This article discusses the use of narrow-bore column fast gas chromatography (GC) combined with a rapid-scanning quadrupole mass spectrometer (qMS) to analyse flavour and fragrance (F&F) samples. Sample and peak identification was achieved with the support of two MS libraries. The first library contained specific pure spectra, relative to entire F&F volatile fractions. Each F&F spectrum was attained by fast gas chromatography mass spectrometry (GC–MS) analysis and can be considered, in terms of concept, in the same way as a direct ion-source injection of the sample. Once the F&F was correctly identified the second MS library was applied: each peak was assigned on the basis of spectral similarity and linear-retention-index data. The narrow-bore column fast GC–qMS method developed was subjected to validation.  

The use of gas chromatography mass spectrometry (GC–MS) is common in the field of flavour and fragrance (F&F) analysis. GC–MS is a highly valuable tool and can be regarded as a bidimensional technology: the separation dimension delivers sample fractions to the MS, which subjects the fractions to fragmentation [usually through electron ionization (EI)], and then separates the generated ions on a 

The analysis of an unknown F&F sample is a challenging task, requiring basically two identification levels: (1) the sample must be correctly identified at mixture level; (2) the individual sample components must be correctly identified at the molecular level. Considering level 1, one way to identify an unknown flavour or fragrance could be to introduce the sample directly into the MS ion source and directly identify the attained spectrum (if you are an experienced MS analyst) or carry out a library search process (if you have an EI-MS library with F&F spectra). Depending on the degree of volatility, samples can be introduced into the ion source by using introduction systems such as a direct insertion probe, a direct exposure probe or a heated reservoir.

  Once a F&F has been identified through EI-MS, then one must pass to the second identification stage; generally achieved through GC–EI-MS. In contemporary F&F analysis, stationary phases of various polarities are used, while identification is usually carried out by exploiting MS spectra and calculating linear retention indices (LRI).

  In previous research, a novel double-filter MS library has been developed and applied to the analysis of F&F volatiles.

The double filter worked in the following manner: at the end of the analysis the GC–MS software automatically calculated LRI values relative to each peak. After — and during — the MS library search process the same software deleted matches with a reference LRI, with respect to that of the unknown, outside a pre-defined LRI range (e.g., ±5 units). The latter is dependent on the type of GC stationary phase used: apolar phases are characterized by lower LRI variations, compared with polar ones. The LRI values contained in the library were derived from GC–MS analysis carried out on a 30 m × 0.25 mm i.d., 0.25 µm film thickness (5% diphenyl + 95% polydimethylsiloxane) column.  

The present research is focused on four main objectives: (1) the development and validation of a fast GC–EI-MS method, using an apolar narrow-bore 10 m × 0.1 mm i.d. capillary, for the analysis of F&F samples; (2) the evaluation of the effectiveness of the previously reported double-filter MS library, under the fast GC–EI-MS conditions developed. Specific attention was devoted to the definition of an LRI window for fast GC–EI-MS analysis, using a silphenylene polymer (equivalent in polarity to a 5% diphenyl + 95% polydimethylsiloxane phase) column; (3) the development of a GC–EI-MS F&F library, defined as EI-MS F&F, for the identification of unknown samples at mixture level. The library was constructed by subjecting genuine F&F samples to the fast GC–EI-MS method developed and then deriving a single averaged spectrum comprising all the peaks in the retention time range. The F&F spectrum attained can be considered as a sample fingerprint and is altogether comparable, in terms of concept, to direct EI-MS analysis; (4) evaluation of the fast GC–EI-MS method combined with the two MS libraries for the rapid quality control of F&F samples. 

The genuine essential oil samples were kindly provided by Citrus Vita (Pace del Mela, Messina, Italy), Simone Gatto (San Pier Niceto, Messina, Italy) and Dr Pappas (Essential Oil University, New Albany, USA). All samples were stored at +4°C. Prior to analysis, each essential oil was diluted 1:10 (

 alkanes and pure standard compounds listed in Table 1 were supplied by Sigma-Aldrich (Milan, Italy). A C

 solution, at the 100 ppm level, was prepared in 

All GC–MS applications were carried out on a Shimadzu GCMS-QP2010 Plus instrument (Kyoto, Japan), equipped with an AOC-20s auto sampler and an AOC-20i auto injector. 

The column used was an SLB-5ms 10 m × 0.10 mm i.d. × 0.10 µm 

 (silphenylene polymer) and was kindly provided by Supelco (Milan, Italy). The temperature programme used was from 50–250 °C at 10 °C/min; the carrier gas (helium) was delivered at an initial pressure of 343.2 kPa (constant linear velocity mode: 50 cm/s). Injected sample volume: 0.5 µL; split ratio: 400:1.  

The column used was an SLB-5ms 30 m × 0.25 mm i.d. × 0.25 ×m 

, and was kindly provided by Supelco. The temperature programme used was from 50–250 °C at 3 °C/min; the carrier gas (helium) was delivered at an initial pressure of 32.0 kPa (constant linear velocity mode: 30.8 cm/s). Injected sample volume: 1 µL; split ratio: 100:1.  

The sample was analysed in the full scan mode with a scan speed of 3333 amu/sec, a mass range of 40–350 

 and a sampling frequency of 10 spectra/s, under fast GC–MS conditions; a scan speed of 700 amu/sec, a mass range of 40–350 

 and a sampling frequency of 2 spectra/s, were applied under conventional GC–MS conditions; interface and ion source temperatures were 250 °C and 200 °C, respectively. 

Electron ionization (EI); detector voltage: 1.0 kV. Data were collected by the GCMSsolution software (Shimadzu). The MS libraries used for spectral matching were the EI-MS F&F and the FFSNC 1.4 (Chromaleont, Messina, Italy).  

In the field of vacuum-outlet fast GC, it is well-known that the use of short mega-bore capillaries is an attractive option for the rapid analysis of low-complexity samples; in fact, low-pressure conditions extend across the entire capillary length, analyte-gas diffusion coefficients are increased and hence, optimal linear velocities are higher. Some very rapid fast GC–MS separations have been reported in the literature.

  However, the use of megabore columns in fast GC–MS experiments is not advisable in the analysis of medium or high-complexity matrices, such as F&F ones. In that situation, it is more convenient to employ a narrow-bore column, offering a much higher plate number. In capillary GC, a reduction in internal diameter enables a substantial increase in column efficiency: for columns with a high phase ratio, the minimum 

 value approaches the column internal diameter. Consequently, a 10 m × 0.10 mm i.d. × 0.10 µm narrow-bore capillary generates the same theoretical plate number (100000), with respect to a 25 m × 0.25 mm i.d. × 0.25 µm column, if both are operated under optimal conditions. Furthermore, the ascending portion of the Golay curve for a narrow-bore capillary raises very gradually and, hence, higherthanoptimal gas velocities can be applied with little loss of column efficiency. Finally, the optimal linear velocity for a given column increases with an i.d. reduction. Hence, short narrow-bore columns enable rapid GC analysis and can provide results altogether comparable to conventional GC experiments, in terms of resolution.

The GC method described here is a relatively fast one: an optimal constant He velocity of 50 cm/s was applied, with a linear temperature gradient of 10 °C/min. Initially, a C

 alkane mixture was subjected to rapid analysis, for LRI calculation. After, a genuine lemon essential oil was analysed: a TIC GC–qMS chromatogram, with a run time of less than 9 min (around 4–5 times faster than a conventional GC–MS experiment), is reported in Figure 1. 

Figure 1: TIC fast GCâqMS chromatogram of lemon essential oil. Peak identity: 1) Î±-thujene; 2) Î±-pinene; 3) camphene; 4) sabinene; 5) Î²-pinene; 6) myrcene; 7) octanal; 8) Î±-phellandrene; 9) Î±-terpinene; 10) p-cymene; 11) limonene; 12) Î²-ocimene (Z); 13) Î²-ocimene (E); 14) Î³-terpinene; 15) cis-sabinene hydrate; 16) terpinolene; 17) linalool; 18) trans-sabinene hydrate; 19) nonanal; 20) camphor; 21) citronellal; 22) terpinen-4-ol; 23) Î±-terpineol; 24) decanal; 25) nerol; 26) neral; 27) geranial; 28) perillaldehyde; 29) undecanal; 30) citronellyl acetate; 31) neryl acetate; 32) geranyl acetate; 33) Î±-bergamotene cis; 34) caryophyllene (E); 35) Î±-bergamotene trans; 36) Î²-farnesene (Z); 37) Î²-farnesene (E); 38) Î²-santalene; 39) bicyclogermacrene; 40) Î±-bisabolene; 41) Î²-bisabolene. 			

Band widths were rather wide for a 6σ) for α-thujene (

 = 7.341 min) were 1.8 s, 2.4 s and 2.7 s, respectively. The 10 Hz sampling frequency of the rapid-scanning qMS, generated a sufficient number of spectra per peak for proper peak re-construction. Moreover, the spectra profiles were consistent over each peak and, hence, peak skewing was not observed. Forty-one well-known lemon oil volatiles were identified (see Figure 1) by using the double-filter FFNSC MS library; which was, as previously mentioned, constructed through conventional GC–qMS analysis. The first filter eliminated possible library matches with a spectral similarity < 90%. For the second filter, an initial wide LRI window was applied (±20 units) to evaluate the differences between the experimental (automatically calculated through the GC–qMS software) and library values. In conventional GC–qMS analysis, using the same narrow-bore column stationary phase, a ±5 LRI unit window is usually applied. In the fast GC–qMS analyses, it was observed that a ±5 unit window would have been sufficient for all the identified compounds except for octyl acetate (+9 units), caryophyllene (E) (–9 units), and bicyclogermacrene (–6 units). Consequently, it was concluded that a ±10 LRI unit range would be suited for the analysis of six additional essential oil types, namely mandarin (green, yellow, red), bergamot, sweet and bitter orange. Each citrus oil was subjected to five fast GC–MS experiments, on the same day and on five consecutive days; the C

 alkane mixture was analysed prior to each application. The final results attained indicated that the ±10 LRI unit range was suitable for the fast GC–qMS method; however, a widened range of ±15 LRI units would be more advisable because a few analytes were characterized by a 10 LRI unit difference, with respect to the library value. On the basis of the authors' experiences, the differences between conventional and fast GC LRI values tend to be more substiantial when the difference between total analyses times increases. Hence, the ±15 LRI window used is valid for the fast GC–qMS method herein developed; a wider LRI range (e.g., ±20, ±25, etc.) would be necessary for "faster" GC–qMS experiments (i.e., very-fast GC).  

The fast GC–qMS method was subjected to validation (lemon oil was used) in terms of intra- and inter-day (n = 10) retention time and peak % area precision. CV% values for 10 lemon oil compounds (peaks 1, 3, 5, 9, 13, 14, 16, 23, 27 and 31) were never over 0.2% (intra-day) and 0.3% (inter-day) if 

 precision is considered, and always lower than 9.8% (intra-day) and 10.6% (inter-day) for peak % area repeatibility. Intra- and inter-day LRI values were also very stable: a C

 alkane series was analysed prior to the lemon essential oil; the process was carried out five times on the same day and on five separate days. No LRI CV% value, relative to the same 10 peaks, was found to be over 0.2%. Finally, the minimum concentration value, at which the 10 compounds (analysed as pure standards) were identified with a minimum MS similarity of 90% (n = 10), was 10 ppm, apart from γ-terpinene which was identified with a 90% similarity at the 5 ppm level.  

At this point, the same fast GC–qMS method was used for the construction of a further MS library, defined as EIMS F&F. To date, approximately 200 genuine essential oils have been subjected to fast GC–qMS and a single spectrum, derived from each chromatogram, is appended in the library. The procedure for the construction of the EIMS F&F library is based on the averaging of the spectra relative to all the chromatogram peaks, from the first to last, not including the solvent, without subtracting an average spectrum relative to the baseline. The baseline spectrum was not subtracted because it would be highly difficult to obtain an average baseline spectrum relative to the entire chromatogram elution range. In terms of concept, each EIMS F&F spectrum can be considered in the same way as a direct MS injection, even though no direct comparison was made between the two methodologies. It is obvious that the EI-MS F&F library can accurately identify an unknown sample but not its constituents (the ability of the approach to deal with natural compositional variation will be addressed below). The essential oils analysed were all diluted 1:10 (

-hexane, injected in a 0.5 µL volume, with a 400:1 split ratio. A 90% similarity filter is applied using this novel library, whereas the LRI filter is obviously not applicable. The EI-MS F&F library was tested by analysing 10 blind samples provided by essential oil companies, all correctly identified by the EI-MS F&F library, with MS % similarities of at least 96%. A single example is shown in Figure 2: the averaged spectrum was subjected to library matching, and the unknown oil was identified as geranium oil (

Figure 2: EI-MS F&F library result: the experimental spectrum is the upper one.  			

At this point, the fast GC–qMS methodology, in combination with the FFNSC and EI-MS F&F libraries, was evaluated. Another of the ten blind samples was subjected to recognition; the fast GC–qMS chromatogram relative to the unknown sample is illustrated in Figure 3(a). 

Figure 3: a) TIC fast GCâqMS chromatogram relative to an unknown F&F sample; b) EI-MS F&F library result.  			

The EI-MS F&F search provided an excellent 100% spectral similarity for green mandarin oil [Figure 3(b)]. However, as can be observed, various green mandarin oils, produced in different harvesting periods (during autumn 2008), were present in the "hit" list. In this specific case, the correct match was the first in the list (harvesting period: 30 October–12 November 2009), although in several other cases, the MS library procedure could not distinguish between green oils produced in different periods. Moreover, yellow and red mandarin essential oils were also present in the "hit" list with excellent similarities and, hence, it was concluded that the EI-MS F&F library cannot reliably distinguish between green, yellow and red mandarin oils. However, careful evaluation of target constituents can be of great help in the differentiation of mandarin oil samples. For example, it is known that the limonene content increases during the production season, ranging from an average value of 68% in October to 74% in February. Furthermore, some monoterpene hydrocarbons (γ-terpinene, β-pinene, and α-pinene), the sesquiterpene hydrocarbons and the oxygenated compounds decrease during the production season.

The green mandarin oil chromatogram was then subjected to peak-to-peak recognition, with a total number of 34 compounds identified (Table 1). 

Table 1: Green mandarin oil volatiles identified using fast and conventional GCâqMS; library (Lib.), fast and conventional (Conv.) LRI values; fast and conventional MS similarity (%) values. 			

With regards to the library LRI values (a ±15 unit window was used), maximum differences of + and – 10 LRI units were observed for limonene and α-selinene, respectively. Moreover, an average deviation of 3.3 LRI units (absolute value), with respect to the library LRIs, was observed. Considering the spectral quality, 14 mandarin constituents (~41%) were identified with an MS similarity value of at least 95%. The peak-to-peak identification results were then compared with those derived from a conventional optimized GC–qMS experiment on green mandarin oil. The total analysis time was more than five times higher (~50 min) and, as expected, the higher resolving power of a 30 m × 0.25 mm i.d. capillary (circa 120000 N) enabled the separation and identification of a higher number of compounds, namely 41 (Table 1). With regards to LRI values (a ±5 unit window was applied), maximum differences of 5 LRI units were observed for various terpenes, suggesting that a wider window would be advisable. The average LRI deviation, with respect to the library LRIs, was improved, with a calculated value of 1.6. Finally, spectral quality was very good, with 31 volatiles (~76%) identified with an MS similarity value of minimum 95%. Again, the better similarity values attained in the conventional application are certainly due to the increased column efficiency. Finally, an averaged spectrum was derived from the conventional GC–qMS chromatogram and was subjected to an EI-MS F&F library search: 94% and 93% similarity matches were attained for yellow and green mandarin oils, respectively. Further EI-MS F&F library searches were carried out on other conventional F&F GC–qMS chromatograms and it was concluded that, although the EI-MS F&F library can be used in conventional analysis, the best results were certainly attained with the fast GC–qMS methodology. 

The results attained in this investigation can certainly be considered as a positive contribution to the F&F field; in fact, the step-wise process proposed enables a rapid identification and evaluation of unknown F&F matrices. The newly-developed EI-MS F&F library, which is currently under continuous growth, is certainly a promising and innovative tool. Moreover, the dualfilter MS library, initially developed for conventional GC–qMS analysis, has been proven suitable for the validated, rapid GC–qMS approach. The use of both libraries, combined with the fast method, can provide a reliable analytical result in less than an hour, comprising sample preparation, alkane mixture and F&F fast GC–qMS analysis and data analysis.  

Future research will be devoted to the development of other dedicated MS libraries, to be used in the field of monodimensional and bidimensional GC–qMS analysis. 

The Project was funded by the Italian Ministry for the University and Research (MUR) with a PNR 2005-2007 Project n. RBIP06SXMR "Sviluppo di metodologie innovative per l'analisi di prodotti agroalimentari".  

The Authors gratefully acknowledge Shimadzu Corporation and Supelco Corporation for the continuous support. 

is attending the final year in the Ph.D. course entitled "Food Chemistry and Safety" at the University of Messina and is engaged in the development of MS databases for food analysis. 

 is assistant professor of food chemistry at the University of Messina. His main interests are the analysis of food constituents using fast gas chromatography, as well as classical multidimensional and comprehensive chromatographic techniques.  

 is principal scientist of Chromaleont, a spin-off society located at the University of Messina. She received a Ph.D. in "Production and Safety of Food of Animal Origin" from the University of Sassari, Italy.  

 is associate professor of food chemistry at the University of Messina and at the "Campus Biomedico" in Rome. Her research interests include the development of comprehensive chromatography and high throughput separation methods for the study of food components.  

 is a full professor of food chemistry at the University of Messina. His main research interests are focused on the development of innovative chromatographic techniques and mass spectrometric methods for the study of complex food samples.  

 is a full professor of analytical chemistry at the University of Messina and at the "Campus Biomedico" in Rome. His main research interests are focused on comprehensive chromatographic techniques, fast separation methods and mass spectrometric detection. 

, (SpringerVerlag, Berlin-Heidelberg 2004), pp.206–213 

2. P.J. Marriott, R. Shellie and C. Cornwell, 

Articles

Using narrow-bore column fast GC combined with rapid scanning qMS to analyse flavours and fragrances is described.

Rapid Quality Control of Flavours and Fragrances using Fast GC–MS and Multi-MS Library Search Procedures

The in-depth study of polyphenols is hampered by their sheer complexity, combined with the inherent limitations of conventional high performance liquid chromatography (HPLC) separation methods. In this paper, the beneficial application of a number of recent developments in HPLC for polyphenol analysis is highlighted. Small particle-packed columns operated at elevated pressures and temperatures are used to improve the speed and/or resolving power of conventional HPLC methods. In addition, comprehensive two-dimensional LC combining hydrophilic interaction chromatography (HILIC) and reversed phase LC (RP-LC) is shown to be a powerful technique to increase the resolving power. Examples of the successful application of these technologies for improved analysis of procyanidins, flavonols and anthocyanins in wine, tea, cocoa and blueberry samples are presented. 

Polyphenols represent one of the most studied classes of natural compounds. Interest in these ubiquitous secondary metabolites, in recent times in particular, stems from their physiological contribution to the human diet and the numerous health benefits associated with their consumption. In addition, polyphenols are responsible for a range of organoleptic properties, including colour, astringency, bitterness and mouth-feel, as well as ageing properties.

Clearly, the analysis of these compounds is of significant importance in various fields, particularly in the food and beverage and neutraceutical industries. However, the accurate analytical determination of phenolic compounds is hampered by the sheer complexity of the numerous chemical classes and species classified as polyphenols. These broadly include phenolic acids (hydroxybenzoic and cinnamic acids) and flavonoids, the latter class being subdivided into flavonols, flavanols (and their polymeric derivatives, the proanthocyanidins) and anthocyanins (anthocyanin-glycosides) (Figure 1). 

Figure 1: General structures of the principal classes of phenolics. R is commonly H, OH or OCH3, while R1 may be OH, O-glycosyl, or O-acyl-glycosyl.  

The investigation of polyphenols is therefore closely related to the development of suitable analytical techniques for their identification and quantification. High performance liquid chromatography (HPLC), and reversed phase LC (RP-LC) in particular, is most often used for the routine analysis of phenolics, while in recent years the hyphenation of HPLC to mass spectrometry (MS) has become an indispensable tool for the detailed investigation of these compounds.

The resolving power of chromatographic methods is commonly measured in terms of peak capacity: the number of peaks that can theoretically be resolved with unit resolution using a given separation method. The peak capacity (

) for gradient separations can be calculated according to:

 the average (4σ) peak width. Common gradient RP-LC methods typically provide peak capacities in the order of 100–150 (for ~1 h analysis times). Considering that the peak capacity should significantly exceed the number of randomly distributed analytes in a given sample to provide a high likelihood of separation,

  and the fact that typical phenolic extracts contain in excess of 100 species, the limitations of HPLC are evident.  

Importantly, in recent years a number of significant developments in HPLC have contributed to deliver improved performance. These include the use of ultra high pressure liquid chromatography (UHPLC), new developments in stationary phase morphologies (monolithic columns and superficially porous particles), high temperature liquid chromatography (HTLC) and multidimensional LC separations. In this contribution we discuss the beneficial application of some of these new technologies to improve the analysis of polyphenols in a variety of samples.  

 Cocoa beans, wine, blueberries and green tea were purchased from a local supermarket. Epicatechin, catechin and uracil standards were from Sigma-Aldrich (Atlasville, South Africa), and anthocyanin standards from Polyphenols Laboratories (Sandnes, Norway). Dimeric procyanidins were isolated by HILIC fractionation from a cocoa extract. Mobile phases were prepared from HPLC-grade solvents (Sigma) and Milli-Q deionized water (Millipore, Milford, Massachusetts, USA). 

 Extracts of green tea and cocoa were prepared as reported previously.

  For extraction of blueberry anthocyanins, 6.681 g blueberries were frozen and ground under liquid nitrogen and extracted with methanol/water/formic acid (60:37:3), followed by evaporative pre-concentration. Filtered wine samples were directly injected. 

 HPLC–UV analyses were performed on an Alliance 2690 HPLC equipped with a photodiode array (PDA) detector (Waters, Milford, Massachusetts, USA). UHPLC experiments were performed on an Acquity UPLC system with PDA detector (Waters). HPLC–ESI-MS analyses were performed on a UPLC system coupled to a Waters Ultima  Q-TOF mass spectrometer operated in positive ionization (PI) mode.

  Blueberry anthocyanins were tentatively identified by MS and MS

  spectra and comparison of retention times with literature reports.

  The following columns were used: XBridge C18 (250 × 4.6 mm, 5 µm) and Acquity BEH C18 (50 and 100 × 2.1 mm, 1.7 µm, both Waters), Zorbax SB C18 (50 × 4.6 mm, 1.8 µm, Agilent, Waldbronn, Germany) and Develosil Diol-100 (250 × 1 mm, 5 µm, Nomura Chemical, Japan). 

Cocoa procyanidins and green tea flavonols:

 HPLC analyses at 25 °C were performed using 0.1% formic acid (A) and acetonitrile (B) at a flow rate of 1 mL/min: 0–0.63 min (2%B), 0.63–27 min (2–18%B), 27–45 min (18–25%B), 45–51.75 min (25–100%B), 51.75–56.25 min (100%B). UHPLC analyses were performed on a 100 mm Acquity column at 50 °C using the following gradient at 0.3 mL/min: 0–0.17 min (2%B), 0.17–7.4 min (2–18%B), 7.4–12.48 min (18–25%B), 12.48–14.53 min (25–100%B), 14.53–15.6 min (100%B). UV detection was performed at 280 and 370 nm for procyanidins and flavonols, respectively. 

 Mobile phases consisted of 7.5% formic acid (A) and acetonitrile (B). HPLC analyses were performed on the XBridge column at 25 °C and 1 mL/min using the following gradient: 0–1 min (1%B), 1–12 min (1–13.5%B), 12–24 min (13.5–23.5%B), 24–28 min (23.5–28.5%B),  28–35 min (28.5%B). UHPLC analyses were performed on two coupled 100 mm Acquity columns at 50 °C and 0.06 mL/min as follows: 0–3 min (1%B), 3–34 min (1–13.5%B), 34–67 min (13.5–23.5%B), 67–78 min (23.5–28.5%B), 78–98 min (28.5%B). UV detection was performed at 500 nm.  

 Analyses were performed on the XBridge column using the same mobile phases as for wine anthocyanins. A flow rate of 1 mL/min was used throughout with the following gradients: 0–50 min (1–25%B), 50–55 min (25–40%B) at 25 °C; and 0–50 min (1–20%B), 50–55 min (25–40%B) at 70 °C. A post-column split (1:4) was used prior to eluent introduction into the MS. 

For procyanidins, experiments were performed on the XBridge column at 25 °C and on a 50 mm Acquity column at 25 °C and 50 °C, and for anthocyanins on the XBridge column at 25 °C and 70 °C. Mobile phases were as specified for each of these samples above, with the composition varied to ensure similar retention factors at all temperatures. Uracil was used as unretained marker, and all experiments were performed in duplicate. van Deemter curves and kinetic plots were constructed according to reference 8. 

 Since the inception of HPLC, there has been a continuous drive to improve the performance of the technique. The most widely practiced approach is to reduce the particle size (

) of column packing. The benefit of this approach is illustrated by the van Deemter curve, which plots chromatographic efficiency, in terms of plate height (

 is the number of theoretical plates) as a function of linear velocity of the mobile phase, 

, is that mobile phase velocity (flow rate) which delivers the minimum plate height (

) and therefore maximum efficiency. The effect of 

 on the van Deemter curve is demonstrated in Figure 2(a) for a dimeric procyanidin analysed on 5 µm and 1.7 µm columns. It is clear that 

, implying that faster and more efficient analyses are possible on the smaller phase for equal column lengths. 

Figure 2: (a) Comparison of van Deemter curves for procyanidin dimer on 5 and 1.7 Âµm columns at 25 Â°C and 50 Â°C; (b) Corresponding kinetic plots of efficiency versus  analysis time.   

In fact, this picture is not entirely accurate, since decreasing 

 leads to a very important decrease in column permeability. Pressure constraints ultimately limit the column length and therefore efficiency attainable, so that very high efficiencies are easier to achieve for larger particle sizes (a result of the higher permeability of the larger 

 phases). Note though that the price to pay for such high efficiencies is a very significant increase in analysis time. 

This dilemma has been partially overcome by the availability of a new generation of high-pressure instrumentation. In Figure 2(b) the isocratic efficiency as a function of total analysis time for the procyanidin dimer is compared for the same 5 µm and 1.7 µm columns, the latter operated at pressures up to 1000 bar. Note the significant decrease in analysis time achieved on the UHPLC column for an efficiency of 25000 plates (equal to that provided by a conventional 250 mm, 5 µm column). Moreover, UHPLC clearly offers faster analysis for efficiencies up to 

It is relatively straightforward to practically exploit the benefits of UHPLC because HPLC methods may nowadays easily be adapted to UHPLC column formats and instrumentation, essentially providing the same separation mechanism. To date this approach has received comparatively little attention for polyphenol analysis,

  with the focus primarily on very fast analysis on short columns, essentially providing the same efficiency as conventional HPLC columns. 

Increasing the temperature of HPLC separations provides an additional means of improving performance.

  This is primarily a result of faster analyte diffusion in the mobile phase at higher temperatures. The effect of this on plate height behaviour is demonstrated once again for the dimeric procyanidins on the 1.7 µm column in Figure 2: while the maximum column efficiency (= 

) remains roughly unchanged, the optimal linear velocity increases and efficiency-loss at high flow rates is reduced. At higher analysis temperature the flow rate should be increased to operate close to 

, in this manner providing the same efficiency faster. Note that even though the mobile phase viscosity is inversely proportional to temperature, the requirement of operating at higher flow rates means that the overall pressure remains roughly constant. 

Figure 3: Comparison of HPLC analysis of cocoa procyanidins and green tea flavonols at 25 Â°C on a 5 Âµm XBridge column (a) and (b) respectively, and at 50 Â°C on a 1.7 Âµm column (c) and (d) respectively. Detection was performed at 280 nm and 370 nm for procyanidins and flavonols, respectively. ÎPmax = 193 and 505 bar for HPLC and UHPLC analyses.  

In fact, elevated temperature offers a complementary means of further increasing the speed of UHPLC separations for efficiencies up to 

 ~80000. The benefits of this approach are illustrated in Figure 3, where conventional HPLC analysis of cocoa procyanidins and green tea flavonols are compared to UHPLC analysis at 50 °C. This approach provides similar separation in a third of the analysis time, demonstrating obvious advantages for routine analysis of polyphenols. 

Figure 4: van Deemter plots for four anthocyanins on a 5 Âµm XBridge column at (a)  25 Â°C and (b) 70 Â°C and (c) corresponding kinetic plots of efficiency versus retention time for malvidin-3-O-glucoside  

For anthocyanins (anthocyanidin-glycosides, Figure 1), analysis temperature plays a particularly important role. These compounds are known to exist in solution in a number of different chemical forms.

  On-column inter-conversion between these species causes significant band broadening,

  leading to the relatively poor efficiency commonly observed for HPLC analyses of anthocyanins. Higher analysis temperatures speed up the inter-conversion reactions — leading to less band-broadening and improved efficiency. This is demonstrated for the van Deemter curves of four common anthocyanins in Figure 4. At conventional flow rates (i.e., 1 mL/min on 4.6 mm i.d. columns), an improvement in efficiency of between 25 and 130% is achieved at 70 °C compared with 25 °C. In this manner elevated temperature may be used to improve the efficiency of conventional gradient RP-LC analysis of anthocyanins, as demonstrated for blueberry anthocyanins in Figure 5. Also note that temperature may affect the selectivity of the separation, as is evident from the relative elution order of some anthocyanins in Figure 5. 

Figure 5: Extracted ion chromatograms for the gradient HPLC-MS analysis of blueberry anthocyanins on a 5 Âµm XBridge column: (a) at 25 Â°C, and (b) at 70 Â°C, both at 1 mL/min. Cy = cyanidin, Dp = delphinidin, Pt = petunidin, Pe = peonidin and Mv = malvidin.  

The previous examples demonstrate how analysis speed for polyphenol analysis may be increased (for the same efficiency) or efficiency increased for the same analysis time. Another approach especially suited for complex phenolic fractions, is to exploit the benefits of UHPLC and elevated temperature for high-efficiency analysis. This is demonstrated for the analysis of red wine anthocyanins on a 200 mm 1.7 µm column operated at 50 °C in Figure 6.

  This set-up provides isocratic efficiencies of up to 40000 plates, and gradient analysis delivers much improved resolution for an acceptable analysis time of 90 min. Note that this analysis is performed at pressures below 300 bar, implying that conventional instrumentation may be used (provided system delay and void volumes are optimized for such analyses). 

Figure 6: Comparison of gradient RP-LC analysis of red wine anthocyanins on a (a) 5 Âµm XBridge column at 25 Â°C  and 1 mL/min and a (b) 1.7 Âµm Acquity column at 50 Â°C  and 0.06 mL/min.14 UV detection at 500 nm. ÎPmax = 190 and 295 bar for HPLC and UHPLC analyses.  

However, caution should be exercised in the application of elevated temperature for phenolic analysis. As is well documented in literature, anthocyanins in particular are readily degraded at relatively mild temperatures. Fortunately this aspect may be evaluated quantitively. Using the approach of Thompson et al.

  it at can be demonstrated that analysis times of less than 2 hours at 70 °C do not lead to on-column degradation of anthocyanins.

Finally, in the context of uni-dimensional HPLC analysis, it should be mentioned that a number of additional developments in column technology in recent years hold some promise for HPLC analysis of polyphenols. These include the use of monolithic

  However, limited application of these column formats to polyphenol determination has been reported to date.

Comprehensive two-dimensional liquid chromatography (LC

The challenges associated with HPLC analysis of phenolics may be demonstrated using an example: procyanidins (Figure 1) consist of a mixture of monomeric, oligomeric and polymeric compounds composed of flavanol building blocks. As the degree of polymerization (DP) increases, the number of possible isomers increases exponentially (a result of the possible combinations of monomers as well as different inter-flavanol bonds). For example, 48 isomers of dimeric procyanidins containing catechin and epicatechin building blocks are possible.

  Considering that optimized high-efficiency HPLC methods provide peak capacities of maximally 400, no uni-dimensional HPLC method is capable of providing complete separation of higher molecular weight (MW) procyanidins in a single analysis.  

Comprehensive two-dimensional HPLC (LC×LC) is a promising method for the separation of such complex samples. LC×LC delivers peak capacities an order of magnitude or more than uni-dimensional LC. In LC×LC, two separation methods are combined in such a manner that the separation in each dimension is retained. As a consequence, the overall peak capacity in a two-dimensional space is (ideally) the product of peak capacities in each dimension. Admittedly, this ideal is only achieved if certain criteria are met, most importantly that both separations are based on different (orthogonal) mechanisms.  

Comprehensive 2-D LC may be performed in off-line, on-line or stop-flow modes.

  In on-line LC×LC, fractions from the first dimension are transferred in real time to the second dimension column, usually using multi-port two-position switching valves. This approach provides relatively fast (approximately equal to 1-D HPLC methods) separations, although resolution in the second dimension is sacrificed to meet sampling rate requirements. Off-line LC×LC, by contrast, results in much longer analysis times, but since no constraints are placed on the second dimension separation much higher peak capacities may be obtained. (Stop-flow LC×LC is less often applied, and provides peak capacities and analysis times in between the off-line and on-line approaches). 

In fact, the application of on-line LC×LC using a variety of separation modes has been demonstrated for diverse phenolics in wine, beer and herb samples.

  In our group, we have investigated the off-line coupling of hydrophilic interaction chromatography (HILIC) and RP–LC to improve procyanidin determination.

  Off-line coupling was chosen to obtain the maximal practical peak capacity through optimal second-dimension analyses. In HILIC, procyanidins are separated according to polarity, which effectively provides molecular weight separation based on the number of hydroxyls (individual isomers are co-eluted).

  RP-LC, in contrast, provides isomeric separation according to hydrophobicity — although complete separation of isomers in not possible in a single analysis. HILIC and RP-LC therefore provide highly orthogonal separations. The off-line comprehensive HILIC×RP-LC analysis of cocoa procyanidins is illustrated in Figure 7, where it is evident that the combination of MW (HILIC, y-axis) and isomeric (RP-LC, x-axis) information delivers a complete picture of the oligomeric procyanidin content of this extract. The practical peak capacity for this separation (according to reference 24) is ~3500.  

Figure 7: HILICÃRP-LC analysis of cocoa procyanidins. Numbers correspond to degree of polymerisation of procyanidins, A indicates A-type procyanidins. Refer to reference 5 for further details.  

The same approach may also be used beneficially for the analysis of other classes of phenolic compounds. This is illustrated in Figure 8 for flavonol glycosides in green tea (practical peak capacity 2500). A very structured elution pattern reminiscent of comprehensive GC is evident from this figure: retention in HILIC increases with the number of attached sugars, while the different flavonol moieties as well as individual mono-, di- and tri-glycosides are resolved in the RP-LC dimension. It may be concluded that HILIC×RP-LC, and LC×LC in general, holds significant promise for the in-depth investigation of complex phenolic fractions such as encountered in numerous natural products.  

Figure 8: HILICÃRP-LC analysis of green tea flavonols. Data taken from reference 6. Myr = myricetin, Q = querectin, K = kaempferol, gal = galactoside, glc = glucoside, rha = rhamnoside, xyl = xyloside, rut = rutinoside and hex = unkown hexose.  

The analysis of polyphenols represents a severe analytical challenge, and important advances in natural phenolic research hinge on the development of suitable separation strategies. Fortunately, recent developments in HPLC show promise for improving polyphenol analysis. 

A summarized comparison of conventional HPLC, UHPLC and/or elevated temperature LC and LC×LC analyses of different phenolic compounds is presented in Table 1. These examples demonstrate that the combination of small 

 columns and/or temperature primarily delivers improved peak capacity production rates. This advantage can be practically exploited in one of two ways: (1) to achieve similar peak capacities to conventional HPLC ~3–4× faster (e.g., cocoa procyanidins, green tea flavonols); and (2) to improve the peak capacity for a given analysis time (e.g., blueberry anthocyanins). These techniques show particular promise in improving current routine HPLC methods for polyphenol determination. In addition, application of UHPLC columns operated at elevated temperatures allows improved total peak capacity for complex phenolic fractions (e.g., red wine anthocyanins).  

Practical HPLC, HILIC and RP-LC methods of analysing common food and beverages containing polyphenols are presented.

Improving HPLC Separation of Polyphenols

will be held again in Montreux, Switzerland, on the banks of the Lake Geneva in the Montreux Music and Convention Centre. The continued success of the symposium in the field of LC–MS has allowed it to grow over the years to one of the premier platforms for the exchange of ideas and the introduction of new instrumentation and is attended and supported by opinion leaders, vendors, experts and newcomers in the field. The event will be held from 

Important trends such as miniaturization and automation will be on the programme, as well as highlights of strategic industrial developments affecting those working in the field of LC–MS. Novel approaches in drug discovery and development are expected to be discussed, including proteomics, metabolomics, systems biology, target and biomarker discovery, novel bioanalytical methodology and impurity screening. Additionally, developments in agriculture, chemical biotech and environmental areas will be covered. 

World leaders in the field will provide new insights and a future outlook of key domains: George Poste will deliver the "Roland W. Frei" opening lecture titled 

The Evolution of Personalized Medicine: Opportunities and Challenges

. Other keynote speakers will include Ian Wilson, Astra Zeneca, presenting 

Fishing for Biomarkers by Metabolite Profiling using LC–MS

Chemistry Gets Personal: Assessing human health

Molecular Microscopy: Multi-modal Tissue Imaging in Biomedicine

A special expert hearing will be organized by Karl-Siegfried Boos of the University of Munchen on 

How to Sample and Prepare Whole Blood for LC–MS–MS Analysis

Comparison of Cell-disintregrated Blood (CDB) and Dried Blood Spots DBS for LC–MS–MS Analysis

 Sampling Using Micropipettes: Handling and Analysis of Small Volume Blood Samples

 Application of Dried Blood Spots in Pharmaceutical R&D

 Metabolomics — Emerging New Technology Platforms

 Direct Surface Analysis with Automated Nanoelectrospray Mass Spectrometry

 Balancing Sensitivity, Specificity and Separation Speed for the LC/electrospray–MS–MS analysis of Mycotoxin Residues in Grains

 The Origin and Fate of (Prebiotic) Osligosaccharides by coupling of HPAEC and MS and High-resolution LC–MS of 13C-labelled Metabolites

The exhibition will feature many major companies involved in LC–MS, while ample time is available in the programme for discussions and introduction into novel concepts and instruments. 

New developments, novel instrumentation and analytical strategies have often been introduced during the meeting and this is again anticipated. An oral and poster session will take place in a relaxed format to allow maximum interaction between the participants. Introductory courses will also take place; these are designed to stimulate newcomers to the world of LC–MS, data interpretation, sample pretreatment and multivariate statistics/chemometrics.  

Further details including registration can be found on-line at  

21st Annual International Light Scattering Colloquium

Four Seasons Biltmore Resort, Santa Barbara, California, USA

http://wyatt.com/events/ilsc/21st-annual-international-light-scattering-colloquium.html

XII Chemometrics in Analytical Chemistry Conference (CAC 2010)

Recent Advances in Sample Preparation Techniques

Rhone Conference Centre, Amsterdam, The Netherlands		

Departments

Event News

 In Part 1 of this two-part series, John Hinshaw discussed the resolution of a pair of symmetrical peaks and the effects that relative peak size have on area measurements. This month's "GC Connections" examines the effects of peak tailing on resolution and peak overlap. 

  reviewed the calculation of the resolution of two adjacent peaks, repeated here in equation 1. A version of the resolution equation commonly used in practice is given in equation 2. 

 is the peak width-at-base as measured at 13.4% of the peak height, and 

 is the peak width at it's half-height. The subscript numbers indicate which peak. Measurements of the peak retention times and widths for the purposes of determining resolution are shown in Figure 1 for a pair of peaks with 

Figure 1: Peak measurements for calculation of resolution. Peak 1 and peak 2 are identical nontailing Gaussian peaks with Ï = 4.0 s and Ï = 0.0 s. The peaks are spaced 8Ï apart (32 s), and Rs = 2.0.  

These relationships are cast in terms of a pair of identical symmetrical peaks, but the definition of resolution does not limit the relative sizes of the peaks or their shapes. Resolution calculations are performed routinely on pairs of peaks that can be quite different in size and shape, yet the effects of disparities in peak shapes on quantitative measurements are rarely assessed. Part 1 of this series

  showed that increasing the size of the first peak up to 10 times the size of the second did not materially affect quantitative measurements, as long as the peak resolution was at the "baseline" level of 1.5 or greater and a simple vertical peak boundary at the valley point — I'll call this method valley integration — delineated the first peak from the second. With a less-than-baseline resolution of 1.0, however, where the difference in the retention times of symmetrical peaks is equal to the average of their widths at base, significant measurement errors of up to 10% for the smaller peak's area were incurred for valley integration. Even greater errors were caused by either tangential or exponential skimming methods of peak integration. Thus for this limited set of examples, a requirement that symmetrical peak pairs have resolution of 1.5 or greater is well justified. 

In reality, chromatographic peaks are not truly Gaussian in nature. This symmetrical peak shape arises from purely theoretical and idealized treatments of mechanisms of retention and transport through a chromatographic system. As soon as significant dead volumes, adsorption, nonlinear mass transfer, thermal gradients or other real-world effects occur, peak shapes depart from the ideal. Non-Gaussian peak profiles, tailing or fronting peaks, and other distortions are the rule rather than the exception. Of these, peak tailing is perhaps the most commonly encountered effect. Tailing can be simulated by the convolution of a Gaussian peak profile with an exponential decay function. Granted, this technique takes idealized symmetrical peaks and adds idealized peak tailing, and so does not represent real-world peaks any better than the purely Gaussian shapes, but it comes close and it certainly provides a useful model for discussion. 

Figure 2: Exponentially modified Gaussian (EMG) peak model. Three peaks with increasing tailing and identical areas and retention times (peak apex). (a) Symmetrical Gaussian peak with Ï = 4.0 and Ï = 0.0, the same as in Figure 1; (b) same as peak 1 with Ï = 4.0; (c) same as peak 1 with Ï = 8.0.  

Figure 2 illustrates the effect on a Gaussian peak of imposing increasingly strong peak tailing. The degree of peak tailing is determined by an exponential decay factor τ, sometimes referred to as the exponential time constant, using an exponentially modified Gaussian peak mathematical model.

  Figure 2(a) shows a purely Gaussian peak, Figure 2(b) shows the effect of an exponential decay factor τ equal to the peak's standard deviation σ, and Figure 2(c) shows an exponential decay factor of twice the standard deviation. From experience, most gas chromatographers would agree that peaks which lie between the purely symmetrical shape of Figure 2(a), where the asymmetry factor 

 = 1.0, and the slightly tailing shape of Figure 2(b), where 

 = 1.4, would be acceptable, while the more strongly tailing peak of Figure 2(c), where 

 = 2.1, might be considered as exhibiting excessive tailing. Table 1 lists measurements of the widths and asymmetry factors for these three peaks. In particular, it is significant that the ratio of the peaks' widths at base to the widths at half-height are roughly constant. This implies that we can apply the alternative version of the resolution equation given in equation 2 for determining the resolution of slightly to moderately tailing peaks, with asymmetry factors ranging from 1.0 to 2.0. The measurement of single peak asymmetry and the effects of asymmetry on peak area determination were discussed in two previous "GC Connections" columns.

  Now let's consider the effects of peak tailing on the resolution of a pair of peaks with the characteristics of the peak shown in Figure 2(b). 

Table 1: Peak measurements from Figure 2. Measurement of As is at 10% of peak height.2 

The resolution of a pair of peaks is defined in terms of their separation and widths. Peak tailing increases the widths of affected peaks, so to maintain a proscribed resolution level, the separation between a pair of peaks must increase in proportion to the amount of peak tailing. In the previous "GC Connections" instalment the characteristics of a pair of symmetrical peaks were measured at 

 = 1.0, 1.5 and 2.0. The symmetrical peak retention time data is given here in the first part of Table 2 and the peak overlap data is given in the first part of Table 3. These symmetrical peaks have the peak shape from Figure 2(a). 

Figure 3: Resolution and overlap of tailing peaks. Peak shapes as in Figure 2(b). (a) Resolution = 1.0; (b) resolution = 1.5; (c) resolution = 2.0.  

For comparison, the second parts of Tables 2 and 3 show how far apart the asymmetrical peaks must stand to achieve the stated resolution, and to what degree the peaks overlap when a moderate degree of tailing, with 

 = 1.4, is applied to both peaks by using the peak shape from Figure 2(b). Looking first at Table 2 for the retention data, an increase in the second peak's retention time of 4–7 s is required, depending upon the selected resolution level, to maintain the same resolution as peak tailing increases. Thus, considering only the resolution measurement for the moment, it is evident that when designing a robust chromatographic method that anticipates a certain degree of peak tailing, the degree of separation of the peaks must be significantly greater than would be required by accommodating only nontailing peaks.  

Table 2: Retention times to attain specified resolution for equal-size peaks. The symmetrical peaks are those shown in Figure 2(a), and the tailing peaks are those shown in Figure 2(b). The data on symmetrical peaks are from the first part of this series.1 (a), (b) and (c) refer to the parts of Figure 3.  

But now consider the meaning of peak resolution. It is tied directly to the degree of overlap of a pair of peaks. The goal is to provide an acceptable tradeoff between area measurement inaccuracies and the degree of separation. The expectations set by the overlap of symmetrical peaks are listed in the first part of Table 3. Here we can see that the overlap of a pair of symmetrical peaks with resolution of 1.5 is about 0.14%. This low overlap is the desired result of separating peaks at baseline resolution. It is the basis for the familiar guideline that baseline resolution allows for adequately accurate area measurements.  

Table 3: Peak area measurements from Figure 3. Overlap is the percentage of the area of one peak that extends into the other. The data on symmetrical peaks are from the first part of this series.1 The peaks all have the same area. (a), (b) and (c) refer to the parts of Figure 3.  

When peaks start to tail, however, this assumption breaks down. The first peak in the pair of tailing peaks under consideration, when separated sufficiently to produce a measured resolution of 1.5, extends into the second peak with an area that is 7.6× greater than is obtained for the pair of symmetrical peaks at the same resolution. At a resolution of 2.0, the overlap area of the first asymmetrical peak into the second is about equal to the degree of overlap for the pair of symmetrical peaks at a resolution of 1.5. Thus, the degree of peak tailing has a direct effect on the meaning of baseline resolution. Higher resolution is required for tailing peaks to achieve the same degree of area overlap as nontailing peaks. 

For the development of robust methods, a good guideline is to require the initial resolution of critical peak pairs to be at least 2.0, and preferably higher. Then, as peaks start to tail while columns age or inlets become contaminated with use, there will still be sufficient resolution available to achieve an acceptable separation. Providing a somewhat higher resolution also grants some leeway for drifting retention times due to ageing, and it facilitates the acceptability of new columns over the lifetime of a method as column manufacturer's normal variabilities come into play. 

This instalment of "GC Connections" characterized the behaviour of symmetrical and nonsymmetrical peaks in terms of the effects of peak tailing on measured resolution and area overlap between peak pairs. For the moderately tailing peaks examined, greater differences in retention time are required to achieve a given resolution compared to symmetrical peaks, as might be expected. But the degree of overlap from the first peak into the second peak is greater for tailing peaks than for symmetrical peaks at the same resolution level, so additional resolution is required to achieve baseline separation of tailing peaks. The next installment will examine the effects that peak tailing has on measured areas and will take a look at some metrics other than resolution for characterization of the quality or degree of separations. 

 is a senior research scientist at Serveron Corp., Hillsboro, Oregon, and a member of 

's editorial advisory board. Direct correspondence about this column should be sent to Alasdair Matheson, editor, 

, Advanstar Communications, Poplar House, Park West, Sealand Road, Chester CH1 4RN, UK or e-mail: 

Data Analysis and Signal Processing in Chromatography

 (Elsevier, Amsterdam, The Netherlands, 1998). 

GC Connections

Part 2 of the series looks at the effects of peak tailing on resolution and peak overlap.

When Peaks Collide (Part 2)

When something doesn't look right in an LC Method, better check it out. 

I recently received an inquiry from a reader complaining about the difficulty he was having meeting system suitability for a liquid chromatography (LC) method that had been transferred into his laboratory. The system suitability test called for a column plate number, 

, of ≤1.5. The column was a 150 mm × 4.6 mm reversed-phase column packed with 5 µm diameter particles. The mobile phase was acetonitrile–buffer, with a buffer of triethylamine adjusted to pH 5.3 with dilute phosphoric acid. He could meet system suitability with a new column, but after fewer than 50 injections, the method would no longer pass. His inquiry had to do with what he could do to improve things without changing the method so that it needed to be revalidated. 

As I look at the system suitability requirements, I'm not surprised that the method fails. Neither of the required performance criteria seem to be too demanding on the surface, but let's pull back and examine the potential for problems with this method. To do this, we have to look at what is reasonable and ideal performance for a column in terms of plate number and then how the tailing factor plays into this. The bottom line is that I think the only way out of his predicament is to live with short column life or rework the method. 

Figure 1: Measurement of column plate number at the peak baseline and half-height (equations 1 and 2).  

Most of us are familiar with the column plate number, or column efficiency, which we abbreviate as 

is by drawing tangents to the sides of the peak and measuring the peak width at the baseline between where the tangents intersect, as indicated in Figure 1. Then 

 is the width at the half-height. The half-height method is more convenient, because tangents don't need to be drawn. The value of 

 obtained at the baseline and half-height should be about the same, because the tangents usually coincide with the curve at the half height. 

, which can also be referred to as the height-equivalent of a theoretical plate, HETP. This is done by dividing 

If something doesn't look right you need to investigate!

LCGC Europe-10-01-2010

Rapid Quality Control of Flavours and Fragrances using Fast GC–MS and Multi-MS Library Search Procedures

Improving HPLC Separation of Polyphenols

Event News

When Peaks Collide (Part 2)

Are the Method Requirements Reasonable?