Fatty Acid Composition and Characterisation of Commercial Vegetable Oils with Chemometric Approaches

International Food Research Journal 27(2): 270 - 279 (April 2020) Journal homepage: http://www.ifrj.upm.edu.my

Fatty acid composition and characterisation of commercial vegetable oils with chemometric approaches

*Kim, N. H., Jung, S. Y., Park, Y. A., Lee, Y. J., Jo, J. Y., Lee, S. M. and Oh, Y. H.

Food and Drug Division, Seoul Metropolitan Government Research Institute of Public Health and Environment, 30 Jangkunmaul 3-gil, Gwacheon-si, Gyeonggi-do 13813, Korea

Article history Abstract

Received: 2 September 2019 The fatty acid (FA) composition of 11 types of vegetable oils (n = 115) marketed in South Received in revised form: Korea was investigated using GC-FID. Unique characterisation of FA profiles was observed 20 February 2020 in the vegetable oils. Coconut oil showed the highest content of saturated FA (SFA) (93.30%). Accepted: 20 March 2020 The content of monounsaturated FA (MUFA) and polyunsaturated FA (PUFA) were highest in olive oil (76.44%) and perilla oil (80.14%). Rice bran oil (1.69) and olive oil (0.56) showed the ideal ratio of PUFA/SFA. Multivariate statistical techniques including principal compo- Keywords nent analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA) were fatty acids, applied to the chromatographic data of FAs. The differences and similarities between the commercial vegetable vegetable oils were clearly observed with PCA and CA. A dendrogram using Ward linkage oils, showed that the vegetable oils were grouped into three clusters. The adulterated samples of principal component sesame oil and perilla oil with cheaper oils such as corn oil and soybean oil could be sensitive- analysis, cluster analysis, ly detected by LDA even at a 2% mixing level. Stearic acid (Wilk’s λ = 0.632) and palmitic linear discriminant acid (Wilk’s λ = 0.407) showed the highest discriminant power for adulterated sesame oil and analysis perilla oil, respectively. © All Rights Reserved

Introduction The composition and content of FAs in dietary vegetable oils differ depending on their origins and Edible fats and oils are the main sources of thus influence their functional and physical properties energy (9 calories/g) and essential nutrients for man- (Lerma-Garcia et al., 2011). The characteristics of FAs kind (Ganesan et al., 2018). In total, 95 - 98% of dietary can be utilised to identify the botanical origin and inten- vegetable oils are composed of triacylglycerol, which tional adulteration of vegetable oils (Li et al., 2011; consists of a single unit of glycerol molecule and three Galão et al., 2014; Zhang et al., 2014). Meanwhile, it fatty acids (FAs) (Lerma-Garcia et al., 2011). FAs can has been reported that there are clear limitations to generally be categorised into saturated FA (SFA), detecting the adulteration of vegetable oils with just monounsaturated FA (MUFA) and polyunsaturated analysing the composition of FAs due to the similarity FA (PUFA) based on the presence and number of of FA composition in some edible oils and the wide double bonds. The ratio of unsaturated to saturated range of adulterated edible oils (El-Hamdy and El-Fiz- FAs in dietary oils and fats is a major factor that influ- ga, 1995; Li et al., 2011). ences human nutrition associated with plasmatic Even if the adulteration of edible oils, which cholesterol and obesity (Kostik et al., 2013). General- usually involves the mixing of expensive oils with ly, SFA, which have no double bonds, are known to cheaper oils, is not severely harmful to human health, increase the level of low-density lipoproteins, and are it is related to economic motivation and consumer’s one of various factors that can increase the risk of coro- right for buying “value-for-money” (Ulberth and Buch- nary heart disease (CHD) (World Health Organization, graber, 2000). Much research has been conducted into 2008). Meanwhile, dietary MUFA and PUFA not only effectively detecting the adulteration of edible oils such have beneficial effects on the blood lipid profile but as adulterated olive oil and flaxseed oil, with a combina- also show the ability to decrease the risk of CHD (Met- tion of analytical techniques and multivariate statistical calf et al., 2007). PUFA including linoleic (C18:2n-6) approaches through characterising FA profiles or and α-linolenic (C18:3n-3) are regarded as essential FAs triacylglycerol (Ruiz-Samblás et al., 2012; Monfreda that should be supplied through dietary food as they et al., 2014; Sun et al., 2015; Jabeur et al., 2017). In cannot be synthesised in our bodies (Dorni et al., the case of sesame oil, which is largely consumed in 2018). Asian countries including South Korea and China, the

*Corresponding author. Email: [email protected] 271 Kim, N. H., et al./IFRJ 27(2) : 270 - 279

problem of its adulteration has existed for a long time soybean oil (n = 7), and sunflower oil (n = 5). Korean due to economic profits (Seo et al., 2010). Several markets sell a variety of sesame oil and perilla oil works have reported the detection techniques for adul- brands. terated sesame oil (Lee et al., 2001; Park et al., 2010; Peng et al., 2015). Non-invasive analytical techniques Chemicals and reagents including Nuclear Magnetic Resonance (NMR) and The analytical standard of FAs, fatty acids

Fourier Transform Infrared Spectroscopy (FT-IR) methyl ester (FAME) mix (C4–C24), was purchased have been recently developed and applied to detect from Supelco (Bellefonte, PA, USA). Analytical grade the adulteration of perilla oil with cheaper oils because ethanol, methanol, chloroform, and iso-octane were of its high retail price (Kim et al., 2018; Park et al., purchased from Fisher Scientific Korea Ltd. (Seoul, 2019). Korea). The 14% boron trifluoride-methanol solution

Chemometric methods have emerged as an and undecanoic acid (C11:0) methyl ester as an internal efficient tool for the authentication and classification standard were purchased from Sigma-Aldrich Co. (St. of edible vegetable oils; these usually utilise multivari- Louis, MO, USA). Sodium sulphate, sodium hydrox- ate statistical and mathematical approaches to extract ide, 0.01 M sodium thiosulfate solution, and Wijs meaningful information from complicated analytical reagent were purchased from Wako Pure Chemical data (Brodnjak-Vončina et al., 2005; Esteki et al., Industries Ltd. (Osaka, Japan). Distilled water was 2018). Among the various tools of multivariate statis- purified using the Milli-Q integral 5 system (Millipore tics, principal component analysis (PCA) and cluster Co., Billerica, MA, USA). A Standard Reference analysis (CA) are commonly used to differentiate and Material (SRM 3274-4) of perilla oil containing ome- group complex data; these can be very helpful to inter- ga-3 and omega-6 FAs was used to validate the analyti- pret food analytical data with visual representation cal method. (Giacomelli et al., 2006; Chudzinska and Baralkiew- icz, 2011; Kafaoğlu et al., 2014). Linear discriminant Esterification of FAs analysis (LDA) has been frequently adopted for food The AOAC official method 969.33 (AOAC, chromatographic data to predict geographical origins 2000) was modified for the preparation of FAME. and varieties, and to identify the adulteration of vegeta- About 25 mg of the vegetable oil sample was accurately ble oils (Lee et al., 1998; Lanteri et al., 2002; weighed in a screw-capped glass tube and 1 mL of the

Bosque-Sendra et al., 2012; Callao and Ruisánchez, internal standard (C11:0, 1 mg/mL iso-octane) was 2018). added. Then, 1.5 mL of 0.5 N of methanolic sodium The main objective of the present work was hydroxide solution was added to the tube and it was to evaluate the FA composition of 11 types of edible mixed after blowing in nitrogen gas. The tube was vegetable oils that are commonly consumed in the heated in a dry block bath (MG-2200, Tokyo Rikakikai South Korean market via GC-FID analysis. Multivari- Co., Ltd., Japan) at 100°C for 5 min. After cooling, 2 ate statistical techniques like PCA and CA were also mL of a 14% boron trifluoride-methanol solution was performed to differentiate the characterisation of the added to the tube, and it was immediately capped, vegetable oils’ FA profiles. The LDA tool was applied vortexed, and heated at 100°C for 30 min. After cooling to select FA variables showing the most discriminant to 30 - 40°C, 1 mL of iso-octane solution was added power for authenticating sesame oil and perilla oil. to the tube and it was capped and mixed vigorously The present work also observed the possibility of for 30 s. Afterward, 5 mL of saturated sodium chloride discriminating the adulterated samples of sesame oil solution was added to the tube and it was agitated and perilla oil that were blended with cheaper oils such thoroughly. After cooling to room temperature, the as corn oil and soybean oil at the concentration range layer of iso-octane separated from the aqueous layer of 2 - 40% from authentic ones by LDA. was dehydrated with anhydrous sodium sulphate and was used as the final solution for analysis. Materials and methods GC instrumental condition Samples FAME analysis was performed with a model A total of 115 vegetable oil samples were of GC-2010 plus gas chromatography equipped with obtained from retail markets in Seoul, Korea which AOC-20i auto injector and flame ionisation detector included local and imported products; canola oil (n = (Shimadzu Corp., Japan). A SP-2560 fused silica capil- 11), coconut oil (n = 9), corn oil (n = 4), grapeseed oil lary column (100 m × 0.25 mm × 0.2 µm) was used (n = 7), flaxseed oil (n = 2), olive oil (n = 16), perilla for separating FAs. The GC-FID operating conditions oil (n = 25), rice bran oil (n = 2), sesame oil (n = 27), were as follows: injector temperature, 260°C; detector Kim, N. H., et al./IFRJ 27(2) : 270 - 279 272 temperature, 260°C; initial oven temperature, 140°C perilla oils were significantly higher than the other for 5 min, which was then raised at 3°C/min to 240°C oils, because those two oils have been used traditional- and held at 240°C for 13 min. The total analysis time ly in Korea for a long time and produced by a large was 51.3 min. He was used as a carrier gas at a 0.7 number of small-scale producers. mL/min flow rate. Hydrogen and air were kept at 40 Recovery test was conducted for analytical and 400 mL/min flow rates, respectively. The injection quality assurance with SRM 3274-4. Major FA volume was 1.0 µL, and the split ratio was 1:5. contents including palmitic (C16:0), stearic (C18:0), oleic

(C18:1), linoleic (C18:2), and linolenic (C18:3) acids were Measurement of iodine value (IV) analysed with the same analytical procedures, and the The IV was measured by the Korean Food recovery rates were obtained in the acceptable range Standards Codex (MFDS, 2016) to determine the of 82.8 - 100.2%. degree of unsaturation. Approximately 0.2 g of each The ten SFAs and seven USFAs were totally oil sample was weighed in a 250 mL triangular flask quantified in the tested oil samples, and it was with lid and then 20 mL of chloroform and 25 mL of confirmed that canola oil and rice bran oil comprised Wijs (iodine monochloride) solution were added into the largest number of FAs (12 kinds of FA). The coco- the flask. Following incubation in the dark for 1 h, 20 nut oil showed the highest percentage of SFA (93.30%) mL of 1 N KI solution and 100 mL of water were added of all vegetable oils, and lauric acid (C12:0), which is into the flask. Finally, the flask was titrated with 0.1 known as the indicator of quality control of coconut N sodium thiosulfate solution. A blank test was oil, was observed as the main FA (49.61%). This result performed using the same procedures. is very similar to that of Dorni et al. (2018), in which coconut oil contained 49.57% lauric acid. Olive oil Multivariate statistical analysis showed the highest content of MUFA (76.44%), Multivariate statistical analyses including followed by canola oil (60.58%). Olive oil was already PCA, CA, and LDA were carried out using IBM SPSS reported in a previous study (Kostik et al., 2013), which Statistics 24.0 software (SPSS Inc.). PCA was demonstrated that it had the highest proportion of performed to generate new variables, principal compo- MUFA of all vegetable oils (78.4%). It is well known nents (PCs), from five major FA variables (palmitic, that diet containing rich MUFA such as olive oil could stearic, oleic, linoleic, and linolenic). CA was conduct- lower the risk of CHD (Hu and Willett, 2002), and for ed to show the organised clusters of the targeted vegeta- that reason olive oil has been recognised as a healthy ble oils. Their nearness in the multidimensional plot food because of its cardio protective effect. The aver- was presented in a dendrogram using Ward linkage. age content of PUFA was the highest in the perilla oil Two-group LDA was performed to find the coeffi- (80.14%) within the range of 77.39 - 82.89% followed cients of independent variables that maximised the by flaxseed oil (73.45%) and grapeseed oil (70.44%). difference in variance between the groups compared In contrast to that, the average SFA content of the peril- to the variance in the group. The adulterated samples la oil was the lowest (6.58%). Perilla oil yielded the of sesame oil and perilla oil were made with blending highest proportion of linolenic acid (68.16%), and has them with corn oil and soybean oil at different concen- been considered as a beneficial vegetable oil to human trations of 2, 5, 10, 20, 30, and 40%. Next, the LDA health, despite being easily oxidised due to high PUFA was applied to discriminate authentic sesame oil and content (Longvah et al., 2000). Meanwhile, grapeseed perilla oil from the adulterated samples. oil and sesame oil yielded the lowest proportion of linolenic acid, 0.30% and 0.32%, respectively. In the Results and discussion present work, the contents of linolenic acid of perilla oil were in the range of 65.57 - 70.75%, which are FA composition of vegetable oils higher than that reported in a previous study (53.6 - A total of 115 samples from 11 types of edible 64.0%, Yoon and Noh, 2011). The variation of the vegetable oils collected from the local markets were content of linolenic acid can be explained by the study analysed for characterising FA composition. The FA conducted by Were et al. (2006), in which the authors percent composition and the specific ratios of tested proposed that the content of linoleic acid and oleic samples are shown in Table 1. The acquired peak areas acid in sesame oil might be affected by origin, genetic from GC analysis were normalised using response variance, refining process, and cultivation environ- factor, and the FA percent contents were calculated ment. with the FAME conversion factor. The FA contents The ideal ratio of TPUFA/TSFA recommend- less than 0.1% were excluded from the analysis. As ed by the WHO is 0.8 - 1.0 (WHO, 2008), and in the shown in Table 1, the number of items of sesame and present work, the rice bran oil (1.69) and olive oil 273 Kim, N. H., et al./IFRJ 27(2) : 270 - 279

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1 2 8 0 4 2 1 4 4 2 3 1 3

2 2 5 2 2 2

) ...... 4 2 0 0 0 0 0 e ...... 0

7 1 1 0 1 1 1

0 0 0 0 0 0 0 1 1 m 2

± ± ± ± ± ± 2 a

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= s N D N D N D N D N D N D N D N D N D 6 5 5 8 7 5

4 3 4 4 1 0 2 3 2 e 0 9 4 2 2 9 n 1 ...... 6 1 5 1 3 1 3 S ( ...... 9 5 4 9 6 5 8 5 0 0 0 0 0 3 4 1 3 4 4

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± ± ± ± ± ± ± ± ± ± .

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3 8 4 1 3 8 1 e 0 4 6 3 6 8 1 1 9 2 1 2 9 9 n P ...... 9 5 2 1 5 . ( . . . . .

3 7 3 0 2 7 1 4 1 0 0 6 1 1 6 1 8 1 6 D ) S

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......

a 6 3 1 3 1

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1 5 4

5 0 8 7 0 7 9 2 0 1 9 2 1 O 3 0 4 5 n 9 3 1 8 7 7 2 5 7 7 . . . . ( TPUFA: total polyunsaturated fatty acids. The content of fatty acids which were not acids of fatty content The acids. fatty polyunsaturated total TPUFA: ...... 5 5 6 % , 1 d 2 0 0 0 7 0 0 8 7 0 ( 1 7 1 7

t n

t 2 6 3 9 8 3 9

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5 0 2 7 1 0 2 1 2 1 1 2 1 2 2 7 8 2 7 9 8

0 0 0 0 1 0 1 ......

) ......

0 0 0 0 0 0 0

7 4 n 0 0 0 0 0 0 0

5 r ± ± ± ± ± ± ± . 2 = ± ± ± ± ± ± ±

2 N D N D N D N D N D N D N D N D 5 8 0 8 5 7 0 4 4 1 3 7 7 0 7 n C o 5 8 4 8 3 7 8 8 ( 7 4 1 1 0 2 0 ...... 0 9 5 3 9 6 5 1 0 0 0 1 0 1 TMUFA: total monounsaturated fatty acids; fatty monounsaturated total TMUFA: 1 2 5 1 2 5 5 c

4 9 6

4 6 8 9 7 9 8 9 8 8 4 6 2 t

0 4 3 6 1 7 4 7 4 4 . . . ) ...... u

1 0 1

9 0 0 0 0 0 0 0 0 0 0

1 n

± ± ± 0 o - = ± ± ± ± ± ± ± ± ± ±

c N D N D N D N D N D N D N D N D N D 1 4 0 0 6 9 2 3 3 4 6 4 6 6 n 6 4 3 ( . . . 5 3 0 3 9 5 1 5 1 1 ...... C o Table 1. Fatty acids composition and specific ratios of commercial vegetable oils analysed by GC-FID. Table 9 8 3 0 7 6 8 2 5 1 5 1 1 4 1 9

5 8 8 4 5 9 2 1 5 3 1 9 5 9 3 6 5

5 4 3 3 5 ) . . . . . 1 1 2 0 0 0 2 7 2 0 2 7 ......

1 1 1 1 1 1 l a

0 0 0 0 0 0 0 0 0 0 0 0 3 7 1

± ± ± ± ± 6 3

± ± ± ± ± ± ± ± ± ± ± ± n . . =

N D N D N D N D

8 4 8 1 3 N D 4 2 2 1 5 0 3 7 9 8 9 4 1 8 0 1 5 4 7 n . . . . . 1 8 5 3 1 1 5 6 1 1 0 6 C a ( ...... 9 2 0 2 2 4 1 0 0 0 0 0 9 1 0 7 9 5 2 6 3 2

) )

TSFA: total saturated fatty acids; fatty saturated total TSFA:

)

) ) ) i c -

b i c

)

) a i c n ) n i c i c

) ) i c ) ) i c A i c r g ) i c o i c

l e l e l e

i c t F e i c l e e n i c i c c l i c n i d n i t c d

i c 6 3 o r o r r c o o o S y - - e i s b h e a m r A n n p i t o u r o r A n s v m a a i c i h i p l a A r a n p / T y t e o g g a e m

L O l e L a a / O F a L r l g L C U F e e - S ( - A U F i ( i c ( 6 B ( P M S a ( C N e γ C α - A ( ( ( ( P m m L ( E ( ( M ( P ( T a ( ( ( U F T 1 8 : T O O g - 6 - 3 1 2 : 0 1 0 : 1 8 : 0 6 : 0 8 : 0 P 2 : 0 1 6 : 0 1 4 : 0 e C 2 4 : 1 C 2 0 : C C C 1 8 : 2 n - 6 2 4 : 0 2 0 : 1 T C 1 6 : C C C m C C 1 8 : 3 n C 1 8 : 3 n C C C O C C ND: not detected; a larger than 0.1% (w/w) was omitted from the Table. than 0.1% (w/w) was omitted from the larger Kim, N. H., et al./IFRJ 27(2) : 270 - 279 274

(0.56) yielded values closest to that. On the other hand, cumulatively accounted for 76.87% of the total the perilla oil (12.23) and flaxseed oil (8.93) yielded variance, and it was confirmed in the score graph that the highest TPUFA/TSFA ratios. It is generally known the eigenvalues of these two PCs were higher than that the high ratio of omega-6/omega-3 FAs in the diet 1.0. Unlike other FAs, linolenic acid (C18:3) only can increase the incidence of many diseases, such as showed negative loading value on the plane of PC1 cardiovascular diseases, cancers, and inflammatory and PC2 in the loading plot. As shown in Figure 1, diseases (Simopoulos, 2002). The WHO recommends the 2-dimensional score plot of PCA of the first two a consumption ratio of omega-6/omega-3 FAs of 5 - PCs extracted from correlation matrix demonstrated 10, whereas the ratio of the modern western diet is that the distinct clusters of 11 vegetable oils were 15.0 - 16.7 owing to the high consumption of omega-6 formed based on their different five major FAs com- FAs. The soybean oil and olive oil showed similar position. Li et al. (2011) suggested that corn and values, 8.65 and 10.95, respectively. In contrast, the sesame oils cannot be distinguished in 3-dimensional sunflower oil (594.71) showed the highest ratio, PCA score plot owing to their similar FA composi- followed by the grapeseed oil (235.32) and sesame oil tions. Meanwhile, the positional difference between (144.10). the two oils could be identified in the present work despite the very similarity of their FA composition. Principal component analysis (PCA) and cluster This similarity is the reason why adulterated sesame analysis (CA) oil has been intentionally made with the addition of PCA was carried out in order to establish and corn oil by unscrupulous food manufacturers. visualise the correlations of five major FA (C16:0, The dendrogram using Ward linkage

C18:0, C18:1, C18:2, and C18:3) variables acquired from produced by CA is shown in Figure 2. The dendrogram the FA analysis of 11 types of vegetable oils. Gener- clearly shows the clusters of vegetable oils having ally, PCA is used to generate new reduced variables, similar FA profiles. With the rescaled distance of 5 - principal components (PCs), which can represent 20, the vegetable oils were grouped in three clusters most variables from the original data (Lee et al., (group 1: sesame, soybean, corn, grapeseed, and 1998). The PCA was conducted for identifying the sunflower oils; group 2: coconut, canola, perilla, and degree of similarity and difference of vegetable oils flaxseed oils; and group 3: olive and rice bran oils). It with their five major FA components. The correla- could be confirmed that these results show similar tion of variables in the present work was significant tendency, like the PCA plot. Looking at the ratio of at p < 0.05, and the Kaiser-Meyer-Olkin (KMO) test TPUFA/TSFA of each group, group 1 showed a indicated sampling adequacy was suited for factor range of 3.21 - 6.63, while group 3 showed the lower analysis, with 0.655. Figure 1 shows the score and value of 0.56 - 1.69. In the case of group 2, the ratio loading plot of PCA of the first two PCs which of TPUFA/TSFA was much higher than the other

Figure 1. Principal component scores (a) and loadings (b) plot for the fatty acids of commercial vegetable oils. 275 Kim, N. H., et al./IFRJ 27(2) : 270 - 279

Figure 2. Dendrogram of cluster analysis using Ward linkage for commercial vegetable oils. groups, at 8.93 - 12.23, with the exception of canola varying by their origin and culture region. As means and coconut oils. With the PCA and CA, the similari- for inspecting the incorporation of heterogeneous oil ties, and differences of vegetable oils based on the into sesame oil, the linolenic acid content (less than FA composition could be visualised and interpreted 0.5%) and iodine value (103 - 118) have been set as in a statistical graph. In addition, the results of PCA quality indicators in the Food Standards Codex by and CA provided the appropriate information for the Korean Food and Drug Administration (KFDA). assessing the quality and authenticity of vegetable As shown in Table 2, it was confirmed that the oils. linolenic acid content of the adulterated sesame oils met the specification standard when the addition of Linear discriminant analysis (LDA) for adulterated corn oil into sesame oil was less than 20%. In other oils words, there is no way to check the adulteration of The accurate detection technique for adulter- sesame oil if corn oil was added at 20% or less. The ated oils was suggested by combining chromato- iodine values of the adulterated sesame oils were in graphic FA analysis with linear discriminant analysis the range of 110.3 - 115.3, which conformed to the (LDA). The adulterated samples of sesame oils and specification standard of authentic sesame oil. The perilla oils were made with the addition of corn oil values were very similar to the previous results and soybean oil in the different percentages of 2, 5, reported by Joo et al. (2017), in which they suggested 10, 20, 30, and 40% in order to observe the changes that the iodine values of adulterated sesame oils of five major FAs composition and iodine value. blended with corn oil (5 - 25%) were in the range of Table 2 shows the major FA composition and iodine 107.5 - 109.9. Through these results, even if a large values of the blended oils. As the percentage of amount of vegetable oils having similar unsaturation added corn oil increased in the adulterated sesame were blended with sesame oil, it was difficult to oil, the content of TSFA slightly decreased from distinguish the adulteration of sesame oil by only 15.05 to 14.23%, but the content of TPUFA measuring the iodine value. increased from 44.99 to 49.16%. Park et al. (2010) In the case of perilla oil, it is stated by the proposed that the comparison of relative ratios (e.g. KFDA that the iodine value (160 - 209) is the unique linoleic acid/stearic acid, etc.) from FA contents quality parameter for inspecting the adulteration of could be a useful tool to distinguish the adulterated perilla oil. As shown in Table 2, the content of sesame oil blended with soybean oil. Although the linoleic acid apparently increased as the addition of difference of relative ratios was somewhat effective soybean oil into perilla oil increased, while the to screen the adulterated sesame oils, there are limita- content of linolenic acid dramatically decreased. tions considering the FA composition of sesame oils Meanwhile, all the iodine values were measured in Kim, N. H., et al./IFRJ 27(2) : 270 - 279 276

3 5 2 5 9 3 . 3 7 2 . . . 1 1

2 . .

1 0 1 7

0 0

9 2

± 4 0 ± ± ±

3 7 . ± ±

. . 4 7 2 . 9 5 0 9 7 0 5 7 2 8 3 9 9 3 . . . . . 6 7 8 3 6 2 1 1 2 4

1 6 2 7 4 2 . 3 2 7 . . 1 1 .

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± 7 0 ± ± ±

7 4 . ± ±

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l 2 1 7 i 8 5 1 . 2 6 3 o . . . 1 1

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0 0

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± 9 l 0 ± ± ±

0 3 . i ± ±

. . 2 0 4 r . 9 2 5 8 9 3 7 e 7 5 7 6 1 0 8 . . . p . . 7

6 9 5 6 1 1 1 1 5 t h i

w 4 3 4

5 3 8 l . 2 5 4 i 1 1 . . .

. . 2

1 0 1 9 6

0 0

± 2 4 n 0 ± ±

± 2 . . ± ±

. a 1 4 6 0 . 6 6 4 7 e 6 3 7 1 8 2 3 7 b 5 6 . . . . . 7 y 6 5 0 5 1 1 o 1 1 6 s

2 2 1 n 4 2 1 . i 1 4 4 . . . 1 1

1 . . x

1 0 1

i 4 7

0 0 1

9 9 ± ± ±

m . . 0 5 ± ±

. 9 6 0 f . 8 5 3 7 0 2 7 1 o 0 8 2 5

4 5 . . . . . 8 5 3 3 5 1 1 % 1 1 6

7 6 8 5 0 1 . 4 0 2 1 . 1 . .

. 2 .

0 1 1 3 4 0

± 3 2 ± ±

± 8 . . ± 2 ±

0 7 1 . 4 6 1 6 8 0 1 7 1 1 6 0 4 . 3 . . . . 8 5 5 2 1 5 1 6 1 1

1 3 1 8 5 4 . 2 4 5 . . 1 1 .

4 . .

1 1 0 7 2

0 0

± TPUFA: total polyunsaturated fatty acids. TPUFA: 5 5 ± ±

± 7 . . c 0 ± ±

. 8 7 1 . 5 0 2 6 6 6 1 7 5 5 2 2 1 4 . . . . . 8 5 6 1 5 1 1 1 1 6

5 8 0 4 5 8 . 6 6 . . 3 1 0

. . . 0

0 0 3 6

0 0 0

± 2 1 0 ± ±

4 . . ± ± ±

. 4

3 4 9 9 7 . 3 6 0 9 1 4 5 3 5 . . 5 8 5 . . . 1 6 8 1 9 3 0 3 4

2 8 7 9 4 7 . 5 5 2 1 0 . .

. . . 0

0 0 1 8 0 0 0

± 4 0 0 ± ±

3 . . ± ± ±

. 3

8 4 8 0 7 . 3 1 4 2 1 4 3 5 4 . . 4 1 5 . . . 1 7 7 1 9 4 0 3 4

2 3 2 6 4 9

. 6 6 l 3 1 0 . . i

. . . 0

0 0 o 8 0 0 0 0

TSFA: total saturated fatty acids; TSFA:

± 6 1 b 0 ± ±

e 2 . .

± ± ± . 2

0 4 7 0 6 . m 3 6 9 4 1 4 0 6 0 a . . 2 4 4 s . . . 1 8 6 e 1 9 4 0 3 4 s

t h 4 1 i 6 0 5 3 . 5 6 3 1 0 . .

w 1 . . .

0 0 3 2 0 0 0 l

± i 8 9 0

± ± . . 1 o

± ± ± . 1 8

4 5 4 5 . 3 2 4 7 n 1 4 0 5 9 . . r 1 8 3 . . . 0 9 5 o 9 4 0 1 3 4 c

8 5 1 6 8 x 3 . 5 5 i 3 1 . . 0

. . 0

0 0 9 5 m 0 0

0 6

± 8 5 f ± ±

0 . . ±

5 ± ± .

2 4 5 5 2

. 3 3 3 1 1 4 3 2 0 3 . . 0 0 . % . . 1 9 5 0 1 9 5 3 4 Table 2. Major fatty acids composition, relative ratios, and iodine values of the blended oil. Table

4 7 2 5 9 5 . 6 5 0 1 . . 3

. . 0 .

0 0 2 3 0 0

0 3

± 9 2 ± ±

0 . . ± 2

± ± .

4 5 4 2 . 3 6 1 1 1 4 6 9 0 9 . . 1 3 . . . 1 9 4 8 5 1 0 3 4

a 3 5 1 3 9 8 . 0 1 2 0 . .

3 . . 0

1 1 5 9 0 0

9 0

0 9 ± ±

9 . . 0

± ± . ±

1 5 4

6 0 . 2 8 9 1 4 5 7 7 0 . . 1 2 9 . . 1 . 9 4 1 5 0 8 3 4

)

) ) 3

) 0 2

: A ) 0 e 8 s 1

1 F u 1 6 : 1 8 :

d c

l 1 8 : ) i S C b 1 8 : a ( A c ( C ( C T

A v A

( C / F a c

( C

F c c i F

e i i c A U n t S c n e F i i r i e P % i l t y l T ( e a d o U T a l m o e l n t P n : all experiments were triplicated and expressed as mean ± SD; F i O a I o i a S T P L L 277 Kim, N. H., et al./IFRJ 27(2) : 270 - 279

the range of 162.7 - 181.0, regardless of the amount whereas if Z < 0, a sample is assigned to the adulter- of soybean oil added, which was within the standard ated oil group. In the case of perilla oils, the value of specification. It was also confirmed that when blend- Z was the opposite. The Fisher’s linear discriminant ed vegetable oils had a similar degree of unsatura- function was employed to classify the group for each tion, it was impossible to detect the presence of heter- oil. As shown in Table 3, the correctly classified rate ogeneous oil in the perilla oil by just measuring the was 100% for sesame oils and 96.8% for perilla oils. iodine value. Only the adulterated perilla oil mixed with 2% To solve these limits for distinguishing the soybean oil was misclassified into the authentic oil authentic oils from the adulterated samples of sesame group. oil and perilla oil, LDA was performed on the results of FA analysis to establish a predictive classification Conclusion model. The prediction results for authentic and adulterated oil samples blended with cheaper oils, corn The goal of the present work was to investi- oil, and soybean oil, are summarised in Table 3. gate the FA composition of 11 types of edible vegeta- Among the five variables (palmitic, stearic, oleic, ble oils marketed in South Korea, and to characterise linoleic, and linolenic acids), stearic acid (Wilk’s λ = their FA distribution pattern. The unique FA profiles 0.632) and palmitic acid (Wilk’s λ = 0.407) showed of vegetable oils showed the differences in the value the highest discriminant power for adulterated of SFA, MUFA, and PUFA. Coconut oil (93.3%) sesame oils and perilla oils, respectively. The first showed the highest value of SFA, whereas canola oil discriminant function was used for statistical analysis (60.58%) and olive oil (76.44%) showed the highest (Wilk’s λ = 0.246, p = 0.000, and canonical correla- value of MUFA due to the large presence of linoleic tion = 0.868 for the adulterated sesame oils; Wilk’s λ acid. The perilla oil (80.14%), flaxseed oil (73.45%), = 0.300, p = 0.000, and canonical correlation = 0.837 and grapeseed oil (70.44%) showed the highest for the adulterated perilla oils). The unstandardised content of PUFA which has been considered as a canonical discriminant function coefficient (Z) beneficial vegetable oil owing to the action of lower- assigns a sample to predicted group (authentic oil or ing cholesterol level. The content of α-linolenic acid adulterated oil). The value of Z was calculated by of perilla oil (68.16%) was comparatively higher these equations: Z = -1003.505 + (8.899 × palmitic) than that of the other vegetable oils, and the unique + (13.719 × stearic) + (9.772 × oleic) + (10.246 × characteristics can make it possible to be used as linoleic) + (13.525 × linolenic) for the sesame oils; Z functional oil in spite of the drawback of easily being = -9.385 + (1.494 × palmitic) + (-4.697 × stearic) + oxidised. Rice bran oil (1.69) and olive oil (0.56) had (0.342 × oleic) + (0.343 × linoleic) for the perilla oils. the ideal ratio of TPUFA/TSFA recommended by The variable of linolenic acid was not used for the WHO. The ideal ratio of omega-6/omega-3 FAs was LDA of perilla oils. Generally, If Z > 0, a sample is observed in soybean oil (8.65) and olive oil (10.95). assigned to the authentic oil group for sesame oils, The FAs data of vegetable oils could be used for

Table 3. Summary of prediction results obtained by linear discriminant analysis for authentic and adulterated oil samples.

Predicted group Actual group Correctly classified Authentic oil Adulterated oil rate (%) Authentic sesame oils (n = 27) 27 0 100 Adulterated sesame oils (n = 6) 0 6 100 Total correct classification (%) (27+6)/33 = 100

Authentic perilla oils (n = 25) 25 0 100 Adulterated perilla oils (n = 6) 1 5 83.3

Total correct classification (%) (25+5)/31 = 96.8 Kim, N. H., et al./IFRJ 27(2) : 270 - 279 278 assessing the nutritional intake of Korean population, 2018. Fatty acid profile of edible oils and fats and will provide basic information for recommended consumed in India. Food Chemistry 238: 9-15. daily consumption. Chemometric approaches utilise El-Hamdy, A. H. and El-Fizga, N. K. 1995. Detec- multivariate statistics to extract and collect useful tion of olive oil adulteration by measuring its information from numerous analytical data. In the authenticity factor using reversed-phase present work, multivariate statistical techniques high-performance liquid chromatography. Jour- including PCA and CA were applied to the experi- nal of Chromatography A 708(2): 351-355. mental data obtained from GC-FID for differentiat- Esteki, M., Shahsavari, Z. and Simal-Gandara, J. ing and visualising the groups of vegetable oils. The 2018. Use of spectroscopic methods in combina- principal component scores plot and dendrogram tion with linear discriminant analysis for authen- showed that the eleven kinds of vegetable oils are tication of food products. Food Control 91: grouped into three clusters. It was confirmed that the 100-112. linolenic acid content and iodine value, the specifica- Galão, O. F., Carrão-Panizzi, M. C., Gontijo Manda- tion of quality inspection for sesame oils and perilla rino, J. M., Santos Júnior, O. O., Maruyama, S. oils distributed in Korea, are not appropriate for A., Figueiredo, L. C., ... and Visentainer, J. V. detecting the adulterated oils which are blended with 2014. Differences of fatty acid composition in a small amount of cheaper oils. Meanwhile, the com- Brazilian genetic and conventional soybeans bined approach of using LDA and of analysing FA (Glycine max (L.) Merrill) grown in different contents could be an alternative tool for distinguish- regions. Food Research International 62: ing the economically motivated adulteration of 589-594. sesame oil and perilla oil from authentic ones, and it Ganesan, K., Sukalingam, K. and Xu, B. 2018. will also act as a critical parameter in quality control. Impact of consumption and cooking manners of vegetable oils on cardiovascular diseases - a Acknowledgement critical review. Trends in Food Science and The present work was financially supported Technology 71: 132-154. by the Seoul Metropolitan Government Research Giacomelli, L. M., Mattea, M. and Ceballos, C. D. Institute of Public Health and Environment (2018). 2006. Analysis and characterization of edible oils by chemometric methods. Journal of the American Oil Chemists’ Society 83(4): 303-308. References Hu, F. B. and Willett, W. C. 2002. Optimal diets for prevention of coronary heart disease. Journal of Association of Official Analytical Chemists American Medical Association 288(20): (AOAC). 2000. Method 969.33 - fatty acids in 2569-2578. oils and fats. United States: AOAC. Jabeur, H., Drira, M., Rebai, A. and Bouaziz, M. Bosque-Sendra, J. M., Cuadros-Rodríguez, L., 2017. Putative markers of adulteration of high- Ruiz-Samblás, C. and de la Mata, A. P. 2012. er-grade olive oil with less expensive pomace Combining chromatography and chemometrics olive oil identified by gas chromatography com- for the characterization and authentication of fats bined with chemometrics. Journal of Agricultur- and oils from triacylglycerol compositional data al and Food Chemistry 65(26): 5375-5383. - a review. Analytica Chimica Acta 724: 1-11. Joo, J.-Y., Yeo, Y.-H. and Lee, N. 2017. Comparison Brodnjak-Vončina, D., Kodba, Z. C. and Novič, M. of quality characteristics of sesame oil and blend 2005. Multivariate data analysis in classification oil by using component analysis and NIR spec- of vegetable oils characterized by the content of troscopy. Journal of the Korean Society of Food fatty acids. Chemometrics and Intelligent Labo- Science and Nutrition 46(6): 739-743. ratory Systems 75(1): 31-43. Kafaoğlu, B., Fisher, A., Hill, S. and Kara, D. 2014. Callao, M. P. and Ruisánchez, I. 2018. An overview Chemometric evaluation of trace metal concen- of multivariate qualitative methods for food trations in some nuts and seeds. Food Additives fraud detection. Food Control 86: 283-293. and Contaminants Part A 31(9): 1529-1538. Chudzinska, M. and Baralkiewicz, D. 2011. Applica- Kim, J. H., Lee, H. J., Kwon, K., Chun, H. S., Ahn, S. tion of ICP-MS method of determination of 15 and Kim, B. H. 2018. A 43 MHz low-filed elements in honey with chemometric approach benchtop 1H nuclear magnetic resonance method for the verification of their authenticity. Food to discriminate perilla oil authenticity. Journal of and Chemical Toxicology 49(11): 2741-2749. Oleo Science 67(5): 507-513. Dorni, C., Sharma, P., Saikia, G. and Longvah, T. Kostik, V., Memeti, S. and Bauer, B. 2013. Fatty acid 279 Kim, N. H., et al./IFRJ 27(2) : 270 - 279

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