Screening and Determination of Potential Risk Substances Based on

Subscriber access provided by University of South Dakota

Article

Rapid screening and determination of potential risk substances based on liquid chromatography-high resolution mass spectrometry Yanqing Fu, Yanhui Zhang, Zhihui Zhou, Xin Lu, Xiaohui Lin, Chunxia Zhao, and Guowang Xu Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b01153 • Publication Date (Web): 12 Jun 2018 Downloaded from http://pubs.acs.org on June 17, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

Rapid screening and determination of potential risk substances based on liquid chromatography-high resolution mass spectrometry

Yanqing Fu1,3, Yanhui Zhang2,Zhihui Zhou1,3, Xin Lu1,3, Xiaohui Lin2*, Chunxia Zhao1,3*, Guowang Xu1,3* 1

CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China

2

School of Computer Science & Technology, Dalian University of Technology, Dalian 116023, China

3

University of Chinese Academy of Sciences, Beijing 100049, China

* Address correspondence to: Prof. Dr. Guowang Xu, CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China. Tel. / Fax: 0086-411-84379530. E-mail: [email protected]. Dr. Chunxia Zhao, CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China. Tel. / Fax: 0086-411-84379559. E-mail: [email protected]. Prof. Xiaohui Lin, School of Computer Science & Technology, Dalian University of Technology, Dalian 116024, China. Tel.: 0086-411-84706002-3920. E-mail: [email protected].

1

ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 26

ABSTRACT Nontargeted analysis is a useful strategy for the discovery of unknown risk compounds. However, how to rapidly screen and determine risk substances is still a big challenge. In this study based on high-performance liquid chromatography (HPLC)-high resolution mass spectrometry (HRMS) a strategy for the rapid screening and determination of risk substances was established. First, the distribution characteristic of every feature from HRMS in all samples was studied by the mean deviation ratio (MDR) calculation. Features with MDR more than 20 were thought to be the first match of obvious suspected substances. Second, the structure characteristics of specific classes of substances which were summarized from our in-house risk substance (IHRS) database with about 500 different additives and drugs were used to rapidly screen the unknown suspected substances with specific structure classes. To further identify above suspected risk substances, IHRS retrieval was carried out. For the unknown features not included in IHRS database, the exclusion of endogenous substances and multiple network databases searching were firstly performed, the remaining substances had to be identified with comprehensive methods. To test the usefulness of the established screening and identification method, 42 meat samples were analyzed and six risk substances were discovered and identified, usefulness of the method was confirmed.

Keywords: nontargeted screening, liquid chromatography-mass spectrometry, mean deviation ratio, characteristic fragmentation retrieval, in-house risk substance database

2


Page 3 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


INTRODUCTION In recent decades food safety has become a global concern. Due to the frequently occurring food incidents 1, food safety has attracted more and more public attention. Developing efficient detection methods for potential risk substances is urgent and significant. Meat and its products are one important kind of diet in the human foods. To pursue the maximization of interests, various additive drugs (such as hormones, clenbuterol, antibiotics and other veterinary drugs) have been used in the feeding process of livestock and poultry. However, long-term intake of livestock and poultry meat with above additive drugs is not conducive to human health. Mild cases can cause food poisoning and allergic reactions while long-term accumulation will be teratogenic, carcinogenic and mutagenic, which have a serious impact on health. In order to avoid the ill effects of risk substances and ensure food safety, different organizations have established different standards which regulated the maximum residue limits (MRLs) for most drug compounds, such as Commission Regulation (EU) 37/2010 established by European Union 2. To monitor whether the content of risk drugs exceeds the specified MRLs in samples, many researchers have developed different analytical methods to detect the potential risk substances. For example, different detection methods for penicillins, cephalosporins, nitroimidazoles, benzimidazoles,

chloramphenicols,

coccidiostats,

beta-agonists,

beta-lactam

antibiotics and some other veterinary drugs in different livestock and poultry meat have been established 3-7. However, targeted analysis only helps to detect specific target additive drugs. For unknown potential risk substances, comprehensive analytical and screening methods need to be developed. The aim of nontargeted screening is to find as much useful information as possible and identify new unknown differential compounds. 3



Page 4 of 26

Nontargeted analysis method has been used to find differential metabolites or biomarkers in metabolomics study environment analysis

8-10

and determine hazardous substances in

11-13

. There have been reports on nontargeted screening method

in food safety analysis to screen and identify potential risk substances

14-16

. The

combination of liquid chromatography (LC) with mass spectrometry (MS) has become an important and powerful analysis tool in food analysis field

1,17,18

. In

particular, because high resolution mass spectrometry (HRMS) has the higher mass resolution and accuracy, wider coverage of analysis information, and better qualitative and identification ability, it has been used in nontargeted screening analysis of contaminants in food more and more frequently 19-21. Nontargeted screening is helpful in extracting more compounds information from the complex food matrix. However, how to extract useful information effectively and determine potential risk substances quickly needs good data processing strategy. In order to achieve this goal, removal of redundant ions is the first vital step. Now the conventionally used methods to remove interfering ions include sample analysis replicates

15,22

, solvent extraction blank and control sample blank14,22,23, which can

remove the unstable interfering ions, the noise ions from analytical process and the useless ions in sample. Zeng et al. developed an ion fusion strategy to subtract the isotopic ions, adduct ions and fragment ions coming from the same compound 24,25. To further quickly determine risk compounds, on one hand, in-house database usually was used

14,26,27

. However, the number of compounds in such in-house databases is

limited. Researchers used differential analysis such as multivariate and univariate methods to analyze samples and discover potential risk compounds

14,15,28,29

.

Nevertheless, multivariate or univariate analysis commonly needs blank control samples in which interested compounds don’t exist. In the absence of blank control 4


Page 5 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


samples, how to rapidly screen and determine risk substances is still a big challenge. Therefore, facing the sample analysis without blank control samples, we developed a universal strategy for the rapid screening and determination of potential risk substances. This strategy mainly included mean deviation ratio (MDR) calculation and characteristic fragmentation rules (CFR) matching. In the MDR calculation ions with the ratio more than 20 were thought to be obvious suspected substances. CFR matching could help to rapidly screen the unknown suspected substances belonging to specific classes of compounds with the characteristic structure. Further identification and confirmation of risk substances were performed on our in-house risk substance (IHRS) databases and multiple network databases. The screening experiments of standards mixture solution and the spiked samples were used for method establishment. And 42 meat (pork, beef and chicken) samples were collected from different markets and analyzed to show the usefulness of the whole nontargeted screening method.

EXPERIMENTAL SECTION Materials and Chemicals. HPLC-grade methanol (MeOH) and acetonitrile (ACN) were obtained from Merck (Darmstadt, Germany). Ultrapure water (H2O) was prepared from a Milli-Q system (Millipore, Billerica, MA, U.S.A.). Formic acid (FA) and compound reference standards (including internal standards, additive standards and drug standards, and so on) were purchased from Sigma-Aldrich (St. Louis, MO, U.S.A.) and J&K Scientific Ltd. (Beijing, China).

Sample Information and Sample Pretreatment. In this study, except for the screening experiment of standards mixture solution (Supporting Information), one 5



Page 6 of 26

blank fish sample spiked with different drug standards and other twenty–three blank fish samples were also taken to establish the screening and identification method. The blank fish samples were marine fishes, which had been tested to contain no risk substances

14

. The drug standards added in the standards mixture solution and the

spiked samples included sulfapyridine, sulfamethoxypyridazine, enrofloxacin, sparfloxacin, penicillin G, cephalexin, tetracycline, clindamycin, albendazole, clenbuterol and tiamulin (Table S1). And their concentrations were all 100 ng/mL. In addition, forty-two meat samples (including pork samples, chicken samples and beef samples) were used for showing the usefulness of the established analysis method, they were randomly collected from different supermarkets and markets. And no priori

addition information of these samples was obtained before experiment. Sample pretreatment method referred to the direct solvent extraction with bead-beating disruption (DSE-BBD) which was established and used for the first time in our previous work 14. The details are as follows. One mL of ACN with 1% FA and 10 µL internal standards solution (1.6 µg/mL) were added into 200 mg (±1 mg) homogenized meat samples which had been weighed and placed in 2 mL centrifuge tubes. For the spiked sample, 10 µL mixture solution of standards with the corresponding concentrations (Table S1) was also added into a blank marine fish meat sample which was tested without known risk substances. After that, zirconia beads were added into the mixture. Further homogeneous extraction was performed in a mixed grinding apparatus (MM400, Retsch, Germany). The instrument parameter of homogeneous extraction process was set to 25 Hz for 1 min, five times. Subsequently centrifugation was performed under the condition of 14000 rpm at 4 ºC for 10 min in a Sorvall Biofuge Stratos centrifuge system (Thermo Fisher Scientific). Finally 1 mL aliquot of the upper layer was transferred and freeze-dried by using the 6


Page 7 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


LabconcoFreeZone 4.5 freeze-dry system (Kansas, U.S.A.) and stored in – 80 ºC for further injection analysis. Before sample injection, 20% ACN (200 µL) was added into the freeze-dried residue and 1 min of vortex was performed followed by centrifugation under the condition of 15000 rpm at 4 ºC for 10 min twice. The supernatant was transferred to sample bottle for LC-MS analysis. The standards mixture solution and blank solvent analysis were analyzed directly by LC-MS.

UHPLC-HRMS Analysis Method. Sample analysis was achieved in an ACQUITY Ultra Performance Liquid Chromatography (UPLC, Waters, Milford, MA, U.S.A.) – Q Exactive HF Mass Spectrometry (Thermo Fisher Scientific, Rockford, IL, U.S.A.) system. Liquid chromatography separation was performed by using a reversed-phase column (BEH C18 column, 2.1 mm ×50 mm, 1.7µm, Waters, Milford, MA, U.S.A.). The mobile phases A and B were H2O with 0.1% FA and ACN with 0.1% FA, respectively. The settings of LC analysis conditions were the same as those in our previous work

14

. The elution gradient started with 2% phase B. After 1 min, the

gradient was linearly increased to 40% B at 8 min, to 95% B at 9 min and maintained for 2 min, followed by equilibration with 2% B for 1 min. Total analysis time was 12 min and the flow rate was 0.4 mL/min. The temperature of column and sample manager was set as 45 ºC and 4 ºC, respectively. Sample injection volume was 5 µL. Parameter settings of mass spectrometry were consistent with those which had been used in our previous work 14. The scan mode chose full scan MS/data-dependent MS/MS (ddMS2) with the resolutions of 120 000 and 30 000, respectively. Scan range was set at m/z 73.4-1100. Normalized collision energy (NCE) was 30%. And the 7



Page 8 of 26

TopN was set to 5. The spray voltage in positive and negative modes was set as 3.5 kV and 3.0 kV, respectively. And in our experiment, the positive and negative modes were analyzed separately. The flow rate of sheath gas and aux gas was 45 and 10 (in arbitrary units), respectively. The capillary temperature was 300 ºC and the aux gas heater temperature was 350 ºC. S-lens rf level was 50.0.

Data Processing. Peaks alignment and frame were performed by the Sieve software (version 2.2, Thermo Fisher Scientific, Rockford, IL, U.S.A.). Trace Finder software (version 3.2, Thermo Fisher Scientific, Rockford, IL, U.S.A.) and IHRS database were used for identification of potential risk substances. The IHRS database was developed in our laboratory and contained about 500 drugs and additives

14

,

including veterinary drugs, antibiotics, toxins, hormones, food additives, coloring agents, sweetening agents, etc. And the compound information in the IHRS database contained compound name, chemical formula, CAS number, extracted mass, retention time, fragment ions, and so on. Origin 9.2 was employed for plotting. OSI-SMMS database (One-step Solution for

Identification

of

Small

Molecules

in

Metabolomics

Studies)

and

OSI-SMMS_Export_MS2.7z program (home developed) were used for rapid qualitative analysis of small molecular compounds and export of MS/MS information from raw data, respectively. And the principle of OSI-SMMS software development had been described in another work of our laboratory

30

. In OSI-SMMS, the

information of retention time, precursor ions and its product ions acquired in different collision energies of about 2000 endogenous compounds were contained. The identification of endogenous compounds can be achieved through precursor ion fusion, MS and MS/MS extraction, retention time calibration, database searching and scoring, 8


Page 9 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


automatically

30

. IdentifyClass program was developed to screen and identify risk

substances by using CFR.

RESULTS AND DISCUSSION Aim of nontargeted screening is to find useful information as comprehensive as possible. In food contaminants analysis, nontargeted screening is helpful for identification of potential risk substances. In this study, a rapid and effective screening and identification method was developed to discover potential risk substances. It mainly includes the following steps: 1) Effective data processing to screen and determine potential risk substances based on the MDR calculation and CFR matching; 2) Identification of the screened risk substances by IHRS database searching; and 3) Identification of unknown risk substances with multiple network databases searching. The screening and determination flowchart of potential risk substances is displayed in Figure 1.

Figure 1. Workflow of screening, determination and identification for potential risk 9



Page 10 of 26

substances.

Rapid Screening and Determination of Unknown Risk Substances. The detailed information of the data processing of the standards mixture solution experiment given in the Supporting Information, 100% of standards were identified. Here we take the spiked sample experiment as an example to explain the method establishment process. The spiked sample experiment included one spiked sample and twenty blank samples. After sample pretreatment and UHPLC-HRMS analysis, peak alignment and frame of the acquired raw data were performed by Sieve software, and the main parameters M/Z width (ppm), Base Peak Minimum Intensity and Background SNwere set as 5, 100000 and 3, respectively. The output peak list contained about ten thousand ions. Because of the complexity of the matrix, there were many interfering ions and redundant ions. And for better screening risk substances, interfering ions and redundant ions needed to be removed. In the sample pretreatment process, extraction solvent blank was used to help to discard the interfering ions from the experiment process including the used tubes, extraction solvent and analytical column. Here the ions with response more than 5,000 in extraction solvent blank were removed. Moreover, in all samples the ion response should be greater than 200,000 in at least one sample, otherwise it would be discarded. In this study, ions belonging to adduct type M+H (M: the compound molecular weight) were remained for further analysis. After above filtration, ion fusion was used to remove the redundant ions including neutral loss, adduct ion and isotopic distribution, and so on. After ion fusion24, only about two thousand ions remained for further analysis.

Screening of Risk Substances with MDR Calculation. In previous research, 10


Page 11 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


statistics methods were often used in metabolomics study, such as PCA, PLS-DA, t-test, z-score, and so on

31-34

. Here, in the screening analysis of samples without

negative control we calculated the mean deviation ratio (MDR) of each feature ion to find potential risk substances in samples by referring to the modified z-score method in literature 31. The calculation formula of MDR is displayed in formula (1). MDR = |(x-x)|/x

(1)

Where x represents the peak response of each ion in single sample, x represents the mean value of above ion in all samples, MDR represents the ratio of deviation of each ion from the mean level. According to above formula, x values of feature ions could be obtained after peak matching. And x value was calculated from the x of each ion. Further the MDR values could be obtained. Then ions were arranged according to the MDR values size. Ions with MDR value more than or equal to 20 were preferentially selected for future identification study. Through above analysis, forty-five ions were defined as obvious outliers which could be potential risk substances. Their identification will be performed in subsequent validation work.

Screening of Risk Substances with CFR Matching. Each class of compound with the characteristic structure can generate its own characteristic fragment ions or neutral losses. On the basis of the CFR, the characteristic fragment matching method can be developed for rapid screening and determination of the unknown suspected substances belonging to the corresponding substance classes but not in the in-house risk database. The workflow of this method mainly consisted of two steps. The first step was filtration process of MS information and MS/MS information. MS data files were the peak lists (including information of tr, exact m/z and intensity), which were obtained after noise filtration. MS/MS data files (.mgf) were obtained from sample raw data 11



Page 12 of 26

through the conversion of OSI-SMMS Export MS2.7z, and it contained information of tr (RTINSECONDS), exact m/z of MS ions (PEPMASS) and exact m/z of MS/MS fragment ions. The aim of the filtration process is to remove the noise of MS/MS data files by referring to the processed MS information. In order to obtain good filtration results, two parameters including mass accuracy and tr difference in the matching of the MS data information after noise filtration and the MS/MS raw data information should be less than 5 ppm and 10 s, respectively. Then the filtered MS/MS information could be used for further matching process. The second step was the matching process of the characteristic fragment ions or neutral losses. Here we take sulfonamides as an example for a detailed description. Sulfonamides have the basic structure p-aminophenylsulfamide which can generate characteristic fragment ions of m/z 156.01138, m/z 108.04439, m/z 92.04948 and m/z [MH-65.97755] (Table 1). According to the MS/MS results of sulfonamide reference standards, it could be found that characteristic fragment ions of m/z 156.0113 and, m/z 108.04439 almost existed in all sulfonamides, while m/z 92.04948 and m/z [MH-65.97755] maybe not appear in some sulfonamides. Then we used fragment ions of m/z 156.01138 and m/z 108.04439 as diagnostic fragment ions 35 and fragment ions of m/z 92.04948 and m/z [MH-65.97755] as auxiliary qualitative fragment ions. In the matching process, diagnostic characteristic fragment ions or neutral losses were mandatory and auxiliary qualitative fragment ions or neutral losses were optional. Compounds attribution could be determined as long as the above mandatory conditions were met. And the optional auxiliary conditions could help for further analysis. Because mass accuracy was the main parameter to determine whether it was the same ion, in the matching process the mass accuracy was defined to be less than 10 ppm. The whole workflow of the filtration process and matching process is shown 12


Page 13 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


in Figure S1.

Table 1. Characteristic fragment ions (CFI) of some classes of drugs.

Besides sulfonamides, according to the acquisition information of MS and MS/MS of other drug and additive standards, CFR of the other three specific classes of compounds, including quinolones, beta-blockers and tetracyclines, were also extracted through our summary (Table 1). Quinolones had characteristic neutral losses fragment ions m/z [MH-43.98983], m/z [MH-43.98983-(43.04220+n*14.01565)] (n=1,2,3…,20), m/z [MH-18.01056] and m/z [MH-20.00623]. Beta-blockers have characteristic fragment ions m/z 56.04948, m/z 74.06004, m/z 98.09643, m/z 116.10699, m/z [MH-42.04695] and m/z [MH-56.06260]. Tetracyclines have characteristic fragment ions m/z 154.04987, m/z 126.05495, m/z 98.06004, m/z [MH-18.01056] and m/z [MH-17.02655]. On the basis of above established matching method, one program named as IdentifyClass based on C++ code description was designed and developed for identification of unknown risk substances belonging to specific classes of compounds. And exhaust algorithm was used in the filtration and matching process. Through the analysis of characteristic matching, compounds of m/z 250.0642 and m/z 281.07 in the 13



Page 14 of 26

spiked sample were identified to be sulfonamides. Compounds of m/z 360.1712 and m/z 393.1726 were identified to be quinolones. Compound of m/z 445.1596 was identified to be tetracyclines. Through above MDR calculation and characteristic fragmentation retrieval, the suspected substances were defined. And the next step was to identify them.

Identification of Risk Substances with IHRS Database. In our previous studies an IHRS database containing drugs and additives was created for identification and confirmation of risk compounds

14

. These drugs and additives included both

exogenous substances (veterinary drugs, antibiotics, toxins, etc.) and endogenous substances (hormones, etc.). Now more compounds including food additives, coloring agents and sweetening agents have been added into the IHRS database and the number of compounds reached nearly 500 in the database. The compound information in the IHRS database included compound name, chemical formula, CAS number, extracted mass, retention time, fragment ions, and so on. In the IHRS database identification for potential risk substances five main parameters including accurate extracted mass (m/z), retention time (tr), fragment ions (FI), isotopic pattern (IP), and library search (LS) were to be set to the appropriate values. Mass error of parent ions and fragment ions was set as 5.0 ppm. The deviation of retention time could not exceed 15s. The fit threshold and allowed mass deviation for IP settings were set as 75% and 5.0 ppm, respectively. The match score threshold of library search for supplementary validation was set as 60%. Detailed setting information in the previous study of our laboratory could be referenced 14. Through IHRS database searching for the suspected ions, it was found that the added standards including sulfapyridine (m/z 250.0642), sulfamethoxypyridazine (m/z 14


Page 15 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


281.07), enrofloxacin (m/z 360.1712), sparfloxacin (m/z 393.1726), penicillin G (m/z 335.1053), cephalexin (m/z 348.1007), tetracycline (m/z 445.1596), clindamycin (m/z 425.1864), albendazole (m/z 266.0955), clenbuterol (m/z 277.0867) and tiamulin (m/z 494.3289), were all determined in the screening process and identified in the database searching. And the CFR retrieval also correctly determined the compound attribution of m/z 250.0642, m/z 281.07, m/z 360.1712, m/z 393.1726 and m/z 445.1596.

Identification of Unknown Risk Substances with Multiple Network Databases Searching. The Exclusion of Endogenous Substances. After above screening and IHRS database identification, thirty-four unknown compounds were still not determined. Then the combined retrieval of multiple databases including OSI-SMMS and HMDB was used for exclusion of endogenous substances. In our laboratory we developed a LC-MS2 database, OSI-SMMS

30

, by using about 2000

metabolite standards, most of these metabolites are endogenous compounds and small parts are exogenous substances. Through OSI-SMMS and HMDB retrieval, twenty-six compounds were preliminarily determined to be endogenous substances according to literatures and our accumulated experience in biological sample analysis. Because known endogenous risk substances have been identified in the IHRS database searching, the endogenous substances identified here were removed, they will be not considered as risk substances. After above retrieval, there were still eight compounds that were not identified. Multiple network databases Searching. The next identification of unknown risk substances was performed by searching online network databases, mainly including DrugBank, Metlin, mzCloud Database, and so on. After multiple network databases searching, the remaining eight compounds were still not identified. At this moment we 15



Page 16 of 26

can’t know whether they are false positives. But both the high success rate of 100% obtained in the standards mixture solution experiment (Supporting Information) and the detection of all 11 added compounds in the spiked sample experiment validated the effectiveness of the established analysis workflow.

Method Application in Screening Risk Substances in Different Meat Samples without Negative Control. To further show the usefulness of our established analysis method, forty-two meat samples without any priori information were collected from local markets and analyzed. Through the Sieve software more than twenty thousand ions were extracted, then interfering ions were deleted, and ion fusion was performed to remove the redundant ions. After that, 2981 ions remained for further analysis. According to the rule of MDR≥20, 384 ions were defined as obvious outliers which could be potential risk substances. Several typical differential ions were displayed in Figure 2. In addition, on the basis of the CFR matching with IdentifyClass, compounds of m/z 279.0901 (Figure 2a, MDR>20) and m/z 321.1005 (Figure 2e, MDR>20) in sample 13 were identified to be sulfonamides. Compounds of m/z 332.1392 (Figure 2c, MDR>20) in sample 38, m/z 360.1708 (Figure 2d, MDR>20) in sample 38 and m/z 362.1498 (MDR20), ciprofloxacin (m/z 332.1393, MDR>20), enrofloxacin (m/z 360.1718, MDR>20), albendazole sulfoxide (m/z 282.0895, Figure 2b, MDR>20), and ofloxacin (m/z 362.1498, MDR20), ofloxacin 16


Page 17 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(quinolones, MDR20), and albendazole sulfoxide (benzimidazoles, MDR>20) as examples, these results are displayed in Figures S2-S5, respectively. These validation results also validated the compound attribution of m/z 279.0901, m/z 332.1393, m/z 360.1708 and m/z 362.1498 obtained from CFR retrieval.

Figure 2. Distribution of MDR for suspected substances taking a. m/z 279.0901, b. m/z 282.0895, c. m/z 332.1393, d. m/z 360.1708, and e. m/z 321.1005 as examples. Black circles show the MDR of risk substances in suspected samples. Blue circles show the MDR distribution for the rest samples. The red square represents the mean. The left and right red vertical lines represent the minimum and maximum values of the data.

Except for above completely specified risk substances (sulfamethazine, ciprofloxacin, enrofloxacin, albendazole sulfoxide and ofloxacin), there were still 379 unknown compounds including compound of m/z 321.1005 that was not identified. Next the exclusion of endogenous substances was performed by using the combined retrieval of multiple databases including OSI-SMMS and HMDB. After retrieval, about 200 compounds were preliminarily determined to be endogenous substances. 17



Page 18 of 26

To further identify the remaining compounds, online network databases searching was performed. Because of the complement information of different network databases, the combined use of multiple online network databases searching is necessary. In this study the mainly used online databases included DrugBank, Metlin, mzCloud Database, and so on. After multiple network databases searching, results showed that compound of m/z 321.1005 in sample 13 was likely to be N4-acetylsulfamethazine. It happened that in the CFR retrieval, compound of m/z 321.1005 in sample 13 was identified to be sulfonamides because it had characteristic fragmentation ions m/z 108.04433, m/z 156.01138 and m/z 255.12296 ([M+H-H2SO2]). For further confirmation of its identity, N4-acetylsulfamethazine reference standard was purchased for the validation experiment. Figures 3A and 3B represent compound of m/z 321.1005 detected in sample and N4-acetylsulfamethazine reference standard, respectively. From the figure, it could be found that the matching of retention time and MS/MS spectrum was perfect. Compound of m/z 321.1005 was finally confirmed to be N4-acetylsulfamethazine. Except for the structures of characteristic fragment ions, structures of other fragmentation ions were also simulated in Mass Frontier software (Thermo Fisher Scientific, Rockford, IL, U.S.A.) and shown in Figure 3A. Elucidation of Completely Unknown Substances. Identification of completely unknown compounds has always been the most difficult and challenging work. In order to validate them well and accurately, more technologies and strategies need to be developed. For example, Multiple-stage tandem mass spectrometry (MSn) spectral trees

38,39

and fragmentation trees

40-42

16,36,37

,

are all useful methods for compounds

authentication and structure elucidation. In addition, researchers developed computational tools, such as iMet, for structure annotation of metabolites which were not described in database (both network database and in-house database)

43

. Except 18


Page 19 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


for the LC-MS analysis platform, other platforms, such as NMR, gas chromatography-MS, FTIR etc. can also be used for structure elucidation of risk compounds.

Figure 3. Extracted ion chromatograms, MS/MS spectra, and structure elucidation of N4-acetylsulfamethazine.

(A)

N4-acetylsulfamethazine

in

sample;

(B)

N4-acetylsulfamethazine standard. (MDR>20, and belonging to sulfonamides, but not included in IHRS database)

Quantitative Results. Quantitative experiment was performed to determine the contents of above six risk compounds. Linearity calibration curves of mixed standards were prepared for quantification. The results are shown in Table 2. Referring to Commission Regulation (EU) No. 37/2010, maximum residue limits (MRLs) of 19



Page 20 of 26

albendazole and enrofloxacin in animal muscle are both 100 µg/kg. Albendazole sulfoxide and ciprofloxacin are the metabolites of albendazole and enrofloxacin, respectively. According to the requirement, the sum of albendazole and its metabolites and the sum of enrofloxacin and ciprofloxacin in animal muscle cannot be more than 100 µg/kg. The total content of sulfonamides in animal muscle cannot exceed 100 µg/kg. However, there is no MRLs for ofloxacin in the regulations. Combined with quantitative results, it could be found that the contents of above risk compounds were within the allowable range except for sulfamethazine and its metabolites N4-acetylsulfamethazine.

The

total

content

of

sulfamethazine

and

N4-acetylsulfamethazine in sample 13 was more than 500 µg/kg, which had far exceeded the prescribed MRLs of sulfonamides. From the quantitative results, we could find that in most samples the contents of potential risk compounds were in the permitted range.

Table 2. Quantitative results of additives detected in meat samples (units: µg/kg) N4-Acetylsulfamethazin

AlbendazoleSulfoxid Enrofloxaci Ciprofloxaci Number

Ofloxacin Sulfamethazine e

e (ABZSO)

n

n

Sample-3

2.66

-

-

-

-

-

Sample-7

-

-

-

1.90

-

-

Sample-8

-

-

-

1.92

-

-

Sample-11

-

-

-

0.85

-

-

-

-

-

-

237.9

24.24

1.06

-

-

-

-

-

-

13.52

1.65

-

-

-

Sample-1 3 Sample-2 1 Sample-3 8

20


Page 21 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


CONCLUSIONS The issue of food safety is becoming more and more prominent and it threats to public health. Nontargeted screening analysis is a useful method for the discovery of potential risk substances. In this study the standards mixture solution and the spiked sample were used to establish the screening and identification method. Based on the established UHPLC-HRMS analysis method, a new universal screening and determination strategy combining MDR calculation and CFR matching was developed to rapidly screen and determine risk substances under the situation of the absence of blank control samples. MDR selected ions with ratio more than 20 as obvious risk substances, which can greatly reduce the subsequent qualitative workload, and determine risk substances more efficiently and quickly. In addition, according to Commission Regulation (EU) No. 37/2010, the maximum residue limits (MRLs) of most pharmacologically active substances in foodstuffs of animal origin were from 1 µg/kg to 1000 µg/kg. In the quantitative results of our experiment, it could be found that the contents of the screened substances ranged from about 1 µg/kg to 500 µg/kg. Based on this, we thought that 20 could help to discover substances exceeding the MRLs, which were considered to be of great risk. CFR retrieval method developed according to the summarized characteristic rules could help to identify specific classes of risk substances quickly. Both IHRS database searching and multiple network databases searching were performed well in the validation of risk substances. The high screening success rate of 100% in standards mixture solution and spiked samples experiments validated the effectiveness of the method. And in the evaluation analysis of 42 different meat samples, analysis results also showed the usefulness of the established method. Through the screening, determination, identification and validation, six potential risk substances were determined and 21



Page 22 of 26

quantified. It can be predicted that the developed method can also be used in other nontargeted screening fields.

ASSOCIATED CONTENT Supporting Information Supplementary information includes the standards mixture solution experiment for method establishment, Table S1 detailed information of 11 compounds in standards mixture solution; Table S2 identification information of the determined 17 substances in the standards mixture solution experiment; and Figures S1-S5 characteristic fragmentation structures of specific classes of compounds and confirmation results of risk substances.

ACKNOWLEDGEMENT This work was financially supported by the key project (No. 21435006) and projects (No. 21775147, 21675154, 21575140) from the National Natural Science Foundation of China.

REFERENCES (1) Malik, A. K.; Blasco, C.; Pico, Y. J. Chromatogr. A 2010, 1217, 4018-4040. (2) Commission Regulation (EU) No 37/2010, Off. J. Eur. Union, L15: 1-72. (3) Becker, M.; Zittlau, E.; Petz, M. Anal. Chim. Acta 2004, 520, 19-32. (4) Xia, X.; Wang, Y.; Wang, X.; Li, Y.; Zhong, F.; Li, X.; Huang, Y.; Ding, S.; Shen, J. J. Chromatogr. A2013, 1292, 96-103. (5) Barreto, F.; Ribeiro, C.; Hoff, R. B.; Costa, T. D. Talanta 2017, 168, 43-51. (6) Li, J.; Chen, Y.; Su, Y.-Q.; Ding, X.-M.; Xia, W.-S.; Liu, H.-M.; Zhang, Y.-B. Food Anal. Method2017, 10, 2185-2193. 22


Page 23 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(7) Yoshikawa, S.; Nagano, C.; Kanda, M.; Hayashi, H.; Matsushima, Y.; Nakajima, T.; Tsuruoka, Y.; Nagata, M.; Koike, H.; Sekimura, K.; Hashimoto, T.; Takano, I.; Shindo, T. J. Chromatogr. B, 2017, 1057, 15-23. (8) Liu, X.; Zheng, P.; Zhao, X.; Zhang, Y.; Hu, C.; Li, J.; Zhao, J.; Zhou, J.; Xie, P.; Xu, G. J Proteome Res.2015, 14, 2322-2330. (9) Dai, W.; Yin, P.; Zeng, Z.; Kong, H.; Tong, H.; Xu, Z.; Lu, X.; Lehmann, R.; Xu, G. Anal. Chem. 2014, 86, 9146-9153. (10) Chen, J.; Zhao, X.; Fritsche, J.; Yin, P.; Schmitt-Kopplin, P.; Wang, W.; Lu, X.; Haring, H. U.; Schleicher, E. D.; Lehmann, R.; Xu, G. Anal. Chem.2008, 80, 1280-1289. (11) Krauss, M.; Singer, H.; Hollender, J. Anal. Bioanal.Chem.2010, 397, 943-951. (12) Masia, A.; Campo, J.; Blasco, C.; Pico, Y. J. Chromatogr. A 2014, 1345, 86-97. (13) Ibáñez, M.; Sancho, J. V.; Hernández, F.; McMillan, D.; Rao, R. TrAC-Trend Anal.Chem. 2008, 27, 481-489. (14) Fu, Y.; Zhou, Z.; Kong, H.; Lu, X.; Zhao, X.; Chen, Y.; Chen, J.; Wu, Z.; Xu, Z.; Zhao, C.; Xu, G. Anal. Chem.2016, 88, 8870-8877. (15) Knolhoff, A. M.; Zweigenbaum, J. A.; Croley, T. R. Anal. Chem.2016. (16) Knolhoff, A. M.; Croley, T. R. J. Chromatogr. A2016, 1428, 86-96. (17) Kang, J.; Park, S. J.; Park, H. C.; Hossain, M. A.; Kim, M. A.; Son, S. W.; Lim, C. M.; Kim, T. W.; Cho, B. H. Appl. Biochem.Biotech.2017, 182, 635-652. (18) Pérez-Ortega, P.; Lara-Ortega, F. J.; Gilbert-López, B.; Moreno-González, D.; García-Reyes, J. F.; Molina-Díaz, A. Food Anal. Method 2016, 10, 1216-1244. (19) Kaufmann, A. Anal. Bioanal. Chem.2012, 403, 1233-1249. (20) Zedda, M.; Zwiener, C. Anal. Bioanal. Chem.2012, 403, 2493-2502. (21) Jia, W.; Chu, X.; Chang, J.; Wang, P. G.; Chen, Y.; Zhang, F. Anal. Chim. Acta 2017, 957, 29-39. (22) Bade, R.; Causanilles, A.; Emke, E.; Bijlsma, L.; Sancho, J. V.; Hernandez, F.; de Voogt, P. Sci. Total Environ.2016, 569-570, 434-441. (23) Solliec, M.; Roy-Lachapelle, A.; Sauve, S. Rapid Commun. Mass Sp.2015, 29, 2361-2373. (24) Zeng, Z.; Liu, X.; Dai, W.; Yin, P.; Zhou, L.; Huang, Q.; Lin, X.; Xu, G. Anal.Chem.2014, 86, 23



Page 24 of 26

3793-3800. (25) Luo, P.; Dai, W.; Yin, P.; Zeng, Z.; Kong, H.; Zhou, L.; Wang, X.; Chen, S.; Lu, X.; Xu, G. Anal. Chem. 2015, 87, 5050-5055. (26) Guo, B.; Wang, M.; Liu, Y.; Zhou, J.; Dai, H.; Huang, Z.; Shen, L.; Zhang, Q.; Chen, B. J. Agr. Food Chem.2015, 63, 6954-6967. (27) Dasenaki, M. E.; Bletsou, A. A.; Koulis, G. A.; Thomaidis, N. S. J. Agr. Food Chem.2015, 63, 4493-4508. (28) Inoue, K.; Tanada, C.; Sakamoto, T.; Tsutsui, H.; Akiba, T.; Min, J. Z.; Todoroki, K.; Yamano, Y.; Toyo'oka, T. Food Chem.2015, 181, 318-324. (29) Inoue, K.; Tanada, C.; Hosoya, T.; Yoshida, S.; Akiba, T.; Min, J. Z.; Todoroki, K.; Yamano, Y.; Kumazawa, S.; Toyo'oka, T. J. Sci. Food Agric.2016, 96, 3876-3881. (30) Zhao, X.;Zeng, Z.;Chen, A.; Lu, X.; Zhao, C.; Hu, C.; Zhou, L.; Liu, X.; Wang, X.; Hou, X.; Ye, Y.; Xu, G., Anal. Chem. 2018, doi: 10.1021/acs.analchem.8b01482.

(31) Guo, L.; Milburn, M. V.; Ryals, J. A.; Lonergan, S. C.; Mitchell, M. W.; Wulff, J. E.; Alexander, D. C.; Evans, A. M.; Bridgewater, B.; Miller, L.; Gonzalez-Garay, M. L.; Caskey, C. T. Proc. Natl. Acad. Sci. U.S.A.2015, 112, E4901-4910. (32) Worley, B.; Halouska, S.; Powers, R. Anal. Biochem.2013, 433, 102-104. (33) Graham, S. F.; Chevallier, O. P.; Roberts, D.; Holscher, C.; Elliott, C. T.; Green, B. D. Anal. Chem.2013, 85, 1803-1811. (34) Vinaixa, M.; Samino, S.; Saez, I.; Duran, J.; Guinovart, J. J.; Yanes, O. Metabolites 2012, 2, 775-795. (35) Xia, B.; Liu, X.; Gu, Y. C.; Zhang, Z. H.; Wang, H. Y.; Ding, L. S.; Zhou, Y. J. Am. Soc. Mass Spectr.2013, 24, 789-793. (36) Wang, S.; Qiao, L.; Shi, X.; Hu, C.; Kong, H.; Xu, G. Anal.Bioanal.Chem.2015, 407, 331-341. (37) Rojas-Cherto, M.; Kasper, P. T.; Willighagen, E. L.; Vreeken, R. J.; Hankemeier, T.; Reijmers, T. H. Bioinformatics 2011, 27, 2376-2383. (38) Ridder, L.; van der Hooft, J. J.; Verhoeven, S.; de Vos, R. C.; van Schaik, R.; Vervoort, J. Rapid Commun. Mass Sp.2012, 26, 2461-2471. 24


Page 25 of 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(39) Ridder, L.; van der Hooft, J. J.; Verhoeven, S.; de Vos, R. C.; Bino, R. J.; Vervoort, J. Anal. Chem.2013, 85, 6033-6040. (40) Vaniya, A.; Fiehn, O. TrAC-Trend Anal. Chem. 2015, 69, 52-61. (41) Rasche, F.; Svatos, A.; Maddula, R. K.; Bottcher, C.; Bocker, S. Anal. Chem. 2011, 83, 1243-1251. (42) Bocker, S.; Duhrkop, K. J. Cheminform.2016, 8, 5. (43) Aguilar-Mogas, A.; Sales-Pardo, M.; Navarro, M.; Guimera, R.; Yanes, O. Anal. Chem.2017, 89, 3474-3482.

25



Page 26 of 26

For TOC only

26


Screening and Determination of Potential Risk Substances Based on

Recommend Documents