Compounds In Complex Samples Research Articles

Utilization of UiO-66-NH2@cellulose hybrid aerogel for solid-phase extraction of sildenafil in health products

研究建立了一种基于UiO-66-NH2@纤维素复合气凝胶的高灵敏度固相萃取新方法,与高效液相色谱法联用用于保健品中西地那非的检测。先将纤维素进行醛基和酰肼基的功能化,然后将两种功能化的纤维素通过交联并负载UiO-66-NH2形成复合气凝胶。将此复合气凝胶作为固相萃取的吸附剂使用时,易于收集且不需要外加磁场或者抽真空的辅助作用,操作简单。研究对制备所得的UiO-66-NH2@纤维素复合气凝胶进行了X-射线粉末衍射、扫描电镜、红外光谱和N2吸附等表征,结果显示UiO-66-NH2成功负载于气凝胶的孔道中,纤维素气凝胶掺杂了UiO-66-NH2之后其孔道结构变得规整,并且比表面积增大。研究优化了复合气凝胶中UiO-66-NH2的负载量对萃取的影响,高比例的负载量有利于西地那非的富集,并且最高的负载率为50%。研究优化了影响西地那非富集效率的实验条件,包括溶液pH、萃取时间、洗脱剂类型、洗脱时间、洗脱体积和离子强度。采用安捷伦Zorbax Eclipse Plus C18色谱柱(150 mm×4.6 mm, 5 μm)进行分离,以含0.1 mol/L三乙胺的磷酸盐水溶液(pH=6.50)-乙腈(30∶70, v/v)为流动相进行洗脱,检测波长为292 nm。在最佳的萃取条件下(pH为9.0,萃取时间为60 min,洗脱剂为乙腈,洗脱液体积为3×2 mL,洗脱时间为40 min),该分析方法的线性范围为10~2000 ng/mL (相关系数R2=0.9949),检出限(LOD, S/N=3)为2.85 ng/mL,富集因子为59.17。将该方法用于保健品中西地那非的萃取,所得回收率为74.93%~89.12%,相对标准偏差为2.8%~5.3%,表明了方法的回收率和精密度良好,说明了该方法具有应用于保健品中西地那非日常检测的潜力。

Direct and Sensitive Electrochemical Detection of Bisphenol A in Complex Environmental Samples Using a Simple and Convenient Nanochannel-Modified Electrode.

Rapid, convenient, and sensitive detection of Bisphenol A (BPA) in complex environmental samples without the need for tedious pre-treatment is crucial for assessing potential health risks. Herein, we present an electrochemical sensing platform using a simple nanochannel-modified electrode, which enables the direct and sensitive detection of BPA in complex samples. A vertically ordered mesoporous silica-nanochannel film (VMSF) with high-density nanochannels is rapidly and stably grown on the surface of a electrochemically activated glassy carbon electrode (p-GCE) by using the electrochemically assisted self-assembly (EASA) method. The high antifouling capability of the VMSF/p-GCE sensor is proven by investigating the electrochemical behavior of BPA in the presence of model coexisting interfering molecules including amylum, protein, surfactant, and humic acid. The VMSF/p-GCE sensor can sensitively detect BPA ranged from 50 to 1.0 μM and 1.0–10.0 μM, with low detection limits (15 nM). Owing to the electrocatalytic performance and high potential resolution of p-GCE, the sensor exhibits high selectivity for BPA detection in the presence of common environmental pollutants, including bisphenol S (BPS), catechol (CC), hydroquinone (HQ), and 4-nitrophenol (4-NP). In combination with the good antifouling property of the VMSF, direct detection of BPA in environmental water samples and soil leaching solution (SLS) is also realized without separation pretreatment. The developed VMSF/p-GCE sensor demonstrated advantages of simple structure, high sensitivity, good antifouling performance, and great potential in direct electroanalysis of endocrine-disrupting compounds in complex samples.

A hybrid variable selection and modeling strategy for the determination of target compounds in different spectral datasets

In this paper, a hybrid technique is proposed to establish quantitative models for the determination of target compounds in different spectral datasets. The proposed hybrid method is the hybridization of interval partial least squares (iPLS) method with gradient descent (GD) algorithm. Here, the novelty of the proposed method is that the iPLS method is applied to variable selection and the GD algorithm is employed to establish quantitative models based on the selected optimal variables. In the application of the hybrid iPLS-GD method, the factors, i.e., the number of the interval for the iPLS method and the learning rate, the number of iterations for the GD method, that affect the quantitative accuracy of the method are optimized and determined. Then three spectral datasets, including the near-infrared spectroscopy (NIR) dataset, nuclear magnetic resonance (1H NMR) dataset and excitation-emission matrix fluorescence (EEM) dataset, are used to test and verify the performance of the iPLS-GD method. We compare the hybrid iPLS-GD method with the PLS and iPLS methods from the aspects of modeling ability and predictive ability. The results demonstrated that the iPLS-GD method can be used as an effective and promising tool for the determination of target compounds in complex samples in practice.

Application of imine covalent organic frameworks in sample pretreatment

亚胺类共价有机骨架(I-COFs)是有机单体根据席夫碱(Schiff-base)反应原理缩合形成的一类新型多孔晶体有机材料。I-COFs具有骨架密度低、比表面积大、孔隙率高、单体种类丰富、孔径尺寸可控、结构可功能化、合成方法多样和物化稳定性好等优点。近年来,I-COFs已成为材料科学领域的研究前沿,并广泛用于气体吸附、存储、催化、传感、光电材料等方面。I-COFs材料优异的物理化学性能使其非常适于用作复杂样品中痕量目标物的分离富集介质,其高比表面积、高孔隙率性能赋予了它极高的吸附负载量,这些性能使得目标分析物可被高效富集;通过控制有机单体的链段长度、几何结构、掺杂元素、取代基团等方面精确调控I-COFs的孔洞结构和功能化基团,从而实现目标痕量物质的选择性富集。目前,I-COFs材料在样品前处理领域作为新型萃取介质已引起了极大关注。该文综述了近年来I-COFs材料的主要类型、合成方法及其在固相萃取、磁性固相萃取、分散固相萃取和固相微萃取方面的研究进展,同时展望了I-COFs在样品前处理领域的发展前景。

Metabolomics-based mass spectrometry methods to analyze the chemical content of 3D organoid models.

Metabolomics, the study of metabolites present in biological samples, can provide a global view of sample state as well as insights into biological changes caused by disease or environmental interactions. Mass spectrometry (MS) is commonly used for metabolomics analysis given its high-throughput capabilities, high sensitivity, and capacity to identify multiple compounds in complex samples simultaneously. MS can be coupled to separation methods that can handle small volumes, making it well suited for analyzing the metabolome of organoids, miniaturized three-dimensional aggregates of stem cells that model in vivo organs. Organoids are being used in research efforts to study human disease and development, and in the design of personalized drug treatments. For organoid models to be useful, they need to recapitulate morphological and chemical aspects, such as the metabolome, of the parent tissue. This review highlights the separation- and imaging-based MS-based metabolomics methods that have been used to analyze the chemical contents of organoids. Future perspectives on how MS techniques can be optimized to determine the accuracy of organoid models and expand the field of organoid research are also discussed.

Native mass spectrometry-based metabolomics identifies metal-binding compounds.

Although metals are essential for the molecular machineries of life, systematic methods for discovering metal-small molecule complexes from biological samples are limited. Here, we describe a two-step native electrospray ionization-mass spectrometry method, in which post-column pH adjustment and metal infusion are combined with ion identity molecular networking, a rule-based data analysis workflow. This method enabled the identification of metal-binding compounds in complex samples based on defined mass (m/z) offsets of ion species with the same chromatographic profiles. As this native electrospray metabolomics approach is suited to the use of any liquid chromatography-mass spectrometry system to explore the binding of any metal, this method has the potential to become an essential strategy for elucidating metal-binding molecules in biology.

Development of Microchip Isotachophoresis Coupled with Ion Mobility Spectrometry and Evaluation of Its Potential for the Analysis of Food, Biological and Pharmaceutical Samples.

An online coupling of microchip isotachophoresis (µITP) with ion mobility spectrometry (IMS) using thermal evaporation interface is reported for the first time. This combination integrates preconcentration power of the µITP followed by unambiguous identification of trace compounds in complex samples by IMS. Short-chain carboxylic acids, chosen as model analytes, were first separated by the µITP in a discontinuous electrolyte system at pH 5–6, and subsequently evaporated at 130 °C during their transfer to the IMS analyzer. Various parameters, affecting the transfer of the separated sample components through the evaporation system, were optimized to minimize dispersion and loss of the analytes as well as to improve sensitivity. The following analytical attributes were obtained for carboxylic acids in the standard solutions: 0.1–0.3 mg L−1 detection limits, 0.4–0.9 mg L−1 quantitation limits, linear calibration range from the quantitation limit to 75 mg L−1, 0.2–0.3% RSD of the IMS response and 98–102% accuracy. The analytical potential of the developed µITP-IMS combination was demonstrated on the analysis of various food, pharmaceutical and biological samples, in which the studied acids are naturally present. These include: apple vinegar, wine, fish sauce, saliva and ear drops. In the real samples, 0.3–0.6% RSD of the IMS response and 93–109% accuracy were obtained.

Current applications of capillary electrophoresis-mass spectrometry for the analysis of biologically important analytes in urine (2017 to mid-2021): A review.

Capillary electrophoresis coupled online with mass detection is a modern tool for analyzing wide ranges of compounds in complex samples, including urine. Capillary electrophoresis with mass spectrometry allows the separation and identification of various analytes spanning from small ions to high molecular weight protein complexes. Similarly to the much more common liquid chromatography‐mass spectrometry techniques, the capillary electrophoresis separation reduces the complexity of the mixture of analytes entering the mass spectrometer resulting in reduced ion suppression and a more straightforward interpretation of the mass spectrometry data. This review summarizes capillary electrophoresis with mass spectrometry studies published between the years 2017 and 2021, aiming at the determination of various compounds excreted in urine. The properties of the urine, including its diagnostical and analytical features and chemical composition, are also discussed including general protocols for the urine sample preparation. The mechanism of the electrophoretic separation and the instrumentation for capillary electrophoresis with mass spectrometry coupling is also included. This review shows the potential of the capillary electrophoresis with mass spectrometry technique for the analyses of different kinds of analytes in a complex biological matrix. The discussed applications are divided into two main groups (capillary electrophoresis with mass spectrometry for the determination of drugs and drugs of abuse in urine and capillary electrophoresis with mass spectrometry for the studies of urinary metabolome).

Improved annotation and quantification of metabolites in rice (Oryza sativa L.) seeds using two-dimensional gas chromatography\u2013time-of-flight mass spectrometry

Two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS) is a powerful tool for identification of compounds in complex samples. Herein, we compared the GC × GC-TOFMS and GC-TOFMS systems for polar metabolite profiling of rice seeds. Forty-seven and thirty-six metabolites were detected in a Korean rice cultivar, Dongjin, using GC × GC-TOFMS and GC-TOFMS, respectively. The limits of detection of shikimic, p-coumaric, and sinapinic acids were 30.0-, 1.6-, and 2.5-times lower, respectively, with GC × GC-TOFMS than with GC-TOFMS. The overlapped peaks of glycerol and phosphoric acid in GC-TOFMS were separated in GC × GC-TOFMS. Polar metabolite profiling of two white and six red rice cultivars was performed using GC × GC-TOFMS and the obtained data were subjected to principal component analysis. Remarkably, principal component 1 separated Heugdaegu from other cultivars, indicating that Heugdaegu has high levels of caffeic, sinapinic, and vanillic acids. Findings from this work may aid breeding programs aimed at improving the quality of rice seeds.

Separation of monomeric and dimeric phenolic compounds in lignosulphonate lignin on different stationary phases using ultrahigh-performance supercritical fluid chromatography

Lignin is a promising renewable resource and its valorization could help to reduce our dependency on fossil carbon resources. Especially the production of small molecular weight and economically valuable compounds, such as vanillin, are of interest. A good separation of the sample components is crucial for a confident identification of compounds in complex sample mixtures using for instance mass spectrometry. In this work, the resolving power and selectivity of five different stationary phases for ultrahigh-performance supercritical fluid chromatography were studied for the class separation of lignin monomers (LMs) and dimers (LDs). A separation of LMs and LDs will help to identify such compounds in complex technical lignin samples. It could be shown that stationary phases with both hydrogen-bonding acceptor and donator groups offer high overall resolving power, while π-π-interactions are advantageous for the separation of the two compound classes. An almost complete separation combined with an improved overall resolving power was achieved with the 1-aminoanthracene stationary phase, which offers both hydrogen-bonding interactions and π-π-interactions.

Exploratory analysis using MRM profiling mass spectrometry of a candidate metabolomics sample for testing system suitability

Multiple reaction monitoring (MRM) profiling is an exploratory mass spectrometry (MS) method applicable to the initial screening of complex samples for small molecules based on their chemical functionalities. We report on the applicability and quality of this method to screen for metabolites, lipids and exogenous compounds in a candidate reference material, the Metabolomics System Suitability Research Grade Material (RGM 10122) which is being developed by the National Institute of Standards and Technology (NIST). In an initial discovery experiment, we recorded data using eighty neutral loss (NL) and precursor ion (Prec) MS/MS scans, selected from literature data as likely of value in recognizing the presence of potential lipids and metabolites. Then the NIST sample was re-examined combining precursor-to-product ion transitions from the discovery experiment with a list of 1357 known literature-based metabolite MRMs. This MRM profiling experiment gave a small set (191) of high-quality lipid specific MRMs for the sample. Similar experiments gave 104 and 17 metabolite and exosome MRM's. These MRM experiments, with a few exceptions, showed relative standard deviations (RSD) under 25% for individual tentatively assigned compounds. At a data acquisition rate of 50 compounds/min, using two MRMs for each compound, this approach allows quick surveys for easily detectable compounds in complex samples.

Bioactivity‐guided separation of antifungal compounds by preparative high‐performance liquid chromatography

Bioactivity-guided chromatographic methods are of great significance for the isolation of the active compounds in complex samples. In this study, four anti-fungal compounds were located by activity screening and successfully isolated from a microbial fermentation sample by preparative high-performance liquid chromatography. Separation performance of columns including C18, positively charged C18, negatively charged C18 and C8 were firstly investigated. And it showed a better capacity of mixed-mode stationary phases for retention and separation. Therefore, the positively charged C18 column was used to separate the sample into several fractions, among which the active one was identified by the antifungal test. And then the active fraction was enriched and separated again by successively using the negatively charged C18 and C8 columns to obtain four compounds, which were identified as polyoxins A, K, F and H. With activity verification, four polyoxins were found to have good inhibitory effects against the three fungal plant diseases including rice sheath blight, tomato grey mould disease, and apple spot leaf disease.

Deep-Learning-Assisted multivariate curve resolution

Gas chromatography-mass spectrometry (GC-MS) is one of the major platforms for analyzing volatile compounds in complex samples. However, automatic and accurate extraction of qualitative and quantitative information is still challenging when analyzing complex GC-MS data, especially for the components incompletely separated by chromatography. Deep-Learning-Assisted Multivariate Curve Resolution (DeepResolution) was proposed in this study. It essentially consists of convolutional neural networks (CNN) models to determine the number of components of each overlapped peak and the elution region of each compound. With the assistance of the predicted elution regions, the informative regions (such as selective region and zero-concentration region) of each compound can be located precisely. Then, full rank resolution (FRR), multivariate curve resolution-alternating least squares (MCR-ALS) or iterative target transformation factor analysis (ITTFA) can be chosen adaptively to resolve the overlapped components without manual intervention. The results showed that DeepResolution has superior compound identification capability and better quantitative performances when comparing with MS-DIAL, ADAP-GC and AMDIS. It was also found that baseline levels, interferents, component concentrations and peak tailing have little influences on resolution result. Besides, DeepResolution can be extended easily when encountering unknown component(s), due to the independence of each CNN model. All procedures of DeepResolution can be performed automatically, and adaptive selection of resolution methods ensures the balance between resolution power and consumed time. It is implemented in Python and available at https://github.com/XiaqiongFan/DeepResolution.

Automated needle-sleeve based online hyphenation of solid-phase microextraction and liquid chromatography

A novel approach for the online coupling of solid-phase microextraction (SPME) and liquid chromatography (LC) is introduced. An innovative Si@GO@βCD coated needle-sleeve extractant device was developed and then employed in the automated online SPME-LC-UV determination of estrogen-like isoflavones from human urine samples. The extractant SPME device is easily attachable at the endpoint of an analytical syringe needle and operated by a lab-made autosampler. Fully automated online SPME-LC is accomplished by proper autosampler programming to perform the following steps: i) the analytes extraction by direct immersion of the extractant device into the stirred sample, ii) a rinsing step iii) the analytes desorption/enrichment, iv) the online transference of the extract to the LC injection valve. Besides allowing the online SPME hyphenation, this extraction modality efficiently addressed the drawbacks associated with the clogging and dispersion of graphene-based microextraction techniques performed in packed-bed and dispersive formats. The main extraction parameters and the performance of the automated online SPME-LC method developed were carefully studied. The results show a good sensitivity, reliability, and straightforward analytical strategy for the determination of organic compounds in complex samples. The detection limit of the method was 20 μg L1 for DAI and 10 μg L−1 for GEN, FOR and BIO. The intra-day RSD was below 10% and inter-day RSD was below 13%. The total analysis time was less than 17 min per sample.

Trace multi-class organic explosives analysis in complex matrices enabled using LEGO®-inspired clickable 3D-printed solid phase extraction block arrays.

The development of a new, lower cost method for trace explosives recovery from complex samples is presented using miniaturised, click-together and leak-free 3D-printed solid phase extraction (SPE) blocks. For the first time, a large selection of ten commercially available 3D printing materials were comprehensively evaluated for practical, flexible and multiplexed SPE using stereolithography (SLA), PolyJet and fused deposition modelling (FDM) technologies. Miniaturised single-piece, connectable and leak-free block housings inspired by Lego® were 3D-printed in a methacrylate-based resin, which was found to be most stable under different aqueous/organic solvent and pH conditions, using a cost-effective benchtop SLA printer. Using a tapered SPE bed format, frit-free packing of multiple different commercially available sorbent particles was also possible. Coupled SPE blocks were then shown to offer efficient analyte enrichment and a potentially new approach to improve the stability of recovered analytes in the field when stored on the sorbent, rather than in wet swabs. Performance was measured using liquid chromatography-high resolution mass spectrometry and was better, or similar, to commercially available coupled SPE cartridges, with respect to recovery, precision, matrix effects, linearity and range, for a selection of 13 peroxides, nitramines, nitrate esters and nitroaromatics. Mean % recoveries from dried blood, oil residue and soil matrices were 79 ± 24%, 71 ± 16% and 76 ± 24%, respectively. Excellent detection limits between 60 fg for 3,5-dinitroaniline to 154 pg for nitroglycerin were also achieved across all matrices. To our knowledge, this represents the first application of 3D printing to SPE of so many organic compounds in complex samples. Its introduction into this forensic method offered a low-cost, 'on-demand' solution for selective extraction of explosives, enhanced flexibility for multiplexing/design alteration and potential application at-scene.

A strategy for the determination of flavor substances in goat milk by liquid chromatography-high resolution mass spectrometry

A new analytical method for untargeted screening of flavor compounds in goat milk and milk flavor using mass spectrometry was developed and validated. All compounds of low molecular weight were considered as candidate chemicals, instead of focusing only on compounds that already have a known affect on the flavor quality of goat milk. High performance liquid chromatography coupled with high resolution quadrupole orbitrap mass spectrometry based on external standard was used to analyse flavor compounds in complex samples. The untargeted screening strategy is data pre-processing, automated componentization, suspect library spectra searching and the retained analytes confirming based on the reference standard solutions. Forty-two flavor compounds were confirmed and analyzed. The established method was validated by considering the guidelines specified in Commission Decision 2002/657/EC and European SANCO/12571/2013 Guideline 2013. This validated method was successfully applied to samples detection, and some unique flavor compounds were found between goat milk and milk flavor samples, which can provide some useful references for the illicit addition of flavors and fragrances in dairy products.

Predicting a Molecular Fingerprint from an Electron Ionization Mass Spectrum with Deep Neural Networks.

Electron ionization-mass spectrometry (EI-MS) hyphenated to gas chromatography (GC) is the workhorse for analyzing volatile compounds in complex samples. The spectral matching method can only identify compounds within the spectral database. In response, we present a deep-learning-based approach (DeepEI) for structure elucidation of an unknown compound with its EI-MS spectrum. DeepEI employs deep neural networks to predict molecular fingerprints from an EI-MS spectrum and searches the molecular structure database with the predicted fingerprints. We evaluated DeepEI with MassBank spectra, and the results indicate DeepEI is an effective identification method. In addition, DeepEI can work cooperatively with database spectral matching and NEIMS (fingerprint to spectrum method) to improve identification accuracy.

On the performance of multivariate curve resolution to resolve highly complex liquid chromatography–full scan mass spectrometry data for quantification of selected immunosuppressants in blood and water samples

Liquid chromatography triple-quadrupole mass spectrometry (LC–MS/MS) in multiple reaction monitoring (MRM) mode is a commonly used approach for quantification of target compounds in complex samples. Regarding the multivariate advantages that can be obtained in the light of full spectra recording, the potential and performance of a smart strategy which is a post-processing of LC–MS full scan data sets with multivariate curve resolution-alternating least squares (MCR-ALS) algorithm for determination of cyclosporine-A (CsA) and tacrolimus (TAC) as typical immunosuppressants in human blood and organic micropollutants in surface water samples was investigated in the current study. Before peak resolution and quantification, LC–MS data compression was performed on each individual and augmented data matrices based on searching the regions of interest (ROIs) in the m/z domain. Then, due to presence of matrix effect, MCR-ALS modeling was performed using a matrix-matched calibration strategy. Here, the serious LC–MS issues such as background drift, chromatographic shifts and co-elution of non-calibrated sample constituents were resolved by implementation of MCR-ALS method on ROI based compressed data. It was shown that the strategy is a powerful tool for the quantitation of CsA and TAC in complex matrices and in the presence of uncalibrated interferences with highly overlapped peaks. However, the overall results indicated that performance of the multivariate modeling of LC–MS in full scan mode for drug quantitation strongly depends on the selected drug, as in the current case superiority of the method for analysis of CsA over TAC, with different therapeutic concentration ranges, was observed.

Pyrolytic profiling nitrosamine specific chemiluminescence detection combined with multivariate chemometric discrimination for non-targeted detection and classification of nitroso compounds in complex samples

The problems of contamination of many products by nitroso compounds have been discussed since 1970′s and have been partially solved, namely, the contamination by carcinogenic volatile N-nitrosamines. However, there is still a gap in knowing non-volatile nitroso compounds in terms of both the determination of these compounds and the description of their toxicity. Therefore, a procedure for their detailed non-targeted study is necessary to be developed. Based on these facts, a new method permitting the detection and the classification of nitroso compound groups, such as N-nitroso, C-nitroso, and interfering substances in the nitrosamine specific chemiluminescence detection after previous gas chromatographic separation, was developed. The method is based on signal profiling of chromatographic peaks recorded by a chemiluminescence detector at different pyrolytic temperatures and subsequent multivariate chemometric classification. The resulting classification function by linear discriminant analysis shows good performance with total accuracy of 96.12% after the method validation. The method was successfully applied and demonstrated on a non-targeted beer sample analysis. Nitroso compounds detected by the method were selected for detailed structural analysis by GC-MS/MS. The combination of the presented method with the MS/MS instrumentation provides a really powerful analytical tool for the identification of unknown nitroso compounds in complex samples. This study represents a valuable contribution to the protocols of identification of organic compounds with the nitrogen functional groups – toxicologically and analytically important nitroso compounds.

Preparation and application of guanidyl-functionalized graphene oxide-grafted silica for efficient extraction of acidic herbicides by Box-Behnken design

A highly selective and efficient extraction material was synthesized through the functionalization of guanidyl onto the graphene oxide-grafted silica via a simple chemical modification, which was designed and proposed to improve the enrichment capacity for acidic herbicides. The extraction material was confirmed by scanning electron microscopy, Fourier transform infrared spectrometry, X-ray photoelectron spectrometry, thermal gravimetric analyzer and zeta potential analysis. Theoretical adsorption energies, static- and dynamic-state binding experiments, and comparative experiments with various adsorbents were investigated to elucidate the adsorption mechanism. The introduction of guanidyl endowed the sorbent with stronger Lewis base property and electron-donating ability, hence, excellent extraction recoveries for acidic herbicides could be obtained. Besides, electrostatic and π-π interactions were considered as two major driving impetuses in the adsorption process. Single-factor experiment and response surface methodology were utilized for the optimization of extraction and desorption conditions. Under the optimized conditions, the wide linearities were obtained with correlation coefficients ranging from 0.9904 to 0.9980, and the method detection limits were in the range of 0.5–2 μg L−1. The relative standard deviation values of the recoveries of five different extractions were 3.0–7.1%. Coupled with high performance liquid chromatography, the as-proposed method was successfully applied to detect five acidic herbicides in Lycium barbarum (Goji). It turned out that the proposed method provided a promising perspective for the selective extraction and determination of polar acidic compounds in complex samples.