The essence of
`chromatography
`
`Colin F. Poole
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`Department of Chemistry, Wayne State University, Detroit, MI 48202, USA
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`
`
`2003
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`ELSEVIER
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`First edition 2003
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`ISBN:
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`FRESENIUS KABI 1032-OOO2
`
`

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`Contents
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`Preface .
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`Chapter 1. General Concepts in Column Chromatography .
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`Introduction .
`1.1.
`1.2. Family Tree of Chromatographic Methods
`1.3. Zone Migration .
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`1.4. Retention .
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`1.5. Band Broadening .
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`1.6. Resolution .
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`1.7. Separation Time .
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`1.8. Principles of Quantification .
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`Chapter 2. The Column in Gas Chromatography .
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`2.3. Stationary Phases
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`2.4. Retention in Gas—Liquid Chromatography .
`2.5. Preparation and Evaluation of Open~Tubular Columns .
`2.6. Preparation and Evaluation of Packed Columns .
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`Chapter 3. Instrumental Aspects of Gas Chromatography .
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`FRESENIUS KABI 1032-OOO3
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`V1
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`The Essence 0fCI1romar0grapI1_v
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`3.7. Vapor Sample Inlets .
`3.8. Coupled—Column Gas Chromatography .
`3.9. Column Connectors and Flow Splitters
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`Chapter 4. The Column in Liquid Chromatography .
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`4.1.
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`Chapter 5. Instrumental Aspects of Liquid Chromatography .
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`5.1.
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`5.8. Postcolumn Reaction Systems
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`Chapter 6. Thin-Layer Chromatography .
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`501
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`527
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`531
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`569
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`573
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`FRESENIUS KABI 1032-OOO4
`
`

`
`Contents
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`7.3. Stationary Phases
`7.4. Kinetic Optimization .
`7.5. Retention .
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`7.7. Related Techniques .
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`7.8. References .
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`Chapter 8. Capillary-Electromigration Separation Techniques
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`8.1.
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`Introduction .
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`8.2. Capillary Electrophoresis .
`8.3. Micellar Electrokinetic Chromatography .
`8.4. Capillary Electrochromatography .
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`8.6. Capillary Isoelectric Focusing .
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`8.8. Method Development .
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`Chapter 9. Spectroscopic Detectors for Identification and Quantification .
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`719
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`Introduction .
`9.1.
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`9.2. Mass Spectrometry .
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`9.3. Fourier Transform Infrared Spectrometry .
`9.4. Nuclear Magnetic Resonance Spectroscopy .
`9.5. References .
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`Chapter 10. Separation of Stereoisomers .
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`10.1. Introduction .
`10.2. Enantioselectivity and Absolute Configuration .
`10.3. Separation of Enantiomers .
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`10.6. Complexation Chromatography .
`10.7. Separation of Enantiomers as Covalent Diastereomer Derivatives .
`10.8. Liquid—Crystal1ine Stationary Phases
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`10.9. References .
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`Chapter 11. Laboratory-Scale Preparative Chromatography .
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`11.1. Introduction .
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`11.2. Thin—Layer Chromatography .
`11.3. Column Liquid Chromatography .
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`720
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`821
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`830
`834
`837
`839
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`847
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`848
`848
`850
`
`FRESENIUS KABI 1032-OOO5
`
`

`
`V111
`
`The Essence QfChromatography
`
`11.4. Supercr1tica1F1uid Chromatography .
`11.5. Gas Chromatography .
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`Subject Index .
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`884
`886
`889
`893
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`901
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`FRESENIUS KABI 1032-0006
`
`

`
`This material may be protected by Copyright law (Title 17 U.S. Code)
`
`62
`
`The Essence 0fCI1r0m(1t0grap/1y
`
`phase velocity is increased beyond its optimum value (see Figure 1.9). Both features are
`favorable for fast separations. Low column permeability and the limited inlet pressure
`dictate that short columns are essential, as well as favorable for fast separations, but
`total plate numbers are low. These short columns (1 ~— 5 cm) usually packed with
`3-irm particles and operated with fast gradients (1-5 min) have found a niche in
`the pharmaceutical industry for rapid screening of combinatorial libraries and drug
`metabolite profiling [257,258]. Mass: spectrometric detection is frequently used to
`track analytes to accommodate poor peak separations. It is the synergy between mass
`separation and chromatographic separation that provides acceptable identification of
`analytes with separation times typically between 1 to 10 minutes per sample. These
`miniaturized separation systems require adapted instrumentation because of the small
`extracolumn volumes and narrow peak profiles.
`The fast separation of macromolecules requires special nonporous or perfusive
`stationary phases with favorable retention and mass transfer properties [l38, 259].
`Combined with elevated temperatures, steep gradients, high flow velocities and short
`columns these stationary phases are very effective for fast separations of biopolymers.
`Supercritical fluids have more favorable kinetic properties for fast separations than
`liquids but are restricted to operating conditions where the density drop along the
`column is minimal to avoid unfavorable increases in solute retention with migration
`distance [260]. Compared with gas chromatography narrow—bore open tubular columns
`are a poor compromise for fast separations using supercritical fluids because they yield
`small retention factors and provide low efficiency per unit time. Packed columns with
`a specific ratio of length to particle diameter to provide the desired resolution are
`generally used.
`Separation speed in capillary electrophoresis is governed by a different set of
`dynamics compared to liquid chromatography. Efficiency is independent of the column
`length but migration time is proportional to the length squared [236,261,262]. It is the
`voltage applied across the capillary column that determines the plate number. Short
`columns can provide both high plate numbers and fast separations with the minimum
`column length established indirectly by extracolumn contributions to band broadening
`and the capacity of the capillary to dissipate the heat generated by the current passing
`through the column. The migration time (t;) is given by L] / uV where V is the voltage
`applied across the column of length L, I is the distance migrated by the sample from
`the point of injection to the point of detection (generally less than L when on—column
`detection is used), and it is the mobility of the ion. Separations on a millisecond time
`scale are possible, if far from routine, using very short capillaries and microstructures
`(section 8.9.7).
`
`1.8 PRINCIPLES OF QUANTIFICATION
`
`This section reviews the basic performance characteristics of chromatographic detectors
`and the various methods of obtaining quantitative information from their signals.
`
`FRESENIUS KABI 1032-OOO7
`
`

`
`General Concepts in Column C/Iromafograp/1y
`
`63
`
`1.8.1 Signal Characteristics
`
`The fundamental properties of the detector signal of general interest are sensitivity, limit
`of detection, dynamic and linear ranges, response time and noise characteristics. It is
`convenient to divide chromatographic detectors into two groups based on their response
`characteristics. Concentration sensitive detectors are non—destructive and respond to a
`change in mass per unit volume (g/inl). Mass sensitive detectors are destructive and
`respond to a change in mass per unit time (g/s). Many liquid phase detectors, such as
`UV—visible, fluorescence, refractive index, etc., are concentration sensitive detectors,
`while many of the common gas—phase detectors (e.g. flame—based detectors) are mass
`sensitive. Sensitivity is defined as the detector response per unit mass or concentration
`of test substance in the mobile phase and is determined as the slope of the calibration
`curve for detectors with a linear response mechanism. For a concentration sensitive
`detector the sensitivity, S, is given by S = AF / w and for a mass sensitive detector
`by S = A / w, where A is the peak area, F the detector flow rate, and W the sample
`amount. A detector with a high sensitivity, corresponding to a larger slope, is better
`able to discriminate between small differences in the amount of analyte. Sensitivity is
`often incorrectly used for limit of detection (or limit of determination). Colloquially a
`detector with a low limit of detection is sometimes incorrectly referred to as a sensitive
`detector when inferring that the detector provides a useful response to a small amount
`of sample. A sensitive detector would be one that is able to distinguish a small range
`of sample sizes. The limit of detection is defined as the concentration or mass flow of a
`test substance that gives a detector signal equal to some multiple of the detector noise.
`A value of 2 or 3 is commonly used as the multiple. The limit of detection (D or LOD)
`is given by D = aN / S, where a is the multiple assumed in the definition of the limit
`of detection. When the test substance is also specified it can be used to compare the
`operating characteristics of different detectors under standard conditions.
`The detector output contains signal associated with the response of the detector to the
`analyte and noise originating from the interaction of the detector with its environment
`and from its electronic circuitry [l83,263,264]. There are three characteristic types of
`noise recognized as short term, long term and drift with properties that can change
`depending on whether they are determined under static or dynamic conditions. Static
`noise represents the stability of the detector when isolated from the chromatograph.
`Dynamic noise pertains to the normal operating conditions of the detector with a flowing
`mobile phase. Ideally, the static and dynamic noise should be similar, and if not detector
`performance is being degraded by the other components of the chromatograph. The
`noise signal is measured over a period of time with the detector set to its maximum
`usable operating range. The observed noise will be different depending on the recording
`device because virtually all—normal laboratory recording devices include some form
`of noise filter. Short—term noise is defined as the maximum amplitude for all random
`variations of the detector signal of a frequency greater than one cycle per minute,
`Figure l.l7. It is calculated from the recording device by dividing the detector output
`into a series of time segments less than one minute in duration and summing the vertical
`displacement of each segment over a fixed time interval, usually 10 to 15 minutes.
`
`FRESENIUS KABI 1032-OOO8
`
`

`
`64
`
`The Essence of Chromatography
`
`SHORI-TERM NOISE
` [Z2
`LONG-lERM NOISE l
`
`l
`I
`
`. 10 Minutes. 3F“
`
`.I
`10 Minutes
`
`
`
`DRIFT
`
`wfiirTJteT ‘‘‘‘ T " .
`i-—————..—________._.,
`
`Figure 1.17. Methods for calculating noise for chromatographic detectors.
`
`Long—term noise is the maximum detector response for all random variations of the
`detector signal of frequencies between 6 and 60 cycles per hour. The long-term noise is
`represented by the greater of Z1 and Z2 in Figure 1.17. The vertical distances Z1 and Z2
`are obtained by dividing the noise signal into ten-minute segments and constructing
`parallel lines transecting the center of gravity of the baseline deflections. Long-term
`noise represents noise that can be mistaken for a late eluting peak. Drift is the average
`slope of the noise envelope measured as the vertical displacement of the baseline over
`a period of 1h. For spectrophotometric detectors, the signal response is proportional to
`the path length of the cell and noise values are normalized to a path length of 1 cm.
`The dynamic range of the detector is determined from a plot of detector response or
`sensitivity against sample amount (mass or concentration). It represents the range of
`sample amount for which a change in sample size induces a discernible change in the
`detector signal. For many, although not all, chromatographic detectors the relationship
`between response and analyte mass or concentration is linear for a wide range of analyte
`concentration. It is the extent of this range that is of most interest to analysts. The linear
`range is commonly used for all quantitative determinations. It is the range of sample
`amount over which the response of the detector is constant to within 5%. It is usually
`expressed as the ratio of the highest sample amount determined from the linearity plot
`to the limit of detection for the same compound (Figure 1.18).
`
`FRESENIUS KABI 1032-0009
`
`

`
`General Concepts in Column Chromatography
`
`65
`
`Extrapolated Response
`Response LINEAR RANGE
`///Response - 5%
`/
`
`
`II L
`
`IIII i
`
`I
`
`gnear Range = Ch I Cm
`
`Cm= Minimum Delectable
`Concentration
`
`Ch= Highest Linear
`Concentration
`
`
`
`RECORDERDEFLECTION
`
`2 x Noise
`
`SThorterm
`'
`NW
`
`C
`
`m
`
`E
`2
`'2
`Ln
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`.— -
`I
`I
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`
`
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`C
`h
`CONCENTRATION
`
`LINEAR RANGE
`0
`— -
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`— —
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`_
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`?9ss
`
`I
`I
`
`5
`"’
`
`'
`Linear Range
`'
`IMinimuIn_
`Upper Limit I
`;DetectabIIIty
`of Llnearlty I
`
`MASS FLOW RATE
`
`Figure l.l8. Methods for calculating the linear response range for chromatographic detectors.
`
`Table 1.1 1
`
`Comparison of manual methods for determining peak areas
`H = peak height, H’ = peak height by constmction (see Fig. 1.4) and wh = peak width at half height, wb at
`base, W0_15 at 0.l5H, W025 at 0.25H and W075 at 0.75H
`
`Method
`Gaussian peak
`Triangulation
`Condol—B0sch
`EMG
`Planimetry
`Cut and weigh
`
`Calculation
`Hwh
`H’wb/2
`H(w0_| 5 + w0_75 )/2
`0.753Hw0.25
`
`\
`
`True area (%)
`93.9
`96.8
`100.4
`100
`100
`100
`
`Precision (%)
`2.5
`4.0
`2.0
`2.0
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`1.8.2 Integration Methods
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`For quantitative analysis it is necessary to establish a relationship between the
`magnitude of the detector signal and sample amount. The detector signal is measured
`by the peak area or height from the chromatogram. A number of manual methods are
`available for calculating peak areas, Table 1.11 [3,l84,264,265]. For Gaussian peaks the
`product of the peak height and the width at half height or the method of triangulation
`can be used. In both cases the calculated area is less than the true peak area. This is
`only important if the absolute peak area is used in further calculations. In this case
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`the calculated area should be multiplied by the appropriate factor to give the true
`peak area. When peak area ratios are used, as in calibration, this is not important.
`The Condol—Bosch and exponentially modified Gaussian (EMG) models provide a
`reasonable estimate of the peak area for Gaussian and moderately asymmetric peaks.
`Up to an asymmetry factor of 3 in the case of the EMG method. No single method
`is perfect and common problems include the difficulty of accurately defining peak
`boundaries, variable precision between analysts and the need for a finite time to make
`each measurement. A major disadvantage of manual measurements is the necessity that
`all peaks of interest must be completely contained on the chart paper (or adjusted to
`remain on the chart paper by varying the detector attenuation during the separation).
`This severely limits the dynamic range of solute composition that can be analyzed. For
`those methods that depend on the measurement of peak widths narrow peaks are usually
`difficult to measure with acceptable accuracy using a magnifying reticule or comparator
`unless fast chart speeds are used to increase the peak dimensions.
`Planimetry and cutting out and weighing of peaks require no assumptions about the
`shape of the peak profile and can be used to determine the area of asymmetric peaks.
`The proper use of a planimeter (a mechanical device designed to measure the area of
`any closed plane by tracing out the periphery of the plane with a pointer connected
`by an armature to a counter) requires considerable skill in its use. Even so, obtaining
`accurate results requires repetitive tracing of each peak with the totals averaged. The cut
`and weigh procedure depends critically on the accuracy of the cutting operation. The
`homogeneity, moisture content and weight of the paper influence precision. Copying
`the chromatogram onto heavy bond paper, with expansion if possible, will preserve
`the original chromatographic record of the separation and enhance the precision of the
`weighings.
`Dedicated electronic integrators and personal computers with appropriate software
`for integration are routinely used for recording chromatograms [l83,184,264,266—27l].
`Since manual methods for determining peak information are tedious and slow, this is
`hardly surprising. It is important, however, to understand the limitations of electronic
`integration. Computer-based systems are increasingly used since they can combine
`instrument control functions with chromatogram recording and integration as well
`as providing electronic data storage. Computer—based systems also provide flexible
`approaches for reporting results and for performing advanced data analysis techniques
`using additional resident software.
`The continuous voltage output from chromatographic detectors is not a suitable signal
`for computer processing. The conversion of the detector signal to a computer readable
`form requires an interface usually resident as an expansion card in the computer. The
`interface scales the detector output to an appropriate range, digitizes the signal, and
`then transfers the data to a known location in the computer. The original input signal
`is transformed into a series of voltages on a binary counter and is stored as a series of
`binary words suitable for data processing.
`The important characteristics of the analog to digital conversion device are its
`sampling frequency, resolution and range. The accurate recording of chromatographic
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`67
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`peaks requires that at least l0 data points are collected overthe peak width. The
`sampling frequency must match the requirements for the narrowest peak in the
`chromatogram. For modern devices with sampling frequencies of 5 Hz or better
`undersampling is usually only a problem in higl1—speed chromatography [250,268].
`Analog to digital converters used for chromatographic applications are usually auto—
`ranging, meaning that the detector signal is automatically divided into ranges so as
`to provide sufficient resolution near the baseline for peak detection while accurately
`registering the peak maximum for large signals. The resolution of the analog to digital
`converter is the smallest change in the analog signal that can be seen in the digital output
`(specified as the number of bits).
`Converted data is usually averaged (bunched) to minimize storage space. Using the
`local peak width to determine the frequency of bunching creates uniform sampling
`density throughout the chromatogram. Long stretches of stable baseline are stored in
`one bunch represented by a single datum and the number of times it recurs. Stored
`data is initially smoothed and peak locations identified by a slope sensitivity function.
`Small peaks and baseline artifacts are removed using a threshold function or later
`by a minimum area reject function. Standard procedures may fail in the case of
`complex baselines, tailing peaks or excessive peak overlap. Computer-based integration
`software often tries to compensate for this problem by providing the possibility of video
`integration. The operator can set any integration boundaries desired by moving a cursor
`on the video monitor. Choosing boundaries though is arbitrary, and it is quite likely that
`different operators will choose different boundaries. Because this procedure can not be
`validated it is unsuitable for regulatory and general quality assurance problems. It can
`be useful in other circumstances to correct blunders made by the software in logically
`interpreting the correct boundary positions.
`Most often,
`the derivatives of the smoothed signal are used for peak detection
`[l83,l84,270—275]. Peaks are detected because the detector signal amplitude changes
`more rapidly when peaks elute than the baseline signal does between peaks. There
`is a threshold of slope below which peaks are not detected as different to baseline
`fluctuations, and this value is set by the slope sensitivity factor. Differentiation of the
`detector signal enhances the changes within the signal facilitating the accurate location
`of peak start and end positions. The starting point of the peak is detected when the first
`derivative has reached a predetermined threshold value. The peak maximum and also
`the valley between partially resolved peaks are then indicated where the derivative falls
`to zero and the end of the peak where the derivative drops below the threshold and the
`signal returns to the baseline. In order to avoid the detection of false peaks due to abrupt
`changes caused by baseline noise a minimum peak width is usually predefined and the
`detected peak is accepted only if the difference between the start and end of the peak
`exceeds the threshold. Some systems use a two step peak-indicating algorithm. In the
`first step a coarse estimation of peak positions is made when the first derivative exceeds
`the threshold for two or more consecutive data points. Then the second derivative of
`the signal is analyzed backwards and peak positions reassigned with respect to the
`chosen threshold in the usual way. The choice of threshold Value is Very important
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`The Esserice of Chromatography
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`and is often user selectable. Alternatively, the integrator may use the average value of
`the signal fluctuations at the start of the chromatogram toautomatically self—program an
`appropriate threshold value. With a low threshold, noise cannot be distinguished from
`peaks, and too many signals will be reported. With too high a threshold, small peaks are
`overlooked and there will be a late start to the integration of detected peaks.
`The area between the start and end positions identified for the peak is integrated
`using a summation algorithm usually based on Simpson’s rule or trapezoidal integration.
`Simpson’s rule is more accurate because it fits a quadratic function to groups of three
`consecutive data points whereas the trapezoid method involves fitting a straight line
`between data points. An adequate sampling frequency is also important since this
`determines the number of slices across the peak (i.e. the data points that are integrated).
`Subtracting the baseline area from the accumulated integral count completes the peak
`area calculation.
`
`In the absence of significant baseline noise most integrators should be capable of
`high precision and accuracy when integrating isolated symmetrical