Melissa Churley

Estrogens play a key role in developing and maintaining the female phenotype, germ cell maturation, and pregnancy. Processes that are not gender specific, such as overall body growth, nervous system maturation, bone metabolism/remodeling, and endothelial responsiveness are also mediated by estrogenic compounds.. Although present in both genders, estrogen concentrations are significantly higher in females. Estrone (E1) and estradiol (E2) are the two major biologically active estrogens in nonpregnant humans, while a third bioactive estrogen, estriol (E3), is the main pregnancy estrogen (1). The clinical determination of serum estrogen levels often is used to monitor low-dose female hormone replacement therapy (HRT) and assess fracture risk in postmenopausal women. In premenopausal women, estrogen levels can be monitored to assess ovarian status, including follicle development, for assisted reproduction protocols such as in vitro fertilization. Estrogens also play a role in precocious as well as delayed puberty in females, and can be monitored as part of estrogen replacement therapy in hypogonadal premenopausal women, in conjunction with luteinizing hormone (LH) measurements. Estrogen level measurements are taken as well during aromatase inhibitor treatment of hormone-dependent breast cancer. 

Some of the metabolites of the estrogens have been implicated in disease development, such as increased systolic blood pressure in both males and females. Because the metabolites can have a longer half life than the estrogens themselves, there is interest in using them as biomarkers of disease. There is a clear association between excessive exposure to estrogens and the development of cancer in hormone-sensitive tissue such as breast and endometrium (2). The estrogen quinone metabolite, E2-3,4-quinone, has been identified as a potential risk factor, and a subset of women with particular cytochrome P450 enzyme haplotypes have an increased risk of breast cancer as a result (3). The catechol metabolites of estrogens are considered intermediates in estrogen-induced carcinogenesis and their mode of action might be to induce deoxyribonucleoside adducts (4,5). Estradiol is a potent preventative against oxidative neurodegenerative disease by virtue of the hyperoxide-dependent hydroxylation of E2 to its catechol metabolites. In addition, E2 and its metabolites 2-hydroxyestrone (2-OHE1) and 16 a-hydroxyestrone (16-OHE1) also are believed to curtail oxidative stress that can lead to disease conditions including atherosclerosis (6,7). Utilization of the estrogen metabolites as biomarkers will require reliable detection at ultra-low concentrations. 

Distinguishing serum E2 levels within the low postmenopausal range of <1 pg/mL to 30 pg/mL is a valuable tool in the study of common diseases of older women such as osteoporosis, cognitive dysfunction, cardiovascular disease, and breast cancer (8). Ultralow estrogen measurements  also are needed to diagnose inborn errors of sex steroid metabolism, disorders of puberty, and estrogen deficiency in men. Reaching the lowest levels also might be important in predicting risk of fractures (9) as well as monitoring response to antiestrogens for prevention of breast cancer (10) and degree of E2 suppression in women receiving aromatase inhibitor therapy (11,12).  

During second-line treatment of hormone-dependent breast cancer, basal and suppressed serum E2 levels are monitored in women receiving aromatase inhibitor therapy. However, assays for E2 were developed originally for younger women — that is, with a range of interest higher than 50 pg/mL. At decreased postmenopausal levels, these assays have varied in accuracy and have lacked standardization. Moreover, aromatase inhibitor therapy requires measurement of E2 at an order of magnitude lower than normal basal levels in postmenopausal women. Ultrasensitive cell bioassays can reach the required levels but are difficult to employ, not widely available, and are not completely specific. Isotopic kinetic methods have been used to determine the degree of E2 suppression but are not practical for clinical use (12). 

Direct and indirect immunoassays have been employed widely in clinical settings to measure E2. Indirect assays such as RIAs require an initial extraction step, whereas direct assays do not require extraction. However, both types of immunoassays have overestimated E2 concentrations in postmenopausal women versus mass spectral methods, and are less reproducible. 

Liquid chromatography–electrospray ionization tandem mass spectrometry (LC–ESI-MS-MS) methods have been developed to measure estrogens. ESI typically offers one the ability to analyze a compound, or panel of compounds, without the need for derivatization techniques commonly used in gas chromatography (GC)–MS applications. However, for an LC–MS-MS method to approach the detection limits described herein, derivatization is required. One such report states a detection limit of 1 pg/mL of dansyl derivatized E2 in serum (13). GC–MS-MS methods in the electron ionization (EI) mode also have been developed, but again do not have the sensitivity required for these applications. 

GC–MS-MS in NCI Mode Delivers Selectivity and Ultralow Detection

The power of negative chemical ionization (NCI) to eliminate chemical interferences provides the ability to quantify therapeutic and subtherapeutic levels of estrogens in complex matrices, when utilized on a GC–MS-MS platform (14,15). These assays typically provide much lower limits of quantification than the LC–MS-MS counterparts, which do not have the sensitivity required for these applications.  

Assays for estrogens performed on a GC–NCI-MS-MS platform usually require isolation of the analytes by solid-phase extraction (SPE) followed by derivatization. With automated extraction procedures and advancements in derivatization techniques, GC analysis has become competitive with LC sample preparation and sample analysis cycle times.  

Operation in NCI mode using halogenated derivatives allows for electron-capture processes to take place. Compared with EI mode, there is less fragmentation of the molecular ion, hence the term, "soft ionization." The result is a high degree of selectivity and sensitivity, with significantly improved detection limits. In NCI mode, selectivity for the analyte is higher relative to that for the matrix and other common interfering ions observed in EI or positive chemical ionization (PCI) modes. The result is less chemical noise, which translates directly into improved detection limits.  

Therefore, the key to sensitive detection when using GC–MS-MS in NCI mode is the optimal derivatization moiety. The pentafluorobenzoyl ester derivatives of estrogens have been shown to be stable and provide diagnostic negative molecular ions (16). These derivatives have proven to be superior to trifluoroacetyl, heptafluorobutyl, pentadecafluorooctanoyl, and perfluorotolyl derivatives (17).  

Table I: Regression analysis for estradiol and estrone standard curves*			

Sensitive, Precise, and Accurate Quantification of Estrogens 

An estrogen-specific assay has been developed on the GC–NCI-MS-MS that can detect as little as 0.15 pg/mL of estradiol reliably. Derivatization of the estrogens was carried out using pentafluorobenzoyl chloride and N-methyl-N-trimethylsilyltrifluoroacetamide (MSTFA), which resulted in the formation of the benzoyl ester for the phenol groups, and the trimethylsilyl (TMS) ether for the 17-hydroxyl group of estradiol. Figure 1 shows the structures of the resulting derivatives of estradiol and estrone. The GC method analysis time is approximately 5 min and the cycle time is approximately 6 min.  

The assay is repeatable as well as reproducible. Nearly 100 injections were made without the need for any routine maintenance. The signal-to-noise ratio (S/N) is greater than 10 and chemical interferences that could affect quantification are not detected. The assay is linear from 0.3 to 80 pg/mL of estrone and 0.75 to 200 pg/mL of estradiol, giving an R

  > 0.996 for both E2 and E1. Precision (%CV) and bias (% difference from expected) are less than 15% across the quantification range for both analytes as well (Table I). Three concentrations of serum QC samples were prepared and analyzed that bracketed the quantification range of the assay for both estrogens. Precision and bias are less than 11% (accuracy >89%) at all concentrations for estradiol, and less than 15% for all concentrations of estrone (Table II). Figure 2 illustrates the ease of detection at the lowest limit of detection (LLOD) for estradiol, 0.15 pg/mL (S/N = 41), as well as the lowest limit of quantification (LLOQ), 0.3 pg/mL. The LLOQ for estrone using this assay is 0.8 pg/mL (Figure 3). 

Table II: QC serum sample results for estradiol and estrone*			

There is a current and growing need to detect very low levels of estrogens and their catechol metabolites reliably. Traditional immunoassays cannot provide the selectivity needed, and LC–MS-MS systems, the current industry standard for bioanalytical analysis, fall short in the ability to chromatographically separate interferences and provide the sensitivity required to quantify levels of certain endogenous compounds. Analysis by GC–NCI-MS-MS has been the primary analytical tool for monitoring endogenous levels of analytes considered to be biomarkers. The selectivity of the derivative, the high performance separation of GC, the response of the halogenated derivatives in NCI mode, and the elimination of matrix interference by SRM analysis provide the needed performance in the development of a biomarker assay.  

There also have been significant advancements in the past few years in GC–MS-MS technology that maximize ion transmission and detection, and the use of fast GC would further reduce runtime. When the ultimate level of detection and quantification is required to measure an ultra-low level endogenous compound, then a GC–NCI-MS-MS system might be the best alternative. 

 are application scientists with Agilent Technologies, Santa Clara, California. 

 is with Taylor Technology, Inc., Princeton, New Jersey.  

(1) Mayo Medical Laboratories web site, accessed 3/2/2010.  

http://www.mayomedicallaboratories.com/test-catalog/Clinical+and+Interpretive/81816

Articles

A number of clinical situations now call for high-sensitivity measurement of estrogens, including monitoring during female hormone replacement therapy, antiestrogen treatment, and estrogen deficiency in men. Traditional immunoassay methods and liquid chromatography–tandem mass spectrometry (LC–MS-MS) do not provide the sensitivity and selectivity required for these applications. In contrast, a gas chromatography–negative chemical ionization–tandem mass spectrometry (GC–NCI-MS-MS) platform can provide detection limits below 1 pg/mL when used in conjunction with the appropriate derivatization protocol, with very short cycle times.

Ultralow Detection of Estrogenic Compounds by GC–NCI-MS-MS

Drug doping testing requires methods that are highly sensitive, specific, and quantitative, particularly for steroid analysis. The demand for such methods will only increase in the future, and technologies must continue to evolve. Gas chromatography–triple quadrupole mass spectrometry (GC–QQQ) meets this challenge by providing doping control laboratories with the ability to do screening, confirmation, and quantification in one analysis, in complex sample matrices, without the need to change their current extraction and GC separation methods.

The recent Beijing Olympic Games reminded us of the value of international sport competition for fostering peace and mutual understanding worldwide. Unfortunately, we are also reminded that there are those athletes who will sacrifice their careers and violate the trust of their countrymen by using performance-enhancing drugs to give them a competitive edge, and cheating is as old as the Olympic Games.  

The use of performance-enhancing drugs in sporting events, either in the form of supplements or highly purified and potent compounds, eventually resulted in the establishment of systematic screening protocols to detect and prevent the use of such substances. This practice of chemical enhancement of athletic performance is commonly referred to as "doping." The death of British cyclist Tommy Simpson in 1967 from an amphetamine-induced heart attack sparked the introduction of routine, in-competition drug testing by most international sports federations, and in 1972 it made its debut at the Olympic Games in Munich. After the formation of the medical committee of the International Olympic Committee (IOC) in 1967, a list of prohibited substances was established. The list is updated frequently and supervised by the World Anti-Doping Agency (WADA), which was formed in 2000. The list currently includes five categories of prohibited substances (anabolic agents, hormones and related substances, beta-agonists, anti-estrogenic agents, and diuretics and other masking agents); three prohibited methods (enhancement of oxygen transfer, chemical or physical manipulation of samples, and gene doping); and four categories of substances prohibited only during competition (stimulants, narcotics, cannabinoids, and glucocorticosteroids). A minimum required performance limit (MRPL) is established for each prohibited compound, examples of which are shown in Table I. 

Table I: Minimum required performance limits 

WADA-accredited laboratories are required to demonstrate that they can routinely perform screening and confirmatory analyses at the MRPL established for each prohibited compound, following general guidelines prescribed by WADA. Procedures to account for the integrity of each specimen and preserve the chain of custody are in place to track handling and storage from the point of collection to final disposal of the specimen. These laboratories are responsible for assuring chain of custody by recording receipt of the samples, assuring proper handling and storage through intralaboratory identification and tracking, and ensuring confidentiality. The testing is then performed, the data archived, and results communicated observing the same strict protocols governing chain of custody. Ongoing original research and method refinement activities are also required of WADA-accredited laboratories to optimize performance and develop even more effective methods to identify new prohibited substances. Consequently, the methods and analytical technologies of these laboratories are constantly evolving. 

GC–MS and LC–MS Are the Testing Platforms of Choice

WADA-accredited laboratories use methodologies that have been optimized to screen large numbers of athlete samples (usually urine) for the presence of prohibited substances, then confirm any presumptive positive result with an orthogonal method that is both highly selective and unequivocally confirmatory. The majority of standard protocols used in doping control laboratories are based on an initial chromatographic separation of the complex urine extract using either gas chromatography (GC) or high performance liquid chromatography (HPLC), followed generally by mass spectrometry (MS) analysis of the chromatographic eluent to detect and quantify the vast range of prohibited substances. GC is suitable for compounds that can be vaporized easily without degrading or decomposing the compound, and was the first chromatographic method to be used extensively in doping control laboratories. HPLC is more suitable for thermolabile and highly polar compounds that cannot be readily vaporized, and for separation of large biomolecules such as proteins and peptides. 

The combination of GC or HPLC with MS provides a highly sensitive, selective, and versatile tool for the detection of WADA-prohibited compounds. A wide range of MS analyzers is available, each having advantages for particular applications. The most common type of MS analyzer used in doping analysis is the single-quadrupole MS system, which can be used in either the full-scan or selected ion monitoring (SIM) mode in screening protocols. Mass spectrometers with more than one mass analyzer (tandem MS or MS-MS systems) provide the next higher level of confidence in the identification of banned substances. These instruments first isolate the precursor ion or other intense fragment ion present in the full-scan MS spectrum and then fragment this precursor target ion into smaller product ions. The resulting product ion mass spectrum can be compared with that obtained from an authentic standard (or interpreted 

 if no standard is available) as part of the identity confirmation process. The ion trap MS-MS system (single analyzer, tandem-in-time operation) has found utility in many doping control laboratoriess for screening due to its ability to provide both MS spectral and MS-MS product ion spectral data, and in particular its sensitivity for acquiring product ion spectra. This performance feature means that the ion-trap mass analyzer can be used for analyte confirmation in selected circumstances, but co-eluted substances with isobaric precursor masses can result in a complex product ion spectrum of questionable value for confirmation. The tandem quadrupole mass spectrometer (sometimes called a "triple quadrupole" or "QQQ" system) is the definitive tool for high-confidence screening of target analytes, their confirmation, and quantification of these prohibited compounds in a single GC–MS-MS analysis.  

GC was the first analytical technique used to detect prohibited substances in athletes, and GC–MS has become the workhorse of doping control laboratories worldwide. It is routinely used to test for anabolic agents as well as beta-2 agonists, anti-estrogens, diuretics, stimulants, narcotics, cannabinoids, and beta-blockers (1). Screening for these compounds is performed using either the full-scan mode or the selected ion monitoring (SIM) mode with a single quadrupole mass analyzer. SIM is also used for confirmation of a suspect positive by determining the relative ratios of multiple ions being monitored for each target compound and comparing these to ratios obtained for authentic standards. GC–MS-MS with an ion trap mass analyzer is used in a number of doping control laboratories as well, to provide added sensitivity and specificity. The recent availability of GC–QQQ systems promises to extend the capability of drug doping laboratories by providing the ability to screen, confirm, and quantify prohibited compounds in one run, even in complex matrices. 

Doping control efforts focus to a significant extent on anabolic steroids, as they are probably the most widely abused category of prohibited substances. The earliest drug doping test methods developed were for steroid detection, resulting for example in the confirmation of stanozolol in a urine sample from sprinter Ben Johnson in the1988 Olympic Games, with the subsequent forfeiture of his gold medal and two world records. More recently, the detection of an elevated testosterone/epitestosterone (T/E) ratio led to the stripping of the 2006 Tour de France title from Floyd Landis, and the imposition of a two-year ban from sanctioned competition. The analysis of this group of compounds also receives close attention in the laboratory, as they must be detected at such low levels (Table I), requiring ongoing development of more highly selective and sensitive testing methods. 

Many doping control laboratories have traditionally used high resolution mass spectrometry (HRMS) or GC–ion trap MS-MS to meet the challenge of identifying and quantifying very low levels of very similar steroid compounds. However, HRMS is a technology that is expensive to acquire and requires a great deal of training and expertise to operate. Ion-trap MS-MS is a more economical option and somewhat easier to operate, but for some analytes, it may require the use of MS-MS-MS to achieve the requisite specificity, potentially resulting in a failure to achieve required sensitivity. On the other hand, the steroid detection and quantification requirements of the doping control laboratory are an excellent fit for the capabilities of the GC–QQQ platform. Selected reaction monitoring (SRM) with a QQQ system provides detection of trace amounts of target compounds in complex matrices, providing screening, confirmation, and quantification in one analysis. The precursor ion of interest generated by the source is selected and isolated in the first (Q1) quadrupole. A hexapole collision cell (Q2) fragments the selected ion, and the product ions are selected in Q3. By choosing product ions from this transition that are characteristic for the steroids of interest, chemical noise is separated from the signal, providing very high sensitivity and selectivity, even in very dirty matrices. With careful selection of a unique precursor to product ion transition for each steroid, it is practical to quickly and easily create SRM assay methods that can be used to identify and quantify simultaneously many of the prohibited steroids in a sample. 

Table II: GCâMS conditions used routinely for steroid analysis are similarly adopted for analysis by GCâQQQ 

Doping control laboratories must be able to detect 2 ppb or less of each of the following steroids: clenbuterol, 19-norandrosterone, 17α-methyl-5β-androstane-3α, 17β-diol, epimetendiol (methandienone), and 3'-hydroxystanozolol (stanozolol). Methods have been developed previously in certain doping laboratories, utilizing the GC–ion trap MS-MS platform, that have demonstrated the potential to screen and confirm less than 2 ppb each of these compounds in a low-level quality control (LQC) standard. This is generally accomplished by examining the extracted ion current profiles of the target ions present in MS spectra or in MS-MS (or MS-MS-MS) product ion spectra. However, many laboratories conduct routine screening analysis of steroids in urine using GC–MS operating in the SIM mode. These laboratories can use the same chromatographic separation protocols to detect and confirm as many as 120 analytes in one run using SRM on a GC–QQQ system (Table II). This includes the components of the LQC standard, which can have the same GC retention times on a GC–QQQ system as they do on a GC–MS system. 

Table III: Reconstructed SRM spectrum SRM product ion ratios of the 3-hydroxyl and 4-hydroxyl metabolites of stanozolol, as purified standards, components of the LQC standard, or in urine samples (previously confirmed positive specimens 1, 2, 3, and 5) 

Positive Identification — Anabolic Steroids

Using SRM on the GC–QQQ system, steroids can be reliably separated, detected, identified, and quantified. Figure 1 demonstrates separation and identification of the components in the LQC standard, all present at only 2 ppb. Very similar compounds such as the 3-hydroxystanozolol and 4-hydroxystanozolol metabolites of stanozolol can be identified and quantified, using the same four ion transitions with SRM, even though they have very similar retention times (Figure 2). The two derivatives give very different relative comparative abundances of the four selected product ions. The data demonstrate that peak height ratios of the product ions, versus a reference product ion, are characteristic of each metabolite, allowing positive identification of both. These ratios are very reproducible, whether using a standard containing only the 3-OH or 4-OH metabolite, the LQC standard, or positive urine samples containing various levels of the metabolites (Table III), allowing reliable identification. 

Doping control laboratories must be able to detect prohibited steroids against a background of several endogenous steroids whose levels must be accurately quantified. For this purpose, an endogenous quality control standard (EQC) with known quantities of endogenous steroids can be used to validate detection limits and screening efficacy. Two of the most significant components of this standard are testosterone and epitestosterone. Epitestosterone is an inactive stereoisomer of testosterone, and in an average adult male the ratio of the quantities of the two is about 1:1. If exogenous testosterone is being taken as a performance-enhancing drug, the T/E ratio increases. WADA has set a ratio of 4:1 as the threshold above which further investigation is required to confirm that the elevated level is the result of exogenous testosterone administration by the athlete. Accurate estimation of this ratio is dependent upon the ability to accurately identify and quantify each of these two steroids. Identification is accomplished by GC–QQQ SRM analysis of the two stereoisomers, providing nine transitions with which to quantify testosterone and epitestosterone. The peak height ratios of the extracted product ion profiles for three of the principal product ions from these transitions (for example, SRM transition 432 > 209; 432 > 301; 432 > 196) provide essentially the same ratios, giving accurate and reliable estimation of the T/E ratio (Figure 3). 

Other endogenous steroid metabolites of testosterone including androsterone, etiocholanolone, dehydroepiandrosterone (DHEA), and 5α- and 5β-androstanediols may be quantified in a similar approach using various transitions for the quantification of these analytes. 

The sports community is moving toward the establishment of a "passport" for each athlete, based on the principle that biological monitoring throughout an athlete's career should make any illegal preparation harder to implement. Such monitoring will ensure that routine quantification of endogenous steroids becomes even more important. Basing this quantification on SIM acquisition techniques may result in inaccurate determinations of these levels, as SIM may be more prone to interferences from the matrix. The seven aforementioned endogenous steroids can indeed be reliably and reproducibly analyzed by SRM on a GC–QQQ instrument, paving the way to realizing the goal of closer scrutiny for athletes both during competition as well as out of competition. This athlete's passport then becomes a historical record of endogenous steroid levels, permitting any anomalies that may result from doping to be recognized quickly. 

As long as athletes look to performance-enhancing drugs to get an edge on their competition, there will be a need for doping control laboratories that have an ever-growing arsenal of technologies and methodologies with which to detect current prohibited substances, as well as substances that may be developed in the future. The demand for sensitivity, specificity, and accurate quantification will only increase, and technologies must continue to evolve to meet the challenge. GC–QQQ is well-suited to meet this challenge because it provides doping control laboratories with the ability to increase sensitivity and specificity in complex matrices, with a modest investment in new equipment, and without the need to change their current extraction and GC separation methods. 

 is Director of the Doping Control Center in Penang, Malaysia. 

 is an Applications Chemist at Agilent Technologies, Santa Clara, California. 

The authors discuss the use of GC-MS in drug doping testing.

The Role of Triple Quadrupole GC–MS in Steroid Analysis

The global focus on keeping food safe has intensified, resulting in major changes in the number of pesticides that are being regulated and monitored, as well as the allowable levels of those pesticides in food. These new regulations drive a need for a wide range of comprehensive and complementary analytical methods and instrumentation that possess the throughput, sensitivity, and breadth of pesticide compound coverage to meet the demand of testing laboratories all over the world. This article provides an overview of the instrument platforms, tools, and workflow for analyzing pesticides.

With the globalization of trade, food production and distribution have become truly international businesses. When we dine out, the fish might come from Japan, the rice from Australia, the spices from China, and the strawberries from Mexico. We take it for granted that the food we eat is safe and free from contamination that could make us seriously ill. 

One of the 20th-century advancements that made this cornucopia possible was the development of pesticides. Crops that were previously devastated by pest damage can now be produced at very high yields to meet worldwide demand. However, all pesticide compounds are potentially harmful to humans, and safeguards must be in place to assure that consumers are not exposed to dangerous levels of these pesticides. 

Many countries have had regulations regarding the use of pesticides, and their allowable levels in foods, for many years. With the increasing globalization of the food industry, there is greater scrutiny on food safety, resulting in major changes in the number of pesticides that are being regulated and monitored, as well as the allowable levels of those pesticides in food.  

The Food Quality Protection Act (FQPA) of 1996 updated federal regulation of pesticides in the United States to implement a single health-based standard for all pesticides in foods. It provides special protection for infants and children, expedites approval of safe pesticides, and requires periodic evaluation of pesticide registrations and tolerances to ensure that the scientific data supporting registrations will remain up to date in the future. Under this mandate, the US Food and Drug Administration (FDA) performs regulatory monitoring by sampling individual lots of domestically produced and imported foods and analyzes them for pesticide residues to enforce established tolerances (

http://www.cfsan.fda.gov/~dms/pes04-06.html

). Domestic samples are collected at the point of production, and import samples are collected at the point of entry into U.S. commerce. Products typically are analyzed as the unwashed, whole, raw commodity. The FDA also performs a total diet study (TDS) on four regional market baskets per year, each basket comprising about 300 foods that represent the average U.S. consumer's diet. The foods are prepared as they would be consumed (table-ready) and tested for regulated pesticides. The FDA also performs "focused sampling" that is generally used to follow up on suspected problem areas or acquire residue data on food commodities that are not usually covered during regulatory monitoring. There are more than 1000 registered pesticides in the U.S., and approximately 400 with tolerances established by the Environmental Protection Agency and enforced by FDA.  

The European Union (EU) has also recently updated a harmonized regulation of pesticide residues in food, with the new standard taking effect on September 8, 2008 ( 

http://ec.europa.eu/food/plant/protection/pesticides/index_en.htm

). This regulation sets maximum residue levels (MRL) for food and animal feed. The MRL is the highest level of a pesticide residue that is legally tolerated. The regulation covers around 1100 pesticides currently or formerly used in agriculture in or outside the EU. For pesticides that are not specifically mentioned, a general default MRL of 0.01 mg/kg (10 ppb) applies. The EU designates the minimum number of samples to be taken by each member state, and EU reference laboratories coordinate, train staff, develop methods of analysis, and organize tests to evaluate the skills of the various national control laboratories. 

Japan also adopted a stricter pesticide regulation in 2006, referred to as the Positive List system, which establishes MRLs for about 500 pesticides ( 

http://www.mhlw.go.jp/english/topics/foodsafety/positivelist060228/introduction.html

 ). Produce with pesticide residues exceeding these MRLs or the default tolerance of the "uniform limit" of 0.01 ppm (10 ppb) for those pesticides for which there is no Japanese MRL cannot be marketed in Japan. The regulation also lists 16 pesticides that must not be detected in food. Japan tested more than 200,000 food samples in 2007. 

The increased international focus on pesticide contamination in food impacts food producers in virtually any country, since the food market is a global one. Producers in Thailand, for example, must test their products to assure compliance with FQPA, the EU regulation, and the Japan Positive List, if they hope to sell their products in the U.S., Europe, and Japan, which generate much of the world's demand for imported food. These new regulations drive a global need for analytical methods that possess the throughput, sensitivity, and breadth of pesticide compound coverage to meet the demand of testing laboratories all over the world that must ensure compliance. 

A Broad Range of Complementary Analytical Methods Is Needed

Food testing laboratories need the ability to detect and quantify hundreds of pesticides, in myriad foodstuffs, at very low levels of contamination. They might be screening for a set of hundreds of known "target" compounds, using a defined test method for each target in the set. Alternatively, a testing laboratory might wish to look for the presence of any pesticide ("unknowns"), without the need for defining a method for each possible compound. They are often called on to do both types of testing, and no single analytical approach can provide the flexibility required to meet this need. Wide variations in the chemical properties of pesticide contaminants and the necessity to detect a very large number of compounds require a range of chromatography and mass spectrometry (MS) systems. 

For pesticides that can be vaporized easily without degradation, gas chromatography (GC)–MS (single quadrupole) is an ideal analytical tool, due to the availability of large libraries of pesticide spectra and deconvolution software. This approach can be used to screen, confirm, and quantify both target and unknown compounds. Many foodstuffs, such as garlic and ginger, are very complex, or "dirty," due to the presence of a very large number of background compounds. GC– triple quadrupole MS (QQQ) is very useful for screening, confirming, and quantifying trace-level target compounds in these complex matrices. 

Pesticides that are quite polar, not easily vaporized, thermally labile, or not easily derivatized are best analyzed using liquid chromatography (LC) methods. LC–QQQ is particularly useful for analyzing sets of known target compounds. A unique fragment ion for each pesticide is routinely used for screening of each target compound — a second precursor to product ion transition can then be used to confirm the identity of the compounds. The transition with the greatest response is typically used to quantify the pesticides. 

Laboratories may be faced with the challenge of determining all pesticides, if any, present in a food sample. LC–time of flight MS (TOF) screening methods provide extremely accurate molecular mass information (typically < 2 ppm) that can be searched against exact mass databases of thousands of compounds. Compounds found in this type of screen can be confirmed and quantified using LC–MS-MS analysis on either QQQ or quadrupole time-of-flight (Q-TOF) instruments. 

One of the most common activities in a food-testing laboratory is analysis for a set of target pesticides of specific interest. A finite set of pesticides might be used for production of a particular food crop, or a specific set might be especially toxic to children, necessitating the routine analysis for those compounds. GC–MS (single quadrupole) utilizing deconvolution software has broad applicability for this type of analysis. The Automated Mass spectral Deconvolution and Identification System (AMDIS) was developed by the National Institute of Standards and Technology, and is incorporated into deconvolution reporting software (DRS) available from Agilent Technologies. Deconvolution separates unresolved peaks at any given retention time into sets of "cleaned" component spectra that can be matched to library spectra. The analyst runs the method that was used to generate the Pesticide and Endocrine Disruptor Database Library and locks the method, so that retention times will match those stored in the database. DRS then uses deconvolution and locked retention times to identify pesticides from the database.      

Figure 1: GCâMS analysis of p,p'-DDE in spinach. Data were generated using a J&W DB-5ms column, a model 7890A GC system, and a model 5975C MSD mass spectrometer (Agilent Technologies). 

This is particularly useful in the analysis of extracts from foods such as spinach (Figure 1). In this case, one of the pesticides of interest is 

'-DDE), a breakdown product of DDT. The total ion chromatogram (TIC) does not show a discernible peak at the 

'-DDE retention time (24.07 min). Deconvolution analysis found 768 different components over the whole TIC. After a pesticide library search, 12 of these had a match, including 

'-DDE. The black ions in the bottom window of Figure 1 represent the raw scan, before deconvolution. The white ions are a deconvoluted component that matches the known spectrum of 

'-DDE. Plotting the extracted ion chromatograms for this component (246, 248, 316, and 318) in the top window of Figure 1 also helped to confirm the presence of the 

'-DDE peak in the TIC. Figure 2 illustrates the extraction of the 

'-DDE spectrum from the raw spectrum, and its match to the library spectrum, resulting in a positive confirmation of the presence of this pesticide in the spinach sample. The use of powerful integrated deconvolution software such as DRS, in combination with specialized database libraries such as the Japanese Positive List Pesticide Library (1) or the more comprehensive Pesticide and Endocrine Disruptor Library (2) (Agilent Technologies, Palo Alto, California), can increase the efficiency of a food testing laboratory greatly. These GC–MS methods can screen for hundreds of compounds and quantify a subset of them in as little as 2 min after the run using DRS. With traditional GC–MS data processing, an analyst would need as much as 15 min per sample to review the data and confirm pesticide hits. 

Figure 2: Confirmation of p,p'-DDE in spinach using DRS and the Pesticide and Endocrine Disruptor spectral library (Agilent Technologies). 

LC–MS methods are also very useful for analysis of target pesticides that are too polar or thermally labile for GC–MS. LC–QQQ is especially suited to very complex food matrices that can interfere with the analysis. 

Sample preparation is a significant challenge with food analysis and makes up a considerable portion of the total pesticide analysis time. Robust analysis methods that can tolerate very complex sample matrices without loss of sensitivity or accuracy are a must to reach throughput goals.  

Multiple reaction monitoring (MRM) with a QQQ system provides detection of trace amounts of target compounds in complex matrices, providing screening, confirmation, and quantification in one analysis (although a replicate analysis is usually required in most laboratories for confirmation). The precursor ion of interest generated by the source is selected and isolated in the first (Q1) quadrupole. A hexapole collision cell (Q2) fragments the molecule, and the product ions are selected in Q3. By choosing product ions from this transition that are characteristic for the pesticides of interest, chemical noise is separated from signal, providing very high sensitivity and selectivity, even in very dirty matrices. With careful selection of highly specific product ions for each pesticide, it is possible to create an MRM assay that can be used to identify and quantify hundreds of pesticides in a sample simultaneously. 

Efficient use of LC–QQQ MRM methods requires optimization of a few parameters, including voltages on the fragmentor and the collision cell. Parameters must be optimized to generate the most intense product ions representative for each target compound, and automated method optimization software simplifies this task enormously. 

LC–QQQ is an excellent platform for screening, confirmation, and quantification of pesticides. A screening method uses one transition per compound to look for a large number of pesticides on the target list. Confirmation of pesticides detected in the screening is done using two transitions per compound. For example, 301 pesticides have been rapidly identified in one analysis with most having a limit of detection (LOD) of 0.01 mg/kg (10 ppb), which is the MRL for baby food in the U.S. and the general default MRL for foods in the European Union. These levels were reached in a single analysis using positive ion electrospray with 99 transitions per time segment, and a quantifying and qualifying ion for each compound (3). Two types of vegetables (pepper and tomato) were evaluated, and a linearity of response over three orders of magnitude was shown for quantification (

Figure 3: Calibration curve for diazinon in pepper using a six-point curve from 0.1 to 100 ng/mL with a linear fit and no origin treatment. Data were generated using a ZORBAX SB-C-18 1.8-Âµm LC column with a model G6410A QQQ system in the positive ESI mode (Agilent Technologies). 

GC–QQQ also provides sensitive and reproducible screening, confirmation, and quantification of pesticides that are not highly polar, particularly in very complex matrices such as garlic and ginger. Despite the high background of these matrices, pesticide compounds can be detected at levels as low as 0.5 ppb, with very high signal to noise and peak area precision as good as 6% RSD (Figure 4). For certain "active" pesticides such as acephate, the use of chemical protectants can reduce adsorption of analytes during the analysis, increasing sensitivity and improving peak shape. The result can be a marked improvement in accuracy of quantification in complex matrices (4). 

Figure 4: Precision of GC-QQQ quantification of 500 fg of cyanophos spiked in garlic. Data were generated using an HPâ5ms UI (Ultra Inert) 30 m x 0.25 mm, 0.25-Âµm film thickness GC column and a model 7000A QQQ MS system (Agilent Technologies). The pesticide cyanophos was spiked into a garlic matrix at 0.5 ppb and replicate analyses performed at 500 fg on-column. The peaks are superimposed for the five runs, with the replicate peak areas, mean peak area, and RSD listed in the table to the left.  

Backflushing the GC column, rather than using oven bakeout, also removes many high-boiling interfering substances in the sample (Figure 5) without altering retention times in subsequent runs or shortening the life of the column (5). Backflushing improves data quality while shortening cycle time and increasing column life. It also eliminates ghost peaks and reduces ion source contamination by keeping excess column bleed and heavy residues from being introduced into the mass spectrometer.  

One key to the successful rapid analysis of large numbers of pesticides is the dwell time of the QQQ mass spectrometer — the time spent to measure the signal of any one precursor to product ion transition. The shorter the dwell time, the more transitions that can be analyzed per unit time, and the more pesticides that can be quantified per unit time. 

Figure 5: Backflushing the GC column for higher signal to noise and shorter cycle times. Comparison of backflushing using capillary flow (red chromatogram) versus column bake-out (blue chromatogram) utilizing a model 7890A GC system and an HP-5ms GC column (Agilent Technologies).  

Often, food-testing laboratories want to determine the entire spectrum of pesticides that might be present in a sample, rather than just a limited set of target compounds. While GC–MS using deconvolution is an effective tool to screen for over 900 pesticides (2), LC–TOF or LC–QTOF can be used to screen for any number of LC–MS-amenable compounds.  

TOF and QTOF are extremely accurate MS techniques, routinely achieving <2 ppm mass accuracy. Analysis software such as the Molecular Feature Extractor (MFE) from Agilent Technologies can find all ions representing real compounds in the sample. Noise and other extraneous ions are excluded. The resulting list of masses is then searched against a database of theoretical exact masses of compounds based on their molecular formulas and selected adducts, to identify pesticide compounds (Figure 6). This method has been used to screen food extracts for 600 pesticides and their degradates. The limit of detection (LOD) was determined for 100 of the 600 pesticides, and varied from <0.01 mg/kg (10 ppb) for 34% of the compounds, to <0.5 mg/kg (500 ppb) for 95% of the compounds (6). Using the MFE algorithm streamlines the data analysis for screening of hundreds of compounds at sensitive levels to minutes, compared with hours or even days using a manual approach. TOF can be used to identify an unlimited number of ionizable compounds, and the sensitivity is not impacted by the number of compounds screened. While LC–TOF can provide screening and quantification of both unknown and targeted pesticides, LC–QTOF or another MS-MS approach is required to confirm identification. 

Figure 6: Pesticide screening for unknowns using LCâQTOF. The top panel shows the full spectrum total ion chromatogram (TIC) using a model 1200 LC system, a ZORBAX Eclipse XDB, 5-Âµm column, and a model 6510 LCâMS QTOF system (Agilent Technologies). The Molecular Feature Extractor found 510 compounds in the TIC, 15 of which had hits from the EXACT MASS database search (bottom panel). The three highlighted compounds were further confirmed by MSâMS analysis. 

One need only look to recent events to appreciate the importance of pesticide testing in food. A series of poisonings in Japan in December of 2007 and January of 2008 that sickened 10 people was linked to methamidophos pesticide contamination of gyoza dumplings imported from China. The impact on Japanese food buying habits was immediate, as importation of Chinese vegetables fell 40% shortly after the source of the poisoning was determined.  

In another example, pet food vegetable proteins contaminated with melamine were imported into the United States from China in early 2007, resulting in the deaths of several cats and dogs. More recently, the same contaminant has appeared in baby formula in China. 

Food-testing laboratories require a broad range of complementary MS-based analytical technologies and methods to screen, confirm, and quantify the presence of as many as 1000 pesticide compounds. Screening methods can be used to detect as many contaminants as possible in a single run, and these methods don't necessarily have to quantify all the compounds. The screening method can be followed by target compound analysis that is very selective, sensitive, and quantitative. For LC-amenable pesticides, this means running LC–TOF or LC–QTOF followed by LC–QQQ. For GC-amenable pesticides, this means running GC–MS with DRS for screening followed by GC–QQQ for target compound analysis at low levels. The method used by any given laboratory is determined by its specific pesticide analysis goals, existing instrumentation, the budget for acquisition of new instrumentation, and required LODs. 

When choosing new instrumentation, laboratories doing pesticide testing also should consider whether a particular manufacturer has a complete solution that includes all of the above platforms, and whether those platforms share common, interchangeable ion sources, a unified software platform, and transferable collision voltages, thus providing a uniform workflow. The robustness of a manufacturer's systems is a key factor, including the tolerance of the ion source to contamination and its required frequency of cleaning and maintenance. Utility of the software and its integration with comprehensive pesticide spectra libraries and exact mass databases are also keys to success. Finally, the manufacturer's reputation for excellence in GC and LC–MS instrumentation should be considered.  

(1) P.L. Wylie, Screening for pesticides in food using the Japanese Positive List Pesticide Method, Agilent Application Note 5989-7436 (2007). 

(2) P.L. Wylie, Screening for 926 Pesticides and Endocrine Disruptors by GC/MS with Deconvolution Reporting Software and a New Pesticide Library, Agilent Application Note 5989-5076 (2006). 

(3) J.A. Zweigenbaum, Multiresidue analysis of 301 pesticides in food samples by LC/Triple Quaduprole mass spectrometry, Agilent Application Note 5989-8614 (2008). 

(5) C.-K. Meng, Improving Productivity and Extending Column Life with Backflush, Agilent Application Note 5989-6018 (2006). 

(6) J.A. Zweigenbaum, Automated screening of 600 pesticides in Food by LC/TOF MS using a molecular–feature database search, Agilent Application Note 5989-5496 (2006). 

This article provides an overview of the instrument platforms, tools, and workflow for analyzing pesticides.

Melissa Churley

Articles

Ultralow Detection of Estrogenic Compounds by GC–NCI-MS-MS

The Role of Triple Quadrupole GC–MS in Steroid Analysis

Comprehensive Screening, Confirmation, and Quantification of Organic Pesticides in Foods by GC–MS and LC–MS