Anal. Chem. 1983, 5 5 , 216-220
216
made up in 0.5 M of either hydrochloric acid or nitric acid. Up to 1.5 mg of gallium can be separated from other elements on 3.0 g of AG 50W-X4 resin, but larger amounts of gallium (up to 20 mg) can be separated from other elements on 10 g of AG 50W-X4 resin as can be seen from Figures 3 and 4, respectively. As little as 10 pg have been separated quantitatively; but there seems to be little doubt that the separation should be applicable to submicrogram amounts and could also be useful for the separation of carrier-free radioisotopes of gallium from irradiated cyclotron targets.
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less background interferences were found with the acetylene-nitrous oxide flame than with the normally used airacetylene flame. The standard and sample solutions should contain 2.0 mg of potassium/mL to suppress ionization and
LITERATURE CITED (1) Strelow, F. W. E. Talanfa 1980, 27,231. (2) Kraus, K. A.; Nelson, F.; Smlth, G. W. J . Phys. Chem. 1954, 5 8 , 11. (3) Ivanova, M. M.; Oglobllna, I . P.; Genel', S. A.; Mitina, V. V.; Kalinin, A. I.; Lambrev, V. G. Zh. Anal. Khim. 1877, 32, 1066. (4) Strelow, F. W. E. Anal. Chlm. Acta 1980, 773,323. (5) Nelson, F.; Murase, T.; Kraus, K. A. J . Chromafogr. 1984, 73,503. (6) Nelrlnckx, R. D.; Van der Merwe, M. J. Radiochem. Radioanal. Lett. 1971, 7,31. (7) Victor, A. H.; Strelow, F. W. E. Anal. Chim. Acta 1982, 738, 265.
RECEIVED for review August 17,1982. Accepted October 20, 1982.
Determination Limits and Distribution Function of Ultraviolet Absorbing Substances in Liquid Chromatographic Analysis of Plant Extracts L. J. Nagels" Laboratorium voor Algemene Scheikunde, RiJksuniversitair Centrum Antwerpen, Groenenborgerlaan 171, 8-2020 Antwerpen, Belgium
W. L. Creten and P. M. Vanpeperstraete Laboratorium voor Experimentele Natuurkunde, Rijksunlversitair Centrum Antwerpen, Groenenborgerlaan 17 1, 8-2020 Antwerpen, Belgium
A study was made of the accuracy of quantltatlve hlgh-performance llquld chromatographic (HPLC) determlnatlons of UV absorblng substances In plant extracts. By use of gradient elution on a reversed-phase column and UV detectlon, 62 extracts from plant leaves were lnvestlgated. The obtained chromatograms provided a dlstrlbution functlon of the relatlve abundance of observed peak areas. A computer slmulatlon of such plant extract analyses permltted the estlmatlon of the real dlstrlbutlon function of component absorbances. By use of the same slmulations, the probablllty that a glven determlnatlon could be performed with success was obtained. These data should be helpful to analysts working on the chromatographic analysls of such samples to estlmate the accuracy of a partlcular determlnatlon In quantltative terms. Reallstlc determlnation llmlts for phenolic compounds In plant extracts were defined and computed. The same method can be applied to the analysis of other blological samples and to other chromatographic technlques.
Qualitative and quantitative determinations of components in biological extracts can be complicated because they are composed of hundreds of products. Analytical chromatographic techniques are powerful tools with which to solve such
problems, eventually in combination with various cleanup procedures. However, these chromatographic methods offer only limited "peak capacities" (n,for definitions see ref 1and 2) to resolve these complex mixtures. Peak capacities can be highly increased by using chromatographic techniques in series (3). Application of these methods could, however, be quite impractical. In chromatography of biological samples, the probability of obtaining an accurate quantitative determination strongly depends on the available peak capacity and on the characteristics of the matrix of interfering substances. Actually, judging the accuracy of a specific chromatographic determination of a component in a biological sample depends on the investigators' experience and intuition. A scientific approach is almost impossible as there are no criteria. Our aim is to express the quality of such a determination in more quantitative terms. As far as we know, no investigations have been carried out in this direction. Therefore we simulated high-performance liquid chromatographic (HPLC) analyses of biological samples by using a microcomputer. The determination of phenolic compounds in plant leaves was taken as a concrete example.
EXPERIMENTAL SECTION Instrumentation. All separations of plant extracts were performed with a Hewlett-Packard 1084B liquid chromatograph, with variable wavelength detector and a recorder-integrator. For
0003-2700/63/0355-0216$01.50/00 1983 American Chemical Society
ANALYTICAL CHEMISTRY, VOL. 55, NO. 2 , FEBRUARY 1983
217
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Flgure 1. (a) Typical chromatogram of an 80% ethanolic plant extract with gradient HPLC, UV detection at 280 nm. (b) Typical computergenerated chromatogram.
the detector wavelength, 280 nm was chosen because this is the wavelength most frequently used for plant phenolics analyses. We used a reversrrd-phase C8 column of 25 cm length and an internal diameter of 9.6 nnm, filled with Lichrosorb RP-8 (E. Merck, Darmstadt GFR). It was eluted with a linear solvent strength gradient (LSS,see ref 1) from 5% B to 40% B in 20 min. Solvent A was 10 mM H3P04and solvent B was methanol. For more details of the method see ref 4. A chromatogram was judged to be acceptable when the smallest peaks (0.3% relative area) were still accurately measured by the detector and integrator equipment. Peak areas were measured only in the linear range of the detector. Extracts that were too concentrated or too diluted were properly adjusted Plant Material1 and Extraction. During the summer, plant leaves were collected from trees, shrubs, and herbs in the neighborhood of the univerfiity. Typically, 5 g of fresh plant leaves was extracted withi lOO-mL, of boiling 80% ethanol. The extract was concentrated in vacuo to ca.5 mL and the resulting H20phase was centrifuged a t 25000g for 10 min. The phenolics were present in the supernatant fraction which was filtered through an ultrafiltration membrane cone (Amicon CFSOA). Of this solution, 10 p L was injected into the chromatographi. Determination of a Distribution Function for Component U V Absorbances in Plant Extracts. We analyzed 62 different extracts of plant leaves (Figure la). The analysis was stopped after a 20-min gradient elution, because nearly all plant phenolic compounds were found in this “window” (see also ref 4). The gradient being of the LSS type, all chromatographic peaks were Gaussian and had nearly the same width, regardless of their retention time (1) This particular system had a peak capacity of 47 from 0 to 20 min analysis time. The maximum peak capacity of this system was in fact more than twice this number, as we did not run the gradie tit up to 100% B solvent. Because practically all plant phenolic compounds appear in the first part of this gradient, the effective peak capacity for these samples was only 47. Each analysis yielded a chromatogram showing a great number of partly separated peaks which will be called “observed peaks”(Figure la). We assumed that such a chromatogram was the result of the accumulation of many more absorption peaks to which we will refer as the “component peaks”. Observed peaks are actually composed of several superimposed component peaks. In order to estimate the reliability of component peak determinations, we investigated the relation between the component peaks and the observed peaks. Therefore, the frequency distribution of the relative areas of the observed peaks was derived from our 62 plant extract chromatograms (Figure 2). The relative peak areas were obtained from the recorder-integrator, coupled to the UV detector (280 nm). They were expressed as a per. centage, the sum of all peak areas in the chromatogram being 100%. Small area peaks were very frequent, while high area peaks were scarce. A significant lack of fit could be shown when trying to describe these experimental data with an exponential or a y distribution. For the best estimate, the distribution was smoothed the resulting frequency distribution (Table I), will be referred to as the frequency distribution of observed peaks (FDO). A computer simiilation program was developed (5) to estimate the frequency distribution of component peaks (FDC). Gaussian peaks, which were given a retention time at random (uniform retention time distribution), while their area distribution was
Figure 2. Distribution of relative peak areas (expressed as a percentage, the sum of the peak areas in the whole chromatogram being 100%) observed in HPLC: determinations of phenolic compounds in 62 different extracts of plant leaves. Peak capacity n = 47.
Table I. Frequency Distribution of Peak Areas Observed in 62 HPLC Plant Extract Cnromatograms (FDO), First Estimate of the Cumulative Relative Frequency Distribution of Component Absorbances in Plant Extracts (FDFE), and Computed Cumulative Relative Frequency Distribution (FDC) of UV Absorbing Components in Plant Extracts FDFE re1 peak FDO area class (smoothed) cumulaboundaries, % ‘obsd freqs tive, % 0.00-0.05 0.05-0.1 0 0.10-0.30 0.30-0.50 0.50-0.7 0 0.70-0.90 0.90- 1.10 1.10-1.30 1.30-1.50 1.50-1.70 1.7 0-1.9 0 1.90-2.30 2.30-2.70 2.7 0-3.10 3.10-3.70 3.70-4.50 4.50- 5.7 0 5.7 0-7.00 7.00-9.00 9.0 0-1 1.00 11.00-20.00 20.00-30.00 30.00-40.00 40.00- 50.00 50.00-60.00 60.00-65.00
no info no info no info 225.00 197.00 172.00 150.00 131.00 116.00 101.00 88.00 143.00 105.00 79.00 91.00 89.00 75.00 46.50 57.00 28.00 33.00 15.00 7.00 3.50 1.50 0.25
3.034 5.961 16.817 26.397 34.784 42.108 48.494 54.071 59.010 63.310 67.057 73.145 77.616 80.979 84.854 88.643 91.836 93.816 96.243 97.435 98.840 99.478 99.777 99.926 99.989 100.000
FDC cumu 1a tive, %
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dictated by a “standing estimate” of FDC, are combined into complete chromatograms. As a first “standing estimate” of FDC, we used FDO, extended with small area peaks (proportional area 0.50, AL > 0.90 and AL > 0.95 (for class boundaries see Table I). The probability PL,(x) that the most abundant
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component peak is present in the observed peak for a t least an amount p is then given by
where N ( x ) = total number of observed peaks in class x , and N,(x) = number of observed peaks in class x for which AL > p . The results obtained from the 90 000 simulated chromatograms (n = 47) are presented in Figure 3. The probabilities PL0,&,PLo,9,and PLo.95are plotted as functions of the relative peak areas of the observed peaks. In a study of the influence of the peak capacity on the probabilities PL,, chromatograms were also simulated for peak capacities up to n = 575. Figure 4 shows the probability PLo,9as a function of the relative peak area of observed peaks for different peak capacities. These figures may be useful to analysts to express the quality of a quantitative determination in exact terms. The probabilities are dependent on the available peak capacity (Figure 4). All curves in Figure 3 have analogue shapes, and all show a minimum for peak areas of about 3%. Probabilities are high for extremely low and for extremely high peak areas. This result was quite obvious for the large peaks (relative area >20%) but rather unexpected for the small ones (relative area < I % ) . I t meant that small observed peaks as well as large observed peaks were likely to correspond to a single component. Intermediate peaks (relative area about 1to 10%) were shown to be the most unreliable. They were often created by coinciding small peaks. For n = 47, the probability that an observed peak of 3% relative area was built up for 90% by a single component peak was only 0.1 (Figure 3); it was about 0.6 for observed peaks of 20%. When the peak capacity was increased, the chance that an analysis was reliable also increased (see Figure 4). The probability that an observed peak of 3% relative area was built up for 90% by one component, increases from 0.1 a t n = 47 to about 0.4 a t n = 150 and to 0.6 at n = 250. So, even in the most efficient chromatographic methods, considerable overlap is to be expected even for the most abundant components (relative absorption of about 10%)
ANALYTICAL CHEMISTRY, VOL. 55, NO. 2, FEBRUARY 1983 PC
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LFigure 5. Probabilities PCo,5,PCo,,, and PCo,os as functlons of the relative peak area of component peaks. They are respectively the probabilities that a component peak is measured with a relative error smaller than 0.5, O:l, and 0.05. Peak capacity n = 47.
Figure 6. Probability PCo,, asi a function of the relative peak area of component peaks. IDifferent peak capacities n = 47, 150, and 250 present in biologicid extracts. Such an overlap can be observed by using detector ratioing techniques (6),but this does not solve the quantification problem. Determination Accuracy of Component Peaks, Analysis of the same simulated chromatograms, from the point of view of component peaks, provided a means of answering the second question. Again all component peaks were identified as belonging to a certain cluster, that formed an observed peak. Now a quantity AC was computed for each component peak, where AC is the ratio of the area of the component peak to the total integrated area of the observed peak to which it contributes. All generated component peaks were classified according to their relative peak areas. For each class the total number of component peaks was counted, as well as the number of peaks for which AC > 0.50, AC > 0.90, and AC > 0.95 (class boundaries are given in Table I). The probability PCl,(x) that a component peak with relative abundance x is determined with an error smaller than 1 - p is given by
where NC(x) = total number of component peaks in class x , and NCp(x) = number of component peaks in class x for which AC > p . Computations were done for all component peaks of about 90000 chromatograms (n = 47). Figure 5 presents the probabilities l?C0,5,P&, and PC0.05 as functions of the relative peak areas of the component peaks. It is clear that the smaller the peak area (which means low concentration and/or low UV extinction coefficient), the smaller the probability of obtaining a certain accuracy. Logically, this probability increases wlhen the accuracy demands are lowered (i.e., larger errors are aidmitted) or the peak capacity is increased (Figure 6). So, a small “observed peak” has a high probability of being “UV pure”, but small ”component peaks” have a low probability of being detected. Determination Limits for Component Peaks. Normally, when biological samples are analyzed with a chromatographic technique, the peak heights of even the smallest component peaks largely exceed the detection limit of the detector. In
219
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Flgure 7. Determinatlon limits DLO~sO,os, DLO.sO,,,and DLO,gOas functions of the peak capacity n. such samples, it is the matrix of interfering substances which establishes what can be accurately determined and not thle detector characteristics. There is no advantage to have a very sensitive detector when the peak capacity of the chromatographic system is too low to resolve the complex mixture’. Although the detector was designed to measure components which are present in low concentrations, such components will be masked by the matrix of interfering substances. It seemed useful to us, therefore, to define a determination limit DLWU,,,, as the minimum relative abundance of a component in an extract, necessary so as to have a probability w to do a determination on which the error is smaller than l - p . This minimum relative abundance is most easily expressed as percent absorbance units (at 280 nm in our case), with the absorbance of the whole extract being 100%. Such a limit is clearly distinct from the “detection limit” (7). One could say that a component can be accurately determined by i3 chromatographic technique, when its concentration is high enough to exceed both limits. Simulations done for different peak capacities up to n = 575 enabled computation of determination limits DLo.’o.o5, DL0.90.1,and DL0,g0.5.These determination limits are presented in Figure 7 as functions of proved to the peak capacity n. Determination limits DLO~go.l be 36%, 21%, and 12% for peak capacities being, respectively, 50, 100, and 200. This means that our HPLC system (n == 47) was only capable of doing accurate determinations (error
