In the Laboratory
Rapid, Simple Quantitation in Thin-Layer Chromatography Using a Flatbed Scanner
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Mitchell E. Johnson Department of Chemistry and Biochemistry, Duquesne University, Pittsburgh, PA 15282-1530;
[email protected] Thin-layer chromatography (TLC) is widely used for qualitative purposes in undergraduate laboratories, particularly organic chemistry lab. The use of TLC for quantitation is relatively rare, owing in part to the fact that TLC plate scanners and spotters are too expensive to justify using with a relatively crude separation technique (this does not apply to highperformance TLC). However, TLC is a valuable separation technique because of its simultaneous, multisample capability, its speed, and the availability of near-universal visualization techniques. We demonstrate that a simple flatbed scanner attached to an ordinary computer functions quite well as a “densitometer” for separations and for stains that yield visibly colored spots. This approach has been taken for the analysis of ginseng for quality control purposes (1). Consistent spotting is achieved relatively easily by aspirating from a low-volume glass pipet. This simple approach makes TLC available as a lowcost quantitative technique for undergraduate laboratories. We have endeavored to include a higher degree of quantitative analysis in traditionally “qualitative” labs such as organic labs on the principle that such practices are best undertaken throughout the course of undergraduate chemical education lest they be forgotten with disuse. Cholesterol and cholesteryl esters stained with iodine serve as an illustrative example. While other, more sophisticated devices based on chargecoupled device (CCD) cameras have been used for similar purposes, none possess the inherent ease of use and access that this approach does (2–5). Experimental Procedure
TLC Plate Loading and Development Cholesterol (Aldrich, Milwaukee, WI, 95%), cholesteryl acetate (Acros Organics, Pittsburgh, PA, 97%), and cholesteryl pelargonate (C9, Aldrich, Milwaukee) standards were prepared by dissolving approximately 100 mg of each in 1.00 mL of toluene and serially diluting 1:2 with toluene. Samples were loaded with a glass 10-µL (10-λ) pipet. Several pipets were marked with a line that represented approximately 1/10 the distance from the tip to the 10-µL mark (i.e., at ~1 µL). The lines were marked all at once to ensure consistency of loading. The pipets were filled by capillary action (normally about 2–3 µL) and blotted down to the mark on a Kimwipe before loading onto the TLC plate by aspirating the pipet contents onto the plate when the pipet was very nearly touching the plate. Lo has described a similar method (6 ). This step takes some practice before it is expedient. Silica gel TLC plates (Whatman, Maidstone, Kent, England; PE SIL G/UV, 250-µm-thick gel on polyester backing, coated with 254-nm fluorophore, cut to 5 × 10 cm) were loaded 1 cm from the bottom and developed in 10:90 2-propanol/heptane to a line drawn 4 cm from the start (about 5 min). The development chamber was a typical 0.5-L lidded round jar containing 25 368
mL of mobile phase in the bottom and folded upright filter paper for controlling the humidity of the chamber.
Staining and Scanning Plates were removed from the development chamber and dried, either by passive evaporation or by evaporation assisted with an ambient-air blower. The plates were placed into the iodine staining chamber face down at a 45° angle, with the top of the plate on the bottom of the chamber. The chamber was identical to the TLC development chamber; approximately 2 g of iodine crystals was placed in the bottom. Timing was controlled with a stopwatch. At the proper time, the plate was removed and immediately placed face down on the bed of the scanner to ensure minimal evaporation of iodine (this practice works similarly to a common practice of covering the stained plate with a microscope slide; see ref 7 ). The scanner (UMAX UC1260, UMAX Data Systems, Taiwan, R.O.C.; 600 dpi optical resolution, 24 bit color at 8 bits/channel) was operated in the reflective mode, color (RGB) scan, 100% size, at 300 dpi. Prescanning to determine the scan limits was performed on a blank TLC plate to minimize iodine photobleaching and evaporation effects. Scanning was performed with MagicScan v. 2.4.1 (UMAX) running as a Plug-in under Photoshop v. 3.0.5 (Adobe Systems, Inc., Mountain View, CA). The images were cropped, converted to grayscale, and saved in compressed TIFF format for further analysis. The images were loaded into Igor Pro v. 3.13 (Wavemetrics, Eugene, OR) and plotted with a contour overlay. Profiles (i.e., pixel value along a specified path) were taken with the Line Profile procedure from Wavemetrics. The image was integrated (binned) over 8 pixels (chosen arbitrarily; FWHM ≈ 20 pixels). The resulting profile was inverted, the baseline was subtracted by fitting baseline regions (specified manually) with a third-order polynomial, and the peaks were integrated either by fitting with multiple Gaussians by nonlinear least squares or by applying manual integration limits and integrating numerically (trapezoidal method). All peak analysis procedures were modified from Wavemetrics Technical Notes. Example files, procedure files, and detailed instructions for using Igor Pro for these tasks are available online as supplemental material.W Results and Discussion
Image Analysis Figure 1 shows a typical scanned image of the TLC separation of the test compounds at various concentrations. The images were initially scanned in color in order to preserve as much information as possible, but for quantitative analysis all images were converted to grayscale using a linear map (the default map for all image analysis programs). In many cases, advanced image processing (e.g., filtering, thresholding) can be performed to identify the position of spots if resolution
Journal of Chemical Education • Vol. 77 No. 3 March 2000 • JChemEd.chem.wisc.edu
In the Laboratory
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micrograms of each test compound was loaded into six separate lanes. bRSD = relative standard deviation. The values in parentheses were computed without data from lane 1 (see text and Fig. 5). Quantitation used manual peak limit setting, and backgrounds were fit to a cubic polynomial and subtracted before quantitation. cFWHM = full width at half maximum. FWHM was calculated using cursors to find the points most closely corresponding to half maximum height, and is therefore subject to quantization (counting, or shot) noise; therefore the RSD is an overestimate.
often underestimated by fitting. Peak height was somewhat less reliable for calibration, especially with saturated spots. Students in a sophomore-level lab will probably be more comfortable with manual integration than with peak-fitting because it is somewhat more intuitive and avoids problems encountered when initial guesses lead to nonsensical program errors. Peak fitting is best left to cases where the peaks are well formed and the baseline is relatively smooth (many capillary electrophoresis experiments, for example).
Chromatography of Cholesterol and Cholesteryl Esters The cholesterol species were chosen to represent a set of compounds that are useful in teaching chromatographic principles, are easily separated by normal phase TLC, and are amenable to iodine staining. The compounds eluted in the expected order: cholesteryl pelargonate, being the least polar, had the highest R f, whereas cholesterol, with its free hydroxyl, was strongly retained and had the smallest Rf (see Table 1). At low concentrations, the compounds were reasonably well separated and the peak shapes were Gaussian, or nearly so. Cholesteryl
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or sensitivity is problematic, but such processing must be taken into account or removed during quantitation. Development in this case was quite uniform, and, as a consequence, a single line profile was usually sufficient for all three species. Igor Pro also has segmented or Bezier-type profiles for curved elution paths (not uncommon when using inexpensive TLC plates), but these routines do not allow for lateral integration. Figure 1b shows the line profile limits typically used for this work. Note that the image is integrated over a portion of the peak laterally (i.e., normal to the direction of the profile path). The contour plot overlay (Fig. 1b) was very useful for visualizing peak maxima for positioning the line profile limits. In Igor Pro, as in other software packages (National Instruments’ LabVIEW/IMAQ packages, for example), the line profile is simply a data series that can be further manipulated. Igor Pro and LabVIEW have a full suite of peak-finding, fitting, and integration routines. The analyses in this work were performed on Igor Pro primarily owing to a much lower cost and a much shorter program development time. Instructions, tips, and an Igor Pro demonstration file are available online as supplemental material.W Figure 2 shows the line profile indicated in Figure 1b with the baseline subtracted. Two integration methods are shown. One method involved setting, with cursors, the integration limits and integrating numerically. Shading indicates the area under the cholesteryl acetate peak and clearly shows the limits of integration. Because these limits were chosen by the user, they were somewhat arbitrary. For this method and these analytes, the peak features proved to be very reproducible (see below) and limit setting was at least consistent. The dashed line is the fit using four Gaussian peaks (the shoulder on the cholesteryl pelargonate peak was fit with the fourth Gaussian). Fits were generally quite reasonable (see residuals in Fig. 2) for short staining times (see below); however, manual integration was computationally and procedurally much simpler, and yielded results that correlated quite well with the fit areas (Pearson’s r = .998, slope = 0.96). In fact, non-Gaussian peak shapes, such as those obtained at high concentration (fronted peaks), were
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migration distance, pixels Figure 1. (a) Scanned image of TLC plate after iodine staining for 2 min, 24 s. Spot series, from left to right, represent a sample load (in each compound) of approximately 100, 50, 25, 12, 6, and 3 µg (delivered in a volume of 1 µL), spotted at line on top of image and developed to straight line at bottom. (b) The same image, cropped to show the spot details with an overlaid contour plot (contours every 10 intensity units) and the line profile limits (solid vertical lines) for the profile in Fig. 2.
Figure 2. The line profile obtained as indicated in Fig. 1 (8 pixel lateral integration). Solid line is raw profile with the baseline subtracted after a cubic baseline fit. Peak identities, right to left (start to finish), are cholesterol, cholesteryl acetate, and cholesteryl pelargonate. The fine dashed line is a nonlinear least squares fit of the data between 130 and 450 pixels with four Gaussians and no other constraints. The dashed line at the top shows the absolute residuals from the fit (right axis).
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pelargonate seemed to have an impurity, which appeared as extensive tailing (also see Fig. 3) and was less reliably quantitated. At high concentrations, cholesterol experienced severe fronting, probably due to stationary-phase “overload”. This is illustrated in Figure 3a. The mobile phase composition was optimized with respect to the percentage of 2-propanol but not to type of eluent. Any amount of 2-propanol between 5 and 10% was found to be useful. Further optimization would be required for longer-chain esters.
Staining and Quantitation Figure 3 shows elution profiles resulting from two extremes in staining time. These times should be taken as relative times only, because other staining chambers or methods will give different results. Several features are of note. Longer staining gave a higher baseline, in general, but not egregiously so, and peak features were much more easily identified. For example, the lowest-concentration sample was barely quantifiable at short stain times, but at longer stain times (20–40 min), the peaks were quite distinct from the baseline. Figure 4a shows calibration curves for cholesteryl acetate. Calibration curves for cholesterol were very similar, in spite of peak fronting; but those for cholesteryl pelargonate, while similar, had high scatter, possibly due to the integration procedure and the impurity/peak tail. As shown in Figure 4b, the peak areas increased linearly with concentration in a log–log plot (Pearson’s r > .99 in all cases). At short stain times for the least concentrated samples (i.e., for the lowest iodine loads), values were less reliable for calibration. Log– 370
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log slopes ranged from 0.6 to 0.4, decreasing monotonically with stain time (except for the shortest stain time). There is no a priori reason to assume a logarithmic relationship. The Kubelka–Munk relationship between diffuse reflectance of an absorbing species and concentration suggests a linear relationship for areas obtained from background-subtracted and inverted profiles, but nonlinearities due to stray light, multiple-angle excitation and collection, and polychromatic aberration are expected for this method (but see refs 3, 5). The extent of polarization effects due to strain birefringence in the glass of the scanner is unknown. There was also a saturation effect on the staining of the spots. Figure 4c shows the effect of stain time on peak area for a moderate concentration. Except for the 10-min case, which appeared to be an anomaly (see below), peak area increased with stain time, but not in a linear fashion—suggesting that, indeed, staining
Journal of Chemical Education • Vol. 77 No. 3 March 2000 • JChemEd.chem.wisc.edu
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In the Laboratory
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saturated at long stain times. Therefore, the log–log correlation was perhaps fortuitous and could have been an artifact caused by flattening in a log plot. To achieve a high degree of accuracy for an unknown sample, close concentration bracketing of the unknown with standards is required. Since the total time required for development and analysis is less than 30 min when only a rough estimate of concentration is required, this simple feedback procedure does not place an undue strain on the analyst. Detection limits were likewise quite respectable for such a simple method. The sample loads ranged from 3 to 100 µg. For a 5-min stain, 2 µg was the limit of detection based on a signal-to-noise ratio (SNR) of 3 (see Fig. 3a). The SNR of the cholesterol peak was approximately 5, making the minimum detectable sample load for a 40 min stain 0.5 µg, as extrapolated from the data in Figure 4c. Surprisingly, the noise on the baseline did not appear to increase with longer stain times (though the overall level was somewhat higher; Fig. 3), which suggested that the limiting noise source was irreproducibility of plate manufacture, not uniformity of exposure to iodine fumes. Linear dynamic range was at least two orders of magnitude for the longer stain times. While more sensitive methods are certainly available—fluorescence (2), chemiluminescence, scintillation counting—none possess the inherent low cost and simplicity advantages, not to mention the universality, of this technique.
Reproducibility Reproducibility is a significant issue in TLC quantitation. With the method described, there appeared to be several potential contributors to irreproducibility: loading, integration, and background. Reproducibility within a single run was tested by loading six separate spots of 25-mg/mL standard with six separate pipets and developing and staining for 5 min (actual time 5:16). The raw line profiles are shown in Figure 5a with the cholesterol acetate peaks aligned to show reproducibility of quantitation. Clearly, the baseline differed substantially across the plate (also see Fig. 1), and quantitation was affected. Even though the baseline was subtracted before integration (Fig. 5b), the background was not additive with the peaks. The compound in the spot probably inhibited nonspecific staining of the spot, probably in a concentrationspecific manner. Therefore, low background peaks (e.g., solid line in Fig. 5) had higher net peak areas (see Fig. 5b). Table 1 shows peak area and height precision with and without the first lane (solid line in Fig. 5). Standard deviations extrapolated from these values are shown in the error bars in Figure 4a. The precision was quite acceptable, even with cheap plates and unsophisticated staining, and in fact is not much worse than that obtained with much more sophisticated methods (2). In addition to nonuniform background, baseline subtraction was somewhat arbitrary in the sense that baseline fluctuations were not necessarily random but rather included spots and streaks from the plates. The baseline fit and its resultant subtraction therefore contributed to irreproducibility in quantitation, though to what extent is unknown. Spot loading was probably not a significant source of imprecision given these factors, and any method that deposits a relatively uniform amount of compound would be suitable. The relative precision of the peak width was a good indicator that consistent spots were deposited on the plates. Peak height and width were similar in relative standard deviation, while peak area was significantly worse. This suggests that the peak integration procedures contributed to overall imprecision. Plate-to-plate reproducibility was also examined by loading three spots of cholesteryl acetate at 25 µg onto four separate plates, developing, and staining for 10.0 min in a fuming chamber separate from that used for the other experiments. Peak area, height, and width precision for a given plate were similar to those in Table 1, but the relative standard deviation for all 12 peaks was much worse, 14%. Absolute peak height, measured without baseline subtraction, was much better, 6.4%. This tends to confirm the hypothesis that nonspecific staining of the plates, and the background thus produced, was the largest cause of uncertainty in quantitation. The 10-min data in Figure 4, therefore, was clearly an anomaly; visual examination of the original color scans showed a pinkish-red background, which was much more intense than the usual yellow background. These findings underscore the need for running a set of standards on the same plate as the analyte. Retention factor (R f) reproducibility was also examined. Figure 5c shows the same profiles aligned at the start, and Table 1 shows the precision of R f (measured at peak maxima). Migration precision was also quite acceptable, and was more than sufficient to allow loading of standard lanes separate from the sample lanes. The use of internal standards for quantitation and for correction of migration distance was not tested, and in fact appeared to be unnecessary, given the
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precision of R f and the low number of theoretical plates (N = 180 for cholesteryl acetate). Conclusions We have demonstrated a readily realized means of quantitation for thin-layer chromatography. A scanner with true 12-bit intensity resolution (bit depth) and 600 dpi minimum linear resolution, along with Igor Pro and a fast PC, can be purchased for under $2500. Although not shown here, almost 50 small plates can be scanned at a time; with distributed analysis, a single scanner system can easily handle the workload from a single laboratory section, and perhaps several. (This is also a good place to introduce the students to the use of networked, distributed computing if they have not already been.) Note that computer memory (RAM and hard drive) becomes an issue when scanning large numbers of plates or large plates, and reliable, high-volume storage and backup devices are required. Longer stain times or a more efficient staining process is indicated for small sample loads (< 10 µg). Improvement of quantitation requires more consistent plate preparation, which can be achieved, for example, by preparing plates by hand. It is possible that other commercial plate preparations, particularly those without fluorophore or phosphor treatments, will give lower and more uniform backgrounds. Other means of iodine staining that give low backgrounds and fast stains have been reported (8). Furthermore, better chambers can be devised that give more consistent exposure to iodine vapor. Note that other types of colorimetric stains will also work with this method, including sulfuric acid–heat and ninhydrin. With these minor improvements, TLC becomes a very useful and accessible analytical technique in addition to being a tool for synthetic chemists. This method could also work for fluorescence if one were adventuresome enough to try to attach an acetate color filter sheet to the light source
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in the scanner. Emission “colors” are handled by the color CCD detector or its filter wheel. Response is optimal in the visible, and this technique will not work in the UV without substantial modification of the equipment. Any means of reproducibly spotting the plate is viable for this technique (e.g., see ref 9). Further analysis of the spots and a higher degree of quantitative accuracy can be achieved by scraping the spots and analyzing them with a reflectance accessory to a spectrophotometer or fluorometer (10). W
Supplemental Material
The following supplemental material for this article is available in this issue of JCE Online: example files, procedure files, and detailed instructions for using Igor Pro for these tasks. Literature Cited 1. Chan, B. T.-P.; Leung, A. K.-M.; Chau, F.-T.; Chan, W. W.L.; Wu, J.-Y.; Kwok, I. M.-Y.; But, P. P.-H. Abstracts of Papers, 215th National Meeting of the American Chemical Society, Dallas, TX, 1998; American Chemical Society: Washington, DC, 1998; ANYL-073. 2. Liang, Y.; Baker, M. E.; Yeager, B. T.; Denton, M. B. Anal. Chem. 1996, 68, 3885–3891. 3. Petrovic, M.; Kastelan-Macan, M.; Lazaric, K.; Babic, S. J. AOAC Int. 1999, 82, 25–30. 4. Vovk, I.; Prosek, M. J. Chromatogr., A 1997, 779, 329–336. 5. Vovk, I.; Prosek, M. J. Chromatogr., A 1997, 768, 329–333. 6. Lo, J. M. J. Chem. Educ. 1982, 59, 66. 7. Maheshwari, N.; Krishnamurty, H. G. J. Chem. Educ. 1987, 64, 1066. 8. Boden, R. M. J. Chem. Educ. 1978, 55, 61. 9. Fisher, T. L. J. Chem. Educ. 1983, 60, 989. 10. Frodyma, M. M.; Frei, R. W. J. Chem. Educ. 1969, 46, 522– 524.
Journal of Chemical Education • Vol. 77 No. 3 March 2000 • JChemEd.chem.wisc.edu
