Food Chemistry 143 (2014) 40–47

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Food Chemistry

journal homepage: www.elsevier .com/locate / foodchem

Discrimination of commercial cheeses from fatty acid profiles andphytosterol contents obtained by GC and PCA

0308-8146/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.foodchem.2013.07.083

⇑ Corresponding author. Tel.: +82 43 719 5303; fax: +82 43 719 5300.E-mail addresses: kimjinho1@yahoo.com, heide2@empas.com (J. Kim).

Nam Sook Kim, Ji Hyun Lee, Kyoung Moon Han, Ji Won Kim, Sooyeul Cho, Jinho Kim ⇑Advanced Analysis Team, Ministry of Food and Drug Safety, Osong Health Technology Administration Complex, Chungcheongbuk-do 363-700, South Korea

a r t i c l e i n f o

Article history:Received 12 April 2013Received in revised form 27 June 2013Accepted 18 July 2013Available online 27 July 2013

Keywords:Fatty acid compositionPhytosterolsCheesesDerivatizationGCGC–MSPCA

a b s t r a c t

In this study, a method for discriminating natural mozzarella cheese from cheese substitutes, using fattyacid profiles, phytosterol contents, and statistical comparison, was developed. A total of 27 cheeses wereevaluated: eight natural mozzarella cheeses (NMCs), four imitation mozzarella cheeses (IMCs), 12 pro-cessed cheeses (PCs) and three mixed cheeses (MCs) composed of NMCs and IMCs. The fatty acid compo-sition of the NMC class was distinct from those of the IMC and MC classes, but statistically similar(p < 0.05) to that of the PC class. The phytosterol content of the NMC class, determined via gas chroma-tography–mass spectrometry, was distinct from the IMCs, but similar (p < 0.05) to a portion of the PCs.Principal component analysis (eigenvalue P 1) indicated that the NMCs can be differentiated from theIMCs, but discrimination between the NMCs and the PCs could not be achieved.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

In recent years, the demand for natural cheese, processedcheese, and cheese substitutes has been ever increasing (Jana &Mandal, 2011; Jana, Patel, Suneeta, & Prajapati, 2010). As defined,natural cheeses are made from milk or milk products, whereas pro-cessed cheese must contain more than 50% milk solids, and cheesesubstitutes, also called imitation cheeses, are generally manufac-tured from non-dairy fats or proteins to make cheese-like products(Bachmann, 2001; Jana & Mandal, 2011; Ntakatsane, Liu, & Zhou,2013). In this study, ‘‘mixed cheese’’ is simply a mixture of naturalcheese mixed with imitation mozzarella cheese. This type ofcheese product, commonly used as a pizza topping in Korea, is rel-atively cheap and has high storage stability (Jana et al., 2010; KoreaFood, 2012; Ntakatsane et al., 2013). In addition to the three broadclasses listed above, cheeses can be further segmented by fat con-tent and other characteristics.

Mixed cheese and imitation mozzarella cheese consumption ison the rise because they are easily prepared and cost considerablyless than natural mozzarella cheese, as milk is substituted with rel-atively inexpensive vegetable products. Mozzarella cheese substi-tutes are used widely, especially in pizza and related products,and the lower quality has the potential to impact human health

(Pellegrino, Resmini, De Noni, & Masotti, 1996). Currently, it is dif-ficult to discriminate between natural and imitation mozzarellacheeses (Middleton, 1989), as analytical differentiation methodsare lacking (Pellegrino et al., 1996).

Cheese is composed of many chemicals, some of which may beused as markers. Furosine, formed in the initial stages of the Mail-lard reaction, is a marker for lactose (Resmini, Pellegrino, & Mass-otti, 1993), and lysinoalanine, which occurs in commercialcaseinate and milk, can be used to differentiate between naturaland imitation mozzarella cheeses (Pellegrino et al., 1996). Addi-tionally, fatty acid composition can be compared to help differen-tiate cheese products (Ntakatsane et al., 2013; Prandini, Sigolo, &Piva, 2011). To date, there have been few studies investigating phy-tosterols, except for studies on the nutritional benefits of phytos-terol-enriched products (García-Llatas & Rodríguez-Estrada, 2011).

Chemometrics is a combination of logic-based methods, such asmathematics and statistics, to effectively handle and interpretlarge amounts of chemical data (Haswell, 1992), and principalcomponent analysis (PCA) is a commonly used multivariate che-mometric method. PCA can be used to identify patterns and to ex-plain similarities and differences in data. Also, PCA can be used toshow relationships that exist between objects and arbitrary princi-pal components (Kadegowda, Piperova, & Erdman, 2008; Matoset al., 2007). Recently, Ntakatsane et al. (2013) used PCA to effi-ciently screen milk products for adulteration.

N.S. Kim et al. / Food Chemistry 143 (2014) 40–47 41

In this study, a method to discriminate natural mozzarellacheese from cheese substitutes is reported. Specifically, natural,imitation, processed, and mixed cheeses were evaluated based onfatty acid profiles and phytosterol contents via gas chromatogra-phy-flame ionisation detector (GC-FID) and GC–mass spectrometry(GC–MS) experiments, and PCA data analysis.

2. Materials and methods

2.1. Materials and chemicals

2.1.1. SamplesCheese and cheese products were purchased from local super-

markets (Chungcheongbuk-do, Korea) and on-line (http://item-page3.auction.co.kr). In total, eight natural mozzarella cheeses(NMCs), four imitation mozzarella cheeses (IMCs), 12 processedcheeses (PCs), and three mixed cheeses (MCs), composed of NMCsand IMCs, were evaluated. The cheeses, described in Table 1, werestored at �50 �C and defrosted at room temperature prior toanalysis.

2.1.2. Standards and reagentsPUFA-2 (Supelco, Bellefonte, USA), a fatty acid sourced from ani-

mals, and undecanoic acid (C11:0, Sigma, Saint Louis, USA), aninternal standard, were purchased. Brassicasterol (99%), campes-terol (98%), stigmasterol (98%), b-sitosterol (78.6%) and 5a-choles-tane (99.9%) were acquired from Sigma (Saint Louis, USA). All stock1 mg/mL phytosterol solutions were prepared separately in etha-nol and stored at �20 �C. Working standards were prepared dailyby diluting stock solutions with ethanol. Butylated hydroxytoluene(BHT; Sigma, Saint Louis, USA), sodium chloride (NaCl), potassiumhydroxide (KOH), chloroform, methanol, isooctane, ethanol, and n-hexane were obtained from Merck (Darmstad, Germany), andwater was obtained from a Milli-Q� Advantage A10� SYSTEM(EMD Millipore Corporation, Billerica, USA). A 14% boron trifluor-ide–methanol solution and N,O-bis-(trimethylsilyl)trifluoroaceta-mide (BSTFA) with 1% trimethylchlorosilane (TMCS) werepurchased from SIGMA (Saint Louis, USA).

2.1.3. InstrumentsTo prepare samples, a PT 2100 homogenizer (Kinematica AG,

Luzern, Switzerland), a Vibracell™-VCX 750 ultrasonicator (Sonics& Materials Inc., Newton, USA), a 5810R Centrifuge (Eppendorf,Hamburg, Germany), and a NE-1001 rotary evaporator (EYELAco., ltd, Tokyo, Japan), were used. Analyses were carried out withan Agilent 7890A GC System (Agilent Technologies Inc., SantaClara, USA) equipped with a 7693 autosampler and FID and an Agi-lent 7890A GC System (Agilent Technologies Inc., Santa Clara, USA)interfaced with a 5975 mass-selective detector and a 7683 auto-

Table 1The list of commercial cheese samples.

Classification Quanti

Natural mozzarella cheeses NMC 8

Imitation mozzarella cheeses IMC 4

Processed cheeses PC 12

Mixed cheeses MC 3

Other ingredients: refined sugar, starch, flour, emulsifier, refined salt

sampler, controlled by Chem Station (Agilent Technologies Inc.,Santa Clara, USA).

2.2. Determination of fatty acid composition

2.2.1. Lipid extractionA modified version of Folch’s method (Christie, 1989) was used

for lipid extraction. To start, 2.5 g of cheese were added to a 25 mLchloroform–methanol mixture (2:1, v/v). Butylated hydroxytolu-ene (0.001%), an antioxidant, was then added to the mixture. Themixture was homogenised (30 mm polytron, 2500 rpm, 30 min),ultrasonicated (Amplifier 35%, 20 min), and 10 mL of saturatedNaCl solution were added. The suspension was then centrifugedfor 20 min at �4 �C and 4000 rpm. The chloroform phase wasrecovered and transferred into a round flask (25 mL), and eachfat extract was dried via rotary evaporator at 45 �C under vacuum.

2.2.2. Preparation of fatty acid methyl estersThe extracted fats were esterified using a modified esterifica-

tion method (Christie, 1989; Christie, Sébédio, & Juanéda, 2001).First, 1 mL of undecanoic acid (5.05 mg/mL to chloroform) wasadded to each fat extract. Then 1.5 mL of 0.5 N methanolic NaOH,a dissolving agent, were added. The flask was then shaken vigor-ously for 30 s. The mixture was transferred into a Teflon-linedscrew-top test tube, heated at 100 �C for 5 min, and cooled at�20 �C for 3 min. Then, 2 mL of 14% methanolic BF3 were added,and the reaction was allowed to proceed for 20 min at 100 �C. Afterthe reaction, the reactant was cooled at �4 �C; 2.5 mL of isooctaneand 5 mL of saturated NaCl were added to the reaction and themixture was stirred. Two phases appeared and were separated,one of which contained fatty acid methyl esters (FAME). The hex-ane phase was filtered with a PTFE syringe filter (0.2 lm, What-man, Kent, UK), dehydrated with anhydrous sodium sulphate(Na2SO4), and the filtrate was diluted to half with isooctane forGC analysis. The same methylation procedure was carried out on200 lL of the PUFA-2 standard solution (10 mg/mL to chloroform)to obtain FAMEs.

2.2.3. Gas chromatographyGC analysis was conducted in accordance with a previously de-

scribed method (Prandini et al., 2011). Qualitative analysis ofFAMEs was carried out on a GC equipped with a FID and a SP™-2560 capillary column (100 m � 0.25 mm i.d.; 0.25 mm film thick-ness; Supelco, Bellefonte, USA). An initial oven temperature of120 �C was maintained for 1 min. Then, the temperature was in-creased to 200 �C at a rate of 25 �C/min and the oven temperaturewas held at 200 �C for 5 min. Then, the temperature was increasedat a rate of 2 �C/min to reach 240 �C, and retained for 15 min. FAME(1 lL) was injected into the instrument using a split ratio of 100:1.The injector port and detector temperatures were set at 240 �C and250 �C, respectively. The column flow rate was 1.0 mL/min, and

ty Material description from product labels

Mozzarella cheese 99%Other ingredients 1%Vegetable oil (palm oil, palm olein oil) 99%Other ingredients 1%Mozzarella cheese 70–80%,Vegetable oil (palm oil, palm olein oil) 19–29%Other ingredients 1%Mozzarella cheese 50–70%,Imitated mozzarella cheeses 29–49%Other ingredients 1%

, citric acid and so on.

Table 2Fatty acid composition of PUFA-2, animal source as a qualitative standard.

Fatty acids Retentiontime (min)

Composition(mol%)

Myristic acid C14:0 15.163 0.79Palmitic acid C16:0 16.890 25.16Palmitoleic acid C16:1n7 17.800 1.67Stearic acid C18:0 19.060 17.50Oleic acid C18:1n9 20.081 22.29Vaccenic acid C18:1n7, trans 20.198 1.93Linoleic acid C18:2n6 21.604 9.62c-Linolenic acid C18:3n6 22.854 2.31Linolenic acid C18:3n3 23.500 2.38Eicosenoic acid C20:1n9 22.945 0.36Arachidonic acid C20:4n6 27.311 8.31Eicosapentaenoic acid C20:5n3 29.710 1.77Docosatetraenoic acid C22:4n6 31.386 2.85Docosahexaenoic acid C22:6n3 35.619 3.08

PUFA-2 standard was from animal source, which had a detail description for onlyqualitative identification. Undecanoic acid (C11:0, 13.278 min) was used as ainternal standard.

42 N.S. Kim et al. / Food Chemistry 143 (2014) 40–47

nitrogen was used as the carrier and make up gas. Identification offatty acids was achieved by comparing the relative retention timeswith the internal and PUFA-2 standards. PUFA-2 standards in-cluded 14 fatty acids and allowed only for qualitative analysis.Fatty acid compositions (mol%) were determined based on the rel-ative chromatographic areas to compare only intergroup differ-ences in the fatty acid compositions. All measurements wereperformed in triplicate.

2.3. Determination of phytosterols

2.3.1. Sample preparationCheese (2.5 g) was mixed with 25 mL of chloroform–methanol

(2:1, v/v), and 50 lL of the internal standard solution. After extrac-tion, as described above, the liquid phase was collected from themixture. The liquid phase was dried via rotary evaporator at45 �C in vacuo. Then 40 mL of ethanol and 10 mL of 0.1 N ethanolicKOH were added, and saponification, at 95 �C for 1 h, was carriedout according to the methods described in the Health FunctionalFood Code (Korea Food & Drug Administration, 2012; Santoset al., 2007). Saturated NaCl (5 mL) and 150 mL of n-hexane wereadded and the fraction not saponified was extracted with a separa-tion funnel. The separated hexane phase was filtered (Whatman™,circles 185 mm U), dehydrated with anhydrous sodium sulphate,and dried by rotary evaporator at 45 �C under vacuum. After dis-solving the extract in 3 mL of ethanol, the mixture was filteredwith a PTFE syringe (0.2 lm) and dehydrated with anhydrous so-dium sulphate. The filtrate was then dried under a gentle streamof nitrogen at 60 �C. The dry residue was derivatized with 500 lLof BSTFA:TMCS (99:1) at 60 �C for 30 min. Finally, 1 lL of the deriv-atized solution was analysed by GC (Schummer, Delhomme,Appenzeller, Wennig, & Millet, 2009; Wu, Hu, Yue, Yang, & Zhang,2009).

2.3.2. Gas chromatography–mass spectrometryThe MS detector transfer line was kept at 280 �C and tuning was

conducted on a daily basis with a perfluorotributylamine (PFTBA)standard composed of three masses (m/z 69, 219, 502). Samples(1 lL) were injected automatically in split mode (10:1) at 300 �C.An Agilent J&W DB-5MS (crosslinked 95% dimethyl/5% phenylpolysiloxane) capillary column (30 m � 0.25 mm i.d., 0.25 lm filmthickness; Agilent Technologies Inc., Santa Clara, USA) was used forseparation, and helium (99.9999%) was used as the carrier gas at aflow rate of 1 mL/min. The column temperature was initially set to200 �C and then held for 1 min. Then, the column was heated to280 �C at a rate of 10 �C/min, and held at 280 �C for 11 min. The col-umn was then heated to 300 �C at a rate of 4 �C/min and retainedfor 5 min. The total run time was 30 min. The samples were ionizedvia electron impact ionisation (EI) with the following conditions:70 eV electron energy; 230 �C electron source temperature; and150 �C quad temperature. Retention times and characteristic frag-ments were determined by total ion monitoring (SCAN) in therange m/z 50–500. Abundant ions and/or parent ions withoutapparent cross-contribution and interferences were chosen as tar-get ions for SIM mode quantification (Fiamegos, Nanos, Vervoort, &Stalikas, 2004). When quantification was complete, the peak areaswere normalised to the internal standard, 5a-cholestane, peak.

2.3.3. Validation methodLinearity, accuracy, precision, limits of detection (LODs), lim-

its of quantitation (LOQs), and recoveries were determined tohelp validate the method. The LODs and LOQs were determinedas the lowest concentration that could be calculated for themajor ions at a signal-to-noise ratio (peak-to-peak S/N) greaterthan 3 and 10, respectively, using the MSD chemstation (AgilentTechnologies Inc., Santa Clara, USA). Linearity was calculated

over a variety of ranges: brassicasterol (2.5–50 lg/mL); campes-terol (2.5–50 lg/mL); stigmasterol (2.4–30 lg/mL); b-sitosterol(2.5–120 lg/mL).

Accuracy and precision were determined at six different con-centrations for the standard solutions: brassicasterol (2.5, 5, 10,20, 30, 40 lg/mL); campesterol (2.5, 5, 10, 20, 30, 40 lg/mL); stig-masterol (2.4, 5, 10, 15, 20, 25 lg/mL); b-sitosterol (2.5, 10, 20, 50,100, 120 lg/mL). Interday (n = 3) and intraday (n = 3 + 3 + 3) accu-racy and precision were calculated as percentages and percentagesof the relative standard deviation (%RSD) at the differentconcentrations.

Natural mozzarella and imitation mozzarella cheese sampleswere spiked at three different concentrations: brassicasterol (5,20, 40 lg/mL); campesterol (5, 20, 40 lg/mL); stigmasterol (5, 15,25 lg/mL); b-sitosterol (5, 50, 100 lg/mL). Sample extractionswere carried out on two spiked cheese samples and two blankcheese samples, according to the extraction method describedabove. The spiked and blank samples were spiked with the internalstandard solution before sample preparation. Recoveries (%) wereobtained in triplicate at each concentration, and determined bycomparison of the mean peak area ratios of analytes with the inter-nal standard spike peak area (Lu et al., 2010; Santos et al., 2007).

2.4. Statistical analysis

The data are expressed as a mean ± standard deviation (SD). Themean fatty acid and phytosterol content values were comparedusing the Kruskal–Wallis test; as a method of median was appliedwhich was one of the nonparametric statistics as distribution-freetests in common with the one-way ANOVA. This test does not relyon parameter estimates or assumptions about variable distribu-tions. Scheffe’s method was used for a posteriori tests. PCA, withVarimax rotation, was conducted in two factors, on the fatty acidprofiles and phytosterol levels for the cheese classes. SPSS software(IBM� SPSS� ver. 18, Armonk, USA) was used for statistical analysis(Galina, Osnaya, Cuchillo, & Haenlein, 2007; Mapekula, Chimonyo,Mapiye, & Dzama, 2011).

3. Results and discussion

3.1. Fatty acid profiles by GC

Representative examples of FAMEs from cheese extracts arepresented in Table 2. Fourteen unique fatty acids were identifiedbased on retention times obtained using a PUFA-2 animal-sourcedqualitative standard and undecanoic acid (C11:0) as an internal

Table 3Fatty acid profiles (mol%) of commercial cheese samples.

Classification C14:0 C16:0 C16:1 C18:0 C18:1 C18:1 C18:2 C18:3 C18:3 C20:1 C20:4 C20:5 C22:4 C22:6n7 n9 n7 n6 n6 n3 n9 n6 n3 n6 n3

Natural mozzarella cheeses NMC1 mean 13.25de 37.68a 1.71f 15.31i 26.72d 0.87hi 3.39d 0.20ijk 0.50bcd 0.06b 0.23g 0.04c 0.05f <0.00ab

SD 0.07 0.08 0.01 0.07 0.07 0.01 0.03 0.00 0.06 0.00 0.00 0.00 0.00 0.00NMC2 mean 15.31fg 41.14de 1.96g 13.94f 24.16abc 0.59abcde 1.67ab 0.18h 0.76def 0.04a 0.12e 0.10d 0.02c 0.01cd

SD 0.21 0.13 0.03 0.15 0.13 0.01 0.06 0.00 0.01 0.00 0.00 0.00 0.00 0.00NMC3 mean 13.00d 37.56a 1.70ef 15.37i 26.75d 0.92i 3.55d 0.20k 0.57cde 0.07b 0.23g 0.05c 0.04ef <0.00ab

SD 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00NMC4 mean 13.01d 37.49a 1.69ef 15.43i 26.82d 0.93i 3.58d 0.20jk 0.46abcd 0.07b 0.23g 0.04c 0.04ef <0.00bc

SD 0.01 0.01 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00NMC5 mean 13.06d 37.46a 1.69ef 15.42i 26.82d 0.92i 3.57d 0.20jk 0.46abcd 0.06b 0.23g 0.05c 0.05f <0.00bc

SD 0.04 0.02 0.00 0.01 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00NMC6 mean 15.34fg 41.12de 1.97g 13.94f 24.14abc 0.64defg 1.61a 0.18h 0.76def 0.04a 0.12e 0.11d 0.02c 0.01d

SD 0.01 0.02 0.03 0.02 0.02 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00NMC7 mean 14.95f 40.97de 1.91g 14.17fg 24.39bc 0.64defg 1.73ab 0.18hi 0.76def 0.04a 0.12e 0.10d 0.02c 0.01d

SD 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00NMC8 mean 15.31fg 41.30e 1.98g 13.81f 24.11abc 0.66fg 1.63a 0.18h 0.75def 0.04a 0.12e 0.10d 0.02c 0.01d

SD 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Imitation mozzarella cheeses IMC1 mean 1.11a 44.64i 0.17a 4.50a 38.97g 0.61bcdef 9.69g 0.04a 0.15a 0.14ef nd nd nd ndSD 0.02 0.07 0.00 0.01 0.06 0.01 0.01 0.00 0.00 0.00

IMC2 mean 1.19a 40.57d 0.19a 4.46a 41.76h 0.64defg 10.83h 0.05ab 0.17ab 0.15ef nd nd nd ndSD 0.00 0.06 0.00 0.02 0.04 0.00 0.00 0.00 0.00 0.00

IMC3 mean 1.12a 44.63i 0.17a 4.51a 38.86g 0.60bcdef 9.78g 0.04a 0.15ab 0.13e nd nd nd ndSD 0.00 0.10 0.00 0.02 0.08 0.01 0.01 0.00 0.00 0.00

IMC4 mean 1.40a 43.70h 0.20a 4.70a 38.90g 0.64cdefg 9.98g 0.06bc 0.29abc 0.15f nd nd nd ndSD 0.02 0.15 0.00 0.02 0.13 0.00 0.02 0.00 0.00 0.00

Processed cheeses PC1 mean 12.69d 38.66b 1.69def 13.99f 27.41d 0.81h 3.73d 0.19hij 0.47abcd 0.06b 0.21f 0.04c 0.04def <0.00ab

SD 0.09 0.04 0.01 0.04 0.08 0.00 0.05 0.01 0.02 0.00 0.00 0.00 0.00 0.00PC2 mean 16.57gh 39.75c 1.69def 14.63gh 23.47ab 0.59abcde 1.63a 0.14f 1.24g 0.04a 0.10d 0.12e 0.01b 0.01e

SD 0.90 0.22 0.05 0.38 0.75 0.04 0.52 0.01 0.23 0.01 0.01 0.01 0.00 0.00PC3 mean 14.39ef 42.14f 1.59d 12.40e 25.02c 0.53a 2.63c 0.16g 0.90efg 0.06b 0.07c 0.11d 0.01b 0.01d

SD 0.72 0.45 0.04 0.14 0.64 0.04 0.42 0.00 0.23 0.01 0.01 0.00 0.00 0.00PC4 mean 12.61d 38.60b 1.68def 14.02f 27.44d 0.88i 3.75d 0.19hijk 0.46abcd 0.07b 0.21f 0.05c 0.04d <0.00ab

SD 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00PC5 mean 12.57d 38.58b 1.68def 14.04f 27.42d 0.89i 3.81d 0.19hijk 0.46abcd 0.07b 0.21f 0.05c 0.04de <0.00ab

SD 0.01 0.04 0.00 0.01 0.03 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00PC6 mean 12.58d 38.60b 1.68def 14.05f 27.44d 0.89i 3.74d 0.19hijk 0.46abcd 0.07b 0.21f 0.04c 0.04de <0.00ab

SD 0.01 0.01 0.00 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00PC7 mean 17.02h 39.89c 1.71f 14.92hi 23.04a 0.63cdefg 1.28a 0.14f 1.09fg 0.04a 0.09d 0.13e 0.01b 0.01e

SD 0.01 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00PC8 mean 17.03h 39.88c 1.71f 14.91hi 23.04a 0.63cdefg 1.28a 0.14f 1.09fg 0.04a 0.09d 0.13e 0.01ab 0.01e

SD 0.02 0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00PC9 mean 17.01h 39.88c 1.71f 14.92hi 23.05a 0.63cdefg 1.29a 0.14f 1.09fg 0.04a 0.09d 0.13e 0.01b 0.02e

SD 0.01 0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00PC10 mean 14.64f 42.34fg 1.61de 12.52e 24.74c 0.58abcd 2.40bc 0.16g 0.76def 0.06b 0.07c 0.11d 0.01ab 0.01d

SD 0.00 0.01 0.00 0.00 0.01 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00PC11 mean 14.64f 42.34fg 1.61de 12.53e 24.74c 0.58abcd 2.39bc 0.16g 0.76def 0.06b 0.07c 0.11d 0.01ab 0.01d

SD 0.01 0.01 0.00 0.00 0.01 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00PC12 mean 14.55ef 42.35fg 1.60de 12.58e 24.80c 0.56ab 2.39bc 0.16g 0.76def 0.06b 0.07c 0.11d 0.01ab 0.01d

SD 0.01 0.01 0.00 0.01 0.02 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Mixed cheeses MC1 mean 6.54c 42.06f 0.87c 9.15d 33.36e 0.69g 6.70e 0.11e 0.30abc 0.10cd 0.09d 0.02b 0.02c <0.00a

SD 0.05 0.07 0.00 0.04 0.08 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00MC2 mean 6.78c 42.91g 0.90c 7.89c 33.78e 0.57abc 6.52e 0.08d 0.39abc 0.10c 0.03a 0.05c <0.00a <0.00ab

SD 0.44 0.15 0.05 0.25 0.40 0.00 0.23 0.00 0.02 0.00 0.00 0.00 0.00 0.00

(continued on next page)

N.S.K

imet

al./FoodChem

istry143

(2014)40–

4743

Tabl

e3

(con

tinu

ed)

Cla

ssifi

cati

onC

14:0

C16

:0C

16:1

C18

:0C

18:1

C18

:1C

18:2

C18

:3C

18:3

C20

:1C

20:4

C20

:5C

22:4

C22

:6n

7n

9n

7n

6n

6n

3n

9n

6n

3n

6n

3

MC

3m

ean

4.00

b44

.44i

0.53

b6.

79b

35.2

9f0.

65ef

g7.

81f

0.06

c0.

25ab

c0.

11d

0.04

b0.

01a

0.01

bn

dSD

0.01

0.04

0.00

0.01

0.04

0.00

0.01

0.00

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44 N.S. Kim et al. / Food Chemistry 143 (2014) 40–47

standard. Table 3 reports the fatty acid compositions (mol%) of thefat extracts from 27 commercial cheeses.

Arachidonic, eicosapentaenoic, docosatetraenoic, and docosa-hexaenoic acids could not be detected in the IMCs. Also, docosa-hexaenoic acid could not be detected in one of the mixed cheeses(MC3).

The oleic acid, vaccenic acid, and linoleic acid profiles for eachcheese class were found to be consistent with those from otherstudies (Prandini, Sigolo, & Piva, 2011; Zhang, Mustafa, & Zhao,2006).

NMCs had oleic acid, 24.11–26.82 mol%; vaccenic acid, 0.59–0.93 mol%; and linoleic acid, 1.61–3.58 mol%, respectively. IMCscontained oleic acid, 38.86–41.76 mol%; vaccenic acid, 0.60–0.64 mol% and linoleic acid, 9.69–10.83 mol%, respectively. PCscontained oleic acid, 23.04–27.44 mol%; vaccenic acid, 0.53–0.89 mol%, and linoleic acid, 1.28–3.73 mol%. The mixed cheeses(MCs) consisted of oleic acid, 33.36–35.29 mol%; vaccenic acid,0.57–0.69 mol%, and linoleic acid, 6.52–7.81 mol%.

The proportion of oleic and linoleic acids in NMCs were statisti-cally similar (p < 0.05) to those of PCs, and these acids were signif-icantly (p < 0.05) more common in NMCs and PCs than in the MCs.The proportion of oleic and linoleic acids in MCs was lower thanthat of IMCs but higher than that of others. There were no signifi-cant differences (p < 0.05) among the concentrations of vaccenicacid in the four cheese classes. From the fatty acid profiles, morefat (p < 0.05) was present in the MCs and IMCs than in the NMCsand PCs.

3.2. Phytosterol analysis by GC–MS

3.2.1. Phytosterol contentRetention times and m/z ratios of four phytosterols from the

cheese extracts and the internal standard 5a-cholestane, used forquantitative purposes, are given in Table 4. The major fragmentswere identified by GC–MS in SCAN mode (Fig 1). For 5a-choles-tane, peaks at m/z 372 (the parent ion, M), 357 (M � 15), and217 (M � 155), were used for quantitation. Four peaks for each tri-methylsilyl derivative were used for quantitation; the derivativeswere brassicasterol (m/z 470 = M + 72, 380 = M � 18,255 = M � 143), campesterol (m/z 472 = M + 72, 382 = M � 18,367 = M � 33), stigmasterol (m/z 484 = M + 72, 394 = M � 18,255 = M � 157) and b-sitosterol (m/z 486 = M + 72, 396 = M � 18,381 = M � 33). The fragmentation patterns observed for the tri-methylsilyl ethers were consistent with those from previous stud-ies (Schummer et al., 2009; Wu, Hu, Yue, Yang, & Zhang, 2009).

Plant sterol contents (lg/g), determined by SIM-mode GC–MS,for the 27 cheese samples are given in Table 5. Quantitation wasbased on the major ions for brassicasterol, campesterol, stigmas-terol, b-sitosterol, and 5a-cholestane at m/z 255, 382, 255, 396,and 217, respectively. For all of the samples tested, campesterolconcentrations ranged from 1.17 to 13.49 lg/g, stigmasterol con-centrations ranged from 0.47 to 7.02 lg/g, and b-sitosterol concen-trations ranged from 0.59 to 47.70 lg/g. The brassicasterol contentcould not be quantified in all of the samples, and stigmasterolcould not be quantified in NMC1 and PC2.

The NMCs had 1.17–1.43 lg/g campesterol, 0.47–0.49 lg/g stig-masterol, 0.59–1.05 lg/g b-sitosterol and 1.77–2.95 lg/g total con-tent. The IMCs had 10.90–13.49 lg/g campesterol, 5.95–7.02 lg/gstigmasterol, 40.76–47.70 lg/g b-sitosterol and 57.61–68.21 lg/gtotal content. The PCs contained 1.60–3.18 lg/g campesterol,0.51–1.19 lg/g stigmasterol, 0.85–5.88 lg/g b-sitosterol, and2.45–10.04 lg/g total content. The MCs contained 5.88–8.45 lg/gcampesterol, 3.22–5.77 lg/g stigmasterol, 18.06–28.10 lg/g b-sitosterol, and 27.17–42.32 lg/g total content.

Phytosterols, the most common being b-sitosterol, are found infats and oils (Contarini, Povolo, Bonfitto, & Berardi, 2002). Phytos-

Table 4Main characteristic ions with relative abundances and retention times of thederivatized phyterosterols in the GC–MS full-scan mass spectra.

Target compounds Diagnostic ions Retentiontime (min)

5a-Cholestane 217 (100%), 357 (22.6%), 372 (15.0%) 11.105Brassicasterol 255 (100%), 380 (38.6%), 470 (29.1%) 15.868Campesterol 382 (100%), 367 (70.0%), 472 (47.5%) 17.087Stigmasterol 255 (100%), 394 (33.6%), 484 (26.3%) 17.731b-Sitosterol 396 (100%), 381 (73.6%), 486 (59.3%) 19.089

N.S. Kim et al. / Food Chemistry 143 (2014) 40–47 45

terols in NMCs are derived from milk fats, whereas phytosterols incheese substitutes are derived from vegetable oils (Bachmann,2001). Overall phytosterol contents in NMCs and PCs were lower(p < 0.05) than in the IMCs and MCs. The total phytosterol contentsin PCs were statistically similar (p < 0.05) to those of NMCs. This re-sult corresponds with product description said NMCs was used forone of the materials for PCs at 70–80%. The highest (p < 0.05) phy-tosterol levels were found in the IMCs, sterols which were likelyderived from vegetable fats. The MCs had 40–50% the same phytos-terol content as IMCs, and confirmed that the MCs were blends ofNMCs and IMCs. The campesterol, b-sitosterol, and total phytos-terol contents for the MCs were distinct from the IMCs. These re-sults are consistent with the results obtained from the fatty acidprofiles reported above.

3.2.2. Phytosterol determination method validationLinearity, accuracy, precision, LODs, LOQs and recovery data for

the GC–MS analysis of plant sterols are given in Tables 6 and 7. Therapid GC–MS method (30 min) was capable of separating fourplant sterols and the internal standard (Fig. 1). The LODs of brass-icasterol, campesterol, stigmasterol, and b-sitosterol were all lowerthan 1 lg/mL. LOQs for brassicasterol, campesterol, stigmasterol,and b-sitosterol were 2.64, 2.67, 2.31, and 2.43 lg/mL, respectively.Linearity, obtained from the relative mass ratios of each phytos-terol to the internal standard, were 2.5–50 lg/mL for brassicasteroland campesterol, 2.4–30 lg/mL for stigmasterol, and 2.5–120 lg/mL for b-sitosterol. The correlation coefficients obtained from thelinearity plots were greater than 0.995 for all of the samples. Intra-day precision for the method (expressed as the RSD) ranged from0.6% to 8.7%, while interday precision ranged from 0.6% to 9.4%.

Fig. 1. Total ion chromatogram of derivatized phytosterols in GC-MS. (1) 5a-cholestansitosterol.

Intraday accuracy ranged from 93.1% to 108.2%, and interdayaccuracy ranged from 92.6% to 108.4%. Recoveries for thephytosterols, added prior to sample preparation were greater than80% at the spike levels evaluated.

3.3. Principal component analysis

PCA was performed with SPSS software, and the Kruskal–Wallistest was used to compare multiple independent groups. The vari-ance eigenvalue was greater than 1. The loadings (or factor scores)corresponding to the principal components were calculated fromthe correlation matrix (Massart, Vandeginste, Deming, Michotte,& Kaufman, 1988). The PCA results were used to identify importantexperimental factors, and factor score plots were used to indicatesimilar, dissimilar, typical, or outlier data (Qiu et al., 2007; Shin,Craft, Pegg, Phillips, & Eitenmiller, 2010).

PCA plots for the data obtained are shown in Fig. 2 (A). The prin-cipal component contributed 58.910% and was composed primarilyof nine fatty acids: myristic acid, palmitoleic acid, stearic acid, oleicacid, linoleic acid, linolenic acid, eicosenoic acid, eicosapentaenoicacid, and docosahexaenoic acid (Table 8). The secondary compo-nent accounted for 40.901%, and was composed of palmitic acid,vaccenic acid, c-linolenic acid, arachidonic acid, and docosatetrae-noic acid. In the PCA plot with two components, three distinctgroups were identifiable. Group A was correlated with the IMCclass, group B with the MC class, and group C was associated withthe NMC and PC classes. Using this analysis, it was possible to dis-criminate between NMCs and IMCs, and between IMCs and MCs.On the other hand, it was not possible to differentiate betweenthe PC class and NMCs.

The score plot for the phytosterols generated from comparisonof the two principal components (eigenvalue P1) is depicted inFig. 2 (B). The primary component contributed 51.974%, and con-sisted mainly of campesterol and b-sitosterol. The secondary com-ponent contributed 47.496%, and was composed primarily ofstigmasterol. From the PCA plot, three groups were identified.Group D was correlated with NMCs and a part of the PC class(PC2 and PC7–9), group E was associated with the rest of the PCclass (PC1, PC3–6, and PC10–12), and Group F was composed ofthe IMC and MC classes. From the PCA plot, it was possible to dis-criminate between the NMCs and the IMCs. On the other hand, it

e (Internal standard), (2) Brassicasterol, (3) Campesterol, (4) Stigmasterol, (5) b-

Fig. 2. PCA score plot for 27 commercial cheeses. (A) Fatty acid profiles (B) Phytosterol contents. NMC, natural mozzarella cheeses; IMC, imitated mozzarella cheeses; PC,processed cheeses; MC, mixed cheeses of NMC and IMC.

46 N.S. Kim et al. / Food Chemistry 143 (2014) 40–47

was difficult to differentiate between some of the PCs from theNMC class and between the MC and IMC classes.

4. Conclusion

PCA of the fatty acid profile and phytosterol content data en-abled the differentiation of NMCs from IMCs. Unfortunately, thePC and MC classes were not completely separated from the NMCand IMC classes. The presented method to discriminate naturalmozzarella cheese from cheese substitutes is interesting approach,but more research is needed to enable the complete differentiationof the cheese classes investigated in this work.

Acknowledgements

This research was supported by a grant from the Criminal Inves-tigations Office of the Korea Food & Drug Administration (KFDA) in2012.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.foodchem.2013.07.083.

N.S. Kim et al. / Food Chemistry 143 (2014) 40–47 47

References

Bachmann, H.-P. (2001). Cheese analogues: A review. International Dairy Journal, 11,505–515.

Christie, W. W. (1989). Gas chromatography and lipids—a practical guide. Dundee:The Oily Press.

Christie, W. W., Sébédio, J. L., & Juanéda, P. (2001). A practical guide to the analysisof conjugated linoleic acid. Inform, 12, 147–152.

Contarini, G., Povolo, M., Bonfitto, E., & Berardi, S. (2002). Quantitative analysis ofsterols in dairy products: Experiences and remarks. International Dairy Journal,12, 573–578.

Fiamegos, Y. C., Nanos, C. G., Vervoort, J., & Stalikas, C. D. (2004). Analyticalprocedure for the in-vial derivatization—extraction of phenolic acids andflavonoids in methanolic and aqueous plant extracts followed by gaschromatography with mass-selective detection. Journal of Chromatography A,1041, 11–18.

Galina, M. A., Osnaya, F., Cuchillo, H. M., & Haenlein, G. F. W. (2007). Cheese qualityfrom milk of grazing or indoor fed Zebu cows and Alpine crossbred goats. SmallRuminant Research, 71, 264–272.

García-Llatas, G., & Rodríguez-Estrada, M. T. R. (2011). Current and new insights onphytosterol oxides in plant sterol-enriched food. Chemistry and Physics of Lipids,164, 607–624.

Haswell, S. J. (1992). Introduction to chemometrics. In Practical guide tochemometrics (pp. 1–3). New York: Marcel Dekker Inc.

Jana, A. H., & Mandal, P. K. (2011). Manufacturing and quality of mozzarella cheese:A review. International Journal of Dairy Science, 6, 199–226.

Jana, A. H., Patel, H. G., Suneeta, P., & Prajapati, J. P. (2010). Quality of casein basedmozzarella cheese analogue as affected by stabilizer blends. Journal of FoodScience Technology, 47, 240–242.

Kadegowda, A. K. G., Piperova, L. S., & Erdman, R. A. (2008). Principal component andmultivariate analysis of milk long-chain fatty acid composition during dietinduced milk fat depression. Journal of Dairy Science, 91, 749–759.

Korea Food and Drug Administration (2012). Health Functional Food Code.Notification No.68. URL: <http://www.mfds.go.kr/gwangju/index.do?mid=26&seq=18826&cmd=v>.

Lu, J., Wang, S., Dong, Y., Wang, X., Yang, S., Zhang, J., et al. (2010). Simultaneousanalysis of fourteen tertiary amine stimulants in human urine for dopingcontrol purposes by liquid chromatography–tandem mass spectrometry andgas chromatography–mass spectrometry. Analytica Chimica Acta, 657, 45–52.

Mapekula, M., Chimonyo, M., Mapiye, C., & Dzama, K. (2011). Fatty acid, amino acidand mineral composition of milk from Nguni and local crossbred cows in SouthAfrica. Journal of Food Composition and Analysis, 24, 529–536.

Massart, D. L., Vandeginste, B. G. M., Deming, S. N., Michotte, Y., & Kaufman, L.(1988). Principal components and factor analysis. In Chemometrics: A textbook(pp. 339–370). New York: Elsevier Science Publishing Company Inc.

Matos, L. C., Cunha, S. C., Amaral, J. S., Pereira, J. A., Andrade, P. B., Seabra, R. M., et al.(2007). Chemometric characterization of three varietal olive oils (Cvs.Cobrancosa, Madural and Verdeal Transmontana) extracted from olives withdifferent maturation indices. Food Chemistry, 102, 406–414.

Middleton, J.L. (1989). Process of using rennet casein for producing imitationcheese. United States Patent 4 822 623 (pp. 1–8).

Ntakatsane, M. P., Liu, X. M., & Zhou, P. (2013). Short communication: Rapiddetection of milk fat adulteration with vegetable oil by fluorescencespectroscopy. Journal of Dairy Science, 96, 2130–2136.

Pellegrino, L., Resmini, P., De Noni, I., & Masotti, F. (1996). Sensitive determinationof lysinoalanine for distinguishing natural from imitation mozzarella cheese.Journal of Dairy Science, 79, 725–734.

Prandini, A., Sigolo, S., & Piva, G. (2011). A comparative study of fatty acidcomposition and CLA concentration in commercial cheeses. Journal of FoodComposition and Analysis, 24, 55–61.

Qiu, Y., Su, M., Liu, Y., Chen, M., Gu, J., Zhang, J., et al. (2007). Application of ethylchloroformate derivatization for gas chromatography–mass spectrometrybased metabonomic profiling. Analytica Chimica Acta, 583, 277–283.

Resmini, P., Pellegrino, L., & Massotti, F. (1993). Evaluation of the Maillard reactionextent for quality control of low-heat treated dairy products. IDF Special Issue,9302:153. Brussels: International Dairy Federation.

Santos, R., Limas, E., Sousa, M., Castilho, M. C., Ramos, F., & Silveira, M. I. N. (2007).Optimization of analytical procedures for GC–MS determination of phytosterolsand p4, phytostanols in enriched milk and yoghurt. Food Chemistry, 102,113–117.

Schummer, C., Delhomme, O., Appenzeller, B. M. R., Wennig, R., & Millet, M. (2009).Comparison of MTBSTFA and BSTFA in derivatization reactions of polarcompounds prior to GC/MS analysis. Talanta, 77, 1473–1482.

Shin, E.-C., Craft, B. D., Pegg, R. B., Phillips, R. D., & Eitenmiller, R. R. (2010).Chemometric approach to fatty acid profiles in Runner-type peanut cultivars byprincipal component analysis (PCA). Food Chemistry, 119, 1262–1270.

Wu, J., Hu, R., Yue, J., Yang, Z., & Zhang, L. (2009). Determination of fecal sterols bygas chromatography–mass spectrometry with solid-phase extraction andinjection-port derivatization. Journal of Chromatography A, 1216, 1053–1058.

Zhang, R. H., Mustafa, A. F., & Zhao, X. (2006). Effects of feeding oilseeds rich inlinoleic and linolenic fatty acids to lactating ewes on cheese yield and on fattyacid composition of milk and cheese. Animal Feed Science and Technology, 127,220–233.