Environ. Sci. Technol. 1998, 32, 294-298
Quantitative Monitoring of Volatile Organic Compounds in Water Using an Evanescent Fiber Optic Chemical Sensor DIANNA S. BLAIR* AND JEANNE BANDO Sandia National Laboratories, P.O. Box 5800, Albuquerque, New Mexico 87185-0755
This work evaluates the usefulness of two linear chemometric algorithms, principle component regression and partial least-squares analysis, for modeling the responses of an evanescent fiber optic chemical sensor to aqueous mixtures of organic analytes with individual concentrations ranging from 50 to 200 ppm. Two data sets were examined. One contained trichloroethylene, 1,1,2 trichloroethane, toluene, and chloroform. The second set contained these four analytes as well as tetrachloroethene. Both chemometric algorithms performed comparably on a given data set with cross-validated root mean squared errors of prediction (RMSEP) for trichloroethylene, 1,1,2 trichloroethane, toluene, and chloroform of approximately 6, 9, 6, and 16 ppm from the first set and 7, 11, 13, and 31 ppm from the second set with tetrachloroethene RMSEP of 31 ppm. The decrease in the quantitative performance of the algorithm for modeling toluene and chloroform upon addition of tetrachloroethene to the sample solutions is due to increased intensity of cladding absorption features in the spectral response matrix. These features overlap with the analyte absorption features of toluene and chloroform and reveal one of the limitations with this type of sensing format.
Introduction Quantitative and qualitative monitoring of volatile organic compounds (VOC), at parts-per-million (ppm) concentrations in aqueous solutions can be both difficult and time consuming. These measurements are inherently difficult due to potential interference from water. In a typical analysis of such a sample, the VOC analytes are separated from the water by sample pretreatment. For example, the EPAapproved method for this type of analysis, method 8260, requires purge-and-trap sample preparation, which volatilizes the analytes of concern and subsequently traps them onto sorbent material, which is then rapidly heated for injection into a gas chromatograph. Another approach is to separate and concentrate the organic analytes from the water matrix using a solvent-free extraction method. This has been performed using a silicone-coated silica core fiber mounted on a microsyringe in which volatile analytes preferentially partition in the silicone film before thermal injection into a gas chromatograph (1). Whereas this technique eliminates the gas phase stripping step of the sample preparation, it can still be time and labor intensive. An alternative to these staged methods of sample pretreatment prior to a traditional * Author to whom correspondence should be addressed. Fax: (505) 844-0116; e-mail:
[email protected]. 294
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analysis method is to use polymeric membranes directly integrated into a sensor platform where VOCs can preferentially partition. For platforms such as the quartz crystal microbalance and surface acoustic wave devices, probing changes in the membranes, i.e., acoustic frequency shifts caused by chemical absorption, provides inferential information about the presence of chemical analytes (2, 3). Another approach that has been examined extensively in the past decade employs coating optical wave guides for measurements involving evanescent wave interactions. Polymers have been used for the detection of organic molecules in aqueous solutions (4-7), and sol-gels have been used for pH measurements (8). On the basis of the principal of evanescent wave spectroscopy, this approach can use commercially available fiber optics as the optical wave guide element, resulting in a relatively inexpensive sensor format for alternatives to the traditional aqueous analysis methods in both environmental and industrial applications. In this work, we examine the quantitative response of an evanescent fiber optic chemical sensor, EFOCS, constructed from commercially available plastic-clad silica fibers. These types of fibers have been shown to be useful as wave guides in the near-infrared spectral region which can be used for quantitative monitoring of organic compounds. Specifically, this work evaluates the usefulness of linear chemometric algorithms for building models based on sensor response to aqueous mixtures containing trichloroethylene (TCE), 1,1,2trichloroethane (TCA), toluene, chloroform (chloro), and tetrachloroethene (PCE). These compounds were chosen due to their frequent presence at environmental contamination sites with subsurface solvent contamination.
Theory Light is guided in an optical element, or wave guide, at high efficiencies due to a phenomenon known as total internal reflection. Though the light undergoes total internal reflection, a small amount of energy actually enters the second medium. This energy, known as the evanescent wave, has been demonstrated to exist by a number of researchers and techniques (9-11). It has a defined penetration depth (dp) into the second medium (12)
dp )
λ1 2π(sin θ - n122)1/2 2
(1)
where light intensity is reduced to 1/e of its value at the interface. The angle θ is the angle of incidence at the interface with respect to normal, λ1 is the wavelength of light in the wave guide medium, and n12 is the refractive index ratio of the wave guide and surrounding medium, n1/n2. Infrared active species present in the second medium and within the penetration depth of the evanescent wave, in the region known as the evanescent field, absorb energy, resulting in a decrease in the intensity of light guided in the fiber. Recently, a general theory for fiber optic evanescent wave spectroscopy based on geometric optics has been published (13). This theory addresses the phenomenon of attenuated total reflection for straight, unclad, step-index multimode fibers. However, for this discussion, eq 2, which represents a Beer’s Law-type relationship between analyte concentration and sensor response, is adequate to describe the phenomenon (14):
()
-log
( )
NA2o I ) ReLc + log Io NA2
S0013-936X(97)00242-3 CCC: $15.00
(2)
1998 American Chemical Society Published on Web 01/15/1998
FIGURE 2. Single-beam evanescent reference spectrum of plastic clad silica fiber in near-infrared spectral region.
FIGURE 1. Evanescent fiber optic chemical sensor holder. In the above equation, I is transmitted light intensity after analyte exposure, Io is reference intensity with no analyte present, L is active fiber length exposed to analyte, c is molar concentration, Re is the effective molar absorptivity defined as a function of the analyte molar absorptivity and the fraction of light entering the fiber that is present in the cladding as the evanescent field (15). The terms NA and NAo are numerical apertures of the fiber with and without analyte present, respectively. Methods for Modeling Spectral Data. Chemometrics is used to efficiently extract information about a sample from chemical data. Using statistical methods, an empirical model is generated that correlates instrumental responses to known variables. Specifically, with spectroscopic data, the calibration process builds a model that correlates a physical parameter, such as analyte concentration, with spectroscopic responses for a set of samples. A number of excellent books can be found that describe chemometric techniques (16). Two specific techniques were used in this study: principle component regression (PCR) and partial least-squares (PLS) analysis.
Experimental Section The sensor is constructed by winding jacketed fiber in a 6.4 cm diameter loop onto a slotted, anodized aluminum holder designed to support the fiber in a rigid configuration, as shown in Figure 1. Of primary concern in designing this holder was breakage of the fiber element. During early stages of EFOCS development, it was found that the silica core of these fibers, stripped of their protective nylon jackets and wound in a relatively tight geometry, underwent breakage at stress points due to reaction with water. Therefore, care was taken to eliminate any tight bends in the wound fiber. Commercially available nylon-jacketed 200 µm silica core fibers clad with 230 µm silicone, with refractive indices of 1.4571 and 1.41, respectively, were purchased from Fiberguide Industries. The jacket was removed from the active length
of fiber by placing the wound fiber into boiling 1,2propanediol for 5 min. The stripped length of fiber used for the results reported here was 8 m. To minimize changes in modal distribution of the sensing fiber caused by temperature fluctuations and fiber movements, 400 µm silica core/480 µm silica-clad fibers were used to couple the sensing fiber to the optical bench. Glass-on-glass fibers were used to allow deployment of the sensing fiber in remote locations. Coupling between fibers was accomplished using bulk head type connectors with 1,2-propanediol as a coupling gel. A stirrer was incorporated into the EFOCS holder design to reduce the water phase diffusion times of analytes, and solution temperatures were monitored using an Omega RTD. All chemicals were purchased from Aldrich Chemical Co. Aqueous solutions of toluene, TCE, TCA, chloroform, and PCE were made volumetrically. On the basis of earlier diffusion studies (7), it was determined that 20 min exposure time was sufficient for analyte concentration in the 230 µm silicone-cladding fiber to reach equilibrium. Therefore, the EFOCS was exposed to the mixture solution for 20 min prior to analysis. The experimental designs that were used to generate the calibration data for prediction modeling are based on seven levels of concentration and four or five chemical factors, sets 1 and 2, respectively, depending on the number of analytes in the test mixtures (17). A total of 35 samples was present in each data set. Spectroscopic instrumentation consisted of a Bomem MD-155 Fourier-transform infrared (FT-IR) spectrometer configured for the near-infrared. A KCl beam splitter, quartz halogen near-infrared source, and a liquid nitrogen-cooled InSb detector were used. The EFOCS was coupled to the spectrometer using a fiber optic interface accessory manufactured by Bomem. Fibers were physically attached to the interface using standard fiber optic connectors. The number of scans coadded per spectra were 64 at 8 cm-1 resolution using cosine apodization. Data collection, processing, and storing was performed on a PC using a Galactic software package.
Results and Discussion All reference spectra were taken with the EFOCS in distilled water. A typical single-beam reference spectrum is shown in Figure 2. This spectrum illustrates the wavelengthdependent light loss determined by source characteristics, absorption and scattering losses in the optical path, and detector responsivity. The decreased light transmission observed between 5500 and 6000 cm-1 is a result of energy absorption by the overtone bands of the fundamental methyl functional groups present in the silicone cladding. The light transmission properties of the fiber are the primary factors contributing to the sensor detection limit. Total analyte signal VOL. 32, NO. 2, 1998 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 3. FT-IR transmission spectra of neat analytes in 2 mm cuvette for (1) TCE, (2) TCA, (3) toluene, (4) chloroform (intensity divided by 6), and (5) PCE.
FIGURE 4. EFOCS response spectra to 50 ppm aqueous solutions of (1) TCE, (2) TCA, (3) toluene, (4) chloroform, and (5) PCE. is a function of fiber length, as shown in eq 2. However, light transmission in the C-H overtone spectral region, where the analytes of concern absorb energy, determines the useful fiber length. For example, Figure 3 shows the five nearinfrared spectra of the pure organic compounds studied taken in transmission mode using a 2 mm quartz cuvette. The active spectral region for these compounds can be observed between 5500 and 6200 cm-1. The equilibrium sensor responses to 50 ppm aqueous solutions of the five analytes studied are shown in Figure 4. Sensor response to these analytes is a combination of factors. One factor is the molar absorptivity which is analyte specific. However, an equally important factor for this sensor is the partitioning coefficient. Defined as the ratio of equilibrium 296
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analyte concentration in the silicone cladding to the aqueous concentration, it provides a means of preconcentrating the analyte of concern in the evanescent field, thus improving its detection limits. Therefore, the resultant sensor response to any analyte is a combination of the above factors. In general, the sensor responses to TCE, TCA, toluene, and chloroform solutions resemble the neat analyte spectra in Figure 3. Due to relatively low partitioning, the chloroform analyte signal is significantly lower than the other three analytes. Also, each spectra has additional features associated with the silicone cladding. This is due to analyte present in the cladding increasing its effective refractive index and subsequent penetration depth, eq 1. This is demonstrated most strongly in the sensor response to PCE. It has no spectral
FIGURE 6. (1) First weight loading vector from PCE model, and (2) ratioed EFOCS spectra with silicone clad fibers of different length.
FIGURE 5. First eigenvectors generated from response matrices for (1) set 1 and (2) set 2.
TABLE 1. Modeling of EFOCS Response Data from Two Data Sets Using Different Chemometric Algorithms
analyte toluene TCE TCA chloro PCE
PLS RMSEP, set 1
no. of factors
PCR RMSEP, set 1
no. of factors
6 6 9 16
5 3 4 8
6 7 9 19
9 2 6 13
features in the region of interest but has a relatively high refractive index, 1.51. As this analyte diffuses into the cladding, the effective refractive index of the cladding increases with a concomitant increase in the penetration depth. Ratioing the resultant spectrum after PCE exposure to a reference spectrum, i.e., with no analyte present, results in what appears to be negative cladding absorption features and a positive baseline shift. Without PCE absorption
PLS RMSEP, set 2
no. of factors
PCR RMSEP, set 2
no. of factors
13 7 11 31 31
5 3 6 6 3
12 7 13 28 28
7 4 9 7 4
features, the presence of this chemical in a mixture of other analytes represents an extremely difficult system to model. It was observed that the sensor response to TCE, TCA, toluene, and chloroform solutions exhibited negative baseline shifts. This appears to contradict eq 1 and what was observed for PCE solutions since all these analytes have refractive indices greater than the silicone cladding. With additional experimentation, it was determined that the baseline shifts VOL. 32, NO. 2, 1998 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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were caused by lack of temperature control in the exposure bath. While the reference spectra were taken with the sensor immersed in a water bath at room temperature, sensor responses to the analytes were taken in water continually stirred using the sensor motor. This motor warmed during use and was observed to raise the water temperature up to 6 °C. Such temperature changes are known to increase the throughput of fiber optic cables due to the temperature dependent material dispersions (18). Sensor response data were collected on aqueous mixtures based on the experimental design described in the Experimental Section. Set 1 contains four analytes, TCE, TCA, toluene, and chloroform. Set 2 contains all compounds in set 1 with the addition of PCE. Similar spectral features were observed in both data sets as well as baseline shifts caused by the temperature effect and cladding refractive index perturbation. However, spectra from set 2 show greater positive base line offsets than set 1 due to the presence of PCE. Examining the first eigenvector for each data set is useful to determine the spectral frequencies that undergo the greatest variations, Figure 5. The eigenvectors are obtained by performing a singular valued decomposition (19), on the matrix of sensor responses for each data set. Of interest is how the first eigenvectors resemble what could be viewed as an average spectrum for the data sets. For mean-centered data, this suggests that path length changes in the data set are significant sources of spectral variation. This is further substantiated by inclusion of absorption features unique to the silicone-cladding material in the eigenvectors. Furthermore, differences in the relative intensity of absorbance features can be observed between the two data sets. Without PCE present in the mixture, set 1, the spectral features directly attributable to TCE, TCA, toluene, and chloroform have higher relative intensities when compared to the absorbance features associated with the silicone cladding. Two linear chemometric techniques, PLS and PCR, were used to generate calibration models on both data sets. All models were generated on mean-centered data using crossvalidation. Outlier detection was performed by examining the concentration F-ratio statistic (20). The results of these calibrations are summarized in Table 1 with performance quantified by the cross-validated root mean squared error of prediction, RMSEP. No significant difference in the performance of the models generated using either algorithm for a particular data set was observed. In general, it can be observed that PCR required a higher number of factors, i.e., sources of variation used to model the data set, to obtain comparable performance to PLS. This is a result of the incorporation of chemical information earlier in the model building with PLS (20). However, the models generated on data set 1 performed significantly better, as determined by F-ratio test at the 95% confidence limit, for toluene and chloroform than the models generated on data set 2. As discussed above, the presence of PCE in the solution mixtures resulted in the cladding spectral features contributing to spectral variation in the sensor response. This relationship between cladding absorption bands and PCE concentration is best illustrated in Figure 6, where the first PLS weight-loading vector for the PCE model, set 2, is plotted. The weight-loading vector, which results from the decomposition of the spectral matrix, is related to the spectral variations that result from concentration differences of that component. Also included in the figure is the ratioed spectrum of two fibers of different lengths which shows the spectral contribution due to the silicone cladding. The similarities between these two spectra clearly demonstrates that the model generated for PCE concentration is related primarily to cladding absorption bands. Furthermore, for analytes with absorption features that overlap the cladding 298
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absorption features, such as toluene and chloroform, degradation in performance of models generated in the presence of PCE occurs due to variations in the cladding band features masking the analyte spectral features. This is particularly true at low concentrations where spectral contribution from the analyte of concern is minimal compared to the spectral variance caused by the PCE. In contrast, comparable performance between data sets was observed for TCE and TCA prediction models generated using PLS or PCR. A contributing factor to this robustness could be the presence of analyte spectral features well shifted from the cladding features. Examination of the data indicates larger errors at higher concentrations. This lack of fit may be caused by using linear methods to model a nonlinear system. The reference analyte concentrations used in generating all models were aqueous analyte concentrations not the true evanescent field concentrations which could be a nonlinear function of solution concentrations. Furthermore, cosolvency effects could be present due to the high concentration of other analytes. Also, as discussed earlier, a change in sample path length occurs when the relatively high index analytes diffuse into the cladding. These nonlinear effects have not been incorporated into the models generated using either algorithm.
Acknowledgments The authors would like to thank Drs. David Haaland and Edward Thomas, of Sandia National Laboratories (SNL), for their scientific contributions to this work and Wayne Einfeld, of SNL, for his critical review of this paper. This work was performed at SNL supported by the U.S. Department of Energy under Contract DE-AC04-94AL85000.
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Received for review March 17, 1997. Revised manuscript received October 28, 1997. Accepted October 30, 1997.X ES9702428 X
Abstract published in Advance ACS Abstracts, December 15, 1997.
