Experimental designs for modeling retention patterns and separation efficiency in analysis of fatty acid methyl esters by gas chromatography–mass spectrometry

An exploratory study of volatile and semi-volatile organic compounds in PM2.5 atmospheric particles from an outdoor environment in Brazil

The development of methods for determining volatile and semi-volatile organic compounds in public spaces has become necessary to identify potential health and environmental risks. This study presents a practical methodology for sampling, extracting, detecting, and identifying these compounds in a vehicular traffic region in Belo Horizonte, Brazil. The methodology uses direct-immersion solid phase microextraction (DI-SPME) and static headspace (SHS) to extract SVOCs/VOCs. Comprehensive time-of-flight gas chromatography mass spectrometry (GC×GC/Q-TOFMS) and gas chromatography coupled to mass spectrometry (GC/MS) were used to detect and identify compounds. The analysed samples, collected with a high-volume sampler (Hi-Vol) with quartz filters and in which particulate matter (PM2.5) was retained, showed the presence of more than 200 compounds, both biogenic (natural origin) and anthropogenic (human origin). Regarding the distribution of chemical classes, aromatic compounds were predominantly found at 29.2%, followed by esters at 20.8%, non-aromatic hydrocarbons at 5.6%, and carboxylic acids at 9.4%. Static headspace gas chromatography (HS-GC) enabled the identification and quantification of 21 volatile compounds, including BETX, dichloromethane, chloroform, and naphthalene, which are currently regulated by the US Environmental Protection Agency (EPA).

Enhanced biosynthesis of coated silver nanoparticles using isolated bacteria from heavy metal soils and their photothermal-based antibacterial activity: integrating Response Surface Methodology (RSM) Hybrid Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies

This study explores the biosynthesis of silver nanoparticles (AgNPs) using the Streptomyces tuirus S16 strain, presenting an eco-friendly alternative to mitigate the environmental and health risks of chemical synthesis methods. It focuses on optimizing medium culture conditions, understanding their physicochemical properties, and investigating their potential photothermal-based antibacterial application. The S16 strain was selected from soils contaminated with heavy metals to exploit its ability to produce diverse bioactive compounds. By employing the combination of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies, we optimized AgNPs synthesis, achieving an improvement of nearly 2.45 times the initial yield under specific conditions (Bennet's medium supplemented with glycerol [5 g/L] and casamino-acid [3 g/L] at 30 °C for 72 h). A detailed physicochemical characterization was conducted. Notably, the AgNPs were well dispersed, and a carbonaceous coating layer on their surface was confirmed using energy-dispersive X-ray spectroscopy. Furthermore, functional groups were identified using Fourier-transform infrared spectroscopy, which helped enhance the AgNPs' stability and biocompatibility. AgNPs also demonstrated efficient photothermal conversion under light irradiation (0.2 W/cm2), with temperatures increasing to 41.7 °C, after 30 min. In addition, treatment with light irradiation of E. coli K-12 model effectively reduced the concentration of AgNPs from 105 to 52.5 µg/mL, thereby enhancing the efficacy of silver nanoparticles in contact with the E. coli K-12.

Polymeric ionic liquid sorbent coatings in thin film microextraction: Insight into sorbent selectivity for pesticides and cannabinoids

Polymeric ionic liquid (PIL) sorbent coatings consisting of polymerizable cations and anions were employed as sorbent coatings in thin film microextraction (TFME) for the extraction of pesticides and cannabinoids. The blades consisted of a thin film of PIL sorbents chemically bonded to vinyltrimethoxysilane-functionalized nitinol sheets. The imidazolium- or ammonium-based PIL sorbents contained aromatic benzyl moieties as well as polar hydroxyl groups or aliphatic functional groups within the chemical structure of the IL monomer. The chemical structure of the IL crosslinkers of the PILs were kept constant across each sorbent, except for the anion, which consisted of either bis[(trifluoromethyl)sulfonyl]imide ([NTf2-]), p-styrenesulfonate ([SS-]), or 3-sulfopropyl acrylate ([SPA-]). Temperature, salt content, and methanol content were optimized as extraction conditions to maximize pesticide-cannabinoid selectivity using Doehlert design of experiments (DOE). Effects of these three factors on selectivity and extraction efficiency are discussed. The optimal extraction conditions consisting of sample temperature (31°C), sodium chloride (30% w/v), and methanol content (0.25% v/v) are compared to initial sorbent screening conditions at a sample temperature of 40°C, 15% (w/v) sodium chloride, and 2.5% (v/v) methanol content. PIL sorbent swelling behavior at different salt and methanol content conditions and its effect on extraction efficiency are hypothesized. Selectivity factors for the sorbents indicated that aromatic moieties within the IL monomer may enhance pesticide-cannabinoid selectivity under optimized conditions, but the extraction efficiency of pesticides that are known to coelute with cannabinoids in the chromatographic separation may be enhanced by employing sorbent coatings with [SPA-] anions.

Method development for optimizing analysis of ignitable liquid residues using flow-modulated comprehensive two-dimensional gas chromatography

The abundance and composition of matrix compounds in fire debris samples undergoing ignitable liquid residue analysis frequently leads to inconclusive results, which can be diminished by applying comprehensive two-dimensional gas chromatography (GC × GC). Method development must be undertaken to fully utilize the potential of GC × GC by maximizing separation space and resolution.. The three main areas to consider for method development are column selection, modulator settings and parameter optimization. Seven column combinations with different stationary phase chemistry, column dimensions and orthogonality were assessed for suitability based on target compound selectivity, retention, resolution, and peak shapes, as well as overall peak capacity and area use. Using Box-Behnken design of experimentation (DoE), the effect of modulator settings such as flow ratio and loop fill capacity were evaluated using carbon loading potential, dilution effect, as well as target peak amplitude and skewing effect. The run parameters explored for parameter optimization were oven programming, inlet pressure (column flow rate), and modulation period. Comparing DoE approaches, Box-Behnken and Doehlert designs assessed sensitivity, selectivity, peak capacity, and wraparound; alongside target peak retention, resolution, and shape evaluation. Certified reference standards and simulated wildfire debris were used for method development and verification, and wildfire debris case samples scrutinized for method validation. The final method employed a low polarity column (5% diphenyl) coupled to a semi-polar column (50% diphenyl) and resulted in an average Separation Number (SN) exceeding 1 in both dimensions after optimization. Separation Numbers of 18.16 for first and 1.46 for second dimension without wraparound for compounds with at least four aromatic rings signified successful separation of all target compounds from varied matrix compositions and allowed for easy visual comparison of extracted ion profiles. Mass spectrometry (MS) was required during validation to differentiate ions where no baseline separation between target compounds and extraneous matrix compounds was possible. The resulting method was evaluated against ASTM E1618 and found to be an ideal routine analysis method providing great resolution of target compounds from interferences and excellent potential for ILR classification within a complex sample matrix.

Retention Modelling of Phenoxy Acid Herbicides in Reversed-Phase HPLC under Gradient Elution

Phenoxy acid herbicides are used worldwide and are potential contaminants of drinking water. Reversed phase high-performance liquid chromatography (RP-HPLC) is commonly used to monitor phenoxy acid herbicides in water samples. RP-HPLC retention of phenoxy acids is affected by both mobile phase composition and pH, but the synergic effect of these two factors, which is also dependent on the structure and pKa of solutes, cannot be easily predicted. In this paper, to support the setup of RP-HPLC analysis of phenoxy acids under application of linear mobile phase gradients we modelled the simultaneous effect of the molecular structure and the elution conditions (pH, initial acetonitrile content in the eluent and gradient slope) on the retention of the solutes. In particular, the chromatographic conditions and the molecular descriptors collected on the analyzed compounds were used to estimate the retention factor k by Partial Least Squares (PLS) regression. Eventually, a variable selection approach, Genetic Algorithms, was used to reduce the model complexity and allow an easier interpretation. The PLS model calibrated on the retention data of 15 solutes and successively tested on three external analytes provided satisfying and reliable results.

Optimizing the relationship between chromatographic efficiency and retention times in temperature-programmed gas chromatography

A methodology that can maximise the chromatographic efficiency that can be achieved within a defined time frame in temperature-programmed gas chromatography is described. The efficiency can be defined as the inverse of peak widths measured in retention index units. This parameter can be described by a model similar to the van Deemter equation, which is expanded to account for the effect of the temperature rate in addition to the effect of carrier gas velocity. The model of efficiency is found by response surface methodology, where the temperature rates and the carrier gas velocities are systematically varied in the experiments. A second model that accurately explains the retention time of the last eluting compound can be found from the same experiments, and optimal conditions are found by combining the two models. The methodology has been evaluated with four capillary columns and three carrier gases, using fatty acid methyl esters as analytes. All experiments showed that there is a fairly linear decrease in efficiency with increasing temperature rates. At any temperature rate, optimal velocity is only marginally higher than the velocity that maximises chromatographic efficiency, since the carrier gas velocity has a limited effect on the retention times.

Detection of GM soybean by multiplex-touchdown PCR-microchip capillary electrophoresis with response surface methodology optimization

The combination of the molecular technique, the multivariate strategy and microchip capillary electrophoresis (MCE) was applied to rapid and sensitive analysis of genetically modified (GM) soybean in food samples. A multiplex-touchdown polymerase chain reaction (PCR) system was developed for simultaneously amplifying three target sequences in Roundup Ready soybean (RRS). Response surface methodology was introduced to determine the optimal separation condition in MCE with good resolution and short analytical time. The detection of the PCR products of RRS was completed within 4 min under the optimal conditions. The specificity of the method was evaluated by testing non-GM soybean materials and three GM maize varieties (MON810, Bt176 and Bt11). A sensitivity of 0.1% GM organisms content was obtained, which was remarkably lower than the labeling threshold for transgenic food defined as 0.9% in the European regulation. The relative standard deviation of migration time was in the range of 0.17-0.95%. The proposed method was rapid, sensitive and specific and can be used to identify and detect GM soybean in food samples.

Chemometric optimization of cation-selective exhaustive injection sweeping micellar electrokinetic chromatography for quantification of ractopamine in porcine meat

An online stacking capillary electrophoresis (CE) method, cation-selective exhaustive injection sweeping micellar electrokinetic chromatography (CSEI-sweep-MEKC), is developed and optimized for analysis of ractopamine (RP) and its homologue dehydroxyractopamine (DRP) in porcine meat. Chemometric experimental design was used to achieve the best possible optimization and reduce the number of trials and errors. The CSEI-sweep-MEKC method enables nanogram per gram level analysis, with limits of detection (LODs) in meat of 5 ng/g for RP and 3 ng/g for DRP (S/N = 3). A higher conductivity buffer (HCB) zone was injected into the capillary, allowing for the analytes to be electrokinetically injected at a voltage of 9 kV for 12 min. Using 125 mM sodium dodecyl sulfate and 15% methanol in the sweeping buffer, RP and DRP were well-separated. The method was validated with a linear calibration curve of 10-300 ng/g (r > 0.994). In comparison to the normal capillary zone electrophoresis method (1 psi for 10 s), this stacking strategy resulted in 900 times sensitivity enhancement. This technique was further applied for analyzing seven kinds of commercial meats, and the residual RP was detected in one (5.76 ng/g of RP). The data were corresponding to the data analyzed by the commercial testing kit and mass spectrometry spectra. This method was successfully used on real samples and is considered feasible for serving as a tool for routine examination in markets.