face challenges due to process complexity and the difficulty of models in capturing the dynamic evolution of time series data. This study introduces an advanced method for predicting LIBs SOH by integrating two-dimensional (2D) convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) with the Gramian angular field (GAF) technique. Health indicators (HIs) are derived from the incremental capacity and charging voltage curves, which are transformed into two-dimensional images using GAF. Bayesian optimization determines the optimal hyperparameters for the model, which uses image data of two health factors as inputs. The results demonstrate that the proposed dual-channel(CH2) image data input model, combined with voltage data during the charging phase, achieves superior performance, with lower errors and higher accuracy, evidenced by an average root mean square error (RMSE) of 0.0112 and an average mean absolute error (MAE) of 0.0087. The model's generalization capability and comparative analysis with existing methodologies affirm its practical significance.

Research Progress on the Activity Stimulation of Lithium Slag in Concrete

Lithium slag (LS), an industrial waste byproduct generated during lithium salt production, is characterized by its harmful trace elements, significant stockpiles and low pozzolanic activity. By 2003, the annual discharge of lithium slag in China surpassed 15 million tons, creating an urgent need for established large-scale disposal technologies. One of the primary strategies for the effective utilization of LS is its application as an auxiliary cementitious material in concrete. However, the low reactivity of LS and challenges associated with its large-scale application impede its effective utilization. Enhancing the pozzolanic activity of LS is pivotal for its substantial incorporation into concrete. This study begins by analyzing the physicochemical properties and volcanic ash reactivity of LS derived from various lithium extraction techniques. It subsequently explores the diverse activation techniques aimed at improving the reactivity of LS within concrete. Ultimately, this paper highlights the significance of synergistic activation strategies, particularly physicochemical co-excitation and multi-exciter composite excitation. These approaches are identified as critical pathways for enhancing the activity of LS. Through this exploration, this study aims to unveil innovative strategies that bolster the resource utilization efficiency of LS, thereby facilitating its effective application in the concrete domain.