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Molecules 2019, 24(16), 2918;

Determination of 107 Pesticide Residues in Wolfberry with Acetate-buffered Salt Extraction and Sin-QuEChERS Nano Column Purification Coupled with Ultra Performance Liquid Chromatography Tandem Mass Spectrometry
College of Food Science and Engineering, Shandong Agricultural University, Taian 271018, China
Jieke Testing Service Co., Ltd., Yantai 265231, China
Agricultural College, Shandong Agricultural University, Taian 271018, China
China National Center for Food Safety Risk Assessment, Beijing 100017, China
Author to whom correspondence should be addressed.
Received: 4 July 2019 / Accepted: 9 August 2019 / Published: 12 August 2019


A multi-residue method for the determination of 107 pesticide residues in wolfberry has been developed and validated. Similar pretreatment approaches were compared, and the linearity, matrix effect, analysis limits, precision, stability and accuracy were validated, which verifies the satisfactory performance of this new method. The LODs and LOQs were in the range of 0.14–1.91 µg/kg and 0.46–6.37 µg/kg, respectively. The recovery of analytes at three fortification levels (10 µg/kg, 50 µg/kg, 100 µg/kg) ranged from 63.3–123.0%, 72.0–118.6% and 67.0–118.3%, respectively, with relative standard deviations (RSDs) below 15.0%. The proposed method was applied to the analysis of fifty wolfberry samples collected from supermarkets, pharmacies and farmers’ markets in different cities of Shandong Province. One hundred percent of the samples analyzed included at least one pesticide, and a total of 26 pesticide residues was detected in fifty samples, which mainly were insecticides and bactericide. Several pesticides with higher detection rates were 96% for acetamiprid, 82% for imidacloprid, 54% for thiophanate-methyl, 50% for blasticidin-S, 42% for carbendazim, 42% for tebuconazole and 36% for difenoconazole in wolfberry samples. This study proved the adaptability of the developed method to the detection of multiple pesticide residues in wolfberry and provided basis for the research on the risks to wolfberry health.
multi-residue; pesticide; wolfberry; Sin-QuEChERS Nano; UPLC/MS/MS

1. Introduction

Lycium barbarum, belonging to the Solanaceous family, also called Gouqizi in Chinese or wolfberry in English, has been widely cultivated in the northwest of China and the Mediterranean region [1,2]. Wolfberry is a kind of traditional Chinese medicine (TCM) and food homologous plant with health-protection functions. It has been widely used for the prevention and treatment of various diseases for more than thousands of years [3,4]. In recent years, wolfberry, a functional dietary supplement with nutritive value, has become increasingly prevalent in the Western countries and regions [5]. Wolfberry is rich in minerals, proteins, polysaccharides, amino acids, carotenoids, flavonoids and so on [6]. This characteristic makes it vulnerable to diseases and insect pests during its growth. Rust mite, gall mite, psylla, mealybug, aphid, anthracnose and powdery mildew are often encountered at the same time or alternately. As chemical agents for diseases and insect pests control as well as plant growth regulation, pesticides play an important role in the growth process and standardized management of wolfberry. Traditional Chinese medicine, as a gem of the Chinese nation, has a unique concept of treating diseases, which has been by degrees accepted both at home and abroad [7]. With the rapid growth of import and export trade of Chinese herbal medicines, China has developed into the largest supplier of wolfberry products [8,9,10]. Artificial planting has become an inevitable trend due to the large demand and the lack of wild wolfberry resources. Consequently, the issue of pesticide residues which are inevitably caused by the extensive use of pesticides in plant cultivation [11], has attracted wide attention of the international community [12,13,14]. Organophosphorus and pyrethroid pesticides are frequently applied to improve the quality and yield during the course of the growth of wolfberry. At present, there are more than 2000 kinds of pesticides registered in the world, of which about 500 are commonly used, while new pesticides are also being developed and applied. Biodegradation, photolysis, chemical oxidation and plant metabolism will decompose most pesticides, but there will still be a minor part of pesticide residues in plants and soil [15]. This small fraction of pesticide not only has serious adverse effects on the quality of wolfberry, but also gives rise to tremendous potential hazard to human health [16]. Simultaneously, there are increasing requirements and new challenges for pesticide residue detection technology [16,17].
Pesticide residue analysis is a complex trace analysis technology in which the pretreatment method plays a vital role in the detection process. The traditional extraction and purification technology cannot meet the requirements of modern pesticide residue analysis, especially under the condition that not only is the concentration of food pollutants getting lower [18,19], but also the matrices are becoming more and more complex [20]. Appropriate pretreatment technology can improve the sensitivity, detection range, precision and accuracy of the detection [21,22,23,24]. In recent years, mass spectrometry (MS) technology has been developing continuously, among which LC coupled with tandem MS (LC-MS/MS) is an effective and sensitive method for the detection of pesticide residues [25,26]. Common sample pretreatment techniques including liquid-liquid extraction (LLE) [27], solid phase extraction (SPE) [28,29], microwave-assisted extraction (MAE) [29,30], supercritical fluid extraction (SFE) [31], accelerated solvent extraction (ASE) [32,33,34], magnetic solid phase extraction (MSPE) [35,36], gel permeation chromatography (GPC) [37,38,39], as well as the QuEChERS method (quick, easy, cheap, effective, rugged and safe) which was firstly published by Anastassiades et al. in 2003 [40]. Nowadays, the QuEChERS protocol has been widely used for the determination of pesticide residues due to its significant advantages [41]. After the continuous improvement, two buffer versions, namely AOAC Official Method 2007.01 (acetate buffering) [42] and European Committee for Standardization (CEN) Standard Method EN 15,662 (citrate buffering) [43], were gradually formed on the basis of QuEChERS technology and widely utilized in sample pretreatment process prior to chromatographic analysis. Multi-pesticide residue analysis mostly used modified QuEChERS method [44,45,46]. Multi-walled carbon nanotubes is a new type of pretreatment adsorption material, which can effectively remove pigments and hydrophobic substances by combining with target molecules as non-covalent interaction. In recent years, it has attracted great attention in the purification process with the advantages of good chemical stability, high surface area, strong adsorption capacity, wide application range of pH value and low cost [47,48,49]. Multi-walled carbon nanotubes (MWCNTs) [50,51,52] and magnetic amino (m-MWCNTs-NH2) [53] or amino (MWCNTs-NH2) [20] modified multi-walled carbon nanotubes are extensively used as sorbent materials for dispersed solid phase extraction in the detection of multi-pesticide residues. Sin-QuEChERS Nano purification column is a new sample preparation product based on the QuEChERS method. The clean-up column with sorbents as multi-walled carbon nanotubes and PSA packed in has good purification effects of plant pigments, lipids, some sugars, sterols, phenol, wax, alkaline interferers, organic acids, etc. with the function of dehydration. With the extraction and purification combined into one-step, the loss of target components via solvent transfer can be avoided, resulting in fewer interferences during determination and longer instrument maintenance periods, meanwhile the sample preparation time is greatly saved. After the organic extract enters the liquid storage tank during the purification, the liquid level rises, and the air in the liquid storage tank is discharged through the vent hole. The Sin-QuEChERS Nano purification column is reversely guided in the downward process of the centrifuge tube, and the purified extract is stored in the liquid storage tank, then the built-in water blocking filter is bonded to the strong hydrophobic water blocking group to ensure that the aqueous solution does not contact with the purification packing and reservoir. The seal is used for sealing between the cylinder and the centrifuge tube.
In this study, in order to monitor the quality of wolfberry in China, a method with acetate-buffered salt extraction and Sin-QuEChERS nanocolumn purification coupled with ultra performance liquid chromatography tandem mass spectrometry was developed for the determination of pesticide residues in wolfberry samples. Different versions of buffer salts for sample extraction and several SPE cartridges for clean-up were compared. The linearity, matrix effect, analysis limits, precision, stability and accuracy were validated in detail. Finally, the method was applied to determinate 50 wolfberry samples to validate the feasibility.

2. Results

2.1. Optimization and Comparison of the Extraction Procedure

In this study, three versions of buffer salts were compared, and the extraction efficiency was evaluated using three levels (10, 25, 50 µg/kg) spiked recovery with five parallel samples. The results, were in accordance with previous studies [54], where most pesticides except for pH-sensitive ones gave excellent results when extracted with three different versions of buffer salts. As shown in Figure 1, the QuEChERS version using acetate buffering or citrate buffering more often gave higher recoveries compared with the unbuffered method for pH-dependent pesticides. For acephate, dimethoate, fenobucarb, methamidophos, omethoate and profenofos that are unstable in alkaline medium, it is easier to obtain higher recovery in buffer salt system with the pH of the matrix was maintained between 5.0 and 5.5 throughout the experiment. In addition to, as Figure 1 results demonstrate, the recovery rate of pesticides in acetate buffer version (AOAC) is slightly higher than that in citrate buffer version (CEN), but the difference is not obvious. Further, referring to previous research results [42,54], the ionization efficiency of acetonitrile and the ability of the matrix itself to interfere with acidity and alkalinity is enhanced in the acetate-buffered version, so subsequent experiments used acetate buffer version.

2.2. Optimization and Comparison of the Clean-up Procedure

Five kinds of SPE cartridges were selected for optimization and comparison of the purification procedure, and were activated by 4 mL methanol before use. Extraction solution obtained as described in Section 3.3 was passed through the SPE cartridges and 4 mL of eluent was collected for the next process which involved treatment the same as the filtrate in Section 3.3. As the Figure 2 results demonstrate vividly, compared with the other three SPE cartridges, the solution purified by the Sin-QuEChERS Nano and HyperSep NH2 catridges showed a colorless and transparent state. PSA was used for adsorbing pigments, organic acids, some carbohydrates and fatty acids while C18 and MWCNTs absorbed the pigments. The Sin-QuEChERS Nano cartridge is packed with a certain amount of MWCNTs in addition to PSA, which can better remove carotene, lutein and other colored interferents in wolfberry with less co-extracted compounds. Recoveries with the different purification processes are displayed in Figure 3, where most pesticides displayed higher recoveries for the Sin-QuEChERS Nano cartridge in comparison with other SPE columns, while all pesticides also gave satisfactory recoveries when purified by HyperSep NH2 columns.
Some pesticides gave unsatisfactory recoveries (>120% or <70%) with the other three SPE cartridges owing to matrix enhancement or inhibition effects caused by matrix co-extracted compounds. Therefore, considering the recoveries, the purification effect and the simplicity of operation, Sin-QuEChERS Nano cartridges were considered the optimal choice for purification of wolfberry.

2.3. Method Validation

2.3.1. Linearity, LOD and LOQ

The linearity was assessed by calibration curves at eight concentration levels, prepared by diluting the mixed stock solution. As shown in Table 1, the linear range of most pesticides was 5–1000 µg/kg with eight points, except for acetochlor, bendiocarb, clothianidin, cyproconazole, flutriafol, metalaxyl, propham were 2–1000 µg/kg, butralin, diethofencarb, fenbuconazole, fosthiazate, isocarbophos, phorate-sulfoxide, propiconazole were 2–500 µg/kg, and cartap, iprodione, phenthoate, phorate, thifluzamide, triflumizole were 10–1000 µg/kg. The results indicated that all targets obtained satisfactory linearity with the correlation coefficient (R2) of the regression curve was better than 0.9900.
Under the optimal experimental conditions, the lowest concentration or the lowest content of the target component can be detected is considered as the detection limit (LOD). The limit of quantitation (LOQ) is the lowest concentration or the lowest quantity of the components to be measured in the sample by analytical method. The LOD and LOQ for each analytes were determined at the lowest concentration at a signal-to-noise ratio (S/N) of 3 and 10, respectively. The results were shown in Table 1 that the LOD and LOQ were 0.14–1.91 µg/kg and 0.46–6.37 µg/kg, respectively.

2.3.2. Precision and Stability

In this study, the stability and precision of the established method were tested. The precision was evaluated by measuring the intra-day and inter-day variations in the relative standard deviation (RSD%) of the peak area of each analyte. In order to estimate intra-day precision, the blank spiked samples at concentration levels of 10 and 100 µg/kg were pretreated and analyzed in one day, with each spike level with five parallel samples. Inter-day precision was achieved by measuring the spiked samples at concentration levels of 10 and 100 µg/kg once a day within five consecutive days. As shown in Table 2, the relative standard deviation values (RSD) of intra-day were 0.6–9.4% (10 µg/kg) and 0.8–9.1% (100 µg/kg), inter-day were 0.8–12.6% (10 µg/kg) and 1.5–11.7% (100 µg/kg). The stability was evaluated by injecting the same volume of wolfberry spiked samples (100 µg/kg) into the UPLC/MS/MS system at 0, 2, 4, 8, 12, 18 and 24 h under the same condition in one day. The RSD values were less than 11.7 for all analytes (Table 2), which indicating that the sample solutions were stable and unchanged.

2.3.3. Accuracy

The accuracy of this method was tested by the blank spike recovery experiment of 107 pesticides. And the spiking levels were 10, 50, and 100 µg/kg, respectively, meanwhile the determination had five parallel samples for each spiking level. Average recoveries of 107 kinds of pesticides at three fortification levels using the analytical procedure, as presented in Table 2, were 63.3–123.0%, 72.0–118.6%, and 67.0–118.3%, respectively, which were within the range of the acceptable values. Under the premise of ensuring the accuracy and reproducibility of the results, a better recovery rate can be obtained, which is fully in line with the AOAC 2007.01 and EN 15,662 standards. All samples were analyzed on the same day and the RSD values below 15.0% at the three fortification levels indicated the accuracy was acceptable.

2.4. Matrix Effects

Matrix effects are caused by the co-elution of matrix constituents that play an important role in multi-residue analysis of pesticides, which can affect the ionization efficiency of target pesticides [55,56] and then influence the quantitative results by matrix enhancement and attenuation effects caused by quality of chromatographic separation, the ionization type, the amount and the type of the sample matrix, and sample preparation procedure [57,58]. For complex matrix like wolfberry is wealthy in mineral substance, proteins, polysaccharose, amino acids, carotinoid, flavonoids and so on [6], it is critical to eliminate or attenuate matrix effects by minimizing matrix co-extractives through the sample preparation procedure. In this research, the matrix effect (ME) was calculated by the following equation:
ME   = A Matrix A s   ×   100 %
where AMatrix is the peak area of matrix standard sample and As is the peak area of pure solvent standard sample. The matrix effect of the individual pesticide was studied at the level of 10 µg/kg with five parallel samples, which was deemed to be ignored if the ME value is between 90% and 110%, while it was regarded to be matrix suppression or enhancement effect when the value was less than 90% or greater than 110%, respectively [55,58,59]. The results showed that 71.96% of the pesticides presented a negligible ME, whereas 27.10% of the analytes presented matrix suppression effect, and only one pesticide (carbofuran) showed a matrix enhancement effect. Table 2 shows the specific ME values of each pesticide. Most of the compounds showed negligible matrix effect or mild matrix inhibition effect, suggesting that this method was suitable for the determination of 107 pesticide residues in wolfberry. However, there are still several pesticides with strong matrix effect: ME < 80% (boscalid, hexythiazox, metconazole) or ME > 120% (carbofuran). Therefore, to overcome and compensate for these matrix effects, matrix-matched standard curves were used in the quantitative analysis.

2.5. Real Samples Analysis

The applicability of the developed method was evaluated by analyzing a total of fifty wolfberry samples collected from supermarkets, pharmacies and farmers’ markets in different cities of Shandong Province and three replicates of each sample were analysed. One hundred percent of the samples analyzed included at least one pesticide, and a total of 26 pesticide residues was detected in fifty samples, which mainly were insecticides and bactericides. Ninety-six percent of the samples was found to be acetamiprid with concentration ranging from 19.54 to 63.15 µg/kg, and imidacloprid was detected in eighty-two percent of the samples with concentration ranging from 7.80 to 60.26 µg/kg. In addition, several pesticides with higher detection frequency in wolfberry were thiophanate-methyl (54%), blasticidin-S (50%), carbendazim (42%), tebuconazole (42%) and difenoconazole (36%). The detection frequency of cyromazine and metalaxyl in wolfberry is only 8%, but the detection concentration is as high as 46.78–64.56 µg/kg and 64.75–86.22 µg/kg, respectively. Moreover, carbendazim (44.36 µg/kg), myclobutanil (51.95 µg/kg) and thiamethoxam (69.21 µg/kg) were found higher concentration in some samples occasionally. All pesticide residues detected in wolfberry were below the MRLs specified by the EU and China, except for pesticides without MRLs. The residue level of imidacloprid with a detection frequency of up to 82% was lower than the MRL (1 mg/kg) set by China (GB 2763-2016). Figure 4 reveals the types, frequency and concentration range of 26 pesticides detected in 50 wolfberry samples. From this test, the adaptability of the developed method to the detection of multiple pesticide residues in wolfberry was determined.

3. Materials and Methods

3.1. Chemicals and Reagents

Pesticide reference standards of all analytes were of purity > 98%, purchased from Aldrich-Sigma (Shanghai, China) and Dr. Ehrenstorfer GmbH (Augsburg, Germany). Each kind of pesticide standard substances was accurately labeled as 10 mg in a 100 mL volumetric flask, then dissolved with methanol and fixed to the scale line. A composite sample working standard solution was prepared by combining aliquots of each stock solution and diluting in methanol to obtain a final concentration of 10 mg/L, a series of matrix-matched standard solutions was obtained by gradually diluting the stock solution with blank matrix solution. All solutions were stored at 4 °C before use.
Acetonitrile (MeCN) and methanol (MeOH) were of HPLC grade, purchased from Fisher Scientific (Pittsburgh, PA, USA). High purity water was obtained using a Milli-Q POD water purification system (Millipore, Schwalbach, Germany). LUMTECHTM Sin-QuEChERS Nano catridges (2 g Na2SO4, 0.6 g MgSO4, 90 mg PSA, 15 mg MWCNTs) and the three kinds of salting out packages for the traditional QuEChERS method (4 g MgSO4, 1 g NaCl), AOAC 2007.01 Method (6 g MgSO4, 1.5 g NaOAc) and EN 15,662 Method (4 g MgSO4, 1 g NaCl, 1 g TSCD, 0.5 g DHS) respectively were provided by Lumiere Technologies (Beijing, China). HyperSep C18 and HyperSep NH2 solid phase extraction catridges were purchased from Thermo Scientific (Waltham, MA, USA), SampliQ Florisil solid phase extraction catridges were obtained from Agilent Technologies (Santa Clara, CA, USA), and ProElut GLASS PSA solid phase extraction catridges were obtained from Dikma Technologies (Beijing, China), respectively.

3.2. Instrument

An ACQUITY Quattro Premier XE system (Waters Corp., Milford, MA, USA) was used for analysis. Chromatographic separation was performed at 35 °C with an ACQUITY UPLC HSS T3 (1.8 μm, 2.1 × 100 mm, Waters Corp., Milford, MA, USA). The mobile phase A was 0.1% formic acid solution and the mobile phase B was 0.1% formic acid acetonitrile. Initial composition of the mobile phase was 80% of solvent A and 20% of solvent B, reaching the 95% of solvent B at 6 min. The concentration of B remained at 95% for 2 min before returning to the initial state in 1 min. Re-equilibration of the column was performed for 1 min before the next injection was conducted. The flow rate of the eluent was 0.4 mL/min and the injection volume was 1 μL. The mass spectrometer was equipped with an electrospray ionization (ESI) interface operating at positive (ESI+) or negative (ESI−) mode, and MRM mode was adopted for date collection. The electrospray voltage, the atomizing gas pressure, the auxiliary airflow speed and the ion source temperature were set as 3000 V, 7.0 Bar, 150 L/h and 400 °C, respectively. The 107 analytical parameters of liquid chromatography tandem mass spectrometry are listed in Table 1.

3.3. Sample Preparation

Wolfberry samples were collected from supermarkets, pharmacies and farmers’markets in different cities of Shandong Province, then the samples were ground into powder, passed through mesh screen (0.42 mm). The prepared samples were stored at 4 ℃ and analysed withinn 24 h following the procedure described below. Wolfberry (pesticide-free) obtained from an organic production base was used as blank matrix for preparing standard curve and the recovery studies.
Five grams of ground wolfberry sample were weighed exactly into a 50 mL polypropylene centrifuge tube and soaked with 10 mL water for 5 min. Subsequently, 15 mL acetonitrile was added, next vortexed for 2 min, after following AOAC 2007. 01 extraction salt pack was added, after that vortexed for 1 min, succeeding centrifuged at 5000 rpm for 5 min. The Sin-QuEChERS Nano purification tube was taken into the 50 mL centrifuge tube, which was filled with extraction liquid, eventually the purification tube was slowly pressed down by an automatic apparatus to making up about 4 mL supernatant in the storage tank of the purification pipe. The structure and usage of Sin-QuEChERS Nano catridges were shown in Figure 5. The supernatant of 4 mL was accurately transferred to 10 mL centrifugal tube, dried with nitrogen gas stream and resolved with 1 mL methanol. Before UPLC/MS/MS analysis, the solution was filtered through a 0.22 µm filter (Dikma, Technologies). The flow chart of sample preparation procedure is presented in Figure 6.

4. Conclusions

In this work, a simple and rapid method has been developed for the determination of 107 pesticide residues in wolfberry samples using UPLC/MS/MS analysis. The qualitative and quantitative analysis of 107 pesticides can be accomplished in 10 min at a time with one injection. The Sin-QuEChERS Nano purification column can not only effectively remove pigments, organic acids, alkaline interferents, fat and water, but also save sample preparation time and avoid the losses caused by solvent transfers. It greatly simplifies the purification process of the samples, while improving the detection efficiency and accuracy. The linearity, matrix effect, analysis limits, precision, stability and accuracy were validated in detail. The LODs and LOQs of the target pesticides obtained by this method were in the range of 0.14–1.91 µg/kg and 0.46–6.37 µg/kg, respectively. Under the premise of ensuring the accuracy and reproducibility of the results, the average recoveries (80%–120%) and RSD (<15%) of most target pesticides at the three spike levels were acceptable, which met the requirements of conventional pesticide residues screening. Fifty commercial wolfberry samples were tested for pesticide residues by the developed method, all of which were positive, and 26 pesticides were detected. These results demonstrated that the proposed method was sensitive, fast, simple and reliable for the simultaneous determination of 107 pesticide residues in wolfberry, furthermore did not require complex purification process to allow routine analysis of a large quantity of samples. Furthermore, the developed approach can further expand the types of target pesticides and be applied to the detection of pesticide residues in more other traditional Chinese medicine. This will be the focus of our future work.

Author Contributions

J.-N.C. carried out the experiments, data acquisition and analysis, and wrote the manuscript; Y.-J.L. collated experimental data and provided technical assistance; Y.-R.Z. carried out data analysis and revised the manuscript; M.-H.W. carried out date acquisition and analysis; X.-Q.Z. provided technical assistance; J.-H.W. provided the wolfberry samples; Y.-N.W. provided technical assistance and revised the manuscript; M.-L.W. designed the experiments and carried out data analysis.


This research was funded by National Key Research and Development Plan for the “Thirteenth Five-Year Plan” (Multi-target high-throughput screening technology research on potential hazardous materials for high-value agricultural products), grant number 2017YFF0211304), and Shandong Modern Agriculture Industry Technology System Project, grant number SDAIT-20-04.


The authors would like to thank the Agricultural College of Shandong Agricultural University for helping to collect the wolfberry samples for analysis.

Conflicts of Interest

The authors declare no conflict of interest.


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Sample Availability: Samples of the compounds are available from the authors.
Figure 1. Average recoveries of 15 pesticides using the three different versions of QuEChERS (n = 5), (a) 10 µg/kg; (b) 25 µg/kg; (c) 50 µg/kg.
Figure 1. Average recoveries of 15 pesticides using the three different versions of QuEChERS (n = 5), (a) 10 µg/kg; (b) 25 µg/kg; (c) 50 µg/kg.
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Figure 2. Colour comparison of extracts purified by different SPE catridges. (a) Sin-QuEChERS Nano; (b) SampliQ Florisil; (c) HyperSep NH2; (d) HyperSep C18; (e) ProElut GLASS PSA.
Figure 2. Colour comparison of extracts purified by different SPE catridges. (a) Sin-QuEChERS Nano; (b) SampliQ Florisil; (c) HyperSep NH2; (d) HyperSep C18; (e) ProElut GLASS PSA.
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Figure 3. Comparison for recoveries of samples with different purification process. (a) Sin-QuEChERS Nano; (b) SampliQ Florisil; (c) HyperSep NH2; (d) HyperSep C18; (e) ProElut GLASS PSA.
Figure 3. Comparison for recoveries of samples with different purification process. (a) Sin-QuEChERS Nano; (b) SampliQ Florisil; (c) HyperSep NH2; (d) HyperSep C18; (e) ProElut GLASS PSA.
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Figure 4. Detection results of pesticide residues in real samples with the developed method.
Figure 4. Detection results of pesticide residues in real samples with the developed method.
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Figure 5. A schematic diagram of the structure and use of Sin-QuEChERS Nano catridges.
Figure 5. A schematic diagram of the structure and use of Sin-QuEChERS Nano catridges.
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Figure 6. Scheme of pesticide extraction procedures from wolfberry samples.
Figure 6. Scheme of pesticide extraction procedures from wolfberry samples.
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Table 1. Liquid chromatography tandem mass spectrometry analysis parameters, LODs, LOQs, Linear range and Calibration Curve Coefficients (R2) of 107 pesticides.
Table 1. Liquid chromatography tandem mass spectrometry analysis parameters, LODs, LOQs, Linear range and Calibration Curve Coefficients (R2) of 107 pesticides.
Precursor ionRetention Time (min)Precursor ions
Cone voltage (V)Collision energy
Linear range
1AbamectinC48H72O14[M + NH4]+8.65890.7305.118221.483.945–10000.9998
2AcephateC4H10NO3PS[M + H]+0.79184.049.210161.173.915–10000.9981
3AcetamipridC10H11ClN4[M + H]+2.29223.056.122100.461.545–10000.9999
4AcetochlorC14H20ClNO2[M + H]+5.14270.0148.115200.270.902–10000.9995
5AldicarbC7H14N2O2S[M + Na]+2.80213.189.125160.451.505–10000.9697
6Aldicarb-SulfoneC7H14N2O4S[M + H]+1.30223.086.025100.722.395–10000.9990
7Aldicarb-SulfoxideC7H14N2O3S[M + H]+0.89207.189.010140.682.275–10000.9911
8AmetrynC9H17N5S[M + H]+2.89228.168.125250.501.665–10000.9998
9AtrazineC8H14ClN5[M + H]+3.66216.196.122180.903.005–10000.9996
10AzoxystrobinC22H17N3O5[M + H]+4.63404.0329.025250.331.085–10000.9995
11BendiocarbC11H13NO4[M + H]+3.38224.1109.017180.240.792–10000.9987
12Blasticidin-SC17H26N8O5[M + H]+2.76423.5189.318120.983.275–10000.9994
13BoscalidC18H12Cl2N2O[M − H]4.75342.9139.932200.602.015–10000.9994
14BupirimateC13H24N4O3S[M + H]+5.06317.0108.031280.481.615–10000.9995
15BuprofezinC16H23N3OS[M + H]+5.36306.1116.020100.822.745–10000.9997
16ButralinC14H21N3O4[M + H]+8.19296.3222.220200.210.702–5000.9997
17CarbarylC12H11NO2[M + H]+3.58202.0117.019280.682.265–10000.9998
18CarbendazimC9H9N3O2[M + H]+6.32192.0132.017221.113.705–10000.9996
19CarbofuranC12H15NO3[M + H]+3.42222.1123.022120.712.385–10000.9991
20CarbosulfanC20H32N2O3S[M + H]+4.27381.1117.924180.963.205–10000.9987
21CartapC7H15N3O2S2[M + H]+0.63238.061.125221.575.2210–10000.9999
22ChlorantraniliproleC18H14BRCl2N5O2[M + H]+4.20484.1286.125200.421.395–10000.9998
23ChlorfenvinphosC12H14Cl3O4P[M − H]5.30358.999.020300.963.225–10000.9989
24ChlorprophamC10H12ClNO2[M + H]+4.85214.6126.020150.722.415–10000.9983
25Clodinafop-propargylC17H13ClFNO4[M + H]+6.78350.0266.022200.862.875–10000.9997
26ClothianidinC6H8ClN5O2S[M + H]+2.16249.9132.022150.220.742–10000.9995
27CyantraniliproleC19H14BrClN6O2[M + H]+3.77475.0286.025150.311.035–10000.9995
28CyazofamidC13H13ClN4O2S[M + H]+5.34325.0107.920200.361.195–10000.9991
29CymoxanilC7H10N4O3[M + H]+2.55199.0111.015180.862.875–10000.9989
30CyproconazoleC15H18ClN3O[M + H]+5.32292.270.230180.220.732–10000.9996
31CyprodinilC14H15N3[M + H]+4.09226.093.030260.842.815–10000.9992
32CyromazineC6H10N6[M + H]+2.66167.260.028201.033.435–10000.9992
33DiazinonC12H21N2O3PS[M + H]+6.99305.196.930350.632.105–10000.9990
34DiethofencarbC14H21NO4[M + H]+4.49268.3124.019400.260.862–5000.9989
35DifenoconazoleC19H17Cl2N3O3[M + H]+5.40406.0111.120600.601.995–10000.9994
36DimethoateC5H12NO3PS2[M + H]+2.25230.1125.022100.401.345–10000.9991
37DimethomorphC21H22ClNO4[M + H]+4.25388.1165.035300.200.655–10000.9994
38DiniconazoleC15H17Cl2N3O[M + H]+6.30326.170.237250.712.385–10000.9994
39DinotefuranC7H14N4O3[M + H]+1.00203.0129.013120.862.885–10000.9983
40DiuronC9H10Cl2N2O[M + H]+3.76233.046.328140.270.915–10000.9974
41EpoxiconazoleC17H13ClFN3O[M + H]+4.68330.0101.025500.551.845–10000.9997
42EthionC9H22O4P2S4[M + H]+8.05384.997.030460.802.665–10000.9994
43FenbuconazoleC19H17ClN4[M + H]+4.89337.070.132200.240.822–5000.9990
44FenhexamidC14H17Cl2NO2[M + H]+4.74302.155.332380.341.155–10000.9998
45FenobucarbC12H17NO2[M + H]+4.38208.094.916140.872.895–10000.9966
46FlonicamidC9H6F3N3O[M + H]+1.78230.2148.135251.364.525–10000.9992
47Fluazifop-butylC19H20F3NO4[M + H]+6.32384.1282.128200.451.495–10000.9985
48FluazinamC13H4Cl2F6N4O4[M + H]+6.17465.0338.123470.270.915–10000.9997
49FluopicolideC14H8Cl3F3N2O[M + H]+4.82386.2173.035250.431.455–10000.9995
50FlusilazoleC16H15F2N3Si[M + H]+5.93316.0165.030280.421.395–10000.9993
51FlutriafolC16H13F2N3O[M + H]+4.24302.170.230180.140.462–10000.9999
52FosthiazateC9H18NO3PS2[M + H]+3.63284.0104.020180.200.682–5000.9998
53Haloxyfop-methylC16H13ClF3NO4[M + H]+5.82376.091.125250.230.775–10000.9998
54HexaconazoleC14H17Cl2N3O[M + H]+6.05315.070.131220.461.525–10000.9992
55HexythiazoxC17H21ClN2O2S[M + H]+6.53353.0168.124260.902.985–10000.9995
56ImazalilC14H14Cl2N2O[M + H]+2.93296.9158.925200.602.005–10000.9999
57ImidaclopridC9H10ClN5O2[M + H]+2.13256.1175.122200.311.025–10000.9999
58IndoxacarbC22H17ClF3N3O7[M + H]+5.83528.0150.025220.371.235–10000.9993
59IprodioneC13H13Cl2N3O3[M + H]+4.98330.0244.715161.575.2310–10000.9995
60IsocarbophosC11H16NO4PS[M + H]+4.30291.1121.112300.210.702–5000.9992
61IsoprocarbC11H15NO2[M + H]+3.91194.195.118140.331.095–10000.9981
62IsoprothiolaneC12H18O4S2[M + H]+5.04291.1188.815180.220.725–10000.9991
63IsoproturonC12H18N2O[M + H]+3.72207.072.028220.300.995–10000.9982
64MalathionC10H19O6PS2[M + H]+4.99331.099.014240.210.715–10000.9998
65MetalaxylC15H21NO4[M + H]+4.49280.1192.130170.210.702–10000.9997
66MetconazoleC17H22ClN3O[M + H]+5.05320.170.025220.521.735–10000.9993
67MethamidophosC2H8NO2PS[M + H]+0.74142.094.022100.441.485–10000.9999
68MethomylC5H10N2O2S[M + H]+1.51163.088.020100.321.065–10000.9992
69MethoxyfenozideC22H28N2O3[M + H]+4.90369.1149.125180.531.785–10000.9994
70MetolachlorC15H22ClNO2[M + H]+6.27284.1176.130250.401.355–10000.9997
71MetribuzinC8H14N4OS[M + H]+3.86215.0131.010180.622.075–10000.9987
72MyclobutanilC15H17ClN4[M + H]+4.59289.170.228180.351.185–10000.9992
73OmethoateC5H12NO4PS[M + H]+0.83214.0125.020220.632.095–10000.9983
74PaclobutrazolC15H20ClN3O[M + H]+5.15294.170.230200.401.345–10000.9995
75PenconazoleC13H15Cl2N3[M + H]+4.99284.070.128160.381.265–10000.9999
76PhenthoateC12H17O4PS2[M + H]+6.88321.0135.030201.916.3710–10000.9999
77PhorateC7H17O2PS3[M + H]+5.82261.075.010121.625.4010–10000.9949
78Phorate-sulfoneC7H17O4PS3[M + NH4]+4.29293.0115.016240.511.715–10000.9979
79Phorate-sulfoxideC7H17O3PS3[M + H]+3.55277.096.918320.150.502–5000.9989
80PhoximC12H15N2O3PS[M + H]+5.77299.0129.012130.451.495–10000.9977
81Piperonyl-butoxideC19H30O5[M + NH4]+6.27356.3119.020300.571.915–10000.9999
82Pirimiphos-methylC11H20N3O3PS[M + H]+6.90306.1108.130320.551.845–10000.9991
83ProchlorazC15H16Cl3N3O2[M + H]+9.87376.1266.022100.892.975–10000.9978
84ProfenofosC11H15BrClO3PS[M − H]7.40372.9127.930401.143.805–10000.9999
85PrometrynC10H19N5S[M + H]+3.45242.0158.020160.692.315–10000.9995
86ProphamC10H13NO2[M + H]+4.07180.077.018200.260.882–10000.9991
87PropiconazoleC15H17Cl2N3O2[M + H]+5.15342.069.025200.160.542–5000.9996
88PropoxurC11H15NO3[M + H]+3.36210.0111.012160.441.455–10000.9968
89PyridabenC19H25ClN2OS[M + H]+7.00365.1147.022200.240.805–10000.9985
90SemiamitrazC10H14N2[M + H]+3.94163.096.318101.324.405–10000.9921
91SethoxydimC17H29NO3S[M + H]+6.42328.3254.323151.504.995–10000.9976
92SimazineC7H12ClN5[M + H]+3.47202.096.025200.300.995–10000.9999
93SpirodiclofenC21H24Cl2O4[M + H]+7.10411.171.225130.782.615–10000.9997
94TebuconazoleC16H22ClN3O[M + H]+4.82308.070.134220.240.795–10000.9996
95TebufenozideC22H28N2O2[M + H]+5.25353.0105.012220.280.955–10000.9996
96ThiabendazoleC10H7N3S[M + H]+1.03202.0131.025250.300.995–10000.9998
97ThiaclopridC10H9ClN4S[M + H]+2.69253.090.125300.200.675–10000.9997
98ThiamethoxamC8H10ClN5O3S[M + H]+1.68292.0132.022220.461.535–10000.9999
99ThifluzamideC13H6Br2F6N2O2S[M + H]+6.53526.8148.020251.695.6310–10000.9993
100Thiophanate-methylC12H14N4O4S2[M + H]+3.20343.193.020460.722.395–10000.9996
101TolfenpyradC21H22ClN3O2[M + H]+6.11384.2171.030201.133.785–10000.9997
102TriadimefonC14H16ClN3O2[M + H]+5.74294.169.330200.200.685–10000.9988
103TriazophosC12H16N3O3PS[M + H]+6.19314.1118.930350.491.635–10000.9996
104Tribenuron-methylC15H17N5O6S[M + H]+4.09396.1154.918140.301.005–10000.9885
105TridemorphC19H39NO[M + H]+4.29298.157.040281.244.145–10000.9997
106TrifloxystrobinC20H19F3N2O4[M + H]+5.94409.0145.025250.481.585–10000.9994
107TriflumizoleC15H15ClF3N3O[M + H]+4.92346.0277.913101.755.8210–10000.9998
Table 2. Recovery (RE) values, Precision, Stability and Matrix effect (ME) of 107 Pesticides.
Table 2. Recovery (RE) values, Precision, Stability and Matrix effect (ME) of 107 Pesticides.
No.Compound10 µg/kg50 µg/kg100 µg/kgIntra-day Precision
(RSD%, n = 5)
Inter-day Precision
(RSD%, n = 5)
(RSD, %)
ME (%)
10 µg/kg100 µg/kg10 µg/kg100 µg/kg100 µg/kg10 µg/kg
81Piperonyl butoxide81.73.585.

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