In the days before electrospray (ES) and the rapid growth of mass spectrometry as a premier analysis tool in the life sciences sector, petroleum, polymers and environmental samples were the focus of mass spectrometry (MS) and vacuum gas-phase techniques were the most commonly used. This month's column looks at some of the problems that the practicing mass spectrometrist encounters in the chemical industry today. What tools  they use to successfully overcome these problems and which tools they look forward to using — or wish they had?

Mass spectrometry (MS) has been a cornerstone technology in the chemical industries, especially in petroleum research and the commercial production of polymers. The American Society for Mass Spectrometry (ASMS) will celebrate its 60th anniversary next year. Although christened under its current name in 1969, it began as "ASTM Committee E-14" in 1952. The work by John Fenn and others that gave us electrospray ionization (ESI) and heralded the modern era where the use of MS for biological analysis predominates, did not start until the 1980s. Before the 1980s gas-phase techniques were the most widely used and techniques were commonly performed in a vacuum as opposed to the atmospheric conditions used today and performed many times as a set experiment using a solids probe, rather than a flowing or chromatographic serial sample introduction. 

What interests MS practitioners today? What generates the most problem-solving attention in the chemical industry at the moment? What tools do they use successfully and which ones do they look forward to — or wish they had? I had an opportunity to speak with Colin Moore, Fellow and Technology Leader in Mass Spectrometry at Chemtura Corporation (Middlebury, Connecticut, USA) about the types of analytical problems that his group is asked to solve and what they learned in the process of solving them; this discussion evolved into a short tutorial on chemical industry practice.  

A UK native, Moore worked for Shell Research for seven years doing analytical work on agrochemicals and simultaneously became a graduate of the Royal Society of Chemistry. Postgraduate studies at the University of Southampton were followed by three years at Warwick University and a PhD with Professor Keith Jennings. He joined Uniroyal (now Chemtura Corporation) in 1994 as a member of the MS group, becoming manager in 1997 and Research Fellow in 2002. He has 20 publications in peerreviewed journals and 24 posters and presentations at conferences to his credit.  

Sample analysis in Moore's laboratory generally falls into one of the following areas:  

Confirmation of the chemical structure of the main components and identification of impurities to help synthetic chemists improve the synthesis.

Identification of minor components in a product that shouldn't be there (for example, colour bodies).

Identification of additives in a polymer or oil (for example, antioxidants in engine oil).

Figure 1: GCâMS response for an oil sample (upper is a methanolic extract; lower is the oil sample dissolved in methylene chloride). (Courtesy of Colin Moore, Chemtura Corporation.)  

For highly pure samples, analysis by nuclear magnetic resonance (NMR) spectroscopy is probably the best way to obtain detailed structural information and therefore most of the samples that mass spectrometrists are asked to analyse are mixtures. The analyst's first decision, therefore, is to determine which separation technique will be used: gas chromatography (GC), liquid chromatography (LC), gel permeation chromatography (GPC), solid-phase microextraction (SPME) or some type of liquid–liquid extraction (LLE). For example, if an engine oil sample in methylene chloride is shaken with methanol then the phenolic and aminic antioxidants will be concentrated in the methanol. GC–MS analysis of the methanol extract makes it easy to identify the major antioxidants in the oils (Figures 1 and 2).  

Figure 2: EI spectrum of peak at 13.82 min in the methanol extract shown in Figure 1. (Courtesy of Colin Moore, Chemtura Corporation.) 

An interesting aspect of identifying unknowns that, according to Moore, "I've not seen in the literature," stems from making use of both electron ionization (EI) and ESI spectra of an unknown, since the fragmentation patterns can be "complementary" (see the EI and ESI spectra in Figure 3). 

Figure 3: The complementary diagnostic nature of EI and ESI spectra of the same compound. (Courtesy of Colin Moore, Chemtura Corporation.) 

In the EI spectrum the first major loss is a C

 alkyl radical whereas in the ESI spectrum it is loss of the C10 alcohol. For those interested in a quick overview on mass spectra a recent column was devoted to that topic

  that, in addition, highlights James Little's insights from his experiences solving problems at Eastman Chemical in Tennessee, USA.  

A significant difference in analytical practice between the pharmaceutical and specialty chemical industries is the level of dependence of the latter on GC–MS. Today the pharmaceutical world favours LC–MS. For those interested in the aspects of how LC–MS became what we think of as open access in the pharmaceutical world, I chronicled the insights of a few practitioners on how LC–MS transitioned from a relatively obscure novelty to become the beneficial tool we know today.

  A recent article also examined the ongoing need for development in MS ionization and related technologies to support work in areas where "electrospray just isn't enough" to do the job.

  According to a survey among practitioners in various disciplines, those identified with the chemical materials industry arguably rely on GC three times more than LC. A quick look around Moore's laboratory supports the observation. 

One issue that all laboratories need to address is productivity. Two of Moore's GC–MS systems, an Agilent 5970 (Agilent Technologies, Palo Alto, California, USA) and a Waters GCT (Waters Corporation, Milford, Massachusetts, USA), are fitted with two GC columns in the injection port using a two-hole ferrule. One column is connected to the mass spectrometer and the other to an ancillary detection system such as flame ionization detection (FID). As Moore explained "As soon as you tell somebody what an unknown peak is in a GC–MS trace, the next question is usually, how much [of it] is there? Collecting the FID data in parallel with the GC–MS data allows us to give our customers semi-quantitative data as well as MS identification in a single experiment." Such a practice is analogous to LC–MS data acquisition with an in-line UV detector. 

Moore's future interests include installing a nitrogen–phosphorus detection (NPD) system, as well as FID, on one GC system because many of the antioxidants that Chemtura makes are amines. Amines as a class are often easily ionized by ESI. Yet in some cases the analytes are just not amenable to the technique. Insufficient polarity, the inability to capture a proton, or excessive volatility that precludes transport and separation by condensed phase means in an LC could all contribute to ESI failure. Great strides have occurred recently to increase the sensitivity, resolution and overall usefulness of LC–ESI-MS instruments. Current techniques — such as atmospheric pressure gas chromatography (APGC) and atmospheric solids analysis probes (ASAP) — which are analogous to the vacuum solids probes used for years in GC–MS, are viable without sacrificing performance, which was not the case years ago.

The ability of software-driven applications to amass increasingly refined data streams has unleashed a data handling problem that crosses into all practices and disciplines. So much so that handling complex data has become a recurring workshop topic at the Conference on Small Molecule Science (CoSMoS) in recent years (

Moore's laboratory uses MassLynx mass spectrometry software (Waters Corporation) to process its MS data. For GC–MS data, workers export the files using the NetCDF converter option in ChemStation software in the three Agilent systems (Agilent Technologies) and then convert the files to the MassLynx format using the Waters DBridge program. NetCDF does not produce a file for the FID system that can be read by MassLynx, and therefore processing the FID trace has to be done using ChemStation. On the GCT system the FID trace is recorded as analogue data by MassLynx that can be processed with the MS data. The retention time differences between the two traces can be time aligned in the chromatogram plots (the peaks come out earlier in the MS data because the vacuum system of the MS increases the helium flow-rate in the column connected to the mass spectrometer). 

Understanding the Problem and Designing the Experiment

As an analytical problem, identifying colour bodies in polymers is neither trivial nor obvious. In a 2005 paper,

  Moore shows there is often more than one way to solve a problem using mass spectrometry. Analysis by LC–MS with an inline photodiode-array (PDA) detector, is probably the best way to identify a new colour body. Techniques like time-of-flight secondary ionization mass spectrometry (TOF-SIMS) can be used to quickly confirm that the colour body on fresh samples is the same as on previously determined samples. 

Moore's 2005 work detailed the analysis of a yellow discolouration on the surface of a compounded ethylene–propylene–diene monomer (EPDM) rubber sample. Surface discolouration of a polymer can result from various phenomena, including contamination, component migration, oxidation and other chemical reactions. Components rising to the surface can give rise to "bloom," a process in which one component of a polymer mixture (usually not a polymer) undergoes phase separation and migration to an external surface of the mixture, according to the IUPAC definition. Protective waxes can be beneficial, but thiazoles (mercaptobenzothiazole) leading to discolouration of the product are undesirable. For example, oxidation of antioxidants can form colour bodies (that is, phenolic antioxidants can form quinone methides).

  The first step in identifying a colour body is to separate it from the polymer, often by washing the surface with a suitable solvent. 

The washings result in a complex mixture of the colour bodies, additives and other surface contaminants. Identification of the coloured components requires further separation of the mixture, analysis of the separated components, and the ability to ascertain which of the components are coloured. The combination of LC–MS–MS with an inline PDA detector is able to do the complete analysis in a single experiment. Moore recalls in 1994 when he joined Uniroyal Chemical, identifying a colour body often involved pooling fractions from multiple LC runs to acquire enough material for the particle beam LC–MS system (an early 1990s rather short-lived technique that, although not very sensitive, produced EI spectra from typical LC-amenable analytes not volatile enough for GC–MS). The much greater sensitivity of ESI sources and TOF mass spectrometers has made the process much quicker and easier, illustrating a central point in mixture analysis: the importance of matching the separation technique with the sensitivity of the final analysis technique. 

Matching aspects of the analytical technique is not as simple as it sounds. When light reflects off a coloured substance, the reflected light has the complementary colour to the wavelength or wavelengths absorbed. Yellow light covers the wavelength range 570–585 nm, but the complementary colour to yellow is indigo over the range 420–430 nm. So when processing the LC–MS data, Moore looked for a component with strong absorbtion over that range of wavelengths. He found one component that yielded the UV and LC–MS spectra shown in Figure 4. Note that the colour body is the neutral Cu(II) dibutyl dithiocarbamate, but for it to be detected by the LC–MS system it must be oxidized in the electrospray process to the positively charged Cu(III) compound. A quick review of spectral colour properties can be found at 

www2.chemistry.msu.edu/faculty/reusch/VirtTxtJml/Spectrpy/UV-Vis/spectrum.htm

Figure 4: Mass spectrum of the colour body (inset) and its UV spectrum. (Courtesy of Colin Moore, Chemtura Corporation.) 

Colour bodies are often polar molecules, which means that they are easy to ionize in an ESI source. For some time now, as the instruments' capabilities improved, accurate mass MS has been recognized as an efficient means of enhancing separation of closely related chemical species. As Moore points out, when "the electrospray spectrum contains few (if any) fragment ions, identifying unknowns requires that either an LC–MS–MS spectrum is acquired and/or exact mass measurements

  are performed to get the elemental formula of the pseudo-molecular ion." An inline UV detector is a useful adjunct that is often overlooked in LC–MS. Here the colour of the offending samples, of course, indicates distinct chromophoric benefits. Components separated by the high performance liquid chromatography (HPLC) column absorb at the appropriate wavelength to give the observed discolouration with the added advantage of well-characterized algorithms used by PDA detectors to distinguish differences in homologous components on the samples that the unaided eye cannot. 

Moore noted another frequently overlooked element in the analytical chemist's trade that bears mentioning: Structure elucidation is made much easier if the analyst has a thorough knowledge of the sample chemistry and its history. 

He also points out that "imaging mass spectrometry is a powerful technique for mapping the concentration of a compound on the surface of a matrix." The technique applies in many fields, including the analysis of inorganic materials, polymers and biological materials. An early publication discusses TOF-SIMS analysis in which a TOF system measures secondary ions produced by bombarding a surface with high-energy particles.

  TOF-SIMS has been used to detect light stabilizers

  on the surface of a polymer as well as to characterize the bulk polymer.

In the few years since Moore published his work, a number of techniques operating by various mechanisms on or near the surface of a material (as opposed to techniques requiring analytes of interest be in solution — desorption electrospray ionization [DESI], direct analysis in real time [DART], ASAP and a few others) have been examined in some detail in this column.

DESI can be used in combination with chemical reactions to improve the selectivity and sensitivity of the analysis. Moore's studies with Keith Jennings and attending presentations by Graham Cooks and John Beynon that emphasized the use of chemical reactions in MS encouraged him to attempt using novel chemical ionization (CI) reagent gases to help solve problems. "Many antioxidants are alkylated aromatic amines and therefore a paper by Buchanan

 was of great interest", he says. As shown by the spectra in Figure 5, the technique not only gives the number of aminic protons in the molecular ion, but it also helps identify fragment ions. The 

Figure 5: Deuterated (ND3) CI and EI spectrum of 4,4'-methylenedianiline. (Courtesy of Colin Moore, Chemtura Corporation.) 

Moore has also updated a method first reported by Morgan and colleagues

  for analysing zinc dialkyldithiophosphate (ZDDP) in engine oils. Engine oils are complex mixtures of base oils and performance enhancing multifunctional additives, like ZDDP. They are excellent antiwear agents and effective oxidation and corrosion inhibitors (Figure 6).  

Figure 6: Potential alkyl group positions. (Courtesy of Colin Moore, Chemtura Corporation.) 

The original work used negative ion CI to produce chloride ion adducts of the oil without any prior separation. Moore has used an atmospheric pressure chemical ionization (APCI) source and a mobile phase containing methylene chloride to give similar results.  

Figure 7: Chloride ion APCI spectrum of an engine oil sample dissolved in methylene chloride. (Courtesy of Colin Moore, Chemtura Corporation.) 

Note that the mass spectrum in Figure 7 yields two complementary pieces of information about the ZDDP sample and gives the molecular weight of any phenolic antioxidants in the oil. The phenolic antioxidant present in the oil is evident by the response at 

Table 1: Understanding the comparative diagnostic value of the ZDDP spectrum*. 

The chloride adduct pseudomolecular ion [ZnL2Cl]

  permits calculating the total number of carbon atoms in the four alkyl groups (R

, tell us if the ZDDP was prepared by blending individual ZDDPs or was produced using a mixture of alcohols. Table 1 illustrates how we can deduce that a mixture of C

 alcohols were used to prepare the ZDDP.  

Figure 8: Generic structure for PAMAs. (Courtesy of Colin Moore, Chemtura Corporation.) 

Though effective for determining carbon chain length of the R groups, the chloride adduction technique does not show whether the chains are linear or branched. However, collision-induced dissociation (CID)–ion mobility spectrometry (IMS)–CID data may hold the key to solve that puzzle.

Figure 9: GPC trace for oil sample. 99.5% of the sample yields an Mp of 564 and 0.5% having an Mp of 8630 (Mp, molecular weight, as reported by WatersGPC). (Courtesy of Colin Moore and John Mannello, Chemtura Corporation.) 

If the chromatographic conditions are not ideal for interfacing with MS or if other analytical techniques are going to be used to assist in the identification, then LC fraction collection may be the best methodology. Polyalkylmethacrylates (PAMAs) are used as viscosity modifiers in oils (Figure 8). Moore has used fraction collection from a GPC system (Figure 9), then pyrolysis GC–MS (Figure 10) and IR analysis to identify PAMAs. If R

 is one of a mixture of alkanes, pyrolysis of this type of polymer gives two series of fragment ions: alkenes and alkyl methacrylates. Thus if R

 then one gets dodec-1-ene and dodecyl methacrylate (Figure 11).  

Figure 10: Pyrolysis GCâMS at 550 Â°C of a PAMA standard (top) and the high mass component from the oil. (Courtesy of Colin Moore, Chemtura Corporation.) 

Moore visited Graham Cooks at Purdue to try using a DESI source to detect the colour body.

  Simply spraying the yellow polymer with acetonitrile indeed gave a small signal for the Cu dibutyl dithiocarbamate. Nevertheless, the signal was enhanced when the oxidizing agent I

Figure 11: Acquired EI spectrum (top) and best library match for the peak at 11.79 min found in the oil extract (Figure 10). (Courtesy of Colin Moore, Chemtura Corporation.) 

Articles

MS &#151; The Practical Art

This month's column looks at some of the problems that the practising mass spectrometrist encounters in the chemical industry today.

Problem-solving in the Chemical Industry

 in Hungary. The symposium will be organized by the Hungarian Society for Separation Sciences.  

36th International Symposium on High Performance Liquid Phase Separations and Related Techniques

 is an international forum for scientific discussion of the methods of high performance liquid chromatography in its various forms, along with the complementary separation techniques such as electrophoresis, electrochromatography, field-flow fractionation, supercritical fluid chromatography,  sample preparation and hyphenated techniques, such as LC–MS and CZE–MS. The field of microfluidics or lab-on-a-chip is also included. 

 all kinds of application areas relevant to the use of liquid phase separations, as well as fundamental studies on advances in separation theories will be covered. Special emphasis will be put on current research areas characterized by innovative development. The HPLC symposia are definitely valuable resources for analytical chemists, biochemists, molecular biologists and those in the separation sciences seeking practical solutions. 

The scientific programme will focus on state-of-the-art technology and the methodology of separation science. It will include plenary and keynote lectures. Lecturers both from academia and industry — at the forefront of the field — will present hot topics and state-of-the-art methods of separation science.  

Discussions and exchange during poster sessions will constitute an essential and vital component of the symposium. The best poster presentations will be awarded prizes. 

Short courses and tutorial lectures are an integral part of the scientific programme, which act as educational lectures for students and others new to various separation disciplines, and provide the opportunity to broaden one's understanding of separation science. Moreover, the short courses will provide the opportunity to meet and discuss separation problems with the real experts.  

Exhibition and vendor seminars are intended to inform the participants about the latest developments in the field in terms of instrumentation, technology and applications.  

Biopharmaceutical and Biomedical Analysis

Clinical, Forensic and Toxicological Analysis

Oligonucleotide Separations; Planar Chromatography

 Retention Mechanisms and Retention Models

 will include opening and closing receptions, symposium dinner, an organ concert and various optional activities.  

For more information please visit the symposium website at 

. The website includes information on abstract submission, registration, exhibition and sponsorship opportunities.  

For further details contact Attila Varga, Symposium Manager of HPLC 2011 Budapest, e-mail: 

			Hotel Palacio Estoril, Estoril, Portugal  

6th International MS and Related Techniques Symposium

			Hilton Manchester Deansgate, Manchester, UK  

VII International Ion Chromatography Conference

Institute of Environmental Engineering of Polish Academy of Science  

New EU GMP Annex 11 Regulations for Computerized Systems

Departments

Event News

After a 13-year absence, Pittcon will be returning to the vibrant city of Atlanta, Georgia, USA — the entertainment and cultural centre of the southern United States. Atlanta is home to a robust food and beverage industry, one of the largest concentrations of colleges and universities in the southern United States, and the site of a rapidly expanding biotech and pharmaceutical sector. If you haven't been to Atlanta in the past three years, you haven't seen it, according to the organizing committee. 

 includes a wide-array of symposia, short courses, poster sessions, networking opportunities and, of course, an exhibition featuring leading analytical and chromatography suppliers. Pittcon 2011 will be held from 13–18 March 2011 in Georgia World Congress Center, Atlanta, Gerogia, USA. 

For those seeking to develop new skills or learn more about chromatographic techniques, an attractive feature of Pittcon is the informative short courses. There are about 61 short courses associated with liquid chromatography and gas chromatography. Several of these are new to Pittcon this year and include topics such as 

LC–MS Strategies for the Identification of Impurities

Method Development and Applications to Small Molecules

New Concepts of TOF-MS for Fast GC and GC

. Pittcon short courses are offered from 12–18 March. Any individual that pays a registration fee for three short courses, receives free conference registration.  

The Conference itself includes 53 invited symposia which showcase nationally and internationally recognized scientists. Some of the symposia of interest to 

Quality by Design in HPLC: The Balance Between Chromatography and Chemometrics

 by Loren Wrisley, Mike McBrien, Imre Molnar, Moheb Nasran and John Dolan, which will focus on the QbD steps related to chromatographic separations. It will include examples of the application of QbD, such as determining the most robust method, deciding on the best selectivity and transferring methods.

Second Century Innovations on Chiral Separations

 by Daniel Armstrong, Isiah Warner, Shahab Shamsi, Gregory Webster and Bingcheng Lin, which will discuss the state-of-the art chiral separations in GC, HPLC, SFC, CE, CE–MS and microchip electrophoresis technology.

Analysis of Pharmaceuticals Present as Water Pollutants: LC–MS Challenges and Solutions

 by Susan Richardson, Helene Budzinski, Brian Buckley, Rebecca Trenholm and Rebeca Pinhancos, which will discuss where and how pharmaceuticals and their metabolites/degradants enter the environment and how the newly introduced high resolution liquid chromatography, such as UHPLC, with high resolution mass spectrometers (Q-TOF and Orbitrap) and fast scanning mass spectrometers (Q-TOF and Q-TRAP) allow the detection of pharmaceuticals present at trace levels in water. 

Advances in Microscale Liquid Chromatography

 by Barry Karger, Stephen Weber, J Michael Ramsey, Gert Desmet and Mary Wirth, which will cover recent advances in the application, theory and instrumentation for miniaturized liquid chromatography systems including capillary, microfluidics and monolithic columns.

New Enantiomeric Separation Approaches for the Pharmaceutical Industry

 by Daniel Armstrong, Christopher Welch, Antal Peter, Kyung Gahm and J.T. Lee, which will describe the newest chromatographic separation methods, including new chiral stationary phases, screening procedures, preparative separations and the separation of unusual chiral entities (such as nonprotenic amino acids).

Implementing Ultra-high-pressure LC (UHPLC) in Pharmaceutical Analysis: Best Practices and Potential Issues

 presented by Michael Dong, Naijun Wu, Klaus Witt, Qinglin Tang and Taylor Zhang, which will include an overview of the fundamentals and benefits of UHPLC, its impacts on method development and sample analysis, operational nuances and pitfalls, best practice in ICH compliant impurity testing and quality control, potential issues and mitigation strategies (precision for small-volume injections, viscous heating, blending noise in high-sensitivity analysis, compatibility to conventional HPLC columns and injector issues for impurity methods).

As well as the invited symposia, there are an additional 200 papers that are related to chromatography in the organized contributed, and poster sessions and workshops. The advance registration fee of $105 allows unlimited access to all parts of the technical programme as well as the exhibition. Pittcon also offers free one-day registration on 17 March 2011. 

Posters will be displayed in two opposite sections on the floor, the Red and the Blue Poster Areas. New for 2011 is Centennial Park, located in the middle of the floor that will provide a place for conferees to relax, network, view exhibitor videos, enjoy an interactive Coca-Cola display and pick up complimentary copies of leading industry publications. 

As social networking and mobile devices are on the rise and fast becoming invaluable tools for business and scientific communities, both will play a significant role in Pittcon this year. For the first time, Pittcon will have a Twitter Café located in 

where conferees can send Pittcon Tweets that will appear on a large screen monitor in Centennial Park. Pittcon has also implemented a mobile application by the name of 

 for attendees to build an agenda and to access exhibitor, technical programme and short course information prior, during and after the event. Local maps, restaurants and Atlanta attraction listings will help visitors find their way around the city after conference hours.  

An important function of Pittcon is to recognize and honour scientists who have made outstanding contributions to analytical chemistry and applied spectroscopy. This year Janusz Pawliszyn, professor of chemistry at the University of Waterloo, Waterloo, Ontario, Canada and a member of 

 editorial advisory board (EAB) will receive the prestigious Dal Nogare Award for 2011, presented by The Chromatography Forum of Delaware Valley (CFDV), for his development of SPME (Solid Phase Micro-Extraction) technology. Awardees are chosen on the basis of contributions to the fundamental understanding of the chromatographic process. Sessions presented in this award include 

Unified Theory of Extraction – Where Does SPME Fit

Ionic Liquids in Separations and Mass Spectrometry

Directions in Separation Science to Register an Agricultural Product

Nitrophenols: Measurement in Air and Rainwater by 2-Dimensional; 

 Chromatography and Very Long Path Detection

Philip Britz-McKibbin, associate professor of bio-analytical chemistry and chemical biology at McMaster University, Hamilton, Canada, will receive the 2011 ACS Division of Analytical Chemistry Award for Young Investigators in Separation Science. Philip's research interests have included fundamental studies of separation science involving capillary electrophoresis and mass spectrometry, which are aimed at the development of novel methodologies relevant to metabolomics, metabolite profiling and biomolecular interactions. Sessions included in this award presentation are 

New Advances in Capillary Electrophoresis–Mass Spectrometry for Metabolomics

Mass Spectrometry-based Metabolomics as a Unique Biochemical Approach for Therapeutic Discovery

Towards a Standardized Metabolomics Repository; Role of Separation Science in Biomarker Discovery: Opportunities and Pitfalls

What the Heck is that? The Challenge of Metabolite Identification in Metabolomics. 

Pittcon attracts nearly 20000 attendees from industry, academia and government from 90 countries worldwide. Each year thousands of scientists take advantage of the opportunity to see the latest in instrumentation all on one exhibition floor. At this year's Exposition, 1000 exhibiting companies, including leaders in chromatography instrumentation and accessories, will be present at the 

Georgia World Congress Center from 14–17 March 2011

A complete list of exhibitors is available on the Pittcon website at 

 Georgia World Congress Center, Atlanta,  Georgia, USA  

Article

A look at what's on offer at this year's Pittcon.

New Horizons in Atlanta

Comprehensive analysis of both volatile and non-volatile metabolites in food combined with information on sensory properties and multivariate statistics can be a valuable tool in understanding and improving the taste of food. Performing food metabolomics studies is, however, challenging and requires the analytical measurements to be of a very high quality. Although in some cases more targeted approaches are adequate, it is expected that new developments in analytical chemistry will increase the value of food metabolomics in the future.

Metabolomics plays an important role in systems biology. It enables better understanding of complex interactions in biological systems. There are many definitions and interpretations of metabolomics but generally it aims to analyse as many — if possible all — small molecules (< 1500 Da) in a biological system. The idea is that the biochemical level of the metabolome is closest to that of the daily function of a cell. It is an emerging tool in many disciplines such as plant physiology, drug discovery, human disease and nutrition and others. Developments in analytical chemistry, such as high-resolution mass spectrometry (HRMS), ultra-high pressure liquid chromatography (UHPLC), and software programs for fast processing of large analytical data sets have been responsible for the popularity and evolution of metabolomics.  

Metabolomics has also found its place in food science as recently reviewed by Wishart

  Within the area of food science, three main applications of metabolomics can be distinguished: 					

Food consumption monitoring and/or physiological monitoring of diet and nutrition focuses mainly on analysing nutrients and their metabolites in biofluids and/or health effects of food on an organism. Although this is a very exciting application, concomitant analysis is performed on biofluids or tissue of humans or animals and not on the food itself. 

Food authenticity. This type of analysis is also performed on food itself and the main purpose is to classify samples according to, for example, origin and age. The advantage of this approach is that one is only interested in accurate classification and that can, in many cases, be achieved by a single method like NMR covering a limited number of metabolites. Furthermore, understanding the reason why different samples are separated and thus identifying discriminating metabolites is not of primary interest. 

Food safety or quality improvement where the analyst wants to correlate a specific property, for example, taste, to metabolite patterns using biostatistics. Taking taste as an example, the main goal is often to understand the taste of food in terms of chemical composition and physical properties so it can be optimized by processing and growth conditions, or to find markers that can be measured routinely and easier than taste itself. This puts strong demands on the experimental design. First of all, the specific property, such as sensory scores, should be quantifiable and the variance herein should be well represented by the different samples. Furthermore, the coverage of the analytical methods used should be high, in order to detect all relevant metabolites that lead to understanding and optimizing the specific property. Dependent on the research question, in some cases the relevant metabolites can be analysed by a target analytical platform, while in other cases a more holistic view (i.e. multiple platforms) is required.

The typical workflow of a metabolomics experiment is shown in Figure 1. Every aspect of this workflow has to be optimized in order to make metabolomics studies a success. With respect to the analytical chemistry involved in metabolomics, sample work-up, analysis of the samples and data-preprocessing are items that have to be dealt with.  

Figure 1: Schematic diagram of the different steps in a food metabolomics experiment.			

The sample preparation procedures strongly depend on the type of matrix and compounds of interest. In the case of volatiles, food samples are preferably analysed directly by headspace, solid phase microextraction (SPME) or stir bar sorptive extraction (SBSE) in combination with gas chromatography–mass spectrometry (GC–MS). Choices should be made with respect to adding agents to stop enzymatic reactions, like salt-solutions. This can have both positive and negative effects on the response depending on the specific compound.  

For less volatile and non-volatile compounds the sample extraction is determined by the polarity range of interest. In the case of metabolomics, where all metabolites can be of importance, extraction using CHCl

O mixtures are most often used to extract both polar and non-polar compounds. Samples should be quickly frozen prior to extraction to stop any further reactions. Extraction should always be performed at low temperature to prevent loss of volatile compounds or thermal degradation. 

Usually, a single analytical method will not be sufficient to analyse all relevant metabolites and thus a combination of different methods should be applied to increase the coverage. In the case of taste, it is essential to cover both volatile and non-volatile compounds. Hence the combination of methods for very volatile components, like SPME–GC–MS, and less- or non-volatile components, like GC–MS and LC–MS, is essential but can hardly be found in literature. 

In this article, an example of food metabolomics in food quality will be described using tomato sensory properties as a demonstration case. Some highlights of this study will be shown; an extensive paper on this study can be found elsewhere.

  The challenges of this approach will be described, especially from an analytical chemical point of view, and recommendations will be made for future developments in food metabolomics with respect to analytical chemical requirements. 

Nineteen different tomato samples from different origins were used. From each sample 22 sensory attributes were quantified in duplicate by a sensory panel consisting of nine assessors. 

For SPME–GC–MS, tomatoes were cut into equal parts and stored for 20 min to allow enzymatic reactions to take place. Next, the samples were pulped on ice and liquid nitrogen was added to stop enzymatic reactions. Prior to analysis an equal weight of saturated CaCl

The sample extraction method applied for GC–MS and LC–MS was adapted from a procedure developed for microbial metabolomics samples where it is essential to quench samples as quickly as possible.

  For oximation silylation-GC–MS (OS-GC–MS), and LC–MS, tomatoes were freeze-dried and grinded followed by extraction using a CHCl

O (20:11:9) mixture. After extraction for 30 min at –40 °C, the extracts were centrifuged. The aqueous phase was pipetted off and freeze-dried. Prior to analysis the freeze-dried samples were redissolved in H

O:MeOH 3:1 v/v for LC–MS or derivatized using a solution of ethoxyamine hydrochloride in pyridine followed by silylation with MSTFA.

Instrumentation and operational conditions:

 GC–MS was performed on a Agilent 6890 gas chromatograph with an Agilent 5973 mass selective detector (Agilent Technologies, Palo Alto, California, USA). Detection was performed using MS detection in electron impact mode. For SPME–GC–MS, volatiles from mixed tomato/CaCl

 samples were absorbed on a 50/30 μm DVB/Car/PDMS fibre (Supelco, Bellefonte, Pennsylvania, USA) for 15 min at 50 °C. Desorption was performed at 250 °C for 10 min followed by splitless injection on a HP5-MS column (30 m × 0.25 mm i.d., 1 μm; Agilent) with a temperature gradient from 0–320 °C at a rate of 10 °C/min. For OS-GC–MS, derivatized samples were analysed by 1 μL PTV injection on a HP5-MS column (30 × 0.25 mm i.d., 0.25 μm; Agilent) with a temperature gradient from 70–325 °C at a rate of 15 °C/min. LC–MS was performed on a LTQ linear ion-trap system consisting of a Surveyor AS autosampler, Surveyor MS pump and LTQ LT–10000 mass detector with an Opton ESI probe (Thermo Fisher Scientific, San Jose, California, USA). Separation of tomato-extracts (10 μL) was performed on a Xbridge C18 column (100 mm × 2.1 mm i.d., 3.5 μm, Waters, Millford, Massachusetts, USA) using a linear mobile phase gradient from 0.1% formic acid in water (100%) to 0.1% formic acid in acetonitrile (30%) in 30 min with a flow-rate of 300 μL/min. Compounds were detected by ESI in both positive and negative mode (

 SPME–GC–MS was chosen as the method for volatiles for the tomato samples [Figure 2(a)] and resulted in the analysis of 101 metabolites, including aldehydes, alcohols and thiazoles. Various SPME fibres were tested but the fibre used here showed, on average, the best results for a broad range of volatile compounds. In general SPME shows better sensitivity and coverage than headspace GC–MS but requires more method optimization with respect to adsorption conditions and the type of SPME fibre. Other techniques like stir bar sorptive extraction (SBSE) seem to have superior performance over SPME. Coupling of these techniques to GC×GC–TOF-MS will increase the sensitivity and coverage significantly but non-targeted metabolite quantification of GC×GC–TOF-MS data is still a challenge and very time-consuming.

Figure 2: Typical chromatograms of tomato samples obtained by (a) SPMEâGCâMS; (b) GCâMS; (c) LCâMSâ(ESI+).			

Extreme volatile metabolites are of course difficult to analyse by techniques like SPME–GC–MS and more specific techniques have to be used when it is expected that these types of compounds might be of interest.  

A common method used in metabolomics, in many application areas, is GC–MS in which metabolites are first oximated and silylated.

  With this method various classes of small polar compounds can be analysed by GC–MS, such as organic acids, amino acids, sugars, sugar monophosphates, alcohols, aldehydes, amines and acyl monophosphates. These classes of compounds are present in almost every biofluid or food sample, which explains the popularity of this method. As a result this method can be easily applied to a new (food) matrix without extensive optimization. Moreover, large databases exist of mass spectra of reference compounds facilitating identification. Derivatized water/methanol extracts of tomato analysed by OS-GC–MS resulted in the detection of 112 metabolites, including organic acids, amino acids, sugars, amines, sugar phosphates and nucleosides [Figure 2(b)]. However, a significant number of metabolites could not be identified using existing databases. The coverage and sensitivity of the OS-GC–MS method can be increased significantly by using GC×GC–TOF-MS as has been demonstrated for other matrices.

  However, as mentioned before, non-targeted metabolite quantification of GC×GC–TOF-MS data in large scale studies is mainly hampered by the limitation of existing data preprocessing tools.  

Non-volatile metabolites are usually analysed by LC–MS. Reversed-phase (RP) LC–MS methods using a C18 column with a mobile phase gradient from water to acetonitrile or methanol are most commonly used for aqueous extracts of food and plant material. Tomato extracts were analysed by RPLC–MS in both the positive ionization (PI) and negative ionization (NI) mode [Figure 2(c)]. Data preprocessing finally resulted in 1394 features that were used for further data analysis. Compounds identified were mainly secondary metabolites like (poly)phenolic compounds and derivatives thereof. 

Small differences exist between methods used in literature and usually include additives used for the mobile phases and different types and suppliers of C18 columns. Untargeted screening is performed using ion-trap or time-of-flight mass spectrometers (TOF-MS), preferably in the PI and NI mode to increase the coverage. These methods are especially capable of analysing larger, non-volatile compounds such as phenolics and peptides. These classes of compounds do exist in many different foods and plant materials, but the exact structures are very type-specific. Due to this and the fact that many LC–MS methods exist with somewhat different experimental conditions, no databases are available that can be used for identification. Databases have been set-up, but primarily starting from a specific matrix, for example tomato.

  However, these are often of little use for other matrices. As a result many peaks can often be detected by LC–MS but many remain unidentified. This is a key problem in the success of LC–MS in metabolomics studies. Consequently, standardization of LC–MS methods in combination with proper databases and successful identification strategies are essential for the future of the use of LC–MS in (food) metabolomics.  

Recent developments in liquid chromatography, such as columns with sub-2 μm particles in UHPLC have led to increased throughput, better separation and sensitivity in metabolomics applications.

  The issue with respect to the large number of unknown peaks and their identification will then become even more urgent. Other developments, like robust HILIC and mixed-mode columns or ion-pair methods, may lead to the use of more than one LC–MS method in metabolomics studies resulting in increased coverage.

  Ideally these methods will be used for high-throughput detection of small polar metabolites without derivatization, in addition to the larger non-polar metabolites detected with RPLC–MS. Some examples do exist in literature, especially in the field of microbial metabolomics, and have shown that it is possible to detect a wide range of underivatized small polar compounds in complex matrices with LC-based methods.

 One limitation of these LC–MS methods is their separation efficiency compared with GC–MS, especially when dealing with isomers like monosaccharides and sugar-phosphates. However, the speed, sensitivity and sample preparation of the LC–MS methods can be superior to GC–MS, for example by coupling of UHPLC to high-resolution MS or MS–MS in hydrophilic interaction liquid chromatography (HILIC) or ion-pair (IP) mode. Quantification of many metabolites in a complex matrix is challenging for both types of methods. While in LC–MS one always has to deal with ion-suppression, the GC–MS method with oximation and silylation shows strongly deviating analytical performance for different classes of compounds, mainly due to the characteristics of the derivatization step 5. Furthermore, with the GC–MS method the results obtained for some metabolites in calibration solutions are very different from that in a complex matrix.

  As a result, the use of labelled standards for LC–MS methods might be sufficient for quantification while this is not true for the GC–MS method, although each metabolite will require its own labelled standard. Therefore these profiling methods are unlikely to be used for absolute quantification in the near future. Future developments in both GC–MS and LC–MS will determine whether LC–MS methods might replace the existing GC–MS methods used in metabolomics. 

After collection of the data the next step in the metabolomics workflow is data preprocessing (i.e., converting the raw data to reliable peak areas/peak lists). Various programs exist for the different analytical techniques. For GC–MS methods either peak picking or deconvolution is applied. Due to the nature of electron impact ionization, peak picking routines usually lead to enormous peak lists, represented by a mass and retention time, in which a metabolite is represented by many entries which is not ideal for statistical analysis. Therefore deconvolution routines are much more practical and ideally lead to metabolites lists — one entry per metabolite represented by the whole mass spectrum and area based on all masses of the EI mass spectrum. The SPME–GC–MS and OS-GC–MS data of the tomato samples were deconvoluted using TNO-DECO

  and resulted in 101 and 112 metabolites, respectively. For LC–MS data, deconvolution is under development but currently peak picking is usually applied using different freeware software packages (e.g., MetAlign, XCMS, MzMine). Due to the soft ionization in ion-trap and TOF equipment, the mass spectrum of a metabolite often contains one main ion, such as [M–H]

  depending on the nature of the metabolite and the mobile phases used. Thus, the metabolite can be represented by one mass without losing to much information. Software tools exist that combine masses belonging to one metabolite taking into account chemical rules and knowledge, like natural isotopes and common adduct. For the LC–MS data of the tomato samples, in-house developed alignment and peak picking software was used, resulting in 1181 peaks excluding isotopes and adducts for as much as possible. These are typical numbers of entries obtained with LC–MS in combination with peak-picking algorithms but so far it is not known what percentage of the number of entries are real metabolites as no one has annotated these peak lists.  

Figure 3: Standardized analysis scheme used for metabolomics studies.			

Many different analytical methods have been developed and applied in metabolomics-related research areas. Interestingly, attention has only very recently been paid to the data quality of these methods using quality control (QC) samples, such as pooled study samples (Figure 3). These samples reflect the average metabolite concentrations and the performance of the analytical platform can be assessed using the relative standard deviation (RSD) of the metabolites in these samples. This approach has only been applied in a qualitative way — by visual inspection — and no attempt was made to really improve the data quality in a quantitative way. However, an explicit way of reducing the analytical error is better.

  Its workflow first consists of data normalization using internal standards (IS) which reduces differences in sample extraction, derivatization and injection volume. With the use of the relative standard deviations (RSDs) of the analyte response in the QC samples to quantify the amount of analytical variation, the best IS is the one that gives a minimum RSD. Adjustments of the analytical instrument between batches of samples can be the cause of analytical errors. This behaviour results in different response factors between and even within batches. Using two types of QC samples the data quality can be improved further. The first type, calibration QC samples, is used to perform a one-point calibration, and the second type, validation QC samples, is used to assess how well the calibration procedure improved the data quality. Figure 4 shows the result of the batch calibration procedure on both the study samples and QC samples. It can be seen that both the intra- and inter-batch trends are corrected for by the procedures. Table 1 shows the result of the calibration procedures on the data quality of the OS-GC–MS tomato data. It can be seen that the raw data are already of good quality and that the calibration procedures lead to further improvement of the data quality.  

Figure 4: Improvement of the analytical data quality by (a) internal standard (IS) normalization and (b) batch correction.			

It is essential to integrate the analytical data coming from the different analytical platforms prior to statistical analysis to find better correlations with the taste attributes. This so-called data fusion procedure

  has been applied to the three different data sets obtained for the tomato samples.  

Table 1: Relative standard deviation (RSD) of 118 metabolites in QC tomato samples in raw data and after calibration procedures.			

Multivariate data, such as metabolomics data, can be analysed best by multivariate statistics. In contrast to univariate statistics, which looks at one metabolite at the time, multivariate statistics analyses all metabolites simultaneously. This approach has shown to be more powerful and sensitive because it exploits existing relations between metabolites. Principal component analysis (PCA) is a common multivariate statistical method that is often used to summarize and to visualize the relations that exist within variables, such as metabolites or sensory scores, within samples and between samples and variables. Methods like regression analysis that build models on both metabolomics and sensory data are much more suitable for food metabolomics studies because it explicitly models the relation between two data sets (i.e., multivariate metabolomics data and a selected sensory attribute). The quality of the regression model determines if the model is good enough for further interpretation. For that the correlation coefficient (R

) between the predicted and the true outcome is used. Double cross validation is used to determine the R

Both the sensory data and (fused) analytical data were analysed by typical multivariate techniques such as PCA and partial least squares (PLS). Figure 5(a) shows a so-called PCA biplot displaying both the tomato samples and the sensory attributes. In this plot it can be directly seen which tomato samples score high or low on specific taste attributes. For example, samples 1 and 3 score high on sour, while samples 8 and 12 score high on sweet. It is also clear that tomatoes 16 and 17 are clearly different from the other tomatoes. A similar PCA plot can be made of the fused analytical data [Figure 5(b)]. Again, it can be seen that tomato samples 16 and 17 are different from the others. From these plots it can be concluded that these samples have a significantly different taste and a significantly different metabolite pattern. However this is not true for all samples. Samples 4, 14 and 19 cluster together in Figure 5(a) but in Figure 5(b) sample 4 is separated significantly from samples 19 and 14. In this case the taste of these three samples is similar but the metabolites patterns do show significant differences. One other striking example is samples 8 and 10. In Figure 5(b), these samples are almost on the same spot indicating very similar metabolite patterns. However, in Figure 5(a) samples 8 and 10 are clearly separated, sample 8 scoring high on sweet and sample 10 high on bitter. From this it can be concluded that despite the large differences in taste, the metabolite patterns are very similar indicating that taste differences can be caused by very subtle differences in metabolites. Summarizing, the relative positions of the tomato samples in the sensory PCA plot and metabolomics PCA plot are very different. Hence it can be concluded that PCA analysis does not suffice in correlating tomato sensory data and instrumental metabolomics data. Nevertheless from the overview of Cevallos-Cevallos,

  it is seen that methods like PCA are still used.  

Figure 5: (a) PCA plot of the sensory data showing both the tomatoes (numbers) and the attributes to enable interpretation about the relations. In total, 63% of the variation of the sensory data is represented by PC1 (39%) and PC2 (24%); (b) PCA plot of the fused metabolomic data. In total, 39% of the variation of the sensory data is represented by PC1 (25%) and PC2 (14%).			

PLS was carried out for different sensory attributes. For sourness R

 = 0.92 as can be seen in Table 2, which indicates that at least 92% of the sensory attribute variation is independently described by the metabolomics data. For other taste attributes similar high R

  values were found after variable selection (see Table 2). From these models the most important metabolites can be determined that contribute most to the model (i.e., explain the most variation in the sensory attribute). Table 3 shows an example of such important metabolites for the PLS model of sourness including the analytical platform they were derived from and whether the metabolites showed a positive or negative correlation with the sensory score. The results in Table 3 demonstrate the added value of applying and integrating different analytical platforms while all methods used in this study are represented. It should be mentioned that part of the important metabolites found for the different taste attributes could not be identified and these were mostly metabolites detected by LC–MS. It was discussed earlier that identification of metabolites, especially those detected by LC–MS, is one of the critical issues in metabolomics which should be solved in the near future in order to make metabolomics a more successful technology. It should be noted that the metabolites themselves do not necessarily lead to the sensory experience with which they correlate. They can also be precursor molecules, cause synergetic or masking effects with other metabolites or have a regulatory role. In this study most of the important metabolites could be identified and of those 29 compounds, 20 were reported earlier to occur in tomatoes.  

Table 2: Double cross validated correlation coefficients (R2) for PLS models obtained for several taste attributes.			

The next step in the whole process is the biological interpretation of the results (i.e., can one understand the observed relation between the identified metabolites and the specific taste attribute). This of course needs profound knowledge of the chemistry of taste in the specific matrix.  

Table 3: Selected metabolites on basis of PLS regression on metabolomics data and sensory data on sourness.			

Validation is another key step in the metabolomics workflow. In order to demonstrate the validity of the results obtained, a separate second study with new samples and sensory data should be conducted and treated in the same way as described here. With respect to the analytical chemistry involved in metabolomics, this requires good reproducibility of the analytical methods used. For bioanalytical methods it is standard procedure to validate analytical methods according to FDA guidelines including reproducibility. This part has been largely overlooked in metabolomics and only recently has more attention been paid to the stability of analytical methods over long time periods for, for example, large-scale metabolomics studies.

After positive validation, the results obtained can be used to optimize or improve the taste of food products, for example by adapting growth or food processing conditions. 

The different aspects of food metabolomics were described using tomato taste as an example. It is shown that the different parts of the metabolomics workflow should be of high quality in order to be successful. With respect to analytical chemistry, it is essential to have as much metabolite coverage as possible. Therefore different complementary analytical techniques should be used and the data should be integrated. Also aspects like data quality, data preprocessing and metabolite identification are essential aspects within a food metabolomics study. This makes food metabolomics studies often laborious, tedious and costly. However, in cases where targeted approaches using existing knowledge are not sufficient, the food metabolomics approach can be a valuable tool in food science. New technological developments in analytical chemistry and statistics will increase the value of food metabolomics even further. 

We would like to thank Karin Overkamp, Bianca van der Werf, Jan Jetten, Luco Ravensberg, Miriam Kort, Richard Bas, Bas Muilwijk, Maarten Hekman, Marc Tienstra, Marloes ter Beek, Wilbert Oostrom and Jack Vogels for their contribution to the different aspects of the metabolomics workflow. Toon van de Ven (Monsanto Holland B.V., Antwerp, Belgium) is acknowledged for his contribution to the tomato sensory study. 

It is expected that new developments in analytical chemistry will increase the value of food metabolomics in the future.

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