Traditional Extraction Methods Research Articles

Droplet-based optical trapping for cell separation in mock forensic samples.

Optical tweezers have a wide range of uses for mechanical manipulation of objects in the microscopic range. This includes both living and static cells in a variety of biomedical and research applications. Single-focus optical tweezers, formed by focusing a laser beam through a high numerical aperture immersion objective, create a significant force, which enables controlled transport of a variety of different cell types and morphologies in three dimensions. Optical tweezers have been previously reported to capture and separate spermatozoa from a reconstituted simulated postcoital sample. We report herein the development of a simplified, more efficient cell transfer protocol that can separate and isolate both spermatozoa as well as leukocytes, with similar efficiencies as those previously reported. The new cell transfer method was used to separate sperm cells from a reconstituted mixture of spermatozoa and vaginal epithelial cells, with complete STR profiles developed from 50 cells with little evidence of contribution from the female contributor to the mixture. This modified protocol was then used to separate 21 samples of enriched leukocytes, with trapped cells ranging from 5 to 22 cells. Complete STR profiles were developed from as few as 10 leukocytes. Thus, with minimal sample preparation and a short trapping time, this method has the potential to provide an alternative to traditional differential extraction methods for separation of sperm:nonsperm mixtures while also providing versatility for separation of cells with differing morphologies.

Enhanced extraction of bioactive compounds from propolis (Apis mellifera L.) using subcritical water

The bioactive compounds and antioxidant activities of propolis extracts were investigated using subcritical water extraction (SWE). SWE was performed by varying temperature (110–200 °C) and time (10–30 min). SWE using only water as solvent successfully to extracted bioactive compounds from propolis using high-purity glass thimbles. The concentrations of galangin (16.37 ± 0.61 mg/g), and chrysin (7.66 ± 0.64 mg/g) were maximal at 200 °C for 20 min, and 170 °C for 20 min, respectively. The antioxidative properties from propolis increased with the increasing extraction temperature and extraction time on SWE. The maximum yields of the total phenolics (226.37 ± 4.37 mg/g), flavonoids (70.28 ± 1.33 mg/g), and antioxidant activities (88.73 ± 0.58%, 98.86 ± 0.69%, and 858.89 ± 11.48 mg/g) were obtained at 200 °C for 20 min. Compared with using ethanol extraction (at 25 °C for 24 h, total phenolics = 176.28 ± 0.35, flavonoids = 56.41 ± 0.65, antioxidant activities = 72.74 ± 0.41%, 95.18 ± 0.11%, 619.51 ± 8.17 mg/g), all yields of SWE extracts obtained at 200 °C for 20 min were higher. SWE is suitable for a much faster and more efficient method extracting bioactive compounds from propolis compared to traditional extraction method.

Algorithm and simulation study of oil painting classification based on visual perception and improved embedded learning

This paper presents an in-depth study and analysis of oil painting classification and simulation using an improved embedded learning fusion vision perception algorithm. This paper analyzes and models the image quality evaluation problem by simulating the human visual system and extracting quality perception features as the main entry point to improve the prediction accuracy of the overall algorithm. This paper proposes a multi-classification method of CCNN, which uses the similarity measure based on information first to achieve multi-classification of artwork styles and artists, and this part is the main part of this paper. This paper uses the wiki art repository to construct a dataset of oil paintings, including over 2000 works by 20 artists in 13 styles. CNN achieves an accuracy of 85.75% on the artist classification task, which is far more effective than traditional deep learning networks such as Resnet. Finally, we use the network model of this paper and other network models to train the classification of 3, 4, and 6 categories of art images. The accuracy of art image classification by this paper’s algorithm is higher than that of the current mainstream convolutional neural network models, and the extracted features are more comprehensive and more accurate than traditional art image feature extraction methods, which do not rely on researchers to extract image features. Experiments show that the proposed method can achieve excellent prediction accuracy for both synthetic distorted images and distorted images.

A rapid LC-MS/MS method for multi-class identification and quantification of cyanotoxins

Cyanobacteria can form harmful blooms in specific environmental conditions due to certain species producing toxic metabolites known as cyanotoxins. These toxins pose significant risks to public health and the environment, making it critical to identify and quantify them in food and water sources to avoid contamination. However, current screening methods only focus on a single class of cyanotoxins, limiting their effectiveness. Thus, fast and sensitive liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) method was developed to analyze eighteen cyanotoxins simultaneously. A simplified extraction procedure using lyophilized samples of cyanobacterial biomass was also used, eliminating the need for traditional solid-phase extraction methods. This method uses multiple reaction monitoring and allows accurate determination and quantification of eighteen cyanotoxins, including anatoxin-a, homoanatoxin-a, cylindrospermopsin, deoxy-cylindrospermopsin, nodularin, guanitoxin, seven microcystins (RR, [D-Asp3] RR, LA, LR, LY, LW, and YR), and five saxitoxins (gonyautoxins - GTX-1&4, GTX-2&3, GTX-5), decarbamoylgonyautoxin (dcGTX-2&3), and N-Sulfocarbamoylgonyautoxin (C1&C2), all in a short acquisition time of 8 min. Therefore, this method provides a simple and efficient approach to identify and quantify harmful compounds produced by cyanobacteria. Hence, this represents the first method to detecting guanitoxin among cyanotoxins. By expanding the range of toxins analyzed, this method can help ensure high-quality food and drinking water and protect recreational users from exposure to cyanotoxins.

A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data

Power systems are prone to a variety of events (e.g. line trips and generation loss) and real-time identification of such events is crucial in terms of situational awareness, reliability, and security. Using measurements from multiple synchrophasors, i.e., phasor measurement units (PMUs), we propose to identify events by extracting features based on modal dynamics. We combine such traditional physics-based feature extraction methods with machine learning to distinguish different event types. Including all measurement channels at each PMU allows exploiting diverse features but also requires learning classification models over a high-dimensional space. To address this issue, various feature selection methods are implemented to choose the best subset of features. Using the obtained subset of features, we investigate the performance of two well-known classification models, namely, logistic regression (LR) and support vector machines (SVM) to identify generation loss and line trip events in two datasets. The first dataset is obtained from simulated generation loss and line trip events in the Texas 2000-bus synthetic grid. The second is a proprietary dataset with labeled events obtained from a large utility in the USA involving measurements from nearly 500 PMUs. Our results indicate that the proposed framework is promising for identifying the two types of events.

Study on Extraction and Purification of Acanthopanax senticosus Polyphenols by an Ionic Liquid-Assisted Aqueous Two-Phase System.

This study aimed to extract and purify polyphenols from Acanthopanax senticosus. A new green method was developed, in which ionic liquids (ILs) were used as aqueous two-phase (ATP) adjuvants to extract the polyphenols from A. senticosus. An ionic liquid-assisted aqueous two-phase system (IL-ATPS) was established. The purification of the polyphenols from the extraction fluid by AB-8 macroporous resin was conducted, and the kinetic mechanisms were studied. The reuse of ionic liquids was executed. The results showed that an [OMIM]Br-assisted ethanol/NaH2PO4 system (IL-ATPS) was the best extraction solvent. In this study, the following optimal extraction conditions were determined: 32 wt.% ethanol, 25 wt.% NaH2PO4, 9 wt.% additional ionic liquid, a solid-liquid ratio of 1:40 g/mL, an extraction temperature of 50 °C, a pH of 4.0, an extraction time of 50 min, and an extraction rate of the polyphenols at 15.90 mg/g. The optimum adsorption parameters of the macroporous resin AB-8 were as follows: a flow rate of 3.5 BV·h-1, a sample volume of 40 mL, an elution flow rate of 3.5 BV·h-1, an eluent volume of 80 mL, and an eluant that was constituted by an 85% volume fraction of ethanol. The decolorization effect of 4% activated carbon was better than the other amounts; in addition, a decolorization rate of 76.81% and an ionic liquid recovery rate of 81.12% were found to be the most optimal. Compared with the traditional extraction methods, IL-ATPS has the advantages of requiring simple operation, saving time, and high efficiency. In addition, it can be used for the extraction of the polyphenolic compounds.

Microwave-assisted extraction in closed vessel in food analysis.

Microwave-assisted extraction (MAE) is an important technique in analytical chemistry. It offers several advantages over traditional extraction methods, such as improved extraction efficiency, shorter extraction times, reduced solvent consumption, and enhanced analyte recovery. Using microwaves, heat is directly applied to the sample, leading to rapid and efficient extraction of target compounds by enhancing the solubility and diffusion of the target compounds, thus requiring lower solvent volume. Therefore, MAE can be considered a more environmentally friendly and cost-effective option facilitating the transition toward greener and more sustainable analytical chemistry workflows. This contribution systematically reviews the application of MAE to a selection of target compounds/compounds classes of relevance for food quality and safety assessment. As inclusion criteria, MAE active temperature control and molecularly-resolved characterization of the extracts were considered. Contents include a brief introduction of the principles of operation, available systems characteristics, and key parameters influencing extraction efficiency and selectivity. The application section covers functional food components (e.g., phenols, diterpenes, and carotenoids), lipids, contaminants (e.g., polycyclic aromatic hydrocarbons and mineral oil hydrocarbons), pesticides, veterinary drug residues, and a selection of process contaminants and xenobiotics of relevance for food safety.

Fourier‐transform infrared spectroscopy for soil organic matter analysis

AbstractThe study of soil organic matter (SOM) can benefit from the use of Fourier‐transform infrared spectroscopy, an analytical method that complements traditional fractionation and extraction methods. This review provides guidance on the use of diffuse reflectance infrared Fourier transform (DRIFT) in the mid‐infrared region (MIR: 4000–400 cm−1). Two distinct applications of DRIFT spectroscopy are reviewed: soil organic matter (1) characterization and (2) quantification. Characterization of SOM involves the qualitative to semi‐quantitative measurement of functional groups that constitute organic matter, and quantification employs chemometrics to predict fractions of SOM. Guidance on decision‐making in how methods are conducted based on sample type and research question, and on interpretation of results are provided.

Recent Advances on Key Enzymes of Microbial Origin in the Lycopene Biosynthesis Pathway.

Lycopene is a common carotenoid found mainly in ripe red fruits and vegetables that is widely used in the food industry due to its characteristic color and health benefits. Microbial synthesis of lycopene is gradually replacing the traditional methods of plant extraction and chemical synthesis as a more economical and productive manufacturing strategy. The biosynthesis of lycopene is a typical multienzyme cascade reaction, and it is important to understand the characteristics of each key enzyme involved and how they are regulated. In this paper, the catalytic characteristics of the key enzymes involved in the lycopene biosynthesis pathway and related studies are first discussed in detail. Then, the strategies applied to the key enzymes of lycopene synthesis, including fusion proteins, enzyme screening, combinatorial engineering, CRISPR/Cas9-based gene editing, DNA assembly, and scaffolding technologies are purposefully illustrated and compared in terms of both traditional and emerging multienzyme regulatory strategies. Finally, future developments and regulatory options for multienzyme synthesis of lycopene and similar secondary metabolites are also discussed.

Ionic liquid-based ultrasonic-assisted extraction coupled with HPLC to analyze isoquercitrin, trifolin and afzelin in Amygdalus persica L. flowers

This study aimed to establish a method for the simultaneous determination of isoquercitrin, trifolin and afzelin in A. persica flowers by high performance liquid chromatography (HPLC) with ionic liquid as extractant and ultrasonic-assisted extraction. The effects of ionic liquid concentration, solid–liquid ratio, number of crushing mesh, ultrasonic time, extraction temperature, and ultrasonic power on the extraction yield of three target compounds were investigated using the extraction yield of target analytes as the index. According to the results of single factor experiment, the Box-Behnken design-response surface methodology (BBD) was used to optimize the extraction method and compared with the traditional extraction method. The results showed that, calibration curves had excellent linearity (R2 > 0.9990) within the test ranges. In combination with other validation data, this method demonstrated good reliability and sensitivity, and can be conveniently used for the quantification of isoquercitrin, trifolin and afzelinin A. persica flowers. And the contents of isoquercitrin, trifolin and afzelin were 64.08, 20.55 and 75.63 μg/g, respectively. The optimal process obtained by BBD was as follows: ionic liquid concentration was 1.0 mol/L, solid–liquid ratio was 1:40 g/ml, mesh sieve was 50 mesh, ultrasonic time was 40 min, extraction temperature was 50 °C, and ultrasonic power was 400 W. Under the optimal conditions, the theoretical predicted total extraction yield of the three target compounds was 159.77 μg/g, which was close to the actual extraction value (160.26 μg/g, n = 3), this result indicating that the optimal process parameters obtained by response surface methodology analysis were accurate and reliable. The method was simple, accurate and rapid for determination the contents of three active ingredients in A. persica flowers.

The Efficient Extraction Method of Collagen from Deteriorated Leather Artifacts.

Collagen is the most crucial component of leather artifacts and analyzing collagen can provide vital information for studying and conserving such artifacts. However, collagen in leather artifacts often faces challenges such as degradation, denaturation, and contamination, which make it difficult to achieve an ideal protein extract using traditional extraction methods. This study aimed to find an efficient collagen extraction strategy for aging leather by comparing and improving commonly used methods. The results of comparing different extraction methods indicated that a NaOH solution was highly effective in extracting collagen from aged leather. To determine the optimal conditions for collagen extraction from the NaOH solution, we conducted orthogonal experiments. The results revealed that a NaOH concentration of 0.05 mol/L, a dissolution temperature of 80 °C, and a dissolution time of 12 h were the most favorable conditions. To validate the effectiveness of this method, we performed SDS-PAGE and biological mass spectrometry tests on collagen extracts from leather samples with varying degrees of aging. All collagen extracts exhibited distinct bands in the gel, and the molecular weight of collagen in each sample exceeded 20 kDa. Furthermore, even with a reduced sample mass of 1 mg (micro-destructive sampling), biological mass spectrometry identified 124 peptides in the protein extract. Notably, four of these peptides were unique to cattle hide collagen and were not present in the collagen of pig, sheep, horse, deer, or human skins. These experimental findings confirm the efficacy of the NaOH solution for extracting collagen from aging leather, suggesting that it can serve as a significant method for collagen identification and analysis in leather artifacts.

Evaluation of the QuEChERS extraction approach for the analysis of active compounds of pharmaceuticals in olive tree portions

BackgroundA modified version of the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) method has been tested for quantifying six active compounds of pharmaceuticals (PhACs), i.e., two different antibiotics, two anti-inflammatories, one antifungal, and one anti-depressant, extracted from roots, leaves and stems, pulp, pits, and oil obtained from olive trees. The different matrices have been polluted with all contaminants at 25, 50, and 250 µg L−1 and the recoveries were determined by liquid chromatography tandem–mass spectrometry. The validation of the method has been carried out by determining linearity, recovery, precision, limits of detection (LODs), and limits of quantification (LOQs) values. A matrix-matched calibration for each matrix has been adopted in order to avoid the matrix effect at the aforementioned levels of fortification.ResultsThe recoveries of PhACs from the different matrices were always above 70% and the relative standard deviation (RSD) always ≤ 20%, conditions required for the validation of the method. The LOD and LOQ values were always lower than 25 µg L−1, i.e., always lower than the minimum concentration used in the experiment; therefore, the method can be validated at 25, 50, and 250 µg L−1.ConclusionsThis method can represent a valid alternative to the traditional extraction methods to quantify pharmaceuticals extracted also from fatty matrices.Graphical

Unlocking the Full Potential of Clove (Syzygium aromaticum) Spice: An Overview of Extraction Techniques, Bioactivity, and Future Opportunities in the Food and Beverage Industry

This review article provides an overview of the bioactive compounds of clove, their health benefits, and their potential application in food and beverages. Cloves are rich in phenolic compounds, mainly eugenol, which exhibit antioxidant, anti-inflammatory, antimicrobial, antifungal, and wound-healing properties. Traditional methods of clove extraction, such as Soxhlet and maceration, have limitations. Green extraction methods, such as ultrasound-assisted extraction, pressurised liquid extraction, and microwave-assisted extraction, have shown promising results. The potential application of clove extract in various food and beverage products are also discussed. Finally, future perspectives and challenges for clove extraction are highlighted. Overall, the review highlights the potential of clove extract as a natural source of bioactive compounds for various applications in the food and beverage industry.

Quantum Bayesian computation

AbstractQuantum Bayesian computation is an emerging field that levers the computational gains available from quantum computers. They promise to provide an exponential speed‐up in Bayesian computation. Our article adds to the literature in three ways. First, we describe how quantum von Neumann measurement provides quantum versions of popular machine learning algorithms such as Markov chain Monte Carlo and deep learning that are fundamental to Bayesian learning. Second, we describe quantum data encoding methods needed to implement quantum machine learning including the counterparts to traditional feature extraction and kernel embeddings methods. Third, we show how quantum algorithms naturally calculate Bayesian quantities of interest such as posterior distributions and marginal likelihoods. Our goal then is to show how quantum algorithms solve statistical machine learning problems. On the theoretical side, we provide quantum versions of high dimensional regression, Gaussian processes and stochastic gradient descent. On the empirical side, we apply a quantum FFT algorithm to Chicago house price data. Finally, we conclude with directions for future research.

Evaluation of cold extracted bioactive compounds from tomato pomace and its use in food fortification

ABSTRACT The purpose of this study was to find a method for the cold extraction of biologically active compounds from tomato pomace, as there are no acceptable, affordable and environmentally friendly extraction methods, and its ability to maintain their vital functions and properties. This method was then evaluated as compared to the traditional thermal extraction methods. The limits of traditional extraction techniques have been overcome by the advent of numerous non-thermal new extraction techniques over the past 20 years. Three methods were used to help extract these compounds, which are blending the frozen (BF), microwave-assisted (MA) and Ohmic heating (OH). The results showed that the BF method was the best extraction method, and the amount of lycopene 487.4 µg/g, β-carotene 115.8 µg/g, total phenolics 1297.4 µg GAE/g, total flavonoids 462.5 µg QE/g and antioxidant activity 89.1%. It was found that the most abundant phenolic compounds in tomato pomace were gallic acid and ellagic nacid, as it contains seven essential amino acids, and that glutamic acid was the dominant acid. Fatty acid estimation showed that linoleic cis and oleic cis were the dominant acids, as they were 585.50 and 164.67 µg/g total fatty acids, respectively. It also contained on unsaturated fatty acids 820.44 µg/g.

Anomalies in Infrared Outgoing Longwave Radiation Data before the Yangbi Ms6.4 and Luding Ms6.8 Earthquakes Based on Time Series Forecasting Models

Numerous scholars have used traditional thermal anomaly extraction methods and time series prediction models to study seismic anomalies based on longwave infrared radiation data. This paper selected bidirectional long short-term memory (BILSTM) as the research algorithm after analyzing and comparing the prediction performance of five time series prediction models. Based on the outgoing longwave radiation (OLR) data, the time series prediction model was used to predict the infrared longwave radiation values in the spatial area of 5° × 5° at the epicenter for 30 days before the earthquake. The confidence interval was used as the evaluation criterion to extract anomalies. The examples of earthquakes selected for study were the Yangbi Ms6.4-magnitude earthquake in Yunnan on 21 May 2021 and the Luding Ms6.8-magnitude earthquake in Sichuan on 5 September 2022. The results showed that the observed values of the Yangbi earthquake 15 to 16 days before the earthquake (5 May to 6 May) exceeded the prediction confidence interval over a wide area and to a large extent. This indicates a strong and concentrated OLR anomaly before the Yangbi earthquake. The observations at 27 days (9 August), 18 days (18 August), and 8 days (28 August) before the Luding earthquake exceeded the prediction confidence interval in a local area and by a large extent, indicating a strong and scattered OLR anomaly before the Luding earthquake. Overall, the method used in this paper extracts anomalies in both spatial and temporal dimensions and is an effective method for extracting infrared longwave radiation anomalies.

Extraction process optimization of essential oil from Mellissa officinalis L. using a new ultrasound-microwave hybrid-assisted Clevenger hydrodistillation

The limited efficiency of traditional hydrodistillation extraction methods has hindered the development of essential oils (EOs). This study introduces an innovative method for enhancing the production of EOs from Mellissa officinalis L. The method involves ultrasonic and microwave-coupled Clevenger hydrodistillation, which utilizes a proprietary distillation device (PDD) extraction process. Yield optimisation was determined systematically by response surface methodology following single-factor experiments. The maximum extraction yield (0.21 ± 0.0019%) was achieved when the liquid-to-solid ratio, microwave extraction power, and microwave extraction time were set to 9.74 mL/g, 559.60 W, and 75 mins, respectively. The new process produced 74.79% and 73.55% more EOs than the electric furnace heating-assisted PDD and the microwave-assisted traditional Clevenger hydrodistillation. The combination of microwave and a modified PDD showed better results for EOs extraction, and it has great potential to improve the production efficiency of M. officinalis EOs.

Formulation and Evaluation of Herbal Kajal for its Anti-Inflammatory, Anti-Microbial, Anti-Acne properties

Kajal is an ancient eye cosmetic, traditionally used in mascara. It is widely used in the Middle East, the Mediterranean, South Asia, and the Horn of Africa as eyeliner to contour and/or darken the eyelids. Some did this to "strengthen the child's eyes", and others believed it could prevent the child from being cursed by the evil eye. With consideration to all these facts, herbal Kajal was formulated and evaluated. Herbal Microwave assisted extracted Kajal (MAEK) and Traditional method extracted Kajal (TMEK) were formulated by using Anethumgraveolans, Yasthimadhu, Ricinuscommunis, Ocimum sanctum, Castor oil and almond oil but the methods of extraction of active constituents was different and evaluated on various parameters. In MAEK all the herbs were extracted by using Microwave assisted extraction method. Green chemistry and microwave assisted extraction was used to obtain high yield, short extraction time, reducing waste and promoting efficiency in extraction. In TMEK all the herbs were extracted by using traditional methods of extraction. The pH, viscosity values proved the significant evaluation. The base satisfied the evaluated parameter values and physical evaluation was suggestive of a cosmetically appealing product.Composition of nutrient agar I.P and cylinder plate method was employed; MAEK showed higher level of zone of inhibition in microbial contamination as compared with TMEK. MAEK was able to inhibit protein denaturation in a concentration-dependent manner as compared with TMEK. Inhibition % of protein denaturation of the formulations was within the range from 53.0% to 76.0% at the concentration range of 25–100 ug/ml. MAEK exhibited a significantly higher level of inhibition compared to TMEK showed the lowest inhibition levels. Herbal Kajal prepared by using Microwave assisted extraction method showed better evaluation parameters as compared with TMEK.

Occurrence of fatty acids in Camellia genus: Extractions technologies and potential applications: A review

The Camellia genus (Theaceae) comprises more than 200 species, including the most famous Camellia sinensis (L.) Kuntze, Camellia oleifera Abel, and Camellia japonica (L.). The commercial interest in these plants linked to their seed fatty acid content increased in the last decades due to their quality and health-enhancing properties, which significantly depend on different aspects such as environmental conditions. Nowadays, the traditional extraction methods of fatty acids from camellias include mechanical press extraction and solvent extraction, which have a high environmental impact. Therefore, it is essential to develop extraction techniques to achieve the maximum lipid yield with the minimum environmental impact and cost. These innovative methods include enzymatic extraction, supercritical fluid extraction (SFE), microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE). However, they are often limited to the laboratory or pilot scale due to economic or technical bottlenecks. This article aims to explore recent advances and innovations related to the extraction of fatty acids from Camellia.

Use of Photo isomers to enhance the removal of lignin from woody biomass hydrolysate: UV irradiated photo isomerization of 4,4-diethoxy-azobenzene enhances precipitation of organosolv lignin from extraction solvents

Conversion of lignocellulose to bioproducts and biofuels is often initiated with a hydrolytic pretreatment process that helps to depolymerize the native polymers in biomass and produce a cellulose rich pulp stream. Extracting dissolved components (lignin) from this hydrolysate is an important step to derive value from the biomass components other than cellulose. In this investigation we propose a novel method to enable precipitation of desired solutes using photo-isomers in the extraction solvent. This photo-switchable solvent enables dissolution or precipitation of desired components using alternating UV or visible light to modify the solubility of the compounds of interest. It was found that among different combinations of three solvents and three photo-isomers, the combination of acetone with 4,4′-diethoxy azobenzene solution was most efficient at precipitating dissolved Organosolv lignin in response to UV light exposure. To our knowledge this is the first demonstration of this approach to separating lignin from organic solvents. This method is significant because it minimizes the high energy demands of traditional lignin extraction methods that involve distillation or large amounts of dilution using antisolvents.