Research article

Received: 9 November 2011 Revised: 11 February 2012 Accepted: 1 March 2012 Published online in Wiley Online Library

(wileyonlinelibrary.com) DOI 10.1002/jrs.4076

Rapid detection of gasoline by a portableRaman spectrometer and chemometricsXiaofang Zhang, Xiaohua Qi, Mingqiang Zou* and Jingwei Wu

Identification of the gasoline purity is important for quality control and detection of gasoline adulteration. Principal componentanalysis and Raman spectroscopy were used to authenticate gasoline adulterated with methyl tert-butyl ether (MTBE) andbenzene. Gasoline could be clearly distinguished from gasoline adulterated with MTBE and benzene by a plot of the firstprincipal component (x-axis) against the second principal component (y-axis). And the radial basis function neural networkwas used for quantitative prediction of the volume percentages of MTBE and benzene in gasoline based on Raman Spectra.The correlation coefficient (r) and mean absolute percentage error between predictive values and spiked values were 0.9907and 0.9934 and 15.73 and 8.19%, respectively. Moreover, the Raman spectra of the samples were obtained with a portableRaman spectrometer. Therefore, the method is simple, effective, fast, does not require sample pre-processing, and is promisingfor rapid gasoline detection. Copyright © 2012 John Wiley & Sons, Ltd.

Keywords: Raman spectroscopy; gasoline; authentication; quantity

* Correspondence to: Mingqiang Zou, Chinese Academy of Inspection andQuarantine, Beijing, 100123, China E-mail: mingqiangz@sina.com

Chinese Academy of Inspection and Quarantine, Beijing, 100123, China

Introduction

With the development of world economy, the requirement ofgasoline has increased sharply with reserves decreased rapidly.The search for clean and renewable energy source has become achallenge to sustainable human development. Therefore, theproduction and sale of clean gasoline have become one of themajor events for the world’s developed countries. Currently, methyltert-butyl ether (MTBE) has been used as high-performance addi-tives to improve the octane number of gasoline. However, MTBEdoes not degrade easily and is potentially poisonous and carcino-genic. Therefore, it has been confined in reality use in somecountries such as the USA, Japan, the EU, Russia, and China,[1] whichhave promulgated the motor gasoline hazardous substancescontrol standards and limited the MTBE maximum content of7%. Meantime, the addition of benzene to gasoline is strictlylimited to the maximum content of 2.5% as it is poisonous andcarcinogenic. However, for high profit margins, some merchantssell inferior gasoline that has been adulterated with a largeamount of these additives to improving the octane number. Thepricing of this adulterated gasoline is not fair to consumers, andadulteration could cause safety problems. Although traditionalmethods such as gas chromatography and mass spectrometry[2–4]

for gasoline analysis are accurate and have low detection limits,they are complicated, time-consuming, expensive, and requireaccess to specialized laboratory facilities. Therefore, simple, rapid,and cheap alternative methods for analysis are urgently neededto detect adulterants in gasoline.

Compared with traditional methods, spectral analysis is rapid,cheap, and nondestructive and is ideal for gasoline analysis. Infra-red and Raman spectroscopy can be used to analyze petrochemicalproducts and obtain molecular vibration information on thehydrocarbons they contain. Because these techniques work bydifferent mechanisms, for symmetrical functional groups, suchas –C�C–, –C=C–, the peaks in Raman spectra can be strongerthan those in infrared spectra. Currently, Raman spectroscopy is

J. Raman Spectrosc. (2012)

widely used to analyze various quality indicators in gasoline.[5–12]

Despite much progress in this field, those studies relied mainly onlarge Raman spectrometers, which are bulky, expensive, slow, anddifficult to be used in practical application.

Principal component analysis (PCA) has been used in manyfields for accurate sample classification and identification.[13–20]

PCA is nonparametric, and the result is independent of anyhypothesis about the data probability distribution. In PCA, thedata is converted by an orthogonal linear transformation to anew coordinate system, which reduces a large number ofvariables to a small number of principal components that explainmost of the variability in the data. The first principal component(PC1) is responsible for the greatest variance, followed by thesecond principal component (PC2) and so on.[21] Meantime,radial basis function neural network (RBFNN) forms a unifying linkbetween function approximation, regularization, classification, anddensity estimation. It is also the case that training RBFNN is usuallyfaster than training multilayer perceptron networks. Combinationof Raman spectroscopy and PCA could be used to develop a rapidand accurate analytical technique with low detection limit foranalysis of the adulterants in gasoline. Considering PCA is used toinvestigate the effect of data progress,[10] better predictions areobtained by using PCA combinedwith RBFNN for gasoline detection.

In this study, a portable Raman spectrometer was used fornondestructive analysis of samples of gasoline adulteratedwith MTBE and benzene. The samples were tested withoutpre-processing. Based on Raman spectral data, PCA was used toauthentic gasoline from the adulterated gasoline, and RBFNN wasused to detect the volume percentage of MTBE and benzeneadulterated in gasoline.

Copyright © 2012 John Wiley & Sons, Ltd.

X. Zhang et al.

Experimental procedures

Instruments

Data were collected using a portable Raman spectrometer(Chinese Academy of Inspection and Quarantine, Beijing, China),equipped with a near infrared diode laser operating at 785 nm asan excitation light source. The spectral resolution is 13 cm-1 withthe scanning range of 200~2000 cm-1. Sample vials (ø 1.2 cm) werepurchased from the National Scientific Company (Rockwood, TN).

Samples

Three gasoline samples were collected by Liaoning Entry–ExitInspection and Quarantine Bureau (China). MTBE and benzenewere purchased from the Beijing Chemical Reagent Company(China). The three different gasoline samples were adulteratedwith MTBE volume percentage of 1, 2, 4, 5, 10, and 20% andbenzene volume percentage of 1, 2, 3, 4 and 5%. The totalnumber of adulterated samples was 33.For analysis, the gasoline samples analyzed were placed in the

sample vials in the Raman spectrometer sample holder.

Data collection and processing

The laser power was set at 300mW, and the data acquisitiontime was set at 10 s to achieve optimal Raman peak intensity.Each spectrum was the average of five consecutive scans ofthe sample. It took about 1min to complete each samplemeasurement. The data processing used was MATLAB R2007a(Mathworks, Natick, MA).

Confidence region

An ellipsoid for the confidence region was established byhypothesis testing of the mathematical expectation for atwo-dimensional normal distribution. The density function ofa two-dimensional normal distribution is described in Eqn. 1.

f x; yð Þ ¼ 1

2psxsyffiffiffiffiffiffiffiffiffiffiffiffiffi1� r2

p � exp �1

2 1� r2ð Þx � uxð Þ2

s2x� 2r

x � uxsx

� y � uxsy

þ y � uxð Þ2s2y

" #( )(1)

where ux and uy are the means of the vertical and horizontalcoordinates of the sample data, respectively, and r is thecorrelation coefficient (Eqn. 2).

r ¼ sxysxsy

(2)

In Eqn. 2, sx and sy are the medium error of x and y, respec-tively, and sxy is the covariance of x and y.

Evaluation of model reliability

In the present paper, the correlation coefficient (r) and meanabsolute percentage error (MAPE) between predictive valuesand spiked values were used to evaluate the capability of model,which are defined by

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r ¼Pni¼1

yi � �yð Þ y i � �y� �

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPni¼1

yi � �yð Þ2s ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPn

i¼1y i � �y� �2s (3)

MAPE ¼ 1

n

Xni¼1

yi � y ij jyi

(4)

In these formulas, yis the spiked value, and yis its prediction foreach of the N test set spectra.

Results and discussion

Data processing

In the experimental, the same sample were measured at least fivetimes and averaged to get a reliable value. For each of the fivemeasurements, the relative standard deviation of the intensityof only specific bands was below 5% and the 200–1800 cm-1

region was used for analysis. The original Raman spectra of gaso-line samples adulterated with various volume percentages ofMTBE and benzene were shown in Fig. 1. The characteristics ofthe Raman bands for MTBE are at 725 and 1445 cm-1 (Fig. 1A),and the major bands for benzene are at 996 and 1173 cm-1

(Fig. 1B). The characteristic bands of saturated hydrocarbons,aromatics, and olefin are at 1400–1510, 1550–1630, and1630–1700 cm-1, respectively.

It was difficult to detect the differences between the gasolinesamples in the original Raman spectra. Raman spectroscopyemploys a scattering technique; the band intensities are depen-dent on the analyte concentration and a number of other factorssuch as laser power and instrumental effects, which cannot be reli-ably reproduced. The impaction of fluorescence is large for Ramanspectroscopy. However, derivativeness of the Raman spectral data

can be used to overcome the influence of focal length, tempera-ture, and fluorescence. After comparison of derivative spectra, asmoothing filter algorithm was not used as this reduced the qualityof modeling. The first derivatives of the spectra are shown in Fig. 2,and the intensity was divided by the 103.

Identification of adulterated gasoline from PCA

In the present research, PC1 and PC2 were calculated from thefirst derivatives of the spectra (200–1800 cm-1) of gasoline andthat adulterated with MTBE and benzene. Figure 3 shows theloading plot for the spectral region 700–1100 cm-1 for the MTBEadulterated gasoline (Fig. 3A) and 900–1100 cm-1 for the benzeneadulterated gasoline (Fig. 3B). In Fig. 3A, the PC1 and PC2explained 54.64 and 29.40% of the variance, respectively, and intotal accounted for 84.04% of the variance, which is most of thevariance in the data matrix. The bands at 780 and 998 cm-1 ofPC1 is mainly associated with gasoline, and the bands at 720and 730 cm-1 of PC2 is mainly associated with MTBE. Figure 3B

2 John Wiley & Sons, Ltd. J. Raman Spectrosc. (2012)

400 800 1200 1600

-4.0k

-2.0k

0.0

2.0k

4.0k

Inte

nsity

Wavenumber / cm-1

Wavenumber / cm-1

gasoline 1% MTBE 2% MTBE 4% MTBE 5% MTBE 10% MTBE 20% MTBE

720

780

998

1026

400 800 1200 1600

-4.0k

-2.0k

0.0

2.0k

4.0k

Inte

nsity

gasoline 1% benzene 2% benzene 3% benzene 4% benzene 5% benzene

780

998

1026

A

B

Figure 2. First derivative spectra of gasoline and gasoline adulteratedwith various volume percentage of (A) methyl tert-butyl ether and(B) benzene.

0.0

20.0k

40.0k

60.0kR

aman

inte

nsity

Wavenumber / cm-1

Wavenumber / cm-1

gasoline 1% MTBE 2% MTBE 4% MTBE 5% MTBE 10% MTBE 20% MTBE 100% MTBE

725

780

998

1026

120513761445

16081669505

344848

913

400 800 1200 1600

400 800 1200 16000.0

20.0k

40.0k

60.0k

Ram

an in

tens

ity

gasoline 1% benzene 2% benzene 3% benzene 4% benzene 5% benzene 100% benzene

760780

996

10261173

13761445

16081669

603

844 1587 1606

A

B

Figure 1. Raman spectra of gasoline adulterated with various volumepercentage of (A) methyl tert-butyl ether and (B) benzene.

Rapid detection of gasoline

shows the loading plots of gasoline adulterated with benzene.The first two principal components (PC1 and PC2) accountedfor 89.73% of the variance for benzene adulterated samples.PC1 was mainly associated with gasoline and PC2 was associatedwith benzene, but they were not exactly the same as gasolineadulterated with MTBE. These results show that the gasolinedifferences in quality were affected by the addition of MTBEand benzene. The different functional groups present in MTBEand benzene allow the adulterated gasoline to be distinguishedfrom gasoline. Therefore, the PCA plot can be used to quicklydetermine if a gasoline sample has been adulterated with MTBEand benzene.

The Raman spectral data was analyzed by PCA, and the PC1scores (x-axis) were plotted against the PC2 scores (y-axis) (Fig. 4).All the gasoline samples are clustered in the middle of the scatterplot, whereas the gasoline samples adulterated with MTBE orbenzene are distributed in the top of or below in the scatterplot. As the volume percentage of adulterant in the gasolineincreased, the data points move further away from those ofunadulterated gasoline. An ellipsoid for the 95% confidenceregion of the gasoline is shown in the figure. The center of theellipsoid is at ux; uy was calculated from the gasoline coordinates.All of the gasoline samples adulterated with MTBE are outside the95% confidence region, and they have the larger distribution overthe scatter plot. As the MTBE volume percentage adulterated ingasoline increased, the sample coordinates move further awayfrom the edge of ellipsoid. The gasoline samples adulterated withbenzene have the smaller distribution over the scatter plot, and itsdata points are located closer to those for gasoline. The gasoline

J. Raman Spectrosc. (2012) Copyright © 2012 John Wiley

samples’ data points with the 1 and 2% benzene are in the 95%confidence region. We have performed other three unadulteratedgasoline samples as test gasoline (the dots of Tgasoline are inFig. 4) to check the method, and the results show that they alsoappear in the same region of the PC1 versus PC2 plot. These resultsconsist with the effect of PC1 and PC2 in loading plot. Therefore,the prediction results obtained by PCA based on Raman spectracan be used to distinguish adulterated gasoline samples fromunadulterated samples.

Quantity of adulterated gasoline by radial basis functionneural network

The RBF network model is

f xð Þ ¼Xmj¼1

wjhj xð Þ: (5)

The model is linear in the hidden-to-output weights wj

� �m

j¼1,

and m is the number of hidden unit. The characteristics featureof RBF networks is radial nature of hidden unit transfer function,

hj� �m

j¼1, which depend only the distance between the input x and

cj of each hidden unit, scaled by a metric Rj,

hj xð Þ ¼ Φ x � cjð ÞTRj�1 x � cjð Þ� �

(6)

where Φ is some function which is monotonic for non-negative numbers. Traditionally, restricting attention to diagonal

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700 800 900 1000 1100-2

-1

0

1

2Lo

adin

g

Wavenumber / cm-1

Wavenumber / cm-1

gasoline MTBE PC1 PC2

720

780

998

900 950 1000 1050 1100

-2

-1

0

1

2

Load

ing

gasoline benzene PC1 PC2

998

982

A

B

Figure 3. Loading plot of the contributions from PC1 and PC2. (A)Gasoline adulterated with methyl tert-butyl ether, and (B) gasolineadulterated with benzene.

-0.7

-0.2

0.3

0.8

1.3

1.8

-3 -2 -1 0 1 2 3PC1

PC

2

Figure 4. Principal component analysis distribution charts of unadulter-ated gasoline and gasoline adulterated with methyl tert-butyl ether andbenzene. The confidence regions of the genuine gasoline samples areindicated by ellipsoids with a=0.95.

X. Zhang et al.

metrics and Gaussian basis functions, the transfer functions thenbe written as

hj xð Þ ¼ exp �Xnk¼1

xk � cjkð Þ2rjk2

" #(7)

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where ri is the radius vector of the j-th hidden unit (j is thej-th hidden unit and i is the i-th sample). Radial unit is definedby its center point and radius. The hidden units respond(nonlinearly) to the distance of points from the center repre-sented by the radial unit. The nearer the distance from thesample to center is, the stronger the response of the radialunit gives out.

For gasoline adulterated with MTBE, there are in total18 samples and leave-one-out cross-validation was used totest the trained network models. For test, one sample wasused as a prediction sample and the other 17 samples wereused for building the weight matrix. For the next test, anothersample was left, which was different from the previous one asprediction sample. This procedure was repeated so that everysample was used once for prediction, 17 times for trainingsample. Nine samples adulterated with benzene in gasolinewere studied, which adulterated the volume percentage of3, 4, and 5%; the prediction method was the same as previ-ously mentioned.

In this work, the new radial basis function was used to trainthe network; the first six PCs were used as input data. Withthis function, there are four parameters related to the RBFNN.The parameters are display-frequency (df), maximum-neuron(mn), error-goal (eg), and spread-constant (sc). For the MTBE,we discussed the optimization value of df, mn, eg, and sc thatwere set to 4, 16, 0.005, and 3, respectively. And the optimalparameters of benzene were 3, 9, 0.005, 3 for df, mn, eg, andsc, respectively. The optimal parameters were set by comparisonof the correlation coefficient and MAPE. The larger correlationcoefficient and the smaller MAPE are computed, the betterprediction results can be obtained and the parameters chosen.The predictions results of MTBE and benzene adulterate ingasoline are shown in Table 1, which were obtained with theoptimal parameters. The RBFNN[22] program was compiled inthe MATLAB language.

The correlation coefficients between prediction values andspiked values of MTBE and benzene were 0.9908 and 0.9934,respectively. The MAPEs between prediction values and spikedvalues of MTBE and benzene were 15.7 and 8.19%, respectively.For MTBE adulterate gasoline, although the error of predictionvolume percentage 1, 2, 10, and 20% are little bigger, that isbecause the scope of volume percentage is wide and the datadistribution is dispersive. The method is easy, quick, low cost,and without loss to be a reliable method for quality control ofgasoline. Therefore, the prediction results obtained by RBFNNbased on Raman spectra can be used of quantitative analysisfor adulterated gasoline.

Conclusions

Raman spectra of gasoline samples were obtained withoutsample pre-processing by a portable Raman spectrometer. TheRaman spectral data (200 ~ 1800 cm-1) were analyzed usingPCA. From a plot of PC1 (x-axis) and PC2 (y-axis), unadulteratedgasoline could be distinguished from gasoline adulterated withMTBE and benzene. The combination of PCA and RBFNN can beapplied to quantitative prediction of MTBE and benzene basedon Raman spectra. Moreover, each sample analysis took onlyabout 1min to complete. Therefore, the technique is promisingfor quality and quantity analysis of gasoline.

2 John Wiley & Sons, Ltd. J. Raman Spectrosc. (2012)

Table 1. Prediction results for methyl tert-butyl ether and benzene with radial basis function neural network

Numbers Spiked value Prediction Relative error (%) r MAPE (%)

MTBE

1 0.01 0.0074 �26.00 0.9908 15.73

2 0.02 0.0171 �14.50

3 0.04 0.0386 �3.50

4 0.05 0.0499 �0.20

5 0.1 0.1090 9.00

6 0.2 0.1760 �12.00

7 0.01 0.0139 39.00

8 0.02 0.0193 �3.50

9 0.04 0.0428 7.00

10 0.05 0.0690 38.00

11 0.1 0.1073 7.30

12 0.2 0.1580 �21.00

13 0.01 0.0120 20.00

14 0.02 0.0239 19.50

15 0.04 0.0365 �8.75

16 0.05 0.0548 9.60

17 0.1 0.0730 �27.00

18 0.2 0.1656 �17.20

Benzene

1 0.03 0.0253 �15.67 0.9934 8.19

2 0.04 0.0384 �5.33

3 0.05 0.0503 1.00

4 0.03 0.0261 �13.00

5 0.04 0.0412 4.00

6 0.05 0.0514 4.67

7 0.03 0.0243 �19.00

8 0.04 0.0371 �9.67

9 0.05 0.0504 1.33

MTBE, methyl tert-butyl ether.

Rapid detection of gasoline

Acknowledgements

This research was supported by grants from the InternationalScience and Technology Cooperation and Exchange Foundation(Grant No. 2008DFA40270), a Strategic 11th five-year Scienceand Technology Supporting Grant (Grant No. 2009BAK58B01)and Special Funded Projects of the Fundamental Research Fundsfrom the Chinese Academy of Inspection and Quarantine of China(Grant No. 2010JK017). The authors thank Doctor Mingyang Liu(Liaoning Entry-Exit Inspection and Quarantine Bureau) forproviding the gasoline samples.

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