Extraction Method Research Articles

Feasibility study on time-domain extraction method for zero-frequency pulse signal of nonlinear Lamb waves and early fatigue damage detection

Feasibility study on time-domain extraction method for zero-frequency pulse signal of nonlinear Lamb waves and early fatigue damage detection

Extraction and Concentration of Spirulina Water-Soluble Metabolites by Ultrafiltration.

Spirulina (Arthospira platensis) is known for its rich content of natural compounds like phycocyanin, chlorophylls, carotenoids, and high protein levels, making it a nutrient-dense food. Over the past decade, research has aimed to optimize the extraction, separation, and purification of these valuable metabolites, focusing on technologies such as high-pressure processing, ultrasound-assisted extraction, and microwave-assisted extraction as well as enzymatic treatments, chromatographic precipitation, and membrane separation. In this study, various extraction methods (conventional vs. ultrasound-assisted), solvents (water vs. phosphate buffer), solvent-to-biomass ratios (1:5 vs. 1:10), and ultrafiltration (PES membrane of MWCO 3 kDa, 2 bar) were evaluated. The quantities of total protein, phycocyanin (PC), chlorophyll a (Cla), and total carotenoids (TCC) were measured. The results showed that ultrasound-assisted extraction (UAE) with phosphate buffer at a 1:10 ratio yielded a metabolite-rich retentate (MRR) with 37.0 ± 1.9 mg/g of PC, 617 ± 15 mg/g of protein, 0.4 ± 0.2 mg/g of Cla, and 0.15 ± 0.14 mg/g of TCC. Water extraction in the concentration process achieved the highest concentrations in MRR, with approximately 76% PC, 92% total protein, 62% Cla, and 41% TCC. These findings highlight the effective extraction and concentration processes to obtain a metabolite-rich retentate from Spirulina biomass, reducing the volume tenfold and showing potential as a functional ingredient for the food, cosmetic, and pharmaceutical industries.

Object-oriented U-GCN for open-pit mining extraction from high spatial resolution remote-sensing images of complex scenes

ABSTRACT Precise extraction of open-pit mines is crucial for resource management and ecological and environmental dynamic monitoring. Current methods for extracting open-pit mines encounter challenges such as low accuracy and difficulty detecting complex scenes of open-pit mining. To address these issues, this paper proposes an object-oriented intelligent extraction method for complex mining scenes using Gaofen-2(GF-2) high-resolution remote-sensing images, which expresses the pixel-level features at the object level and utilizes the unique feature propagation and aggregation capabilities of the graph structure for the extraction of the mines. First, an object-oriented feature expression strategy is introduced to express multi-level pixel-level features as object-level features by constructing objects with appropriate multi-resolution segmentation parameters. This approach effectively reduces the impact of isolated pixel noise and outliers on classification results. Second, this paper proposes a U-GCN-based open-pit mining extraction method that combines the powerful multi-level feature extraction capabilities of U-GCN to propagate and aggregate information in a graph structure, effectively modelling spatial relationships between different objects. This method achieves high-precision extraction of open-pit mining areas. In experiments conducted on two study areas of varying scales, the F1 scores for open-pit mine extraction reached 93.32% and 83.06%. Comparative experiments demonstrate that the proposed object-oriented U-GCN method performs superiorly in terms of accuracy, stability and robustness across mining scenes with different levels of complexity. The proposed open-pit mine extraction method offers new insights and methodologies for current extraction practices.

Pectin Microwave Assisted Extraction from Pumpkin Peels: Process Optimization and Chemical-Physical and Rheological Characterization.

Recently, pectin, a versatile polysaccharide with different industrial applications, has gained significant attention as an eco-friendly and functional ingredient. This study investigates pumpkin peels (Cucurbita maxima L., Mantua variety) as a novel source of pectin, using a microwave-assisted extraction method with citric acid-acidified water as solvent. The extraction conditions were optimized using a Design of Experiments approach, considering the solvent-to-solid ratio (SSR), pH, temperature, and extraction time. The optimized conditions (94.8 °C, 5 min, pH 1.5, and 46 mL/g SSR) resulted in a pectin yield of 18.05%. A comprehensive characterization of the extracted pectin was performed, including FT-IR spectroscopy, DSC, TGA, rheological properties, and techno-functional assessments such as water holding capacity and fat binding capacity. The results indicated a high degree of esterification (56.19 ± 0.87%), classifying the pumpkin peels (PP) extract as a high methoxyl pectin. PP pectin demonstrated potential as a stabilizer and emulsifying agent, although its high methoxyl content limits its use as a carrier for targeted bioactive delivery. The findings support the viability of using agricultural by-products to obtain valuable polysaccharides, contributing to waste valorization and sustainable industrial practices.

Protein extraction from the industrial solid residue of brown seaweed after alginate extraction

The growing interest in the “blue-bioeconomy” and the valorization of macroalgae has driven research and industrial efforts to develop valorization strategies for the by-products generated during alginate extraction. Sequential extraction methods, such as acid/alkali treatment, enzymatic, ultrasound, and pH-shift process, were investigated and compared in order to obtain protein extracts from the solid residues after alginate extraction from two brown seaweed species (Ascophyllum nodosum and Saccharina latissima). Applying a sequential extraction, combining several methods, allowed the recovery of more than 29.5 % of the total protein from the residues. The resulting extracts exhibited optimal functional properties in terms of oil and water retention and outstanding emulsifying and foaming capacity. In addition, the protein extracts showed considerable polyphenol content, remarkable antioxidant activity, and an enhanced ability to inhibit angiotensin-converting enzyme (ACE). This study demonstrated that the residues after alginate extraction are a sustainable and promising source of protein with potential applications in the food industry.

Integrating AI with medical industry chain data: enhancing clinical nutrition research through semantic knowledge graphs.

In clinical nutrition research, the medical industry chain generates a wealth of multidimensional spatial data across various formats, including text, images, and semi-structured tables. This data's inherent heterogeneity and diversity present significant challenges for processing and mining, which are further compounded by the data's diverse features, which are difficult to extract. To address these challenges, we propose an innovative integration of artificial intelligence (AI) with the medical industry chain data, focusing on constructing semantic knowledge graphs and extracting core features. These knowledge graphs are pivotal for efficiently acquiring insights from the vast and granular big data within the medical industry chain. Our study introduces the Clinical Feature Extraction Knowledge Mapping ( ) model, designed to augment the attributes of medical industry chain knowledge graphs through an entity extraction method grounded in syntactic dependency rules. The model is applied to real and large-scale datasets within the medical industry chain, demonstrating robust performance in relation extraction, data complementation, and feature extraction. It achieves superior results to several competitive baseline methods, highlighting its effectiveness in handling medical industry chain data complexities. By representing compact semantic knowledge in a structured knowledge graph, our model identifies knowledge gaps and enhances the decision-making process in clinical nutrition research.

Research on Rotating Machinery Fault Diagnosis Based on Multi-Strategy Feature Extraction

This study proposes a novel feature extraction method that leverages multi-strategy optimization algorithms to enhance the accuracy and efficiency of rotating machinery fault diagnosis. By introducing an improved slime mold algorithm (LTSSMA), it optimizes the penalty factor and decomposition layers in variational mode decomposition (VMD), achieving more precise signal decomposition. The sample entropy generated by VMD forms the basis of the feature vector, and the nonlinear dimensionality reduction algorithm (t-SNE) is applied to reduce dimensions. Finally, the optimized features are classified using a random forest (RF) model, resulting in an 11.3% improvement in fault diagnosis accuracy compared to traditional methods. This method not only accelerates the diagnostic process but also significantly improves fault identification reliability.

Improving the activity of an inulosucrase by rational engineering for the efficient biosynthesis of low-molecular-weight inulin.

Inulin, a widely recognized prebiotic, has diverse applications across various industrial sectors. Although inulin is primarily produced through plant extraction, there is growing interest in enzymatic synthesis as an alternative. The enzymatic production of inulin from sucrose, which yields polymers with degrees of polymerization similar to those of plant-derived inulin, shows potential as a viable replacement for traditional extraction methods. In this study, an inulosucrase from Neobacillus bataviensis was identified, demonstrating a non-processive mechanism specifically tailored for synthesizing inulin with polymerization degrees ranging from 3 to approximately 40. The enzyme exhibited optimal activity at pH 6.5 and 55°C, efficiently producing inulin with a yield of 50.6%. Ca2+ can improve the activity and thermostability of this enzyme. To enhance catalytic total activity, site-directed and truncated mutagenesis techniques were applied, resulting in the identification of a mutant, T149S, displaying a significant 57% increase in catalytic total activity. Molecular dynamics simulations unveiled that the heightened flexibility observed in three surface regions positively influenced enzymatic activity. This study not only contributes to the theoretical foundation for inulosucrase engineering but also presents a potential avenue for the production of inulin.

Automatic Refactoring Approach for Asynchronous Mechanisms with CompletableFuture

To address the inherent limitations of Future in asynchronous programming frameworks, JDK 1.8 introduced the CompletableFuture class, which features approximately 50 different methods for composing and executing asynchronous computations and handling exceptions. This paper proposes an automatic refactoring method that integrates multiple static analysis techniques, including visitor pattern analysis, alias analysis, and executor inheritance structure analysis, to conduct precondition checks. Distinct from existing Future refactoring methods, this approach considers custom executor types, thereby extending its applicability. Using this method, the ReFuture automatic refactoring plugin was implemented within the Eclipse JDT framework. The method was evaluated in terms of the number of refactorings, refactoring time, and error introduction, alongside a side-by-side comparison with the existing method. The refactoring outcomes for nine large applications, including ActiveMQ, Hadoop, and Elasticsearch, show that ReFuture successfully refactored 639 out of 813 potential code structures, achieving a refactoring success rate of 64.70% without introducing errors. This tool effectively facilitates the refactoring to CompletableFuture and enhances refactoring efficiency compared to manual methods.

Extracting Sentence Embeddings from Pretrained Transformer Models

Pre-trained transformer models shine in many natural language processing tasks and therefore are expected to bear the representation of the input sentence or text meaning. These sentence-level embeddings are also important in retrieval-augmented generation. But do commonly used plain averaging or prompt templates sufficiently capture and represent the underlying meaning? After providing a comprehensive review of existing sentence embedding extraction and refinement methods, we thoroughly test different combinations and our original extensions of the most promising ones on pretrained models. Namely, given 110 M parameters, BERT’s hidden representations from multiple layers, and many tokens, we try diverse ways to extract optimal sentence embeddings. We test various token aggregation and representation post-processing techniques. We also test multiple ways of using a general Wikitext dataset to complement BERT’s sentence embeddings. All methods are tested on eight Semantic Textual Similarity (STS), six short text clustering, and twelve classification tasks. We also evaluate our representation-shaping techniques on other static models, including random token representations. Proposed representation extraction methods improve the performance on STS and clustering tasks for all models considered. Very high improvements for static token-based models, especially random embeddings for STS tasks, almost reach the performance of BERT-derived representations. Our work shows that the representation-shaping techniques significantly improve sentence embeddings extracted from BERT-based and simple baseline models.

B-159 Fast Cleanup for 19 Steroid Analytes in Serum Using Supported Liquid Extraction (SLE)

Abstract Background Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a powerful tool that holds promise in designing the future of steroid analysis in the clinical laboratory. It is already widely used in steroid analysis in endocrinology and is being utilized increasingly in newer applications for individual steroids and panels relevant in clinical research. LC-MS/MS is gaining user base owing to greater sensitivity and specificity and gradually even replacing some of the legacy test methods. Improved sample preparation, modern chromatography methods, and sensitive, faster scanning mass spectrometers have all played a role in improving LC-MS/MS. Steroid analysis for clinical research can require very low limits of detection which demand high recoveries and very clean extracted samples. Supported Liquid Extraction (SLE) can provide very clean extracted samples with less method development and a streamlined workflow compared to solid phase extraction (SPE). Laboratories desire high-throughput methods which consolidate analytes into one panel with fast chromatographic run times. Chromatographic separation of steroid isomers with the same m/z is necessary for accurate quantitation by LCMS/MS. Meeting these criteria can be challenging in a single LC-MS/MS method. Methods Each 200 µL serum sample was pretreated with 200 µL of pH 6 Ammonium Acetate buffer (aq) before being loaded onto a Novum™ PRO SLE, MAX 96-well plate, following the standard SLE protocol. Samples were eluted using 2 × 900 µL of Dichloromethane or Ethyl Acetate. After evaporation of the elution solvent, samples were reconstituted in 100 µL of 0.5 mM Ammonium Fluoride / Methanol (60:40, v/v). A Kinetex™ 2.6 μm C18, 50 x 3.0 mm column using a gradient of 0.5 mM Ammonium Fluoride (aq) and Methanol on an Agilent® 1290 Infinity UHPLC and SCIEX® 7500 Triple Quad™ were used for quantitation. Results Here, we present an effective sample extraction method and LC-MS/MS analysis method for the quantitation of a 19-analyte steroid panel from serum. The Kinetex 2.6 μm C18 column provided a fast 8-minute chromatographic separation and good selectivity for steroid analytes in both positive ion mode and negative ion mode. Calibration curves with a linear fit and 1/x weighting, showed good linearity with R2 of >0.99 for all analytes except 17-OH-Pregnenolone and DHEAS when using Dichloromethane as the elution solvent, and R2 of >0.980 for all analytes when using Ethyl Acetate as the elution the solvent. Accuracy of samples using Dichloromethane as the elution solvent were 80-112 %, except for 17-OH-Pregnenolone and DHEAS. Accuracy of all samples using the Ethyl Acetate elution solvent were 91-118 %. Conclusions The-96 well plate extraction method was robust, reproducible, and can be automated. SLE using Novum Pro MAX combined with a fast LC method provides a simple, rapid, high-throughput workflow for identification and quantitation of 19 steroids from serum.

An Optimized Liquid Chromatography-Mass Spectrometry Method for Ganglioside Analysis in Cell Lines.

Gangliosides are glycosphingolipids composed of a sialylated glycan head group and a ceramide backbone. These anionic lipids form lipid rafts and play crucial roles in regulating various proteins involved in signal transduction, adhesion, and cell-cell recognition. Neuroblastoma, a pediatric cancer of the sympathetic nervous system, is treated with intensive chemotherapy, radiation, and an antibody targeting the GD2 ganglioside. Gangliosides are critical in neuroblastoma development and serve as therapeutic targets, making it essential to establish a reliable, rapid, and cost-effective method for profiling gangliosides, particularly one capable of isomeric separation of intact species. In this study, liquid chromatography-mass spectrometry (LC-MS) was optimized using standard gangliosides, followed by the optimization of sphingolipid extraction methods from cell lines by comparing Folch and absolute methanol extraction techniques. Percent recovery and the number of identified sphingolipids were used to evaluate the analytical merits of these methods. A standard gangliosides calibration curve demonstrated excellent linearity (R2 = 0.9961-0.9975). The ZIC-HILIC column provided the best separation of ganglioside GD1 isomers with a 25 min runtime. GD1a elutes before GD1b on the ZIC-HILIC column. Absolute methanol yielded better percent recovery (96 ± 7) and identified 121 different sphingolipids, the highest number between the two extraction methods. The optimized method was applied to profile gangliosides in neuroblastoma (COG-N-683), pancreatic cancer (PSN1), breast cancer (MDA-MB-231BR), and brain tumor (CRL-1620) cell lines. The ganglioside profile of the neuroblastoma cell line COG-N-683 showed an inverse relationship between GD1 and GD2. Ceramide, Hex1Cer, GM1, and GM3 were highly abundant in CRL-1620, PSN1, and MDA-MB-231BR, respectively. These results suggest that our method provides a sensitive, reliable, and high-throughput workflow for ganglioside profiling across different cell types.

The Influence of Solvent Choice on the Extraction of Bioactive Compounds from Asteraceae: A Comparative Review

While the potential of Asteraceae plants as herbal remedies has been globally recognized, their widespread application in the food, cosmetic, and pharmaceutical industries requires a deeper understanding of how extraction methods influence bioactive compound yields and functionalities. Previous research has primarily focused on the physiological activities or chemical compositions of individual Asteraceae species, often overlooking the critical role of solvent selection in optimizing extraction. Additionally, the remarkable physiological activities observed in these plants have spurred a growing number of clinical trials, aiming to validate their efficacy and safety for potential therapeutic and commercial applications. This work aims to bridge these knowledge gaps by providing an integrated analysis of extraction techniques, the diverse range of bioactive compounds present in Asteraceae, and the influence of solvent choice on isolating these valuable substances. By elucidating the interplay between extraction methods, solvent properties, and bioactivity, we underscore the promising potential of Asteraceae plants and highlight the importance of continued research, including clinical trials, to fully unlock their potential in the food, cosmetic, and pharmaceutical sectors.

Improved and Novel Methods for Investigating Organophosphate Esters in Particulate Matter

A pressurized liquid extraction (PLE) method for the extraction of 31 organophosphate esters (OPEs) and novel organophosphate esters (NOPEs) has been developed. Unlike previously published methods, this method utilizes the high-throughput nature of PLE (as opposed to Soxhlet or sonication methods) without using potentially harmful organic solvents like methylene chloride. Combinations of hexane and acetone and hexane and ethyl acetate at various temperatures were examined. Extracts were concentrated and analyzed via gas chromatography–mass spectrometry. The final optimized method utilized 1:1 v/v hexane/ethyl acetate at 100 °C for three static cycles (5 min each) at 80% flush volume and a 100 s N2 purge. This provided average surrogate corrected target analyte percent recoveries in spike and recovery experiments (n = 6) for OPEs and NOPEs of 106 ± 13%, with average surrogate recoveries of 88.6 ± 7.3%. The developed method was further validated using standard reference materials and was then applied to atmospheric particulate matter samples collected in the city of Providence, RI. The dataset reflected ambient concentrations of 16 OPEs and NOPEs (reported in pg m−3) for the first time in the greater Providence metropolitan area, including one of the first reports of NOPEs in atmospheric particulate matter in the U.S.

Application of SPNGO-VMD-SVM in rolling bearing fault diagnosis

Abstract Traditional bearing fault feature extraction and fault classification methods have low recognition accuracy and limited recognition capability in noisy environments. To address this problem, this paper proposes an improved northern eagle algorithm (SPNGO) to optimize the variational modal decomposition (VMD) and support vector machine (SVM) to achieve bearing fault diagnosis. Firstly, to overcome the disadvantages of NGO, such as easy fall into local optimal solutions and slow convergence speed, the Sine Cosine Strategy (SCA) and Position Optimisation Search Algorithm (POS) are introduced to optimize the NGO, and the superiority of the SPNGO algorithm is proved through the comparison of different algorithms. Then, SPNGO-VMD is used to adaptively decompose the vibration signals of faulty bearings and generate multiple modal components IMF. The effective IMF components are screened based on the craggy principle to reconstruct the signals. Finally, the reconstructed feature signals are input into SPNGO-SVM for fault classification and compared with other fault diagnosis models. The study results show that the fault diagnosis accuracy of SPNGO-VMD-SVM can reach 96.67%, which can effectively improve the accuracy of bearing fault diagnosis.

B-229 Extraction-free Nucleic Acid Sample Preparation from Inhibitor Rich Samples via Hyperbaric Heating

Abstract Background Extraction-based nucleic acid sample preparation for PCR is limited by long processing times, liquid transfers, cost, and storage requirements. Extraction-free approaches enable streamlined sample prep but are severely impacted by inhibitors present in biological sample matrices. We previously reported the use of Hyperbaric Heating (HBH) for effective organism lysis and demonstrate here its effectiveness to prepare nucleic acids for PCR from various inhibitor-rich sample matrix types. Methods HBH exposes the sample to high temperature/high pressure environment for a short duration. This process effectively lyses organisms and denatures inhibitors within seconds. Samples are sealed within a pressure-tight metallic vessel, then rapidly heated using magnetic induction. Within the sealed metallic vessel, vaporization of the aqueous sample raises the internal pressure, enabling an internal temperature higher than 100°C. Proprietary sample processing beads containing chelating agents and other proteins protect nucleic acids from fragmentation during heating. Results The efficacy of HBH for PCR amplification was tested against commercial kits and extraction-free methods. HBH treatment of nasal swab samples spiked with inactivated SARS-CoV-2 elicited detection down to 125 viral copies/mL. Viruses such as RSV and Influenza spiked in nasal swab samples demonstrated comparable PCR results as extraction prepped samples. HBH treatment of microbe samples, including Candida albicans spiked in urine and Bacillus subtilis spiked in serum, had comparable PCR threshold cycles as microbe specific DNA extraction methods (Table 1). Conclusions HBH is a generalizable extraction-free nucleic acid sample prep technology affording detection capabilities comparable to commercial sample prep methods. We demonstrated the use of HBH sample prep for detection of several infectious microorganisms in common biological matrices (nasal swab, serum, urine) without additional sample purification. With its ease of use, speed, and lower cost, HBH will be a key tool in applications where nucleic acid amplification is pivotal for diagnostic and research use.

Enhanced extraction of antioxidant phenolics: Quercetin, Kaempferol, Gallic Acid, Syringic Acid, and Epicatechin from fresh berries and their waste using Ultra-Turrax and Ultrasonication

This study aimed to improve extraction of antioxidant phenolics from fresh berries and berries waste. A combination of Ultra-Turrax milling and ultrasonication, both efficient and eco-friendly techniques widely used in the extraction of bioactive compounds, produced the best extraction of quercetin, kaempferol, gallic acid, syringic acid and epicatechin. These methods, which rely on mechanical disruption and ultrasonic waves to enhance cell wall breakdown, allow for greater release of phenolic compounds while minimizing heat damage. Ultra-Turrax 14,000 r/min, 5 min combined with 1 h ultrasonication led to total phenolics and total flavonoids concentration of 42.29 mgGAE g−1dw, 21.85 mgRE g−1 for blueberry, 97.68 mgGAE g−1dw, 27.62 mgRE g−1 for raspberry and 44.28 mgGAE g−1dw, 16.5 mgRE g−1 for blackberry. In waste, 72.01 mgGAE g−1dw, 41.61 mgRE g−1 from blueberry, 101.18 mgGAE g−1dw, 19.76 mgRE g−1 from raspberry and 96.60 mgGAE g−1dw, 28.90 mgRE g−1 from blackberry were obtained. Syringic acid and epicatechin were the main recovered compounds with 1945 mg g−1dw and 2028 mg g−1dw from fresh raspberry and 884 mg g−1dw and 966 mg g−1dw from blackberry waste. Extracts from blueberry, raspberry and blackberry had antioxidant activities of 832.87, 2757.64 and 583.32 µM TE g−1 dw correspondingly. This study highlights an efficient extraction method that can support more sustainable practices in the food industry by maximizing the recovery of valuable phenolic compounds from both fresh berries and their processing waste.

A Review on the Roles of Extracellular Polymeric Substances (EPSs) in Wastewater Treatment: Source, Mechanism Study, Bioproducts, Limitations, and Future Challenges

Biological treatment is currently a favorable option to treat wastewater due to its environmentally friendly methods and minimal toxic by-products. The majority of biological wastewater treatment uses bacteria as treatment agents, which are known to have excellent capabilities for removing various pollutants. Researchers have extensively explored the use of extracellular polymeric substances (EPSs) generated by bacteria in wastewater treatment. This review focuses on the sources of EPSs, factors influencing their production, and their role in wastewater treatment. Bacterial species, nutrient availability, pH, temperatures, and the presence of toxins were mentioned to be the factors influencing EPS production by bacteria in wastewater treatment. Produced EPSs by bacteria may promote the aggregation, adsorption, decolorization, and degradation of pollutants. This review highlights the challenges of discovering new potential bacterial species and complex EPS extraction methods, as well as the importance of mass production for larger-scale applications.

Tribological performance of used engine oil treated with cement and celite adsorbents by solvent extraction-adsorption method

Purpose As lubricating oils are used, their performance deteriorates and they become contaminated. The purpose of this paper is to investigate the lubrication performance of reclaimed 5 W-30 a fully synthetic used engine oil (UEO) with wear tests after refining it from a solvent-based extraction method using solvent (1-PrOH) and adsorbent materials such as cement, celite and deep eutectic solvent (DES). Design/methodology/approach The treated oil mixtures were prepared by blending engine oils with various adsorbent materials at 5% (w/w) in organic 1-PrOH solvent at a UEO: solvent ratio of 1:2 (w/w). The measurement of kinematic viscosity, density, the total acid number (TAN) and elemental analysis of oil samples was done by the ASTM standards D445/D446, D4052, D974 and D6595, respectively. Adsorbents and treated oil samples characterized by SEM-EDX, FTIR and UV analysis, respectively. Meanwhile, lubricating performance in tribological applications was evaluated through the wear test device using a rotating steel alloy 1.2379 cylinder and a stationary 1.2738 pin under 20, 40 and 80 kg load conditions. Worn surface analysis was done with SEM and 2.5D images. Findings It was found that when using the combination of cement and celite as an adsorbent in the reclamation of used engine oil demonstrated better lubricant properties. The properties of used engine oil were improved in the manner of kinematic viscosity of 32.55 from 68.49 mm2/s, VI (Viscosity index) value of 154 from 130, TAN of 3.18 from 4.35 (mgKOH/g) and Fe content of 11 from 32 mg/L. The anti-wear properties of used engine oil improved by at least 32% when 5% cement and 5% celite adsorbent materials were used together. Research limitations/implications The paper is based on findings from a fully synthetic 5 W-30 A5 multi-grade engine lubrication oil collected after driving approximately 12.000 km. Practical implications The results are significant, as they suggest practical regeneration of used engine oil is achievable. Additionally, blending fresh oil with reclaimed used engine oil in a 1:1 ratio reduced wear loss by over 10% compared to fresh oil. Social implications Reusing used engine oils can reduce their environmental impact and bring economic benefits. Originality/value This study showed that the properties of UEO can be enhanced using the solvent extraction-adsorption method. Furthermore, the study provided valuable insights into the metal concentrations in engine oil samples and their impact on lubrication performance. The order of the number of the grooves quantity and the possibility of the observed scuffing region trend relative to the samples was UEO > 5W-30 fresh oil > Treated oil sample with the adsorbent cement and celite together. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0209/

B-154 Development and Evaluation of an Automated High-Throughput Magnetic Bead-Based pre-treatment Platform for Lipid-Soluble Vitamins in Clinical Mass Spectrometry

Abstract Background Mass spectrometry has become a critical tool in clinical diagnostics due to its unparalleled sensitivity, specificity, and ability for multiplex analysis, redefining standards in small molecule detection. However, its efficacy is limited by complex sample preparation requirements, such as purification and enrichment. Traditional pre-treatment methods often involve labor-intensive, error-prone procedures that can dilute samples, thereby necessitating enhanced sensitivity in mass spectrometry. Addressing these challenges, we developed an automated, high-throughput pre-treatment platform using magnetic bead technology to streamline the preparation of small molecules for analysis via liquid chromatography-tandem mass spectrometry (LC-MS/MS). This study assesses the platform's effectiveness and reliability using lipid-soluble vitamins as a test case. Methods We developed a magnetic bead-based extraction process for lipid-soluble vitamins (Vitamins A, 25-hydroxyvitamin D2, 25-hydroxyvitamin D3, E, and K1) from clinical serum samples, utilizing Zybio's EXM3000 for automation. Protein-bound vitamins were dissociated using methanol and isopropanol, followed by magnetic bead extraction. The extracted vitamins underwent LC-MS/MS analysis. Comparative analysis with traditional liquid-liquid extraction methods, using 20 clinical samples and commercially available kits, evaluated this novel approach's analytical efficacy. Results The method showed excellent linearity (r > 0.99) for the target compounds. The imprecision ranged from 1.5% to 9.3%, and the accuracy (recovery rate) was between 91.5% and 113.4%. The limit of quantification, repeated six times, had a deviation of -8.5% to 13% and a CV of 1.5%-6.4%, meeting industry standards and EP document requirements. Comparisons with commercial kits in 20 clinical samples showed deviations within ±15%, and the 95% confidence interval for expected bias met the testing requirements. The method also demonstrated high correlation with the commercial kits, as evidenced by regression equations for Vitamin A: y = 0.9732x - 1.7985, 25-hydroxyvitamin D2: y = 1.0163x - 0.0055, 25-hydroxyvitamin D3: y = 1.0597x - 0.7968, Vitamin E: y = 1.0569x - 0.2917, and Vitamin K1: y = 1.0055x - 0.0039. Conclusions This study showed excellent performance of a novel automated high-throughput magnetic bead sample preparation platform for small molecules in clinical mass spectrometry. This technology overcomes the complexities associated with sample pre-treatment in clinical mass spectrometry and holds broad clinical application prospects in mass spectrometric analysis.