Journal of separation science最新文献

Fluorinated liquid crystal monomers, owing to persistent, bioaccumulative, and toxic properties, have been detected in environmental and biological samples, posing potential hazards to the environment and humans. However, current pretreatment methods predominantly rely on volatile organic solvents, necessitating cumbersome purification and prolonged nitrogen evaporation time. Moreover, the trace distribution characteristics of fluorinated liquid crystal monomers in the environment demand pretreatment methods that combine highly selective extraction capabilities with significant enrichment efficiency. Consequently, developing efficient extraction methods is crucial for enhancing the detection of fluorinated liquid crystal monomers in the environment. Temperature-responsive deep eutectic solvents represent a novel class of designable extraction solvents developed in recent years. Under temperature induction, temperature-responsive deep eutectic solvents reversibly switch between polar and non-polar states, achieving separation and enrichment effects. According to the biphenyl structure of fluorinated liquid crystal monomers, phenolic compounds were selected as precursors to prepare a series of temperature-responsive deep eutectic solvents for extracting 15 fluorinated liquid crystal monomers from water samples. During the transition from polar to hydrophobic non-polar states, temperature-responsive deep eutectic solvents extract fluorinated liquid crystal monomers from water via π-π interactions and intermolecular hydrogen bonding. Single-factor screening identified temperature-responsive deep eutectic solvents (M_{4-chlorophenol}:M_{4-ethylphenol} = 1:2) as the optimal extractant, with extraction conditions optimized using response surface methodology. Method's LOD and LOQ ranged from 0.08 to 0.30 µg/L and 0.26 to 0.98 µg/L, respectively. In analysis of surface water samples and wastewater treatment inlet/outlet samples, the spiked recovery rates of fluorinated liquid crystal monomers ranged from 70.5% ± 3.5% to 125.8% ± 1.6%, with detection concentrations spanning 0.44–2.67 µg/L. Compared to conventional methods, this approach achieves one-step extraction without requiring purification or additional dispersing solvents. It reduces extractant consumption and shortens extraction time while maintaining high extraction efficiency, offering a novel approach for detecting fluorinated liquid crystal monomers in environmental water.

In this study, a novel effervescent-assisted magnetic micro solid-phase extraction (EA-MSPE) method was developed for the detection of four organochlorine pesticides (OCPs) in water samples. The adsorbent for this method consisted of magnetic biochar particles, which were created by loading Fe₃O₄ onto pomelo peel biochar. It combined effervescent compression technology to complete the automatic extraction process and utilized an applied magnetic field to achieve solid-liquid separation, thereby establishing the EA-MSPE method. Several experimental parameters that affect the extraction efficiency, including biochar dosage, acid-base conditions, pH, eluting solvent, and salt content, were investigated. The results indicated that the optimal conditions were as follows: a biochar dosage of 0.06 g, a ratio of sodium dihydrogen phosphate to sodium bicarbonate of 1:3, a pH of 10, an elution solvent composed of ethyl acetate and n-hexane (2:1), and 10% NaCl. The recoveries of the four OCPs ranged from 63.56% to 87.52%. The limits of detection varied between 0.17 and 0.95 µg/L. The relative standard deviations ranged from 6.0% to 9.2%. This method was based on effervescent technology and magnetic particles to achieve an automatic extraction and separation process, saving time, energy, and being environmentally friendly. The produced magnetic effervescent tablet was easy to carry, more suitable for field sampling and extraction, and has strong practicability.

This study proposed a general strategy of “pre-column derivatization-chiral selector synergy” and achieved preparation-level separation in the gossypol derivative system for the first time. In this study, gossypol acetate was derived by chloramphenicol base (d-(-)-threo-1-(p-nitrophenyl)-2-amino-1,3-propanediol), and the derivatives were separated by high-speed countercurrent chromatography using HP-β-CD (Hydroxypropyl-β-cyclodextrin) as chiral selector. According to the test results, a two-phase solvent system with a volume ratio of 7:13:8:12 water: methanol: ethyl acetate: cyclohexane was selected. The factors influencing the separation process were investigated. The final conditions are 65% methanol content in aqueous phase, 60% ethyl acetate content in organic phase, HP-β-CD concentration was 0.05 mol/L, pH value was 2.37, and temperature was 20°C. The purity of the enantiomers of gossypol acetate is more than 95% (it was calculated by the area normalization method), and the recoveries of gossypol derivatives are between 80% and 90%.

Endogenous glycopeptides in human serum are valuable candidates for disease biomarker discovery, but their low abundance and structural complexity pose significant analytical challenges. In this study, we developed and optimized a robust workflow for enriching and in-depth characterizing the endogenous glycopeptidome using a novel hydrophilic interaction liquid chromatography (HILIC) sorbent. The HILIC-based strategy effectively enriched glycopeptides from mono-glycosylated IgG and avidin digests, as well as multi-glycosylated horseradish peroxidase (HRP), demonstrating high selectivity, femtomolar-level sensitivity (down to 1 fmol), reproducibility (RSD > 1), and reusability for up to four cycles. Comprehensive analysis by mass spectrometry (MS) identified 334 endogenous intact N-linked glycopeptides, including 318 N-glycosites from 289 glycoproteins. In addition, 242 endogenous intact O-linked glycopeptides from 38 glycoproteins are identified. These represent one of the most extensive endogenous glycopeptide datasets to date. N-glycans were primarily complex biantennary types, while Core 1 structures dominated O-glycans. Motif analysis revealed a strong preference for canonical N-glycosylation motifs (NXT/NXS) and a nearly equal distribution of O-glycosylation on serine and threonine residues. Gene Ontology analysis indicated functional enrichment in membrane-associated, catalytic, and immune-related processes. This optimized HILIC-MS platform enables sensitive and reproducible glycoproteomic profiling, providing a valuable tool for biomarker discovery.

The objective was to achieve accurate origins discriminations of flos of Lonicerae japonicae, especially those from the genuine and non-genuine producing areas. An integration strategy was proposed based on machine learning, combining ultra-high performance liquid chromatography-time-of-flight mass spectrometry and gas chromatography-ion mobility chromatography data fusion for geographical origins discrimination of flos of Lonicerae japonicae. Sixty-one batches of flos of Lonicerae japonicae samples from different origins were determined by ultra-high performance liquid chromatography-time-of-flight mass spectrometry and gas chromatography-ion mobility chromatography. Multivariate statistical analysis, including principal component analysis and partial least-squares discriminant analysis were performed for the classification with an accuracy of 80.33% and 96.72%, respectively. Variable importance in projection (VIP>1) screened 9 nonvolatile differential constituents and 32 volatile differential constituents, respectively. The results of machine learning models combined with ultra-high performance liquid chromatography-time-of-flight mass spectrometry and gas chromatography-ion mobility chromatography data fusion indicated that the multilayer perceptron, logistic, gradient boosting decision tree, and Decision Tree (CART)) combined with a middle-level data fusion approach, achieve 100% classification accuracy with the training set. Among them, the multilayer perceptron algorithm achieved 100% accuracy for both the training and testing sets. These findings provide methodology support for tracing the origin of flos of Lonicerae japonicae.

Casein-based ingredients are widely used and labeled in cosmetic products, especially facial masks. However, a reliable determination method has not been established, and label authenticity cannot be readily verified. Therefore, in the present study, we selected four previously verified marker peptides corresponding to the four milk casein subtypes (αs1, αs2, β, and κ) and developed a quantitative UHPLC–ESI–QqQ–MS method for determining caseins in facial mask samples. Compared with organic-solvent precipitation and isoelectric precipitation, an optimized in-gel enzymatic digestion using 8% polyacrylamide, followed by three cycles of ultrasound-assisted extraction with 0.1% formic acid–acetonitrile, afforded the highest recovery. αs1-, αs2-, and κ-casein showed good linearity from 0.1 to 4 µmol/L, while β-casein was linear from 0.2 to 8 µmol/L (all R² > 0.990). For β-casein, the detection limit and quantitation limit were 10 and 30 nmol/L, respectively. Matrix effects (ME) were calculated as (response in matrix/response in neat solution) − 1. The ME values were 0.5, 0.4, 0.3, and 0.4 for the selected peptides of αs1-, αs2-, β-, and κ-casein, respectively, indicating that the corresponding peak areas in the milk matrix were 50%, 40%, 30%, and 40% higher than those obtained in neat solution. Therefore, all four peptides exhibited moderate-to-strong signal enhancement rather than ion suppression. Method accuracy and precision were satisfactory, with recoveries of 87.0%–108.0% and relative standard deviations of 2.8%–8.2%. Applied to 20 batches of facial masks labeled as containing milk protein extract, casein was detected in 11 batches. In conclusion, the method is suitable for authenticity verification of casein in cosmetic products.

Nano liquid chromatography–mass spectrometry has come to be a key enabling technology in shotgun proteomics due to the combination of exceptional separation power, sensitivity, and comprehensiveness. However, the know-how of setting up proteomics methods to deliver robust, reliable, and meaningful results to large-scale life science experiments has remained somewhat ambiguous. This protocol outlines guidance for establishing nano-LC–MS/MS workflows focusing on comprehensive and untargeted deep proteome profiling, using state-of-the-art column technology and mass spectrometry. Employing a second-generation micropillar-array column, a trade-off is demonstrated between analysis time and chromatographic resolving power, which in turn impacts peptide and protein identification scores from a commercial HeLa reference standard. Furthermore, a straightforward workflow to develop a data-independent acquisition (DIA)-parallel accumulation—serial fragmentation (PASEF) analytical method is proposed, with a special focus on the optimization of the ESI source settings. Besides the method development, the study discusses the use of segmented gradients, and an MS-compatible surfactant in the sample diluent is also explored. Finally, the robustness of the developed method is demonstrated through consistently identifying 7558 protein groups (CV = 0.3%) as maintaining high repeatability peptide retention times (mean CV = 0.2%) and system pressure (CV = 0.4%) over 21 consecutive analyses.