The abuse of novel psychoactive substances (NPS) has been increasing over the last few years especially as these products have become so readily available through the Internet. These products are emerging mostly as analogues or precursors of well-known drugs of abuse, such as amphetamine. However, a major problem that is more complicated than their abusive potential is that these products may not contain what is on the label. In fact, they have been shown to contain a mixture of drugs and excipients that may be toxic or even lethal. The number of deaths caused by NPS products has been increasing over the last year. This increase stimulates the need for sensitive techniques to identify these substances and detect any organic impurity in their products. Hyphenated mass spectrometric techniques, such as gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) offer some advantages. These techniques can both separate the constituents as well as confirm the chemical identity of the individual constituents in the product. 

Novel psychoactive substances (NPS), also called "legal highs," represent a major threat to society, particularly to the younger generation. The threat caused by these drugs ranges from chronic psychological or physical illnesses to death. These drugs emerge as analogues or precursors of well-known drugs of abuse to bypass regulations, and the popularity of abusing them is increasing worldwide (1–4). The European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) witnessed the emergence of 41 NPS in 2010, which was high compared to 24 substances in 2009 and 13 substances in 2008 (1). This was the largest number of substances reportedly introduced in a single year and included the following derivatives: 15 cathinones, 11 cannabinoids, a synthetic derivative of the plant based substance arecoline, five phenethylamines, one tryptamine, one piperazine, one cocaine, one ketamine, one phencyclidine, one aminoindan, one benzofuran, one aliphatic amine, and a substance that was described as designer medicine (1). The largest threat of these drugs is "amphetamine type" stimulants whose use, according to the United Nations report, has exceeded that of heroin and cocaine (3). 

Several factors contribute to the emergence of these new drug derivatives, such as the drop in the purity of ecstasy, heroin, and cocaine over the last few years (3); the change in the sociological and behavioral context (5); the change in the drug phenomena from life-threatening treatment to lifestyle drugs; and the emergence of unregulated Internet websites. These drugs are marketed in an attractive way and can be found under the names "Legal," "Speed," "Research Chemical," "Plant Food," "Bath Salts," and "Not for human consumption" (6–8). They also are sold at a cheap price. A survey by Schmidt and colleagues showed that an average price of a designer drug in the UK was 9.69 GBP (9). In addition, a survey conducted by Carhart-Harris and colleagues showed that the popularity of common NPS was related to their legal status (2,10). The issue associated with the emergence of NPS is not simply related to increased use, but in the chemical diversity of the products available (1). The EMCDDA warns that the situation could become even more complex if the future witnesses a growing trend in the synthesis of new illicit drugs based on pharmaceutical products (1).  

The toxicity and death case reports caused by these drugs have been increasing over the last few years (1,2,11). According to the United States Poison Control Center, 2237 calls were received from January to May 2011 relating to "bath salts" from 47 states; whereas, the number of calls in 2010 relating to these drugs was 303 (3,12). The cases from the last few years include paranoia and psychotic illnesses (13), acute liver failure (14,15), myocardial inflammation (16), acute sympathomimetic toxicity, hallucinations (1,17–24), and even death (1,20,25–29). The toxic effects associated with these products are not only related to the NPS pharmacological effect and dose, but also to the impurities and adulterants found in them. Often, these products do not only contain what is stated on the product label, or "label claim," but also contain a mixture of drugs and impurities. These additional substances are both deliberate (for example, to counteract a side effect) and accidental (such as, resulting from a poor quality synthesis). They range from organic to inorganic materials and include illicit drugs, pharmaceutical active ingredients, chemical precursors, and common excipients (30). For instance, analysis of products from patients admitted to the emergency department showed that out of 33 products analyzed 19% of nonliquid samples did not contain any drug and 23% contained a pharmaceutical active ingredient (31). The remaining samples contained both classic and novel recreational drugs, including methamphetamine, ketamine, chlorophenylpiperazine, and 1-benzylpiperazine. This highlights a significant issue of confirming the identity of these substances and ultimately the quantity in which they are present in a product.  

Hyphenated mass spectrometric techniques, such as gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS), offer the advantage of selectivity and sensitivity for the analysis of NPS. These techniques can separate and potentially identify NPS of similar chemical structure, in contrast to rapid colorimetric tests that are often unable to discriminate between chemical analogues and isomers. The objective of this review is to highlight the MS methods used in the literature to date for the analysis of NPS. The review focuses on NPS products purchased from the Internet as well as product seizures. Most of the references in the literature cited the use of GC–MS; whereas only one reference utilized the application of LC–MS. 

GC–MS has been the mass spectrometric method of choice for NPS analysis because GC can often separate structural isomers and MS provides fragmentation that is essential in identifying different analogues of a substance. Also, data collected by electron ionization (EI) is easily checked against fragmentation libraries. This is beneficial when analyzing NPS products that are often contaminated with additional analogues, isomers, synthesis precursors, and other active ingredients or organic impurities. 

 Figure 1:  Chemical structures of (a) amphetamine and (b) cathinone.			

GC–MS has been used to identify drug analogues in NPS products seized (32,33) and purchased from the Internet (34). For example, the cathinone "mephedrone" was identified in two products initially believed to be "amphetamine" (Figure 1) (32). Italian police seized these products in the form of an off-white powder and a white tablet, labeled with a dolphin logo (Table I), which initially tested positive for amphetamine using a standard colorimetric test. The products were further analyzed using GC–MS, yet the data did not confirm the presence of amphetamine. Examination of the mass spectrum showed the presence of mephedrone (MW 177) and its major fragment at 

 58, which corresponded to the immonium ion (Table II). The drug had retention times of 6.03 min and 11.06 min before and after derivatization with 2,2,2-trichloroethyl chloroformate. Derivatization of mephedrone increased the major fragment from 

 232, 234, and 236, showing the three chlorine atoms with their clustering pattern. Another case was observed for two samples obtained during a large product seizure submitted by the police department in Australia (33). The GC–MS analysis showed that the samples had very similar mass spectra with the same major ion peak of 

 58 corresponding to cathinone fragmentation, yet the chromatograms showed peaks at different retention times, 5.54 and 6.96 min. The two drugs were identified as the 3-fluoromethcathinone analogues 3,5-difluoromethcathinone and 3,5-dichloromethcathinone, respectively. Another case identified a mixture of cathinone analogues in capsules purchased from the Internet that claimed to contain only the active ingredient mephedrone (34). The products purchased were Spirit, Sub Coca 2, Neo Dove, and Sub Coca. The products all were different colors. However, the results showed that only the first product (Spirit) contained mephedrone (retention time = 4.36 min), the second product (Sub Coca 2) contained a mixture of α-phthalimidopropiophenone (retention time = 6.45 min) and 2-fluoroamphetamine (retention time = 2.56 min), and the third (Neo Dove) and fourth (Sub Coca) contained a mixture of caffeine, mephedrone, 

-ethylcathinone, and α-phthalimidopropiophenone (Table I). The MS spectra of the four substances showed the same fragmentation pattern as the previous cathinones. 

 Table I: Details of the substances found in novel psychoactive substances products			

Because the synthesis of NPS is often done in uncertified laboratories, there is little effort made to control or purify isomeric forms. The incidence of analogues present as isomers, yet still different to that on the label claim of an NPS product, was much more prominent in the literature. These included isomers of fluoromethcathinone (35), methylmethcathinone (36), 

-ethylethcathinone (37), methylenedioxycathinone (38), isocathinone (39), and naphyrone (40).  

A number of studies developed methods using isomer standards to identify a single isomer present in a sample. A method to distinguish the 2-, 3-, and 4-isomers of fluoromethcathinone was developed using GC–EI-MS (35). The chromatogram of the 4′-isomer (9.28 min) was clearly distinguished from the 2′-isomer (9.15 min) and the 3′-isomer (9.23 min). However, the three isomers were not stable so acetylation was needed to improve their stability. In this respect, the retention time of the acetylated derivatives of 4′-, 3′-, and 2′-isomer increased to 11.44, 11.46, and 11.66 min, respectively. The fragmentation of the three isomers was the same as the cathinone derivatives with a major ion peak corresponding to the immonium ion at 

 58. The presence of the 4′-isomer of fluoromethcathinone was then confirmed in three capsule products labeled as Sub Coca Dragon, High Spirit, and Neo Dove 2. However, in this case GC–MS could not detect caffeine and methylamine salt, which were present in the capsules and were confirmed using nuclear magnetic resonance (NMR) and infrared (IR) spectroscopy. A method to distinguish 2-, 3-, and 4-isomers of methylmethcathinone in NPS products also was developed (36–38). All isomers showed the same MS fragmentation pattern with the major ion peak at 

 58 (36), yet they were successfully separated 2<3<4 by GC between 6 and 9 min, respectively. The seized product analyzed was found to contain the 4- methylmethcathinone (mephedrone) isomer as well as benzocaine and showed GC retention times of 8 and 12 min, respectively. 

Table II: Ions observed from the analysis of novel psychoactive substances using chemical ionization (CI) and electron ionization (EI) mass spectrometry 			

In some cases, samples contained a substance and an isomer of the substance, demonstrating the typically poor quality of the product obtained. Isomers of mephedrone and ethcathinone were detected in two seized products using GC–EI-MS (37). The isomers were detected at different retention times than mephedrone (8.2 min) and ethcathinone (6.5 min) and were seen at 6.4 min for isomephedrone and 5.4 min for isoethcathinone. Both isomers had a major ion peak at 

 134 corresponding to isocathinone instead of 

 58, which corresponds to cathinone (Table II). Benzocaine also was found in these products. Methods to distinguish 3,4-methylenedioxymethcathinone and butylone isomers found in two seized products using GC–EI-MS also was developed (38). The chromatogram of the first sample showed the separation of 3,4-methylone and 3,4-MDPV from two other impurities, benzocaine and phenacetin, and were between 12 and 16 min (Table I). On the other hand, the second sample lacked these two impurities and the GC data showed the separated isobutylone and 3,4-butylone between 13 and 15 min. The mass spectra showed base peaks for methylone, butylone, and MDPV at 

 58, 72, and 126, respectively (Table II). Isomers of pentedrone and pentylone, were seen in two NPS product seizures: white powder seized from the customs department at the airport in Austria and green capsules brought to the police from a head shop in Germany (39). For the white powder, the mass spectrum in chemical ionization (CI) mode for both isomers showed the same [M+H]

192, but the mass spectrum in EI mode showed a difference in the major ion peak of the characteristic immonium ion that was 

 120 for isopentedrone (Figure 2 and Table I). Similarly, the capsules showed to contain pentylone and iso-pentylone had similar results with [M+H]

 236 in CI mode and showed different major ion peaks in EI mode of the characteristic immonium ion that was at 

 86 and 164 for pentylone and isopentylone (Figure 2), respectively. A recent study using GC–ion trap-EI-CI-MS to distinguish α- and β-isomers of naphyrone also was done (40). In this case, both isomers showed similar mass spectra with a major ion peak at 

 126 corresponding to the formation of the iminium ion in EI mode, and [M+H]

 282 in CI mode. However, both isomers had different retention times at 12.41 and 12.86 min corresponding to α and β-naphyrone respectively. The β-isomer was detected in one powder sample from the Internet, whereas both isomers were found in a second powder sample (Table I). 

Figure 2:  Chemical structures of (a) pentedrone, (b) isopentedrone, (c) pentylone, and (d) isopentylone. 			

In many cases, NPS products contain mixtures of isomers, analogues, and organic and inorganic impurities. This was observed with the analysis of 24 NPS products purchased from the Internet (41). Most of these products were labeled as naphyrone (NRG-1) but contained one or a binary mixture of other cathinone analogues or isomers, an anesthetic, a stimulant, or other substances with no psychoactive effect. The cathinones found included 4-methyl-N-ethylcathinone, butylone, flephedrone, MDPV, mephedrone, and naphyrone (Table I). The substances with no psychoactive effects included benzocaine, caffeine, lidocaine, procaine, and inorganic materials (Table I). A GC–MS method was developed to separate all substances found in the purchased products. Thus, the retention times for the cathinone derivatives were 7.97, 9.54, 6.73, 11.53, 7.64, and 12.41 min, corresponding to 4-methyl-

-ethylcathinone, butylone, flephedrone, MDPV, mephedrone, and naphyrone, respectively. The mass spectra for these cathinones showed major fragmentations corresponding to the iminium ion at 

 58, 58, 126, 72, 126, and 72, respectively (Table II). On the other hand, benzocaine, caffeine, lidocaine, and procaine showed retention times of 8.36, 9.9, 10.05, and 10.92 min, respectively. Whereas all these products contained a single substance or binary mixtures of two substances, further binary mixtures and more complicated quaternary mixtures of cathinones were detected in an additional three products. Two of the products were labeled as mephedrone and one was labeled as naphyrone (42). Five different cathinones were identified in these samples (Table I) and had retention times of 6.73, 9.95, 11.12, 11.53, and 9.39 min, corresponding to 4-fluoromethcathinone, pentylone, MDPBP, MDPV, and MPPP, respectively. The mass spectra in EI mode showed a major ion peak corresponding to these cathinones at 

 58, 86, 112, 126, 98, and 164, respectively (Table II). In addition, the [M+H]

  values detected for these cathinones in CI mode were observed at 

 182, 236, 262, 276, and 218, respectively. 

A number of studies used GC–MS to identify substances chemically unrelated to the label claim. In some cases, NPS products contained neither an isomer nor an analogue of their label claim, but instead contained a completely different substance. This was shown with seven products purchased from the Internet and labeled as MDAI (three products), Benzofury, 5-IAI, NRG-3, and E2. Apart from one MDAI sample, the GC–MS analysis of the products showed that they contained completely different ingredients to their label claim (43). These products contained a mixture of BZP, 3-TFMPP, and caffeine, except E2, which contained only caffeine (Table I). The chromatography method was able to separate the compounds present that appeared at 5, 5.3, 5.7, and 7 min corresponding to BZP, 3-TFMPP, MDAI, and caffeine, respectively. The major ion peaks observed in the mass spectra were 

 91, 188, 160, and 194, respectively. In comparison, products claiming to contain legal substances including amino acids, salts, ketones, and minerals contained a NPS (44), usually a cathinone derivative (Table I). Additional molecular components found in theses products included piperazines, ephedrine, and caffeine.  

LC–MS also has been used to develop methods to separate known NPS. Five seized powder samples were obtained from the Hungarian customs and finance guard (45). The five samples contained the cathinones mephedrone, butylone, MDPV, flephedrone, and 4´-methylethcathinone (Table I). The method was developed using these samples and reference standards of methylone and methedrone. The LC method was able to separate the seven substances in less than 8 min. In addition, the mass spectra (in ESI mode) showed characteristic ions [M+H]

 178, 222, 276, 182, 208, 194, and 192, respectively. 

Products of novel psychoactive substances represent a major threat ranging from chronic illness to death. Unlike traditional pharmaceuticals, these products are often made in uncertified laboratories where the synthesis and manufacturing quality is not properly controlled. Hyphenated MS techniques offer rapid and sensitive procedures for identifying a range of molecular constituents found in these products including NPS analogues, NPS isomers, and common anesthetics. Analysis of novel psychoactive substance products showed that more than 60% of products contained a mixture of novel psychoactive substances in addition to their label claim, and more than 25% contained an anesthetic or caffeine.  

 is a senior lecturer in pharmaceutical chemistry in the School of Pharmacy at the University of Hertfordshire in Hertfordshire, UK.  

 is a senior lecturer in analytical science in the School of Pharmacy at the University of Hertfordshire in Hertfordshire, UK.  

 is a postdoctoral research fellow in the School of Pharmacy at the University of Hertfordshire in Hertfordshire, UK. Please direct correspondence to: 

(1) EMCDDA-Europol, Annual Report on the implementation of Council Decision 2005/ 387/ JHA (2010).			

 (2) US Department of Justice, Drug Enforcement Administration, Background, Data and Analysis of synthetic cathinones 2011, Office of Diversion Control, Drug and Chemical Evaluation Section, Washington, D.C. 20537 (2011). 

(3) United Nations Office on Drugs and Crime, 

 (6) Advisory Committee for Misuse of Drugs (ACMD), Consideration of the cathinones (2010).			

 (7) C. Littlejohn, A. Baldacchino, F. Schifano, and P. Deluca, 

 (8) I. Vardakou, C. Pistos, and Ch. Spiliopoulou, 

 (9) M.M. Schmidt, A. Sharma, F. Schifano, and C. Feinmann, 

(10) R.L. Carhart-Harris, L.A. King, and D.J. Nutt, 

 doi:10.1016/j.drugalcdep.2011.02.011 (2011). 

Report of the Risk Assessment of Mephedrone in the Framework of the Council Decision on New Psychoactive Substances

(12) US Department of Justice, National Drug Intelligence Center, 

Situation report: Synthetic Cathinones (Bath Salts): An Emergency Domestic Threat

, doi: 10.1016/j.genhosppsych.2011.05.014 (2011). 

(14) S. Fröhlich, E. Lambe, and J. O'Dea, 

(15) C. Boulanger-Gobeil, M. St-Onge, M. Laliberte, and P.L. Auger, 

(16) P.J. Nicholson, M.J. Quinn, and J.D. Dodd, 

(17) M. Katagi, K. Zaitsu, N. Shima, H. Kamata, T. Kamata, K. Nakanishi, H. Nishioka, A. Miki, and H. Tsuchihashi, 

(18) D. Deburyne, M. Courne, R. Le Boisselier, S. Djezzar, M. Gerardin, A. Boucher, L. Karila, A. Coquerel, and M. Mallaret, 

(19) D.M. Wood, S. Davies, M. Puchnarewicz, J. Button, R. Archer, J. Ramsey, T. Lee, D.W. Holt, and P.L. Dargan, 

(20) New Jersey Division of Consumer Affairs, Designer drugs labeled as "bath salts." Statistics on abuse in New Jersey (2011).			

(21) Morbidity and Mortality weekly report (MMWR), 

Emergency Department Visits After Use of a Drug Sold as "Bath Salts"-Michigan, November 13, 2010–March 31, 2011

(22) D.M. Wood, S. Davies, M. Puchnarewicz, J. Button, R. Archer, H. Ovaska, J. Ramsey, T. Lee, D.W. Holt, and P.I. Dargan, 

(23) D.M. Wood, S.L. Greene, and P.I. Dargan, 

Articles

Gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) techniques offer advantages in separating and confirming the identity of constituents in novel psychoactive substances.

Identification of Novel Psychoactive Substances Using Hyphenated Mass Spectrometric Techniques

Identification and classification of pathogenic microorganisms, particularly of pre-existing or emerging strains of infectious agents such as bacteria or viruses, is an important concern. The main shortcoming of most current methods, which rely on reverse transcription polymerase chain reaction, is that they require known sequence information for detection. Mass spectrometry–based proteomics (MSP) techniques, in contrast, can be used to identify pathogens without any prior knowledge of what is contained in the sample. MSP also can be done without the need to grow cultures, and enable the microorganism to be classified down to strain level. A study of colony collapse disorder in honey bees illustrates these techniques.

Methods based on reverse transcription polymerase chain reaction (RT-PCR) are used extensively in the diagnosis of genetic diseases as well as in the determination of the abundance of RNA molecules within a cell or tissue as a measure of gene expression. However, RT-PCR requires known sequence information to reliably detect and identify pathogenic microorganisms. For example, the technique uses probes that hybridize to a known sequence. If a significant mutation occurs in a viral target, the known effective probes are rendered useless, because they will be unable to hybridize to a mutant sequence. 

By using mass spectrometry (MS)–based proteomics (MSP) techniques, along with rigorous sample processing methods, it becomes possible to effectively identify and quantify thousands of proteins in both healthy and infected samples in a single study. MSP offers an orthogonal and complementary approach to the RT-PCR that has been traditionally used for pathogen identification and classification. The technology is capable of generating unambiguous peptide fragment data that are then processed using bioinformatics tools in which the fragments are compared to a full library of peptide sequences generated from genomics and proteomics research. As a consequence, peptide fragment data acquired by MSP enables the identification and classification of microorganisms without the need for amplification, probes, or primers that are commonly associated with RT-PCR–based methods. It is also of significant importance that MS allows for the identification, quantification, and classification of fungi, bacteria, and viruses in a single analytical run. Phylogenetic classification can be to strain level and is limited only by the level of precision within the proteomic and genomic databases. 

In a recent publication (1), we presented the findings of a study whereby the MSP approach was deployed to identify and quantify thousands of proteins from healthy and collapsing bee colonies. Colony collapse disorder (CCD) is a major cause of concern in the United States because it results in the devastation of numerous honey bee colonies every year. To date, many CCD investigations, using sensitive genome-based methods such as RT-PCR, have identified small RNA bee viruses and the microsporidia 

 in both healthy and collapsing colonies. Nevertheless, the use of these techniques has not yet made it possible to firmly link any pathogen to honey bee losses. 

More than 60 different bee samples were analyzed for pathogens using an MSP technique. One of the samples originated from U.S. honey bee colonies that had been affected by CCD in 2006–2007, when this unusual syndrome first received worldwide press coverage. Another sample was from a collapsing colony in an observation hive fitted with a bidirectional flight counter and was sampled through time as the colony failed. Another was collected from collapsed bee colonies in Florida. Non-CCD Australian honey bees were used as reference sample. A further sample came from an isolated nonmigratory beekeeping operation in Montana with no history of CCD and another consisted of dead bees recovered from inoculation feeding trials with 

 alone, invertebrae iridescent virus (IIV) alone, a mixture comprising 

 plus IIV, and controls that were fed syrup alone. 

The paper describes the sampling methods, inoculation experiments, and MSP protocols that were used during the study (1). The experimental MS-MS spectral data of bacterial peptides were searched using the SEQUEST algorithm (University of Washington) against a constructed proteome database of microorganisms. 

Following MSP analysis of all these samples by ABoid (U.S. Army) (2), a database of more than 3000 identifiable peptides was generated, which represented more than 900 different species of invertebrate-associated microbes. Of those, only 29 were identified as being specific to bees and were used for subsequent analyses. The study focused on viruses, fungi, and microsporidia in the genus 

MSP analysis detected two RNA viruses (Varroa destructor-1 virus and Kakugo virus) that had not been previously reported in North American honey bee populations. A highly significant and also unreported co-occurrence of strains of DNA of IIV was also identified, featuring a microsporidian of the genus 

. The two RNA viruses were only noted occasionally, but the DNA virus was present in virtually all CCD samples. IIV were identified as the most prevalent viral peptides, appearing with 100% frequency and at high peptide counts in failing and collapsed colonies. They were also present in almost 75% of strong colonies, although at lower concentrations. 

Invertebrae iridescent viruses are large double-stranded DNA viruses of the Iridoviridae family, and their potential correlation with CCD had not been noted previously because small RNA viruses have been traditionally associated with the occurrence of most bee diseases. As a result, the detection and identification of IIV constitutes a very important finding and a step forward toward solving the CCD problem. The study suggests that the interaction between 

 and an IIV-6-like virus could contribute to bee mortality. Furthermore, it was noted that IIV and 

 occurred throughout a collapse and their concentrations increased as the severity of the CCD increased. 

Inoculation cage-trial experiments were subsequently used to test the MSP-generated hypothesis that an interaction between 

and IIV may lead to increased bee mortality. The results of these experiments are shown in Figure 1.  

 Figure 1: Survival over a 14-day postinfection period observed in cage-trials of honey bees infected with Nosema ceranae and IIV. Figure represents the combined survival results for four biological replications; N = 30 bees in each group for each biological replicate (1).			

The figure plots the survival over a 14-day post-infection period observed in cage-trials of newly emerged, 1–3 day old honey bees infected with 

 and IIV. The figure represents the combined survival results for four biological replications (

 = 30 bees in each group for each biological replicate). Deaths in control group were confirmed not to be pathogen-related via MS analyses. The survival data were analyzed using the Kaplan-Meier method, with the following results: control vs. 

: P = 0.01; control vs. IIV alone: P,0.01 (0.008); control vs. 

 + IIV: P = 0.04. (Bees that perished within 24 h of inoculation were not included in the survival curve analyses.) These results strongly supported the hypothesis that coinfection with 

 and IIV can be more lethal to bees than either pathogen alone.  

Figure 2: Discriminant function analysis for differences in pathogen peptide counts among strong, failing, and collapsed honey bee colonies. Vertical and horizontal lines mark the non-CCD out-group as a reference set (1). 			

Stepwise discriminate function analysis of count-weighted occurrence data was undertaken to assess whether strong, failing, and collapsed colonies could be differentiated by specific patterns of pathogen occurrence (Figure 2). In the figure, function 1 explains 81% of the discriminating variance and contrasts a higher incidence of iridovirus (IIV), 

 and to a lesser extent, black queen cell virus (BQCV) in failing colonies with a higher incidence of deformed wing virus (DWV) and some Israel acute paralysis virus (IAPV) in the remaining groups. Thus, only two pathogens, namely IIV and DWV, were necessary for significant discrimination among different colonies. A noteworthy finding was that collapsed and strong colonies were not significantly different; however, this finding can be easily explained as the few bees left in colonies at the final stages of collapse are those that are not infected. 

The SEQUEST algorithm was implemented to search the experimental MS-MS spectral data of bacterial peptides against the proteome database of microorganisms. SEQUEST thresholds for searching the product ion mass spectra of peptides were Xcorr, deltaCn, Sp, RSp, and deltaMpep. These parameters provided a uniform matching score of all candidate peptides, the generated outfiles of which were subsequently validated using a peptide prophet algorithm (3,4).  

The mass spectrometry–based proteomics approach is set to play a key role in the life science, medical research, environmental, and food testing markets over the next few years. As the recent use of this method in the study of honey bee colony collapse disorder illustrates, MSP provides an unrestricted and unbiased approach for sensitive and cost-effective detection and identification of pathogenic microorganisms down to strain level.   

The major differentiating advantage of MSP is that it is able to identify bacteria, viruses, fungi, and other cellular material without the need for growing cultures or any prior knowledge about them. In addition, MSP is faster than PCR-based methods. The entire MSP process (sample preparation, mass spectrometry, and software processing) takes about an hour to complete, depending on the complexity of the sample. In addition, the computer file can be analyzed again when the genomic data are updated and in this manner identify previously unknown microbes at a later time without having to re-examine the samples. This method is of significant benefit for infectious disease identification and a range of other potential applications in military, medical, pharmaceutical, food, and public safety areas.  

The data discussed here were previously published in reference 1. 

 is retired from his position as a senior research scientist at the US Army Edgewood Chemical Biological Center, in Aberdeen Proving Ground, Maryland. He can be reached at 

(10) (2010): e13181. doi:10.1371/journal.pone.0013181. 

S5:001. doi:10.4172/2157-7064. S5-001 (2011). 

(3) A. Keller, A. Nesvizhskii, E. Kolker, and R. Aebersold, 

A study of colony collapse disorder in honey bees illustrates how mass spectrometry–based proteomics techniques can be used to to identify pathogens without any prior knowledge of what is contained in the sample.

The Use of Mass-Spectrometry-Based Proteomics for Reliable Detection and Identification of Pathogens as Illustrated by the Study of Bee Colony Collapse Disorder

Accurate mass offers a significant advance over nominal-mass systems for metabolite studies. Accurate-mass data provide an increased ability to identify the metabolites observed through enhanced parent and fragment ion specificity. Examples provided show how accurate-mass data can differentiate between analytes of interest and the matrix. These examples also demonstrate how accurate-mass data of nonradiolabeled samples can address the challenges of in vivo metabolite identification studies previously only attempted with the combination of liquid chromatography–mass spectrometry and flow scintillation analysis. Further, a discussion of the use of accurate mass in glutathione profiling exemplifies the advantages of using accurate mass for in vitro applications.

In recent years, accurate mass–based analyses have become an increasingly important approach for qualitative mass spectrometry (MS) analyses (1–6). The use of accurate-mass applications has steadily replaced the use of nominal-mass approaches (7–11), and many scientists who are new to accurate-mass instrumentation are making greater use of the technologies now available. 

The goal of this paper is twofold. First, it describes the theoretical basis of why accurate-mass approaches offer a significant advance over nominal-mass approaches in the arena of qualitative analysis, and specifically metabolite identification. Second, it describes some of the analytical approaches that can now be conducted in a relatively routine manner — analytical approaches that were far from routine using the nominal-mass instrumentation. 

We should begin the discussion of accurate mass–based analyses with some key definitions, provided below. 					

 is the mass calculated using the average atomic mass of each element weighted for its natural isotope abundance.

 is the integer mass of the most naturally occurring stable isotope.

 is the calculated mass of an ion or molecule calculated using the mass of the most abundant naturally occurring isotope of each element.

is the calculated mass of an ion or molecule containing a single specified isotope of each atom.

 is the measured mass of an ion measured to high mass accuracy.

 is an MS technology that exhibits high mass resolving power.

The average mass is a value that is frequently used by medicinal chemists, but has no meaning in MS. No molecule exists that is represented by the average mass; the various isotopes present in a molecule  generate an isotope pattern in the mass spectrum. The terms 

 are often used interchangeably because the most abundant naturally occurring isotope commonly is used in calculating an exact mass. 

 describes the capability of a mass spectrometer to differentiate between adjacent masses such as 

 1000 and 1001. This term is appropriate when discussing the resolution of a quadrupole or ion-trap mass spectrometer.  

 that is appropriate for quadrupole orthogonal acceleration time-of-flight (QTOF) and orbital trap mass spectrometers is based on full width at half maximum (FWHM). Thus, 

 is defined as the mass being analyzed divided by the peak width of the raw mass data at that mass. So a mass spectrometer that detects an ion at 

 500 that has a peak width of 0.1 Da is operating at a resolving power of 500/0.1, or 5000. A mass spectrometer exhibiting a resolving power of 5000 can differentiate between 

Because mass resolution is a function of the mass of the ion being examined, when an instrument resolution is stated, the mass at which that resolution was determined also must be defined. In the example above, the mass spectrometer is exhibiting a resolution of 5000 at mass 500. 

Mass accuracy is a key parameter of mass spectrometer performance and determines the specificity of the mass measurement being made. The most commonly used definition of mass accuracy is given in parts per million (ppm) and is determined as the difference between an exact (or theoretical) and measured (accurate) mass. Table I displays the ppm errors for three different measured masses that have the same absolute mass error (25 mDa) relative to their exact masses. The ppm error, because it is a relative term, varies with the mass of the ion being examined. Traditionally, a mass error of 5 ppm has been accepted by medicinal chemistry journals as sufficient to provide a definitive elemental composition. Because 

 is a relative term, however, mDa mass accuracy, which is as an absolute measurement of instrument performance, often is used as a convenient day-to-day tool. 

 Table I: Example of ppm and mDa error at different masses 			

A mass spectrometer with high mass resolving power generally is considered an essential tool for achieving good mass accuracy. In reality, high resolving power is used to eliminate chemical interferences and is not otherwise required for obtaining good mass accuracy. In fact, accurate-mass measurements can be achieved routinely with even a quadrupole mass spectrometer operated at low mass resolving power if interferences are not a problem. However, given that the samples being analyzed usually are not pure standards but rather are mixtures or chromatographic peaks from LC–MS analyses, interferences are a frequent occurrence and HRMS is required to obtain quality accurate-mass data. 

The application of accurate-mass technology offers several advantages in conducting metabolite identification studies when compared with the use of nominal-mass instrumentation such as triple-quadrupole or ion-trap mass spectrometers.   

A review of the three main goals in conducting metabolite identification studies creates a framework for understanding these advantages. These goals are the same whether the samples are derived from in vitro or in vivo experiments. 

The first goal of metabolite identification is to differentiate between ions derived from the drug itself and drug-related metabolites and ions derived from the matrix. The second goal is to identify the biotransformation that has taken place, such as addition of an oxygen atom or loss of a methyl group. The third goal is to define which site of the drug molecule has undergone biotransformation. Accurate-mass data enable all three of these goals to be achieved more quickly than when using nominal-mass data. 

A paradigm shift is possible in using accurate-mass approaches because the data are rarely compromised with interferences and thus provide more information about the molecule being analyzed. Compared to nominal-mass studies, the overall result is that conclusions are reached more rapidly, with a higher level of confidence in their accuracy. 

There are four main reasons that accurate-mass data are superior to nominal-mass data in conducting metabolite identification studies, and these are discussed below. 

How is it that having accurate-mass data enables differentiation between the analytes of interest and the matrix? In most instances, the matrix components to be eliminated as false positives are derived from biological sources such as microsomes, hepatocytes, plasma, or urine. Such components are predominantly made of carbon, hydrogen, nitrogen, and oxygen. 

Table II: List of common elements and their accurate masses 			

Most xenobiotics that are being analyzed, though mainly derived of the same elements, often contain a higher level of unsaturation and occasionally incorporate halogens. Table II lists some basic parameters of common elements. Carbon does not contribute to the fractional mass (the numerals after the decimal point) of a molecule's mass, whereas both hydrogen and nitrogen increase the fractional mass, and oxygen, halogens, and sulfur decrease the fractional mass. Thus, the fractional mass of a molecule will change with the elements that constitute the structure of the compound. 

For example, for each double bond introduced into a molecule, the fractional mass will decrease by 0.0156 Da. Because many of the xenobiotics that are drug candidates are more highly aromatic than naturally occurring "interferences" such as peptides, the observed fractional mass is lower. A stylized example of this is shown in Figure 1, in which a tripeptide, Tyr-Gly-Gly, is compared with a possible drug compound of similar nominal mass. The mass difference of 32 mDa would be easily differentiated using a modern accurate-mass instrument. 

Figure 1: Example of a tripeptide and a possible xenobiotic with similar nominal mass. 			

The difference in fractional mass enables elimination of false positive mass "hits" when dealing with biological matrices. Figure 2 reproduces two extracted ion chromatograms from the same data file. The top trace uses a mass window of ±0.5 Da and the bottom trace uses a mass window of ±0.005 Da. The nominal-mass top trace has multiple peaks or "hits" for the parent compound, whereas the accurate-mass bottom trace has only one peak. 

Figure 2: Two extracted ion chromatograms from the same data file. The top trace is generated using a 1-Da wide window. The bottom trace is from using a 10-mDa wide window.			

Accurate-mass data facilitate differentiation of biotransformations that result in an identical nominal-mass shift. For example, when using a nominal-mass instrument, it is impossible to differentiate between M + O – 2H or M + CH

 because the mass shift (in both instances) is +14 Da. When the same comparison is made using accurate-mass data, the difference is very clear, because M + O – 2H (M + 13.9793) or M + CH

 (M + 14.0156) results in a mass shift that differs by 36 mDa. 

An essential step in conducting a metabolite identification study is to determine which fragment ion in the MS-MS spectrum is derived from the correct substructural moiety of the parent molecule. This information is used to compare the MS-MS spectra of a metabolite with the parent drug and thus determine the part of the molecule that has undergone biotransformation. The use of accurate-mass data in this step can prevent errors in localization of the biotransformation, because nominal-mass data cannot differentiate between different substructures in the molecule that provide the same nominal-mass fragment ions. 

Figure 3 shows a molecule that would provide fragment ions with similar nominal mass that would require an accurate-mass analysis to be correctly interpreted. Incorrectly interpreting the MS-MS spectrum of the parent drug would result in incorrect localization of the biotransformation. An incorrect localization of the site of metabolism could lead to synthetic chemisty decisions to address a metabolic instability that would alter the wrong site on the molecule. 

Figure 3: Example of a potential drug molecule that provides multiple fragment ions with the same nominal but different exact masses. 			

Nominal-mass instruments use MS-MS methods to obtain specificity, such as multiple reaction monitoring (MRM) for quantitative assays and precursor ion scans and constant neutral loss scans for qualitative screening or identification. These MS-MS approaches are less sensitive than full-scan experiments, simply as a consequence of transmission efficiency in such MS-MS instrument modes. The application of accurate mass using full-scan experiments is more sensitive than nominal-mass MS-MS methods because equal, or superior,  specificity is obtained without the resultant loss of ion current. 

The Role of Metabolite Identification in Various Stages of Drug Development

Metabolite identification plays an important role throughout the discovery, preclinical, and clinical stages of small-molecule drug development (see Figure 4). 

Figure 4: Schematic of different metabolite identification experiments performed throughout the drug discovery, preclinical, and clinical stages of drug development. 			

In drug discovery, metabolite identification can be used to determine the routes of excretion of a drug and its metabolites, to define the sites on a molecule that are subject to metabolism, to assess the potential for formation of reactive metabolites, and to compare the metabolites formed across various species, including humans. 

In preclinical drug development, in vivo studies, typically in rats, dogs, and monkeys, are designed to confirm that the metabolite profile observed across species in the in vitro studies are also observed in vivo. The mass balance study also illuminates the routes of excretion and their relative quantitative importance. 

The clinical development role of metabolite identification is generally twofold. First, it determines that the circulating metabolites observed in humans were also present in the toxicity studies filed in the investigational new drug (IND) application, thus demonstrating that the toxicity of the human metabolites has been tested. Second, it defines the structure of the metabolites observed in human plasma, urine, and feces and the relative importance of the observed route of excretion. 

Accurate-mass approaches offer a significant advance over nominal-mass approaches in the arena of qualitative analysis, and some of the analytical approaches can now be conducted in a relatively routine manner.

Accurate Mass: Why It's the Best Solution for Metabolite Identification in Discovery, Development, and Clinical Applications

Food allergies are becoming a global epidemic, and to the allergen sufferer, the need to accurately identify an allergen in a food product is a necessity. Bioanalytical methods to detect for allergens in foods can vary significantly, each offering its own advantages and disadvantages, and techniques are continuing to evolve and develop. LC–MS-MS has recently emerged as a new tool for allergen analysis, becoming the first technology that allows confirmatory analysis of multiple allergens in a single analysis. Here, we provide an update on the sample preparation and LC–MS-MS tools available for allergen detection in foods, as well as highlight some of the advantages that LC–MS-MS offers compared to other analytical techniques. We also discuss what the future holds for testing of allergens in foods — both for the scientist and for the allergen sufferer.  

There is no doubt that food allergies are increasing among the global population. Whether this increase is because of the evolution of the food we eat, the introduction of different types or forms of food products into different world regions, genetics, or simply the increased use of sanitation products that weaken the immune system, the rise is definitely palpable (1). It is estimated that more than 150 million people worldwide have an allergy to one or more types of food (2). The most common allergies are related to egg, cow's milk, fish, peanuts, and rice. The only way to prevent an allergic reaction is to avoid the allergen given that there is no cure for food allergies. This means that accurate food labeling is an essential resource for concerned allergen sufferers. 

Allergens almost exclusively consist of proteins, and an allergic reaction — ranging from mild rashes and hives to severe anaphylaxis or loss of consciousness — is the body's response to the presence of that protein. Scientific methods established to identify allergens in food are primarily bioanalytical methods, in which common approaches such as enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) are set to target the intact allergen protein or the DNA that produced the allergenic protein, respectively. Newer methods also have recently emerged using mass spectrometry (MS), providing an alternative approach to allergen detection by analyzing fractions of the allergenic protein in the sample.   

People with allergies rely heavily on food manufacturers and testing facilities to ensure that products are free of allergens and are labeled accordingly. These demands of the public are putting pressure on regulatory agencies that are governing food labeling laws as well as on food testing laboratories, which must strive to use the best technology to achieve accurate and reproducible results. 

Traditional Methods for Detection of Allergens and Their Challenges

The PCR approach for allergen detection utilizes the remnant DNA in the sample from the allergen organism, amplifying its presence using a DNA polymerase enzymatic reaction. This approach, which incorporates unique DNA primers, is highly specific to the allergen of interest. As a result, PCR is less susceptible to cross-reactivity to different allergens. And, because PCR is an amplification reaction, typically only a small quantity of extracted DNA is needed, allowing low levels of allergens to be detected in the food. PCR also allows the detection of multiple allergens in a single sample preparation step through the use of assorted DNA primers that are specific to the series of allergens of interest. 

Unfortunately, PCR has major drawbacks. First, because the method utilizes an indirect analysis of the allergen, quantification is often difficult. Additionally, false negatives can be observed, specifically in heavily processed foods, in which the DNA can be destroyed during processing. PCR also is more sensitive to the presence of food matrix components. Matrix components can often cause significant inhibition with the PCR, which also can produce false negative results. 

Finally, and most importantly for foods such as baked goods, PCR is not suitable to detect egg and milk allergens. Although the approach works quite well for nut allergens, it cannot distinguish by-product proteins (egg and milk) from tissue proteins (chicken and bovine), which have identical DNA (3).  

Although PCR is a good tool for some allergen detection in food, its drawbacks in the detection of egg and cow's milk allergens, two of the most common allergens, make it less suitable as a standard tool for multiple allergen analysis. This methodology also is not suitable for cooked or processed foods, because the processing could destroy the DNA and the matrix could produce high interference levels in the analysis. 

The ELISA approach for allergen detection is based on the interaction of an allergenic protein with an antibody to that protein. In the presence of the allergen, the protein–antibody complex is reacted with an enzyme-labeled conjugate, a reaction causing a color change in the solution that may be detected using a spectrophotometer. ELISA provides a fast and easy-to-use methodology to detect for a single allergen in food, and it can be applied for the quick analysis of allergens in raw ingredients during the food production process. 

However, ELISA methodology is not a catch-all approach. Every allergen of interest must be analyzed by a different ELISA kit specific to that allergen because antibodies are highly specific only to the allergenic protein of interest. This high degree of specificity of an antibody to an allergen protein also leads to the high possibility of false negative results. For example, many proteins are prone to modification during food processing or sample processing. Such modifications could inhibit the antibody–protein interaction, which might cause a negative ELISA result even if the allergenic protein is present. 

ELISA methods also are susceptible to false positive results. For example, some egg proteins may cross react with the antibody for β-lactoglobulin, which is a major cow's milk allergen (4,5), making it difficult to determine if the food product contains egg, cow's milk, or both. Similar cross-reactivity has been noted for various tree nuts as well (5). 

Given these drawbacks, challenges exist in the use of ELISA as a confirmatory analytical method for the determination of allergens in food products. With the expansion of food allergies among the global population, it is likely that regulatory agencies could initiate more stringent food labeling laws, requiring a methodology that would produce accurate, reliable, and confirmatory results. In addition to basic food labeling for the presence of allergens above specified target levels, ongoing studies to determine the correlation between clinical allergic reactions and the actual quantity of allergen present in a food product could encourage enforcement of thresholds or action levels (6). 

Emerging Technologies for Allergen Detection

The newest analytical approach for allergen analysis that is gaining traction in global laboratories uses liquid chromatography–tandem mass spectrometry (LC–MS-MS) for the identification of allergenic peptides in food products. Rather than analyzing the full intact allergenic protein (as done with ELISA), LC–MS-MS allows for the direct analysis of digested peptide fragments of allergenic proteins, which are detected using their characteristic molecular masses.  

There are two specific elements to the successful analysis of allergens by LC–MS-MS. First is the sample preparation, which is perhaps one of the most crucial steps in most analyses of complex matrices, such as food. Second is the proper identification and analysis of peptide markers that are specific to each allergen. Here, we discuss both of these elements. 

Sample Preparation Developments for Allergen Analysis by LC–MS-MS

Food matrices are notoriously difficult to work with because of their many types (solids, liquids), and characteristics (polar, nonpolar). Whether it is a fatty matrix like milk, an oily matrix like peanuts, or a carbohydrate-rich matrix like bread, it is typically ideal to route these samples through a sample preparation process before analysis. Diligence upstream of the analysis with the proper sample preparation technique can lead to an improvement in the accuracy of results by lowering the level of detection (LOD), improving method sensitivity, and reducing the presence of interfering components. 

The preparation of food samples for the analysis of allergens involves a lot of work, with most innovations coming forth in recent years. In 2008, Careri and colleagues published a sample preparation procedure for peanut allergen extraction from snack food products (7). Their innovative work paved the way for additional developments of modified, shorter sample preparation approaches for allergen analysis using LC–MS-MS detection. This new approach, pioneered by Heick and colleagues (8), (outlined in Figure 1) achieves lower limits of detection and tighter coefficients of variation between replicates through the incorporation of solid phase extraction (SPE).  

Figure 1: Sample preparation approach for allergen analysis using SPE and LCâMS-MS.			

Samples are homogenized using a food processor, then mixed with an extraction buffer containing ammonium bicarbonate, urea, and dithiothreitol — designed to extract the allergen proteins from the food in a reduced solvent environment. Samples are processed further using a roller mixer and then centrifuged. Iodoacetamide is added to the extract to serve as an alkylating agent to prevent reformation of disulfide bonds in the allergenic protein. Digestion buffer containing ammonium bicarbonate, acetonitrile, and trypsin is then added, and the proteins are digested into peptide fragments through an overnight incubation.   

The digested sample presents a homogenate riddled with interfering enzymes, sugars, and matrix components, making SPE cleanup necessary at this juncture to remove these components. SPE also is important in this procedure because it concentrates the allergenic peptides, allowing for lower levels of detection. A sample SPE procedure for extraction of allergenic peptides from complex food matrices follows: A pretreated bread or pasta sample is loaded on an SPE cartridge (in this case, a 200-mg, 6-mL Phenomenex Strata-X cartridge) that has been conditioned with 6 mL of acetonitrile containing 0.1% formic acid and equilibrated with 6 mL of water containing 0.5% trifluoroacetic acid. The cartridge in this example is washed with 3 mL of 0.5% trifluoroacetic acid in water, and the analytes are eluted with 6 mL of acetonitrile. The analyte-containing solvent is then evaporated to dryness using nitrogen and reconstituted in 300 μL of 95:5:0.5 (v/v/v) water–acetonitrile–formic acid. 

Innovations in research and development on sample preparation tools will play a fundamental role in the future of allergen analysis using LC–MS-MS. 

Peptide Analysis and Identification by LC–MS-MS

LC–MS-MS is a powerful tool for the detection of allergenic peptides in food products. The primary benefit of using LC–MS-MS for allergen detection is the multiple levels of detection specificity, in which the presence of multiple target peptides as well as the unique masses and fragmentation patterns of those peptides allow a two-tiered allergen identification process, improving confidence and reliability in the detection of the allergen. Other benefits offered by LC–MS-MS are direct analysis of the allergenic proteins through their characteristic masses and fragmentation patterns; the ability to better quantify the allergen than with other methodologies; and the ability to detect multiple allergens in a single sample preparation procedure (9). 

Allergenic peptides are first identified using databases and literature, primarily to ensure that the peptides are specific to the allergen and that they are not susceptible to modification during food processing. After peptides are identified and verified, a multiple-reaction monitoring (MRM)-initiated detection and sequencing (MIDAS, AB Sciex) workflow can be used for LC–MS-MS analysis of those peptide fragments. This workflow incorporates the power of MRM with full-scan linear ion trap MS-MS spectral data to allow the highest level of specificity for peptide identification and confirmation. An example of this workflow is shown in Figure 2.  

Figure 2: Example of a multiple-reaction-monitoring initiated detection and sequencing workflow for allergenic peptide fragment analysis.			

A sample MRM chromatogram of a bread sample fortified with egg and milk allergens using this LC–MS-MS workflow is shown in Figure 3. The workflow triggers the MS-MS spectrum of each peptide identified, offering richer mass spectral information and a higher level of confidence in the identification of that allergenic peptide.  

Figure 3: Sample multiple-reaction-monitoring LCâMS-MS analysis of a bread sample fortified with egg and milk allergens.			

There are several benefits with this MRM approach. First, this approach allows for the detection of peptides from multiple allergenic proteins if the peptides of those allergens are known. This overcomes the limitations of ELISA, in which only a single allergen can be detected at a time. Second, LC–MS-MS offers the ability for a reliable quantitative workflow, with lower limits of detection for milk peptides around 2 ppm and limits between 5 and 10 ppm for egg peptides. Reliable quantitation potential, in addition to the ability to achieve lower limits of detection at levels consistent with or below those identified with transitional methodologies, is another benefit of the LC–MS-MS workflow for allergen analysis. 

With food allergies becoming more and more common, especially among children, it can be speculated that authorities and regulatory agencies will begin to enforce more stringent food labeling laws in the coming years, perhaps also setting action levels (or minimum allergen levels that are acceptable for a product to be labeled "allergen-free") (6). Rigorous food labeling laws will lead to more rigorous testing protocols, and particularly the need for methodology that offers reliable, accurate, and confirmatory identification of food allergens in assorted consumer products. LC–MS-MS for allergen detection is the most ideal methodology to address these needs.  

The LC–MS-MS methodology described here represents the beginning of innovations using this technique for allergen detection in food. As the information on allergenic peptide markers expands for a number of different allergens of interest (10) and innovations in sample preparation techniques evolve, allergen detection by LC–MS-MS will easily surpass traditional techniques for routine allergen detection in foods.   

There are many different analytical methods to detect allergens in food products, and LC–MS-MS is emerging as a front-running methodology because of its robustness, specificity, and reliability relative to conventional methodologies. In this article, we presented recent studies showing the power of LC–MS-MS for allergen detection in food. This approach will continue to evolve to meet the demands of consumers.   

As consumers and allergen sufferers wait to see how regulations will change, scientists and the technology industry will continue to push forward in researching and developing the most accurate and reliable quantitative methodologies for allergen detection. The evolution of advanced methodologies for allergen detection in food has only just begun.  

 is with AB Sciex UK Limited, in Warrington, Cheshire, United Kingdom. 

are with Phenomenex Inc., in Torrance, California. 

is with AB Sciex in Framingham, Massachusetts. Please direct correspondence to: 

(1)  M. Wills-Karp, J. Santeliz, and C.L. Kark, 

http://www.elucidare.co.uk/assignments/Project_Allergy/Global%20food%20allergies%20statistics.pdf

(5) I. Selo, G. Clement, H. Bernard, J. Chatel, C. Creminon, G. Peltre, and J. Wal, 

(7) M. Careri, L. Elviri, M. Maffini, A. Mangia, C. Mucchino, and M. Terenghi, 

(8)  J. Heick, M. Fischer, and B. Popping, 

An update on the sample preparation and LC–MS-MS tools available for allergen detection, as well advantages of those techniques.

Special Issues-03-01-2012

Identification of Novel Psychoactive Substances Using Hyphenated Mass Spectrometric Techniques

The Use of Mass-Spectrometry-Based Proteomics for Reliable Detection and Identification of Pathogens as Illustrated by the Study of Bee Colony Collapse Disorder

Accurate Mass: Why It's the Best Solution for Metabolite Identification in Discovery, Development, and Clinical Applications

Advanced Analysis of Allergens in Foods: LC–MS-MS Methodologies and Future Insights

High-Throughput Screening of Abused Steroids in Urine Using Direct TOF-MS and LC–High-Resolution TOF-MS with Comprehensive Ion Fragmentation

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