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The main sample preparation method for analysis of pesticide residues in fruits is QuEChERS. In this study, a novel sample preparation method using molecular complex-based dispersive liquid-liquid microextraction is introduced with detection of forchlorfenuron by high-performance liquid chromatography coupled with diode array and mass spectrometric detection. Sample treatment involves initial extraction of a 5 g sample with 3 mL acetonitrile, and then the selective concentration of the analyte is performed using 150 µL tributyl phosphate by forming intermolecular hydrogen bonds with the analyte. The extraction mechanism was proved using ATR-FTIR. Under the optimised conditions, recovery rates varied between 88% and 107% for various sample matrices spiked at three levels in the range 0.01-0.1 mg kg-1. Intra-day and inter-day repeatabilities were in the ranges of 2.2-8.0% and 1.6-9.5%, respectively. Detection limit and quantitation limit were 0.33 µg kg-1 and 1.09 µg kg-1 for diode-array detection; 0.01 µg kg-1 and 0.04 µg kg-1 for tandem mass spectrometry detection. This method was successfully applied for the analysis of 149 various fruits. The analyte was found in 4 of the 149 samples and the contents were not over the specific maximum residue limit established by domestic and international regulations.

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For simultaneous analysis of four fat-soluble tocopherols (α-, ß-, γ-, and δ-) in edible oils, an efficient and green method using deep eutectic solvent-based liquid-phase microextraction (DES-LPME) coupled with reversed-phase high-performance liquid chromatography (RP-HPLC) was developed. The DESs formed by different quaternary ammonium salts and ethanol were used as the extractants. Tetrabutylammonium chloride (TBAC)-ethanol DES at a molar ratio of 1:2 achieved the best extraction efficiency. Under the optimized conditions, the detection limits were in the range of 2.1-3.0 ng mL-1. The intra-day and inter-day repeatability were in the ranges of 3.9-5.3% and 4.8-7.1%, respectively, and the recoveries for the real samples varied from 80.7% to 105.4%. The developed method was successfully employed for the determination of all four tocopherol homologues with an RP-HPLC system containing a COSMOSIL π-NAP column in five edible oils collected locally. Graphical abstract.

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The presence of weak interlayers and groundwater are common adverse geological conditions in tunnels. To investigate the modes of failure of rock masses surrounding tunnels owing to weak interlayers and groundwater, model tests and numerical simulations were conducted in this study based on two cases, and a model that considers only the weak interlayer was conducted for comparison. Based on the tests, differences between two models in terms of rock pressure, displacement, cracks and strain were analysed. The results reveal that the presence of groundwater has a significant effect on the space-time distribution of stress, displacement and cracks in the surrounding rock. Furthermore, based on the numerical model, the seepage field was analysed in terms of pore water pressure, permeability and the seepage process to understand the joint action of groundwater and weak interlayer on the failure mechanism of tunnels. The results show that the groundwater and interlayer complement each other to induce the failure mode of the surrounding rock. The water accelerates slip in the interlayer and the development of cracks. Conversely, low strength, muddy weak interlayers serve as the channels of water flow, resulting in deformations and cracks at different locations and different failure modes.

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A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

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The previously reported procedure for the determination of the total phthalate in fatty food involved the extraction of phthalates using chloroform/methanol followed by the removal of the solvents before alkaline hydrolysis requiring 20 h and derivatization of phthalic acid. In this study, a phase-transfer catalyst (tetrabutylammonium chloride) was used in the liquid-liquid heterogeneous hydrolysis of phthalates in oil matrix shortening the reaction time to within 25 min. The resulting phthalic acid in the hydrolysate was extracted by a novel molecular complex based dispersive liquid-liquid microextraction method coupled with back-extraction before high-performance liquid chromatography coupled with photodiode array detection. Under the optimal experimental conditions, the linearity of the method was in the range of 0.5-12 nmol/g with the correlation coefficients (r) >0.997. The detection limit (S/N = 3) was 0.11 nmol/g. Intraday and interday repeatability values expressed as relative standard deviation were 3.9 and 7.1%, respectively. The recovery rates ranged from 82.4 to 99.0%. The developed method was successfully applied for the analysis of total phthalate in seven edible oils.

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A selective and low organic-solvent-consuming method of sample preparation combined with high-performance liquid chromatography with diode-array detection is introduced for analysis of phthalic acid esters in edible oils. Sample treatment involves initial liquid-liquid partitioning with acetonitrile, then QuEChERS cleanup by dispersive solid-phase extraction with primary secondary amine as sorbent. Preconcentration of the analytes is performed by ionic-liquid-based dispersive liquid-liquid microextraction, with the cleaned-up extract as disperser solvent and 1-hexyl-3-methylimidazolium hexafluorophosphate as extraction solvent. Under the optimized conditions, correlation coefficients (r) were 0.998-0.999 and standard errors (S y/x ) were 2.67-3.37 × 10(3) for calibration curves in the range 50-1000 ng g(-1). Detection limits, at a signal-to-noise ratio of 3, ranged from 6 to 9 ng g(-1). Intra-day and inter-day repeatability, expressed as relative standard deviation, were in the ranges 1.0-6.9 % and 2.4-9.4 %, respectively. Recovery varied between 84 % and 106 %. The developed method was successfully used for analysis of the analytes in 28 edible oils. The dibutyl phthalate content of four of the 28 samples (14 %) exceeded the specific migration limit established by domestic and international regulations.

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A very simple, fast and environmentally friendly sample extraction method was proposed for the analysis of phthalate esters (PAEs, di-isobutyl phthalate (DIBP), dibutylphthalate (DBP), butylbenzylphthalate (BBP) and bis(2-ethylhexyl)phthalate (DEHP)) in alcoholic beverages by using conventional ionic liquid dispersive liquid-liquid microextraction. The samples were extracted by 160 µL 1-octyl-3-methylimidazolium hexafluorophosphate in the presence of appropriate amount of ethanol and 10% (w/v) sodium chloride solution; the enriched analytes in sedimented phases were analyzed by high performance liquid chromatography-diode array detector (HPLC-DAD). Under the optimum conditions, a satisfactory linearity (in the range of 0.02-1 µg mL(-1) for white spirits and 0.01-0.5 µg mL(-1) for red wines with the correlation coefficients (r) varying from 0.9983 to 1), acceptable recovery rates (88.5-103.5% for white spirits and 91.6-104.6% for red wines), good repeatability (RSD ≤ 8.0%) and low detection limits (3.1-4.2 ng mL(-1) for white spirits and 1.5-2.2 ng mL(-1) for red wines) were obtained. The developed method was successfully applied for the determination of the four PAEs in 30 white spirits and 11 red wines collected locally, and the DBP content in 63% (19:30) white spirits exceeded the specific migration limit of 0.3 mg kg(-1) established by international regulation.

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A new method was established for the simultaneous determination of the migration amounts of bisphenol A (BPA) and phenol from polycarbonate (PC) bottles based on subcritical water extraction (SWE) and high performance liquid chromatography. The optimum extraction conditions included an extraction temperature of 120 degree C, a pressure of 6.89 MPa (1000 psi), a static extraction time of 1 h and one cycle. Under the conditions, the migration amounts of the BPA ranged from 6.81 to 1116 micro g/g in 11 samples. Phenol was not detectable in 5 samples, and in other ones the migration amounts of phenol varied in the range of 3.25 -6. 08 micro g/g. The traditional soaking extraction experiments showed that PC was subjected to weak hydrolysis after long-time leaching. The BPA and phenol were separated in 8 min. Good linearities were obtained in the range of 0. 05 - 20 mg/L for BPA and 0.02 - 20 mg/L for phenol ( r > 0.999 7). The limits of detection were 7.6 micro g/L for BPA and 2.0 micro g/L for phenol. Intra-day and inter-day repeatabilities (expressed as RSD) were less than 5.21% and 11.63%, respectively. Compared with traditional water soaking extraction, the extraction efficiencies increased 49 - 106 times using this developed SWE method. The procedure is simple, rapid and environment friendly, and can be utilized to determine the migration amounts of BPA and phenol in PC bottles.

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In this study, two novel sample extraction methods for the analysis of bisphenol A (BPA) in edible oils were developed by using liquid-liquid extraction followed by a dispersive liquid-liquid microextraction (LLE-DLLME) and reversed-phase dispersive liquid-liquid microextraction (RP-DLLME). RP-DLLME showed a superior characteristic over LLE-DLLME and other previously reported procedures because of its easy operation, short extraction time, high sensitivity, low organic solvent consumption and waste generation. The optimized extraction conditions of RP-DLLME for 1.0 g of edible oil diluted in 4 mL of n-hexane were: extractant, 100 µL 0.2 M sodium hydroxide solution (80% methanol, v/v); extraction time, 1 min; centrifugation, 3 min. The determination of BPA was carried out by high performance liquid chromatography coupled with a DAD detector. The method offered excellent linearity over a range of 0.010-0.5 µg g(-1) with a correlation coefficient of r>0.997. Intra-day and inter-day repeatability values expressed as relative standard deviation were 1.9% and 5.9%, respectively. The quantitation limit and detection limit were 6.3 and 2.5 ng g(-1). The target analyte was detected in 5 out of 16 edible oil samples. The recovery rates in real samples ranged from 89.5 to 99.7%.

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The traditional for the determination of α-tocopherol in cereal grains includes saponification of a sample followed by liquid-liquid extraction, and it is time- and solvent consuming. In this study, a dispersive liquid-liquid microextraction (DLLME) method was developed to extract α-tocopherol in situ from the saponified grain sample solution. The DLLME experimental parameters including the type and volume of extractants, the volume of dispersers, the addition of salt and the extraction/centrifuging time were examined and optimized. The recommended analytical procedure showed excellent precision (relative SDs of the α-tocopherol amount of 3.1% over intraday and 7.2% over interday), high sensitivity (the detection limit of 1.9 ng/mL), and strong recovery values (88.9-102.5%). In addition, statistical analyses showed no significant difference between the detected amounts of α-tocopherol found by the standardized method and this new procedure. The method was successfully applied to determining the amounts and distribution of α-tocopherol in 14 cereal grain samples.

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