phase-resolved fluorescence spectral fingerprinting of petrolatums

Phase-Resolved Fluorescence Spectral Fingerprinting of Petrolatums

P A T R I C I A M. R I T E N O U R H E R T Z a n d L I N D A B. M c G O W N * Department of Chemistry, P. M. Gross Chemical Laboratory, Duke University, Durham, North Carolina 27706

The first application of multi frequency phase-resolved fluorescence spectroscopy (PRFS) to the characterization of a complex, multicomponent system is described. The incorporation of lifetime selectivity significantly enhances the ability to discriminate between different petrolatum samples, relative to conventional, steady-state fluorescence techniques. Che- mometric data analysis strategies are used to reduce noise contributions and to compare the dynamic spectral features of the petrolatums. Index Headings: Computer applications; Fluorescence; Luminescence; Spectroscopic techniques; Time-resolved spectroscopy.

INTRODUCTION

Fluorescence analysis is uniquely suited to the characterization and fingerprinting of complex samples because of its combination of high sensitivity, multiple dimensions of selective information, and minimal sample perturbation. The latter ability is particularly important for fingerprinting applications, since some of the best spectral features for discriminating between samples may result from dynamic intermolecular interactions in the intact samples. The high sensitivity of fluorescence measurements is also important because the best discriminators may be trace constituents of the samples.

Successful fingerprinting applications have been dem- onstrated for steady-state fluorescence techniques, es- pecially for those involving synchronous excitat ion spectra 1,2 and total luminescence spectra. 3,4 The main limitation of fluorescence approaches has been the sim- ilarity between the spectra of different compounds and the broad, relatively featureless spectral bands, compared to those obtained in vibrational spectroscopy. For- tunately, compounds with very similar or highly overlapping fluorescence spectra often have significantly different fluorescence lifetimes. Thus, the addition of dynamic lifetime information to the fluorescence spectral formats will greatly strengthen the fingerprinting ability of fluorescence spectroscopy, by providing an independent dimension of selectivity in which to distinguish between samples with similar spectral characteristics. Life- time techniques can be used to relatively enhance or suppress the spectral contributions of different components as a function of fluorescence lifetime, in order to selectively view the signals of components that are good discriminators between samples, even if they are minor contributors to the total steady-state fluorescence signal. Finally, lifetime techniques allow us to fully exploit the dynamic, intermolecular interactions and matrix effects that modify the luminescence of the fluorescent corn-

Received 10 August 1990. * Author to whom correspondence should be sent.

ponents in complex samples; these dynamic processes reflect the uniqueness of each sample and are therefore a valuable source of discrimination in fingerprinting applications.

Phase-resolved fluorescence spectroscopy (PRFS) 5-7 is a relatively simple and convenient technique for com- bining fluorescence lifetime and spectral information) Previously, three-way arrays of multifrequency PRFS data were successfully used in the analysis of simple, two-component systems, s Potential applications for complex samples such as crude oils have also been indicated. 1° In this paper, we describe the first systematic study of multifrequency PRFS for dynamic fluorescence spectral fingerprinting of complex samples, including data analysis strategies for sample classification.

A set of petroleum-based lubricants (petrolatums) was used as the complex system in this work. Fingerprinting of petrolatums has both industrial and forensic applications. Discrimination between petrolatums by fluorescence methods is based on variations in the polycyclic aromatic hydrocarbon (PAH) content due to differences in the crude oil source and the refining and processing procedures. In addition, reports on preparation procedures and effects of storage and aging 11 suggest that it may be possible to be able to identify the samples on a jar-to-jar basis, which has forensic significance for evi- dence in rape cases. On the basis of chromatographic analysis, 12 investigators have tentatively identified several PAH components in petrolatums, including ben- zo[e]pyrene, chrysene, t r iphenylene, pyrene, and benz[a]anthracene. Attempts to discriminate between jars, or even preparations, on the basis of steady-state fluorescence spectroscopy have met with limited suc- cess. 13

THEORY

Phase-Resolved Fluorescence Spectroscopy . The PRFS technique uses frequency-domain fluorescence lifetime instrumentation to produce a time-independent, phase- resolved fluorescence intensity (PRFI) that is a function of the spectral and lifetime characteristics of the emitting component: 9

PRFI = Kme,([cos(¢D -- tan-l(o~r))]/[(o~r) 2 + 1] '/=) (1)

where K is the dc (steady-state) component of the emit- ted signal and is a function of the spectral features and concentration of the fluorescent component, as well as the intensity and modulation depth of the exciting light; me= is the modulation depth (ratio of ac amplitude to dc intensity) of the exciting light; o~ is the angular modu-

Volume 45, Number 1, 1 9 9 1 ooo3-7o2s/91/45Ol-OO7352.oo/o APPLIED SPECTROSCOPY 73 © 1991 Society for Applied Spectroscopy

lation frequency of both the excitation and emission signals (note that w = 27r[, where f is the applied linear modulation frequency in Hz); r is the fluorescence lifetime of the component; and 4~D is the phase of the detector and can be adjusted to any value between 0 ° and 360 °.

The PRFI for a particular lifetime component can range from a maximum value, when CD is set to be in-phase with the fluorescence signal, to the minimum value of zero, when ~bD is exactly 90 ° out-of-phase with the fluorescence signal. In addition, the PRFI is also a function of o~, so that there are two independent instrumental parameters that can be used to implement fluorescence lifetime selectivity. Combined with excitation and emission wavelengths, therefore, there are altogether four instrumental parameters that can be controlled in the PRFS experiment, in order to fully exploit the multiple dimensions of dynamic spectral information.

PRFS Data Formats for Spectral Characterization. Steady-state fluorescence spectral data can be represented as a two-way data array, or Excitation-Emission Matrix (EEM), in which intensity is plotted as a function of emission wavelength (kern) and excitation wavelength (ke.) to generate a three-dimensional surface. 14 Fluores- cence lifetime has been incorporated into the EEM by means of both time-domain ~5 and frequency-domain 16 techniques, thereby extending the EEM data into a fourth dimension to produce three-way data arrays. In the frequency-domain approach, PRFI is plotted as a function of emission and excitation wavelength at a given modulation frequency (¢0) to generate a Phase-Resolved EEM (PREEM). The detector phase is generally set to suppress the contribution of scattered light at each frequency. The resulting three-way Excitation-Emission- Frequency Array (EEFA) consists of a stack of PREEMs collected at a series of modulation frequencies. The PRFI at each kern, Xex point in the EEFA varies along the frequency axis according to Eq. 1.

EXPERIMENTAL

Solutions of petrolatums (1.0 mg/mL unless otherwise noted) were prepared in spectral-grade cyclohexane (Ko- dak, Rochester, NY). Five different commercial preparations of petrolatums were used: Vaseline nursery jelly (sample #1, Chesebrough-Pond ' s , Greenwich, CT), K-Mart brand pure petroleum jelly (sample #2, K-Mart, Troy, MI), Safeway brand pure petroleum jelly (sample #3, Safeway Stores, Inc., Oakland, CA), Vaseline brand pure petroleum jelly (sample #4, Chesebrough-Pond's, Inc., Greenwich, CT), and First-Aid Vaseline brand car- bolated petroleum jelly (sample #5, Chesebrough-Pond's, Inc., Greenwich, CT). For fluorescence measurements, the solutions were contained in quartz cuvettes and were not deoxygenated.

An SLM 48000S multifrequency phase-modulation spectrofluorometer (SLM Instruments, Inc., Urbana, IL) was used for all fluorescence measurements. The instrument incorporates a 450-W xenon arc lamp for excitation, an electro-optic (Pockels cell) device for modulation of the excitation intensity, and phase-sensitive P M T detection. A reference channel is used for ratiometric measurements in both the steady-state and phase-resolution modes. An IBM PC-AT is used for on-line data acqui-

sition. Data analysis for this work was performed on a series of interfaced microcomputers, including an H P 9920U, an IBM PC, and a Macintosh SE.

The PRFS data were collected with the detector phase angle adjusted to suppress the scattered light signal from a solution of Kaolin in deionized water, thereby gener- ating a bandpass "filter" that defines a window of fluorescence lifetime at each frequency. 8 For all measurements, the spectrofluorometer sample compartment was maintained at 20.0 ° ± 0.1°C with a Haake A81 temper- ature control unit. Fluorescence intensity was measured as the average of five samplings over a period of several seconds, performed internally by the instrument software. Both the steady-state EEMs and the PREEMs were collected as a series of emission spectra with 4-nm intervals for both excitation and emission wavelengths. The PREEMs were collected at five modulation frequencies: 60, 30, 15, 8, and 5 MHz. The contour and three-dimensional surface plots were generated by Surfer software (Golden Software, Inc., Golden, CO).

RESULTS

Optimization of Concentration. In order to optimize the fluorescence signal of the petrolatums, we compared the steady-state EEMs for several concentrations of lu- bricant in cyclohexane, including 1 mg/mL, which is the concentration used by others, 13 0.1 mg/mL, 0.5 mg/mL, and 10 mg/mL. The petrolatums were easily solubilized in cyclohexane at each concentration, and no blank fluorescence was observed for the solvent in the wavelength region of interest. The 1 mg/mL concentration was found to provide optimal fluorescence intensity in both the steady-state and phase-resolved spectra. Less spectral detail was observed at lower concentrations, along with reduced signal-to-noise at the 0.1 mg/mL concentration. At the highest concentration studied (10 mg/mL), fluorescence intensity was also lower than at I mg/mL, probably due to concentration quenching. On the basis of these experiments, a concentration of 1 mg/mL was used for all subsequent steady-state and phase-resolved fluorescence measurements.

Steady-State Excitation-Emission Matrices (EEMs). For each of the five petrolatums, a steady-state EEM was generated in the region of interest (~e, = 270--430 rim; ~em = 290-450 rim). As shown in Fig. 1, the steady- state EEMs of the different petrolatums are very similar and are all dominated by intense peaks in the lowest wavelength regions of the EEMs. It is difficult to discriminate between the petrolatums by visual examination of the EEMs, which is consistent with results re- ported by others? 3

Enhancement of Signal-to-Noise for Phase-Resolved Excitation-Emission Matrices (PREEMs). In our initial collection of PREEMs for the petrolatums, two observations were made: the signal-to-noise ratios are significantly lower than those for the steady-state EEMs, and the most intense signals in the lowest wavelength regions of the steady-state EEMs are negligible in the PREEMs. Both of these observations are attributed primarily to the Pockels cell modulator, which attenuates the excitation beam and also acts as a high-pass filter that cuts off most of the light below 300 nm. Both effects are

74 Volume 45, Number 1, 1991

FIG. 1.

# 1

• . , , o ~ . :Tgv j ,

q~

Steady-state EEMs for five different petrolatums (1 mg/mL). Excitation: 270-430 nm; emission: 290-450 nm.

A B

2

Fro. 2. Petrolatum #4 (1 mg/mL): (A) raw PREEM; (B) PREEM reconstructed from first three primary eigenvectors. Spectral region as described for Fig. 1.

APPLIED SPECTROSCOPY 75

A

2

-

27 2~

. _ ~ " L , . . . , f \ ~ o * " , ' , , , , ) . , o ~ o, '"

FIG. 3. PREEM at five different modulation frequencies for petrolatum #4 (1 mg/mL): (A) 60 MHz, (B) 30 MHz, (C) 15 MHz, (D) 8 MHz, and (E) 5 MHz. Matrices reconstructed from first three primary eigenvectors. Spectral region as described for Fig. 1.

evident in the P R E E M shown in Fig. 2A, as compared with the steady-state EEMs in Fig. 1.

In order to minimize the noise contribution to the PREEMs, the data matrix corresponding to each PREEM was decomposed into its eigenvectors and eigenvalues by singular value decomposition, followed by matrix reconstruction from the first rank n eigenvectors. The matrix rank n was estimated by a recently described canonical correlation technique, 17 and a rank of three was consis- tently estimated for the PREEMs of the petrolatums. Figure 2B shows the P R E E M that was reconstructed from the first three primary eigenvectors for one of the petrolatums. The use of three eigenvectors provided suf- ficient spectral detail and intensity, while the addition of a fourth eigenvector was found to cause significant deterioration of signal-to-noise. All subsequent PREEMs were therefore reconstructed from the first three eigenvectors.

Effect of Modulation Frequency on the PREEMs. In PRFS, shorter-lived emission components are enhanced at higher frequencies and the longer-lived components are enhanced at lower frequencies. Moreover, at each


frequency, ¢0, the PRFS intensity will be optimal for components with a fluorescence lifetime equal to 1/w. For the petrolatum studies, phase-resolved EEMs were collected for each petrolatum sample at the same wavelengths as the steady-state EEMs. The five modulation frequencies, and the corresponding optimal fluorescence lifetimes, include 60 MHz (2.6 ns), 30 MHz (5.3 ns), 15 MHz (11 ns), 8 MHz (20 ns), and 5 MHz (32 ns). As shown in Fig. 3 for one of the petrolatums, modulation frequency clearly affects the spectral features in the PREEMs. Additionally, the suppression of scattered light by appropriate adjustment of the detector phase angle (¢D) makes it possible to observe fluorescence peaks of interest which otherwise might have been masked or dis- torted by the scatter peak.

Comparison of EEM and PREEM Spectral Finger- prints. Simple visual comparison of the PREEMs for the different petrolatum samples suggests that the PREEMs are better "fingerprints" than the steady-state EEMs. For example, the PREEMs collected at 15 MHz for the five petrolatums (Fig. 4) are much less similar than the corresponding set of steady-state EEMs (Fig. 1).

FIG. 4.

# 1

# 2 # 3

"'~;4.~o~,~ e~' "' ¢'~,7' v ~ ' ~ ~eCx~

"z, ' t~) o 0 ~ e ~,'" " ~ i 30~v e'"

PREEMs of the five different petrolatums at 15 MHz. Matrix reconstruction and spectral region as described for Fig. 3.

In order to accomplish the primary objective of this study, which was to compare the abilities of multifrequency phase resolution with steady-state analysis to differentiate between spectrally similar complex samples, it was desirable to consider quantitative methods for distinguishing between the data sets for the different samples in addition to simple visual discrimination. For quantitative comparisons, we focused on two samples, #2 and #5. Comparisons were based on the normalized eigenvalues of the primary eigenvectors, which were obtained from singular value decomposition of the steady- state EEMs and the PREEMs for replicate runs (nine replicates of SS EEMs, ten of the PREEMs) for the two samples. The eigenvalues represent the fractional contribution of each corresponding eigenvector to the total variance in the data array.

Several methods of data analysis were used to compare the replicate runs associated with the two samples. First, the means of the normalized eigenvalues of each of the primary eigenvectors for the replicate runs of samples #2 and #5 were compared with the use of the Mann- Whitney U-test for significance, is Significant differences (a = 0.05, where a is the significance level, i.e., 95% confidence that the mean values are significantly differ-

ent) between the two samples were found in the steady- state data for eigenvalue three only, and in the PRFS data for the first, second, and fourth eigenvalues at 60 MHz, the second and fourth eigenvalues at 15 MHz, and the second eigenvalue at 5 MHz. Thus, only the eigenvalue of the third eigenvector provides discrimination in the steady-state data, and only the eigenvalue of the second eigenvector provides discrimination at all three frequencies in the PRFS data.

In the second approach to discrimination between samples, the replicates of samples #2 and #5 were plotted in two-dimensional space in which the axes corre- sponded to the eigenvalues of two of the primary eigenvectors. For each of the two samples, the mean, or centroid, of the set of replicates was calculated, along with the Euclidean distance of each point to the centroid. 19 The average Euclidian distance for the set of replicates and the standard deviation was then calculated for both samples. The relative discrimination between the two samples was evaluated as a percentage, in which the sum of the standard deviations of the Euclidian distances for the two samples is divided by the distance between the centroids of the two samples and multiplied by 100. The lower the percent, the better the separation


between the clusters of replicates for the two samples and therefore the better the discrimination between samples.

Plots of the sample replicates in the two-dimensional space spanned by the first and second normalized eigenvalues yielded the following percentages for the different data sets: 53% for steady-state data; 37% for 60 MHz; 39% for 15 MHz; 87% for 5 MHz. Plots for the second and third normalized eigenvalues yielded the following percentages: 42 % for steady-state; 91% for 60 MHz; 13 % for 15 MHz; 32% for 5 MHz. Combinations of other eigenvalues provided less overall discrimination.

Clearly, the best discrimination between samples is achieved by the 15 MHz data, followed by 5 MHz, in the plot of the second and third eigenvalues. The 15 MHz and 60 MHz data sets in the plots of the first and second eigenvalues are tied for a distant third-place in discrim- inatory power. As in the Mann-Whitney U-tests of the one-dimensional plots of individual eigenvalues, the PRFS data provide better discrimination between samples than the steady-state data, and the second eigenvalue provides the best PRFS discrimination. The discriminating power of the second eigenvalue is greatly enhanced by combination with the third eigenvalue; it is also enhanced to a lesser extent by combination with the first eigenvalue. Thus, it appears that two dimensions are better than one.

Finally, the replicates for samples #2 and #5 were plotted in the three-dimensional space spanned by the normalized eigenvalues for three of the primary eigenvectors. These plots were generated by a commercial software package (MacSpin) which allows rotation of the scatter plots in any direction about the three axes. Com- parisons for the three-dimensional data were by visual examination only. The replicates for the two samples form well-separated clusters for the 5 MHz and 15 MHz data, but are slightly overlapping for the 60 MHz data. The steady-state data also form well-separated clusters for the two samples, although the distances involved in the steady-state clusters are much smaller than those for the PRFS data.

Clustering of the PRFS data was also examined for the normalized eigenvalues of a given primary eigenvector (first, second, third, etc.) in three-dimensional plots in which the axes correspond to the three modulation frequencies. For each normalized eigenvalue, a particular frequency axis appeared to provide the best discrimination between the two samples: the first eigenvalue was the best discriminator at 60 MHz, the second eigenvalue at 15 MHz, and the third eigenvalue at 5 MHz. As in the other data representations, best discrimination appears to be provided by eigenvalue two at 15 MHz.

DISCUSSION

Several observations can be made regarding the comparison of samples #2 and #5. The PRFS data indicate that the shorter-lived emission components are respon- sible for the largest share of the variance (i.e., dominate the first eigenvector) in the spectra and provide the best discrimination between samples at the highest frequency (60 MHz). The longer-lived emission components appear to dominate the second and third eigenvectors and be-

come increasingly important for discrimination between samples as modulation frequency is decreased.

Discrimination between samples based on two eigenvectors was significantly better than discrimination based on a single eigenvector, and the use of the second and third eigenvectors at 15 MHz provided the best discrimination, significantly surpassing the degree of discrimination in the steady-state results. This result suggests that relatively minor emission components in the inter- mediate lifetime range of 11 ns are the best discriminators between the two samples, and the discrimination is greatly enhanced by the use of PRFS to amplify the relative contribution of the minor components. In the steady-state data, discriminating information is masked by the dominant contribution to the spectral variance that appears to be very similar for all five samples. Dis- crimination between samples #2 and #5 does not occur until the third eigenvector is considered. It should be noted that the information represented in the third eigenvalue in the steady-state data may in fact correspond to the information in the second eigenvalue in the PRFS data, since the first eigenvalue in the steady-state data is probably dominated by the large peaks below 300 nm that are absent in the PRFS data due to the Pockels cell transmittance characteristics. On the other hand, the signal-to-noise ratio is lower for the PRFS data than for the steady-state data by as much as half an order of magnitude, as a result of the phase-resolution process; very low-level signals from discriminating components are therefore less likely to be detectable in the PRFS data than in the steady-state data.

CONCLUSIONS

This study demonstrates the improved discrimination afforded by selective enhancement of spectral features as a function of fluorescence lifetime, implemented in the frequency domain through phase resolution. Com- parison of the best case of discrimination for the steady- state and PRFS data clearly indicates the advantage of fluorescence lifetime selectivity for the petrolatum samples. Future work will explore other types of samples with spectral features in the visible range, in order to avoid the UV cut-off in the PRFS data. Methods for classification and comparison of samples that will exploit the information contained in the eigenvectors of the decomposed data matrices, as well as the associated eigenvalues, are currently under investigation.

ACKNOWLEDGMENTS

This work was supported by the United States Department of Energy through the Office of Basic Energy Sciences (Grant Number DE-FG05- 88ER13931).

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phase-resolved fluorescence spectral fingerprinting of petrolatums

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