Anal. Chem. 1989, 6 1 , 313-320 (3) Smlth, D. S.; Hassan; Nargessi, R. D. Modern Fluorescence Spectroscopy; Wehry, E. L., Ed.; Plenum: New York, 1981; Vol. 3, Chapter 4. (4) Brlght, F. V. Anal. Chem. 1988, 6 0 , 1031A. (5) Amkraut, A. A. Immunochemrstry 1964, 1 , 231. (6) Bright, F. V.; McGown, L. 8. Telenta 1985, 3 2 , 15. (7) Spencer, R. D. Clln. Blochem. Anal. 1981, 10. 143. (8) Dandllker, W. 8.; Kelly, R. J.; Dandllker, J.; Farquhar, J.; Levin, J. I m munochemlstry 1973, 10, 219. (9) Spencer, R. D.; Toledo, F. 6.;Williams, 6. T.; Yoss, N. L. Clln. Chem. 1973. 19. 838. (10) Perrin. F. J . Phys. Radium 1928, 7 , 390. (1 1) Lakowicz, J. R. Principles of fluorescence Spectroscopy; Plenum: New York, 1983; Chapter 5. (12) Weber, G. A&. Protein Chem. 1953, 8 , 415. (13) Weber, G. J . Chem. Phys. 1977, 66, 4081. (14) Lekowlcz, J. R.; Cherek, H.; Maiiwal, B. P.; Gratton, E. Biochemistry 1985, 2 4 , 376. (15) Lekowicz, J. R.; Gratton, E.; Cherek. H.; Maliwal, 8. P.; Laczko. G. J . Bbl. Chem. 1984, 259, 10967. (16) Gratton. E.; Limkeman, M. Biophys. J . W83, 44, 315.
313
(17) Yguerabide, J.; Epsteln, H. F.; Shyer, L. J . Mol. Bbl. 1970, 51, 573. (18) Holowka, D. A.; Cathou. R. E. Biochemrstry 1976, 15, 3379. (19) Tao, T. Biopo~mefs1989, 8 , 609.
RECEIVED for review August 22,1988. Accepted November 14,1988. This work was supported by BRSG SO7 RR 07066 awarded by the Biomedical Research Support Program, Division of Resources, National Institutes of Health, the donors of the Petroleum Research Fund, administered by the American Chemical Society, a New Faculty Development Award from New York State/United University Professions, a Non-Tenured Faculty Grant from 3M, Inc., a grant from the Health Care Instruments and Devices Institute at SUNYBuffalo, and the Center for Advanced Technology (SUNYBuffalo).
Prediction of Gasoline Octane Numbers from Near-Infrared Spectral Features in the Range 660-1215 nm Jeffrey J. Kelly,' Clyde H. Barlow,' Thomas M. Jinguji, and James B. Callis* Center for Process Analytical Chemistry, Department of Chemistry, BG-IO, University of Washington, Seattle, Washington 98195
The feasibility of predicting the quality parameters of gasoline from Its absorption spectrum in the wavelength range 660-1215 nm was investigated. I n this spectral region, vibrational overtones and combination bands of CH groups of methyl, methylene, aromatic, and olefinic functions were observed. With the aid of multivariate statistics, the spectral features could be correlated to various quality parameters of gasoline such as octane number. As an example, multivariate analysis of the spectra of 43 unleaded gasoline samples yielded a three-wavelength prediction equation for pump octane that gave excellent correlations ( R 2 = 0.95; standard error of estimate, 0.3-0.4 octane number; standard error of prediction, 0.4-0.5 octane number) with the ASTM motor determined octane numbers. Independent multivariate analysis using partial least-squares (PLS) regression yielded similar results. An additional set of nine sampler from the Pacific Coast Exchange Group of the ASTM were examined for ten different quality parameters (research and motor octane numbers, Reid vapor pressure, API gravity, bromine number, lead, sulfur, aromatic, oiefinlc, and saturate contents). Regression analysis of the spectra results in correlation of nine of the ten properties with R 2values ranging from 0.94 to 0.99 and standard errors near the independent reference test values.
Octane number is an experimentally measured, fuel-performance property of gasoline, strongly related to consumer satisfaction. The octane rating of a gasoline is determined by the measurement of a standard knock intensity in specially designed, ASTM-CFR test engines, where the sample's performance is compared to reference fuel blends ( 1 ) . The in-
* Corresponding author.
Permanent address: Department of Chemistry, The Evergreen State College, Olympia, WA 98505. 0003-2700/89/0361-0313$01.50/0
strumentation required for the measurement of octane numbers is expensive (over $100 OOO), requires constant maintenance, needs frequent standardization, consumes approximately 1pt of gasoline per test, and takes 20 min per sample. Such instruments are not well suited for on-line applications such as blending operations. In addition, catalysis research is made more time-consuming and expensive due to the large samples required. Clearly, an alternate test for octane number is highly desirable, especially one that is more rapid, more portable, less expensive, and suitable for on-line use. Clevett (2) has reviewed a number of alternative designs for on-line octane analysis. Many of these instruments use thermocouples to detect the partial oxidation reactions that are the precursors to knocking. These correlative instruments still rely on combustion measurements and are relatively expensive and time-consuming (5-20 min per sample) but do consume smaller amounts of gasoline than the knock engine. An alternative strategy for octane determination is based on the relationship of chemical structure and odane. Straight chain hydrocarbons have low octane numbers (normal heptane defines the zero of the octane scale), branched hydrocarbons have increased octane numbers (isooctane is used to define 100 on the octane scale), and aromatic hydrocarbonsgenerally have high octane numbers. Given the octane numbers of a gasoline's constituents and the amounts of each present in a gasoline, one could hope to evolve an additive relationship that would predict the overall octane number. This approach was employed some years ago by Walsh and Mortimer (3), who used gas chromatography to identify and quantify the amount of each gasoline component and then applied linear multivariate statistics to predict octane number. Although this technique gives excellent predictions for octane number, it is too time-consumingfor on-line or even routine laboratory analysis. Yet another approach relies on the use of the number and types of functional groups in a gasoline sample to predict its 0 1989 American Chemical Society
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physcal properties including octane number. Le and Allen ( 4 ) predicted thermodynamic properties such as heat capacities of hydrocarbons by consulting the thermodynamic literature for relevant group properties and adding the contribution of each. Thus, if one had an experimental technique for determining the types and amounts of functional groups in a gasoline sample, one could hope to predict gasoline properties. This leads to the idea of using one of the various spectroscopic techniques, such as nuclear magnetic resonance (NMR), mass, or infrared spectroscopy, which, while not resolving every component of the sample, do allow identification of spectral regions that yield estimates for functional groups (5).Spectroscopic techniques have the advantage of being much faster than gas chromatography and yield data that are easily digitized. The approach of linear group additivity of gasoline components was employed by Meyer et al. (6) to predict octane numbers from proton NMR spectra at 100 MHz. This work showed a correlation between chemical structure of gasoline components and octane rating of the end product; however, the relationship obtained was not strictly linear. Moreover, these authors used weighting coefficients for the estimation that were based largely on heuristics and intuition. Dolbear (7) determined that the individual integrated areas of the aromatic, aliphatic, and allylic regions of the NMR spectra of gasoline could be used in a linear equation to give good correlation with octane numbers of gasolines. Although the above studies were successful in predicting octane numbers, NMR is not a technique that is well-suited to on-line use. Recent work shows that more robust calibrations and predictions can be obtained when systematic use is made of multivariate methods to analyze vibrational spectroscopic data. The feasibility of this statistical calibration approach was demonstrated by Honigs, Hirschfeld, and Hieftje (81, who showed it is possible to simultaneously determine the heat of formation, average molecular weight, and number of methyl groups per molecule for mixtures of three hydrocarbons by multivariate analysis of the near-infrared (near-IR) absorption spectrum of a sample. The near-infrared spectroscopic region is attractive for gasoline analysis because most of the absorption bands observed in this region arise from overtones or combinations of carbon-hydrogen stretching vibrations of the hydrocarbon molecules. The absorptivity of these bands is largely independent of the remainder of the molecule but does depend on the concentration of the absorbing functional group (9, IO), e.g., methyl, methylene, olefinic, or aromatic CHs. This makes near-IR spectroscopy especially suited for analyses based on hydrocarbon functional groups. The short wavelength portion of this region, roughly 700-1200 nm, has greatly decreased absorptivities, but has the distinct advantages that it can be transmitted over inexpensive fiber optics, can utilize long path lengths through hydrocarbon mixtures (1-5 cm), and can be measured with very inexpensive light sources (tungsten lamps, light-emitting diodes) and detectors (silicon diodes). We have named this spectral region the short wavelength near-infrared (SW-NIR). The above characteristics recommend SW-NIR spectroscopy for rapid, on-line hydrocarbon analysis. We report in this paper a spectroscopic method that employs multivariate statistical analysis and that can successfully predict the results of the desired quality assurance tests for gasoline. In particular we have applied multilinear regression and the method of partial least squares to SW-NIR spectra of gasolines and have obtained calibration equations that predict physical properties, and particularly octane numbers, of gasolines with a precision approaching that of the octane testing motor.
Further testing of a wide range of gasolines and gasoline blending stocks from different seasons, locations, sources of crude and refineries, with different additives, and evaluated by different forms of spectroscopy needs to be undertaken to determine the precise equation applicable to a given sample set within the entire population of gasolines. Nevertheless, this study demonstrates the feasibility of the general approach of applying SW-NIR spectroscopy combined with multivariate calibration methods to quality assurance testing of gasolines. EXPERIMENTAL SECTION
Gasoline Samples. Sixty-fivegasoline samples were donated by A1 Herbert and John Mough of the Division of Measurement Standards, Department of Food and Agriculture, State of California during the week of July 20,1986. Forty-eight samples were received from the Sacramento testing facility and 17 from the Anaheim facility. These samples represent California summer-run gasolines. Each of the gasoline samples was documented as to the values of Research Octane Number or RON (ASTM Method 2699-84), Motor Octane Number or MON (ASTM Method 2700-84), and lead content as determined by the individual testing facility. A value for Pump Octane Number (PON) was calculated by PON = (RON + MON)/2. The gasoline samples were divided into a set of unleaded gasolines (43 samples) with RON’Sranging from 91.7 to 98.4 and MON’s varying from 82.0 to 87.3 and a set of leaded gasolines (22 samples) where RON and MON had the ranges 92.7-98.4 and 82.0-87.4, respectively. Another set of nine gasoline samples was donated by Shauna May TecleMariamof the United StatesOil & Refining Co. in May 1986. They were obtained from the Pacific Coast Exchange Group of the ASTM and collected over a period of 19 months from August 1985 to February 1986. These samples were characterized by 24-34 different laboratories in the Pacific Coast Exchange Group for a series of 10 different ASTM tests for gasoline-RON, MON, AA lead, Reid vapor pressure, API gravity, sulfur, bromine number, and aromatic, olefins, and saturates contents. All gasoline samples were stored in tightly stoppered bottles, sealed with sealing tape and kept at 4 “C in the dark. Samples were removed from storage and warmed to room temperature just before spectral analysis. Hydrocarbon Reagents. Benzene, n-heptane, and isooctane were Baker Reagent Grade. Spectroscopy. SW-NIR spectra (-1215 nm) were recorded on a Pacific Scientific Co. (PSCO) Model 6250 scanning spectrophotometer interfaced to an IBM AT microcomputer. The instrument was equipped with a flexible bifurcated fiber-optic light guide for remote sensing of the sample’s spectrum. The 18in. light guide consisted of a concentric outer bundle of illuminating and inner bundle of analyzing fibers. The end of the light guide butted up against a cylindrical quartz sample cuvette that was backed by a polished aluminum surface. With reflection off the aluminum block, the illumination beam passed twice through the 1.00-cmcuvette for a total of 2.00 cm path length before collection by the analysis fibers and transmission to the silicon detector of the PSCO spectrometer. The spectra were influenced by interference from the instrument’s cut-off filter in the 660-700-nm region and decreased detector sensitivity above 1150 nm. Spectra were collected and digitized (700 points/555 nm) at 2 Hz. Fifty scans were signal-averaged and stored along with the sample constituent values for RON, MON, lead, and PON. Wavelength calibration of the Pacific Scientific spectrophotometer was performed in the PbS-detected,long wavelength 12W25oanm region and had a measured wavelength accuracy of better than fl nm. This, however, resulted in a constant 22-nm offset when wavelength calibration was performed in the silicon-detected, short wavelength 680-1235-nm region. All wavelength values reported here have been corrected for this offset. Statistical Methodology. Stagewise multiple linear regression was performed on the 700-point SW-NIR spectra of gasoline sample sets with an Il3M microcomputer and the analysis routines supplied by PSCO (11). Simple and multiple R values were determined and standard error on regression was calculated. Mathematical treatments of the data prior to multiple regression analysis, such as base-line drift correction, n-point smoothing, and first and second derivatives,were explored to determine the
ANALYTICAL CHEMISTRY, VOL. 61, NO. 4, FEBRUARY 15, 1989 .42 .
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Figure 1. Short wavelength near-infrared (SW-NIR) absorption spectra of 43 unleaded gasollnes.
best fit linear-regression equation for each data set. Intercorrelations between the regression wavelengthswere calculated and high correlations eliminated in order to reduce information redundancy and overfitting. Scatter diagrams, residual plots, correlation spectra, and graphs of regression prediction vs actual value (lab plots) were obtained as visual representations of the results. Partial least-squares (PLS) analysis of the SW-NIR spectra was performed on both a DEC Microvax I1 and an IBM microcomputer using the PLS 2-block modeling program (12-14).The standard error of estimate, R2value, covariance, F-test statistic, and correlation coefficient were determined for each of the constituents (dependent variables). The linear regression equations and PLS results were then used to predict octane numbers of internal subsets and of other gasoline sample sets.
RESULTS AND DISCUSSION Spectroscopic Assignments. Figure 1 presents the SWNIR spectra of the 43 unleaded gasoline samples. The absorption bands seen in Figure 1 are the result of overlap of the many different absorbances of the component hydrocarbons in gasoline. The individual absorption bands can be readily assigned by comparison with the SW-NIR spectra of gasoline components. Figure 2 illustrates representative gasoline component hydrocarbon spectra. In part a of Figure 2, n-heptane with 6 methyl C-H and 10 methylene C-H shows more intense methylene bands than methyl (15,16).Benzene, part b of Figure 2, has aromatic C-H bands that are clearly resolved from those of alkanes. In part a of Figure 2, 2,2,4trimethylpentane (isooctane) with 15 methyl C-H and 1 methylene C-H shows stronger methyl bands than methylene. In the near-IR spectral region, weak absorptions by hydrocarbon C-H bonds arise from overtones and combinations of molecular vibrations. Of primary importance to gasoline studies in the SW-NIR are the overtones of the symmetric and antisymmetric C-H stretching vibrations. The symmetric stretching becomes comparatively weaker and only the antisymmetric persist at higher overtones. In addition, combination bands of stretching with bending vibrations in methyls and stretching with scissoring vibrations in methylenes are found in the near-IR, but these are somewhat less intense than overtone bands in the SW-NIR. Olefins and aromatics exhibit similar C-H overtones in the near-infrared and their combination bands are barely detectable in normal SW-NIR spectra. General assignmenta of the near-IR absorption peaks to specific overtones and combinations of C-H fundamental frequencies have been suggested [see Weyer (17)and Wheeler (18) for reviews]. Table I presents a summary of these assignments for the broad hydrocarbon C-H absorptions observed in gasoline in the short wavelength near-infrared region. Multilinear Statistical Analysis of SW-NIR Spectra of California Unleaded Gasolines. The major observable
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Flgure 2. SW-NIR absorption spectra of selected hydrocarbons: (a) n-heptane; (b) benzene; (c) isooctane.
Table I. Main C-H Absorption Bands in the SW-NIR Wavelength Region (approximate peak maxima in nm) absorption band 2nd overtone combination 3rd overtone 4th overtone 5th overtone
methyl C-H methylene C-H 1190 1015 913 746
1210 1053 934 762 715
aromatic C-H 1145 875 714
variance in Figure 1is base-line offset due to the single-beam nature of the spectrometer and positioning of the cuvette in front of the fiber-optic probe. This variance can be removed either by offset subtraction or by derivative transformation. Offset subtraction was performed by subtracting the absorbance a t 790 nm from the digitized absorbance values at all other wavelengths; 790 nm was selected because of the minimal variance in this region and lack of influence from the cut-off filter and detector limitations.
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Table 11. Three-Wavelength Regression Equations for Octane Numbers of Unleaded Gasolines octane
constants
wavelengths, nm
std error of
no.
K(O)
K(1)
K(2)
K(3)
A1
A2
13
regression
Pump
95.9 71.1 78.4
-513.2 -506.5 -29.7
274.9 135.7 498.0
215.9 302.0 -538.5
932 932 930
896 1164 1012
1032 896 940
0.323 0.388 0.406
research motor
93.0
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91.51
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Figure 3. Mathematical transformation of 15 representative unl ed gasoline SW-NIR spectra-base-llne offset subtraction spectra, using 790 nm as reference wavelength. Wavelengths selected by stagewlse multhear r e g e d o n analysls for pump octane number of the California
97
unleaded summer gasoline data set are Indicated. P
Offset subtraction results in spectra (Figure 3) that reveal more compositional variation between samples than the original spectra and thus more variance with which to attempt correlation to octane number. We found that both gave equally good correlation with gasoline properties. The results reported here are for the analysis of the offset-subtracted (absorption) spectra as these spectra are easier to interpret and the equations are applicable to filter-type on-line analyzers. Linear regression equations of the form octane number = K(0) K(l)-A(hl) K(2).A(X2)
+
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were determined for motor, research, and pump octane numbers on the base-line corrected SW-NIR spectra of the California summer unleaded gasoline sample set. In addition, regressions were performed on the n-point smoothed, first and second derivatives of the spectra, but the straight digitized spectra without math transformations other than offset subtraction provided the best regression correlations. Table I1 summarizes the regression results on each of these constituents in the unleaded sample set. Wavelengths selected by regression analysis for pump octane number are indicated in Figure 3. As seen from Table 11, regression results lead to multiple R2values of 0.973 for RON, 0.953 for MON, and 0.975 for PON.Quite satisfactory correlations are found between octane numbers and linear regression absorption equations by using three wavelengths. Figure 4 presents lab plots of the actual vs regression predicted octane numbers for the California unleaded gasolines. Although there are differences in the wavelengths selected by regression for each of the different octane numbers, it can be seen by correlating the wavelengths in Table I1 with the C-H absorption regions in Table I and Figure 2 that the same major features of gasoline SW-NIR spectra are selected. The first wavelength chosen for each regression equation is the third overtone absorption peak of methylene (930-940 nm) whose regression coefficient is negative. This is consistent with the idea that the major goal of gasoline component
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Figure 4. Lab plots of actual vs three-wavelength regredon predicted octane numbers from SW-NIR spectra of California unleaded gasolines: (a) pump octane number: (b) research octane number; (c) motor octane
number. blending is to reduce the number of methylene groups arising from straight chain compounds and to replace them with branched and aromatic compounds. Therefore, it is not surprising to find that the next wavelengths chosen (896,1012, 1032, and 1164 nm) involve positive correlations to methyl and aromatic groups. From a purely statistical point of view, one is probably not entitled to use three wavelengths for a sample set of this size. However, when one can invoke sound physical reasoning based on established spectroscopic principles, then more wavelengths are justified.
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2 4 6 8 IO NUMBER o f FACTORS RETAINED Figure 5. Prediction residual sum of squares (PRESS) plot of the PLS model of research octane number from SW-NIR spectra of unleaded gasollnes. The y axis displays the variance in the dependent variable (octane number) remaining as a function of the number of Independent Variables on the x axis. 0
Table 111. PLS Regression Statistics for Four Independent Variables
std error of estimate corr coeff R2statistic
research
motor
pump
0.388 0.990 0.981
0.395 0.989 0.977
0.303 0.994 0.989
90 92
Partial Least-Squares Regression Analysis of California Unleaded Gasolines. Partial least-squares (PLS) analysis of the SW-NIR spectra of unleaded gasolines closely parallels the results of multilinear regression analysis. The number of independent variables (absorption values at different wavelengths) needed to account for the majority of the constituent (octane number) without introducing high levels of noise and overfitting was determined by a prediction residual sum of squares (PRESS) plot (12),which is the predictive error, sum of squares value plotted against the number of independent variables. The PRESS plot for research odane number is given in Figure 5. A summary of the PLS regression statistics is presented in Table 111. This analysis confirms that a regression matrix derived from three to four latent variables provides sufficient information to describe the majority of the variance in the unleaded gasoline data set. Lab plots of a mean-centered, four latent variable PLS analysis for each octane number, using the same offset-subtracted unleaded gasoline spectra as in MLR, are found in Figure 6. Comparison with the MLR lab plots in Figure 4 shows strong similarities. Correlations between predictions of the two different statistical treatments can be determined by comparing the octane value each predicts. For multilinear regression (MLR), the three-wavelength equation from Table I1 was used to predict octane numbers. The PLS matrix modeling method program was used to predict values from four latent variables for the same gasoline samples. Table IV illustrates such a prediction of pump octane number for gasoline samples in the California unleaded data set. The lab value is the actual pump octane number as calculated from the RON and MON values supplied by the California Division of Measurement Standards of the Department of Food and Agriculture. This correlation of statistical methods can also be visualized by simultaneously graphing their two lab plots on the same graph. In Figure 7 the predicted data pairs for pump octane numbers are seen to cluster closely, indicating similar predictions by the two methods. Regression of PLS predicted values against MLR values for pump octane numbers yields a standard error of estimate of 0.216 octane numbers and a correlation coefficient of R = 0.994,demonstrating the utility of applying either statistical method to the determination of
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octane numbers from the SW-NIR spectra of gasolines. Plots of PLS regression coefficients at each wavelength multiplied by the average absorption coefficient at that wavelength as a function of near-infrared wavelength provide a spectrum that visualizes the extent of correlation in each region of the spectrum to the reference property. Parts a and b of Figure 8 present such correlation spectra of the 700 data
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ANALYTICAL CHEMISTRY, VOL. 61, NO. 4, FEBRUARY 15, 1989
Table IV. Comparison of Calculated vs Actual Pump Octane Numbers for California Unleaded Gasoline8 by Multilinear Regression (MLR) and Partial Least Squares (PLS) sample no.
lab value
MLR prediction
MLR residual
PLS prediction
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
87.85 88.0 90.95 88.0 90.8 91.8 91.95 89.0 87.1 92.0 92.05 91.1 91.05 88.15 87.4 87.45 87.0 91.6 87.7 89.2 89.85 90.9 90.7 91.35 90.95 91.4 92.0 87.6 90.4 87.9 87.25 87.55 90.35 92.55 92.2 92.25 92.55 87.7 92.15 92.8 87.6 87.5 92.05
87.29 87.50 90.79 87.72 90.65 91.81 91.85 89.35 87.54 92.18 92.03 91.19 91.16 88.58 87.21 87.17 87.06 91.54 87.57 89.82 90.52 91.14 91.01 91.15 90.91 91.20 92.08 87.88 90.42 87.16 87.91 87.48 90.15 92.45 92.08 91.98 92.54 87.79 91.86 92.50 88.00 87.49 91.80
-0.56 -0.50 -0.16 -0.28 -0.15 +0.01 -0.10 +0.35 +0.44 +0.18 -0.02 +0.09 +0.11 +0.43 -0.19 -0.28 -0.06 -0.06 -0.13 +0.62 +0.67 +0.24 +0.31 -0.20 -0.04 -0.20 +0.08 +0.28 +0.02 -0.74 +0.66 -0.07 -0.20 -0.10 -0.12 -0.27 -0.01 +0.09 -0.29 -0.30 +0.40 -0.01 -0.25
87.60 87.53 90.86 87.73 91.17 91.84 91.76 89.58 87.71 91.99 91.93 91.33 91.27 88.61 86.88 87.01 86.79 91.70 87.62 89.72 90.12 90.87 90.68 91.26 90.89 91.11 91.92 87.76 90.38 87.37 87.57 87.78 90.52 92.29 91.97 91.89 92.76 88.00 91.83 92.46 88.07 87.30 92.06
PLS residual
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point SW-NIR spectra for research and motor octane numbers, respectively. Note that the major features of t h e spectrum support the results obtained by step-forward regression analysis. In particular, there is a strong negative correlation in the third overtone methylene band for both RON and MON and that the positive correlation with the third overtone methyl band is stronger for motor than research octane. Similar, b u t less clearly defined correlations are seen i n t h e 1035-1070-nm combination bands with motor again showing a stronger correlation with methyl absorption. T h e second overtone region of methyl groups correlates positively but gives even less resolution of the different hydrocarbon contributions
to octane rating. Aromatics contribute, as determined by their second overtone band at 1140-1160 nm, but appear t o correlate more strongly with research octane number than with motor octane.
Verification of Octane Number Predictions for Gasolines Using Internal Data Sets. In order to test t h e validity of the two statistical methods further, t h e 43 unleaded gasoline sample set was divided into two subsets by using the ujackknife” procedure of placing every other sample into a
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Table V. Internal Predictions of Unleaded Gasoline Octane Numbers research motor pump A. Jackknife Results 1. 4-variable PLS regression (a) odd-numbered training set std error of calibration R2
(b) even-numbered test set std error of prediction 2. 3-wavelength MLR regression (a) odd-numbered training set std error of calibration R2
(b) even-numbered test set std error of prediction B. Sacramento vs Anaheim Results 1. 4-variable PLS regression (a) Sacramento training set std error of calibration
R* (b) Anaheim test set std error or prediction 2. 3-wavelength MLR regression (a) Sacramento training set std error of calibration RZ (b) Anaheim test set std error of prediction
0.383 0.994
0.355 0.993
0.324 0.995
0.480
0.521
0.461
0.367 0.979
0.389 0.957
0.240 0.988
0.574
0.421
0.567
0.417 0.988
0.306 0.991
0.294 0.993
0.469
0.776
0.423
0.391 0.966
0.293 0.972
0.261 0.981
0.421
0.469
0.443
.___
different subset. Multilinear regression analysis was then performed on the odd-numbered samples (22 gasolines) and t h e resulting three wavelength regression equation used to predict octane numbers of t h e even-numbered gasoline samples. Another natural division within the unleaded sample set concerns those gasolines tested in Anaheim versus those tested i n Sacramento. A three wavelength MLR equation determined from the Sacramento-tested subset was used to predict the values for the Anaheim-tested gasolines. A similar validation using these subsets was performed by using t h e
ANALYTICAL CHEMISTRY, VOL. 61, NO. 4, FEBRUARY 15, 1989
319
Table VI. Prediction of ASTM Gasoline Quality Parameters from SW-NIRSpectra test r
O
z
0
-1.2-
-1.6-
:
I-
-2.0-
*
W
0 -2.4
I
I
I
I
0 0.1 0.2 0.3 0.4 0.5 L E A D CONTENT of GASOLINE ( g / g a l )
Flgure 9. Leaded gasolines: deviations from unleaded octane pre-
dictions. four-variable PLS modeling procedure. Table V presents a statistical summary of these predictions. Note that the standard error of prediction is a factor of 2 poorer than the standard error of regression, suggesting little overfitting by the regression analyses employed. Statistical Analysis of the SW-NIR Spectra of Leaded Gasolines. Regression analysis was attempted on the 22 leaded California gasoline samples. A good correlation was not obtained between lead content and SW-NIR absorption ( R = 0.5170 for three wave-lengths), indicating the very low concentration of lead compounds does not have detectable absorption features in this spectral region. As expected, regressions for octane numbers in leaded gasolines were much less successful than for unleaded samples (RON, R2= 0.876; MON, R2= 0.920; PON, R2= 0.921). Since lead content could not be determined from the SW-NIR spectra, the octane value increase by lead compounds would also not be detectable. However, by use of regression equations generated for octane numbers of unleaded gasoline (Table 11), it is possible to predict the “unleaded”portion of the octane values of leaded gasolines. The predicted values are lower than the actual values, as expected. Figure 9 presents the residuals of this prediction (difference between actual leaded gasoline octane number and the predicted “unleaded” value). Actual gasoline lead susceptibility (increased octane rating with added lead compound) is known to be a logarithmic function. Yet over this rather narrow lead concentrationrange, our results suggest a roughly linear increase in octane number with a slope of 4.0 octane number/ (g of Pb/gallon). Regression Analysis for ASTM Quality Features of Gasolines. The nine gasoline samples obtained from the Pacific Coast Exchange Group of the ASTM, although limited in number, provide an opportunity to apply regression analysis of SW-NIR spectra to much more thoroughly analyzed samples. These gasolines samples have been evaluated by from 20 to 34 different testing facilities for 10 quality features of gasoline. Table VI presents the results of three wavelength multilinear regression against the average of the values determined by the ASTM member testing facilities. Although the overall sample set is small, these results suggest that regression analysis of near-IR spectra may be quite useful in the determination of at least eight of these ten standard gasoline tests. The standard errors of regression are quite good compared to the standard deviations of the results from the physical test facilities. This suggests that analytical techniques based on SW-NIR spectroscopy may prove useful in the rapid determination of these gasoline properties.
CONCLUSIONS Our results support the concept that a relationship exists between chemical structure and the fuel-performance property-octane number. Although the weak absorptions by the various types of hydrocarbon C-H bonds are broad overlapping features in the near-IR region, multilinear regression analysis on short wavelength near-IR spectra demonstrates
research octane no. motor octane no. lead (g/gal) REID vapor (psi) API gravity sulfur (wt %) bromine no.
aromatic SD olefins saturates
ASTM range MLR std error MLR of values of calibration corr coeff 91.1-96.3 82.3-84.2 0.0024.739 8.5-12.6 52.3-60.1 0.003-0.056 1.3-37.21 28.0-43.6 0.73-16.5 46.7-66.8
0.367 0.239
0.982 0.958
-a
-a
0.75 0.38 0.002 0.66 0.42 0.57 0.73
0.945 0.995 0.996 0.999 0.998 0.996 0.996
aToo few of the samples contained lead to provide a meaningful regression analysis.
that correlations between physical properties (8)and structure can be obtained without a detailed connecting theory. This, in turn, implies a strong relationship between functional group type and relative absorbance in a gasoline sample and the sample’s octane number. We find that the octane numbers of a gasoline sample exhibit strong negative correlations with methylene content and positive correlations with both methyl and aromatic content. This relationship was first noted by Love11 and Campbell (19). These results are consistent with gasoline blending methods, which decrease straight chain hydrocarbon content and increase branched and aromatic hydrocarbon content in order to increase octane number. The fact that the short wavelength near-infrared spectral analysis can yield three wavelength regression equations that have standard errors in the 0.3-0.4 octane number range demonstrates the potential of near-infrared spectral analysis for octane number determinations. These standard deviations of the residuals of prediction are quite good when compared with the reproducibility standard deviations of 0.2-0.4 octane number (reproducibility of engine test data varies with octane number) for RON and 0.3-0.5 for MON. In addition the ASTM Handbook cites a *0.3 octane number variation obtained when the same gasoline sample is tested in the same test engine and a f0.7 octane number variation when the same gasoline is tested in different test engines (20). Note that among the results obtained in this study, the standard error of calibration of pump octane number is better by -21/2 than either the RON or MON, which is consistent with PON being a quantity derived by averaging the two motor-obtained measurements. In addition, the standard errors of prediction in the validation tests are only slightly greater than the standard errors of calibration, as expected in a model that is highly correlated to real physical and chemical variance and not overly sensitive to random variance. The inherently low molecular absorptivity in the far-visible region of the near-IR allows work with long path lengths. This suggests the potential to perform on-line measurements over inexpensive fiber optics. The results we report here were obtained by using fiber optics, from 50 total scans taking less than 1 min for both scanning and computer data storage. They illustrate the potential for acquiring data from three (or a small number of) near-IR wavelengths to do quality-control measurements in fuels and fuel stocks. The same measurements could be made with fixed wavelength, inexpensive instruments that could be hand-held or placed on-line in the engine of an automobile or in the refinery.
ACKNOWLEDGMENT The authors wish to thank Al Herbert and John Mough of the California Department of Food and Agriculture and Shauna May TecleMariam of the United States Oil & Refining Co. for providing engine-tested gasoline samples. Sonja
Anal. Chem. 1989,61, 320-325
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Kalbfleisch provided technical assistance during the early stages of this work. J.B.C. expresses his appreciation to David E. Honigs for valuable consultation on this project. LITERATURE CITED (1) 7985 Annual Book of ASTM Standards, Volume 05.04 Test Methods for Rating Motor, Dlesei, Aviation Fuels; American Society for Testing and Materials: Philadelphia, PA, 1985. (2) Cievett, Kenneth J. Process Ana/yzer Technology; Wiley: New York, 1 ~ 8 6 pp ; 859-890. (3) Walsh, R. P.; Mortimer, J. V. Hydrocarbon processing 1971, 5 0 , 153-1 58. (4) Li,-T. Alien, D. T. Fuel 1885, 1754-1759. (5) Pettrakis, Leonldas; Allen, DavM T.; Gavalas, George R.; Gates, Bruce C. Anal. Chem. 1983, 5 5 , 1564-1588. (8) Myers, Mark E., Jr.; Stollsteimer, Janis; Wims, Andrew M. Anal. C M . 1975, 47, 2301-2304. (7) Dolbear, G. E. U.S. Patent 3,693,071, 1972. (8) Honlgs, D. E.; Hirschfeld, T.; Hieftje, G. M. Anal. Chern. 1985, 5 7 , 443-445. (9) Hibbard, R. R.; Cleaves, A. P. Anal. Chem. 1949, 27, 486-492.
?I;
(10) Tosi, C.; Pinto, A. Spectrochlm. Acta, PartA 1972, 28A, 585-597. (1 1) Draper, N. R.; Smith, H. Applied Regression Ana/j&s; 2nd ed.;Wiley: New York, 1981; pp 337-341. (12) VeLamp, D.; Kowalski, B. R. Center for Process Analytlytical Chemistry, BG10, University of Washington, Seattle, W A PLS Z-BbdC Modeling. Version 1.0 (IBM-PC) and Verslon 2.0 (MC), 1986. (13) Haaland, D. M.; Thomas, E. V. Anal. C b m . 1988, 6 0 , 1193-1202. (14) Lorber, A.; Wangen, L. E.; Kowalskl, B. R. J . Chemom. 1987, 7 , 19-31. (15) Fox, J. J.; Martin, A. E. Proc. R . SOC.London 1938, A767. 257-281. (16) Fox, J. J.; Martin, A. E. Proc. R . SOC.London 1938, A775, 208-233. (17) Weyer, L. G. Appl. Spechosc. Rev. 1985, 1-43. (18) Wheeler, 0. H. Chern. Rev. 1959, 59. 829-666. (19) Loveii, Wheeler G.; Campbell, John M. 7% Science of Petfoburn; Oxford Unlversity Press: London, 1938; Vol. 4. pp 3004-3023. (20) 7985 Annual Book of ASTM Standards; Volume 05.04 Test Methods for Rating Motor, Diesel, Aviation Fuels; American Society for Testlng and Materials: Philadelphia, PA, 1985, 38, 72.
RECEIVED for review March 25, 1988. Accepted November 10, 1988.
Laboratory Experiments on the Determination of Polycyclic Aromatic Hydrocarbon Coverage of Submicrometer Particles by Laser- Induced Aerosol Photoemission Reinhard Niessner,* Wilfried Robers, and Peter Wilbring
Uniuersity of Dortmund, Inorganic and Analytical Chemistry, P.O. Box 50 05 00, 0 - 4 6 0 0 Dortmund 50, FRG
Flrst measurements of laser-Induced photoelectric charglng of submlcrmeter particles wlth dlfferent polycyclc aromatlc hydrocarbon (PAH) coatlngs are presented. Pure carbon and NaCl partlcles were generated, and a monodisperse fraction was selected for subsequent coating wlth a PAH. The growth of the partlcles due to PAH adsorptlon or condensation was controlled with a screen type dtffuslon battery wlth a resolutlon better than 1 nm. These aerosols were Irradiated wlth an exclmer-laser-pumped dye laser that was frequency-doubled wlth a BBO crystal. Once photoelectrons were emltted, the remaining ptkskhrely charged partlcles were detected with an aerosol electrometer. Wavelengths between 207.5 and 241 nm were Investlgated. Pure NaCl partlcles showed a clearly nonlnear behavior when lrradlated wlth increaslng energy denslty of the laser at a fixed wavelength, while pure carbon particles showed a llnear behavlor. The spectral dependences of the charging of NaCl and carbon particles are given for dtfferent PAH submonolayer coatlngs. Emlsslon of coated NaCl particles Is enhanced for all the PAHs, whlle In the case of carbon particles dlfferent PAHs had a dlfferent Influence on the emisslon.
INTRODUCTION Polycyclic aromatic hydrocarbons (PAHs) are one result of an incomplete combustion of carbon- and hydrogen-containing compounds (1-3). Therefore, many anthropogenic processes are recognized as sources of PAHs. The increased interest in control technologies for PAH emission has its origin in their relevance to human health. During the combustion process ultrafine carbon particles with diameters below 100 nm are generated by homogeneous and heterogeneous nucleation processes (4-6). Furthermore, comparatively large
amounts of gaseous PAH compounds are present in the hot aerosol mixture. Therefore adsorption and/or condensation of PAHs will occur during cooling down in the exhaust system. The result of the adsorption of PAHs onto the “trace catcher” particles is a more or less submonolayer coated particle system. As it is known from numerous studies, especially the five-, six-, and seven-ring PAHs are particle-bound (7-9). The same class of PAHs is presumed to be the most DNA-reactiveone in the metabolization process after inhalation and deposition in the alveolar region (10, 11). The reason for the observed strong activity of metabolized PAHs on human DNA, concluded from their statistically proven ability to cause cancer, i s not yet completely understood (12,13). In order to minimize the PAH emissions, one clearly needs analytical techniques that have the potential of recognizing particle-bound PAHs ”in situ” and “on line” after a combustion process. One technique that partially fulfills these prerequisites is the photoelectric charging of PAH-contaminated submicrometer aerosols. The details of this technique are described by Burtscher et al. (14,15) and Niessner (16,17). From laboratory studies with artificially PAH-coated ultrafine particles (d,, < 100 nm) and pure ultrafine PAH particles ( d p < 100 nm), it is known that the charging rate reflects the surface coverage of particles with photoemitting material (16, 18). The charging rate of particles under irradiation is a function of many different parameters such as energy of UV photons, photoelectric yield, particle radius, etc. (19, 20). These findings have been assured with the so-called photoelectric aerosol sensor at a fixed wavelength (16).The aim of the work presented here was to extend .the knowledge on photoelectric charging of PAH-contaminatedparticles through the application of a tunable UV laser. The principal motivation was the cataloging of the photoemission characteristics of several five- to seven-ring PAHs adsorbed on model particle
0003-2700/89/0361-0320$01.50/00 1969 American Chemical Society
