Efficient Extraction Technique Research Articles

Eco-friendly and efficient extraction of phenolic compounds from Commiphora gileadensis bark using microwave-assisted extraction

Commiphora gileadensis (C. gileadensis) is a medicinal plant with a variety of biological activity; its bark has several therapeutic potentials, including the treatment of wounds, inflammatory diseases, and treating bacterial infections. This study utilized microwave-assisted extraction (MAE) to optimize the recovery and phenolic components of the ethanolic extracts of C. gileadensis bark. Different MAE process parameters (microwave power, irradiation time, sample/solvent ratio, extraction temperature, and solvent (ethanol) concentration) were studied for their impacts on the extraction process using One-Factor-At-a-Time (OFAT) method. The extracts were further characterized for the presence of different phytochemicals using Gas Chromatography-Mass Spectroscopy (FC-MS) and Fourier Transform Infrared Spectroscopy (FTIR) analyses. The experimental findings revealed that the maximum extraction yield, total phenolic content (TPC), and total flavonoid content (TFC) of the ethanolic extract of the of C. gileadensis bark using MAE process were 39.40 ± 0.47 w/w%, 166.41 ± 3.97 mg GAE/g d.w., and 52.83 ± 2.95 mg QE/g d.w., respectively. Furthermore, 30 phenolic constituents were discovered in the extract of C. gileadensis bark for the first time, and these compounds showed good in vitro antioxidant activity. Hence, MAE is a suitable technique for efficient and green extraction of different groups of phytochemicals of pharmaceutical importance from the bark of C. gileadensis.

A field study of pore-network systems on the tight shale gas formation through adsorption-desorption technique and mercury intrusion capillary porosimeter: Percolation theory and simulations

Properly identifying microscopic pore structure properties of shale reservoirs is essential for developing efficient extraction techniques of shale hydrocarbons. To understand the unique and complex pore network of the Northern Guizhou shale reservoir, shale samples from the Wufeng-longmaxi Formation were investigated using different analytical techniques, X-Ray Diffractometry, low-pressure nitrogen gas adsorption-desorption isotherms, and Mercury intrusion capillary porosimeter. Results indicate that the Wufeng-longmaxi formation is mainly composed of a high quantity of quartz ranging from 0.6 % to 64.1 % and an average extent of 55.825 %, the lowest amount of potassium feldspar ranging from 0.6 % to 2.2 % and an average extent of 1.45 % with Total organic contents ranging from 3.672 to 5.209 %. Similarly, the BJH adsorption techniques for pore volume and area were investigated. The results indicate the dominance of the mesopore with pore size ranging from 2.03 nm to 40nm, average pore volume of 0.073715 m3/g, and average pore area of 13.0324 m2/g. The pore throat distributions from MICP showed drain pressure and median pore throat radius ranging from 0.47 to 13.79 MPa and 0.05–0.08 μm, respectively. Suggested complementary experimental techniques reveal the connectivity of pore networks of the Wufeng-longmaxi Formation shale reservoir, which shows a high production potential of hydrocarbons. The findings of this study can provide a valuable understanding of fluid transport properties and storage space of shale gas that can help decision-makers as they set exploration initiatives for shale gas reservoirs.

Early exploration modelling of natural hydrogen systems through the use of existing open source data

Natural hydrogen’s viability as a sustainable energy source will be enhanced through comprehensive geological modelling. This paper integrates existing open-source data to delve into the geological aspects of natural hydrogen exploration and proposes possible workflows. Geological and geophysical modelling entails characterising subsurface formations conducive to natural hydrogen generation and trapping. Utilising geological surveys, field observation, geophysical seismic and gravity data, alongside existing well logs, this analysis looks to identify regions with favourable geological conditions for the generation, migration and structures for accumulation of natural hydrogen. Furthermore, understanding the subsurface geology aids in the development of safe and efficient extraction techniques. By incorporating geological modelling into the evaluation of natural hydrogen, this paper provides a comprehensive overview of its potential as a sustainable energy solution. Leveraging existing open-source data alongside geological insights ensures a robust foundation for decision-making in exploration, production, storage, and utilisation strategies. This integrated approach empowers stakeholders to make informed choices in shaping a greener, more sustainable energy landscape.

Bioactives in Cocoa: Novel Findings, Health Benefits, and Extraction Techniques

Theobroma cacao L. seeds, commonly known as cocoa beans, are the foundation for cocoa and chocolate production. Following harvest, these beans undergo a multi-step processing chain including fermentation, drying, roasting, and grinding. This process yields cocoa solids, cocoa butter, and cocoa powder—all fundamental ingredients in the food and beverage industry. Beyond its sensory appeal (flavor, aroma, and texture), cocoa has garnered significant interest for its potential health benefits attributed to a rich profile of bioactive compounds. Cocoa is a well-documented source of polyphenolics, specifically flavanols, alongside methylxanthines, phytosterols, and dietary fibers. These constituents have been associated with a diverse range of bioactivities, including antioxidant, anti-carcinogenic, anti-diabetic, anti-inflammatory, anti-obesity, and anti-allergenic properties, potentially contributing to overall health maintenance. Efficient extraction techniques are crucial for maximizing the recovery of these valuable bioactive components from cocoa plant material. Modern methods are continuously being explored to optimize this process. This review focuses on the established health benefits associated with the bioactive compounds present in cocoa. Additionally, it will explore and discuss contemporary approaches for the extraction of these bioactive compounds from this plant source.

Cn2a-capsnet: a capsule network and CNN-attention based method for COVID-19 chest X-ray image diagnosis

Due to its high infectivity, COVID-19 has rapidly spread worldwide, emerging as one of the most severe and urgent diseases faced by the global community in recent years. Currently, deep learning-based diagnostic methods can automatically detect COVID-19 cases from chest X-ray images. However, these methods often rely on large-scale labeled datasets. To address this limitation, we propose a novel neural network model called CN2A-CapsNet, aiming to enhance the automatic diagnosis of COVID-19 in chest X-ray images through efficient feature extraction techniques. Specifically, we combine CNN with an attention mechanism to form the CN2A model, which efficiently mines relevant information from chest X-ray images. Additionally, we incorporate capsule networks to leverage their ability to understand spatial information, ultimately achieving efficient feature extraction. Through validation on a publicly available chest X-ray image dataset, our model achieved a 98.54% accuracy and a 99.01% recall rate in the binary classification task (COVID-19/Normal) on a six-fold cross-validation dataset. In the three-class classification task (COVID-19/Pneumonia/Normal), it attained a 96.71% accuracy and a 98.34% recall rate. Compared to the previous state-of-the-art models, CN2A-CapsNet exhibits notable advantages in diagnosing COVID-19 cases, specifically achieving a high recall rate even with small-scale datasets.

Human Activity Recognition Based On Video Summarization And Deep Convolutional Neural Network

Abstract In this technological era, human activity recognition (HAR) plays a significant role in several applications like surveillance, health services, Internet of Things, etc. Recent advancements in deep learning and video summarization have motivated us to integrate these techniques for HAR. This paper introduces a computationally efficient HAR technique based on a deep learning framework, which works well in realistic and multi-view environments. Deep convolutional neural networks (DCNNs) normally suffer from different constraints, including data size dependencies, computational complexity, overfitting, training challenges and vanishing gradients. Additionally, with the use of advanced mobile vision devices, the demand for computationally efficient HAR algorithms with the requirement of limited computational resources is high. To address these issues, we used integration of DCNN with video summarization using keyframes. The proposed technique offers a solution that enhances performance with efficient resource utilization. For this, first, we designed a lightweight and computationally efficient deep learning architecture based on the concept of identity skip connections (features reusability), which preserves the gradient loss attenuation and can handle the enormous complexity of activity classes. Subsequently, we employed an efficient keyframe extraction technique to minimize redundancy and succinctly encapsulate the entire video content in a lesser number of frames. To evaluate the efficacy of the proposed method, we performed the experimentation on several publicly available datasets. The performance of the proposed method is measured in terms of evaluation parameters Precision, Recall, F-Measure and Classification Accuracy. The experimental results demonstrated the superiority of the presented algorithm over other existing state-of-the-art methods.

Extraction of Sesquiterpene Lactones from Inula helenium Roots by High-Pressure Homogenization and Effects on Antimicrobial, Antioxidant, and Antiglycation Activities

AbstractThe sesquiterpene lactones (SL) of Inula helenium (I. helenium) roots were extracted using high-pressure homogenization (HPH) and compared against those derived from maceration. The effect of process conditions on the extraction efficiency of bioactive compounds (alantolactone, isoalantolactone, total phenols, and flavonoids); the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical-scavenging activity, ferric-reducing antioxidant power (FRAP), and antimicrobial activity of the extract; and the inhibition of advanced glycated end product (AGE) formation were assessed. The HPH of 90 MPa for 4 passes demonstrated the highest alantolactone (38.1 ± 0.7 mg/g) and isoalantolactone yields (34.4 ± 0.2 mg/g), respectively, which were significantly higher than the rates obtained using maceration (20.0 ± 0.2 mg/g and 18.9 ± 0.7 mg/g). Under optimal extraction conditions of 90 MPa for 4 passes, inhibition of AGE formation in the extract reached a rate of 97.16 ± 1.86%, with the highest DPPH and FRAP of 231.63 ± 2.96 µg/mL and 949.43 ± 1.86 µmol TE/100 mL, respectively. HPH exhibited lethal activity against all tested bacteria and fungi strains (Escherichia coli, Staphylococcus aureus, Listeria monocytogenes, Aspergillus niger, Fusarium oxysporum, and Penicillum expansum) at a concentration of 15 µL. SEM results proved that HPH severely damaged the cell structures of roots, increased solvent permeability, and improved the extraction rate of bioactive compounds. Additionally, this study also demonstrated that HPH yields high recovery with a specific energy consumption of 0.22 kWh/kg, which is close to the recommended minimum energy demand for an extraction procedure within industrial production processes. Therefore, these findings showed that HPH can be used as an efficient extraction technique for the production of SL in related industries.

Sustainable aqueous biphasic system created using sugar-based deep eutectic solvents/ethyl lactate for the green extraction of biomolecules

In recent years, the use of green solvents with efficient extraction techniques has gained considerable interest in the research community. This study proposed a novel aqueous biphasic system (ABS) consisting of a hydrophilic sugar-based deep eutectic solvent (DES) and biodegradable ethyl lactate (EL). To understand the phase behaviour of sugar-based DES-EL ABS, at 298.15 K and at an atmospheric pressure, the phase diagrams were determined, and the effects of the structure of different saccharides such as HBDs on the phase separation were discussed. By fitting the effective excluded volume theory (EEV) model to phase diagram data, the ability of the monosaccharide/disaccharide-based DES to induce phase separation was determined. Furthermore, the tie-line length (TLL) was measured and analysed for the stability of DES in both phases at equilibrium. Finally, extraction experiments of such systems for five target biomolecules (eugenol, vanillin, vanillic acid, gallic acid and rutin) were conducted. The results indicated that the hydrophilic/hydrophobic nature of biomolecules preferentially drives their partitioning behaviour. Eugenol, vanillin, and vanillic acid preferentially migrated to the more hydrophobic EL-rich phase, whereas gallic acid and rutin entered the hydrophilic DES-rich phase. A combination of factors such as pH, viscosity and TLL of the system can also affect the separation efficiency. The results of the extraction experiments confirmed the good feasibility of the proposed system for biomolecular extraction applications.

Social Media Monitoring Of Airbnb Reviews Using AI: A Sentiment Analysis Approach For Immigrant Perspectives In The UK

This paper presents a novel approach for monitoring social media content related to Airbnb reviews, explicitly focusing on the sentiments expressed by immigrants in the United Kingdom. The proposed system, a Quick Search System, leverages machine learning techniques to perform sentiment analysis on many Airbnb reviews. The system aims to provide timely and insightful information about the experiences and sentiments of immigrants in the UK, as reflected in their Airbnb reviews. By employing state-of-the-art machine learning algorithms, the system enables efficient and accurate sentiment classification, allowing for the identification of key themes and sentiments expressed by immigrant users. The study demonstrates the potential of this approach in gaining a deeper understanding of immigrant perspectives within the context of peer-to-peer accommodation, and its implications for social media monitoring and customer satisfaction management.This present study has conducted a critical analysis utilizing efficient feature extraction techniques, including N-grams and TF-IDF, to optimize identifying positive, neutral, and negative feedback. Furthermore, five different models were utilized, and the training and testing processes were accompanied by parameter tuning. Ultimately, the study concluded that the Random Forest (RF) classifier performed exceptionally well, achieving a 95% accuracy rate.

Highly efficient separation of Sc3+ and Y3+ in acid solution by a graphene oxide membrane with interlayer sieving

Sc and Y are key rare earth elements and are widely used in lamp phosphors, lasers and high-performance alloys. However, highly efficient extraction and separation of Sc3+ and Y3+ is laborious, harmful, slow, and costly, strongly necessitating more efficient extraction and separation techniques. Here, we produced hydrated Sc3+- and hydrated Y3+-controlled graphene oxide (GO) membranes and find that both hydrated cations were completely self-rejected by the membrane. By combining this self-rejection effect of the larger hydrated Y3+-controlled GO membrane and the rapid passage of the membrane through the smaller hydrated Sc3+, we proposed a strategy to separate Sc3+ and Y3+ by using a hydrated Y3+-controlled GO membrane. The experimental results show that the permeation rate of Sc3+ exceeds that of Y3+ when the separation factor reaches 4.02, which can be attributed to the interlayer sieving effects of the GO membrane. Our finding illustrates the use of a forward osmosis process with a GO membrane for the efficient separation of Sc3+ and Y3+ by interlayer sieving, which provides a new effective and eco-friendly method for the separation of rare earth elements.

Can a Fraction of Flour and Sugar Be Replaced with Fruit By-Product Extracts in a Gluten-Free and Vegan Cookie Recipe?

Certain food by-products, including not-good-for-sale apples and pomegranate peels, are rich in bioactive molecules that can be collected and reused in food formulations. Their extracts, rich in pectin and antioxidant compounds, were obtained using hydrodynamic cavitation (HC), a green, efficient, and scalable extraction technique. The extracts were chemically and physically characterized and used in gluten-free and vegan cookie formulations to replace part of the flour and sugar to study whether they can mimic the role of these ingredients. The amount of flour + sugar removed and replaced with extracts was 5% and 10% of the total. Physical (dimensions, color, hardness, moisture content, water activity), chemical (total phenolic content, DPPH radical-scavenging activity), and sensory characteristics of cookie samples were studied. Cookies supplemented with the apple extract were endowed with similar or better characteristics compared to control cookies: high spread ratio, similar color, and similar sensory characteristics. In contrast, the pomegranate peel extract enriched the cookies in antioxidant molecules but significantly changed their physical and sensory characteristics: high hardness value, different color, and a bitter and astringent taste. HC emerged as a feasible technique to enable the biofortification of consumer products at a real scale with extracts from agri-food by-products.

Microwave-assisted extraction of pectin from onion and garlic waste under organic, inorganic and dual acid mixtures

This study aims to investigate pectin extraction from garlic (GW) and onion waste (OW) by microwave-assisted (MAE) and sequential microwave assisted-hot acid extraction (MAHE) methods. All extractions were performed under three different media including organic acids [citric (CA) and acetic (AA)], inorganic acids [sulfuric (H2SO4) and hydrochloric (HCl)] and their mixtures. GW provided more pectin yields compared with OW. While the highest pectin yields from GW and OW by MAE in H2SO4 were respectively 24.62 ± 0.65 and 24.93 ± 0.59%, these yields under MAHE were 27.99 ± 0.36 and 28.43 ± 0.42%, respectively. Higher pectin yields and galacturonic acid (Gal-A) contents were mostly achieved in inorganic acids. However, degree of esterification (DE), methoxyl content (MeO) and equivalent weight (EW) values were higher for the pectins extracted under organic acids. Extraction of pectin from GW and OW was also accomplished in dual acidic media by MAE. Addition of inorganic acids to the organic acid solutions resulted in increasing pectin yields. The highest pectin yields from GW and OW under dual acid solutions were respectively 23.36 ± 0.66 and 21.88 ± 0.52%, and achieved in 1/3 HCl-H2SO4 and 1/3 CA-H2SO4 mixtures by MAE. While increasing inorganic acid contents in dual acid solutions resulted in enhanced Gal-A contents, increasing organic acid volume also generated higher DE and MeO values of the pectins. Obtained successful outcomes indicate that MAHE method could be used as an efficient extraction technique for the higher pectin yields, and utilization of organic-inorganic dual acid mixtures during MAE provides enhanced yields and controlled physicochemical properties of pectin.Graphical abstract

Extraction of 4-hydroxy benzoic acid from potato processing industrial waste

Abstract Due to the substantial volume of waste produced and the potential for high-value bioactive compounds to be extracted from potato peel, the valorization of industrial waste of potato processing industry is the subject of this research. The study presents an innovative technique for efficient extraction of 4-hydroxybenzoic acid (P-HBA), a valuable bioactive molecule, from potato peel extract by utilizing molecularly imprinted chitosan beads (MICB). The MICB was synthesized using environmentally benign cross-linkers such as Tetraethyl orthosilicate (TEOS), Sodium triphosphate (STPP), while P-HBA was is used as a template. The MICB was investigated using FTIR, SEM, and EDAX. Adsorption studies were conducted to optimize parameters like adsorbent dose, solution concentration, time and pH. A recovery rate of 95.07 % is recorded from a synthetic P-HBA solution when measured by HPLC while from the actual potato processing industrial effluent the same is 40 %. The adsorption capacity of MICB is 67.5 mg g−1 and specific binding capacity is 20.62 mg g−1 which remained unchanged for six consecutive cycle reuse. This is the first study to report uptake of a bioactive molecule from actual potato processing industry waste.

New discovery of the coalescence kinetics of sesame oil droplets under a high internal phase: A highly efficient oil extraction technique

Traditional pressing is of low efficiency (< 80 %). A highly efficient sesame oil extraction technique was discovered via micro-hydration of sesame paste (φ = ∼ 75 %) and then agitation with a yield of ∼ 95 %. However, the extraction mechanism is still unknown. To uncover this, microscopic imaging was used, and it found that agitation progressively increased the droplet size of micro-hydrated paste (φ = 74.5 %) from an initial size of < 4 μm. As agitated for 20 min, almost 85 % (v/v) of oil was over 20 μm, which was linearly and positively correlated (R2 > 0.96) with oil yield. Increase in droplet size was due to droplet compression, film rupture, and droplet coalescence. The coalescence frequency based on agitation time followed an exponent curve (R2 > 0.97). This coalescence might be related to the decreased water relaxation time and increased paste viscosity. This study, for the first time, found the oil droplet coalescence in hydrated sesame paste (φ = 74.5 %) during agitation, thereby successfully extracting oil at room temperature. The findings of this work can be a starting point for research on micro-hydration extraction for oil-containing materials from a packing density of oil droplets point view.

A Review: Subcritical Water Extraction of Organic Pollutants from Environmental Matrices.

Most organic pollutants are serious environmental concerns globally due to their resistance to biological, chemical, and photolytic degradation. The vast array of uses of organic compounds in daily life causes a massive annual release of these substances into the air, water, and soil. Typical examples of these substances include pesticides, polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). Since they are persistent and hazardous in the environment, as well as bio-accumulative, sensitive and efficient extraction and detection techniques are required to estimate the level of pollution and assess the ecological consequences. A wide variety of extraction methods, including pressurized liquid extraction, microwave-assisted extraction, supercritical fluid extraction, and subcritical water extraction, have been recently used for the extraction of organic pollutants from the environment. However, subcritical water has proven to be the most effective approach for the extraction of a wide range of organic pollutants from the environment. In this review article, we provide a brief overview of the subcritical water extraction technique and its application to the extraction of PAHs, PCBs, pesticides, pharmaceuticals, and others form environmental matrices. Furthermore, we briefly discuss the influence of key extraction parameters, such as extraction time, pressure, and temperature, on extraction efficiency and recovery.

Impact of Grinding and Sorting Particle Size on Phytochemical Yield in Dipterocarpus alatus Leaf Extract.

The main objective of this study was to investigate the impact of grinding (pretreatment) with a pin mill on the crude extract yields of Dipterocarpus alatus (Yang-Na) leaves. A factorial design in a completely randomized design was conducted to study the combinational effects of sieve sizes (1.0, 1.5, and 3.0 mm) and feed rates (1.0, 1.5, and 3.0 kg min-1), examining the interaction of parameters for grinding oven-dried Yang-Na leaves. Ethanol extraction initially evaluated the influence of Yang-Na leaf powder with diverse particle sizes. When sorting particle size, the crude extract yield increased as the particle size decreased, with 0.038-0.150 mm particles yielding the highest extraction, although yields decline when the particle size is lower than 0.038 mm. The average particle sizes, production capacity, and fineness modulus all exhibited a significant decrease as the sieve size and feeding rate were reduced, while the specific energy consumption showed an inversely proportional relationship with these parameters. Intriguingly, the crude extract yield remained independent of the average particle size. Notably, the highest yield (14.79 g kg-1) was derived from a 0.31 mm average particle size, ground with a 1.5 mm sieve and a 3 kg min-1 feeding rate. This suggests that the pretreatment, involving both grinding conditions and sorting size, has an impact on the performance of the extraction process. However, this study offers an energy-efficient alternative, advocating for using average particle sizes without prior sorting, streamlining the extraction process while maintaining substantial yields. These insights underline the crucial influence of particle size and grinding techniques, advancing our understanding of efficient herbal extraction techniques for industrial applications.

A comprehensive framework for advanced protein classification and function prediction using synergistic approaches: Integrating bispectral analysis, machine learning, and deep learning

Proteins are fundamental components of diverse cellular systems and play crucial roles in a variety of disease processes. Consequently, it is crucial to comprehend their structure, function, and intricate interconnections. Classifying proteins into families or groups with comparable structural and functional characteristics is a crucial aspect of this comprehension. This classification is crucial for evolutionary research, predicting protein function, and identifying potential therapeutic targets. Sequence alignment and structure-based alignment are frequently ineffective techniques for identifying protein families.This study addresses the need for a more efficient and accurate technique for feature extraction and protein classification. The research proposes a novel method that integrates bispectrum characteristics, deep learning techniques, and machine learning algorithms to overcome the limitations of conventional methods. The proposed method uses numbers to represent protein sequences, utilizes bispectrum analysis, uses different topologies for convolutional neural networks to pull out features, and chooses robust features to classify protein families. The goal is to outperform existing methods for identifying protein families, thereby enhancing classification metrics. The materials consist of numerous protein datasets, whereas the methods incorporate bispectrum characteristics and deep learning strategies. The results of this study demonstrate that the proposed method for identifying protein families is superior to conventional approaches. Significantly enhanced quality metrics demonstrated the efficacy of the combined bispectrum and deep learning approaches. These findings have the potential to advance the field of protein biology and facilitate pharmaceutical innovation. In conclusion, this study presents a novel method that employs bispectrum characteristics and deep learning techniques to improve the precision and efficiency of protein family identification. The demonstrated advancements in classification metrics demonstrate this method’s applicability to numerous scientific disciplines. This furthers our understanding of protein function and its implications for disease and treatment.

RESEARCH ON EFFICIENT INDEXING OF LARGE-SCALE GEOSPATIAL DATA BASED ON MULTI-LEVEL GEOGRAPHIC GRID

Abstract. With the implementation of unified natural resource management in China, national geographic conditions monitoring data have been identified as fundamental data for natural resource survey and monitoring. The efficiency of information extraction from massive spatio-temporal data to support natural resource management has emerged as a critical indicator for maximizing the value of geographic conditions monitoring data and enhancing data-driven decision management. Traditional spatial indices are computationally intensive, and when confronted with immense data volume or uneven data scale, issues such as extensive index computations and poor scale adaptability arise, impeding the efficient retrieval of complex geospatial data. In response to the need for efficient indexing of massive geospatial monitoring data at a scale of 100 million, a multi-level geographic spatial index framework based on geographic grids is proposed. Within the geographic conditions spatio-temporal database, a three-level spatial index of "zone-grid-space" is constructed, utilizing massive land cover data for analysis and testing. The results demonstrate that the multi-level spatial index method exhibits excellent scale adaptability, and grid coding dimensionality reduction and numerical operations effectively reduce the computational load of spatial retrievals of complex vector patches. This method significantly improves the retrieval efficiency of large-scale national geographic conditions data, providing an efficient technique for lightweight information extraction of large-scale monitoring geospatial data within spatial computing systems. The method holds reference value for on-demand retrieval, analysis, and decision-making of natural resource spatio-temporal big data.

Adaptive polarity of graphene oxide anchored silica doped with C18 for effective enrichment of aflatoxins from foodstuff

Selecting adsorbent with appropriate polarity is a practicable way to improve the enrichment efficiency for aflatoxins (AFs) during the solid phase extraction in sample pretreatment. In this study, graphene oxide anchored silica was prepared and applied to dop with C18 with suitable polarity for effective pre-concentration and purification of AFs from foodstuff. With systematic optimizing, GO anchored silica/C18 (1/1, w/w) exhibited excellent extraction performance for AFs. As the medium, a facile and efficient pipette-tip solid-phase extraction (PT-SPE) technique was developed for enrichment of AFs. Coupling with high-performance liquid chromatography fluorescence detection (HPLC-FLD), the developed analysis method illustrated the lower limits of detection of 0.075 ng/g and 0.17 ng/g for AFG2, AFB2 and AFG1, AFB1 (R2 > 0.99). The recoveries were calculated as 71.27 ∼ 94.52 % for the spiking level of 1, 5, and 20 ng/g with the intra-day and inter-day precision of 1.30 to 4.01 and 3.56 to 5.78, respectively. Hence, the developed PT-SPE-HPLC-FLD determination method was sensitive, accurate, and convenient for quantitative analysis of AFs at trace contents in foodstuff.

An exhaustive measurement of re-sampling detection in lossy compressed images using deep learning approach

The detection of resampling in digital images is critical for image authentication, but performance can be challenging when dealing with lossy compression. This study proposes an efficient feature extraction technique for detecting resampling (i.e., tampering) in post-JPEG compressed images. Our approach combines compression clues with resampling clues and feeds them to various traditional machine learning (ML) algorithms such as logistic regression, K-nearest neighbours (K-NN), support vector machine (SVM), decision tree (DT), and random forest (RF) to detect and classify doctored images in the re-compression scenario. We propose and evaluate feed-forward deep neural networks (DNN) and 1D convolutional neural networks (CNN) based on evaluation parameters such as accuracy, recall, precision, and F1 score, comparing them with the aforementioned traditional ML algorithms. Our results show that the RF and one-dimensional (1D) CNN are the most efficient models for this task. Furthermore, the 1D CNN outperforms the state-of-the-art techniques, particularly in the most challenging case of downscaling in lossy JPEG compressed images. Our proposed method demonstrates promising results for resampling detection in post-JPEG compressed images, which can be helpful in various image authentication applications.