Peak purity analysis in HPLC and CEusing diode-array technology

Application

Abstract

In terms of quality control and for all quantitative analysis peak purity

is a major task, which can be addressed in different ways. Often only

peak shapes and chromatograms are taken into consideration but a

very elegant possibility without the need to use a mass spectrometric

detector is the comparison of spectra recorded with a diode-array

detector during the registration of a chromatographic peak. Due to the

differences of these spectra within a single chromatographic peak, its

purity can be decided easily. The Agilent ChemStation software offers

an easy to use tool to perform these tasks routinely. In this note, the

theoretical background of peak purity determination is presented as

well as the practical use of the ChemStation software for these purposes.

Mark Stahl

In the elution dimension Signals

A valuable tool in peak purityanalysis is the overlay of separa-tion signals at different wave-lengths to discover dissimilaritiesof peak profiles. The availability ofspectral data in the three-dimen-sional matrix generated by thediode-array detector enables sig-nals at any desired wavelength tobe selected and reconstructed forpeak purity determination afterthe analysis. A set of signals ca beinterpreted by the observer bestwhen, before being displayed, it isnormalized to maximumabsorbance or to equal areas. Agood overlap, where peak shapeand retention or migration timematch, indicate a pure peak whilea poor overlap indicates animpure peak, as demonstrated infigure 1.

In addition to overlaying signals,their ratios ca be calculated andplotted. The resulting ratiogramsare sensitive indicators of peakpurity (figure 2). Any significantdistortion of the ideal rectangularform of the ratiogram indicates animpurity. However, there are somelimitations to be considered whenusing signals for peak purity deter-mination:• The UV-Visible spectra of both

the main compound and theimpurity must be well known inorder to select the most suitablewavelengths for the peak profilecomparison. This fact reducesthe applicability of this type ofimpurity detection to a routinelike search of known impuritiesin known main peaks.

ty detector, peak purity can only bejudged from the peak profile of thissignal. Peak profile however isinfluenced by a variety of parame-ters and depends heavily on chro-matographic or electrophoretic res-olution. Therefore peak puritydetermination based on the peakprofile of a single signal is a veryunreliable and insensitive method.This is especially true in CE, wheremismatched sample and bufferzones always result in peak skew-ing2. As a consequence, a secondapproach involving multiple wave-length detectors acquiring morethan one signal in parallel has beenadopted. Such detectors enableimpurities to be uncovered bymethods that involve overlaying sig-nals to compare peak profiles andcalculate signal ratios. These detec-tors have some disadvantageswhich will be discussed in the fol-lowing section. They can be elimi-nated easily by a third approachbased on diode-array technology1:• on-line acquisition of UV-Visible

spectra during the peak’s elution,• several signals at different wave-

lengths in parallel,• signal extracts from a 3-D data

matrix, containing spectral dataand separation signals, for peakpurity analysis..

Peak purity determination can beperformed in different levels, tai-lored to the complexity of the sepa-ration and the user’s needs.Detailed descriptions of the variouspeak purity routines for signals andspectra, such as normalization andoverlay, the mathematical opera-tions and the different displaymodes will be given in the follow-ing sections.

Introduction

An essential requisite of a separa-tion analysis is the ability to verifythe purity of the separated species,that is, to ensure that no coelutingor comigrating impurity contri-butes to the peak’s response. Theconfirmation of peak purity shouldbe performed before quantitativeinformation from a chromato-graphic or electrophoretic peak isused for further calculations.Neglecting peak purity confirma-tion means, in quality control, animpurity hidden under a peakcould falsify the results or, inresearch analysis, important infor-mation might be lost or scientificobservations rendered void shouldan impurity remain undiscovered.Validated analytical methods usu-ally include the peak purity checkas a major item in the list of theirmethod validation criteria (table 1).

Techniques for peak puritydetermination

Several techniques are currentlyused for peak purity determinationin high performance liquid chro-matography (HPLC) and in capil-lary electrophoresis (CE)1. With aconventional single wavelengthdetector (or a monitor providingjust one single output signal) suchas a refractive index- or conductivi-

2

Method validation criteria

Selectivity (peak purity determination) ✔Linearity ✔Limits of detection and quantification ✔Quality of data (accuracy and precision) ✔Ruggedness ✔

Table 1Peak purity determination – a major criterion in method validation

• The technique is not directlyapplicable to research work ormethod development. The riskof overlooking an unknownimpurity remains even when several wavelengths are selectedin parallel.

Peak purity determination in the thirddimension Spectra

Comparing peak spectra is probablythe most popular method to discover an impurity. If a peak is pure all UV-visible spectraacquired during the peak’s elutionor migration should be identical,allowing for amplitude differencesdue to concentration. The resultsobtained by comparison of thesespectra against each other shouldbe very close to a perfect 100%match. Significant deviations canbe considered as an indication of impurity. Unfortunately theinverse is not necessarily true. Ifthe spectra are not significantlydifferent, the peak can still beimpure for one or more of threepossible reasons:1. The impurity is present in much

lower concentrations than thatof the main compound.

2. The spectrum of the impurityand the spectrum of the maincompound are identical or verysimilar.

3. The impurity completely coe-lutes or comigrates with the maincompound, with both havingexactly the same peak profile.

Any peak purity algorithm canonly confirm the presence ofimpurities and never proveabsolutely that the peak is pure.The likelihood of discovering animpurity rises with increasing dis-

3

tinction between spectra and peakprofile, higher resolution betweenthe main compound and the impu-rity, and with increasing absoluteand relative concentration of theimpurity.

There are no hard and fast rulesas to which concentrations ofimpurities can be detected andwhich not. In general, less than 0.5- 1 % can be determined if thespectra are distinct enough. If thespectra resemble each other very

signal A signal B

impure pure

200

150

100

50

1.61.51.41.31.21.1

7.6 7.8 8.0 8.2 8.4 8.6Time [min]

7.6 7.8 8.0 8.2 8.4 8.6

Ratio A-to-B

mAU

Time [min]

Figure 2 Signal ratiograms for impure and pure peaks

signal Bsignal A

pure impure

Time [min] Time [min]7.5 8.1 7.5 8.1

signal A(offset)

signal B

Figure 1 Normalized signals for pure and impure peaks

closely, and the column or capil-lary system does not resolve theimpurity and main componentwell, then only 5 % impurity maybe feasible.

Before proceeding, the differencebetween the terms spectral impu-rity and peak impurity should beclearly defined. Spectral impurityindicates a distortion of the ana-lyte spectrum by the near - con-stant presence of backgroundabsorbance from solvents, and/ormatrix compounds and/or animpurity. Peak impurity, in con-trast, refers to a distortion of theanalyte spectrum by an additionalcomponent which partially orcompletely coelutes or comigrateswith the major compound3.

Background correction of the peakspectraBefore the spectra are used forthe peak purity analysis, theyshould be corrected for back-ground absorption caused by themobile phase or matrix compounds,by subtracting the appropriate reference spectra. Whether such abackground correction needs tobe applied depends on the separa-tion system employed. For isocraticseparations with a properly bal-anced diode-array detector, thesolvent’s constant spectral contri-bution will be eliminated by theautomatic subtraction of the sol-vent spectrum at the beginning ofthe run. For gradient separationswhere the mobile phase’s contri-bution to absorbance may changewith time, background correctionsshould be made for each peakindividually. The Agilent ChemSta-tion software offers three differentmodes for setting reference spectra(figure 3):

4

Figure 3 Spectral Options, Reference Spectrum window of the ChemStation

without reference spectrum

using nearest baseline

using apex as reference

apex spectrum

baselinespectrum

peak start

peakend

Figure 4 Apex and baseline spectra of a peak

1. No reference:

Spectral operations are per-formed without any reference(recommended for exceptionalsituations only, even isocraticseparations should use a refer-ence spectrum).

2. Manual reference:

One or two reference spectra can be specified by the user.This mode is used for interac-tive spectral evaluations of non-

baseline separated peaks. Onlythe spectra in between the twoselected reference points areused for the purity evaluation.

3. Automatic background

correction:

Depending on the acquisitionparameters chosen either A, Bor C is performed automatically.A) All spectra:

For peak purity, the spectra at the actual peak start and end

are linearly interpolated and subtracted from the peak spec-trum. The first integrated peakstart or end to the left of theselected spectrum that is on thebaseline is taken as the time forleft reference spectrum. For theright reference time the first inte-grated peak start or end on thebaseline to the right is taken (fig-ure 4 and 5). If a peak is not com-pletely resolved from its neigh-bour, automatic selection of refer-ence spectra might lead to a refer-ence selected from the valleybetween the two peaks. Thoughwe already know that an unre-solved peak cannot be pure, wemay want to use the purity test tolook for other hidden components.In that case, the manual referencecan be used to select a referencespectrum from before and afterthe group of peaks.

B) Peak controlled spectra:

The nearest spectrum with typeBaseline to the left and right ofthe selected spectrum is taken asthe reference time. When no leftor right baseline spectrum isfound, the first or last spectrumfrom the data file is taken as thereference (figure 5).

C) FLD spectra:

The nearest spectra at the pointsof inflection on the up-and down-slopes of the peak are taken as thereference spectra. This optimizessignal-to-noise and error correc-tion.

Wavelength rangeThe wavelength range for thespectra can, and should be, select-ed carefully so that only the signif-icant spectral area is under obser-vation (figure 6). This eliminates

5

peak 1 peak 2

peak 3

peak 4

peak 5

peak 6

b1 b3b2 b4 b5 b6

1 b1 to b2 b12+3 b3 to b4 b34 b3 to b4 b35 b3 to b4 b46 b5 to b6 b5

Baseline NearestPeak segment baseline

spectrum

Figure 5Spectral Options, Reference Spectrum window of the ChemStation

Figure 6Reference spectra for the spectra or peak controlled spectra

the high absorbance of eluants inthe lower UV range that normallycause high spectral noise. Higherwavelength ranges should be omit-ted if the compounds show noabsorbance to avoid increasednoise and calculation time.

Normalization of the peak spectraBefore the spectra are comparedthey should be normalized. Threedifferent modes of normalization(figure 7) are possible:

1. Maximum absorbance of thespectrum (or a particular wave-length range of the spectrum)

2. Area of the spectrum (or awavelength range of the spec-trum)

3. Best possible match of theentire spectrum. This type ofnormalization is recommendedto display spectra for peak puri-ty evaluation because it tries tomake the difference of bothspectra as small as possible byshifting and rescaling the spec-tra. The ChemStation normal-izes spectra automatically usingthe best possible match of theentire spectrum.

Selection of the peak spectra forcomparisonTraditionally, three peak spectrasufficed for purity determination:the upslope, the apex and thedownslope spectra. This practicerisks overlooking impurities at thebase of the peak. Agilent’s diode-array detectors can acquire allpeak spectra or peak-controlledspectra with the help of the Agi-lent ChemStation. Peak-controlledacquisition is subject to a certainnoise absorbance threshold, asdescribed in the following sectionand shown in figure 6 and 8, todetect peaks. Acquistion eitherway provides significantly morespectral data for more reliablepeak purity evaluations. The num-ber of spectra per peak to beprocessed can be three or more. Ifthe value is set to three then threespectra are taken at roughlyequidistant points during thepeak’s elution or migration. Ifspectra have been acquired inpeak-controlled mode during therun, you should select All spectrafor purity determination because

6

maximum

minimum

(a) maximumóminimum normalization

(c) area normalization (b) match normalization

difference spectrum

Both spectra have sameabsorbance range

Area of difference spectrum as small as possible

area

maximum

minimum

area

Both spectra have same area

Figure 7Three normalized modes, (a) maximum – minimum normalization, (b) match normalization and (c) area normalization.

Peak controlled

absorbancethreshold

apex

downslopeupslope

All-in-peak

baseline

All spectra

Figure 8Acquisition of spectra at different peak sections.

in most practical cases this corre-sponds to the three to five spectrathat will have been recorded. If all spectra were acquired during therun, a setting of five to seven spec-tra to be processed is wise (figure9). Too many spectra will onlyincrease calculation and displaytime, without providing any moresignificant information.

Absorbance thresholdSetting an absorbance thresholdputs limit to the number of spectraconsidered for peak purity. Thisreduces the contribution of spectralnoise to normalization and overlay(figures 6 and 8).

Spectra processingBefore comparing spectra severalmathematical operations can becarried out to improve their quality(figure 6):1. Smooth factor

This mathematical operationsmoothes the selected spectrumusing the coefficients of Savitzky-Golay: The filter lengths can befrom five data points upwardswith no upper limit. The defaultvalue is 5 and in order not to losetoo many spectral characteristicsthe filter length should notexceed 13 (in most cases).Smoothing is useful to removespectral noise, which makes iden-tification more reliable when thespectral noise is about one fifthor more of the absorbance.

2. Spline factor

This constructs a smooth curvethrough each data point by generating new data points. Thenumber of new data points gener-ated is calculated as follows:

No. of data points -1 x spline factor + 1

7

3 peak spectra

5 peak spectra

7 peak spectra

9 peak spectra

- width23 + width2

3

- width + width

apex

All peak spectra(all spectra recorded during elution of the peak)

- width38

- width45

+ width38

+ width45

- width38 + width3

8

+ width45- width4

5

apex

apex

apex

+ width58

+ width38

+ width78 + width9

8- width98

- width78

- width58

- width38

Figure 9Position of the selected peak spectra for the different peak spectra selection modes (width is the peakwidth at half height).

The higher the spline factor themore data points are generatedbetween the existing ones. Thesplined curve still goes throughall of the original data pointsand merely makes the curvevisually more attractive.

3. Logarithm

This calculates the logarithm ofthe selected spectrum. Logarith-mic spectra reduce theabsorbance scale. It may be use-ful to use logarithmic spectra incases where the absorbancescale is very large.

4. Derivative Order

This calculates the specifiedderivative of the selected spec-

trum. Derivative spectra revealmore pronounced details thanoriginal spectra when compar-ing different compounds. Thederivative of a spectrum is verysensitive to background noise.

Peaks purity determination1. Comparing the peak spectra

After selection, correction forbackground influences and nor-malizing, the spectra can be over-laid to check for possible spectralimpurities. Figure 10 shows thenormalized and overlaid spectra ofa pure peak and figure 11 animpure peak. Any significant dis-similarity encountered in the com-

parison of the spectra recordedacross the peak indicates the pres-ence of an impurity. However, noconclusion can be drawn concern-ing the kind, number and level ofimpurity. An additional aid ininterpreting spectral dissimilari-ties is the display of differencespectra, generated by subtractingthese normalized spectra from theother peak spectra selected. Theprofiles of the difference enable afurther conclusion to be drawn:randomly distributed residual pro-files result from spectral noisewhich may be caused by theinstrument (figure 10, upper sec-tion), whereas systematic trendswill be observed if real spectraldifferences caused by a spectralimpurity occur (figure 11, uppersection).

2. The similarity factor

The ChemStation’s special peakpurity software routine does notonly allow the display of spectraand their differences, it is alsoable to calculate a numerical valueto characterize the degree of dis-similarity of the peak spectra, aso-called similarity factor, basedon the match of the peak spectrato one another.

Several statistical techniques areavailable for comparison of spec-tra. Since UV-Visible spectra con-tain only a small amount of finestructure, the least – square - fitcoefficient of all the absobancesat the same wavelength gives thebest result. The similarity factorused in the ChemStation isdefined as:

SIMILARITY = 1000 x r2

where r is defined as

8

Figure 10Normalized spectra and randomly distributed residual spectra resulting from spectral noise

Figure 11Systematic trends of different spectra indicating spectral impurity

( ) ( )[ ]

( ) ( )

−•−

−•−=

∑∑

∑=

=

=

=

=

=

ni

iavi

ni

iavi

ni

iaviavi

BBAA

BBAAr

1

2

1

2

1

and where Ai and Bi are measuredabsorbances in the first and sec-ond spectrum respectively at thesame wavelength; n is the numberof data points and Aav and Bav theaverage absorbance of the firstand second spectrum respectively(see also figure 12).

At the extremes, a similarity factorof 0 indicates no match and 1000indicates indentical spectra. Gen-erally, values very close to theideal similarity factor (greaterthan 995) indicate that the spectraare very similar, values lower than990 but higher than 900 indicatesome similarity and underlyingdata should be observed morecarefully. Figure 13 shows exam-ples of similarity factors for simi-lar, different, noisy and spectrawith impurity. The slope of theregression lines represents theratio of the concentration of thetwo spectra.

9

0 10 20 30

Absorbance spectrum 2

010

203040

18 mAU

16 mAU

40 Absorbance spectrum 1

50

50

nm260

nm260

Similarity Slope Intercept

999.968 1.06818 0.04693

Figure 12 Similarity of absorbance at the individual wavelength plotted for a pair of spectra gives the similarity factor

Spectral difference 0.6% Spectral difference 55% Spectral difference 0.5%Spectral difference 4.8%

(a) very similar spectra (b) different spectra (c) spectra with impurity (d) spectra with noise

Similarity 999.95Slope 1.12Intercept 0.18

Similarity 45.065Slope 0.44Intercept 12.4

Similarity 983.101Slope 1.61Intercept -6.45

Similarity 992.214Slope 1.61Intercept -0.016

Figure 13Graphical display of similarity factor for different pairs of normalized spectra

3. Improving sensitivity and reli-

ability

The similarity curve and thresholdcurve functions improve the sensi-tivity and reliability of the peakpurity evaluation by using all thespectra acquired during the elu-tion or migration of a peak ratherthan just three or four.• Similarity curve

The mathematical fundamentalsused in the similarity curve cal-culations are those used for thepurity factor, however, they aredisplayed in another format. Allspectra from a peak are com-pared with one or more spectraselected by the operator (figure14), an apex or an average spec-trum for example. The degree ofmatch or spectral similarity isplotted over time during elution.An ideal profile of a pure peak isa flat line at 1000 as demonstrat-ed in figure 15(a). At the begin-ning and end of each peak,where the signal-to-noise ratiodecreases, the constribution ofspectral background noise to thepeak’s spectra becomes impor-tant. The contribution of noiseto the similarity curve as shownin figure 15(b).

• Threshold curve

The influence of noise on weak-ly absorbing spectra can be seenin figure 16. The similarity factordecreases with decreasing sig-nal-to-noise ratio or constantnoise level with decreasingabsorbance range. A thresholdcurve shows the effect of noiseon a given similarity curve. Theeffect increases rapidly towardsboth ends of the peak. Inessence, a threshold curve is a

10

Figure 14Spectral Options, Advanced Peak Purity Options of the ChemStation. Here the spectrum chosen for the calculation of the peak purity level can be selected and the display of the similarity curve can be customized.

Figure 15Similarity curves for a pure peak with and without noise, plotted in relation to the ideal similarity factor (1000) and the user - defined threshold (980).

980

1000

Peak spectrum, 20 mAU Noise, 0.1 mAU

.

(a) peak without impurity and noise

(b) peak without impurity but with noise

similaritycurve

similaritycurve

980

1000

similarity curve with back-ground noise contribution. Figure 17(a) shows both the sim-ilarity curve and the thresholdcurve for a pure peak with noise,figure 17(b) for an impure peak.The determination of noisethreshold is performed automat-ically based on the standarddeviation of pure noise spectraat a specified time with a userselectable number of spectra,usually at 0 minutes with 14spectra. In subsequent analysesyou can define the noise thresh-old as a fixed value based onyour experience.The threshold curve, represent-ed by the broken line, gives therange for which spectral impuri-ty lies within the noise limit.Above this threshold, spectralbackground noise and the simi-larity curve intersects thethreshold curve indicating animpurity providing the referenceand noise parameters have beensensible chosen (figures 17 and21).

The software offers four modes todisplay the similarity and thresh-old curves:• Similarity & Threshold (with

out any transformation): The similarity and threshold curves are displayed as they are calcu-lated using values between 1 and 1000 (figure 18a).

•. Similarity & Threshold as the

natural logarithm: Similarityand threshold curves are dis-played as the natural logarithmof their calculated value. Thiscan give additional details in thehigher match factors (figure18b).

11

Sim

ilarit

y fa

ctor

Maximal deviationcaused by noise

Dissimilarity outsidethe noise level

Similarity withinthe noiselevel

Similarity curvewithout noise

Signal-to-Noise Ratio

Minimal deviationcaused by noise

1 2 3

Compound Spectrum

1

2

3

Spectra withdifferent S/N ratios

Figure 16Similarity factor as function of the noise level

980

1000

Impurity spectrum, 1 mAU

5% impurity

980

1000

Noise, 0.1 mAU

(b) peak with impurity and noise

similaritycurve

thresholdcurve

(a) peak without impurity but with noise

similaritycurve

thresholdcurve

Figure 17Effect of impurity and noise on similarity and threshold curves

impurities. The flexibility of beingable to select a specific targetspectra is valuable in those caseswhere the analyst has to assume

where the impurity is, or needs toimprove the sensitivity of purityevaluation. An example may helpto show how this principle can beapplied. If the impurity is assumedto be located on the peak, select-ing the tail or apex spectrum to becompared with all other spectrawill provide the most significantinformation. Figure 20 gives theratio curve for the front, apex, tailand average spectrum of a peakwhich contains an impurity afterthe response maximum (apex).

•. Similarity / Threshold ratio:

The ratio of the similarity andthreshold curves is displayed asa single curve.

ratio = 1000 – similarity1000 – threshold

. The results for each spectrumare compared to the expectedresult based on the thresholdcalculation. If the ratio is lessthan 1 the test for that spectrumpasses, if it is greater than 1then it fails (figure 18(c)).

•. Purity ratio: The purity valueof each single spectrum is dis-played as the logarithm of thedifference from the thresholdvalue. The chromatographicpeak, similarity and thresholdcurves are displayed in theupper part of the display (figure18(d) and 21). For a spectralpure peak the ratio values arebelow unity (the purity ratio isin the green band) and for spec-tral impure peaks the values areabove unity (the purity ratio isin the red band). The advantageof these modes are that onlyone line is displayed, instead oftwo which makes the interpre-tation more simple. All theexact values for each singlespectrum are not only graphi-cally displayed but can also bereviewed in the Peak Purityinformation window (figure 19) of the ChemStation.

4. Using specific target

spectra

The ChemStation permits calcula-tions of the purity factor and simi-larity curves relative to differenttarget spectra, as shown in figure20 and table 2. As a general rule,the option Compare the averagespectrum provides the most valu-able information for unknown

12

Figure 18Threshold and similarity curves, (a) as calculated, (b) ln (threshold) and ln (similarity), (c) as a ratioand (d) as purity ratio

Figure 19The peak purity information window of the ChemStation gives detailed information of the peak spec-tra recorded

Compare each individual spectrum

➝ to all others➝ to the apex spectrum➝ to the average spectrum (of all peak spectra)➝ to the front spectrum➝ to the tail spectrum➝ to the front and the tail spectra

Table 2Different reference spectra for spectra comparison

13

The front spectrum gives a smallspectral impurity at the end of thepeak. The deviation in this firstratio curve is small since the frontspectrum absorbed little (giving arather high threshold curve). Theapex spectrum gives a low impuri-ty in the front of the peak (theapex spectrum contains only avery small amount of the impuri-ty) and high impurity at the tail.The tail spectrum (with a highamount of impurity) gives a spec-tral impurity at the front of thepeak. The average spectrum (amean of the peak spectra selected,in this case the upslope-, apex-,and downslope spectra) indicatesspectral impurity in the total peak.This average spectrum contains ofcourse, the spectral contributionof the impurity. In this particularcase the average contains morecontribution from the impuritythan the apex spectrum, showinga higher spectral impurity at theelution or migration front, andlower impurity at the tail, com-pared with the ratio curve of theapex spectrum.The profile of the similarity-,threshold- and ratio curvesdepends on the position, level andspectral differences of the impuri-ty and, as such, no general state-ments can be made on shape. Profile will differ from situationto situation.

5. Calculating a purity factor

Two approaches are available,depending on whether you selectto use similarity curves or not.• With fixed threshold

The target spectrum, as speci-fied by the user, is comparedwith all the other selected peakspectra. (3, 5, 7, 9 or all). Themean of all purity values below

Peak with2% impurity

0

1

ratio of front spectrum to all other spectra

ratio of apex spectrum to all other spectra

ratio of tail spectrum to all other spectra

ratio of average spectrum to all other spectra

Figure 20Ratio curves for different target spectra from the same peak

the specified threshold gives thepurity factor. If no value fallsbelow the threshold, the purityfactor is calculated as the meanof all values. A fixed thresholdvalue may be useful in somecases of quality control or if theautomatically calculated thres-hold is too restrictive for yourpurposes

• With calculated threshold curve

The mean of all purity valuesexceeding the calculated thresh-old gives the purity factor, onthe condition that at least threeconsecutive values lie over thethreshold. If you choose to usethe threshold curve, the thresh-old is calculated as the mean ofthe same data points used to cal-culate the purity factor. Thespectra used to construct thepurity curve are indicated byplus and minus marks in the

graphical display (as annotatedin figure 18(d) and 20). The bluemarks denote spectra under thethreshold limit whereas the redones denote those exceeding thethreshold limit. Minus denotesspectra excluded from the simi-larity factor calculation sinceinsufffient neighboring spectralay above the threshold. Plusdenotes spectra included in thesimilarity factor calculationsince at least two more, neigh-borin, spectra also exceed thethreshold.

6. Extracting signals auto-

matically from spectra data

The ChemStation software allowsto automatically select peak sig-nals for display. These peak sig-nals are taken from the user speci-fied signals acquired during theanalysis or are extracted from the

spectral data recorded with allspectra set during acquisition. Theextraction is optimized to find asmuch difference as possible in thecurvature of the signals. This givesan extra conformation for peakimpurity and in many cases anindication of the location of theimpurity (in some cases evenwhen more than one impurity ispresent).

7. ChemStation software win-

dows for peak purity analysis

The peak purity analysis windowcontains different windows asshown in figure 21. The whole sep-aration is displayed at the top ofthe window, and a boxed outlinedesignates the peak currentlyunder scrutiny. Retention ormigration time labelling is option-al. On the left side below an over-lay of the different peak spectra isshown. This clearly shows theirdegree of similarity and thereforealso the purity of the chromato-graphic peak investigated. On theright side below the chro-matogram the similarity andthreshold curves and the purityratio is shown. This allows aneasy, quick and sensitive determi-nation of the peak purity. Also,the differences of the spectra, sim-ilarity and threshold curves, theirlogarithm and the similarity tothreshold ratio can be displayed inmore demanding cases.

Examples

Pure peakFigure 22 shows the selected peakspectra. The signals overlap per-fectly reaffirming the validity ofthe background correction. Thesimilarity curve exhibits a profilevery similar to and within the

14

Figure 21Peak purity analysis of an impure peak. The three main windows (chromatogram, recorded spectra of the peak and the peak purity ratio) are shown.

Figure 22Peak purity analysis of a pure peak

threshold curve limits and, thepeak purity ratio is clear withinthe green band.

Impure peakFigure 23 shows the peak puritydetermination of an impure peakcontaining an impurity with aquite similar spectrum to that ofthe main compound. The overlayof the normalized spectra and theextracted signals indicate thepresence of an impurity. The simi-larity curve exceeds the thresholdcurve between 9.5 and 9.6 min andthe peak purity ratio is clearly inthe red band thus leading to thewarning message. From these sig-nals we can conclude that theimpurity is at the end of the peak.Figures 24 illustrates the Chem-Station’s capabilities to discoververy small impurities of 0.5 % andless with almost indentical spectrato the main analyte. Often thespectra overlay, the residual spec-tra and even the ratiogram do notprovide sensitive enough informa-tion to uncover the impurity, how-ever the similarity and thresholdcurve and the peak purity ratio aremore sensitive and are able toreveal the hidden impurity.

Automation of peak purity determina-tionAll the routines described herecan be performed both interactive-ly and fully automatically. Chosing“full report” as report style createsa purity report for any integratedchromatographic peak. All para-meter preferences can be storedin a single method, and printed asdocumentation of the analysis toaid in the execution of Good Labo-ratory Practices (GLP). Startingsuch a method initiates injection,separation, data analysis andreporting of the samples in one

15

Figure 23Peak purity analysis of a peak containing an impurity.

Figure 24Depending on the spectral differences of the components less than 0.5 % impurity can be determinedwith the ChemStation peak purity software.

5%

0.5%

0.1%

step, unattended. Printed reportscontain all the graphical informa-tion from the screen plus additional numerical results.Despite the ChemStation’s capacityto run sample analyses automati-cally and unattended, we recom-mend that you examine the resultsyourself first visually before passing any purity judgement.Incorrect results most often arisewhen a column or capillary sys-tem fails to resolve peak or theChemStation integrates somebaseline event erroneously leadingto a false reference spectrumbeing selected.

Conclusion

Peak purity determination with adiode-array detector is a powerfultool to check peak purities. Bycomparing spectra from the ups-lope, apex and downslope impuri-ties with less than 0.5 % can beidentified. Therefore the techniquedescribed offers an alternative tousing a mass spectrometric detec-tor for peak purity. The AgilentChemStation software offers aneasy and automated tool to per-form the peak purity check withhighest performance. This can and should be done as a matterof routine to achieve reliable highquality data.

References

1.D. N. Heiger, “High performancecapillary electrophoresis,” Agilent Primer 2000 publicationnumber 5968-9936E.

2.H.-J. P. Sievert and A. C. J. H.Drouen, “Spectral matching andpeak purity” in Diode-Array

Detection in High-Performance

Liquid Chromatography, 1993,

51 – 125, Marcel Dekker, New York.

Copyright © 2003 Agilent TechnologiesAll Rights Reserved. Reproduction, adaptationor translation without prior written permissionis prohibited, except as allowed under thecopyright laws.

Published April 1, 2003Publication number 5988-8647EN

www.agilent.com/chem

Mark Stahl is Application

Chemist at Agilent Technologies,

Waldbronn, Germany