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The analysis of per- and polyfluoroalkyl substances (PFAS) via sum parameters like extractable organic fluorine (EOF) in combination with high resolution-continuum source-graphite furnace molecular absorption spectrometry (HR-CS-GFMAS) is highly promising regarding fluorine sensitivity and selectivity. However, the HR-CS-GFMAS method includes several drying and heating steps which can lead to losses of volatile PFAS before the molecular formation step using e.g., GaF formation. Hence, the method leads to a strong discrimination of PFAS within the EOF depending on their physical/chemical properties and is therefore associated with reduced accuracy. To reduce this discrepancy and to indicate realistic PFAS pollution values, an optimization of the HR-CS-GFMAS method for PFAS analysis is needed. Hence, we determined fluorine response factors of several PFAS with different physical/chemical properties upon application of systematic optimization steps. We could therefore improve the method's sensitivity for PFAS analysis using a modifier drying pre-treatment step followed by a sequential injection of sample solutions. The highest improvement in sensitivity of volatile PFAS was shown upon addition of a Mg modifier during drying pre-treatment. Thereby, during optimization the relative standard deviation of fluorine response factors could be reduced from 55 % (initial method) to 27 % (optimized method) leading to a more accurate determination of organofluorine sum parameters. The method provides an instrumental LOD and LOQ of ß(F) 1.71 µg/L and 5.13 µg/L, respectively. Further validation aimed to investigate several matrix effects with respect to water matrices. Here, substance-specific behavior was observed. For example, perfluorooctanoic acid (PFOA) which was used as calibrator, showed signal suppressions upon high chloride concentrations (>50 mg/L). Hence, a thorough separation of Cl from analytes during sample preparation is needed for accurate sum parameter analysis.

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Deep Mutational Scanning (DMS) assays are powerful tools to study sequence-function relationships by measuring the effects of thousands of sequence variants on protein function. During a DMS experiment, several technical artefacts might distort non-linearly the functional score obtained, potentially biasing the interpretation of the results. We therefore tested several technical parameters in the deepPCA workflow, a DMS assay for protein-protein interactions, in order to identify technical sources of non-linearities. We found that parameters common to many DMS assays such as amount of transformed DNA, timepoint of harvest and library composition can cause non-linearities in the data. Designing experiments in a way to minimize these non-linear effects will improve the quantification and interpretation of mutation effects.

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Spatiotemporal monitoring of pesticide residues in river water is urgently needed due to its negative environmental and human health consequences. The present study is to investigate the occurrence of multiclass pesticide residue in the surface water of the Feni River, Bangladesh, using an optimized salting-out assisted liquid-liquid microextraction (SALLME) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS). The optimized SALLME method was developed and validated following the SANTE/11312/2021 guidelines. A total of 42 water samples were collected and analyzed to understand the spatiotemporal distribution of azoxystrobin (AZ), buprofezin (BUP), carbofuran (CAR), pymetrozine (PYM), dimethoate (DMT), chlorantraniliprole (CLP), and difenoconazole (DFN). At four spike levels (n = 5) of 20, 40, 200, and 400 µg/L, the recovery percentages were satisfactory, ranging between 71.1 % and 107.0 % (RSD ≤13.8 %). The residues ranged from below the detection level (BDL) to 14.5 µg/L. The most frequently detected pesticide was DMT (100 %), followed by CLP (52.3809-57.1429), CAR (4.7619-14.2867), and PYM (4.7619-9.5238). However, AZ and BUP were below the detection limit in the analyzed samples of both seasons. Most pesticides and the highest concentrations were detected in March 2023, while the lowest concentrations were present in August 2023.Furthermore, ecological risk assessment based on the general-case scenario (RQm) and worst-case scenario (RQex) indicated a high (RQ > 1) risk to aquatic organisms, from the presence of PYM and CLP residue in river water. Human health risk via dietary exposure was estimated using the hazard quotient (HQ). Based on the detected residues, the HQ (<1) value indicated no significant health risk. This report provides the first record of pesticide residue occurrences scenario and their impact on the river environment of Bangladesh.

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The effectiveness of commonly used extractants for chromatographic separation of rare earth elements (REEs) was compared. Columns loaded with similar molar concentrations of tributyl phosphate (TBP), di-(2-ethylhexyl) phosphoric acid (HDEHP), and N-Methyl-N, N, N-tri-octyl-ammonium chloride (Aliquat-336), with mineral acid as eluent were evaluated. Retention factors were determined, and separation efficiency was assessed based on the resolution data of the REEs acquired under the same elution conditions for each column. HDEHP demonstrated the best separation efficiency for the entire REE series (mean Rs = 2.76), followed by TBP (mean Rs = 1.52), while Aliquat-336 exhibited the lowest performance (mean Rs = 1.42). The HDEHP-coated column was then used to optimize the extraction chromatographic separation of the REEs. The primary challenge was to completely elute the heavy REEs (Tb - Lu) while maintaining adequate separation of the light REEs (La - Gd) within a reasonably short time. The stepwise gradient elution procedure improved the resolution between adjacent REEs, allowing the complete separation of the entire REE series within 25 minutes. Better separation efficiency for light REEs was achieved at higher column temperatures and a mobile phase flow rate of 1.5 mL/min in the tested domain of 20-60 °C, and 0.5-2.0 mL/min, respectively, resulting in plate heights (H) ranging from 0.011 to 0.027 mm.

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An accurate and sensitive method for the determination of a total of 23 pesticides and their metabolites in human urine has been optimised. The methodology is based on a previously published method based on solid-phase extraction with methanol and acetone followed by ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) in the selected reaction mode (SRM) with both positive and negative electrospray ionization (ESI+/-). The detection settings of the previous method, which allowed to determine the metabolites from 6 organophosphate and 2 pyrethroid pesticides, were optimised in order to include further pesticide groups, such as 11 neonicotinoids, 3 carbamates/thiocarbamates and 2 triazoles. The 5-windows method enduring 22 min was optimized with acceptable results in relation to accuracy (recoveries >75 %), precision (coefficients of variation <26 %) and linearity (R2> 0.9915). The limits of detection ranged between 0.012 ng/mL and 0.058 ng/mL. Samples from the German External Quality Assessment Scheme (G-EQUAS) encompassing 2 pyrethroids, 2 organophosphate and one neonicotinoid (6-chloronicotinic acid, a common metabolite of imidacloprid and acetamiprid) were analysed, and the latter, included in this newest optimization, provided good reference results. The method is optimal as a human biomonitoring tool for health risk assessment in large population surveys.

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While Reinforcement Learning (RL) has already proven successful in performing complex tasks, such as controlling large-scale epidemics, mitigating influenza and playing computer games beyond expert level, it is currently largely unexplored in the field of separation sciences. This paper therefore aims to introduce RL, specifically proximal policy optimization (PPO), in liquid chromatography, and evaluate whether it can be trained to optimize separations directly, based solely on the outcome of a single generic separation as input, and a reward signal based on the resolution between peak pairs (taking a value between [-1,1]). More specifically, PPO algorithms or agents were trained to select linear (1-segment) or multi-segment (2-, 3-, or 16-segment) gradients in 1 experiment, based on the outcome of an initial, generic linear gradient (ϕstart=0.3, ϕend=1.0, and tg=20min), to improve separations. The size of the mixtures to be separated varied between 10 and 20 components. Furthermore, two agents, selecting 16-segment gradients, were trained to perform this optimization using either 2 or 3 experiments, in sequence, to investigate whether the agents could improve separations further, based on previous outcomes. Results showed that the PPO agent can improve separations given the outcome of one generic scouting run as input, by selecting ϕ-programs tailored to the mixture under consideration. Allowing agents more freedom in selecting multi-segment gradients increased the reward from 0.891 to 0.908 on average; and allowing the agents to perform an additional experiment increased the reward from 0.908 to 0.918 on average. Finally, the agent outperformed random experiments as well as standard experiments (ϕstart=0.0, ϕend=1.0, and tg=20min) significantly; as random experiments resulted in average rewards between 0.220 and 0.283, and standard experiments resulted in average rewards of 0.840. In conclusion, while there is room for improvement, the results demonstrate the potential of RL in chromatography and present an interesting future direction for the automated optimization of separations.

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A reversed-phase high-performance liquid chromatographic (HPLC) method was developed for the simultaneous determination of the potential impurities of dexketoprofen, including the distomer R-ketoprofen. After screening the separation capability of four polysaccharide columns (Lux Amylose-1, Lux Amylose-2, Lux Cellulose-1 and Lux Cellulose-2) in polar organic and in reversed-phase modes, appropriate enantioseparation was observed only on the Lux Amylose-2 column in an acidified acetonitrile/water mixture. A detailed investigation of the mobile phase composition and temperature for enantio- and chemoselectivity showed many unexpected observations. It was observed that both the resolution and the enantiomer elution order can be fine-tuned by varying the temperature and mobile phase composition. Moreover, hysteresis of the retention times and enantioselectivity was also observed in reversed-phase mode using methanol/water mixtures on amylose-type columns. This could indicate that the three-dimensional structure of the amylose column can change by transitioning from a polar organic to a reversed-phase mode, which affects the enantioseparation process. Temperature-dependent enantiomer elution order and rare enthalpic/entropic controlled enantioseparation in the operative temperature range were also observed in reversed-phase mode. To find the best methodological conditions for the determination of dexketoprofen impurities, a full factorial optimization design was performed. Using the optimized parameters (Lux Amylose-2 column with water/acetonitrile/acetic acid 50/50/0.1 (v/v/v) at a 1 mL/min flow rate at 20 °C), baseline separations were achieved between all compounds within 15 min. Our newly developed HPLC method was validated according to the current guidelines, and its application was tested on commercially available pharmaceutical formulations. According to the authors' knowledge, this is the first study to report hysteretic behavior on polysaccharide columns in reversed-phase mode.

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Two-dimensional liquid chromatography (2D-LC), and in particular comprehensive two-dimensional liquid chromatography (LC×LC), offers increased peak capacity, resolution and selectivity compared to one-dimensional liquid chromatography. It is commonly accepted that the technique produces the best results when the separation mechanisms in the two dimensions are completely orthogonal; however, the use of similar separation mechanisms in both dimensions has been gaining popularity as it helps avoid difficulties related to mobile phase incompatibility and poor column efficiency. The remarkable advantages of using reversed phase in both dimensions (RPLC×RPLC) over other separation mechanisms made it a promising technique in the separation of complex samples. This review discusses some physical and practical considerations in method development for 2D-LC involving the use of RP in both dimensions. In addition, an extensive overview is presented of different applications that relied on RPLC×RPLC and 2D-LC with reversed phase column combinations to separate components of complex samples in different fields including food analysis, natural product analysis, environmental analysis, proteomics, lipidomics and metabolomics.

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For method development in gas chromatography, suitable computer simulations can be very helpful during the optimization process. For such computer simulations retention parameters are needed, that describe the interaction of the analytes with the stationary phase during the separation process. There are different approaches to describe such an interaction, e.g. thermodynamic models like Blumberg's distribution-centric 3-parameter model (K-centric model) or models using chemical properties like the Linear Solvation Energy Relationships (LSER). In this work LSER models for a Rxi-17Sil MS and a Rxi-5Sil MS GC column are developed for different temperatures. The influences of the temperature to the LSER system coefficients are shown in a range between 40 and 200 °C and can be described with Clark and Glew's ABC model as fit function. A thermodynamic interpretation of the system constants is given and its contribution to enthalpy and entropy is calculated. An estimation method for the retention parameters of the K-centric model via LSER models were presented. The predicted retention parameters for a selection of 172 various compounds, such as FAMEs, PCBs and PAHs are compared to isothermal determined values. 40 measurements of temperature programmed GC separations are compared to computer simulations using the differently determined or estimated K-centric retention parameters. The mean difference (RSME) between the measured and predicted retention time is less than 8 s for both stationary phases using the isothermal retention parameters. With the LSER predicted parameters the difference is 20 s for the Rxi-5Sil MS and 38 s for the Rxi-17Sil MS. Therefore, the presented estimation method can be recommended for first method development in gas chromatography.

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Phosphate oxygen isotope (δ18OP) technique is an effective tool to identify the source and transformation process of phosphorus. The poor applicability of existing δ18OP pretreatment methods for sediments hindered the large-scale application of δ18OP technology. This paper presents an optimization framework for the pretreatment of sediment δ18OP samples based on large-scale applications, using the Fuyang River Basin as a case study. The typical channel landscape outflow lake, South Lake, was selected as the most favorable point for assessing the applicability and optimizing the mainstream δ18OP pretreatment method, which was achieved by clarifying the sediment environmental characteristics of South Lake. To evaluate the suitability of the Blake and McLaughlin methods in South Lake, a comparative study was carried out based on five dimensions: phosphorus recovery rate, removal efficiency of organic matter, removal efficiency of extraction liquid impurity ion, experimental time, and reagent consumption cost. The findings demonstrated that the Blake method outperformed the McLaughlin method across all five dimensions. Based on the environmental characteristics of the sediments of South Lake, the Blake method was optimized from two perspectives, namely the substitution of reagents and adjustment and optimization of experimental procedures. This resulted in an enhancement of phosphorus recovery and organic matter removal efficiency, while also reducing the experimental time required. The optimized method also yielded satisfactory results when applied to the entire watershed. This research paper can thus offer valuable technical support for the widespread application of sediment δ18OP technology.

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The untargeted approach to mass spectrometry-based metabolomics has a wide potential to investigate health and disease states, identify new biomarkers for diseases, and elucidate metabolic pathways. All this holds great promise for many applications in biological and chemical research. However, the complexity of instrumental parameters on advanced hybrid mass spectrometers can make the optimization of the analytical method immensely challenging. Here, we report a strategy to optimize the selected settings of a hydrophilic interaction liquid chromatography-tandem mass spectrometry method for untargeted metabolomics studies of human plasma, as a sample matrix. Specifically, we evaluated the effects of the reconstitution solvent in the sample preparation procedure, the injection volume employed, and different mass spectrometry-related operating parameters including mass range, the number of data-dependent fragmentation scans, collision energy mode, duration of dynamic exclusion time, and mass resolution settings on the metabolomics data quality and output. This study highlights key instrumental variables influencing the detection of metabolites along with suggested settings for the IQ-X tribrid system and proposes a new methodological framework to ensure increased metabolome coverage.

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Emerging polycyclic aromatic nitrogen heterocycles (PANHs) contributes significantly to the health risk associated with inhaling polluted air. However, there is a lack of analytical methods with the needed performance to their determination. This study presents the optimization and validation for the first time of a green microscale extraction procedure for the determination of twenty-one PANHs, including carbazole, indole, and quinolone classes, in particulate matter (PM2.5) samples by gas chromatography-mass spectrometry. A simplex-centroid mixture design and full factorial design (23) were employed to optimize the following extraction parameters: type and volume of solvent, sample size, extraction time, and necessity of a cleanup step. Low limits of detection and quantification (LOD < 0.97 pg m-3 and LOQ < 3.24 pg m-3, respectively) were obtained in terms of matrix-matched calibration. The accuracy and precision of the method were adequate, with recoveries in three levels between 73 to 120% and intraday and interday relative standard deviations from 2.0 to 12.9% and 7.3 to 18.9%, respectively. The green character of the method was evaluated using the Analytical Greenness (AGREE) tool, where a score of 0.69 was obtained, indicating a great green procedure. The method was applied to PM2.5 samples collected from sites with different characteristics; the concentrations ranged from 69.3 pg m-3 (2-methylcarbazole) to 11,874 pg m-3 (carbazole) for individual PANHs and from 2306 to 24,530 pg m-3 for ∑21PANHs. Principal component analysis (PCA) and hierarchical clustering enabled discrimination of the sampling sites according to the PANHs concentrations. The score plots formed two distinct groups, one with samples containing higher concentrations of PANHs, corresponding to sites with a major influence from diesel emissions, and another group with minor PANH contents, corresponding to sites impacted by emissions from urban traffic and industrial activities.

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Analytical data processing often requires the comparison of data, i.e. finding similarities and differences within separations. In this context, a peak-tracking algorithm was developed to compare multiple datasets in one-dimensional (1D) and two-dimensional (2D) chromatography. Two application strategies were investigated: i) data processing where all chromatograms are produced in one sequence and processed simultaneously, and ii) method optimization where chromatograms are produced and processed cumulatively. The first strategy was tested on data from comprehensive 2D liquid chromatography and comprehensive 2D gas chromatography separations of academic and industrial samples of varying compound classes (monoclonal-antibody digest, wine volatiles, polymer granulate headspace, and mayonnaise). Peaks were tracked in up to 29 chromatograms at once, but this could be upscaled when necessary. However, the peak-tracking algorithm performed less accurate for trace analytes, since, peaks that are difficult to detect are also difficult to track. The second strategy was tested with 1D liquid chromatography separations, that were optimized using automated method-development. The strategy for method optimization was quicker to detect peaks that were still poorly separated in earlier chromatograms compared to assigning a target chromatogram, to which all other chromatograms are compared. Rendering it a useful tool for automated method optimization.

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Systematic selection of mobile phase and column chemistry type can be critical for achieving optimal chromatographic separation, high sensitivity, and low detection limits in liquid chromatography electrospray high resolution mass spectrometry (LC/MS). However, the selection process is challenging for non-targeted screening where the compounds of interest are not preselected nor available for method optimization. To provide general guidance, twenty different mobile phase compositions and four columns were compared for the analysis of 78 compounds with a wide range of physicochemical properties (logP range from -1.46 to 5.48), and analyte sensitivity was compared between methods. The pH, additive type, column, and organic modifier had significant effects on the analyte response factors, and acidic mobile phases (e.g. 0.1% formic acid) yielded highest sensitivity. In some cases, the effect was attributable to the difference in organic modifier content at the time of elution, depending on the mobile phase and column chemistry. Based on these findings, 0.1% formic acid, 0.1% ammonia and 5.0 mM ammonium fluoride were further evaluated for their performance in non-targeted LC/ESI/HRMS analysis of wastewater treatment plan influent and effluent, using a data dependent MS2 acquisition and two different data processing workflows (MS-DIAL, patRoon 2.1) to compare number of detected features and sensitivity. Both data-processing workflows indicated that 0.1% formic acid yielded the highest number of features in full scan spectrum (MS1), as well as the highest number of features that triggered fragmentation spectra (MS2) when dynamic exclusion was used.

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A modified QuEChERS method coupled with high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was established for residue analysis of 39 pollutants (34 commonly used multi-class pesticides and 5 metabolites) in medlar matrices (fresh, dried, and medlar juice). Samples were extracted using water with 0.1 % formic acid: acetonitrile (5: 10, v/v). The phase-out salts and five different cleanup sorbents (including N-propyl ethylenediamine (PSA), octadecyl silane bonded silica gel (C18), graphitized carbon black (GCB), Carbon nanofiber (C-Fiber) and MWCNTs) were investigated to improve the purification efficiency. The Box-Behnken Design (BBD) study was employed for an optimal solution of the volume of extraction solvent, phase-out salt, and the purification sorbents for the analytical method. The average recoveries of the target analytes in the three medlar matrices ranged from 70 % to 119 % with relative standard deviations (RSDs) of 1.0 %-19.9 %. Screening of market samples (fresh and dried medlars) collected from the major producing regions in China showed that 15 pesticides and metabolites were detected in the samples at concentrations of 0.01-2.22 mg/kg, and none of which exceeded the maximum residue limits (MRLs) set in China. The results showed that the risk of food safety by consumption of medlar products caused by the use of pesticides was low. The validated method could be used for rapid and accurate screening of multi-class multi-pesticide residues in Medlar for food safety.

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Cystine-enriched food supplements are increasingly popular due to their beneficial health effects. However, the lack of industry standards and market regulations resulted in quality issues with cystine food products, including cases of food adulteration and fraud. This study established a reliable and practical method for determining cystine in food supplements and additives using quantitative NMR (qNMR). With the optimized testing solvent, acquisition time, and relaxation delay, the method exhibited higher sensitivity, precision, and reproducibility than the conventional titrimetric method. Additionally, it was more straightforward and more economical than HPLC and LC-MS. Furthermore, the current qNMR method was applied to investigate different food supplements and additives regarding cystine quantity. As a result, four of eight food supplement samples were found to be inaccurately labeled or even with fake labeling, with the relative actual amount of cystine ranging from 0.3% to 107.2%. In comparison, all three food additive samples exhibited satisfactory quality (the relative actual amount of cystine: 97.0-99.9%). Notably, there was no obvious correlation between the quantifiable properties (price and labeled cystine amount) of the tested food supplement samples and their relative actual amount of cystine. The newly developed qNMR-based approach and the subsequent findings might help standardization and regulation of the cystine supplement market.

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OBJECTIVES: Validation studies in juvenile dental age estimation primarily focus on point estimates while interval performance for reference samples of different ancestry group compositions has received minimal attention. We tested the effect of reference sample size and composition by sex and ancestry group on age interval estimates. MATERIALS AND METHODS: The dataset consisted of Moorrees et al. dental scores from panoramic radiographs of 3334 London children of Bangladeshi and European ancestry and 2-23 years of age. Model stability was assessed using standard error of mean age-at-transition for univariate cumulative probit and sample size, group mixing (sex or ancestry), and staging system as factors. Age estimation performance was tested using molar reference samples of four sizes, stratified by year of age, sex, and ancestry. Age estimates were performed using Bayesian multivariate cumulative probit with 5-fold cross-validation. RESULTS: Standard error increased with decreasing sample size but showed no effect from mixing by sex or ancestry. Estimating ages using a reference and target sample of different sex reduced success rate significantly. The same test by ancestry groups had a lesser effect. Small sample size (n < 20/year of age) negatively affected most performance metrics. DISCUSSION: We found that reference sample size, followed by sex, primarily drove age estimation performance. Combining reference samples by ancestry produced equivalent or better estimates of age by all metrics than using a single-demographic reference of smaller size. We further proposed that population specificity is an alternative hypothesis of intergroup difference that has been erroneously treated as a null.

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Neuromedin U (NmU) and neuromedin S (NmS) are two closely related neuropeptides belonging to the neuromedin family. NmU usually occurs either as a truncated eight amino acid long peptide (NmU-8) or as an 25 amino acid long peptide, although other molecular forms exist depending on the species considered. NmS, on the other hand, is a 36 amino acid long peptide, sharing the same amidated C-terminal heptapeptide with NmU. Nowadays, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is the preferred analytical technique for peptide quantification, because of its excellent sensitivity and selectivity. However, reaching the required quantification limits for these compounds in biological samples remains an extremely challenging task, especially because of their nonspecific binding (NSB). This study highlights the difficulties that are faced when quantifying larger neuropeptides (23-36 amino acids) compared to smaller ones (< 15 amino acids). The first part of this work aims to solve the adsorption problem for NmU-8 and NmS, by investigating the different steps involved in the sample preparation, i.e. the different solvents applied and the pipetting protocol. The addition of 0.05% plasma as an adsorption competitor was found to be primordial to avoid peptide loss due to NSB. The second part of this work focusses on further improving the sensitivity of the LC-MS/MS method for NmU-8 and NmS, by evaluating some UHPLC-parameters, including the stationary phase, the column temperature and the trapping conditions. For both peptides of interest, the best results were achieved when combining a C18 trap column with a C18 iKey separation device containing a positively charged surface. Column temperatures of 35 and 45 °C for NmU-8 and NmS respectively, resulted in the highest peak areas and S/N ratios, while applying higher column temperatures substantially decreased sensitivity. Moreover, a gradient starting at 20% organic modifier instead of 5% significantly improved the peak shape of both peptides. Finally, some compound-specific MS parameters, i.e. the capillary and the cone voltages, were evaluated. The peak areas increased with a factor 2 and 7 for NmU-8 and NmS respectively and peptide detection in the low picomolar range is now feasible.

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This study attempts to explore the suitable conditions for the detection of volatile flavor compounds (VFCs) in Ningxiang pork by headspace solid-phase microextraction and gas chromatography-mass spectrometry (HS-SPME-GC-MS). Ningxiang pigs were harvested from a slaughterhouse and a longissimus dorsi sample was collected from each animal. The VFCs of Ningxiang pork can be strongly impacted by the detection conditions (columns, weight of meat samples, heat treatment time, equilibrium conditions, and extraction conditions) that need to be optimized. Our results also provided the optimal test conditions: weighing 5 g of meat samples, grinding for 30 s in a homogenizer, heat treatment at 100 °C for 30 min, equilibration at 70 °C for 30 min, and extraction at 100 °C for 50 min. Furthermore, the feasibility and representativeness of the test method were confirmed based on principal component analysis and a comparison of the three pork VFCs. These findings offer researchers a unified and efficient pretreatment strategy to research pork VFCs.