Road Traversability Analysis Using Network Properties of Roadmaps* Muhammad Mudassir Khan1, Haider Ali2, Karsten Berns3 and Abubakr Muhammad1 Abstract— Traversability analysis is an important aspect of its traversability which is sometimes not possible. To avoid autonomous navigation in robotics. In this paper, we relate traversing the terrain to find its traversability, exterioceptive
Some reviews of clomid noted that the drug can also cause weight gain, hair loss and vision impairment Brand or Generic? The information is provided for informational purposes only and is not a guide for self .Cialis ne doit pas être prise à tous. Il est important que cialis en ligne est prescrit par un médecin, bien se familiariser avec les antécédents médicaux du patient. Ich habe Probleme mit schnellen Montage. Lesen Sie Testberichte Nahm wie cialis rezeptfrei 30 Minuten vor dem Sex, ohne Erfolg. Beginn der Arbeiten nach 4 Stunden, links ein Freund ein trauriges Ja, und Schwanz in sich selbst nicht ausstehen, wenn es keinen Wunsch ist.
Identification and confirmation of chemical residues in food by chromatography-mass spectrometry and other techniquesTrends in Analytical Chemistry, Vol. 27, No. 11, 2008 Identification and confirmationof chemical residues in foodby chromatography-massspectrometry and other techniquesSteven J. Lehotay, Katerina Mastovska, Aviv Amirav, Alexander B. Fialkov,Tal Alon, Perry A. Martos, Andre´ de Kok, Amadeo R. Ferna´ndez-Alba A quantitative answer cannot exist in analysis without a qualitative component to give enough confidence that the result meets theanalytical needs (i.e. the result relates to the analyte and not something else). Just as a quantitative method must typically undergo anempirical validation process to demonstrate that it is fit for purpose, qualitative methods should also empirically demonstrate thatthey are suitable to meet the analytical needs. However, thorough qualitative method validation requires analysis of a great numberof samples (possibly more than can be reasonably done), which is generally avoided due to the time and the effort involved.
Instead, mass spectrometry (MS) is generally assumed to be the gold standard for qualitative methods, and its results are typically unquestioned. For example, a system was developed by European regulators of veterinary drug residues in food animals (2002/657/EC), in which the number of identification points given in MS analyses depends on the general degree of selectivity of the MStechnique used. This well-defined approach gives a definite answer for decision-makers, so it has grown in popularity.
However, the identification-points system is not scientific. The reality is that each situation requires information gathering and careful deductive thinking on the part of the analyst to make MS identifications. Rather than devise arbitrary requirements that needto be met by an unthinking analyst, we remind the analytical community that confirmation can be given only if two or moreindependent analyses are in agreement, preferably using orthogonally selective (independent) chemical mechanisms.
In this article, we discuss the proper use of terminology, highlight the identification power of various MS techniques, demonstrate how MS identifications can fail if precautions are not taken, and re-assert the value of basic confirmation practices, qualitativemethod validation, information checklists, routine quality-control procedures, and blind proficiency-test analyses.
Published by Elsevier Ltd.
Keywords: Confirmation; Food; Gas chromatography; Identification; Liquid chromatography; Mass spectrometry; Method validation; Pesticideresidue; Qualitative analysis; Tandem mass spectrometry Steven J. Lehotay*, Katerina Mastovska, U.S. Department of Agriculture, Agricultural Research Service, Eastern Regional Research Center; 600 East Mermaid Lane, Wyndmoor, Pennsylvania 19038, USA Aviv Amirav, Alexander B. Fialkov, Tal Alon, School of Chemistry, Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel University of Guelph, Laboratory Services, Chemistry Method Development, 95 Stone Rd. West, Guelph, Ontario, N1H 8J7, Canada VWA - Food and Consumer Products Safety Authority, Chemistry Laboratory, Pesticides and Mycotoxins Analysis R&D Group, Hoogte Kadijk 401, 1018 BK Amsterdam, Amadeo R. Ferna´ndez-Alba Department of Hydrogeology and Analytical Chemistry, University of Almerı´a, La Can˜ada de S. Urbano s/n, 04071 Almerı´a, Spain *Corresponding author.
Tel.: +1 (215) 233 6433; Fax: +1 (215) 233 6642;E-mail: 0165-9936/$ - see front matter Published by Elsevier Ltd. doi:10.1016/j.trac.2008.10.004 Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 sample, is preferable to confirmation of the analyte in theextract, and, when warranted by high enough stakes, multiple laboratories should be involved in the analyses.
The fundamental purpose of analytical chemistry is to Another important term for this article is limit of meet needs for qualitative and quantitative analysis of identification (LOI), which is defined as the lowest con- centration for which the identification criteria are met.
In qualitative analysis, two questions can be posed: The identification criteria may be defined in different 1) ‘‘What is in the sample?'' (general screening, struc- ways depending on fitness-for-purpose, and LOI should ture elucidation, or component analysis); and, be determined empirically, much as the limit of detection 2) ‘‘Is the analyte in the sample?'' (targeted screening (LOD) and limit of quantitation (LOQ) should be vali- and analysis).
dated in quantitative methods. Alternatively, the con- In quantitation, the central question is: ‘‘How much of cepts of lowest calibrated level (LCL) or minimum the analyte is in the sample?'' However, a quantitative required performance limit (MRPL) can be adapted to answer should not be given without an acceptable qualitative analyses, which require demonstration of degree of qualitative knowledge that the measured result acceptable performance at a minimum concentration to relates to the analyte alone and not something else.
suit the purpose for the analysis. In this latter model, if In this article, we mainly focus on this latter issue of the analytical needs dictate that the LOI must be analyte identification/confirmation-which is a major 610 ng/g, then defined identification criteria have to be issue in many regulatory, forensic, clinical and other met routinely for analyses of samples containing 10 ng/g of the analyte(s). System-suitability and quality-control The first consideration to address is that many ana- (QC) tests should be conducted at this level before and lytical chemists use the terms identification and confir- during analysis of a batch of samples.
mation interchangeably, as if they mean the same thing.
In the LOD model, if the identification criteria in an MS However, they are defined differently in English dictio- analysis entail that a minimum of three ions with signal- naries, and scientific usage should also reflect those dif- to-noise ratio (S/N) > 3 are needed to make an identifi- ferences. We wish to encourage widespread use of the cation, then the LOI would be the concentration at terms below with the following definitions: which the least abundant ion gives S/N = 3 (i.e. the LOD 1) indication is a non-quantitative result from a general of the least abundant ion is used as the quantitation ion).
screening method (e.g., immunoassay), for which Typically, the LOD refers to the concentration at which other factors may cause the result (i.e. ‘‘presumed'' S/N = 3 for the response used in quantitation (and LOQ positive or negative); has S/N = 10); however, the analyst may choose to 2) determination is a quantitative result from a method make LOD (or LOQ) = LOI if the application is important that meets the acceptable performance criteria for enough that only acceptably identified analytes be re- the quantitative purpose of the analysis (e.g., chro- ported as detected in a quantitative analysis. Such a matography with an element-selective detector); decision would decrease the rate of false positives at the 3) identification is a qualitative result from a method expense of increasing the rate of false negatives.
capable of providing structural information (e.g.,using mass spectrometric (MS) detection) that meets 1.2. Sources of error acceptable criteria for the purpose of the analysis; In measurement science, there are three sources of error: 1) random variability (precision); 4) confirmation is the combination of two or more anal- 2) systematic bias (trueness); and, yses that are in agreement with each other (ideally, 3) spurious or gross errors (mistakes).
using methods of orthogonal selectivity, at least one The first two forms of error are taken into account of which meets identification criteria).
during method validation to assess the quality of ana- By definition, confirmation requires that one result lytical results expected from a particular method. Ana- must ‘‘confirm'' the other, so at least two analyses are lytical chemists and clients have devised minimal needed. As a result, a single analysis, no matter how method-performance criteria for precision and trueness selective it may be, is not confirmatory. The degree of that must be met during validation, QC procedures, and selectivity required to satisfy ‘‘confirmation criteria'' proficiency testing (PT) to demonstrate that the method must also be fit for purpose, depending on the stakes meets their needs.
involved , and, in most applications, the confirmatory However many analytical chemists tend to forget the methods should use orthogonally selective (independent) occurrence and the impact of spurious forms of errors, approaches based on different chemical mechanisms, even though mistakes cannot be eliminated in real-world such as liquid and gas chromatography (LC and GC) analyses. In light of the high quality of modern analyt- separations. Furthermore, confirmation of the analyte in ical technology and instrumentation, human errors are the sample, which entails re-extraction of a duplicate undoubtedly the greatest source of error with respect to Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 the stakes can be very high, even a matter of life and number of mistakes depends on the diligence and the death, depending on the analytical results. We intend intelligence of the people performing the work, but, even this article to review critically common approaches to for the best analysts, the number of human errors will MS identification, present real-world examples of pit- typically be the limitation in assessing qualitative MS falls when identifying analytes, and remind the reader identifications. For example, this has been found to be the case for DNA testing, which can have theoretical misidentification rates on the order of 1 chance per 100 billion people (except for being unable to distinguishidentical twins), yet the rate of human errors in blindstudies to evaluate the performance of analytical 2. Historical perspective on MS identification processes are measured in terms of percent .
Because human error will typically be the limiting 2.1. Dual-column and element-selective detectors factor during the analytical process, an accurate Prior to the widespread introduction of MS in routine empirical measurement of the rates of false positives and residue laboratories, chemists relied on dual-column or false negatives arising solely from MS techniques is quite separate methods of detection to confirm analytes .
difficult, if not impossible, to determine. Statisticians, This is still the only approach available to many labo- metrologists, or other scientists can go to great lengths to ratories in developing countries, and some regulatory calculate the probabilities of false positives and false guidelines officially permit this approach as a confirma- negatives from a theoretical basis of chemical structures, tory method, but submission of such a technique by a measurement errors, and the numbers of different mol- pesticide or drug registrant to a developed nation would ecules that exist, but no single approach will be valid for be questioned in this era of widespread availability of MS, all situations. In any event, spurious forms of error (e.g., tandem MS (MS2) and multi-stage MS (MSn) instru- mislabeling, laboratory contamination and inadvertent ments. The reason for this is demonstrated in (A spikes) will remain the most common reasons for and B), which plots the similarities between the relative retention times (tR) of 263 (A) or 315 (B) pesticides Due to spurious sources of error, real-world qualitative analyzed using different GC columns and conditions analyses should entail not only confirmation of the Despite the different phases and methods, GC relies presence (or absence) of the analyte in the extract, but on the same physico-chemical mechanism for the sepa- also confirmation that the analyte originates from the ration, so these confirmation methods are not orthogo- sample. This requires re-analysis of a duplicate sample, nally selective. This type of dual-column GC approach ideally using another validated method involving differ- may still be considered confirmatory (which could be the ent chemistries of isolation and/or detection. For many case even if the same method was used twice), but the applications, such as regulatory enforcement actions, value of such a confirmation is limited. The use of dif- knowledge of the chemical form of the analyte in the ferent sample-preparation methods and/or element- sample is equally important (e.g., one metabolite of the selective detectors adds to the value of the confirmations original chemical may be legal, but another may be but without extensive testing, it is questionable if regulated, so the analytical method(s) must be able to the relatively low degree of selectivity in the confirma- distinguish them in order to take valid action).
tion suits the needs of high-stakes applications.
In derivatization methods, the selectivity of the Although MS detection has not been extensively chemical reaction becomes part of the method, and compared in side-by-side, real-world applications with typically provides a lower degree of selectivity than the dual-column GC confirmation, analytical chemists rec- MS detection, so MS cannot overcome the inherent ognize the high degree of selectivity of MS detection in limitation of the derivatization step(s) leading to the final chromatography. It has increasingly been used to re- results. In any analytical method, the selectivity of the place dual-column techniques for confirmation purposes, entire sampling procedure, sample preparation and but this in part is also how terms ‘‘identification'' and analysis must be considered holistically. The range of ‘‘confirmation'' have become confused. MS was tradi- chemicals that can possibly be identified from the overall tionally used to confirm analyte identity after determi- process is limited by the overlapping region among the nations with non-MS methods, but, as with any single subsets of chemicals that ‘‘pass through'' each step in method, MS is not able to ‘‘confirm'' the presence of the process.
analytes in the sample by itself. Nowadays, it may be The topic of analyte identification and confirmation more efficient to use MS to identify chemicals qualita- has been the subject of much discussion tively in an initial screening method, and then use a especially recently as MS instruments have become traditional, non-MS method to make quantitative more routinely available in many laboratories. This determinations and qualitative confirmations at the topic is very important in many applications because Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 a general guideline established through empirical dem- a ve tR of DB-1
onstrations asserted that three ions of the proper ratio give enough selectivity to identify most compounds. The experiences of many analytical chemists supported this conclusion , but few would say that it should be a ‘‘rule'' because many exceptions can be found that indicate a three-ion requirement is either too strict or not strict enough.
For example, Sphon used an early mass-spectral library to compare the spectra of 30,000 compounds in the database with that of diethylstilbesterol (DES). He found that isolating the three most intense ions in the DES spectrum, with liberal relative-abundance criteria, specifically distinguished DES from any other compoundin the library. shows the results from this la ive tR o
f DB-5 and DB-1701
demonstration, including an update from 1997 , and a new set of results using NIST mass-spectral dat- abases. As shows, the use of the three most appropriate ions, even with rather wide ion-ratio con- straints, eliminated the possibility that DES would be confused with any other compound in the libraries. In one case, the use of only two ions with reasonable constraints (m/z 268 and m/z 145 with ±10% permit- RRT DB-1701 1.5
ted ion-ratio variability) isolated DES from all other 107,885 compounds in NISTÕ98 (but two additional compounds would be listed as possible hits among the 163,198 compounds in NISTÕ05). However, also shows that if, in addition to m/z 239 as a qualifierion, the second most intense ion in the DES spectrum, Figure 1. Relative retention time (vs parathion) comparison for GC m/z 107, is chosen as the second qualifier ion, rather analysis of pesticides using different 30 m, 0.25 mm i.d. columns than m/z 145 (the fourth strongest), three other com- and oven-temperature programs by the Dutch Food Inspection Ser-vice A) 263 pesticides comparing DB-1 and DB-5 phases; pounds overlap with DES in a search of the NIST li- and, B) 315 pesticides comparing DB-5 and DB-1701 phases.
braries. The choice of m/z 107 is not unreasonablecompared to m/z 145 in a selected-ion monitoring (SIM)program, except in this case it may lead to more pos- Recent documents about identification and confirma- sible interferences. Moreover, if the molecular ion and tion issues have focused on MS techniques, but confir- base peak, m/z 268, is excluded (e.g., due to an inter- matory approaches should not exclude the use of other ference) and the three most intense fragment ions are methods. For example, the illegal usage of an insecticide chosen instead, two different chemicals can give rise to isofenphos-methyl was uncovered by nitrogen-phos- rather stringent ion ratios shown in for the phorus detection and the elemental information three-ion ‘‘identification'' of DES.
using pulsed flame-photometric detection can be very The above consideration refers only to the possible valuable , particularly when it is employed simul- number of compounds from NIST libraries that can be taneously with MS .
confused with DES, while an unknown quantity ofcompounds that are not in the library could also inter- 2.2. MS-identification criteria fere in the identification. For this general reason, Sphon In the U.S.A., the origins of MS-identification guidelines was careful not to provide anything other than ‘‘guide- in regulatory decisions can be traced to Food and Drug lines'' for MS identification because no single set of rules Administration scientist James Sphon . Prior to would necessarily apply to all situations.
circa 1980, MS instruments were less available, less Despite this, others in the U.S.A. began using what affordable and less used in routine monitoring labora- became known as the ‘‘three-ion criterion'' for mass- tories, and computer technology was in its infancy. Very spectral identification Simply put, at least three limited spectral-library information was available, so ions of the correct m/z and relative-abundance ratio chemists tended to rely on traditional structure-eluci- ±10% (absolute) are desired to make a mass-spectral dation techniques. The uniqueness of a mass spectrum to match. Other factors needed for identification generally a particular molecule depends on a variety of factors, but include proper tR (±2% error factor) and sufficient S/N Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 Table 1. Update of SphonÕs demonstration of selectivity of three ions in the mass spectrum of diethylstilbestrol (DES), for which the referencespectrum has m/z 268 = 100% relative abundance, 107 = 67%, 239 = 56%, and 145 = 55% Constraint; ion (% relative abundance) Number of spectra Number of spectra Number of spectra Number of spectra meeting constraint constraint [NISTÕ98] Number of compounds in library or +239 (50–70) nd = not done.
(>3) for the chromatographic peak of the least intense 3. MS techniques and their relative selectivity and identification power To provide greater stringency and more precaution against false positives, the European Union (EU) ap- Without question, MS is currently the most powerful tool proach required four matching MS ions to make identi- commonly available to analytical chemists for identifi- fication of banned substances in foods, rather than the cation of organic compounds in a variety of matrices. We three ions in the proper ratios recommended by Sphon.
do not wish to disparage the well-known positive attri- In time, this decision led to the establishment of the butes of MS techniques but we want to remind analysts identification-point (IP) system as a ‘‘requirement'' for that MS is not a panacea, as some people would like to identification of organic residues and contaminants in believe. Chemical identification is strongly affected by MS samples of animal origin in the EU regulatory system technology, which is constantly evolving and improving This constitutes a departure from the traditional with time. In this section, we briefly discuss a few approach in which only ‘‘guidelines'' are provided, not relevant MS techniques and compare their capabilities ‘‘requirements.'' For pesticide analysis in the EU, guide- and limitations for identification purposes.
lines are still preferred .
The IP system has some practical benefits in that 3.1. Importance of the molecular ion decisions can be made using clearly defined criteria, but, Some ions (in particular, the molecular ion) may be as in the case of essentially all identification guidelines to given more weight than others in an identification, but date, a critical drawback is that a rigorous assessment how can this weight be assessed? One possibility is to has not been conducted to determine the uncertainty of collect information about the potential number of the approach(es). For example, what are the differences interferences using data libraries and various algo- in the rates of false positives and false negatives by rithms. It is commonly accepted that the presence of the requiring four IPs for banned substances over three IPs molecular ion in a mass spectrum enhances confidence for registered compounds? Why should a high-resolution in the sample identification for the following reasons: ion always be worth two points in the IP system, and a) it is the highest mass ion, so it tends to have the MS2 ions always be worth 1.5, whereas the (pseudo)- least amount of chemical or matrix interferences; molecular ion is only worth 1? What is defined as ‘‘high'' b) it safeguards against misidentification of homolo- gous and degradation products; and,
Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 c) it enables additional tools for identification pur- shows how these 628 compounds give a MW range of poses, either via isotope-abundance analysis (IAA) 303.8–304.4 amu, with a distribution (full-width at half or via accurate-mass-related elucidation of the maximum, FWHM) of 0.166 amu. This does not include empirical formula.
the numerous possibilities for fragment ions in this m/z region that can further complicate the situation, espe- techniques in LC-MS often yield the (pseudo)-molecular cially considering the typically low residue levels for the ion (e.g., [M+H]+), formation of adduct ions with sodium analyte among complex matrix components at high or ammonium ions also occurs frequently. In electron ionization (EI), the molecular ion is practically absent in If the target analyte contains many elements with a about 30% of the mass spectra and such a high large mass defect, such as Cl and Br, then HRMS truly probability of its absence introduces doubt that the excels in the suppression of matrix interference. Whereas highest mass ion present in a mass spectrum is indeed hydrogen adds only +0.008 amu mass defect per H the molecular ion. In fact, the highest mass spectral peak atom, each Cl atom has mass defect of could be a high mass fragment ion or emerge from an Br has an even greater mass defect of impurity. A unique way to enhance molecular ions in EI Other common elements in organic molecules have the is by using supersonic molecular beam (SMB)-MS, which has been reviewed recently With cold EI (EI of vibrationally cold molecules in SMB-MS) the molecular . The relatively large mass defects for Cl and Br cause ion is enhanced and is practically always observed a significant shift from the center of the mass distribution while cluster chemical ionization can be used to for a given MW (see which improves selectivity of ascertain further the validity of the suspected mass- analysis for HRMS. Other MS approaches also give en- spectral peak as the molecular ion hanced detection of halogenated compounds, such asnegative-ion chemical ionization or even EI in full-scan 3.2. High-resolution MS and accurate mass mode with isotope-abundance analysis. However, the High-resolution MS (HRMS) is becoming more popular superior capabilities of HRMS are fully demonstrated in in laboratories, particularly in the form of time-of-flight the analysis of dioxins, PCBs, and similarly multi-halo- (TOF) MS, while it can also be found in magnetic sector, genated analytes Fourier transform (FT) MS, Orbitrap, and even quadru- Otherwise, most organic chemicals (analytes and pole MS with software-calibration enhancements. For matrix components) do not contain Cl or Br, and are this discussion, we must distinguish between accurate located near the center of the histogram, as shown in mass and high resolution. For example, TOF-MS can for diazinon. In this example with resolution of have mass accuracy of 2 ppm while its full-width half- 10,000, about 72 other compounds (11.5% of those maximum resolving power is of the order of only with MW = 304 amu) in the NISTÕ02 library would 10,000. Thus, in comparison with FTMS or Orbitrap, have the same measured mass, so a nine-fold improve- which has similar mass accuracy but a higher resolvingpower of 100,000 TOF-MS has less power to re-duce matrix interferences than its capability to generatepossible elemental formulas.
Accurate-mass TOF is more powerful in LC-API-MS (because the pseudo-molecular ion is nearly alwayspresent in the spectrum) than in GC-EI-MS because, asstated in Section 3.1, about 30% of the chemicals do notexhibit the molecular ion Also, common EI-MSlibrary-search programs require unit-mass resolution towork properly (although that could be simulated withappropriate software).
Usually, the various vendors who sell high-resolution TOF instruments describe the power of high resolution inqualitative terms, which makes it hard to compare it toother options (e.g., MS2), so we wish to provide a more Figure 2. Histogram showing the exact-mass distribution of the quantitative picture of the merit of HRMS. For this 628 compounds with nominal molecular weight of 304 amu in purpose, we used the NISTÕ02 library, making the the NISTÕ02 mass-spectral library. The calculated average mass is assumption that the library accurately represented the 304.123 amu with a standard deviation of 0.083 amu. The exact mass of diazinon is marked, and the depicted region demonstrates MW = 304 amu, the library contained 628 compounds, how 72 other compounds in the library cannot be resolved fromdiazinon with MS resolving power of 10,000.
including our example-target analyte, diazinon. Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 ment in selectivity over unit mass-resolution instru- that the evaluated mass spectral peak is indeed the ments is realized, which is good but not sufficient for full molecular ion.
elimination of matrix interference.
However, the ability to determine accurate mass 3.4. Comprehensive two-dimensional GC·GC-MS enables the analyst to have a table of possible elemental Comprehensive GC·GC-MS serves as another method for formulas listed in declining order of matching the improved sample identification through improved GC experimental mass. Such a measurement provides a separation and hence reduced matrix interference. Typ- strong, independent (orthogonal) tool for analyte iden- ically GC·GC has a theoretical gain in separation power tification, which can serve to confirm other methods of by about 20 through having a second GC time window identification. To a certain extent, accurate mass can of 4 s with an average peak width of 0.2 s How- also be obtained with ‘‘unit'' resolution quadrupole MS, ever, in reality, the GC·GC separation power is signifi- and commercial software algorithms can accurately cantly lower because a proper GC·GC analysis requires locate the center of the mass-spectral peak. If a typical that each first-dimension GC peak width will have to sample mass (e.g., diazinon at m/z 304) is measured with accommodate three to four GC·GC cycles. As a result, 5-ppm accuracy, this translates into 0.0017-amu the GC·GC analysis time is generally longer, due to the accuracy, which reduces the number of possible com- need to generate broader GC peaks, so the GC·GC sep- pounds by an impressive factor of 100. However, the aration power should be compared with a one-dimen- level of confidence in sample identification by HRMS sional GC separation with a longer column. Also, the cannot be greater than the level of confidence that the second-dimension separation is often not fully orthogo- evaluated mass-spectral peak is indeed the molecular nal to that of the first dimension (see Section 2.1).
ion, and this should not be underestimated. If the ana- Despite the above criticism, GC·GC-MS is a powerful lyzed ion is actually a fragment ion, unknown to the analytical tool that can particularly excel in the sepa- analyst, then the identification will be wrong.
ration of polar samples from non-polar matrix interfer-ences, and, as a result, improve library identification.
3.3. Isotope-abundance analysis Furthermore, the added separation power of GC·GC-MS The relative abundances of the various isotopomers may allow high-quality identifications using two ions (in (molecular ions with different isotopes) can provide reconstructed SIM) instead of following the traditional accurate elemental formulas. Traditionally, the IAA ap- guideline of three ions. This would provide an even lower proach has served to help elucidate molecular weights LOI since the third ion is typically the one with the least and chemical structures of synthesized organic com- abundance and greatest chance for matrix interferences.
pounds Recently, a unique IAA method and soft- ware were developed , among other things, to link column also increases S/N, which has the effect of fur- with the NIST MS library and automatically support or ther lowering LOI compared with one-dimensional GC reject the proposed library identification. In case of a rejection, the IAA software independently provides a listof elemental formulas with declining order of matching the experimental data, similar to accurate-mass mea- MS2 is a powerful technique that uniquely combines surements, but IAA does not require costly accurate- improved sensitivity and selectivity; however, like SIM, it mass MS instrumentation.
comes with the price of being a target-based method, Due to the low intensity of the isotopomer ions, key for which misses any compound that is not in its target list use of the IAA approach is very low noise with few (so there is an inherent chance for many false negatives).
chemical and background interferences.
While there are many examples in the literature There is also demand for absence of protonation (due showing how MS2 excels in reducing matrix interference to chemical ionization or self-chemical ionization).
and typically lowers the LOD, we are not aware of any SMB-MS and SMB-MS2 can provide high sensitivity publication that has explored how and when MS2 fails with low background noise while their collision-free fly- and the factor by which it improves selectivity. In EI- through EI process excludes undesirable molecular ion MS2, the inherent dissociation energies for any given protonation which allows the use of IAA even at molecule lead to the same fragments as it generates in EI mass spectra but with different relative intensities. Thus, Although the IAA software was developed and tested the MS2 product ions are not a matter of random dis- mostly with GC-SMB-MS, IAA is also being used effec- tribution of all masses from m/z 1 to the precursor ion tively to complement and to supplement accurate-mass minus 1. If we analyze the NIST library of EI mass data with high-resolution LC-MS. Again, as with accu- spectra, we conclude that, for a parent ion with m/z 250, rate mass, the level of confidence in any sample identi- only about eight masses are preferred in the mass range fication, including combined IAA and NIST library m/z 50–249. For example, hydrogen loss is usually un- search, cannot be greater than the level of confidence likely and the next option is a loss of CH3, which is a Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 difference of 15 amu apart with a void mass space of 14 With respect to choice of ions and their relative amu. In LC-API-MS, the use of (pseudo)-molecular ions selectivity, as shows in the case of the NISTÕ98 as MS2 precursor ions further restricts the number of database, ions of higher m/z have less chance of potential available possibilities for product ions.
interferences than those with lower m/z. In a real Moreover, a small percentage of organic chemicals are application involving the GC-MS analysis of pesticides in not suitable for MS2 analysis, particularly when EI is very complicated spice extracts, the degree of matrix used, due to ion instabilities (or excessive stability to interference was shown to reduce exponentially by a allow further fragmentation with typical instruments), factor of 20-fold per each 100-m/z increase This lack of enough product ions, or low formation of high- study also demonstrated how the presence of the mass ions in MS to allow MS2 (e.g., in terms of identifi- molecular ion in the spectrum greatly enhanced the cation power, MS2 lacks the ability to exclude 150,000 ability to isolate the analyte peak in a complicated other compounds in full-scan spectral libraries). How- matrix; however, this pattern is a general trend and any ever, in terms of targeted analysis, MS2 has a second particular situation depends on the combination of dimension of selectivity in its collision-induced dissocia- analyte, concentration, matrix and method.
tion voltage settings, which is part of the optimization Some chemists have set policies for MS-identification decisions to account for the general trend shown inFor example, some criteria had dictated that ionswith m/z <91 or many ions from a chlorine or bromine 4. Limitations of current guidelines and rules cluster should not be used for identification purposes. The intention of such policies is to reduce the Milman has given an excellent overview of different MS chances of false positives by analysts with poor judg- identification criteria used by different organizations ment, but their arbitrariness can eliminate valuable and we do not repeat it here, but the information is information and preclude the use of new technologies helpful in order to improve understanding of the dis- that could result in improving identifications. For in- cussion in this section.
stance, chlorine-ion or bromine-ion clusters indicateboth the presence and the number of those atoms in the 4.1. Targeted monitoring in MS and choice of ions molecule, and that eliminates a great number of other There are some major difficulties in implementing typical possible chemicals in the identification.
MS-identification guidelines and rules, especially with Another common predisposition is to avoid use of ions GC-quadrupole MS because its use in the analysis of with <10% relative-ion abundances . A very impor- chemical residues tends to require SIM mode to achieve tant aspect to consider when choosing quantitation and the necessarily low LOQ. It is quite common for analytes qualifier ions in MS is to maximize S/N and thereby to yield only one or two ions of adequate intensity, even minimize LOD or LOQ and LOI, but this does not mean in EI, so it is impossible to identify many analytes at that ions with relative abundance <10% will not yield reasonably low concentrations using three or four ions.
higher S/N than ions with higher intensity. As shown in Even when an analyte has P 3 intense ions, some of an example for permethrin the presence of the the ions chosen for analysis over the range of targeted molecular ion at the proper tR with adequate S/N, even if compounds frequently have an interference from thesample extracts at the tR of interest, so different ionsmust be chosen, depending on the analyte-matrix pair, to increase selectivity and minimize LOI. The choice ofSIM ions can be difficult, especially in multi-residueanalysis of complex matrices with scores of analytes in Relative Abundance >5% the method. A trade-off must be made in the number of y = 48608e-0.0111x
analytes that can be included in the method vs time and R2 = 0.9866
the number of qualifier ions, all depending on back- ground matrix interferences.
Furthermore, in targeted approaches (e.g., SIM and Relative Abundance = 100% MS2), ‘‘false negatives'' are guaranteed to occur for non- y = 2013.8e-0.0107x
R2 = 0.9057
targeted compounds if they occur in the sample. Simi-larly, chemicals of interest or importance that are not recovered or detected by any particular method could be considered false negatives if they occur in the sample.
This is why scope of analysis (or analytical range) is Figure 3. Number of spectra in the NISTÕ98 mass-spectral library often the most important feature in a method, particu- plotted vs m/z of the base peak (relative abundance = 100%) andm/z of peaks with relative abundance >5%.
larly in regulatory screening applications.
Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 it was only 1% relative-ion abundance, is a powerful aid ion-ratio restrictions, and higher mass resolution), in the identification of analytes. IAA also works on the which tends to increase selectivity in general, but not premise that small isotopomer ions may be used to assess necessarily in particular. An example is how MSn of the molecular formulas of the chemical to help make iden- same product ion generated from different compounds tifications. Such powerful approaches should not be accumulates more information, but has no value in distinguishing between the different compounds. demonstrates the effect of choosing different MS demonstrates this situation in the case of MS2 of ethion ions in GC-MS (SIM) for the same application in a dif- and terbufos using the same fragment, m/z 231, which is ferent matrix is demonstrated in the analysis of pirimi- the base peak in their full-scan mass spectra. Another phos-methyl in carrot and orange extracts vs added example is that higher mass resolution to obtain exact- concentrations. Note how the relative intensities and mass measurements cannot distinguish between isomers variabilities of the ion ratios ‘‘change'' for the same with the same molecular formula. Some molecules (e.g., analyte in different matrices, due to chemical noise polychlorinated biphenyl or dioxin congeners) produce (matrix interferences). For carrots, m/z 125 should be almost identical mass spectra, and a high-resolution avoided, but, for oranges, the base peak with m/z of 290 chromatographic separation must be relied upon to help has a co-eluting interference. This is not unusual, and make the identification In that case, highly selec- the analyst has to be aware of the different situations tive sample preparation followed by high-resolution GC and take appropriate precautions. However, the wide (or GC·GC) with selective detection of chlorinated com- diversity and large number of food, environmental, pounds would probably be more selective than a rapid forensic, clinical, and other possible matrices makes it GC-MS method using high-resolution TOF. In essence, impractical to choose different qualifier ions depending the central failing of prescribed mass-spectral identifica- on the interferences in each matrix . Ultimately, the tion rules is that they do not apply in all situations .
analyst must take good care and use sound judgment, The lack of a unified theory for the concept of selectivity especially when using SIM to make identifications.
in analytical chemistry is an underlying cause of theproblem . For example, it is impossible to provide the degree of selectivity in a method to such an extent that it The key to identification of chemicals is not necessarily demonstrates true specificity (the result can only originate to characterize the analyte to the greatest extent possi- from the analyte and no other factor). In science, a ble, but to exclude the possibility that any other molecule hypothesis, such as ‘‘this chemical is in the sample,'' possesses the same measured trait(s). Current guidelines cannot be proved with 100% confidence, and experi- and rules narrow the focus (e.g., more ions, MS2, tighter mental evidence can only lend further support to or dis- Concentration in Carrot (ng/g)
vs. m/z 75
Concentration in Orange (ng/g)
Figure 4. Ratios of qualifier ions for pirimiphos-methyl vs the base peak of m/z 290 in GC-MS (SIM) analysis of carrot and orange ex-tracts (n = 4 at each concentration). In carrot, m/z 125 had a Figure 5. MS2 spectra of terbufos and ethion on an ion-trap instru- chemical interferent, and in orange, an interferent occurred at ment using EI. Precursor ion = m/z 231, excitation storage le- vel = m/z 102, excitation amplitude = 62 V, non-resonant mode.
Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 prove the hypothesis, not prove it. Unlike quantitative incorrect. The chemist claimed to have identified oxamyl analysis, qualitative analysis does not provide the actual in the sample by a GC method employing splitless injec- degree of confidence that a result is accurately known tion, but oxamyl is thermally labile and cannot be ana- because chemical selectivity does not follow a normal lyzed directly at the GC conditions used. The method had distribution pattern. We have already mentioned that actually detected oxamyl oxime, which was produced by spurious errors probably predominate and that mistakes thermal decomposition of oxamyl at the hot GC injector.
also do not follow a normal distribution. In trace-residue Typically, LC methods are used for the analysis of analyses in complex matrices, perhaps qualitative an- carbamate pesticides. Alternatively, as shown in , swers with 95% confidence can be determined through they can be analyzed directly by GC-SMB-MS , a practical validation process , but statements of which can provide EI spectra at >99.9% or >99.99% confidence become problematic prominent molecular ion, particularly for thermally- without a solid theoretical foundation for measuring labile chemicals (e.g., oxamyl), and that is a major selectivity and/or the analysis of many samples in care- advantage of SMB over traditional GC-MS.
fully controlled and perfectly conducted experiments.
It is hard to fault the MS expert in misidentifying oxamyl in the conventional GC-MS analysis. Theoxamyl-oxime metabolite appears in the NIST MS 5. Real-world pitfalls in qualitative analysis library, but it is listed as ethanimidothioic acid,2-(dimethylamino)-N-hydroxy-2-oxo, methyl ester. The Everybody makes mistakes, and every analyst can give lower traces in show the mass spectra of oxamyl examples of circumstances that led to a mistake. Learning and its oxime in the NISTÕ02 library (upper trace is the from the mistakes of others is better than learning from mass spectrum of cold oxamyl obtained using GC-SMB- our own mistakes. In this section, we present some MS ). Given the choice between the exotic sounding examples of real situations that have led the analyst either name given above and oxamyl, which was the injected to make a misidentification or to take extra precautions in compound after all, the chemist figured that oxamyl was qualitative MS analyses. We provide these examples of the correct molecule and unwittingly entered the spec- real-world pitfalls to underscore the points made in this trum of oxamyl oxime as oxamyl into the MS data-pro- article, and perhaps help the reader recognize when a set cessing software.
of circumstances leading to a possible identification orconfirmation is not as clear as it may seem.
5.1. Chemical degradation (e.g., GC-MS of carbamatepesticides)In a presentation at a pesticide residue workshop, an MSexpert showed how a targeted pesticide was detected in adifficult pepper sample using GC-MS in full-scan data-acquisition mode with mass-spectral deconvolution andcontemporaneous mass-spectral library matching. Thecalibration curve was linear and no matrix interferenceswere observed. The deconvoluted mass spectrum (whichwas well isolated from the complex background) wasessentially identical with the reference spectrum for thepesticide in his mass-spectral library of targeted analytes,which was generated by contemporaneous injection ofcertified pure reference standards using the same methodon the same instrument. The spectrum obtained with EIpossessed five intense ions of >15% relative abundancethat gave a very similar pattern to the spectrum in theNIST mass-spectral library for the pesticide. The tR wasexactly the same, as was the peak shape, with respect tothe reference standard for the analyte. The combinationof all of these factors met the GC-MS confirmation cri-teria established for chemical residue analysis by several Figure 6. Comparison (top to bottom) of cold EI mass spectrum of organizations so, for all intents and purposes, oxamyl using GC-SMB-MS and the NIST library mass spectra the pesticide was ‘‘identified''.
of oxamyl and oxamyl oxime. Conventional GC-MS using EI does Despite this, knowledgeable and experienced pesticide not yield the m/z 219 molecular ion, and hot splitless injectioncauses the conversion of oxamyl to its oxime in the inlet.
chemists in the audience knew that the finding was Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 This type of mistake is not uncommon; the compound tissues, two analytes with the same nominal molecular injected is not necessarily the compound detected or weight [i.e. albendazole sulfone (MW = 297.328) and assigned in reference libraries (analysts have also hydroxy mebendazole (MW = 297.313)] co-eluted using uncovered misnamed spectra in commercial mass-spec- a C-18 column (15 cm · 3 mm i.d. with 5-lm particles) tral libraries), especially if the molecular ion is absent in with a typical water/MeOH/MeCN buffered reversed- the EI mass spectrum of that compound. There can be phase gradient . shows their structures and other sources of errors (e.g., mislabeling the reference fragmentation pattern in electrospray positive MS2.
standard, assigning the wrong chromatographic peak as Without knowledge of this situation, the analyst would the analyte in a mixture, or not performing background not be able to distinguish between the two chemicals in real samples using typical MS2 instruments with unit- In another example, the MS expert had also included mass resolution. The situation is further complicated methomyl in the list of pesticide analytes in the GC-MS given that hydroxy mebendazole needs a much higher method. Like oxamyl, methomyl is thermally labile and regulatory LOD than albendazole sulfone in the EU (there readily converts to methomyl oxime in certain solutions is no LOD for mebendazole and its important metabolites, (e.g., methanol) and/or during traditional GC analysis at whereas the maximum residue limits (MRLs) for alben- both the injector and column. Again, the analyst ob- dazole and its important metabolites are 100 ng/g in tained a consistent peak with strong ions of m/z 105, 88, milk and 1 lg/g in bovine liver). In this case, a high- and 58, which looked much like the NIST library spec- mass-resolution instrument could help resolve these trum for methomyl. Unlike oxamyl and its oxime, compounds, but improving their chromatographic sep- methomyl oxime is not included in the library and the aration with a narrower column, smaller particles, and/ US tolerance definition for methomyl does not include its or more selective stationary phase would also provide a oxime To further complicate matters, the maxi- good solution.
mum regulatory limit for a separate pesticide, thiodicarb,includes methomyl in its tolerance definition. If the 5.3. Identifying unknowns (e.g., isofenphos-methyl) pepper sample had contained methomyl rather than One of the most difficult jobs for an analyst is to find a oxamyl and/or oxamyl oxime, then it would have ap- completely unknown chemical in a sample. Unscrupu- peared to the analyst that the presence of methomyl was lous athletes, racing-animal trainers, and food producers identified in the sample with little doubt. In fact, the use illegal drugs or pesticides in an attempt to gain a chemist would have unknowingly identified the presence competitive or monetary advantage, and they try to use of methomyl oxime in the sample, which has no regu- chemicals that cannot or will not be detected. For latory bearing in the U.S.A., and which could have example, farmers in Spain apparently obtained an arisen from unquantifiable concentrations of methomyl, unregistered pesticide, isofenphos-methyl, from a man- methomyl oxime, and/or thiodicarb in the original ufacturer in China and applied it to a crop of peppers Since there was no registration for the pesticide, In these cases, the presence of the molecular ion in the the analytical laboratories had no knowledge of it and mass spectra of the parent molecules would have en- there was no reference standard for it. It is common sured that the oxime metabolites were not confused with practice in some chemical-residue laboratories to use the larger pesticide analytes. This example illustrates GC-MS (SIM) and LC-MS2, which can detect only tar- both importance of the presence of the molecular ion and geted analytes. These methods are guaranteed to miss also the selection of an appropriate analytical method- any chemical of possible interest that is not targeted in ology that eliminates undesirable analyte conversion. In most cases, despite identification guidelines that typicallyrequire more ions for structure elucidation, it is actuallythe presence of distinct ions and other critical informa- tion (e.g., analyst knowledge and sample history) that tends to yield better results. Moreover, the use of a more appropriate type of approach, such as LC-MS2, would have provided the evidence needed to find the errors.
These examples also demonstrate the importance of using orthogonally selective (independent) approaches for the purposes of confirmation.
5.2. Similar analytes (e.g., LC-MS2 of anthelmintic residues)In another example involving LC-MS2 method develop- Figure 7. Structures and ESI(+)-MS2 fragmentation patterns ofalbendazole sulfone and hydroxyl mebendazole.
ment for the residue analysis of veterinary drugs in cattle Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 residues were initially found in food extracts as a curious There are certain circumstances in which no blanks extraneous peak in GC using nitrogen-phosphorus can be found because all samples tested contain the detection. Full-scan GC-MS was used to make the iden- suspected chemical. A few examples of this are ultra- tification and to obtain the multi-analysis confirmation trace findings of certain ubiquitous persistent organic after a reference standard of the illegally applied chem- pollutants in environmental samples or semicarbazide in ical had been synthesized.
shrimp using very sensitive MS instruments. In these Some analysts choose to use GC-MS in full-scan mode to cases, the ultralow background levels are averaged, and provide the chance to find unknowns (and increase the a positive finding of interest only occurs when the cal- potential number of analytes targeted). One advantage for culated concentration exceeds an ‘‘action level'' or the those laboratories faced with the isofenphos-methyl situation was that they re-examined the total-ion chro- In other cases (e.g., the occurrence of acrylamide in matograms from earlier analyses to identify the previously certain types of processed food), the parameter of interest non-targeted analyte. In the case of LC, full-scan MS with cannot be isolated in a blank without also generating the API is not so useful because typically only the pseudo- chemical that needs to be identified. In the case of molecular ion appears, and that is insufficient for making acrylamide, no blanks of crackers, potato chips, French identifications even in high-resolution applications. The fries, and similar cooked products containing asparagine ability to assess the MW or the molecular formula of a and reducing sugars could be prepared because chemical is helpful, but additional information is needed acrylamide is formed during the process used in making to make an identification in MS. Ferrer and Thurman the food products, so, when acrylamide was found in showed that it was possible to obtain in-source fragmen- these food products using LC-MS2, there was still a tation in LC-TOF-MS to analyze 101 pesticides while chance that there had been a false identification (or maintaining acceptable sensitivity.
quantitative overestimation) because chemical interfer- In full-scan EI-MS, there are typically so many matrix ents could not be excluded using a true matrix blank.
peaks (with many overlaps) in chromatograms that it is Acrylamide is a small molecule (71 amu), and an much too time consuming to evaluate the spectra for interferent in potato chips (presumably the amino-acid each peak . Deconvolution software exists to help valine that generates the m/z 72 immonium ion) was , but most chemicals in the chromatograms (e.g., observed during sample-preparation experiments .
the examples of isofenphos-methyl and methomyl oxime) In this situation, confirmation by an alternate method, do not appear in even the most extensive mass-spectral namely GC-MS2, was done to verify that the acrylamide libraries. Furthermore, spectral matching with a con- had been found by both distinct methods at similar temporaneously analyzed reference standard is com- concentrations in the samples. But, even then, the monly required to make an identification, and simple analyst had to demonstrate that analytical artefacts had matching with a library spectrum is not sufficient for a not occurred, because acrylamide can be formed from its variety of reasons (e.g., mistakes in the library, differ- starting materials in the extract during the hot injection ences between instruments and conditions, or incorrect process in the GC inlet (or during any sample- tR). This topic has been debated in the literature preparation steps using enough heat). Incredibly, it is and experts in the American Society for Mass Spec- possible that the analyst can follow all standard practices trometry have devised guidelines that emphasize the for two independent MS identifications for orthogonally need for contemporaneously analyzed reference stan- selective confirmation of acrylamide, and still be wrong if an interferent is misidentified in LC-MS2 and acrylamideis formed during sample preparation and/or during 5.4. Situations without a true blank (e.g., acrylamide in injection (split/splitless) in GC-MS2. In these instances, there is no substitute for an informed, careful chemist To ensure the quality of analysis, one of the most using well-validated methods.
important factors, which is commonly absent from typ-ical identification criteria, is that the analyte signal does 5.5. Contaminated reference standards (e.g., erucamide not occur in the analysis of a blank matrix. A basic control experiment in scientific investigations is to isolate One of the most insidious problems in developing tar- the parameter of interest (in this case, ‘‘that the signal geted MS methods (e.g., SIM and MS2) is when a refer- occurs because the analyte is present in the sample, and ence standard is mislabeled, has degraded, or contains a not an artefact''). Analysis of reagent and matrix blanks contaminant. Just because a bottle has a chemical listed is standard practice in QC procedures, which are with a given purity does not mean it is always correct, designed to exclude certain factors (e.g., instrument- but this type of false information can be difficult to un- memory effects, carry over, laboratory or reagent contamination, or misidentification due to matrix In an example, reference standards for 3-acetyl- deoxynivalenol (DON) and 15-acetyl-DON were con- Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 taminated with erucamide. LC-MS2 conditions are typi- rinsed the apparatus very well – but not well enough, it cally optimized using infusion, and the largest peak is turned out. The pesticides were confirmed to be present assumed to be the standard, since it typically has nearly in the extracts by independent GC-MS analyses, but the 100% purity. The erucamide [M+H]+ was m/z 338, with extracts were contaminated during the filtration steps, so its C-13 isotope at m/z 339, and, unfortunately, the the source of the pesticides in the samples had not been [M+H]+ for 3- and 15-acetyl-DON was also m/z 339.
With infusion, one could not therefore optimize the In a blind comparison study of samples shared be- system for either of those compounds, since erucamide at tween two laboratories to compare the different methods m/z 339 created analyte-optimization problems, but an used, a student was asked to review the findings from the uninformed analyst would not recognize the issue.
GC-MS (full scan) because the chemist did not trust the In this example, the analyst was aware of the issue software to do the job automatically. Indeed, the and chromatographically resolved erucamide from the instrument missed a large peak for a pesticide (phosmet, target mycotoxins in LC-MS2 rather than using infusion.
which gives only two strong ions), but the student was Column fractionation of the impurity and analysis by day-dreaming during the tedious review process and also UV/VIS revealed that it gave no UV/VIS signal, yet missed the obvious peak. The chemist was embarrassed fractionation of the standard showed a UV/VIS spec- when the other laboratory identified the pesticide resi- trum. Analysis of the contaminated standard by LC-UV/ due, and the chemistÕs laboratory had not. The studentÕs VIS-MS2 also indicated this. So, it was possible in this excuse was: ‘‘I was part of the method, and if there was a example to have an impurity in the standard that could false negative, then it is still the methodÕs fault.'' be detected by LC-MS2. However, other detectors couldassist the analyst in demonstrating the impurity chro- 5.7. Analyte derivatization (e.g., nitrofurans and matographically had no impact on the identification and quantitation of the target compound, but would have The use of the antibacterial agents nitrofurans is banned resulted in seriously incorrect optimization for the target in many countries, due to their mutagenic and carcin- compounds. Basically, information from an independent ogenic effects. In the analysis of nitrofurans in animal method (e.g., UV/VIS in this example) can be used in tissues, the common analytical method calls for over- addition to MS techniques to aid in identifications as well night acid hydrolysis and derivatization of the sample as making confirmations.
followed by LC-MS2 analysis . Nitrofurans arerapidly metabolized to smaller molecules, [e.g., furazoli- 5.6. Spurious errors (e.g., misteaks) done to 3-amino-2-oxazolidinone (AOZ) or nitrofurazone We mentioned that spurious forms of errors are probably to semicarbazide (SEM)] that bind to proteins in the tis- the most common reasons for misidentifications in sue. For nitrofurazone converting to SEM, shows working laboratories. There are numerous examples of how there are different possibilities that can lead to the human mistakes that we have found, but the more same detected derivatized analyte(s) in the method.
troubling factor relates to the mistakes we have not Another possibility is that the chemicals can already be found. In the following paragraphs, we give just a few present in the sample, but originate from a source other examples that illuminate potential laboratory errors.
An analyst in a laboratory had been preparing a When the new nitrofuran method was first imple- concentrated stock solution of many pesticides using mented, 30% of positive nitrofuran findings by an EU disposable pipette tips. Even though the used tips were veterinary-drug reference laboratory in The Netherlands placed in a box clearly labeled as waste, they were were for SEM, mainly in prawns and shrimp, at typical accidentally confused as being new. A different analyst concentrations of 1 ng/g The other analyte then used these tips to transfer sample extracts for rou- commonly detected was AOZ, which like SEM (MW = 75 tine monitoring to autosampler vials for analysis. Each amu), is a rather small molecule (MW = 102 amu) extract in the sequence was found to contain a different comprising common elements C, H, N, and O. Many pesticide at a rather high level. All identification criteria enforcement actions have been taken on nitrofuran were met in each case, and the matrix and reagent findings in shrimp imported to the EU, which has led to blanks were clean, but the analyst questioned the re- large economic losses by exporters and producers .
sults, and, fortunately, the cause of the problem was In the meantime, SEM was shown to occur in packaged foods, most probably arising from plastic sealing rings In another example, high concentrations of endosul- and carrageenan . However, we should note that fans were found in river water . Unknown to the initially no actions were taken until field investigations analysts, the same pesticide had been applied in an found containers of nitrofurans in the possession of agricultural field trial, and run-off water samples were shrimp producers.
prepared using the same filter apparatus in the labora- This leads to the question of whether the EU require- tory. The other group of analysts thought they had ments, which pertain to the number of IPs achieved in MS Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 analysis, are valid in this sort of situation. The purpose of found in the pet food, did not match the pathologic analysis is to determine if nitrofurans were used, and causes of the pet deaths. It is possible that folic acid was inherent aspects of the analytical method do not eliminate confused with aminopterin, as they have similar struc- other factors that could lead to the same result. The acid tures, as shown in .
hydrolysis and derivatization steps affect the degree of Later, a combination of high concentrations of mela- selectivity in the overall method, and, although the IP mine and cyanuric acid were found to be the cause of system very probably leads to proper MS identification of kidney failure of the pets that had died from eating the the derivatized analytes, those are not the compounds of contaminated pet food.
regulatory interest. The IP rules were developed with the This example illustrates the need for confirmation by a selectivity of different MS techniques in mind, and they do second laboratory in such a high-profile case, but, even not take into account the lesser degree of selectivity of within the same laboratory, use of a second method chemical reactions during sample preparation. An ana- would have made it less likely that aminopterin would lyst can therefore achieve >10 IPs of the end products of have been confirmed, and the press report would not the method using many sophisticated MS analyses, but have been issued.
provide no additional information to determine if nitro-furans were used or not. To address this concern, at leastin the case of chickens, investigations have been con- 6. Assuring data quality ducted to monitor for the parent drug directly in eyeballs,where they may accumulate .
The point of these true stories and real-world discussion When SEM was found to be occurring naturally and as is to emphasize that seemingly straightforward analyses a contaminant in the samples, this created a conundrum and the analytical decisions that come from them can for the regulators because their inflexible IP rules (EU/ still be wrong, despite strict analytical requirements and 657/2002) required that regulatory laboratories take enforcement action when four identification points were Oxamyl and methomyl are just two among scores of met, even at extremely low concentrations of <0.1 ng/g.
pesticides that have ‘‘complicating details'' in their anal- This led to revision of the EU legislation to devise MRPLs, ysis and regulatory control, and any method that ana- which essentially set a limit of 1 ng/g for nitrofuran- lyzes something other than the true compound of interest, marker metabolites .
as in the nitrofuran example, should face increased In a similar situation involved in the regulatory scrutiny and lead to caution in interpretation of results. In analysis to determine if dithiocarbamate fungicides were todayÕs environment, greater pressure is placed on the used illegally in fruit and vegetable production, the most laboratories to follow ISO 17025 practices and obtain common approach has been to analyze CS2 liberation accreditation for their analytical methods, but accredita- from the samples when treated with tin chloride tion alone cannot guarantee data quality. In nearly all the The method detects and quantifies CS2, but there are examples given, a checklist, such as the one discussed in known sources of matrix components leading to CS2 Section 6.2, would have helped the analysts avoid mis- other than just dithiocarbamates, particularly in bras- takes and make more accurate identifications.
sica-vegetable species, and that makes regulatory actions The reliance on recipe-type instructions reduces the questionable. To address this problem, chemists have role of the thinking analyst and displaces the scientific been investigating methods to analyze the dithiocarba- burden of proof (‘‘it wasnÕt me, it was the method'').
mates in the food directly .
That is not to say that analytical procedures should notbe followed precisely or that an organized system and 5.8. Multi-laboratory confirmation (e.g., aminopterin proper documentation should not be in place, but, in any or folic acid, or melamine in pet food?) laboratory, the most critical factor that leads to high- In this last example, sometimes the stakes are so high quality analytical data comes from skilled, informed, that many laboratories are needed to confirm an ana- responsible analysts, who have sufficient resources and lytical result. An example of this was the large interna- time to do their job.
tional investigation to find the cause of a rash of pet Independent of laboratory accreditation, a valuable deaths in 2007, which had been associated with pet food tool for improving the performance of analysts comes Before obtaining confirmation from other labora- from the feedback provided in PT-sample programs, as tories, one organization announced that aminopterin documented in many examples shows an had been identified in the pet food and they believed it to example in the case of European Proficiency Testing be the cause of the deaths. Although a second laboratory (EUPT) among approximately 130 accredited laborato- did confirm the presence of aminopterin at the non-toxic ries for analysis of pesticide residues in fruits and vege- level of <4 ng/g, other laboratories could not detect that tables. The quantitative results improved by about 50% chemical in the pet food. The known toxicological from PT 4 to PT 8 in terms of inter-laboratory repro- symptoms of aminopterin, and the low concentrations ducibility, and the qualitative performance also im- Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 Figure 8. Analytical approach for the analysis of nitrofurazone in animal-tissue samples and possible pathways for false-positive results for thecompound detected, NP SEM.
m/z 441 → 294, 175, 120 labs reported 11
Number of Labs 10
Number of Pesticides
m/z 442 → 295, 176, 120 71% of labs
reported 1 -16 pesti
Figure 9. Molecular structures of aminopterin and folic acid.
proved in terms of fewer false negatives, as shown in for Tests 6 and 8. In many cases, the laboratories Number of Labs
had to expand the scope of analysis to reduce the number of ‘‘false negatives,'' and, in other cases, thefeedback obtained by participating in the programs helped analysts better identify the analytes in blind Number of Pesticides
Figure 10. Improvement in qualitative results from Tests 6 to 8 Also, the number of false positives reported during involving 128–130 laboratories participating in the European Profi- that EUPT program has also reduced over time. In the ciency Testing Program for pesticide residue analysis of fruits and first five EUPT sample sets (1–5), 20 false positives vegetables. In Test 6, there were 13 pesticides present in the were reported among the laboratories in each test round, sample, and, in Test 8, there were 16 pesticides present in the sam- but, in the next five sample sets (6–10), this number decreased to 610 false positives per round among the130 laboratories. This decrease is attributed to the 6.1. Qualitative method validation wider implementation of MS systems and improved skills Due to the lack of a solid theoretical basis for qualitative and experience among analysts in using the instruments identifications, qualitative methods must typically be and the techniques, especially LC-MS2.
assessed empirically, just as quantitative methods are Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 validated. The real chances of false positive and false selectivity of the chromatographic separation; negative results in identifications, which constitute the instrument performance and proper tuning; most important qualitative performance factors, need to be determined with respect to analyte, concentration, samples and that no analyte carryover or matrix, and method parameters. Proposals have been laboratory contamination has occurred; made to do this during method development prior knowledge about the history of the sample and inter-laboratory trials have been performed to in terms of likelihood that the analyte would be measure the confirmatory ability of MS techniques in present, such as its potential use; blind analyses of real-world samples .
whether the MS-fragmentation pattern can be ex- The empirical approach is daunting if the measure- ment is to be made to a high level of confidence (e.g., the detected chemical in the extract makes sense >99%), but a reasonable degree of validation can be (e.g., analyte-stability issues, concentration, clean performed to eliminate the use of poor methods (e.g., up done, and derivatizations); 95% level of confidence) .
consistency of the analytical result with previous A multivariate statistical model has been demon- analyses of the sample(s); strated to lower the rates of both false positives and false prior experience of the analyst; and, negatives in GC-MS identification vs arbitrarily elimination of other possible compounds that chosen criteria , but it may not be practical to could lead to the same result.
analyze enough samples in a blind fashion to determine One must also recognize that more than a single the optimized criteria.
compound is often present at any given time in a chro- Essentially, this practical difficulty leads back to the matogram, which can generate mixed spectra that can simpler concept of confirming analytes in the sample by confuse the identification. For this reason, proper back- repeating the analysis of a duplicate sample using an ground subtraction, which may entail mass-spectral orthogonally selective, validated method (perhaps even deconvolution of co-eluted peaks, is critical to aid and to in a second laboratory if the importance of the analysis improve the process.
6.3. Determination of ion ratios 6.2. Factors in making identifications A common flaw in typical MS-identification guidelines is Section 5 presented several examples of how current the way used to set permissible variability in the relative approaches to identification can lead the analyst astray, ion-abundance ratios. The IP rules dictate that, for GC- not necessarily due to the identification criteria per se, MS in EI mode, relative ion abundances must be within but because not all the factors were considered in the ±10% (relative, not absolute value) of the ion intensity of overall method. In making qualitative decisions, the the reference spectrum for ions >50% relative abun- analyst (and/or software) should take into consideration dance, ±15% for ions <20–50%, ±20% for ions >10– a number of factors, many of which, but not all, appear 20%, and ±50% for ions 610% In chemical ioni- in current guidelines . These factors can be included zation, API and other techniques, a wider acceptable in developing checklists for analysts to compile the degree of variability is permitted. Different confirmation information needed systematically to satisfy fit-for-pur- criteria set different standards for acceptability, as shown pose identification criteria: in a previous report but none are based on specific information about elemental composition gained empirical measurements. In reality, some molecules, from other detectors, isotopic patterns (e.g., Cl techniques and instruments yield variability in the mass and Br), or the nitrogen rule (for molecules com- spectra greater or less than others.
prising C, H, N, O, P, S, Si, and halogens, an odd-numbered ion indicates that an odd number of Nmolecules occurs in the ion, and an even-num- 7. Proposed alternatives bered ion means that no or an even number ofN molecules appears in the structure); 7.1. Determining variability of ion ratios correct isotopomer patterns for the assigned During the validation of a quantitative method using MS molecular formula; detection, many analyses are done to evaluate recover- the choice of qualitative ions and the presence of ies, repeatability, reproducibility, linearity of calibration the molecular ion in the spectrum, even if it is and other factors. The same data can be used to deter- mine the variability of the ion ratios from day-to-day actual trends and variability of the chromato- with respect to concentration in the sample matrix(es).
graphic tR and appearance of peak shape of the Indeed, some chemists have chosen to measure these analyte (e.g., peak shifts, tailing factors, and iso- factors to help set the identification criteria for their Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 The error bars in indicate the standard deviation means to ensure that the criteria are reasonable and (SD) of the measurements of pirimiphos-methyl with realistic. The multivariate statistical approach should n = 4 at each concentration (taken from calibration work even better if enough samples can be evaluated to standards in matrix analyzed on four different se- fit into the model quences). In carrot, the average %relative ion ratios vs The assessment of ion ratios is just one facet of the m/z 290 for concentrations P 38 ng/g were 99 ± 3 for overall process, and the acceptability criteria should not m/z 276, 86 ± 2 for m/z 125, and 68 ± 2 for m/z 305; be set so strictly that they act to increase unnecessarily whereas, in orange, the same approach on the same the LOI without actually providing a greater degree of instrument a few months later led to values of 110 ± 7, selectivity in the analysis. At this time, rather than 108 ± 6, and 73 ± 4 for the same ions vs m/z 290, defining criteria that are assumed to decrease the rate of respectively. The change in mass spectra over time, false positives, such as defining an arbitrary points including the base peak in this case, due to different system and ion-abundance windows, the actual mini- tuning parameters or other factors, is not an uncommon mization of false positives is better addressed through occurrence and should be taken into account in the checklists, many analyses of positive samples and blanks, confirmatory procedures. Thus, reference spectra should analyses of blind PT samples, better analyst training, and be updated frequently, ideally after each tuning.
knowledgeable judgment, taking into account all avail- Assuming normal (Gaussian) distributions of ion able information.
intensities, the setting of variability criteria for relativeion intensity dictates the rate of false negatives that will 7.2. Matching factors occur. For example, at 10 ng/g in carrot, the limiting In library searches, ion ratios are calculated relative to third ion for pirimiphos-methyl (m/z 305, the molecular the base peak, so more emphasis is placed on the base ion) averaged 72 ± 8% relative abundance vs the m/z peak than other ions in calculating the spectral fit. The 290 base peak. This indicates that, if the ion-ratio switch in base peak for compounds, such as pirimiphos- window is set at ±10% (relative value, thus an accept- methyl in , can lead to lower match factors, despite able range of 65–79% relative abundance) as the IP the analyteÕs presence. Endosulfan is an example where system requires, then the rate of false negatives at 10 ng/ its spectrum contains dozens of ions, and its pattern is g in carrot would be 33% according to Gaussian dis- thereby easily recognizable by the analyst (provided it tribution theory. A window of ±2 SD (which should be does not co-elute with chlordane), but the variability of determined from P 16 measurements) can be used in- the relative ion intensities is high, resulting in low stead, corresponding to 5% false negatives. Thus, a matching factors. Identification criteria should not re- relative ion-abundance range of 56–88% for m/z 305 quire high spectral match factors (e.g., >90%) that do would yield an LOI of 10 ng/g for pirimiphos-methyl in not correspond to reality. Just as the ion ratios can be carrot by the GC-EI-MS (SIM) method used. Further- empirically measured during method validation, so can more, an ion-ratio window of ±3 SD relates to a mere match factors vs reference spectra to determine the more 0.3% chance of false negatives, which would lead to a realistic setting for the required match factor at the LOI relative ion-abundance range of 48–96% in the pirimi- needed. As discussed previously, contemporaneous phos-methyl example given.
comparisons with the standard spectra are critical, ide- In terms of potential false positives, comparison of ally from both solvent and matrix-matched standards at these ion ranges in NISTÕ98 with the more narrow a concentration similar to that of the analyte in the window defined by the IP system made no difference in being able to isolate the spectrum of pirimiphos-methyl In practice, matching thresholds for targeted pesticides from all others in the library using the three most in- are typically set very low (e.g., 40%) in order to minimize tense ions of higher mass.
false negatives, so then the analyst can use judgment to We suggest use of an empirical approach such as this: assess whether the preliminary software finding is first, to determine the variability of the ion ratios correct or not. Ideally, software would be trusted to in matrix extracts at the desired concentration; and, identify any (or all) compound(s) in the chromatogram then an ion-ratio window can be defined to set the iden- automatically, but this is seldom the case.
tification criteria for ion ratios at that concentration.
Another important, useful tool is the identification- Instrument software already commonly allows the probability factor provided by the NIST library and the user to set qualifier - ion ratios or spectral matching listed order of hits. It is better to have a worse fit but factors in the method, and the software could be further better hit in the sense of having the suspected analyte devised to measure variability of mass spectra. By using come first in the identification-probability list and with a real data rather than arbitrary criteria, the rate of false high level of confidence than necessarily with a high negatives should be reduced. Afterwards, a general matching factor. A known example to illustrate this assessment of false positives can be made using blanks, concept is the mass spectra of simple normal alkanes blind analyses, mass-spectral library searches, and other that provide very high matching factors to all, but they Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 are usually useless for identification because, without a system suitability and/or QC tests be made before and molecular ion, they all show the same mass spectra. The during analytical sequences to ensure that instrument- use of identification-probability factors (e.g., provided in performance criteria are being met (e.g., proper calibra- NIST mass spectral matching software) is therefore pre- tion, tR, peak shapes, and MS tuning factors).
ferred over simple direct-matching factors, since theidentification probability also takes into account the 7.4. Assessment of false positives and false negatives degree to which other candidates in the library, which An important possible measurement of method perfor- may also have high matching factors, can be excluded as mance is to determine the percentage of sample analyses candidates in the identification. The ability to exclude that give erroneous results using given identification other library candidates through MS library search criteria. One simple approach is to analyze in a blind represents an important advantage of full-scan GC-MS.
fashion a number of samples fortified (or not) withanalyte(s) of interest in matrices of interest. If only final 7.3. Assessment of chromatographic factors extracts of blanks need to be fortified, then time-con- Another important factor in MS detection in combina- suming sample-preparation steps can be minimized. MS tion with chromatography is the selectivity of the sepa- specialists often believe such an approach would be a ration. Direct infusion of the sample into an MS waste of time because they are confident in their instrument may be reasonable for screening methods, instruments and techniques, but when blind analyses but, for complex samples, an analytical separation have been conducted in practice (e.g., PT samples), the should be coupled to the MS detector for increased number of erroneous results reported can be surprising selectivity. Just as the MS-ion ratios can be assessed This approach for perhaps 20 blind samples empirically, the assignment of the acceptable tR window would certainly be useful to indicate a problem with the and peak shape can also be validated empirically during qualitative aspects of a method (or to verify that there is the quantitative method-validation process. Measure- not a serious problem).
ment of tR is a central to chromatography and it is col- One of the authors of this report challenged application lected in each injection sequence with respect to system- chemists from different MS manufacturers to provide re- suitability tests and calibration standards, and that sults using their instruments on a series of fruit and provides a realistic range of values that can be measured.
vegetable extracts that have been fortified (or not) at Moreover, the different ions used in an MS identification relevant concentrations with 16 pesticides. Among five should co-elute with each other (within the scan speed chemists who accepted the challenge, none has been able and dwell times of the detection process).
to perform the task well at the first attempt and all re- provides the average, SD and %relative SD quired feedback to help them learn from their mistakes.
(%RSD) in the tR of 16 pesticides from 24 injections of Independent of instrumentation, qualitative analysis calibration standards in carrot extracts over the course of cannot be done properly without experience with the four sequences without performing instrument mainte- analytes and the matrices. The blind analysis of samples nance. One possibility is for the tR window in identifica- in a realistic situation is an excellent way to train ana- tion criteria to be established based on this kind of lysts, and it is recommended that this practice be empirical measurement. For example, using a ±3 SD window for dimethoate would require that its peak must chemical-residue analysis. Perhaps in future, an auto- have tR of 11.841–12.045 min to be considered for mated system of analyte identification using a neural- identification purposes. Again, contemporaneous assess- network approach, in which the software is trained, ment of tR makes good analytical and practical sense.
much as an analyst is trained by experience, can be Peak shape and isomeric patterns should also be taken developed, but, until that time, analyst training is an into account. For example, the four isomers of cyper- essential part of qualitative analysis.
methrin that occur in the pesticide formulation present aspecific pattern, which, when observed in the correct tR 7.5. Spectra with too few ions window, essentially guarantees that cypermethrin has One of the main difficulties in GC-MS identification by been detected independent of MS information. Peak traditional criteria (either EI or CI in full scan or SIM) shapes, tR values, and asymmetry factors provide further relates to how many pesticides have mass spectra with information to help make an identification. The presence only one intense ion. Notable examples include per- of a tailing peak at the tR for a compound that gives methrin (m/z 183), fenthion (278), methoxychlor (227), sharp peaks (or vice versa) indicates a possible false po- phosmet (160), terbufos (231), pendimethalin (252), sitive. Again, the use of contemporaneous injections of piperonyl butoxide (176), disulfoton (88) and pyri- standards or standard addition to the injected sample (in methanil (198). Many other pesticides [e.g., methida- matrix and at similar concentration level) would ac- thion (m/z 145 and m/z 85), give only two intense ions.
count for such chromatographic changes. In fact, it is To obtain three or more ions that meet typical MS- common for standard operating procedures to require identification criteria leads to LOIs that are >100 ng/g, Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 2) enforcement applications to support legal actions.
Table 2. Reproducibility of tR in the GC-MS analysis of 16 pesti- Different degrees of confidence in identification and cides in calibration standards prepared from blank carrot extracts(n = 24) confirmation results are required for these two purposes.
The current guidelines do not differentiate very well between these different needs, so they do not optimally suit either purpose of analysis.
The current criteria are not based on objective or empirical forms of measurement, but are based on arbitrary criteria using subjective assessments about the degree of selectivity provided by different MS techniques.
The assessments are generally correct, but there are many exceptions, depending on the analyte-matrix pair, the concentration, the MS ions detected, the analytical technique, and the importance of the results.
The current rates of false negatives are thought to be too high in both types of application (enforcement and non-enforcement) because the identification criteria are too stringent. Furthermore, spurious errors are not typ- ically addressed to reduce false positives, which are bestaddressed through confirmatory analysis.
especially in complicated matrices, which is too high for As the word implies, ‘‘confirmation'' requires the risk assessment and other common regulatory purposes.
results from at least two analyses (which should ideally be However, if the one or two strongest ions are used, then orthogonally selective and at least one of them should the LOI can be reduced greatly.
involve MS detection after an analytical separation), Independent of the number of ions, we propose that which, in enforcement applications, must agree with each enforcement actions continue to entail a second injection other in terms of analyte identity and concentration.
of the original, re-extracted sample using a different For non-enforcement purposes, a single analysis using analytical approach (e.g., LC vs GC), column phase (with MS detection should be satisfactory, or two analyses orthogonal selectivity), detector (e.g., element-selective using non-MS methods, provided that there has been an detector vs MS), and/or MS technique (e.g., CI vs EI, or empirical demonstration that acceptably low rates of false MS2 vs MS) plus inclusion of reagent and matrix blanks positives and false negatives occur for the analytes at an . Due to the inability of non-MS methods to provide adequately low concentration in the targeted matrices.
structural information, at least one of the analytical An MS qualitative screening approach may be the best methods should use MS for detection, if the instrumen- option, followed by a different method to make the con- tation is available to the laboratory.
firmation and to improve the quantitative determination.
For analyses that do not involve high stakes, such as We summarize the arguments and the points made in non-violative findings for risk-assessment purposes, a this article in the following proposed sequence for single injection using MS detection should be demon- quantitative and qualitative method validation.
strated as fit-for-purpose. The method-validation process Define the need for the analysis (scope of analytes and would provide a realistic estimate of the LOI, and blind matrices, concentration ranges, acceptable degree of analyses would have to be done to demonstrate that no accuracy, and tolerable rate of false negatives); false positives occurred in P 10 analyses of different Define the primary analytical method(s) to meet sample types using blanks and samples fortified at the the needs of the analysis most effectively and LOI level. Again, non-MS techniques do not provide efficiently (e.g., GC-MS, LC-MS2, GC with selective structural information, so a second analysis should be detection, LC-fluorescence, and others); done using MS or an alternate approach if a non-MS detector is used in the first analysis.
method(s) for the targeted analyte(s) and ma-trix(es) (or representative analytes and matrices)to determine quantitative characteristics of the 8. Summary of proposed confirmation system for method(s) according to accepted practices; chemical residues At the same time as quantitative method validation,empirically determine MS criteria using samples of In the chemical-residue analysis of foods and environ- known concentrations on different days in fortified mental samples, the main purposes of analysis relate to: matrices to achieve the desired identification limit 1) surveillance monitoring and data collection for (with set criteria to minimize false negatives); non-enforcement reasons; and, Trends in Analytical Chemistry, Vol. 27, No. 11, 2008 Measure rates of false positives while also training  A. Gentili, D. Perret, S. Marchese, Trends Anal. Chem. 24 (2005) analysts through analysis of blind fortification sam-  L. Rivier, Anal. Chim. Acta 492 (2003) 69.
ples ( P 10–20 blanks from different sources and a  B. Maralikova, W. Weinmann, J. Chromatogr. B 811 (2004) 21.
similar number of fortified samples at a variety of  B.L. Milman, Trends Anal. Chem. 24 (2005) 493.
concentrations); [Note: this can be done over time  G. Stoev, A. Michailova, J. Chromatogr. A 1031 (2004) 11.
during routine analyses, and such tests should be  E. Soboleva, K. Ahad, A ´ . Ambrus, Analyst 129 (2004) part of the QC requirements];  G. Stoev, A. Mihailova, J. Chromatogr. A 1047 (2004) 263.
Devise a second approach to be used for indepen-  General Inspectorate for Health Protection, Analytical Methods for dent confirmation of suspected violative samples Pesticide Residues in Foodstuffs, 6th Edition, Ministry of Public from the initial analysis, and conduct blind analy- Health Welfare and Sport, Amsterdam, The Netherlands, 1996.
ses of extracts to estimate rates of false positives  M. Mezcua, C. Ferrer, J.F. Garcı´a-Reyes, M.J. Marti´nez-Bueno, and false negatives vs concentration, as in Step (5).
M. Sigrist, A.R. Ferna´ndez-Alba, Food Chem. 112 (2009) 221.
 S. Dagan, J. Chromatogr. A 868 (2000) 229.
The proposed validation sequence covers only general  A.B. Fialkov, A. Gordin, A. Amirav, J. Chromatogr. A 991 (2003) factors, but, in future, we hope that more specific, practical procedures can be described, just as in the  J.A. Sphon, J. Assoc. Off. Anal. Chem. 61 (1978) 1247.
case of quantitative method validation for chemical  W.C. Brumley, J.A. Sphon, Biomed. Mass Spectrom. 8 (1981) 390.
 R. Baldwin, R.A. Bethem, R.K. Boyd, W.L. Budde, T. Cairns, R.D. Gibbons, J.D. Henion, M.A. Kaiser, D.L. Lewis, J.E. Matusik, Further scientific study of the degree of selectivity of J.A. Sphon, R.W. Stephany, R.K. Trubey, J. Am. Soc. Mass MS techniques coupled with analytical separations is Spectrom. 8 (1997) 1180.
needed. Ideally, a systematic approach based on sound  R.A. Bethem, R.K. Boyd, J. Am. Soc. Mass Spectrom. 9 (1998) theory can be devised to assess accurately the degree of accuracy in qualitative MS analysis, but, until that time,  European Commission, Off. J. EU L221 (2002) 8.
 A. Amirav, Org. Mass. Spectrom. 26 (1991) 1.
we believe that the proposed general approach is more  A. Amirav, A. Gordin, M. Poliak, A.B. Fialkov, J. Mass Spectrom.
scientific and better fits the purposes of chemical-residue 43 (2008) 141.
analysis than the arbitrary criteria currently in place.
 A.B. Fialkov, A. Amirav, Rapid Comm. Mass Spectrom. 17 (2003)  M.W.F. Nielen, M.C. van Engelen, R. Zuiderent, R. Ramaker, Anal.
Chim. Acta 586 (2007) 122.
We thank Lutz Alder, David Heller, Eugenia Soboleva,  U.S. Environmental Protection Agency, Tera- through Octa- Charles Stafford and Stephen Stein for their participation Chlorinated Dioxins and Furans by Isotope Dilution HRGC/HRMS, in discussions. This research was supported in part by the Israel Science Foundation (Grant No. 1172/07). This  R.M. Silverstein, G.C. Bassler, T.C. Morrill, Spectrometric Identi- research was further supported by the James Franck fication of Organic Compounds, 4th edition., John Wiley & Sons, Center for Laser Matter Interaction Research, Chicago, New York, NY, USA, 1981.
Illinois, USA, and a grant from the Ministry of Science,  T. Alon, A. Amirav, Rapid Commun. Mass Spectrom. 20 (2006) Culture, Sport of the State of Israel, and Karlsruhe Re-  A.B. Fialkov, U. Steiner, S.J. Lehotay, A. Amirav, Int. J. Mass search Center (FZK), Germany.
Spectrom. 260 (2007) 31.
 J. Cochran, J. Chromatogr. A 1186 (2008) 202.
 M. Kochman, A. Gordin, P. Goldshlag, S.J. Lehotay, A. Amirav, J. Chromatogr. A 974 (2002) 185.
 R.A. Bethem, J. Boison, J. Gale, D. Heller, S. Lehotay, J. Loo,  U.S. Dept. of Agriculture Agricultural Marketing Service, Pesticide S. Musser, P. Price, S. Stein, J. Am. Soc. Mass Spectrom. 14 (2003) Data Program, Laboratory Quality Control SOPs QC-15, Manas- sas, VA, USA, 2003.
 European Commission, Method Validation and Quality Control  S. Stein, D. Heller, J. Am. Soc. Mass Spectrom. 17 (2006) 823.
Procedures for Pesticides Residues Analysis in Food and Feed,  M. Poliak, A.B. Fialkov, A. Amirav, J. Chromatogr. A 1210 Document No. SANCO/2007/3131, 31 October 2007  U.S. Code of Federal Regulations, Title 40, Chapter 1, Part 180  European Commission, Off. J. EU L71 (2003) 17.
 National Academy of Sciences, The Evaluation of Forensic DNA Washington, DC, USA, July 1, 2002. ( Evidence, National Academy Press, Washington, DC, USA, 1996.
 F. Andre´, K.K.G. De Wasch, H.F. De Brabander, S.R. Impens, L.A.M. Stolker, L. van Ginkel, R.W. Stephany, R. Schilt, M. Danaher, A. Furey, Anal. Chim. Acta (in press).
D. Courtheyn, Y. Bonnaire, P. Fu ¨ rst, P. Gowik, G. Kennedy,  I. Ferrer, M. Thurman, J. Chromatogr. A 1175 (2007) 24.
T. Kuhn, J.-P. Moretain, Trends Anal. Chem. 20 (2001) 435.
 C.R. Borges, Anal. Chem. 79 (2007) 4805.
 R.A. de Zeeuw, J. Chromatogr., B 811 (2004) 3.
 E.L. Schymanski, C. Meinert, M. Meringer, W. Brack, Anal. Chim.
´ .J. Pozo, J.V. Sancho, M. Iba´n˜ez, F. Herna´ndez, W.M.A. Niessen, Acta 615 (2008) 136.
Trends Anal. Chem. 25 (2006) 1030.
 J.W. Cochran, J. Chromatogr. Sci. 40 (2002) 254.
 I. Ferrer, J.F. Garcı´a-Reyes, A. Fernandez-Alba, Trends Anal.
 V. Furtula, G. Derksen, A. Colodey, J. Environ. Sci. Health, Part B Chem. 24 (2005) 671.
41 (2006) 1259.
Trends in Analytical Chemistry, Vol. 27, No. 11, 2008  R.A. de Zeeuw, J. Forensic Sci. 50 (2005) 745.
 R.C. Perz, H. van Lishaut, W. Schwack, J. Agric. Food Chem. 48  D.S. Mottram, B.I. Wedzicha, A.T. Dodson, Nature (London) 419  G. Crnogorac, S. Schmauder, W. Schwack, Rapid Commun. Mass  K. Mastovska, S.J. Lehotay, J. Agric. Food Chem. 54 (2006) 7001.
Spectrom. 22 (2008) 2539.
 L. Castle, J. Eriksson, J. AOAC Int. 88 (2005) 274.
 K. Burns, J. Am. Vet. Med. Assoc. 230 (2007) 1600.
 S.J. Lehotay, J.A. Harman-Fetcho, L.L. McConnell, Mar. Pollut.
Bull. 37 (1998) 32 Corrigendum 38 (1999) 1265.
 A. Leitner, P. Zollner, W.J. Lindner, J. Chromatogr. A 939 (2001)  J.-P. Antignac, B. Le Bizec, F. Monteau, F. Andre, Anal. Chim.
Acta 483 (2003) 325.
 C. Bock, C. Stachel, P. Gowik, Anal. Chim. Acta 586 (2007) 348.
 W.J. de Boer, J. van der Voet, W.G. de Ruig, J.A. van Rhijn,  European Food Safety Authority (EFSA), Statement of the K.M. Cooper, D.G. Kennedy, R.K.P. Patel, S. Porter, T. Reuvers, Scientific Panel on Food Additives, Flavourings, Processing Aids V. Marcos, P. Mun ˜ oz, J. Bosch, P. Rodrı´guez, J.M. Grases, Analyst and Materials in Contact with Food (updating advice available on (Cambridge, UK) 124 (1999) 109.
semicarbazide in packaged foods), EFSA, Brussels, Belgium, EFSA/  I. Bobeldijk, ‘‘Identification criteria for the GC-MS analysis of AFC/FCM/17-final, October 1, 2003.
environmental contaminants in various matrices,'' Report KOA  S. Phongvivat, ‘‘Nitrofurans Case Study: ThailandÕs Experience'', 00.100 (in Dutch), Kiwa NV, Nieuwegein, The Netherlands, Joint FAO/WHO Technical Workshop on Residues of Substances November 2000.
 R. Baigorri, A.M. Zamarren ˜ o, M. Fuentes, G. Gonza´lez-Gaitano, J.M. Garcı´a-Mina, G. Almendros, F.J. Gonza´lez-Vila, J. Agric. Food  K. Hoenicke, R. Gaterman, Accred. Qual. Assur. 11 (2006) 29.
Chem. 56 (2008) 5480.
 K.M. Cooper, R.J. McCracken, D.G. Kennedy, Analyst (Cambridge,  M.J. Schneider, D.J. Donoghue, J. Chromatogr. B 780 (2002) UK) 130 (2005) 824.
Gebrauchsinformation: Information für Patienten Senshio® 60 mg Filmtabletten Ospemifen Dieses Arzneimittel unterliegt einer zusätzlichen Überwachung. Dies ermöglicht eine schnel e Identifizierung neuer Erkenntnisse über die Sicherheit. Sie können dabei helfen, indem Sie jede auftretende Nebenwirkung melden. Hinweise zur Meldung von Nebenwirkungen, siehe Ende Abschnitt 4.