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Discriminative analysis of denatured vegetable oils by thermally assisted hydrolysis and methylation-gas chromatography combined with multivariate analysis
Journal of Analytical and Applied Pyrolysis 64 (2002) 187–196
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Discriminative analysis of denatured vegetable oils by thermally assisted hydrolysis and methylation-gas chromatography combined with multivariate analysis S. Okuyama a,*, T. Mitsui a, Y. Ishida b, H. Ohtani b, S. Tsuge b a
Criminal In6estigation Laboratory, Aichi Police H.Q., 2 -1 -1 Sannomaru, Naka-ku, Nagoya 460 -8502, Japan b Department of Applied Chemistry, Graduate School of Engineering, Nagoya Uni6ersity, Furo-cho, Chikusa-ku, Nagoya 464 -8603, Japan Received 5 September 2001; accepted 4 January 2002
Abstract Various denatured vegetable oils were classified into the original oils based on their fatty acid compositions determined by thermally assisted hydrolysis and methylation-gas chromatography in the presence of trimethylsulfonium hydroxide combined with multivariate analyses. As a whole, 132 oil samples subjected to discrimination were obtained from 11 types of vegetable oils after denaturation under various conditions such as indoor exposure under different lightings or denaturation during combustion. Here, peak intensities of the methyl esters of the representative five fatty acids observed in the chromatograms such as palmitic (C16:0), stearic (C18:0), oleic (C18:1), linoleic (C18:2) and linolenic (C18:3) acids were used as input variables for the multivariate analyses such as principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). As a result, first, discriminative analysis among the oil classes was attempted by means of PCA. Although linseed oil samples were clearly differentiated from the other oil samples even after denaturation by developing the 1st and 3rd principal component scores, the distinct discrimination among the oil classes other than linseed oil was difficult. On the contrary, SIMCA was
* Corresponding author. Tel.: + 81-52-951-1611 (exp 4732); fax: + 81-52-951-1611 (exp 4719) E-mail address: [email protected] (S. Okuyama). 0165-2370/02/$ - see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 - 2 3 7 0 ( 0 2 ) 0 0 0 3 0 - X
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successfully used to classify every oil sample into their original vegetable oil much more distinctly even for those after denaturation. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Soft independent modeling of class analogy; Principal component analysis; Vegetable oils; Thermally assisted hydrolysis and methylation-gas chromatography; Denaturation; Trimethylsulfonium hydroxide
1. Introduction In forensic sciences, it is often required to specify the kinds among the various vegetable oils used in crime, especially in arson, mainly through examining the compositions of fatty acid components in the oil samples. However, the change in the oil components by oxidation and denaturation [1– 3] usually makes the specification of the oils used in a criminal event difficult. Generally, the composition of fatty acids in the oil samples is usually analyzed by gas chromatography (GC) after tedious and time-consuming pretreatment [4] which involves hydrolysis and methylation in the presence of an alkali and an esterification reagent. Recently, thermally assisted hydrolysis and methylation-gas chromatography (THM-GC) has proved to be a powerful method [5] for rapid determination of the chemical composition of the fatty acids included in various types of lipids. By this method, fatty acid components contained in the oil samples are changed into their methyl esters as the result of the one-step THM reaction in the presence of an organic alkali such as trimethylsulfonium hydroxide [(CH3)3SOH, TMSH] even for less stable polyunsaturated fatty acids. In this study, various vegetable oils such as soybean, rapeseed, corn, sesame, peanut, olive, castor, sunflower, cottonseed, linseed and safflower oils denatured under various conditions were analyzed by THM-GC in terms of fatty acid composition. The data were evaluated by multivariate analysis such as principal component analysis (PCA) [6– 10] and soft independent modeling of class analogy (SIMCA) [11 –13] in order to discriminate the class of the vegetable oils even for those after denaturation.
2. Experimental
2.1. Samples and reagent Soybean, corn, olive, castor and linseed oils were obtained from Wako Pure Chemical Ind. (Osaka, Japan). Sesame, peanut, safflower, sunflower and cottonseed oils were purchased from Sigma (St. Louis, MO) and rapeseed oil was obtained from Nisshin (Tokyo, Japan). Trimethylsulfonium hydroxide (TMSH) methanol solution (0.2 M) as a hydrolysis/derivatization reagent was supplied by Tokyo Kasei Kogyo (Tokyo, Japan).
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2.2. Denaturation of oils An aliquot of each vegetable oil sample (ca. 0.5 ml) taken onto a glass plate was weathered indoor either on the window side under sunlight, on the window side in the shade or under illumination of a fluorescent lamp for 1, 2 or 3 months. In addition, for the simulation of the arson case, a pile of two sheets of newspaper and cardboard (20 cm× 40 cm), or a block of wood (21 cm× 8.5 cm× 1.2 cm) impregnated with 10 ml of each oil sample was burned until the fire went out spontaneously. Thus, 11 kinds of denatured samples were obtained for each of the vegetable oils. Table 1 summarizes the weathering conditions for the oil samples. Furthermore, a denatured oil sample extracted from the residues in the actual site of an arson case was also examined. Prior to THM-GC measurements, 20 30 ml of the oil denatured by indoor weathering was dissolved in 1 ml of chloroform. The control oil solutions were prepared by dissolving 30 ml of the original oils in 1 ml of chloroform. The residues of oil denatured by combustion were also examined after extraction with 50 ml of diethylether.
2.3. THM-GC conditions A vertical microfurnace pyrolyzer (Shimadzu PYR-4A) was directly coupled to a gas chromatograph (Shimadzu GC-17A) equipped with a fused silica capillary column coated with polyethylene glycol stationary phase (30 m long× 0.25 mm i.d., 0.25 mm film thickness; Hewlett Packard, Palo Alto, CA) and a flame ionization detector (FID). About 1 ml of the oil sample solution in chloroform or diethylether plus 4 ml of the TMSH solution taken in a platinum sample cup was dropped into the heated center of the pyrolyzer at 350 °C under the flow of helium carrier gas. The optimum amounts of the TMSH solution added to the oil samples and the reaction temperature were empirically determined to obtain the highest yield of Table 1 Weathering conditions for oil samples Weathering conditions
Abbreviations
1. Original oil sample 2. Weathering 1 month under sunshine on the indoor window side 3. Weathering 2 months under sunshine on the indoor window side 4. Weathering 3 months under sunshine on the indoor window side 5. Weathering 1 month in the shade on the indoor window side 6. Weathering 2 months in the shade on the indoor window side 7. Weathering 3 months in the shade on the indoor window side 8. Weathering 1 month under fluorescent lamp 9. Weathering 2 months under fluorescent lamp 10. Weathering 3 months under fluorescent lamp 11. Denatured during combustion with newspaper and cardboard 12. Denatured during combustion with wood
Control Exp. A(1) Exp. A(2) Exp. A(3) Exp. B(1) Exp. B(2) Exp. B(3) Exp. C(1) Exp. C(2) Exp. C(3) Exp. D(1) Exp. D(2)
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linoleic acid methyl ester after examining various amounts of the reagent between 1 and 5 ml and temperatures between 250 and 500 °C, respectively, using the control soybean oil sample. The 50 ml min − 1 helium carrier gas flow-rate at the pyrolyzer was reduced to 1.0 ml min − 1 at the capillary column by means of a splitter. The column temperature was initially set at 170 °C and then programmed to 240 °C at a rate of 5 °C min − 1. The interface/injector and detector temperatures were maintained at 240 °C.
2.4. Data treatment and multi6ariate analysis The resulting data from the observed pyrograms were processed using a commercially available software for the multivariate analysis, Pirouette version 2.6 (Infometrix, Inc., Woodinville, WA). Two major pattern recognition methods, PCA and SIMCA were used to discriminate the class of the vegetable oils. Prior to the multivariate analysis, the chromatographic data collected as a set of raw intensities were normalized to the total intensity and then mean-centered in order to scale each peak with a constant variation. PCA analysis was carried out by basically the same method described elsewhere [9,10]. SIMCA is one of the classification methods, where a principal component (PC) model, or a SIMCA box is tentatively constructed for each class of the oil samples, and then the differentiation of unknown samples is performed by projecting the samples into the PC models and then evaluating their fit qualities. The number of relevant PC was determined for each of the classes according to the modeling program of the software [14].
3. Results and discussion
3.1. Typical chromatograms of denatured oil samples obtained by THM-GC Fig. 1 shows typical chromatograms of the denatured soybean oil samples along with that of the control sample. Five kinds of fatty acid methyl esters; palmitic (C16:0), stearic (C18:0), oleic (C18:1), linoleic (C18:2) and linolenic (C18:3) acids are observed mainly in the chromatogram of the control soybean oil after the elution of methanol and dimethylsulfide formed from the excess reagent, TMSH, as the result of the THM reaction [5] (Fig. 1(a)). Among these, the peak of the polyunsaturated fatty acid, C18:3, almost disappears in the chromatogram of every denatured soybean oil sample (Fig. 1(b–d)) due to its highly reactive nature. In addition, the peak of C18:2 is drastically diminished for the denatured samples depending on the denaturation conditions. Moreover, relative peak intensities of C18:1 also slightly decrease during exposure. The preferential losses of the unsaturated fatty acid components during denaturation were also observed in the other vegetable oil samples in the similar manner.
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Fig. 1. Chromatograms of weathered soybean oil samples. (a) Control, (b) weathering 1 month under sunshine [Exp.A(1)], (c) weathering 3 months under sunshine [Exp.A(3)], (d) denatured during combustion with paper [Exp.D(1)].
3.2. Multi6ariate analysis 3.2.1. Discrimination by PCA Normalization of the chromatogramic information for multivariate analysis was carried out using the peak intensities of the representative five methyl esters of C16:0, C18:0, C18:1, C18:2 and C18:3 fatty acids. Here, each peak intensity was
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Table 2 Typical changes of normalized fatty acid peak intensity (sample: soybean oil) Weathering conditionsa
Normalized peak intensity (%)
Control Exp. A(1) Exp. A(2) Exp. A(3) Exp. B(1) Exp. B(2) Exp. B(3) Exp. C(1) Exp. C(2) Exp. C(3) Exp. D(1) Exp. D(2) a
C16:0
C18:0
C18:1
C18:2
C18:3
(Total)
11.6 25.7 34.0 38.4 15.0 33.4 42.0 11.7 12.2 28.4 20.2 12.9
3.9 9.0 11.9 12.8 5.4 11.8 13.8 4.0 4.4 41.0 7.2 4.0
22.4 41.2 43.5 40.0 28.2 45.2 38.2 22.5 23.2 38.7 34.7 23.0
56.7 23.4 10.3 8.9 48.5 9.5 5.8 56.7 55.5 22.2 37.7 55.5
5.4 0.5 0.1 0.0 2.9 0.1 0.0 4.9 4.7 0.4 0.0 4.6
(100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0)
Weathering conditions correspond to those in Table 1.
divided by the total intensity of the five peaks in every chromatogram and thus the normalized peak intensity (%) of each component was calculated. As a whole, 132 data sets (1 control and 11 denatured samples×11 vegetable oils) of the normalized peak intensities were obtained. Table 2 shows the normalized peak intensities of the soybean oil samples as an typical example. This table also clearly indicates that polyunsaturated fatty acid components were preferentially deteriorated during determination. PCA was used to make the data easier to classify into 11 types of the vegetable oils in which a set of normalized intensities obtained from the 5 peaks was transformed to a new set of values, called principal component scores (PCS), regarding the 132 oil samples. Table 3 summarizes the factor loadings and percentage variance explained by each PC for the PCSs thus obtained by use of the data sets for the 132 oil samples. The factor loadings express the weight of the categories Table 3 Factor loading of principal component and percentage variance explained by each principal component (for 132 data sets regarding the 11 kinds of oil samples) Category
C16:0 C18:0 C18:1 C18:2 C18:3 Variance (%)
Factor loading of principal component 1st
2nd
3rd
4th
0.485 0.309 0.416 −0.653 −0.263 37
0.466 0.540 −0.670 0.199 −0.058 28
−0.027 0.147 −0.090 −0.374 0.911 21
−0.610 0.755 0.213 0.076 −0.087 14
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Fig. 2. Score plot of first and third principal components obtained by principal component analysis for 132 vegetable oil samples. , soybean; , rapeseed; , corn; , sesame; ×, peanut; 2, olive; , safflower; , castor; , sunflower; , cottonseed; ", linseed; *, original oil sample (control).
(fatty acid components) influencing the corresponding PC. For example, the fact that the factor loadings of the saturated and the monounsaturated fatty acids for the 1st principal component (PC1) show positive loadings while those of the polyunsaturated fatty acids negative indicates that PC1 is dependent strongly on the variations of relative amounts between the saturated plus the monosaturated fatty acids, and the polyunsaturated fatty acids which are preferentially decomposed during denaturation. Moreover, relatively large negative loading of C18:1 on the 2nd principal component (PC2) and large positive one of C18:3 on the 3rd principal component (PC3) suggest that the relative content of C18:1 and C18:3 is reflected in increased scores on these axes. On the other hand, percentage variance explained by each PC (%) that reveal the proportion of information in the data sets expressed in the corresponding PC obtained for the 132 samples are approximately 37, 28, 21 and 14% for the PC1, PC2, PC3 and 4th principal component (PC4), respectively. The fact that the sum of the corresponding variance amounts to 100% suggests that these four PCs’ values comprise the whole information. Fig. 2 shows a score plot between PC1 and PC3 for the 132 oil samples. It is obviously recognized that linseed oil (") is discriminated clearly from the other oil samples even after denaturation by using this score plot. This observation is greatly attributed to the fact that linseed oil generally contains an exceptionally large proportion of C18:3 which might primarily contribute to PC3. Furthermore, the plots for the denatured linseed oils shift to positive PC1 values from that of the original one due to the fact that the factor loadings for PC1 showed negative loadings for the polyunsaturated fatty acids preferentially decomposed during denaturation. The similar shifts in the plots for the denatured oil samples were observed commonly irrespective of their kinds. However, the distinct discrimination
So, Ca So Sa, So Sa, So So So, Sa So So Ca, So So Sa, So So
Soybean (So)
Ra Ra Ra Ra Ra Ra Ra Ra Ra Ra Ra Ra
Rapeseed (Ra)
Cr Cr, Pe, Cr, Cr, Cr, Cr, Cr Cr Cr Pe, Cr Cr
Pe Cr Pe Pe Pe Pe
Corn (Cr)
Applicable SIMCA box of oil samplesc
Se Se Se Se Se Se Se Se Se Se Se Se
Sesame (Se)
Cr, Pe Pe, Cr, Pe, Pe, Pe, Cr, Cr, Pe Cr, Pe, Pe Cr
Cr Pe Cr Cr So Pe Pe
Pe
Peanut (Pe)
Ol Ol Pe, Ol Pe, Pe, Ol Ol Ol Pe, Pe, Ol Ol Ol
Ol Ol
Ol
Olive (Ol)
Ct, So, Sa, Sa Sa, Sa, Sa Ct, Sa, Sa, Sa Sa, Ct
Sa, Su So So
So So
Sa, Su Sa So
Safflower (Sa)
Ca Ca Ca Ca Ca Ca Ca Ca Ca Ca Ca Ca
Castor (Ca)
Su Su Su Su Su Su Su Su Su Su Su Su
Sunflower (Su)
b
The goodness of fit goes from left to right in decreasing fashion. Weathering conditions correspond to those in Table 1. c Oil samples: So, soybean; Ra, rapeseed; Cr, corn; Se, sesame; Pe, peanut; Ol, olive; Sa, safflower; Ca, castor; Su, sunflower; Ct, cottonseed; Li, linseed.
a
Control Exp. A(1) Exp. A(2) Exp. A(3) Exp. B(1) Exp. B(2) Exp. B(3) Exp. C(1) Exp. C(2) Exp. C(3) Exp. D(1) Exp. D(2)
Weathering conditionsb
Table 4 Estimated classesa of oil samples by SIMCA
Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct
Cottonseed (Ct) Li Li Li Li Li Li Li Li Li Li Li Li
Linseed (Li)
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among the oil classes other than linseed oil is difficult by judging from the score plot in Fig. 2. Moreover, it was also impossible to discriminate unequivocally the 132 oil samples by using the two or three dimensional score plots of any other combinations of PCs even with a rotation procedure. These results suggest that the discriminative analysis of the denatured oil samples is not an easy task by THM-GC combined with PCA alone.
3.2.2. Discrimination by SIMCA Discrimination of the 132 oil samples was attempted by use of another multivariate analytical technique called SIMCA applied to the same data sets obtained by the THM-GC. Firstly, one SIMCA box for a vegetable oil consisting of the control and its 11 denatured samples was constructed. Similarly, SIMCA boxes were constructed for 11 kinds of vegetable oils. Then, the 132 normalized data set for every oil sample was individually calculated again and fitted into the 11 SIMCA boxes. Table 4 shows the results obtained by this procedure, in which the 11 SIMCA boxes corresponding to the 11 vegetable oils are allocated to the 132 denaturated samples. Here, the suggested SIMCA boxes are listed in the order of the better fitting from left to right for a given sample. As for soybean, corn, peanut, olive and safflower oils, some oil samples comprise more than one SIMCA boxes. Although the suggested SIMCA boxes are overlapping to some extent, it is demonstrated that the every denaturated oil sample is much more distinctly classified into the original vegetable oil by SIMCA than by the preceding PCA method using the score plots mentioned above. In particular, all of the denatured samples of rapeseed, sesame, castor, sunflower, cottonseed and linseed oils are unequivocally classified into the original oils. In fact, the observed THM-GC data for a denatured oil sample extracted at the actual site of an arson case, which was known to be a sesame oil, coincided with the SIMCA box corresponding to it. Finally Table 5 summarizes the correlation between the oil classes claimed by SIMCA and the probable ones among the 11 vegetable oils examined in this work. Table 5 Correlation between oil classes claimed by SIMCA and suspected vegetable oils Result of SIMCA
Suspected vegetable oils
Soybean Rapeseed Corn Sesame Peanut Olive Safflower Castor Sunflower Cottonseed Linseed
Soybean, peanut, safflower Rapeseed, Corn, peanut Sesame Peanut, corn, olive, Olive Safflower, soybean Castor, soybean Sunflower, safflower Cottonseed, safflower Linseed
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Even if the measuring result of an unknown sample is judged to be classified into soybean oil by SIMCA, this sample might belong not only to soybean oil but also to peanut and safflower oil. In a similar manner, the decisions by SIMCA to be corn, peanut, safflower, castor, sunflower and cottonseed oils might involve uncertainty to some extent, while the results to be rapeseed, sesame, olive and linseed oils should be unequivocally identified even for the denatured samples. Thus, the proposed technique could be practically effective in forensic sciences in which the oil samples to be analyzed are often exposed under various conditions causing denaturation.
References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]
M. Fujisaki, S. Mohri, Y. Endo, K. Fujimoto, J. Oleo. Sci. 50 (2001) 97. M. Fujisaki, M. Sato, Y. Endo, K. Fujimoto, J. Oleo. Sci. 50 (2001) 121. M. Lazzari, O. Chiantore, Polym. Deg. Stab. 65 (1999) 303. W.M.N. Ratnayake, J. Oleo. Sci. 50 (2001) 339. Y. Ishida, S. Wakamatsu, H. Yokoi, H. Ohtani, S. Tsuge, J. Anal. Appl. Pyrolysis 49 (1999) 267. G. Musumarra, G. Scarlata, G. Romano, S. Clementi, S. Wold, J. Chromatogr. Sci. 22 (1984) 538. C.S. Smith, S.L. Morgan, C.D. Parks, A. Fox, J. Anal. Appl. Pyrolysis 18 (1990) 97. R.S. Sahota, S.L. Morgan, Anal. Chem. 65 (1993) 70. M. Hida, S. Okuyama, T. Mitsui, Y. Minami, Y. Fujimura, Chromatographia 38 (1994) 436. S. Okuyama, T. Mitsui, Jpn. J. Toxicol. Environ. Health 41 (1995) 287. S. Wold, Pattern Recognit. 8 (1976) 127. E.R. Collanres, R. Duta, W.J. Welsh, W.L. Zielinski, J. Brower, Anal. Chem. 69 (1997) 1392. T.P. Valid, M.C. Burl, N.S. Lewis, Anal. Chem. 73 (2001) 321. E.R. Malinowski, Factor analysis in Chemistry, 2nd ed., Wiley, New York, 1991, p. 98.
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Discriminative analysis of denatured vegetable oils by thermally assisted hydrolysis and methylation-gas chromatography combined with multivariate analysis S. Okuyama a,*, T. Mitsui a, Y. Ishida b, H. Ohtani b, S. Tsuge b a
Criminal In6estigation Laboratory, Aichi Police H.Q., 2 -1 -1 Sannomaru, Naka-ku, Nagoya 460 -8502, Japan b Department of Applied Chemistry, Graduate School of Engineering, Nagoya Uni6ersity, Furo-cho, Chikusa-ku, Nagoya 464 -8603, Japan Received 5 September 2001; accepted 4 January 2002
Abstract Various denatured vegetable oils were classified into the original oils based on their fatty acid compositions determined by thermally assisted hydrolysis and methylation-gas chromatography in the presence of trimethylsulfonium hydroxide combined with multivariate analyses. As a whole, 132 oil samples subjected to discrimination were obtained from 11 types of vegetable oils after denaturation under various conditions such as indoor exposure under different lightings or denaturation during combustion. Here, peak intensities of the methyl esters of the representative five fatty acids observed in the chromatograms such as palmitic (C16:0), stearic (C18:0), oleic (C18:1), linoleic (C18:2) and linolenic (C18:3) acids were used as input variables for the multivariate analyses such as principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). As a result, first, discriminative analysis among the oil classes was attempted by means of PCA. Although linseed oil samples were clearly differentiated from the other oil samples even after denaturation by developing the 1st and 3rd principal component scores, the distinct discrimination among the oil classes other than linseed oil was difficult. On the contrary, SIMCA was
* Corresponding author. Tel.: + 81-52-951-1611 (exp 4732); fax: + 81-52-951-1611 (exp 4719) E-mail address: [email protected] (S. Okuyama). 0165-2370/02/$ - see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 - 2 3 7 0 ( 0 2 ) 0 0 0 3 0 - X
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successfully used to classify every oil sample into their original vegetable oil much more distinctly even for those after denaturation. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Soft independent modeling of class analogy; Principal component analysis; Vegetable oils; Thermally assisted hydrolysis and methylation-gas chromatography; Denaturation; Trimethylsulfonium hydroxide
1. Introduction In forensic sciences, it is often required to specify the kinds among the various vegetable oils used in crime, especially in arson, mainly through examining the compositions of fatty acid components in the oil samples. However, the change in the oil components by oxidation and denaturation [1– 3] usually makes the specification of the oils used in a criminal event difficult. Generally, the composition of fatty acids in the oil samples is usually analyzed by gas chromatography (GC) after tedious and time-consuming pretreatment [4] which involves hydrolysis and methylation in the presence of an alkali and an esterification reagent. Recently, thermally assisted hydrolysis and methylation-gas chromatography (THM-GC) has proved to be a powerful method [5] for rapid determination of the chemical composition of the fatty acids included in various types of lipids. By this method, fatty acid components contained in the oil samples are changed into their methyl esters as the result of the one-step THM reaction in the presence of an organic alkali such as trimethylsulfonium hydroxide [(CH3)3SOH, TMSH] even for less stable polyunsaturated fatty acids. In this study, various vegetable oils such as soybean, rapeseed, corn, sesame, peanut, olive, castor, sunflower, cottonseed, linseed and safflower oils denatured under various conditions were analyzed by THM-GC in terms of fatty acid composition. The data were evaluated by multivariate analysis such as principal component analysis (PCA) [6– 10] and soft independent modeling of class analogy (SIMCA) [11 –13] in order to discriminate the class of the vegetable oils even for those after denaturation.
2. Experimental
2.1. Samples and reagent Soybean, corn, olive, castor and linseed oils were obtained from Wako Pure Chemical Ind. (Osaka, Japan). Sesame, peanut, safflower, sunflower and cottonseed oils were purchased from Sigma (St. Louis, MO) and rapeseed oil was obtained from Nisshin (Tokyo, Japan). Trimethylsulfonium hydroxide (TMSH) methanol solution (0.2 M) as a hydrolysis/derivatization reagent was supplied by Tokyo Kasei Kogyo (Tokyo, Japan).
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2.2. Denaturation of oils An aliquot of each vegetable oil sample (ca. 0.5 ml) taken onto a glass plate was weathered indoor either on the window side under sunlight, on the window side in the shade or under illumination of a fluorescent lamp for 1, 2 or 3 months. In addition, for the simulation of the arson case, a pile of two sheets of newspaper and cardboard (20 cm× 40 cm), or a block of wood (21 cm× 8.5 cm× 1.2 cm) impregnated with 10 ml of each oil sample was burned until the fire went out spontaneously. Thus, 11 kinds of denatured samples were obtained for each of the vegetable oils. Table 1 summarizes the weathering conditions for the oil samples. Furthermore, a denatured oil sample extracted from the residues in the actual site of an arson case was also examined. Prior to THM-GC measurements, 20 30 ml of the oil denatured by indoor weathering was dissolved in 1 ml of chloroform. The control oil solutions were prepared by dissolving 30 ml of the original oils in 1 ml of chloroform. The residues of oil denatured by combustion were also examined after extraction with 50 ml of diethylether.
2.3. THM-GC conditions A vertical microfurnace pyrolyzer (Shimadzu PYR-4A) was directly coupled to a gas chromatograph (Shimadzu GC-17A) equipped with a fused silica capillary column coated with polyethylene glycol stationary phase (30 m long× 0.25 mm i.d., 0.25 mm film thickness; Hewlett Packard, Palo Alto, CA) and a flame ionization detector (FID). About 1 ml of the oil sample solution in chloroform or diethylether plus 4 ml of the TMSH solution taken in a platinum sample cup was dropped into the heated center of the pyrolyzer at 350 °C under the flow of helium carrier gas. The optimum amounts of the TMSH solution added to the oil samples and the reaction temperature were empirically determined to obtain the highest yield of Table 1 Weathering conditions for oil samples Weathering conditions
Abbreviations
1. Original oil sample 2. Weathering 1 month under sunshine on the indoor window side 3. Weathering 2 months under sunshine on the indoor window side 4. Weathering 3 months under sunshine on the indoor window side 5. Weathering 1 month in the shade on the indoor window side 6. Weathering 2 months in the shade on the indoor window side 7. Weathering 3 months in the shade on the indoor window side 8. Weathering 1 month under fluorescent lamp 9. Weathering 2 months under fluorescent lamp 10. Weathering 3 months under fluorescent lamp 11. Denatured during combustion with newspaper and cardboard 12. Denatured during combustion with wood
Control Exp. A(1) Exp. A(2) Exp. A(3) Exp. B(1) Exp. B(2) Exp. B(3) Exp. C(1) Exp. C(2) Exp. C(3) Exp. D(1) Exp. D(2)
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linoleic acid methyl ester after examining various amounts of the reagent between 1 and 5 ml and temperatures between 250 and 500 °C, respectively, using the control soybean oil sample. The 50 ml min − 1 helium carrier gas flow-rate at the pyrolyzer was reduced to 1.0 ml min − 1 at the capillary column by means of a splitter. The column temperature was initially set at 170 °C and then programmed to 240 °C at a rate of 5 °C min − 1. The interface/injector and detector temperatures were maintained at 240 °C.
2.4. Data treatment and multi6ariate analysis The resulting data from the observed pyrograms were processed using a commercially available software for the multivariate analysis, Pirouette version 2.6 (Infometrix, Inc., Woodinville, WA). Two major pattern recognition methods, PCA and SIMCA were used to discriminate the class of the vegetable oils. Prior to the multivariate analysis, the chromatographic data collected as a set of raw intensities were normalized to the total intensity and then mean-centered in order to scale each peak with a constant variation. PCA analysis was carried out by basically the same method described elsewhere [9,10]. SIMCA is one of the classification methods, where a principal component (PC) model, or a SIMCA box is tentatively constructed for each class of the oil samples, and then the differentiation of unknown samples is performed by projecting the samples into the PC models and then evaluating their fit qualities. The number of relevant PC was determined for each of the classes according to the modeling program of the software [14].
3. Results and discussion
3.1. Typical chromatograms of denatured oil samples obtained by THM-GC Fig. 1 shows typical chromatograms of the denatured soybean oil samples along with that of the control sample. Five kinds of fatty acid methyl esters; palmitic (C16:0), stearic (C18:0), oleic (C18:1), linoleic (C18:2) and linolenic (C18:3) acids are observed mainly in the chromatogram of the control soybean oil after the elution of methanol and dimethylsulfide formed from the excess reagent, TMSH, as the result of the THM reaction [5] (Fig. 1(a)). Among these, the peak of the polyunsaturated fatty acid, C18:3, almost disappears in the chromatogram of every denatured soybean oil sample (Fig. 1(b–d)) due to its highly reactive nature. In addition, the peak of C18:2 is drastically diminished for the denatured samples depending on the denaturation conditions. Moreover, relative peak intensities of C18:1 also slightly decrease during exposure. The preferential losses of the unsaturated fatty acid components during denaturation were also observed in the other vegetable oil samples in the similar manner.
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Fig. 1. Chromatograms of weathered soybean oil samples. (a) Control, (b) weathering 1 month under sunshine [Exp.A(1)], (c) weathering 3 months under sunshine [Exp.A(3)], (d) denatured during combustion with paper [Exp.D(1)].
3.2. Multi6ariate analysis 3.2.1. Discrimination by PCA Normalization of the chromatogramic information for multivariate analysis was carried out using the peak intensities of the representative five methyl esters of C16:0, C18:0, C18:1, C18:2 and C18:3 fatty acids. Here, each peak intensity was
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Table 2 Typical changes of normalized fatty acid peak intensity (sample: soybean oil) Weathering conditionsa
Normalized peak intensity (%)
Control Exp. A(1) Exp. A(2) Exp. A(3) Exp. B(1) Exp. B(2) Exp. B(3) Exp. C(1) Exp. C(2) Exp. C(3) Exp. D(1) Exp. D(2) a
C16:0
C18:0
C18:1
C18:2
C18:3
(Total)
11.6 25.7 34.0 38.4 15.0 33.4 42.0 11.7 12.2 28.4 20.2 12.9
3.9 9.0 11.9 12.8 5.4 11.8 13.8 4.0 4.4 41.0 7.2 4.0
22.4 41.2 43.5 40.0 28.2 45.2 38.2 22.5 23.2 38.7 34.7 23.0
56.7 23.4 10.3 8.9 48.5 9.5 5.8 56.7 55.5 22.2 37.7 55.5
5.4 0.5 0.1 0.0 2.9 0.1 0.0 4.9 4.7 0.4 0.0 4.6
(100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0)
Weathering conditions correspond to those in Table 1.
divided by the total intensity of the five peaks in every chromatogram and thus the normalized peak intensity (%) of each component was calculated. As a whole, 132 data sets (1 control and 11 denatured samples×11 vegetable oils) of the normalized peak intensities were obtained. Table 2 shows the normalized peak intensities of the soybean oil samples as an typical example. This table also clearly indicates that polyunsaturated fatty acid components were preferentially deteriorated during determination. PCA was used to make the data easier to classify into 11 types of the vegetable oils in which a set of normalized intensities obtained from the 5 peaks was transformed to a new set of values, called principal component scores (PCS), regarding the 132 oil samples. Table 3 summarizes the factor loadings and percentage variance explained by each PC for the PCSs thus obtained by use of the data sets for the 132 oil samples. The factor loadings express the weight of the categories Table 3 Factor loading of principal component and percentage variance explained by each principal component (for 132 data sets regarding the 11 kinds of oil samples) Category
C16:0 C18:0 C18:1 C18:2 C18:3 Variance (%)
Factor loading of principal component 1st
2nd
3rd
4th
0.485 0.309 0.416 −0.653 −0.263 37
0.466 0.540 −0.670 0.199 −0.058 28
−0.027 0.147 −0.090 −0.374 0.911 21
−0.610 0.755 0.213 0.076 −0.087 14
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Fig. 2. Score plot of first and third principal components obtained by principal component analysis for 132 vegetable oil samples. , soybean; , rapeseed; , corn; , sesame; ×, peanut; 2, olive; , safflower; , castor; , sunflower; , cottonseed; ", linseed; *, original oil sample (control).
(fatty acid components) influencing the corresponding PC. For example, the fact that the factor loadings of the saturated and the monounsaturated fatty acids for the 1st principal component (PC1) show positive loadings while those of the polyunsaturated fatty acids negative indicates that PC1 is dependent strongly on the variations of relative amounts between the saturated plus the monosaturated fatty acids, and the polyunsaturated fatty acids which are preferentially decomposed during denaturation. Moreover, relatively large negative loading of C18:1 on the 2nd principal component (PC2) and large positive one of C18:3 on the 3rd principal component (PC3) suggest that the relative content of C18:1 and C18:3 is reflected in increased scores on these axes. On the other hand, percentage variance explained by each PC (%) that reveal the proportion of information in the data sets expressed in the corresponding PC obtained for the 132 samples are approximately 37, 28, 21 and 14% for the PC1, PC2, PC3 and 4th principal component (PC4), respectively. The fact that the sum of the corresponding variance amounts to 100% suggests that these four PCs’ values comprise the whole information. Fig. 2 shows a score plot between PC1 and PC3 for the 132 oil samples. It is obviously recognized that linseed oil (") is discriminated clearly from the other oil samples even after denaturation by using this score plot. This observation is greatly attributed to the fact that linseed oil generally contains an exceptionally large proportion of C18:3 which might primarily contribute to PC3. Furthermore, the plots for the denatured linseed oils shift to positive PC1 values from that of the original one due to the fact that the factor loadings for PC1 showed negative loadings for the polyunsaturated fatty acids preferentially decomposed during denaturation. The similar shifts in the plots for the denatured oil samples were observed commonly irrespective of their kinds. However, the distinct discrimination
So, Ca So Sa, So Sa, So So So, Sa So So Ca, So So Sa, So So
Soybean (So)
Ra Ra Ra Ra Ra Ra Ra Ra Ra Ra Ra Ra
Rapeseed (Ra)
Cr Cr, Pe, Cr, Cr, Cr, Cr, Cr Cr Cr Pe, Cr Cr
Pe Cr Pe Pe Pe Pe
Corn (Cr)
Applicable SIMCA box of oil samplesc
Se Se Se Se Se Se Se Se Se Se Se Se
Sesame (Se)
Cr, Pe Pe, Cr, Pe, Pe, Pe, Cr, Cr, Pe Cr, Pe, Pe Cr
Cr Pe Cr Cr So Pe Pe
Pe
Peanut (Pe)
Ol Ol Pe, Ol Pe, Pe, Ol Ol Ol Pe, Pe, Ol Ol Ol
Ol Ol
Ol
Olive (Ol)
Ct, So, Sa, Sa Sa, Sa, Sa Ct, Sa, Sa, Sa Sa, Ct
Sa, Su So So
So So
Sa, Su Sa So
Safflower (Sa)
Ca Ca Ca Ca Ca Ca Ca Ca Ca Ca Ca Ca
Castor (Ca)
Su Su Su Su Su Su Su Su Su Su Su Su
Sunflower (Su)
b
The goodness of fit goes from left to right in decreasing fashion. Weathering conditions correspond to those in Table 1. c Oil samples: So, soybean; Ra, rapeseed; Cr, corn; Se, sesame; Pe, peanut; Ol, olive; Sa, safflower; Ca, castor; Su, sunflower; Ct, cottonseed; Li, linseed.
a
Control Exp. A(1) Exp. A(2) Exp. A(3) Exp. B(1) Exp. B(2) Exp. B(3) Exp. C(1) Exp. C(2) Exp. C(3) Exp. D(1) Exp. D(2)
Weathering conditionsb
Table 4 Estimated classesa of oil samples by SIMCA
Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct
Cottonseed (Ct) Li Li Li Li Li Li Li Li Li Li Li Li
Linseed (Li)
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among the oil classes other than linseed oil is difficult by judging from the score plot in Fig. 2. Moreover, it was also impossible to discriminate unequivocally the 132 oil samples by using the two or three dimensional score plots of any other combinations of PCs even with a rotation procedure. These results suggest that the discriminative analysis of the denatured oil samples is not an easy task by THM-GC combined with PCA alone.
3.2.2. Discrimination by SIMCA Discrimination of the 132 oil samples was attempted by use of another multivariate analytical technique called SIMCA applied to the same data sets obtained by the THM-GC. Firstly, one SIMCA box for a vegetable oil consisting of the control and its 11 denatured samples was constructed. Similarly, SIMCA boxes were constructed for 11 kinds of vegetable oils. Then, the 132 normalized data set for every oil sample was individually calculated again and fitted into the 11 SIMCA boxes. Table 4 shows the results obtained by this procedure, in which the 11 SIMCA boxes corresponding to the 11 vegetable oils are allocated to the 132 denaturated samples. Here, the suggested SIMCA boxes are listed in the order of the better fitting from left to right for a given sample. As for soybean, corn, peanut, olive and safflower oils, some oil samples comprise more than one SIMCA boxes. Although the suggested SIMCA boxes are overlapping to some extent, it is demonstrated that the every denaturated oil sample is much more distinctly classified into the original vegetable oil by SIMCA than by the preceding PCA method using the score plots mentioned above. In particular, all of the denatured samples of rapeseed, sesame, castor, sunflower, cottonseed and linseed oils are unequivocally classified into the original oils. In fact, the observed THM-GC data for a denatured oil sample extracted at the actual site of an arson case, which was known to be a sesame oil, coincided with the SIMCA box corresponding to it. Finally Table 5 summarizes the correlation between the oil classes claimed by SIMCA and the probable ones among the 11 vegetable oils examined in this work. Table 5 Correlation between oil classes claimed by SIMCA and suspected vegetable oils Result of SIMCA
Suspected vegetable oils
Soybean Rapeseed Corn Sesame Peanut Olive Safflower Castor Sunflower Cottonseed Linseed
Soybean, peanut, safflower Rapeseed, Corn, peanut Sesame Peanut, corn, olive, Olive Safflower, soybean Castor, soybean Sunflower, safflower Cottonseed, safflower Linseed
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Even if the measuring result of an unknown sample is judged to be classified into soybean oil by SIMCA, this sample might belong not only to soybean oil but also to peanut and safflower oil. In a similar manner, the decisions by SIMCA to be corn, peanut, safflower, castor, sunflower and cottonseed oils might involve uncertainty to some extent, while the results to be rapeseed, sesame, olive and linseed oils should be unequivocally identified even for the denatured samples. Thus, the proposed technique could be practically effective in forensic sciences in which the oil samples to be analyzed are often exposed under various conditions causing denaturation.
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