Authentication of edible vegetable oils adulterated with used frying oil by Fourier Transform Infrared Spectroscopy

Food Chemistry 132 (2012) 1607–1613

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Analytical Methods

Authentication of edible vegetable oils adulterated with used frying oil by Fourier Transform Infrared Spectroscopy Qing Zhang, Cheng Liu, Zhijian Sun, Xiaosong Hu, Qun Shen ⇑, Jihong Wu ⇑ National Engineering & Technology Research Center for Fruits and Vegetables Processing, Key Laboratory of Fruits and Vegetables Processing, Ministry of Agriculture, College of Food Science and Nutritional Engineering, Box 112, China Agricultural University, No. 17, Qinghua East Road, 100083 Beijing, China

a r t i c l e

i n f o

Article history: Received 16 November 2010 Received in revised form 5 May 2011 Accepted 30 November 2011 Available online 8 December 2011 Keywords: Edible vegetable oils Used frying oil Adulteration Fourier Transform Infrared Spectroscopy Cluster analysis Discriminant analysis Linear regression analysis

a b s t r a c t The application of Fourier Transform Infrared (FTIR) Spectroscopy to authenticate edible vegetable oils (corn, peanut, rapeseed and soybean oil) adulterated with used frying oil was introduced in this paper. The FTIR spectrum of oil was divided into 22 regions which corresponded to the constituents and molecular structures of vegetable oils. Samples of calibration set were classified into four categories for corn and peanut oils and five categories for rapeseed and soybean oils by cluster analysis. Qualitative analysis of validation set was obtained by discriminant analysis. Area ratio between absorption band 19 and 20 and wavenumber shift of band 19 were treated by linear regression for quantitative analysis. For four adulteration types, LODs of area ratio were 6.6%, 7.2%, 5.5%, 3.6% and wavenumber shift were 8.1%, 9.0%, 6.9%, 5.6%, respectively. The proposed methodology is a useful tool to authenticate the edible vegetable oils adulterated with used frying oil. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction Deep-fat frying is a welcome food processing method and has been prevailed for centuries. Palatable fried products and timesaving process course are the two main reasons for its prevalentness in people’s life. Nevertheless, many harmful reaction products, such as trans configuration (Tsuzuki, Matsuoka, & Ushida, 2010), polymers (Choe & Min, 2007), etc. are produced during the deep-fat frying course and then exist in the products. The more deep-fat frying times conduct, the more harmful to consumers’ health. How to deal with these used frying oil (UFO) is a big problem for the industry. The UFO is supposed to be a very good renewable resource like the raw material of biodiesel, but now it becomes one of the threats of people’s health. This is because that some unscrupulous traders add it to qualified edible vegetable oils just for high profits while the government does not have the corresponding legal regulations. This problem is particularly serious in some areas. Adulteration of edible oil has been a chronic illness in food adulteration for many years. It not only causes a potential harm or threat to the health of consumers, but also undermines the integrity and orderly economy. There are many adulteration ways, for

⇑ Corresponding authors. Tel.: +86 10 62737524; fax: +86 10 62737392 (Q. Shen), tel.: +86 10 62737434 16; fax: +86 10 62737434 12 (J. Wu). E-mail addresses: [email protected] (Q. Shen), [email protected] (J. Wu). 0308-8146/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodchem.2011.11.129

example, high priced oil adulterated with lower priced oil, edible oil adulterated with non-edible oil and qualified vegetable oils adulterated with waste cooking oil. Fortunately, though the adulteration means become complicated and diverse, the corresponding detection techniques go better and are improved faster which have benefited from the development and application of advanced instrumentation. Fast, non-destructive, non-polluting methods of instrumental analysis have replaced the traditional chemical titration in the concept of modern detection means. Therefore, there are many references which focused on the edible oil adulteration. So far, there have been lots of methods such as nuclear magnet resonance (Agiomyrgianaki, Petrakis, & Dais, 2010; Rezzi et al., 2005), dielectric spectroscopy (Lizhi, Toyoda, & Ihara, 2010), gas chromatography (Al-Ismail, Alsaed, Ahmad, & Al-Dabbas, 2010; Brodnjak-Voncina, Kodba, & Novic, 2005), high performance liquid chromatography (Cunha & Oliveira, 2006; Park, Chang, & Lee, 2010), mass spectrometry (Lerma-Garcia, Herrero-Martinez, Ramis-Ramos, & Simo-Alfonso, 2008; Vaclavik, Cajka, Hrbek, & Hajslova, 2009), fluorescence spectroscopy (Poulli, Mousdis, & Georgiou, 2007; Sikorska, Gorecki, Khmelinskii, Sikorski, & Koziol, 2005), near infrared spectroscopy (Downey, McIntyre, & Davies, 2002), mid-infrared spectroscopy (Gurdeniz & Ozen, 2009; Vlachos et al., 2006), Raman spectroscopy (Zou et al., 2009), differential scanning calorimetry (Angiuli et al., 2009; Ferrari et al., 2007), etc. Meanwhile, it had obtained satisfactory adulteration detection

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effectiveness by combining with chemometrics analysis to process the data of these instrumental techniques. These chemometrics include principal component analysis (Lizhi et al., 2010), linear discriminate analysis (Rezzi et al., 2005), partial least square (Gurdeniz, Ozen, & Tokatli, 2010), multiple linear regression (Lerma-Garcia et al., 2008), counterpropagation artificial neural networks (Brodnjak-Voncina et al., 2005), etc. All these methodologies were of extremely important significance in the field of edible oil study. FTIR is one of the best instruments of detection and has been interested by many researchers from the beginning of discriminating work. Fast spectrum acquisition, easy to operate and needing no complex sample preparation are its advantages (Maggio et al., 2009). FTIR was used to authenticate hazelnut oil mixed with different types of oils, the spectral data was analysed by discriminant analysis and partial least-squares analysis. The detection level and correlation coefficient for the PLS model were 2%, 0.99, respectively (Ozen & Mauer, 2002). In addition to its application in the detection of edible oil adulteration, FTIR was also used in the studies of thermal stability of edible oils. (Moros, Roth, Garrigues, & de la Guardia, 2009; Pinto, Locquet, Eveleigh, & Rutledge, 2010). These studies proved the method was a promising research approach for the rapid analysis of the thermal degradation of oils. The oxidised fatty acid concentration in virgin olive was obtained by analysing the result of FTIR spectra using multiple linear regression (Lerma-García, Simó-Alfonso, Bendini, & Cerretani, 2011), the method was effective and the result showed satisfactorily. When the edible oils were heated at high temperature for a long time, the amount of trans fatty acids increased (Tsuzuki et al., 2010), which could be the basis for the authentication of edible vegetable oils adulterated with UFO. Due to the lack of reports that focused on the authentication of edible vegetable oils adulterated with UFO, the aim of this work was to develop and validate an analytical method based on FTIR spectroscopy, in conjunction with multivariate calibration methodologies, for the qualitative and quantitative analysis of the edible vegetable oils adulterated with UFO. 2. Experimental 2.1. Materials The test materials used during the study were corn oil (CO), peanut oil (PO), rapeseed oil (RO), soybean oil (SO) and UFO. The former four oils were purchased from local supermarket; they were all pure and qualified products. The UFO was collected from the sales stand of twisted cruller of the local street, its origin was mainly soybean oil. In this study, in order to get the deep oxidation of the collected UFO we simulated the frying process of twisted cruller for five times and each time it cost 2 h. Potassium bromide (KBr) (Chemical Reagent Beijing Co., Ltd.) that was used to compress a KBr pellet was analytically pure. 2.2. Apparatus The instrument used for the spectrum acquisition was a Perkin Elmer (precisely) 100 FTIR spectrometer (Perkin Elmer Corporation, Norwalk, CT, USA) equipped with a room temperature deuterated triglycine sulphate (DTGS) detector and interfaced to a personal computer operating with Windows-based IR Spectroscopy Version 6.1.0 (PerkinElmer, Inc.). The apparatus adopted to acquire the KBr pellet was an YP-2 tablet press (ShanYue Scientific Instrument Ltd., Shanghai, China) attached with a disc notch model. An oven (Binder ED115, Germany) was used to dry the KBr.

2.3. Sample arrangement According to different adulteration proportion of the qualified edible vegetable oils mixed with UFO, two sets had been arranged: calibration set and validation set. For the calibration set, the adulteration proportions were 0%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 100%. For the validation set, twelve samples of different adulteration proportions for each qualified vegetable oil mixed with UFO were arranged. The codes of these twelve samples were letters of A, B, C, D, E, F, G, H, I, J, K, L.

2.4. Spectra acquisition KBr (powder) was dried at 130 °C for 4 h, and then used to make the KBr pellet by the tablet press. KBr of 0.3 g was accurately weighed to the notch of the model; pressure was set at 27 MPa for 4–5 min. A film of small amount of oil sample (approximately 2 lL) was daubed evenly on a side of the homemade smooth disc of KBr. It used the smooth KBr pellet as the background before obtained the spectra of samples. The spectra were recorded from 4000 to 450 cm 1, the number of scans being 1 at a resolution of 4 cm 1. Spectrum acquisition of each sample repeated three times in the same condition. The operation of infrared spectra acquisition was performed under the ambient temperature.

2.5. Data treatment and statistical analysis All graphs and data treatments were obtained by Origin 7.5 (OriginLab corporation, Northampton, England) and SPSS 17.0 (SPSS corporation, Chicago, USA) respectively.

2.5.1. Data treatment For knowing well about the relation between FTIR spectrum of vegetable oil and its chemical component structures and achieving the convenient analysis of FTIR spectrum, the work divided the entire infrared absorption area into 22 regions and all the specific information, including absorption range and corresponding functional group and vibration mode, was showed in Table 1, whose train of thought was derived from former study (Lerma-García et al., 2011) and target was to know the ralationship between chemical constitution of vegetable oils and their FTIR spectrum. The area of each absorption peak or shoulder was calculated by IR Spectroscopy Version 6.1.0. According to the change of each area, the smallest area change (standard deviation (SD) was 0.0048, the minimum among the 22 SDs) of band 14 was selected as the divisor, dividing another 21 areas. So there were 21 area ratios, which were used as the parameters for the cluster analysis.

2.5.2. Qualitative analysis For achieving qualitative analysis of the adulteration, cluster analysis was used. This work was accomplished by the calibration set. The feasibility of this methodology was validated according to discriminatory analysis by validation set.

2.5.3. Quantitative analysis Quantitative analysis was carried out by remarkable area ratio between two absorption peaks and wavenumber shift of a typical absorption band. This work was completed according to construct linear regression (LR) equations by the calibration set.

Table 1 22 Regions of edible oil’s FTIR spectrum and their details: range, functional group, mode of vibration and so on. Absorption bands No. 1

Range (cm 1)

Nominal frequency (cm 1)

Functional group

Mode of vibration

3550–3450

3473

AC@O (ester)

Overtone

PSUc

Absorption intensityd w

O O

C 2

3035–2989

3025a

@CAH (trans)

C

Stretching

vw

H C

C

C

C

H 3009a

3

2989–2949

2953a

@CAH (cis)

ACAH (CH3)

Stretching

Stretching (asym)

H

m

H

m

H H C H

4

2949–2881

2926a

ACAH (CH2)

Stretching (asym)

vs

H C H

2881–2780

2854a

ACAH (CH2)

Stretching (sym)

vs

H C H 6

2750–2500

2677

Carbonyl group

Fermi resonance

7

1850–1677

1744a

Stretching

1711a

AC@O (ester, aldehyde, ketone, anhydride) AC@O (free fatty acid)

Stretching

C

O

C

O

w vs vw

O C OH

8

1667–1625

1654

C@C (cis)

Stretching

9 10 11 12 13 14 15 16 17 18 19

1486–1446 1446–1425 1425–1407 1407–1390 1390–1371 1371–1331 1290–1215 1215–1147 1127–1111 1111–1072 1006–929

1464b 1450b 1418a 1397b 1377a 1359b 1238 1163 1120a 1099a 968a

ACAH (CH2) ACAH (CH3) @CAH (cis) @CAH (cis) ACAH (CH3) OAH ACAH (CH2) ACAH (CH2) ACAO (ester) ACAO (ester) AHC@CHA (trans)

20

929–885

914a

AHC@CHA (cis)

885–802

b

Scissoring Stretching (asym) Bending (rocking) Bending Bending (sym) Bending (in-plane) Bending Bending Stretching Stretching Bending (out-ofplane) Bending (out-ofplane) Wagging

21

850

@CH2

Disubstituted cis C@C of acyl groups of oleic and linoleic acids The same as 4 The same as 3 The same as 23009 The same as 23009 The same as 3 CAOAH The same as 4 The same as 4 The same as 1 The same as 1 The same as 23025

w m m w w m vw m s m m w

The same as 23009

w

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5

w

H C H

22

802–630

723a

A(CH2)nA AHC@CHA (cis)

a b c

Bending (out-ofplane)

CH 2

CH 2

CH 2

The same as 23009

m

CH 2 w

1609

d

According to Guillen and Cabo (1997). According to Silverstein, Bassler, and Kiemle (2005). PSU meant possible structural units. Absorption intensity: vs, very strong; s, strong; m, mediun; w, weak; vw, very weak.

Rocking

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ketones, carboxylic acids, and short chain alkanes and alkenes. Triacylglyceride monomers, Dimers and polymers, cyclic and epoxy compounds are result from thermal and cyclisation reactions (Sahin & Sumnu, 2009). When UFO was added into the qualified vegetable oils, the chemical constitution of qualified oil was changed. These changes in constituent might generate the FTIR spectrum diversities between the UFO and qualified vegetable oils. At the basis of the above-mentioned comparisons among the spectra of five oil samples, a series of adulteration proportions between UFO and four qualified oils were carried out. Soybean oil adulterated with UFO was used as the instance to elucidate the adulteration discrimination. The FTIR spectra of different adulteration proportion samples were showed by Fig. 1-c. It was hard to find any significant differences among the curves in Fig. 1-c. For thoroughly utilising the information performed by the infrared spectrum of oil and fat, the FTIR spectrum of each sample (calibration and validation set) was divided into 22 regions for the qualitative analysis. The two-step cluster was chosen to classify the adulteration proportions. After the clustering method, discriminant analysis was used by the validation set on the basis of the cluster results. The classifications of other three vegetable oils adulterated with

3. Results and discussion 3.1. Qualitative analysis of adulteration The FTIR spectra of five pure oils were presented in Fig. 1-a. There were less absorption bands in functional groups region (4000–1650 cm 1), but more complicated absorption profiles in fingerprint region (<1650 cm 1). As Fig. 1-a showed, there were not any significant differences among the five spectra, in other words, absorption band positions and absorption intensities of the same wavenumber were very similar. Nevertheless, subtle discrepancies among these spectra did exist. As long as the spectra were observed carefully, small differences about the absorption band position and absorbency intensity of the same band appeared. When the absorption band was amplified, the differences emerged as shown in Fig. 1-b. Many complex chemical reactions (hydrolysis, oxidation and thermal reaction etc.) happen during the process of food frying, especially in the repeated and long time deep frying. Accordingly, many kinds of products are produced. The amount of free fatty acids, mono- and di-acylglycerols, and glycerols is increased by hydrolysis in UFO. Oxidation produces hydroperoxides and then low molecular volatile compounds such as aldehydes,

a

b

CO

Abs

2

0.4

Abs

CO

PO

0.0 0.4

Abs

0 2

Abs

PO

0.0 0.4

RO

Abs

0 2

Abs

RO

0 2

0.0 0.4

SO

Abs

Abs

SO

0 2

0.0 0.4 UFO

Abs

Abs

UFO

0

0.0 4000

3000

2000

1000

1050

Wavenumber (cm-1 )

c

1000

950

900

850

Wavenumber (cm -1 )

2.0

d 1: 0

0.40

2: 2% 3: 5%

0.35

Absorbance

Absorbance

1.5

1.0

4: 10%

7 0.30 3 0.25

5: 20% 6: 50%

4 6

7: 100%

5 0.20

0.5 0.15

1 2

0.10 0.0 4000

3500

3000

2500

2000

Wavenumber (cm -1)

1500

1000

500

1050

1000

950

900

850

Wavenumber (cm -1)

Fig. 1. The FTIR spectra of CO, PO, RO, SO, UFO and a series of adulteration proportions of SO adulterated with UFO. Graph a and b were the direct FTIR spectra and the graph c and d were the amplified spectra around 1050–850 cm 1.

90, 100

L (85.24)

50, 60, 70, 80

H (54.80), I (64.76), J (74.08), K (84.10)

10, 20, 30, 40

D (18.64), E (23.11), F (36.08), G (49.86)

2, 3, 4, 5, 6, 7, 8, 9 A (4.03), B (7.68), C (14.58) 80, 90, 100

L (92.68)

30, 40, 50, 60, 70

F (36.02), G (48.51), H (55.27), I (64.62), J (67.23), K (84.49)

3, 4, 5, 6, 8, 9, 10, 20

B (7.68), C (14.58), D (18.64), E (23.11)

1, 2, 7

A (3.96)

3.2. Quantitative analysis of adulteration

A (2.99), B (8.13) L (92.96) F (36.00), G (48.34), H (55.98), I (65.14), J (67.39), K (84.83) Validation set (%)a

Letters of A, B, C, D, E, F, G, H, I, J, K, L were the code names of the samples set in the validation set. a

J (67.57), K (83.75), L (93.08) D (17.90), E (23.51), F (36.29), G (49.10), I (64.53)

0 70, 80, 90, 100 30, 40, 50, 60

3, 4, 5, 6, 7, 8, 10, 20 C (13.15), H (56.00) 0, 1, 2, 9 90, 100 50, 60, 70, 80

7, 8, 9, 10, 20, 30, 40 B (7.78), C (12.37),E (23.43) 0, 1, 2, 3, 4, 5, 6 A (3.94), D (17.50) Calibration set (%)

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UFO were resolved in the same procedure. The results of clustering and classifying were listed in Table 2.The entire classified categories were the optimised classification. As displayed in Table 2, the cluster results were acceptable basically, except for that of the PO + UFO and RO + UFO. For PO + UFO, samples with the proportions of 0%, 1%, 2% and 9% were assigned to the first category while with the proportions of 3, 4, 5, 6, 7, 8 (<9%), 10, and 20% were assigned to the second category. For RO + UFO, samples with the proportions of 1%, 2%, and 7% were assigned to the first category, but those of 3, 4, 5 6(<7%), 8, 9, 10, and 20% were assigned to the second category. For CO + UFO, samples with the proportions of less than 6% were assigned to the first category. So it was hard to discriminate the adulteration when the proportion was low. This may attribute to the similar fatty acids composition and the nearly the same amount of some fatty acids in vegetable oils (Sherazi, Talpur, Mahesar, Kandhro, & Arain, 2009). For SO + UFO, adulteration could be distinguished when the proportion is as low as 2%, which was a satisfactory cluster result. When it came to the results of validation set by discriminant analysis, it showed there were some erroneous judgements in the four mixed ways. However, the classification results showed all the samples were differentiated besides sample A and D of CO + UFO and sample A and B of PO + UFO. In summary, the detectabilities of the four adulteration ways were 83.33%, 83.33%, 100% and 100%, respectively. In other words, by means of this method, qualitative analysis of vegetable oils authentication may get an effective result.

0, 1

II I I IV III II I

PO + UFO

IV III II I

CO + UFO

No. of cluster

Table 2 Cluster and discriminant results for qualitative analysis of vegetable oils adulteration.

RO + UFO

V IV III II

SO + UFO

V IV III

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Through the observation of Fig. 1-d, the absorption intensity of the spectrum increased accompanied with the increase of adulteration proportion except 2%. After comparing the areas of band 19 and 20 and wavenumber of band 19 of different proportions, it could be found that: (1) the area of band 19 increased and the area of band 20 decreased while the adulteration proportion increased. (2) When the adulteration proportion increased, the peak’s wavenumber of band 19 shifted toward the higher wavenumber position. With the increase in the number of frying times, a series of chemical reactions took place in vegetable oils, including the transformation from cis-configuration to trans-configuration (Tsuzuki et al., 2010). That’s to say, the amount of trans-configuration increased and the amount of cis-configuration decreased. Meanwhile, along with the increase of the amount of trans-configuration, the absorption of trans-configuration in the mid-infrared spectrum needed a higher wavenumber. For further quantitative analysis of adulteration, these two above-mentioned points were used to construct linear regression equations by the calibration set. Fig. 2 showed that there were significant linear correlations between the selected area ratio (A19/ A20), the wavenumber shift of band 19 and adulteration proportion of four mixed ways. Namely, with the increase in the proportion of adulteration, both A19/A20 and wavenumber of band 19 increased. The limit of detection (LOD) was obtained according to the following equation: LOD = (YLOD intercept)/slope, where: YLOD = intercept + 3 Sb, Sb means standard error of the regression statistics (Vlachos et al., 2006). All results of linear regression analysis were listed in Table 3. In Table 3, it was interesting to notice that there were highest coefficient of determination (R2 = 0.9988, 0.9971) and lowest LOD (3.6, 5.6%) in both A19/A20 and wavenumber shift for SO + UFO. By contrast, PO + UFO had the lowest coefficient of determination (R2 = 0.9951, 0.9924) and highest LOD (7.2, 9.0%) in both A19/A20 and wavenumber shift. The differences in the R2 and detection limits could be explained by the origin of UFO used in this study and the differences of component of fatty acids among the different

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10

968.4 Wavenumber (cm -1 )

A19/A20

8 6 CO+UFO PO+UFO

4

RO+UFO SO+UFO

2

968.0 967.6 CO+UFO

967.2

PO+UFO RO+UFO

966.8

SO+UFO

966.4

0

20 40 60 80 100 Adulteration proportion (%)

0

20 40 60 80 100 Adulteration proportion (%)

Fig. 2. The linear relationships between A19/A20, wavenumber shift of band 19 and adulteration proportion of four mixed forms.

Table 3 The results of linear regression analysis between A19/A20, the wavenumber shift of band 19 and adulteration proportion of four mixed forms. Item

A19/A20

CO + UFO PO + UFO RO + UFO SO + UFO

Wavenumber shift of band 19

Regression equation

R2

LOD (%)

Regression equation

R2

LOD (%)

y = 0.0693x + 1.961 y = 0.0525x + 2.066 y = 0.0405x + 3.704 y = 0.0727x + 1.417

0.9959 0.9951 0.9971 0.9988

6.6 7.2 5.5 3.6

y = 0.0156x + 966.7 y = 0.0182x + 966.5 y = 0.0128x + 967.0 y = 0.0164x + 966.7

0.9938 0.9924 0.9955 0.9971

8.1 9.0 6.9 5.6

a

b

c

d

e

f

g

h

Fig. 3. Correlation plot of actual value versus FTIR-LR analysis predicted value of validation set by using the area ratio between band 19 and 20 and the wavenumber shift of band 19. (a) CO + UFO, (b) PO + UFO, (c) RO + UFO and (d) SO + UFO for area ratio; (e) CO + UFO, (f) PO + UFO, (g) RO + UFO and (h) SO + UFO for wavenumber shift.

vegetable oils. In other hand, the R2 and LOD for A19/A20 were better than that for wavenumber shift of band 19, which could be attributed to the subtle change in the wavenumber shift of band 19 and comparatively large change in the area ratio between band 19 and 20. This methodology obtained a satisfactory authentication result. For validating the reproducibility of the regression analysis, predicted value of adulteration proportion of validation set was calculated by the equation of the regression analysis. Then the simple distribution of scatter plots between the actual value and predicted value were obtained and showed by Fig. 3, which corresponded to A19/A20 and the wavenumber shift of band 19 respectively. There was good reproducibility between the predicted values and actual values for A19/A20 of the four mixed forms in Fig. 3(a–d); but in Fig. 3(e–h), there were certain divergences between the predicted values and actual values of the four mixed ways. This could be explained by assuming that the small change among the wave-

number shifts of band 19 of the different adulteration proportions caused the biggish error between the predicted values and actual values.

4. Conclusions The FTIR spectrum of vegetable oils was divided into 22 regions according to the different absorption peaks. Cluster analysis for calibration set and discriminant analysis for validation set were accomplished on the basis of areas of these regions and got a reasonable vegetable oils authentication result. The area ratio between band 19 and band 20 and wavenumber shift of band 19 were used to construct linear regression equations, the LODs of CO, PO, RO and SO adulterated with UFO were 6.6%, 7.2%, 5.5% and 3.6% on the condition of area ratio and 8.1%, 9.0%, 6.9% and 5.6% on the condition of wavenumber shift, respectively. So edible

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vegetable oils adulterated with UFO could be detected by the combination of the information of FTIR spectrum and chemometrics according to the result of qualitative and quantitative analysis obtained in this study. What specific components are produced after repeatedly frying of vegetable oils and what the relation is between the infrared spectrum of vegetable oils and the specific chemical substances need a further study. Acknowledgements The research is supported by project of oil quality and safety control technology research and industrialisation demonstration (2009BADB9B08) of the ‘‘11-5’’ Technology Support Program. The authors would like to thank teacher Zhang of Academy of Science of China Agricultural University and upperclassman who helped us during the experiment and the course of paper writing. References Agiomyrgianaki, A., Petrakis, P. V., & Dais, P. (2010). Detection of refined olive oil adulteration with refined hazelnut oil by employing NMR spectroscopy and multivariate statistical analysis. Talanta, 80(5), 2165–2171. Al-Ismail, K. M., Alsaed, A. K., Ahmad, R., & Al-Dabbas, M. (2010). Detection of olive oil adulteration with some plant oils by GLC analysis of sterols using polar column. Food Chemistry, 121(4), 1255–1259. Angiuli, M., Bussolino, G. C., Ferrari, C., Matteoli, E., Righetti, M. C., Salvetti, et al. (2009). Calorimetry for fast authentication of edible oils. International Journal of Thermophysics, 30(3), 1014–1024. Brodnjak-Voncina, D., Kodba, Z. C., & Novic, M. (2005). Multivariate data analysis in classification of vegetable oils characterized by the content of fatty acids. Chemometrics and Intelligent Laboratory Systems, 75(1), 31–43. Choe, E., & Min, D. B. (2007). Chemistry of deep-fat frying oils. Journal of Food Science, 72(5), 77–86. Cunha, S. C., & Oliveira, M. (2006). Discrimination of vegetable oils by triacylglycerols evaluation of profile using HPLC/ELSD. Food Chemistry, 95(3), 518–524. Downey, G., McIntyre, P., & Davies, A. N. (2002). Detecting and quantifying sunflower oil adulteration in extra virgin olive oils from the Eastern Mediterranean by visible and near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 50(20), 5520–5525. Ferrari, C., Angiuli, A., Tombari, E., Righetti, M. C., Matteoli, E., & Salvetti, G. (2007). Promoting calorimetry for olive oil authentication. Thermochimica Acta, 459(1– 2), 58–63. Guillen, M. D., & Cabo, N. (1997). Characterization of edible oils and lard by Fourier transform infrared spectroscopy. Relationships between composition and frequency of concrete bands in the fingerprint region. Journal of the American Oil Chemists Society, 74(10), 1281–1286. Gurdeniz, G., & Ozen, B. (2009). Detection of adulteration of extra-virgin olive oil by chemometric analysis of mid-infrared spectral data. Food Chemistry, 116(2), 519–525. Gurdeniz, G., Ozen, B., & Tokatli, F. (2010). Comparison of fatty acid profiles and mid-infrared spectral data for classification of olive oils. European Journal of Lipid Science and Technology, 112(2), 218–226.

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