Previous instalments in this series covered basics of valving and its applications in gas chromatography (GC). This month, we will discuss the use of fluidic switching devices for open-tubular column manipulations, such as heartcutting and comprehensive multidimensional GC×GC.

Open-tubular (capillary) gas chromatography (GC) columns challenge mechanical flow switching valves with requirements for higher speeds and lower volumes. Soon after the initial development of capillary columns, researchers recognized that fluidic flow switching devices were well suited for multidimensional capillary GC applications. David R. Deans (1) was one of the pioneers who published papers and patents on the subject in the late 1960s; the term 

 is synonymous with such applications. But practical multidimensional capillary column implementations depend on precise pressure settings and timing intervals, and the mechanical pressure regulators and control systems found in earlier gas chromatographs were not up to the challenges of widespread application. Broader adoption of capillary column multidimensional applications was delayed until the onset of modern solid-state electronics, microprocessor control, and the silicon microelectromechanical systems (MEMS) devices that constitute the pressure and flow components of electronic pressure control systems (EPC) found in modern GC instruments. 

Fluidic switching depends on a fundamental characteristic of fluid flow through an enclosed channel: A pressure differential creates directional fluid flow through the channel in the direction of lower pressure. Reversing the pressure differential reverses the direction of flow. The flow channel can be a passageway inside a switching device or it can be an entire chromatographic column. These obvious statements are no revelation to chromatographers but keep them in mind as an aid in understanding what follows.  

Rotary valves directly connect to sample loops, columns and detectors for detector selection, column-to-column manipulations, and backflushing, to name a few applications. Earlier instalments in this series (2,3) described how chromatographers configure rotary valves to redirect column output to different detectors or other columns. Implementations with rotary valves are robust and generally simpler to set up and maintain than fluidic switches. However, mechanical actuators have limited switching speeds and are subject to mechanical stress and wear over the long run. Valve rotors have temperature limits to be considered as well. Diaphragm valves, which use pressure to actuate and relieve polymeric seals across the valve ports instead of a rotating valve core, switch faster and have extended lifetimes. Mechanical valves are ideal for packed, micropacked and some wider bore capillary columns, and fluidic switches perform best with narrower capillaries and at higher switching speeds. 

The Deans switch originally employed a series of interconnected tees and crosses with control flows driven by electrical solenoids. Modern implementations use more integrated designs that incorporate the minimum number of external unions. Precisely formed internal passageways are allocated to the necessary cross-connections; these also form internal restrictions where desired. These designs have minimum dead volumes and their construction materials present highly inert surfaces to passing solutes. The control pressures are established and modulated over time with electronic pressure control (EPC).  

Figure 1 illustrates how a fluidic switching scheme would work for a single incoming flow with two exits. This generic fluidic switch diagram shows a narrow tube into which an incoming flow is directed. The incoming flow (orange line) is directed by the control flows S1 and S2 (green lines) to either outlet 1 or outlet 2, depending on the relative pressures of the control inputs. The two vertical dashed lines labelled T1 and T2 illustrate where the device would be divided if three discrete tee fittings were employed such as in earlier implementations. 

In Figure 1(a), the control pressure applied at S1 is higher than that applied at S2. As a result, flow inside the device at the input stream's central point of entrance runs from left to right and the input stream is directed to outlet 2. When the pressures at the control points are reversed, as in Figure 1(b), then the flow proceeds from right to left, and the input stream is directed to outlet 1. 

The input stream, which is most often the carrier gas from a first column, is diluted with a small amount of control gas on its way through the switching device. Careful choice of pressures relative to the columns and restrictions minimizes the amount of dilution and normally does not create any problems with sensitivity or peak resolution — in fact, resolution is improved as the result of better peak shapes. A minimum amount of flow at all times from both of the pressure control inputs prevents back flow and flushes the inner passageways. In contrast, the mechanical switching valve arrangements discussed in earlier instalments (2,3) do not contribute any additional gases to the main carrier streams. 

The associated columns and restrictors are not shown in this diagram. The inlet point and each outlet point of the fluidic switch would connect in an application-dependent fashion to a column or restrictor, whose pressure and flow characteristics determine the passage of carrier and control gases through the switching system in concert with the incoming pressure settings. 

Manufacturers offer a wide variety of fluidic switching configurations that range from a simple backflushing device all the way to comprehensive GC×GC systems. The details of each implementation vary according to the chromatographic requirements and corresponding deployment of each instrument's specific inlets, detectors, and fluidic devices. The rest of this "GC Connections" instalment will address one specific instance of a fluidic switching application: a two-dimensional separation of oxygenates from the polar and nonpolar constituents of gasoline. The presented method and its configuration is just one of many ways in which such separations can be accomplished. 

-butyl ether, methanol, and ethanol are routinely added to gasoline formulations to improve combustion characteristics. The quantification of such additives is difficult in the presence of a complex mixture of hydrocarbons such as gasoline: No single GC column will separate the target compounds from all of the matrix constituents. A combination of a nonpolar dimethylsilicone column and a polar tris-cyanoethoxypropane (TCEP) GC column in a heartcut configuration does the job quite well, and such configurations have been routinely applied to packed and micropacked columns. 

Capillary columns with higher theoretical plate counts can better resolve such mixtures, given an appropriate configuration such as the fluidic switching arrangement diagrammed in Figure 2. The device is similar to that shown in Figure 1, and the flow paths are designated in the same colours. Figure 2 adds a discrete flow restrictor between the two sides of the device that creates a positive trickle purge flow through the nonselected branch to sweep out any residual materials and prevent back flow. 

In Figure 2(a) and 2(b), carrier gas flow proceeds forward from a split–splitless inlet at 45 psig through the polar TCEP column. The end of the TCEP column is maintained at 26 psig by the mid-point pressure control module (PCM) flow applied to either side of the device. This amount of pressure drop creates a flow-rate of 2 mL/min at the initial column temperature of 50 °C. The inlets of both the the DB-1 column and a matched restrictor are set to 26 psig by the midpoint pressure, which creates flowrates of 3 mL/min through each. 

In Figure 2(a), the fluidic switch two-way solenoid is turned off, which applies the midpoint pressure to the junction of the DB-1 column inlet and the switching device. The portion of the midpoint flow that does not pass into the DB-1 column sweeps past the connection point of the TCEP column end and the fluidic device. The flow mixes with carrier flow and eluted peaks from the TCEP column and proceeds into the restrictor and onward to flame ionization detector 1. In this position, all of the peaks from the TCEP column will pass into the restrictor, and only carrier gas from the midpoint supply will enter the DB-1 column. 

In Figure 2(b), the fluidic switch control solenoid has been turned on and the midpoint flow is directed in the opposite direction, to the junction of the fluidic device and the restrictor. This is the heartcut operation. The balance of the midpoint flow sweeps past the exit of the TCEP column and brings carrier gas and eluted peaks into the DB-1 column, until the fluidic control solenoid is turned off again. 

To see how the fluidic switch implementation works for oxygenates in gasoline, we need to first look at how the TCEP column retains nonpolar petroleum compounds and polar oxygenates. Figure 3(a) is a portion of the chromatogram obtained by injecting a solution of MTBE in iso-octane with the fluidic switch remaining in the off position throughout the run: the MTBE is eluted in 5.54 min. Having scouted out the retention time of MTBE, the next step is to activate the fluidic switch a moment before MTBE and deactivate it afterwards. Figure 3(b) is the chromatogram of the TCEP column when the fluidic switch is on from 5.48 to 5.76 min after injection; the MTBE peak has disappeared. Figure 3(c) is the chromatogram from the DB-1 column simultaneous with the TCEP chromatogram of Figure 3(b); the MTBE peak is eluted at 7.18 min or 1.64 min after it entered the DB-1 column. 

The result of applying this heartcut timing to a sample of gasoline with MTBE is illustrated in Figure 4. Figure 4(a) shows the TCEP chromatogram. There is a gap where the heartcut is taken. Figure 4(b) shows the MTBE peak at 7.18 min, followed by a small cluster of hydrocarbon peaks that accompanied the MTBE as it left the TCEP column and entered the DB-1 column during the heartcut. All of these hydrocarbons are eluted after MTBE, which is more volatile.  

Figure 4 also illustrates the ease of performing multiple heartcuts in a single run. An internal standard of 1,2-dimethoxyethane (DME) has been used to improve accuracy and repeatability with the very low injection volume of 0.1 μL. DME is eluted well after MTBE from the TCEP column, at about 13.4 min, and a second heartcut is performed around the DME peak at 13.26–13.55 min. The DME peak then appears in the DB-1 chromatogram at 14.8 min. 

The same configuration can be applied to the other oxygenates by modifying the heartcutting times or by adding new heartcuts. It is also possible to pull out benzene, toluene, ethylbenzene and xylenes (BTEX) with appropriate heartcut times. 

Finally, the fluidic switch can be used in conjunction with a pressure-programmable inlet to backflush the TCEP column after the last peak has been eluted from the DB-1 column. The TCEP column is very polar and in the absence of backflusing has to be held at the upper column temperature level for 50 min before all of the remaining gasoline peaks are eluted. Instead, as shown in Figure 5, the midpoint pressure can be raised while the TCEP column inlet pressure is dropped close to zero. This has the effect of reversing the flow through the TCEP column. Instead of a 50 min hold time, a 20 min time or less suffices to remove the remaining peaks, which exit the TCEP column into the splitter and are vented through the split vent. The backflush operation cuts the total analysis time by 30 min or almost 50%.  

Fluidic switching devices for capillary GC columns have come a long way since their inception more than 40 years ago. With modern electronic instrument controls and precisely machined fluidic components the application of fluidics to capillary column heartcutting and backflushing has become much less difficult to use in routine laboratory operations. Like their better known mechanically valved counterparts for packed and micropacked columns, fluidic switching systems are another powerful tool available to chromatographers. 

 is senior research scientist at BPL Global Ltd, Hillsboro, Oregon, USA and a member of 

's editorial advisory board. Direct correspondence about this column should go to 

 editor, Alasdair Matheson, 4A Bridgegate Pavilion, Chester Business Park, Wrexham Road, Chester, CH4 9QH, UK or e-mail: 

Articles

GC Connections

This month the use of fluidic switching devices for open-tubular column manipulations is discussed.

Valves for Gas Chromatography, Part III: Fluidic Switching Applications

Detection, analysis and characterization of low-abundant metabolites remain an unresolved problem in metabolic studies. In this study, we report a novel approach to address this challenge. The current methodology is derived from the predictive multiple reaction monitoring (pMRM) mode available on triple-quadrupole linear ion trap mass spectrometry (MS) systems. The pMRM mode offers the highest sensitivity among various acquisition modes for studying trace levels of metabolites of the herbicide clomazone in plants. Additionally, this method allows for the identification of positional isomers of metabolites. Unknown metabolites were further identified and validated by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM) and data-dependent tandem mass spectrometry (MS–MS) scans on high-resolution liquid chromatography mass spectrometry (LC–MS). During structural assignment of its metabolites, the unique chlorine isotopic signature of clomazone was used as a naturally occurring label to study MS–MS fragmentations. 

Clomazone (2-[(2-chlorophenyl)methyl]-4,4-dimethyl-3-isoxazolidinone), a carotenoid biosynthesis inhibitor, has been used as an herbicide for the last four years in California to control late watergrass [

 (Stapf) Koss]. Clomazone detoxification is known in other species and soil microorganisms (1,2). Biotransformation of clomazone in rice and early watergrass has also been investigated with radiolabelled clomazone, which resulted in schemas of biotransformations and lists of metabolites (3). However, the radiolabelling technique is not sensitive enough to reveal physiological important metabolites and cannot offer structural information on detected entities.  

Currently, the analysis of low-abundant metabolites represents an unresolved problem in metabolic profiling. Although multiple techniques have been used to conduct metabolic profiling (4,5), Liquid chromatography mass spectrometry. (LC–MS)is considered the most promising platform (6,7) because of its superior sensitivity, selectivity, flexibility and wide-range of metabolite detection. However, LC–MS profiling performed in full scan mode, in spite of delivering overwhelming numbers of metabolites detected, is not sensitive enough to detect and characterize metabolites at trace levels. In contrast, liquid chromatography–tandem mass spectrometry (LC–MS–MS) provides excellent sensitivity in multiple reaction monitoring (MRM) mode, but suffers from a lack of structural information and metabolite coverage. 

Recently, a hybrid MS system combining a triple-quadrupole (QQQ) scanning functionality with sensitive linear ion trap (LIT) scans was made commercially available (see Experimental). Working in LIT mode, the system provides improved performance and enhanced sensitivity in full scan (EMS) and product ion scan (EPI) modes. Additionally, the instrument can operate under all of the triple-quadrupole scanning modes including MRM, precursor and constant neutral loss scanning. Therefore, accurate quantification and additional structural information can be obtained simultaneously in a single run by combing MRM and EPI scanning via the built-in information-dependent acquisition (IDA) functionality.  

MRM-IDA-EPI methods have recently been applied in drug discovery (9–13). In addition to previously described methods, predictive multiple reaction monitoring (pMRM) technology using new predicting algorithms has been used in the present study (14–17). The pMRM algorithms generate theoretical metabolite MRMs based on the latest updated database sets of well-known Phase I and Phase II biotransformations. More than 80 biotransformations of exogenous metabolites were described (15). Interestingly, the new pMRM approach can identify positional isomers that are helpful in identification of positional changes in metabolism. For example, clomazone has a molecular ion [M+H]

 ??125, representing the phenyl ring substructure. Di-oxidation adds two oxygen atoms on the parent molecule. After the collision of the parent ion in Q2, the fingerprint fragment may be 

 = 125, corresponding to di-OH-clomazone isomers with two, one and zero oxygen atoms on the phenyl ring substructure of the clomazone.  

Detection and characterization of metabolites are not straightforward processes. In many cases, LC–MS–MS methodology is insufficient to fully describe the molecule of interest. The MS system operates at unit mass resolution, which is not sufficient to deduce unique elemental composition of unknown metabolites. Accurate mass measurements and MS–MS fragmentation patterns may help with structural assignments. Accurate mass measurement using high-resolution MS is necessary for validating newly identified secondary metabolites. High mass accuracy and high resolving power can be obtained [for example, less than 5 ppm; maximum resolving power 100?000 at 

 = 400, full width at half maximum (FWHM)]; however, the data acquired with highresolution MS are insufficient for assigning unique elemental compositions without supporting information on isotope ratios (18,19).  

The isotopic abundance pattern serves as a powerful additional constraint for identification of candidates with very similar elemental composition. Previous studies demonstrated that interpretation of isotopic abundance patterns removes more than 95% of false candidate formulas for molecules smaller than 500 Da (18,19). It was concluded that instruments with 3 ppm mass accuracy and 2% relative error for isotopic abundance pattern outperformed those with less than 1 ppm accuracy that do not include isotope information in the calculation of molecular formulas. Self calibrated lineshape isotope profile search (sCLIPS) algorithms corrected the instrument line-shape and enabled exact isotope modelling when comparing the MS response of an unknown ion against theoretically calculated responses for all possible candidate formulas for data acquired in continuous mode. As long as the isotopic pattern can be measured accurately, sCLIPS algorithms can be used to predict elemental composition of any molecule. Actually, our recent experimental data supported the use of exact isotope modelling as a key filter for the unique elemental formula assignment (20).  

In the present study, we developed a workflow that takes advantage of sensitive MS hybrid hardware and feature-rich software, successfully identifies several lowabundant secondary metabolites, and constructs metabolic biotransformation pathways of the herbicide clomazone in 

LC–MS-grade acetonitrile was purchased from Burdick and Jackson (VWR International, West Chester, Pennsylvania, USA); the purity of each lot was investigated by LC–MS infusion. Extrapure formic acid was purchased from Fluka (Sigma-Adrich, St. Louis, Missouri, USA). The herbicide clomazone and 5-hydroxy clomazone were obtained from FMC (Princeton, New Jersey, USA). Pure water was supplied by a Milli-Q gradient A10 unit (Millipore Corporation, Billerica, Massachusetts, USA) with a residual total organic carbon content of < 5 ppb. Fresh aqueous buffers for LC–electrospray ionization (ESI)-MS were prepared on the working day. Also, on each working day, a clomazone stock solution (0.1 mg/mL) for tuning was prepared freshly in a solvent system with identical composition to the initial LC mobile phase. A 0.2 mg/mL reserpine stock solution was made in methanol.  

 plants hydroponically treated at the two- to three-leaf stage with 50 μM clomazone for 96 h were mixed with 0.5 mL of 4:3:1 (v/v/v) water–acetonitrile–isopropanol, and the mixture was placed into a 2-mL microcentrifuge tube. Tissues were homogenized for 0.5 min at 30 Hz with a stainless steel ball mill (Retsch MM 301, Retsch GmbH & Co., Germany), the tube holder of which had been prechilled to –80 °C; the homogenate was then sonicated in an ultrasonic bath for 1 min and extracted at 4 °C on an orbital shaker at 750 Hz for 5 min in the dark. The mixture was centrifuged at 16100 rcf for 2 min and the supernatant was transferred to a clean tube. The pellets were resuspended in 0.5 mL of the extraction solvent and mixture, centrifuged at 16100 rcf for 2 min and the supernatant was combined with the initial extracted volume. Extracts were injected (10 μL) in the LC instrument.  

Reversed-phase (RP) LC–ESIMS analysis was conducted using an Acquity UPLC system composed of a binary solvent manager, a sample manager, a column manager and a TUV detector (Waters Corporation, Milford, Massachusetts, USA). The system was operated in high performance liquid chromatography (HPLC) mode. Because clomazone has a phenyl group in its structure, a 150 mm × 3 mm, 3-μm 

 Luna phenyl-hexyl column (Phenomenex, Torrance, California, USA) was chosen to separate clomazone and its metabolites assuming that π-π interactions may provide efficient selectivity. Mobile phases used were as follows: A, 0.1% formic acid in water; B, 0.1% formic acid in acetonitrile. After a 2-min isocratic run at 3% B, a gradient to 60% B was concluded at 6 min, and then ramped to 95% B for up to 15 min. Column wash with 100% B was followed by equilibration with 3% B starting at 21 min and maintained until 25 min. All injection volumes were 10 μL, the column temperature was 50 °C, and the flow-rate was 0.3 mL/min. The weak-wash solvent was 1:1 (v/v) water–methanol and the strong-wash solvent was 3:1 (v/v) acetonitrile–isopropanol. The entire effluent from the LC column was directed into the ESI source of an API 4000 QTrap hybrid triple-quadrupole linear ion trap mass spectrometer (Applied Biosystems/MDS Sciex, Foster City, California, USA) equipped with a TurboIonSpray source (a heated electrospray source with an orthogonal source of heated gas to help desolvate the spray). Ion-source parameters were set manually. The scales of the horizontal and vertical axes of the ion source were set at 6. Final TurboIonSource parameters were as follows: curtain gas (CUR), 20 psi; collision gas (CAD), high; ionSpray voltage (IS), 5.2 kV; temperature (TEM), 300 °C; ion source gas 1 (GS1), 50 psi; ion source gas 2 (GS2), 50 psi; and interface heater (IHE), on. 

Using clomazone as the reference, compound-specific parameters were automatically optimized using LightSight software (V2.0, Applied Biosystems/MDS Sciex). The automatic method creation tool in the software was used to generate the following data: positive mode: predictive multiple reaction monitoring (pMRM-IDA), EMS full scan, precursor scan of 125 (the fingerprint fragment of clomazone), precursor scan of 141 (hydroxyl group added on the fingerprint fragment of clomazone), neutral loss of 115 scan (the molecular ion of clomazone is 240, minus the fingerprint fragment of 125), neutral loss scan of 129 for unknown glutathione conjugates; and negative mode: neutral loss scan of 176 for unknown O-glucuronides, precursor scan of 272 for unknown glutathione conjugates. 

The pMRM-IDA method was composed of one positive MRM scan, one IDA criteria and one enhanced product ion (EPI) scan. The known and described biotransformations (15) were used to generate an MRM list. Table 1 shows a truncated list of relevant transitions. The MRM scan had a list of 122 transitions calculated from the predictive algorithm embedded in LightSight 2.0 (Table 1). Parameters for the MRM scan were as follows: declustering potential (DP), 61 V; entrance potential (EP), 10 V; collision energy (CE), 29 V; and collision cell exit potential (CXP), 20 V. Q1 was set as unit resolution and Q3 as low resolution. Dwell time of each MRM channel was 5 ms and pause time was 2.5 ms. IDA criteria were set as the most intense ion exceeding 500 counts triggering an EPI scan to confirm charge state and isotope pattern selection. Parameters for EPI were as follows: scan mode, profile; scan rate, 4000 amu/s; LIT fill time, 5 ms; dynamic fill time, on; DP, 40 V; CES, 25 V; CE, 60 V; CXP, 20 V. Q1 was set as unit resolution. 

Table 1: The predictive MRM (pMRM) transitions list. 

The HPLC method was the same as described earlier. The entire effluent from the HPLC column was directed into the ESI source of an LTQ-Orbitrap MS system (Thermo Fisher Scientific, San Jose, California, USA) operated under Xcalibur software (V2.07, Thermo Fisher Scientific) or an LTQ-FT Ultra hybrid linear ion trap 7.0 T Fourier-transform ion cyclotron resonance (FT-ICR) system (Thermo Fisher Scientific) operated under Xcalibur software (V2.07, Thermo Fisher Scientific). It was realized by our group that the Orbitrap system was 10–100 times more sensitive than the FT-ICR system, but the latter system could achieve a resolution of 1000000 at 

 = 400 and had better mass accuracy (routinely reached to the range of 0.1–2 ppm). Therefore, the Orbitrap system was used to identify low abundant unknown metabolites, whereas the FT-ICR system was used when maximum mass accuracy and resolution were needed. Both systems used identical source and scanning parameters. The ion source voltage was 5 kV. The nitrogen sheath and auxiliary gas flow were 60 and 20 units, respectively. Nitrogen was produced by a nitrogen generator system (Peak Scientific, Billerica, Massachusetts, USA). The ion transfer capillary temperature was 300 °C. Typical ion gauge pressure was 0.90 × 10

. Briefly, the mass spectrometer was operated in the data dependent mode to automatically switch between MS and MS–MS acquisition. Survey full scan MS spectra (from 

 = 200–600) were acquired with the resolution 

 = 50000 (FWHM) in the Orbitrap system and 

 = 100000 (FWHM) in the FT-ICR system at 

 = 400 (after accumulation to a "target value" of 1000000 in the linear ion trap using the automatic gain control (AGC). The most intense ions were isolated and fragmented in the linear ion trap using collisionally induced dissociation (CID) at a target value of 100000. All scan events were acquired with one microscan. Full-scan spectra were acquired with a 200 ms maximum ionization time. Parameters applied in MS–MS scan events were an isolation width of 2 Da, an activation time of 30 ms, a normalized collision energy of 40% and an activation Q of 0.250. Dynamic exclusion was used with the following parameters: repeat count, 2; repeat duration, 15 s; exclusion duration, 45 s. 

Structure Elucidation Based on Mass Spectral Data 

Mass spectra of the feature peaks were spectrally corrected using MassWorks software (version 2, Cerno Bioscience, Danbury, Connecticut, USA) to achieve high mass and spectral accuracy after data acquisition. MassWorks sCLIPS algorithms corrected the instrument's line-shape and enabled exact isotope modelling when comparing the MS response of an unknown ion against theoretically calculated responses for all possible candidate formulas. The use of exact isotope modelling with MassWorks was a key feature to the unique elemental formula identification. The unique elemental formula was searched against the CAS database (American Chemical Society, Washington, D.C., USA) using the strategy of "Explore Substances – Chemical Structure." The chemical structures that corresponded to the elemental formula were saved and imported to Mass Frontier 5.0 (HighChem Ltd, Bratislava, Slovakia) for the MS–MS fragmentation modelling analysis. The Mass Frontier "Fragments and Mechanisms" module is an expert system providing information about basic fragmentation and rearrangement processes based on literature, starting from a user-supplied chemical structure. The theoretical fragments generated by Mass Frontier were compared to those acquired from LC–MS providing assistance with elucidation of the structures of feature peaks. 

Fragmentation Mechanism of Clomazone in ESI Positive Mode: 

Clomazone has one chlorine atom attached to its phenyl ring and shows the unique chlorine signature in its mass spectrum (Figure 1) that can be used to track clomazone metabolites. During an LC–MS experiment using an MS system at unit mass resolution, we demonstrated that in-source fragmentation of clomazone ([M+H]

 = 125, which retained the chlorine signature, suggesting the cleavage of the substructure of phenyl ring of clomazone (Figures 1 and 2). In addition, it was previously shown in well-established environmental LC–MS–MS studies on water contamination of clomazone that 

 = 240/125 is a typical MRM transition (21,22). Extracted ion chromatograms illustrated that clomazone M0 

 = 125 had the same retention time, confirming the origin of the fragment 

 = 240 (Figure 2). The theoretical fragmentation mechanism simulated in Mass Frontier confirmed the above mentioned results. In addition, the theoretical fragmentation mechanism suggested a possibility to produce fragment 

 = 128 without the chlorine atom in this fragment's structure (Figure 1). However, in the clomazone molecule, the C-N bond was easier to break down than the C-C bond, so that 

 = 240/128 was a minor fragmentation pathway. This minor fragmentation pathway explained the intensity elevation in 

 = 128 in the lower left panel of Figure 2. Finally, the fragment of 

 = 125 was used as the fingerprint of clomazone and its secondary metabolites during automatic method creation. 

The following methods were used for data acquisition: positive mode: predictive multiple reaction monitoring (pMRM-IDA), EMS full scan, precursor scan of 125 (the chlorobenzyl ring of the clomazone molecule), precursor scan of 141 (the hydroxyl chlorobenzyl ring, specifically for M4 searching), neutral loss of 115 scan (the alkyl chain of the clomazone molecule), neutral loss scan of 129 for unknown glutathione conjugates; and negative mode: neutral loss scan of 176 for unknown O-glucuronides, precursor scan of 272 for unknown glutathione conjugates. Among all of the above methods, pMRM-IDA-EPI methods detected the greatest number of clomazone-related secondary metabolites, suggesting the highest sensitivity is achieved with the pMRM technique (21,22). With prior knowledge of the parent compound and its fragmentation pathway, the theoretical MRM transitions for metabolites were determined, thus generating a pMRM-IDA method. A complete set of MRM transitions was generated for metabolites that are the result of modifications (Table 1). Two critical components in generating a pMRM method were the algorithm that generated the comprehensive MRM transitions and the new comprehensive biotransformation sets according to the possible clomazone biotransformation known from the literature. These new biotransformation sets had more than 100 metabolic pathways, thus enabling better prediction of MRM transitions of potential metabolites. 

Metabolite Detection and Characterization 

To better understand complex and overlapping metabolic pathways, high-throughput 

 metabolite identification was applied. Although the pMRM approach was good at finding compounds at trace levels because of the high sensitivity of the hybrid triple-quadrupole linear ion trap system in MRM mode, the unit resolution and low mass accuracy of the system warranted further metabolite identification and validation using high-resolution high-accuracy MS. For further confirmation, the MS–MS fragmentation pattern was analysed based on information about basic fragmentation and rearrangement processes in the literature. 

Table 2: Detected secondary metabolites of clomazone. 

Several secondary metabolites were detected, analysed and characterized using the current methodology (Table 2). For example, metabolite M3, the oxidation product of clomazone, 

 = 256, was found using pMRM transition of 256/125. High-resolution MS spectral data showed that M3 had an accurate mass of 256.073 with the single-chlorine-atom isotopic signature. Isotopic modelling in MassWorks suggested the unique elemental formulae of C

Cl. Fragmentation pattern modelling in Mass Frontier indicated that the fragmentation mechanism of M3 was the same as M4 MS–MS spectra, but the fingerprint fragments were different because of the different position of the hydroxyl group in the parent molecule (Figure 4).  

Using the hybrid triple-quadrupole linear ion trap system, enhanced product ion scans (EPI) of M3 in clomazone-treated plant samples and pure standard solutions showed a high degree of similarity, suggesting the existence of M3 in plant samples. In addition, time course data on M3 formation proposed another physiological line of evidence (Figure 5). Therefore, it was concluded that the unknown secondary metabolite M3 was 5-hydroxy-clomazone. Compared with M3, metabolite M4 might have a different location of the hydroxyl group attached to the benzene ring, supported by the facts that M3 and M4 had different retention times and different fingerprint fragments (Figure 5). Metabolite M5, having a transition of 

 = 272/125, with an accurate monoisotopomer mass of 272.06843, had a clear single-chlorine signature (Figure 3). Elemental composition assignment of M5 was offered by best-fit formulas at delta of 2 ppm. However, there were a few isomers with fingerprint fragment 

 = 125 possessing chlorine isotopic patterns. Finally, the most possible candidate (with the highest probability score) was suggested (Table 2). Keep in mind though, M3, M4 and M5 could be found at different retention times due to different positional isomers (Figure 5). 

Other interesting metabolites, M8 and M9, were found using pMRM transitions of 268/125 and 320/125. Unfortunately, these metabolites could not be detected on hybrid triplequadrupole linear ion trap system with any other modes of acquisition. Attempts to detect M8 and M9 with the Orbitrap or FT-ICR MS systems also failed. On the other hand, this result actually demonstrated the higher selectivity of pMRM mode compared to that of the full-scan profiling mode. Regardless, we suggested putative chemical structures to be validated (Table 2). In the future, after HPLC fractionation and concentration, offline nuclear magnetic resonance (NMR) and accurate mass measurement will be applied to confirm the suggested structures of M5, M8 and M9. However, it is known that isolation of unstable metabolites is a challenging process. Therefore, there is no guarantee that the above mentioned plan will be successful in our future endeavors. Other metabolites, M1 (

 = 272/125) were of less physiological importance and were not pursued as diligently as those metabolites mentioned above. 

In summary, we demonstrated a novel workflow that was successfully applied in complex time-course experiments of the weed watergrass treated with different levels of clomazone, revealing more metabolites as well as assigning metabolic pathways (21,22). 

In biological bodies, exogenous compounds go through all kinds of biotransformations; however, most of these biotransformations could not be detected by previous technology (15). In the present study, we demonstrated for the first time that low-abundant clomazone metabolites that were buried under dominating abundant metabolites could be successfully detected, analysed and characterized. Chemical structures of these detected metabolites were proposed, based on MS and MS–MS spectra. The described methodology was applied in complex experiments in which plants were treated with clomazone at different levels in a time-course manner, and was effective in elucidating biotransformations of clomazone. We believe that the current methodology can be extended to study endogenous metabolic pathways. This approach opens an exciting opportunity for metabolic pathways elucidation, thus providing a powerful tool for small-molecule biomarker screening and drug discovery.  

This work was financially supported by the UC Davis Genome Center Pilot Project and partially funded by grants from the California Rice Research Board. Our appreciation is given to Professor Scott D. Stanley from California Animal Health & Food Safety Laboratory for access to the LTQ-Orbitrap mass spectrometer.  

 is with the California State Department of Toxic Substances Control in Berkeley, California. 

 are with the Department of Plant Sciences at the University of California in Davis, California. 

 is with the UC Davis Genome Center at the University of California in Davis, California.   

(2) S.Y. Liu, M. Shocken and J.P.N. Rosazza, 

Metabolic Analysis. Methods in Molecular Biology: Micro and Nano Technologies in Bioanalysis

, J.W. Lee and R.S. Foote, Eds. (The Humana Press Inc., Totowa, New Jersey, 2009) 544, pp. 343–353.  

(7) V.V. Tolstikov, O. Fiehn and N. Tanaka, in 

Application of Liquid Chromatography-Mass Spectrometry Analysis in Metabolomics, in: Methods in Molecular Biology: Metabolomics: Methods and Protocols

 (The Humana Press Inc., Totowa, New Jerseu, 2007) 358, 141–158.  

Describes a new method to address the challenges of detection, analysis and characterization of low-abundant metabolites in metabolomic studies.

Trace Metabolic Profiling and Pathway Analysis of Clomazone Using LC-MS-MS and High-Resolution MS

36th International Symposium On Capillary Chromatography and the 9th GC×GC Symposium

International Symposium on Capillary Chromatography (ISCC)

 has established its reputation as a forum for microcolumn separation techniques. Since the first meeting in Hindelang in 1975, the most important developments in capillary gas chromatography, microcolumn liquid chromatography and electromigration techniques have been presented in this symposium series. 

The format and the atmosphere of the 36th meeting will be similar to the previous successful meetings, held in Hindeleng, Riva del Garda, Monterey, Baltimore, Gifu, Kobe, Wintergreen, Las Vegas, Dalia, Albuquerque, Portland and San Diego. Luigi Mondello from the University of Messina is the chairman of this year's event and Pat Sandra will be honorary chairman this year.  

, Italy, will accomodate the 36th meeting from 

. The four-day event will feature recent findings from leading academic and industrial experts in the form of lectures and posters. The conference offers sessions on capillary gas chromatography, microcolumn liquid chromatography, electromigration methods and microfabricated analytical systems, which are expected to cover lab-on-a-chip, column technology, coupled and multidimensional techniques, comprehensive techniques, hyphenated techniques, sampling and sample preparation, trace analysis and automation. 

Application sessions include environmental applications, energy/petrochemcal/industrial applications, biomedical/pharmaceutical applications and the analysis of natural products, food, flavours and fragrances. Workshop-seminars of instrument manufacturers and an extensive exhibition of instrumenation, accessories and supplies will run in parallel to the scientific programme. 

At the meeting, the 2012 Marcel Golay Award will be presented in recogntion of outstanding contributions in the field of separation science. Outstanding research work presented as oral or poster contributions by youngsters will be awarded with the Leslie Ettre Award for research on capillary gas chromatography applied to environmental or food analyses. 

To encourage scientific exchange and friendship building, the scientific programme will be spiced with the well-known "Riva Social Programme", which consists of a welcome reception, cocktail party, classical concert, cheese and wine evening, disco night and farewell cocktail. 

Considering the interest in comprehensive techniques, the 9th GC×GC Symposium will be organized during the same period to allow scientists to attend both meetings. The 9th GC×GC symposium will start on 27 May 2012 with a course presented by experts in the field covering the fundamental aspects of comprehensive techniques and a plenary session on 28 May 2012. 

For both meetings, abstracts for consideration as lecture or poster presentations can be submitted on-line at 

 All abstracts will be reviewed on the basis of scientific merit, novelty and practical application. Presenters at the meeting may also have their work published in a special volume of the 

Authors intending to submit papers for the symposium will be required to adhere to the following deadlines: A 300-word abstract must be received no later than 

. For abstract submission see the website: 

 Notification of acceptance will be mailed to the authors by 

Clarion Congress Hotel Prague, Prague, Czech Republic

 Institute of Chemical Technology, Czech Republic and RIKILT — Institute of Food Safety, The Netherlands 

CE Method Development and Validation Course

The Convent Hotel, Amsterdam, The Netherlands 

Oud-Sint-Jan' Conference Center, Bruges, Belgium

 Royal Flemish Chemical Society and Royal Society of Chemstry 

Ornage County Convention Centre, Orlando, Florida, USA 

Send any event news to Alasdair Matheson at  

Departments

Event News

The use of aerosol defence pepper sprays for self-defence and as assault weapons has increased.The residue of these sprays is often left behind as physical evidence on a victim’s clothing or personal belongings. A rapid and accurate method has been developed to qualitatively determine capsaicin and its most important analogues – dihydrocapsaicin and nordihydrocapsaicin – on clothing. The methanol solvent extraction (SE) and solid-phase microextraction (SPME), recovery from fabrics, and gas chromatography mass spectrometry (GC–MS) identification of residue from aerosol defence pepper sprays are described. 

Aerosol defence pepper sprays are compounds that cause temporary incapacitation by producing sensory irritation. This results in extreme discomfort (burning, sneezing, tearing, secretions, gagging, vomiting) and pain is associated with the areas affected (nose, eyes and respiratory tract). The effects begin within 3–15 s and can last 15–90 min. 

The three basic components of aerosol defence pepper sprays are: the active ingredient — the irritant oleoresin capsicum (OC) at a concentration range of 0.5–20% — with a carrier and propellant. The carrier acts as a vehicle in which the irritant is suspended or dissolved. Propellants are used to expel the irritant from the canister. The sprays can be expelled with fine stream or misty cone patterns (ranges 2–6 m). The stream can be a liquid, a gel or a foam. Commonly used carriers and propellants include nitrogen, carbon dioxide, propane, butane, ethanol, propylene glycol, secondary butanol, isopropanol, tetrafluoroethane, kerosene, d-limonene, acetone, methylene chloride or 1,1,1-trichloroethane. Sometimes UV/vis dyes can be present or blends with chloroacetophenone (CN) or chlorobenzalmalononitrile (CS) are possible. Figure 1 illustrates a schematic representation of an aerosol defence pepper spray canister.  

Figure 1: Schematic diagram of an aerosol defence pepper spray canister. 

OC is classified as an inflammatory agent and is the primary compound of interest in this paper. Exposure to OC produces inflammation and swelling of the mucous membranes associated with the eyes, nose and throat. OC is a reddish-brown oily liquid derived from the plants of the genus 

., which are commonly referred to as hot peppers or chillies.  

OC contains a group of compounds called capsaicinoids (CAPs), which are responsible for the pungency associated with cayenne and other varieties of peppers. CAPs are the pharmacologically active and pain-producing components of the hot pepper. There are five naturally occurring CAPs: capsaicin, nordihydrocapsaicin, dihydrocapsaicin, homocapsaicin and homodihydrocapsaicin (the active ingredients responsible for the irritation properties are capsaicin and dihydrocapsaicin; they make up to 80–90% of the total). The exact chemical composition of OC varies with the type of pepper, its age, and parts of the plant from which the extract is obtained. Capsaicin is also widely used in food preparation as a herpes treatment and a deterrent to dog fights and swine cannibalism.  

Capaiscin can also be synthesized in the laboratory. OC contains over 100 distinct volatile components in addition to capsaicin.  

The burning capacity is directly related to the concentration of capsaicin and it is evaluated by means of the organoleptic Scoville heat units (SHU) (Table 1). The percentage of capsaicin can, therefore, be converted to SHU with the following equation: 

For example, a pepper spray that indicates "10% OC @ SHU 2.000.000" is calculated as [200,0000 × 0.10 ]  = 200,000 SHU are released at the nozzle when spraying. 

The basic chemical structure of capsaicin and its analogues is a vanilloid ring connected to an amino alkyl chain containing 9–11 carbon atoms. Figure 2 shows the five major CAPs. 

Pure capsaicin is insoluble in water (0.090 g at 37 °C) but soluble in oils and some solvents (alcohols, ethers, ketones, chloroform). Capsaicin forms crystals, has a melting point of 65 °C and a boiling point at 210–220 °C. It has an extrapolated vapour pressure of 1.5 × 10

  mm Hg at 65 °C and a vapour density (relative to air) of 10.5. It exerts its effect on the sensory nerves by interacting with the vanilloid receptor, promoting the release of substance P, as well as other cytokines. The release of these cytokines from the peripheral sensory neurons causes a sensation of intense burning and pain. 

News reports often show criminal actions or law enforcement abuse with aerosol defence pepper sprays. In Belgium aerosol defence pepper sprays are classified as a prohibited weapon, therefore, possession by non-police members is illegal. The extraction and identification of these compounds has become increasingly important in forensic casework. Inadequate extraction may lead to a false negative result. Commonly used extractions methods are solvent extraction (SE) and solid-phase microextraction (SPME). Identification can be performed by gas chromatography–mass spectrometry (GC–MS) or liquid chromatography–mass spectrometry (LC–MS) (1–2).  

Aerosol defence pepper spray canisters were obtained at various police departments in Brussels and Ghent. Table 2 summarizes the analytical conditions we developed based on references (3) and (6–7).  

The methanol solvent was of analytical grade and purchased from VWR (Heverlee, Belgium). Nitrogen for evaporating the extraction liquid was purchased from Messer (Zwijndrecht, Belgium). 

Glass Pasteur pipettes, GC syringes, GC septa, glass jars with metal screw caps and glass vials with screw caps were purchased from VWR (Heverlee, Belgium). Polyethylene bags, nylon-11 bags and solvent free aluminum tape were purchased from Polis-Service / BVDA (Lille, Belgium) and RajaPAC (Tongeren, Belgium). The SPME holder and fibre (50/30 μm divinylbenzene/carboxen/polydimethylsiloxane) were purchased from Supelco (Bornem, Belgium). 

Analysis was performed using a Thermo GCTraceMS+ (Thermo Fisher Scientific, Waltham, Massachusetts, USA). Separation of the analytes was achieved using a Rtx-5MS column, 30 m × 0.32 mm i.d., 1 μm column (Restek, Bellefonte, Pennsylvania, USA). The injection volumes on the gas chromatograph were 1 μL performed in splitless mode (1 min at 260 °C) and 1 mL performed in split mode (30 mL/min at 260 °C). The SPME conditions were 15 min adsorption at 100 °C and 2 min desorption in split mode (30mL/min at 260 °C). The helium carrier gas was set at a constant pressure of 100 kPa. The chromatographic conditions included an initial oven temperature of 60 °C held for 2 min and a temperature ramp of 30 °C/min to 300 °C held for 15 min. The mass spectrometer was operated in the full scan EI+ (70eV) mode from 25–400 amu. The source and transfer line temperatures were set to 200 °C and 250 °C respectively. The detector multiplier was set to 500 V.  

Table 3 represents the mass ions of the CAPs, ethanol and propyleneglycol. (5).  

Table 3: Chromatograms of three major defence sprays. 

 The aerosol defence pepper spray chosen from the different standards was First Defense MK-3 spray (First Defense, Wyoming, USA) (Figure 3) from a police department in Ghent-Belgium. Pure pepper spray liquid (10 μL) was spiked onto a white cotton swab (15 × 15 cm

). This sample was put in a polyethylene bag and a nylon-11 bag. Another cotton swab without spiking was used as blank sample and preserved in the same conditions as above. A solution of 5% pepper spray liquid in methanol was prepared in a glass vial with screw cap. 

The clothing [100%] was confiscated in a polyethylene bag by the police force at the end of 2008. Two years later the whole packaging was placed in a nylon-11 bag in our laboratory. 

Figure 3: Photograph of pepper spray canister investigated.

: GC septa were put on the several bags (clothing 100%; spiked cotton swab and blank cotton swab) with solvent-free aluminum tape and heated at 100 °C for 1 h in an oven. A 1 mL head space sample and an SPME adsorption sample (15 min) was taken and injected with a split of 30 mL/min at 260 °C (SPME for 2 min) (2). 

Methanol solvent extraction conditions (1)

: Parts of the clothing were cut (back part [Control] as a blank, neck part front [N], left pocket front [L] and right pocket front [R]) with a Stanley knife and extracted with 50 mL methanol (a minimal volume to wet the fabrics [Figure 4]).   

The extracts were manually shaken every 15 min for 24 h. The methanol was collected with the aid of a glass Pasteur pipette, in a glass jar. The liquids were evaporated with nitrogen to volumes of 100 μL respectively. These volumes were transferred to glass vials with screw caps and inserts. 

Figure 4: Samples were collected from different parts of the jacket using methanol. 

The case investigated involved a car chase in November 2008; driver X claimed to have been sprayed by driver Y with an aerosol defence pepper spray. The result of this action is driver X assaults driver Y. When the police force arrives, driver Y claims to have been sprayed. The police documented the symptoms of the assault on driver X. Driver Y only showed symptoms of beating. Only the protective cap of an aerosol defence pepper spray canister was found at the location. On interrogation the son of driver Y states that his father has an aerosol defence pepper spray and that because driver Y wasn't with his usual car, driver Y must have had the spray in his clothing. Therefore, the clothing of driver Y was confiscated from January 2009 for further analysis.  

The following assumptions were made to clarify who was the victim or the attacker:   

1. By analyzing the neck part [N] the possibility of a person being sprayed on could be determined. 

2. By analyzing the right pocket part the possibility of a person having the spray in their possession could be determined.  

This also assumes both parties were righthanded. 

Our analytical method is capable of identifying the three major aerosol defence sprays (CN, CS and OC (the active components of CAPs); CN in head space mode, and CS and OC in solvent mode (Figure 5). 

Figure 5:  Chromatogram of three major aerosol defence sprays. 

 By using mass specific ions 31 and 45, retention time comparison with pure liquids and NIST database identification, traces of ethanol and propylene glycol were detected on the clothing (the whole garment in a bag combination) (Figure 6). This indicates that the garment could have been in contact with a pepper spray.

Figure 6: Traces of ethanol and propylenglycol. 

The most common solvents used for extraction of the CAPs are methanol, acetone, methylene chloride and ethyl acetate. Table 3 summarizes the analytical results. By using mass specific ions: 137 (the most intense ion); 152, 195, 293 (the molecular ions of nordihydrocapsaicin); 305 (the molecular ion of capsaicin); and 307 (the molecular ion of dihydrocapsaicin), retention time comparison with pure liquid extracts and NIST database identification, traces of CAPs were only detected on the right pocket part of the clothing (Figure 7) (1) (4) (5). This indicates that the garment belonged to the attacker who was right-handed and that he was never sprayed on. There were no traces on the neck part,  

Figure 7: Traces obtained from pocket part of jacket. 

CAPS can persist on clothing for a relatively long period of time if properly packaged and stored. Glass jars and polymer bags provide the best packaging for the collection and preservation of this type of evidence. Folding of the clothing and minimal air in the packaging are also important factors. 

Describes an analysis that was performed in a forensic science laboratory to assess who was the victim and who was the attacker in a legal dispute.

Analyzing Aerosol Defence Pepper Spray Residues on Clothing and Analysis by GC-MS

Too much variability in your liquid chromatography method? Here's a look at how to track down the source of the problem.

Sometimes, our liquid chromatography (LC) methods do not perform with the level of precision that we need or expect. When this happens, one or more factors may be contributing to the observed lack of precision. In this month's instalment, we'll see how method precision is determined and some of the factors that influence it. Armed with this information, we should be able to track down the source of problems and, hopefully, correct it.  

Before we start, a word about nomenclature. We usually talk about the 

 of a method or a measurement, but what we actually measure is the 

 — lack of precision — or how much error there is in the measurement. So, when we say that a method has 2% precision, we really mean it has 2% imprecision. And what does this mean? Most commonly, imprecision is stated as ±1 standard deviation, with the assumption that the data are normally distributed (unless otherwise stated). From a statistical standpoint, this means that the above example would tell us that the data are within ±2% of the mean approximately 68% of the time, or within ±4% of the mean 95% of the time. Thus, 27% of the time (95%–68%) we would expect the data to be 2% to 4% from the mean. It is important to realize that a stated value for imprecision does not guarantee that the data will fall within any specific limits, but instead gives information about how tightly the data are expected to cluster about the mean value. This is why multiple replicates (typically 6–10) are needed to determine imprecision. Imprecision is most commonly stated in units (millilitres, minutes, milligrams, and so forth) or as percentages. Usually, percentages are stated as the 

, which are equivalent terms: standard deviation divided by the mean. These can be expressed as decimal values or as percentages. I'll use CV in this discussion. 

Usually, there are several sources of imprecision, or error, in a method. For example, there are potential errors in sampling, sample preparation, injection, chromatography and data analysis. Generally, we assume that these are normally (randomly) distributed, so we can combine them to get the overall imprecision by taking the square root of the sum of squares of each individual source: 

 Here, the subscripts refer to the variability of each individual contribution. To have a minor role (< 15%) in the overall imprecision, CV any individual contribution should be no more than 0.5 × CV

. This gives us a practical guideline of what to watch for when we are trying to isolate a source of error or reduce the overall variability of a method.  

To illustrate this, I'll use an example that we use in our LC classes. Let's assume that we need the overall method imprecision to be no more than ±2%, which is to say, CV

,  ≤ 2%. To establish the method precision, we weigh, extract, and analyze 6–10 nominally equivalent samples and find that the standard deviation of the peak areas is ±3.2%. For this example, we'll consider all the sources of error to be included in weighing, sample preparation, injection and integration. We can get a good estimate of each of these contributions, either directly or indirectly, with some simple experiments and estimates. An estimate of weighing error could be obtained by weighing an amount of a strongly UV-absorbing compound equivalent to the sample weight, such as 15 mg. Make six replicate weighings and dilute each in an appropriate solvent and volume, for example, 100 mL of water in a 100-mL volumetric flask. Then check the absorbance with a UV spectrophotometer for several replicates of each sample. By using a different analytical technique, a large dilution volume, and multiple samples and replicates, we have eliminated the chromatographic influences and, we hope, minimized other errors. No matter what you do, you won't eliminate all errors, but this is a good try. Injection error can be checked by making multiple injections of the same sample vial and calculating the standard deviation of the peak area. Integration error can be estimated by dividing the signal-to-noise ratio (

 where the signal is the peak height measured from the middle of the baseline noise to the top of the peak and the noise is the peak-to-peak baseline width. Sample pretreatment error is difficult to measure directly, but if we know the other contributions, we can rearrange a form of Equation 1 and calculate it:  

 where the various subscripts represent sample preparation (spl prep), weighing (weigh), injection (inj), and signal-to-noise (S/N). If we measured CV

There are (at least) two ways to go about reducing method error. I'm always one for trying the easy things first, so let's do that here. Consider uncertainty due to weighing, sample pretreatment, injection, and signal-to-noise ratio. Which are easy to reduce? The answer is everything except sample pretreatment, which can be a lot of work to improve. So, let's reduce the other factors and see what happens. Weighing error is easy to reduce.  

Generally, the error in an analytical balance is fairly constant at different weights, so if we can weigh out more sample, the percent error should drop. Similarly, much of autosampler error is constant, whether we inject 1 μL or 100 μL, so again, a larger injection should reduce the percent error. If we weigh out more sample and inject more, it is likely that the peaks in the chromatogram are going to be larger, increasing the signal while having no affect on the noise, so this will reduce the 

 error. If we reduce each of these sources of imprecision to 0.1%, how does this affect the overall method imprecision?   

 Oops, it looks like the easy way out didn't give us much return on our investment! We reduced the imprecision only from 3.2% to 3.0%, hardly enough for our 2% target value. 

What about the hard way? Let's address sample pretreatment. Perhaps we can improve the imprecision by using an internal standard to compensate for losses, modify an extraction or evaporation step, or make some other change in the sample preparation process. If we can reduce the sample pretreatment error from 3.0% to 1.0% and not change any of the other sources of error, how does this change the overall method performance?  

 You can see that by reducing the sample preparation error, we cut the overall method imprecision in half. 

Now we can add a couple more practical rules of thumb to our collection. First, the overall imprecision will never be smaller than the largest individual imprecision. For example, with sample-preparation imprecision of 3.0%, the overall method will never be more precise than 3%, and most likely will be larger. Second, focus on the largest source of imprecision first when trying to improve method performance. It did no good to address the easy factors first. We only found effective results when the largest source of imprecision, sample preparation, was attacked. After you have reduced a factor below the critical level of 0.5 × CV

, you can look for the next largest source of error. 

I recently received an e-mail from a reader who had a problem with method precision in an isocratic, reversed-phase method for a formulated drug product. The formulation contained an additive to help stabilize it for the delivery process, but caused shortened column lifetime if it was not removed before analysis. Evaporative lightscattering detection (ELSD) was used. This scientist measured the method imprecision based on recovery of the active compound from the formulation. The overall method imprecision was ±5%, which was more than desired, so the reader was looking for ways to reduce the uncertainty. 

The need to remove the additive means that sample preparation was involved; the reader didn't specify, but I suspect it was solid-phase extraction (SPE) or liquid–liquid extraction (LLE). ELSD also throws additional uncertainty into the mix when compared to the more common UV detection. The first thing that I would do here is to check the laboratory records for performance and calibration checks, and repeat any that may need to be updated. Specifically, are the analytical balance and pipettes in calibration? Has the LC system undergone a performance qualification check with a UV detector, such as the one described in reference 2? Such a check will test the system under best-case conditions, but it will assure you that the autosampler imprecision is small (for example, < 0.5%) and that there aren't other basic instrument problems. Has a similar performance check been performed on the ELSD detector? What is the normal level of imprecision for the detector? If it is 5%, there isn't much hope for reducing method imprecision, but if it is 2% or less, performance improvements may be possible.  

After I was confident that the basic LC system was operating properly and not a major source of imprecision, I would start under ideal conditions and move to the method conditions in a stepwise fashion to see if I could figure out where the problem originated. A good place to start would be to make up a solution of reference standard of the drug at the normal concentration and make multiple injections with the same volume as normally used, from a single vial of standard. This would give a value for the variability of the injector, chromatography system, detector, and data processing. Because of the non-linear nature of ELSD, it might be a good idea to repeat this experiment over the expected concentration range of real samples. Compare absolute area precision as well as results obtained using a calibration curve to see if the calibration curve might be the problem (such as using the wrong calibration model). I would then repeat the same experiments, but use a formulated product as the sample source. If possible, extract a single large sample, or alternately, extract several replicates and combine the extracts to obtain sufficient volume for multiple injections. How does imprecision change when the formulation is used instead of the reference standard? Does this give you an idea of the problem source? If imprecision increases, it must be due to the additional materials present in the extract, not the sample-preparation process, because injecting a homogeneous extract eliminates sample-preparation variations. 

Next, I would perform replicate extractions of a single homogeneous sample. Depending on the formulation, excess sample may be available to extract multiple aliquots; otherwise it may be possible to combine and homogenize several individual samples to create a larger, homogeneous sample source for multiple extractions. Analysis of these samples will help to identify if sample preparation problems are present. Multiple injections (for example,  n = 3) may help isolate injection-to-injection variability from extraction-to-extraction variability. Is an internal standard being used? Most of the time, when multiple sample-preparation steps are involved (for example, extraction, evaporation, and reconstitution), an internal standard will reduce imprecision. Check the results with and without using the internal standard for correction. I've seen cases where the internal standard made things worse because there were errors in the way the internal standard was added to the sample. If no internal standard is present, you may be able to use a second peak in the sample as a surrogate internal standard. Track the ratio of the area of this peak to the analyte to see if the ratio is more consistent than the area of the analyte alone. If this is the case, an internal standard should be added to the method. 

By breaking down the problem so that experiments progress from ideal to real, you should find a particular step that results in a large increase in imprecision. You can use the techniques discussed in the previous section to help estimate the contribution of various steps to the overall method imprecision. This, then, will help you decide which factors need to be addressed to reduce overall method imprecision. 

Sooner or later we'll all encounter a method that has unacceptably large imprecision. This discussion has looked a little at the theory of how errors are combined to give the overall method imprecision. We then looked at a hypothetical example as a way to illustrate the contribution of errors of different magnitude. The practical example at the end helped to show how to start from the best-case of instrument-performance checks and then work through from a reference standard to a complex sample-preparation process in an effort to isolate different sources of uncertainty in a method. From this discussion, we can list five guidelines for isolating imprecision problems:  

If we assume errors are random, the overall uncertainty of a method is the square root of the sum of squares of the individual contributions to uncertainty (Equation 1).

To have an insignificant (≤ 15%) contribution to the overall error, an individual source of error can be no more than 50% of the desired method imprecision. 

The overall method imprecision will never be smaller than the largest individual contribution to that error. 

As a result of rules 2 and 3, we should first seek to reduce the largest individual source of error, especially if it is > 50% of the target method uncertainty. 

To isolate the source of imprecision, start from a simple, ideal, well-behaved system and sample, then add complexity in a stepwise fashion until the problem source is identified. 

 (1) L.R. Snyder, J.J. Kirkland and J.L.Glajch, 

Practical HPLC Method Development, 2nd Ed. 

(Wiley, Hoboken, New Jersey, 1997) p. 71.  

, is vice president of LC Resources, Walnut Creek, California, USA. He is also a member of 

's editorial advisory board. Direct correspondence about this column should go to "LC Troubleshooting", 

, 4A Bridgegate Pavillion, Chester Business Park, Wrexham Road, Chester CH4 9QH, UK, or e-mail the editor, Alasdair Matheson, at 

LC Troubleshooting

Too much variability in your liquid chromatography method?

LCGC Europe-11-01-2011

Valves for Gas Chromatography, Part III: Fluidic Switching Applications

Trace Metabolic Profiling and Pathway Analysis of Clomazone Using LC-MS-MS and High-Resolution MS

Event News

Analyzing Aerosol Defence Pepper Spray Residues on Clothing and Analysis by GC-MS

Locating Precision Problems