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Prediction of the identity of fats and oils by their fatty acid, triacylglycerol and volatile compositions using PLS-DA
Food Chemistry 118 (2010) 948–955
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Food Chemistry journal homepage: www.elsevier.com/locate/foodchem
Analytical Methods
Prediction of the identity of fats and oils by their fatty acid, triacylglycerol and volatile compositions using PLS-DA S.M. van Ruth a,*, B. Villegas a,b, W. Akkermans c, M. Rozijn a, H. van der Kamp a, A. Koot a a
RIKILT – Institute of Food Safety, Wageningen University and Research Center, P.O. Box 230, 6700 AE Wageningen, The Netherlands Instituto de Agroquímica y Tecnología de Alimentos, IATA – CSIC, P.O. Box 73, 46100 Burjassot, Valencia, Spain c Biometris, Wageningen University and Research Center, P.O. Box 16, 6700 AA Wageningen, The Netherlands b
a r t i c l e
i n f o
Article history: Received 19 November 2007 Received in revised form 19 May 2008 Accepted 22 October 2008
Keywords: Authenticity Fat composition, identity Prediction models PTR-MS
a b s t r a c t The identity of a variety of animal fats and vegetable oils was predicted by three different analytical techniques with help of chemometrics. The sample material of animal origin consisted of milk fat, cow fat, pig fat and poultry fat. The vegetable oils comprised coconut, palm and palm kernel oils. Each product group was composed of at least eight samples of independent batches. For the identity prediction of the fats/oils several (combinations of) datasets were used: absolute and relative measurements of fatty acid compositions, of triacylglycerol compositions, and of combined fatty acid and triacylglycerol compositions. Volatile organic compound compositions were used as well. Fatty acid and triacylglycerol compositions were determined by gas chromatography. Fingerprints of volatile compositions were acquired using Proton transfer reaction-mass spectrometry. The rates of successful prediction were high and varied between 89 and 100%. The 100% rate was obtained for the absolute combined fatty acid/triacylglycerol dataset. Proton transfer reaction-mass spectrometry resulted in 89% correct classifications, has the advantage that it allows very rapid measurements compared to the other techniques, but requires further studies. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction Quality assessment of raw materials and final products is fundamental for maintaining high quality standards. Different aspects determine the overall quality of foods: quality (in terms of sensory characteristics, stability, and nutritional values), safety (with respect to microbiology, contaminants, and toxins), and authenticity. An authentic product is one which strictly complies with the declaration given by the producer in terms of ingredients, natural components, absence of extraneous substances, production technology, geographical and botanical origin, production year, and genetic identity. Authenticity is an important issue for the food industry due to legal compliance, economic reasons, guarantee of a constant well-defined quality, use of safe ingredients, and religious reasons (Kamm, Dionisi, Hischenhuber, & Engel, 2001). Adulteration is generally motivated by maximizing profit by replacing an expensive with a cheaper ingredient (Cserháti, Forgács, Deyl, & Miksik, 2005). On the other hand, contamination may happen accidentally, e.g. in factories, where several oils are produced or used at the same time. These cross-contaminations are in the range of 1–2% of the total amount. Detection of adulterations involves the recognition of one or several markers typical of * Corresponding author. Tel.: +31 317 480250; fax: +31 317 417717. E-mail address: [email protected] (S.M. van Ruth). 0308-8146/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodchem.2008.10.047
the adulterant. The ideal marker(s) of oils and fats should be specific to the adulterant and absent in the authentic oil/fat. Not many chemical markers fullfil these criteria. In fact, the natural variability of chemical composition prevents having one discriminative marker for each type of oil (Kamm et al., 2001). Edible fats and oils are complex mixtures containing a wide range of compounds. They are mainly composed of triacylglycerols (TAGs), diacylglycerols (DGs), free fatty acids (FFAs), phospholipids, and other minor components. The most important group of compounds is represented by TAGs, which are in chemical terms trihydric alcohols esterified with fatty acids (FAs) (Buchgraber, Ulberth, Emons, & Anklam, 2004). The biosynthetic pathways of lipids, and thus the TAG profiles are species specific (Ulberth & Buchgraber, 2000). The attributes of TAGs are determined by their total carbon number, their degree of unsaturation and the position and configuration of the double bonds in each FA. Each TAG species may be differentiated in regiospecific/stereospecific isomers by determining the exact position of the 3 FAs on the glycerol backbone. The trihydric alcohol glycerol itself has a plane of symmetry and shows, therefore, optical activity when the 2 primary hydroxyl groups are esterified with different FAs. The analysis of the TAG composition of an oil or fat is a challenging task because an enormous number of individual TAG species is possible due to the large number of possible FA combinations on the glycerol backbone. For example a fat containing only two different FAs results in the theoretical number of 8 possible isomers. However, most seed fats
S.M. van Ruth et al. / Food Chemistry 118 (2010) 948–955
from plants contain 5–10 different FAs. Simple seed oils composed of e.g. 5 different FAs may give 125 individual TAG molecules. Vegetable oils consist generally of fewer FAs than animal carcass fats, milk fats or fish oils. They seldom contain odd-numbered, branched-chain, or unsaturated FAs with fewer than 16 carbon atoms. Animal fats may be much more complex consisting of 10– 40 different FAs. In milk fat more than 400 different FAs have been identified. Most of those FAs arise from ruminal microbial metabolism and thus are unique to ruminant fats (Buchgraber et al., 2004). Traditional analytical strategies to uncover adulteration and guarantee quality have relied on wet chemistry to determine the amount of a marker compound or compounds in a suspect material and a subsequent comparison of the value(s) obtained with those established for equivalent material (Karoui & De Baerdemaker, 2007). As the macro- and micro-components of oils are semi-volatile, both HPLC and GC techniques can be employed for their measurement. In the literature TAG and FA have received most attention. A relatively new concept is classification of oils or fats by their volatile profiles. The sensory properties of fats and oils differ considerably, which is mainly due to their volatile flavours. Therefore, volatile profiles may be useful for discrimination of fats and oils. Gas Chromatography (combined with olfactometry/flame ionization detection or mass spectrometry) (Högnadóttir & Rouseff, 2003; Luna, Morales, & Aparicio, 2006), electronic nose systems (Cosio, Ballabio, Benedetti, & Gigliotti, 2006; Hai & Wang, 2006) and proton transfer reaction-mass spectrometry (PTR-MS; Araghipour et al., 2007) are techniques for analysis of volatiles and are employed to generate classification data. As the more advanced evaluation methods used for authenticity tests of oils include a high number of variables (e.g. composition of TAGs), multivariate statistical methods are required for evaluation of the results (Cserháti et al., 2005). In the present study the identities of seven types of fat (milk fat, cow fat, pig fat, poultry fat, coconut oil, palm oil, and palm kernel oil samples) were predicted applying partial least square discriminant analysis (PLS-DA) to (a) their multivariate FA profiles, (b) their TAG profiles, (c) their FA and TAG profiles combined, and (d) their profiles of volatile organic compounds (VOCs) measured by PTR-MS. FA and TAG profiling are rather conventional methods, but the non-targeted, non-biased chemometric approach (the PLSDA) is an interesting new aspect, as well as the combination of multivariate datasets. PTR-MS analyses was explored because of its rapid data generation properties (full spectrum <30s). The success rates of the four approaches (FA, TAG, FA+TAG and PTR-MS) were compared.
2. Materials and methods 2.1. Materials Eight regular and fourteen fractionated butter oil samples (8 soft and 6 hard fractions) were kindly provided by VIV Vreeland (Zelhem, the Netherlands) and Friesland Foods (Noordwijk, the Netherlands). Eight coconut oil, eight palm oil, and eight palm kernel oil samples were supplied by Unilever (Vlaardingen, The Netherlands). Samples originated from different batches. The animal fat samples from the Dutch industry consisted of eight cow fat samples, eight pig fat samples, and eight poultry fat samples. Three of them were commercial mixed samples from Smilde (Heerenveen, the Netherlands) and the others were samples from individual animals. The samples were selected taking into account as much as possible both natural and technology-induced variation. Sample material was stored at 20 °C in absence of light until analysis was carried out.
949
2.2. Methods 2.2.1. FA methyl ester (FAME) analysis The fats were methylated and the fatty acid methyl esters analysed according to the international standard ISO 15885:2002. Nonanoic acid (C9:0) was added as internal standard (Sigma–Aldrich Chemie, Zwijndrecht, the Netherlands). As reference material a home-made standard FAME mixture composed of C4:0, methyl butyrate (Fluka, 19358, Sigma–Aldrich Chemie, Zwijndrecht, The Netherlands); C6:0, methyl capraote (Fluka, 21599); C8:0, methyl caprylate (Fluka, 21719); C10:0, methyl decanoate (Fluka, 21479); C12:0, methyl laurate (Fluka, 61689); C14:0, methyl myristate (Fluka, 70129); C16:0, methyl palmitate (Fluka, 76159); C18:0, methyl stearate (Fluka, 85769); C18:1, methyl oleate (Fluka, 75160); C9:0, methyl nonanoate (Sigma, 245895) was used to calculate calibration factors of the various FAMEs. Absolute concentrations (g fatty acid/100 g fat) as well as relative concentrations (the total FAME measured was normalized to 100%) were calculated. All fats and oils were analysed in triplicate. 2.2.2. TAG analysis The TAG analysis was carried out according to the Draft International Standard ISO/DIS 17678|IDF 202 (Milk fat – Detection of foreign fats by gas chromatographic analysis of triglycerides). Tricaprion (C18) was added to each sample as internal standard. The reference material CRM 519 (IRMM, Geel, Belgium) was used for determining the calibration factor of each triglyceride. Both absolute concentrations of each TAG (g triacylglycerol/100 g fat) and relative concentrations (the total TAG measured was normalized to 100%) were measured. All fats and oils were analysed in triplicate. 2.2.3. PTR-MS analysis For headspace analysis, 5 g of fat or oil was placed in a glass flask (100 ml) at 30 °C for 30 min to allow equilibration. Preliminary experiments showed that 30 min was sufficient for equilibration. Two replicates of each sample were analysed. The volatile organic compounds (VOCs) in the headspace of the samples were analysed at 30 °C by PTR-MS according to the method described by Lindinger, Hirber, and Paretzke (1993). A constant drift voltage of 600 V and a pressure of 2.09 ± 0.01 mbar were maintained in the reaction chamber. The headspace was drawn from the sample flask at a rate of 15 ml/min which was led through a heated transfer line into the high sensitivity PTR-MS for on-line analysis. Data were collected for the mass range m/z 20–149 using a dwell time of 0.2 s mass1. The instrument was operated at a standard E/N (ratio of electric field strength across the drift tube, E, to buffer gas density, N) of 138 Td (1Td = 1017 cm2 V molecule1). Inlet and drift chamber temperatures were 60°C. Each sample was analysed for at least 5 full mass scans. The headspace concentrations of the compounds during the cycles #2, #3 and #4 were calculated as described by Hansel et al. (1995) and background and mass discrimination corrections were applied. Headspace concentrations were subsequently averaged over the three mass scans for further statistical analysis. In preliminary experiments some of the samples were analysed for seven cycles: the results did not show consistent changes in headspace concentrations (especially no decrease) after the first cycle. Therefore, cycles #2, #3 and #4 were selected for calculations. Milk fat, coconut oil, palm oil and palm kernel oil samples were analysed in duplicate. Most animal fat samples were samples from individual animals, lack of sample material did not allow a sufficient number of PTR-MS analysis on the same samples to carry out PLS-DA analyses. Therefore, for the PTR-MS analysis, only milk fat and the vegetable oils were considered for statistical analysis.
0.5 0.5 1.1 1.6 0.1 0.2 0.1 0.6 0.2 0.0 0.1 0.0 0.0 0.0 1.4 3.3 12.6 18.9 1.8 10.5 2.6 0.6 0.6 0.1 0.1 0.0 0.0 0.0 0.6 1.2 2.1 1.7 0.1 0.7 0.1 21.0 31.6 35.2 36.1 7.0 40.7 16.1 1.6 1.7 0.0 0.0 0.0 0.0 0.0 0.4 0.5 0.2 0.3 0.1 0.1 0.0 10.6 24.0 17.6 5.5 3.1 4.8 2.6 1.9 2.4 1.6 4.8 0.0 0.2 0.0
c
a
b
Coefficient of variance (variance between sample means, not between replicates). Mean value of 14 fractionated and 8 unfractionated samples. Mean value of eight samples.
30.4 26.2 27.4 23.9 10.2 48.1 9.0 1.0 0.4 0.1 0.1 0.0 0.0 0.0 1.0 0.5 0.0 0.4 0.0 0.0 0.0 11.5 5.1 1.8 2.5 18.4 1.1 16.5 3.9 0.5 0.3 3.6 49.4 0.2 51.3 3.1 0.1 0.1 0.2 6.6 0.0 3.8 1.4 0.1 0.1 0.1 8.6 0.1 4.2 2.4 0.0 0.0 0.0 0.7 0.0 0.3 Milk fat Cow fatc Pig fatc Poultry fatc Coconut oilc Palm oilc Palm kernel oilc
C18:3 C18:2conj C18:2 C18:1c2 C18:1c1 C18:1 C18:t2 C18:1t1 C16:1 C16:0 C15:0 C14:1 C14:0 C12:0
The FAME compositions of the fats and oils characterized by 18 FAs are presented in Table 1. The FAME compositions of the cow fat (CV = 44%), pig fat (CV = 43%) and poultry fat samples (CV=34%) varied most across samples (CV based on variance between sample means, not between replicates). This is not surprising as these samples originated from different single animals. The FAME data were subsequently subjected to PLS-DA. The best prediction rates appeared to be obtained with auto-scaling of the variables and no scaling of the units. The prediction success rates in single cross-validation runs varied from 74% (1 component) to 100% (100%: 4 and 8 components). It was therefore decided to run the 100 repeated cross-validations for 4 components to gain insight into the stability of the results. On average 1.4% of the fats were misclassified per replication. Fig. 1 shows box plots of the distribution of the 100 Ci-values obtained for each sample (for the definition of C, see the paragraph on ‘Statistical Analysis’). The figure reveals that some samples are occasionally misclassified. Cow fat 8 is misclassified most often (column 38 in
C10:0
3.1. FAME
C8:0
3. Results and discussion
C6:0
(where pit = pim when the classification is correct, and pit = pim2 when the classification is incorrect) has a value between 0 and 0.5 when sample i is incorrectly classified, and between 0.5 and 1 when it is correctly classified. The larger the difference between pm and pm2, the closer the value of C will be to either 1 or 0. If the difference between pim and pim2 is small, C will be near 0.5. Therefore, values near 0 or 1 indicate high confidence, whereas values around 0.5 indicate lower confidence in prediction results. The box plots of Ci (one box plot for each sample) were composed as a summary of the results of the 100 repeated cross validations.
C18:0
pit pim þ Pim2
Table 1 Absolute fatty acid composition of animal and vegetable fats/oils [g fatty acid/100 g fat].
Ci ¼
CVa [%]
2.2.4. Statistical analysis PLS-DA models (Barker & Rayens, 2003) were estimated to predict the identity of the samples using either the FAME, TAG or VOC data. The analyses were carried out in Matlab using the PLS toolbox (Wise et al., 2006). PLS-DA performs a dimension reduction on the predictor variables. The dimensions (components) extracted are composed such that they exhibit maximal correlation with Y (class membership, e.g. milk fat, poultry fat, palm oil, etc.). After estimation of the classification model, its performance was evaluated by means of 10-fold cross-validation: 10% of the samples were randomly removed from the data set, and a model built with the remaining samples was used to classify these left out samples. The procedure was repeated ten times to obtain predictions for all samples. The number of components that is extracted is an important parameter in a PLS-DA model. Therefore, models with 1–8 components were investigated to select the most appropriate (optimal) number of components. Various standardization methods (on variables and on units) were investigated to improve prediction rates, i.e. mean-centering, auto-scaling, and log transformation. After selecting the model, the 10-fold cross validation (which is a random process) was repeated 100 times to get insight into the repeatability of the classification results. The output of the 100 replications is summarized as follows. PLS-DA outputs, for each sample i, a posterior probability pi (pi1, pi2,. . ..pik) for membership in each of the k classes. The sample is assigned to the class with the highest posterior probability. Let pim be the probability for this class, and let pim2 be the probability for the class with the second-largest posterior probability. Then
20 44 43 34 14 15 18
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b
950
951
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Fig. 1. Results for 100 replications of the random cross validation of the predicted classification of fat and oil samples by their absolute fatty acid compositions into identity classes (e.g. cow fat, palm oil, etc.) using PLS-DA models. For each of the samples, a box shows the location of the .25 and .75 quartiles of the quantity C (see text), the dotted lines (whiskers) are the whiskers; they have length 1.5 * the inter-quartile range (or shorter, if there are no more observations), and the crosses are outliers, lying outside the whiskers. The data have been sorted so that the 22 milk fat samples are displayed first (column 1–22), followed by 8 coconut oil (column 23–30), 8 cow fat (columns 31–38), 8 palm kernel oil (column 39–46), 8 palm oil (columns 47–54), 8 pig fat (columns 55–62), and 8 poultry fat samples (columns 63–70).
Table 2 Number and (percentages) of predicted classification of oils and fats into product classes by their absolute fatty acid composition using a four component PLS-DA model. The correctly classified samples in bolda,b. Sample
Milk fat Cow fat Pig fat Poultry fat Coconut oil Palm oil Palm kernel oil a b
PLS-DA classification
Total correctly classified (%)
Milk fat
Cow fat
Pig fat
Poultry fat
Coconut oil
Palm oil
Palm kernel oil
22 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
0 7 0 0 0 0 0
0 0 8 0 0 0 0
0 1 0 8 0 0 0
0 0 0 0 8 0 0
0 0 0 0 0 8 0
0 0 0 0 0 0 8
(0%) (87.5%) (0%) (0%) (0%) (0%) (0%)
(0%) (0%) (100%) (0%) (0%) (0%) (0%)
(0%) (12.5%) (0%) (100%) (0%) (0%) (0%)
(0%) (0%) (0%) (0%) (100%) (0%) (0%)
(0%) (0%) (0%) (0%) (0%) (100%) (0%)
(0%) (0%) (0%) (0%) (0%) (0%) (100%)
100 87.5 100 100 100 100 100
69 out of 70 Samples were correctly classified (98.6%). Variables standardized by means of auto-scaling, no standardization on units applied.
the plot): 22 times out of 100 this sample is incorrectly classified as milk fat. However, in most cases the confidence in the incorrect classification is not very high (Ci closer to 0.5 than to 0). Four of the pig fat samples are occasionally misclassified as cow fats1 (columns 55–62). Four more cow fat samples (columns 31–35) are sometimes incorrectly classified as pig fat. However, for these samples, the probabilities for the correct and incorrect class are almost equal, resulting in Ci -values of near 0.5. Therefore, these misclassifications are hardly visible in the box plot. Finally, one coconut fat sample (column 30) is occasionally misclassified with higher confidence, either as cow or pig fat. More detailed results are now given for one particular cross validation run. For this run, the numbers of samples predicted into the various product groups are listed in Table 2. A scores plot of the first three dimensions of the model is shown in Fig. 2. In this 1 The box plot shows whether a sample is misclassified or not, it does not show into which incorrect class a misclassified sample is placed. This information is available, but due to space considerations, it is impossible to present it in a table. It was therefore decided to mention it in the text, where appropriate.
run, only one cow fat sample was misclassified as poultry fat. But the posterior probability for this classification is not very high (0.13). As the second largest posterior probability is 0.05, this incorrect classification would result in a Ci-value of 0.05/0.18 = 0.28. The scores plot (Fig. 2) shows that the misclassified cow fat is positioned more or less halfway between the cow fat and the poultry fat samples. A similar approach was adopted for the relative FAME data. With 4 components extracted, on average 1% of the classifications was incorrect. 3.2. TAG The TAG compositions of the fats and oils were determined; they were characterized by 17 triglycerides (Table 3). The animal fats (CV = 21–40%) showed a more varied TAG composition than the vegetable oils (CV = 3–10%; CV considering variance between sample means only). TAG composition of the cow fat was generally within the ranges specified by Precht (1992), who developed a method for detection of foreign fat in milk fat. The compositions
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Fig. 2. Scores plot of the first three dimensions of PLS-DA on the absolute fatty acid composition data of seven oils and fats.
Table 3 Absolute triacylglycerol composition of animal and vegetable fats/oils [g triacylglycerol/100g fat].
b
Milk fat Cow fatc Pig fatc Poultry fatc Coconut oilc Palm oilc Palm kernel oilc a b c
Chol.
C26
C28
C30
C32
C34
C36
C38
C40
C42
C44
C46
C48
C50
C52
C54
C56
CVa (%)
0.4 0.1 0.1 0.1 0.2 0.0 0.1
0.3 0.0 0.0 0.0 0.7 0.0 0.9
0.6 0.0 0.0 0.0 1.0 0.0 0.7
1.3 0.0 0.0 0.0 3.0 0.0 1.3
2.7 0.0 0.0 0.0 11.6 0.1 6.2
6.3 0.0 0.0 0.0 15.2 1.1 8.0
11.0 0.2 0.1 0.2 17.9 4.2 20.2
12.7 0.2 0.0 0.2 16.2 2.4 15.5
9.8 0.0 0.0 0.5 10.4 0.1 9.2
6.9 0.2 0.0 1.2 7.7 0.0 8.9
6.5 1.0 0.2 2.3 4.6 0.1 6.7
7.3 3.7 0.6 5.8 2.7 0.6 5.4
9.0 11.8 2.9 9.9 2.5 7.3 6.5
10.8 23.3 15.6 20.5 2.1 38.0 2.9
9.6 38.1 62.6 36.1 1.8 39.6 3.0
4.6 24.7 20.8 26.2 1.0 12.0 3.3
0.3 0.8 1.6 1.2 0.0 0.6 0.1
21 28 25 40 9 10 3
Coefficient of variance (variance between sample means, not between replicates). Mean value of 14 fractionated and 8 unfractionated samples. Mean value of eight samples
of coconut oil and pig fat in the present study showed generally higher concentrations of the longer chain TAGs than those reported by Precht (1992). For the TAG data too, auto-scaling of the variables and no scaling of the units appeared to give the best results. The optimal number of components was larger here than it was for the FAME data before. The cross-validation procedure was repeated 100 times for a solution with 6 components, giving a confidence box plot (Fig. 3). It appeared that all cow fat samples (columns 31–38) are occasionally classified as poultry fat samples, and all poultry fat samples (columns 63–70) sometimes as cow fat samples (see Footnote 1). Apparently, separating these two types of fat by their triglyceride compositions is a challenging task. A similar approach was adopted for the relative TAG data. Here, with 6 components, on average 10 % of the samples was incorrectly classified. Without exception these were poultry and cow fats being confounded. 3.3. Combined FAME and TAG analysis The FAME and TAG datasets were combined for further improvement of the identity prediction of the fats and oils. Optimal results regarding identity prediction were obtained with a six com-
ponent PLS-DA model with auto-scaling of the variables and no scaling of the units. The confidence plot of this combined six component model is presented in Fig. 4. The box plot shows that all confidence levels are between 0.5 and 1.0 which indicates that the combined model classified 100% of the samples correctly. Combination of the two datasets resulted further in higher confidence in the predictions in general than for the single data sets: most values are close to 1. A similar approach was adopted for the combined relative data. They resulted on average in a correct classification of 99 % of the samples. 3.4. VOC Proton transfer reaction-mass spectrometry (PTR-MS) is a promising technique for analysis of volatile compounds and has been used to investigate different issues in food science. For instance the effect of technological conditions on the VOC profiles of orange juices has been studied (Biasioli et al., 2003). Pollien and co-workers used PTR-MS for on-line monitoring of acrylamide (Pollien, Lindinger, Yeretzian, & Blank, 2003). Correlation with sensory data (Biasioli et al., 2006) and real-time in-nose analysis (Fransnelli, van Ruth, Kriukova, & Hummel, 2005) are other
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Fig. 3. Results for 100 replications of the random cross validation of the predicted classification of fat and oil samples by their absolute triacylglycerol compositions into identity classes (e.g. cow fat, palm oil, etc.) using PLS-DA models. For each of the samples, a box shows the location of the .25 and .75 quartiles of the quantity C (see text), the dotted lines (whiskers) are the whiskers; they have length 1.5 * the inter-quartile range (or shorter, if there are no more observations), and the crosses are outliers, lying outside the whiskers. The data have been sorted so that the 22 milk fat samples are displayed first (column 1–22), followed by 8 coconut oil (column 23–30), 8 cow fat (columns 31–38), 8 palm kernel oil (column 39–46), 8 palm oil (columns 47–54), 8 pig fat (columns 55–62), and 8 poultry fat samples (columns 63–70).
Fig. 4. Results for 100 replications of the random cross validation of the predicted classification of fat and oil samples by their combined absolute fatty acid and triacylglycerol compositions into identity classes (e.g. cow fat, palm oil, etc.) using PLS-DA models. For each of the samples, a box shows the location of the .25 and .75 quartiles of the quantity C (see text), the dotted lines (whiskers) are the whiskers; they have length 1.5 * the inter-quartile range (or shorter, if there are no more observations), and the crosses are outliers, lying outside the whiskers. The data have been sorted so that the 22 milk fat samples are displayed first (column 1–22), followed by 8 coconut oil (column 23–30), 8 cow fat (columns 31–38), 8 palm kernel oil (column 39–46), 8 palm oil (columns 47–54), 8 pig fat (columns 55–62), and 8 poultry fat samples (columns 63–70).
important applications. Proton transfer reactions are used to induce chemical ionisation of the vapours to be analysed. The sample gas is continuously introduced into a drift tube, where it is mixed with H3O+ ions formed in a hollow cathode ion source. Volatile compounds that have proton affinities higher than water
(>166.58 kcal/mol) are ionised by proton transfer from H3O+, mass analysed in a quadrupole mass spectrometer and eventually detected as ion counts/s (cps) by a secondary electron multiplier. The outcome is a mass resolved fingerprint of the total volatile profile of a sample. PTR-MS is interesting for this fingerprinting
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units are presented in Table 4. The milk fats, coconut oils and palm oils were correctly classified except for one coconut oil. Palm kernel oils showed poor classification results, with only 50% of the samples being correctly classified. Misclassified palm kernel samples were placed into all product groups, therefore, it seems that the palm kernel oils were not characterized by specific VOCs, but showed great overlap with the VOCs of the other product groups. Overall, 89% of the samples were classified correctly using PTR-MS data. Therefore, classification was reasonably successful, but rates were not as high as for the FAME and TAG analysis. However, PTR-MS analysis has the advantage of being very rapid compared with the other analysis techniques, which would make it a potential technique for screening of particular fats and oils.
Unfractionated milk fat 100000
Concentration [ppbv]
10000
1000
100
10
1 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Mass [m/z] Fig. 5. Mean fingerprint mass spectrum of the volatile organic compounds in the headspace of regular milk fat obtained by proton transfer reaction mass spectrometry.
Table 4 Number and (percentages) of predicted classification of oils and fats into product classes by their volatile organic compound compositions using a three component PLS-DA model. The correctly classified samples in bold a,b. Sample
Milk fat Coconut oil Palm oil Palm kernel Oil
PLS-DA classification
Total correctly classified (%)
Milk fat
Coconut oil
Palm oil
Palm kernel oil
22 (100%) 0 (0%) 0 (0%) 1 (12.5%)
0 7 0 2
0 0 8 1
0 1 0 4
(0%) (87.5%) (0%) (25%)
(0%) (0%) (100%) (12.5%)
(0%) (12.5%) (0%) (50%)
100 87.5 100 50
a
41 out of 46 Samples were correctly classified (89.1%). Variables standardized by means of auto-scaling, no standardization on units applied. b
approach as (i) it requires no pre-treatment of the sample, (ii) it allows rapid measurements (typically <1 min for a complete mass spectrum), and (iii) the technique is extremely sensitive (ppt level). The PTR-MS data of the VOCs covered a range of 129 masses (data not shown). An example of the mean fingerprint mass spectrum of the regular milk fats is presented in Fig. 5. Results for one cross-validation run while applying a three component PLS-DA model with auto-scaling of the variables and no scaling of the
Table 5 Overview of correct classifications of fats and oils by their fatty acid, triacylglycerol or volatile organic compound composition using PLS-DA. Techniques
Number of components in PLS-DA
Correct classifications (%)
Fatty acid composition, absolute data Fatty acid composition, absolute date Fatty acid composition, relative data Triacylglycerol composition, absolute data Triacylglycerol composition, relative data Combined Fatty acid and triacylglycerol composition, absolute data Combined fatty acid and triacylglycerol composition, relative data Volatile organic compound composition measured by proton transfer reaction mass spectrometry
4 6 4 6 6 5
99 97 99 96 90 99
6
100
3
89
4. Conclusions For identity prediction all techniques used were reasonably successful in classifying the various animal fats and vegetable oils (89–100%, Table 5). The identity of the samples was most successfully predicted using the combined absolute FAME and TAG data set (i.e. for the present dataset 100%). The combined FAME-TAG dataset has the additional advantage that a more robust prediction is obtained as the confidence in the predictions is generally higher than for the other datasets. Alternatively, if a certain prediction rate (e.g. 95%) is required, fewer PLS components are sufficient to obtain this success rate. PTR-MS analysis which results in a volatile fingerprint was least successful but still resulted in 89% correct classifications with the additional advantage of the speed of the technique. The technique gave promising results, but further studies are required to confirm its use as a good alternative for FA and TAG analyses. References Araghipour, N., Colineau, J., Koot, A., Akkermans, W., Moreno Rojas, J. M., Beauchamp, J., et al. (2007). Geographical origin classification of olive oils by PTR-MS. Food Chemistry, 108, 374–383. Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17, 166–173. Biasioli, F., Gasperi, F., Aprea, E., Colato, L., Boscaini, E., & Märk, T. D. (2003). Fingerprinting mass spectrometry by PTR-MS: Heat treatment vs. pressure treatment of red orange juice, a case study. International Journal of Mass Spectrometry, 223–224, 343–353. Biasioli, F., Gasperi, F., Aprea, E., Endrezzi, I., Framondino, V., Marini, F., et al. (2006). Correlation of PTR-MS spectral fingerprints with sensory characterisation of flavour and odour profile of ‘Trentingrana’ cheese. Food Quality and Preference, 17, 63–75. Buchgraber, M., Ulberth, F., Emons, H., & Anklam, E. (2004). Triacylglycerol profiling by using chromatographic techniques. European Journal of Lipid Science and Technology, 106, 621–648. Cosio, M. S., Ballabio, D., Benedetti, S., & Gigliotti, C. (2006). Geographical origin and authentication of extra virgin olive oils by an electronic nose in combination with artificial neural networks. Analytica Chimica Acta, 567, 202–210. Cserháti, T., Forgács, E., Deyl, Z., & Miksik, I. (2005). Chromatography in authenticity and trace ability tests of vegetable oils and dairy products: A review. Biomedical Chromatography, 19, 183–190. Fransnelli, J., van Ruth, S. M., Kriukova, I., & Hummel, T. (2005). Intranasal concentrations of orally administered flavours. Chemical Senses, 30, 572–582. Hai, Z., & Wang, J. (2006). Detection of adulteration in camellia seed oil and sesame oil using an electronic nose. European Journal of Lipid Science and Technology, 108, 116–224. Hansel, A., Jordan, A., Holzinger, R., Prazeller, P., Vogel, W., & Lindinger, W. (1995). Proton transfer reaction mass spectrometry: On-line trace gas analysis at the ppb level. International Journal of Mass Spectrometry and Ion Processes, 149(150), 609–619. Högnadóttir, A., & Rouseff, R. L. (2003). Identification of aroma active compounds in orange essential oil using gas chromatography-olfactometry and gas chromatography-mass spectrometry. Journal of Chromatography A, 998, 201–211. Kamm, W., Dionisi, F., Hischenhuber, C., & Engel, K.-H. (2001). Authenticity assessment of fats and oils. Food Reviews International, 17(3), 249–290.
S.M. van Ruth et al. / Food Chemistry 118 (2010) 948–955 Karoui, R., & De Baerdemaker, J. (2007). A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products. Food Chemistry, 102, 621–640. Lindinger, W., Hirber, J., & Paretzke, H. (1993). An ion/molecule-reaction mass spectrometer used for on-line trace gas analysis. Journal of Mass Spectrometry and Ion Processes, 129, 79–88. Luna, G., Morales, M. T., & Aparicio, R. (2006). Characterisation of 39 varietal virgin olive oils by their volatile compositions. Food Chemistry, 98, 243–252. Pollien, P., Lindinger, C., Yeretzian, C., & Blank, I. (2003). Proton transfer reaction mass spectrometry, a tool for on-line monitoring of acrylamide formation in the
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headspace of Maillard reaction systems and processed food. Analytical Chemistry, 75, 5488–5494. Precht, D. (1992). Detection of foreign fat in milk fat. I. Qualitative detection by triacylglycerol formulae. II. Quantitative evaluation of foreign fat mixtures.. Zeitschrift für Lebensmittel Untersuchung und –Forschung, 194, 1–8. Ulberth, F., & Buchgraber, M. (2000). Authenticity of fats and oils. European Journal of Lipid Science and Technology, 102, 687–694. Wise, B. M., Shaver, J. M., Gallagher, N. B., Windig, W., Bro, R., & Koch, R. S. (2006). PLS Toolbox Version 4.0 for use with Matlabtm, Eigenvector Research Inc. WA, USA: Wenatchee.
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Analytical Methods
Prediction of the identity of fats and oils by their fatty acid, triacylglycerol and volatile compositions using PLS-DA S.M. van Ruth a,*, B. Villegas a,b, W. Akkermans c, M. Rozijn a, H. van der Kamp a, A. Koot a a
RIKILT – Institute of Food Safety, Wageningen University and Research Center, P.O. Box 230, 6700 AE Wageningen, The Netherlands Instituto de Agroquímica y Tecnología de Alimentos, IATA – CSIC, P.O. Box 73, 46100 Burjassot, Valencia, Spain c Biometris, Wageningen University and Research Center, P.O. Box 16, 6700 AA Wageningen, The Netherlands b
a r t i c l e
i n f o
Article history: Received 19 November 2007 Received in revised form 19 May 2008 Accepted 22 October 2008
Keywords: Authenticity Fat composition, identity Prediction models PTR-MS
a b s t r a c t The identity of a variety of animal fats and vegetable oils was predicted by three different analytical techniques with help of chemometrics. The sample material of animal origin consisted of milk fat, cow fat, pig fat and poultry fat. The vegetable oils comprised coconut, palm and palm kernel oils. Each product group was composed of at least eight samples of independent batches. For the identity prediction of the fats/oils several (combinations of) datasets were used: absolute and relative measurements of fatty acid compositions, of triacylglycerol compositions, and of combined fatty acid and triacylglycerol compositions. Volatile organic compound compositions were used as well. Fatty acid and triacylglycerol compositions were determined by gas chromatography. Fingerprints of volatile compositions were acquired using Proton transfer reaction-mass spectrometry. The rates of successful prediction were high and varied between 89 and 100%. The 100% rate was obtained for the absolute combined fatty acid/triacylglycerol dataset. Proton transfer reaction-mass spectrometry resulted in 89% correct classifications, has the advantage that it allows very rapid measurements compared to the other techniques, but requires further studies. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction Quality assessment of raw materials and final products is fundamental for maintaining high quality standards. Different aspects determine the overall quality of foods: quality (in terms of sensory characteristics, stability, and nutritional values), safety (with respect to microbiology, contaminants, and toxins), and authenticity. An authentic product is one which strictly complies with the declaration given by the producer in terms of ingredients, natural components, absence of extraneous substances, production technology, geographical and botanical origin, production year, and genetic identity. Authenticity is an important issue for the food industry due to legal compliance, economic reasons, guarantee of a constant well-defined quality, use of safe ingredients, and religious reasons (Kamm, Dionisi, Hischenhuber, & Engel, 2001). Adulteration is generally motivated by maximizing profit by replacing an expensive with a cheaper ingredient (Cserháti, Forgács, Deyl, & Miksik, 2005). On the other hand, contamination may happen accidentally, e.g. in factories, where several oils are produced or used at the same time. These cross-contaminations are in the range of 1–2% of the total amount. Detection of adulterations involves the recognition of one or several markers typical of * Corresponding author. Tel.: +31 317 480250; fax: +31 317 417717. E-mail address: [email protected] (S.M. van Ruth). 0308-8146/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodchem.2008.10.047
the adulterant. The ideal marker(s) of oils and fats should be specific to the adulterant and absent in the authentic oil/fat. Not many chemical markers fullfil these criteria. In fact, the natural variability of chemical composition prevents having one discriminative marker for each type of oil (Kamm et al., 2001). Edible fats and oils are complex mixtures containing a wide range of compounds. They are mainly composed of triacylglycerols (TAGs), diacylglycerols (DGs), free fatty acids (FFAs), phospholipids, and other minor components. The most important group of compounds is represented by TAGs, which are in chemical terms trihydric alcohols esterified with fatty acids (FAs) (Buchgraber, Ulberth, Emons, & Anklam, 2004). The biosynthetic pathways of lipids, and thus the TAG profiles are species specific (Ulberth & Buchgraber, 2000). The attributes of TAGs are determined by their total carbon number, their degree of unsaturation and the position and configuration of the double bonds in each FA. Each TAG species may be differentiated in regiospecific/stereospecific isomers by determining the exact position of the 3 FAs on the glycerol backbone. The trihydric alcohol glycerol itself has a plane of symmetry and shows, therefore, optical activity when the 2 primary hydroxyl groups are esterified with different FAs. The analysis of the TAG composition of an oil or fat is a challenging task because an enormous number of individual TAG species is possible due to the large number of possible FA combinations on the glycerol backbone. For example a fat containing only two different FAs results in the theoretical number of 8 possible isomers. However, most seed fats
S.M. van Ruth et al. / Food Chemistry 118 (2010) 948–955
from plants contain 5–10 different FAs. Simple seed oils composed of e.g. 5 different FAs may give 125 individual TAG molecules. Vegetable oils consist generally of fewer FAs than animal carcass fats, milk fats or fish oils. They seldom contain odd-numbered, branched-chain, or unsaturated FAs with fewer than 16 carbon atoms. Animal fats may be much more complex consisting of 10– 40 different FAs. In milk fat more than 400 different FAs have been identified. Most of those FAs arise from ruminal microbial metabolism and thus are unique to ruminant fats (Buchgraber et al., 2004). Traditional analytical strategies to uncover adulteration and guarantee quality have relied on wet chemistry to determine the amount of a marker compound or compounds in a suspect material and a subsequent comparison of the value(s) obtained with those established for equivalent material (Karoui & De Baerdemaker, 2007). As the macro- and micro-components of oils are semi-volatile, both HPLC and GC techniques can be employed for their measurement. In the literature TAG and FA have received most attention. A relatively new concept is classification of oils or fats by their volatile profiles. The sensory properties of fats and oils differ considerably, which is mainly due to their volatile flavours. Therefore, volatile profiles may be useful for discrimination of fats and oils. Gas Chromatography (combined with olfactometry/flame ionization detection or mass spectrometry) (Högnadóttir & Rouseff, 2003; Luna, Morales, & Aparicio, 2006), electronic nose systems (Cosio, Ballabio, Benedetti, & Gigliotti, 2006; Hai & Wang, 2006) and proton transfer reaction-mass spectrometry (PTR-MS; Araghipour et al., 2007) are techniques for analysis of volatiles and are employed to generate classification data. As the more advanced evaluation methods used for authenticity tests of oils include a high number of variables (e.g. composition of TAGs), multivariate statistical methods are required for evaluation of the results (Cserháti et al., 2005). In the present study the identities of seven types of fat (milk fat, cow fat, pig fat, poultry fat, coconut oil, palm oil, and palm kernel oil samples) were predicted applying partial least square discriminant analysis (PLS-DA) to (a) their multivariate FA profiles, (b) their TAG profiles, (c) their FA and TAG profiles combined, and (d) their profiles of volatile organic compounds (VOCs) measured by PTR-MS. FA and TAG profiling are rather conventional methods, but the non-targeted, non-biased chemometric approach (the PLSDA) is an interesting new aspect, as well as the combination of multivariate datasets. PTR-MS analyses was explored because of its rapid data generation properties (full spectrum <30s). The success rates of the four approaches (FA, TAG, FA+TAG and PTR-MS) were compared.
2. Materials and methods 2.1. Materials Eight regular and fourteen fractionated butter oil samples (8 soft and 6 hard fractions) were kindly provided by VIV Vreeland (Zelhem, the Netherlands) and Friesland Foods (Noordwijk, the Netherlands). Eight coconut oil, eight palm oil, and eight palm kernel oil samples were supplied by Unilever (Vlaardingen, The Netherlands). Samples originated from different batches. The animal fat samples from the Dutch industry consisted of eight cow fat samples, eight pig fat samples, and eight poultry fat samples. Three of them were commercial mixed samples from Smilde (Heerenveen, the Netherlands) and the others were samples from individual animals. The samples were selected taking into account as much as possible both natural and technology-induced variation. Sample material was stored at 20 °C in absence of light until analysis was carried out.
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2.2. Methods 2.2.1. FA methyl ester (FAME) analysis The fats were methylated and the fatty acid methyl esters analysed according to the international standard ISO 15885:2002. Nonanoic acid (C9:0) was added as internal standard (Sigma–Aldrich Chemie, Zwijndrecht, the Netherlands). As reference material a home-made standard FAME mixture composed of C4:0, methyl butyrate (Fluka, 19358, Sigma–Aldrich Chemie, Zwijndrecht, The Netherlands); C6:0, methyl capraote (Fluka, 21599); C8:0, methyl caprylate (Fluka, 21719); C10:0, methyl decanoate (Fluka, 21479); C12:0, methyl laurate (Fluka, 61689); C14:0, methyl myristate (Fluka, 70129); C16:0, methyl palmitate (Fluka, 76159); C18:0, methyl stearate (Fluka, 85769); C18:1, methyl oleate (Fluka, 75160); C9:0, methyl nonanoate (Sigma, 245895) was used to calculate calibration factors of the various FAMEs. Absolute concentrations (g fatty acid/100 g fat) as well as relative concentrations (the total FAME measured was normalized to 100%) were calculated. All fats and oils were analysed in triplicate. 2.2.2. TAG analysis The TAG analysis was carried out according to the Draft International Standard ISO/DIS 17678|IDF 202 (Milk fat – Detection of foreign fats by gas chromatographic analysis of triglycerides). Tricaprion (C18) was added to each sample as internal standard. The reference material CRM 519 (IRMM, Geel, Belgium) was used for determining the calibration factor of each triglyceride. Both absolute concentrations of each TAG (g triacylglycerol/100 g fat) and relative concentrations (the total TAG measured was normalized to 100%) were measured. All fats and oils were analysed in triplicate. 2.2.3. PTR-MS analysis For headspace analysis, 5 g of fat or oil was placed in a glass flask (100 ml) at 30 °C for 30 min to allow equilibration. Preliminary experiments showed that 30 min was sufficient for equilibration. Two replicates of each sample were analysed. The volatile organic compounds (VOCs) in the headspace of the samples were analysed at 30 °C by PTR-MS according to the method described by Lindinger, Hirber, and Paretzke (1993). A constant drift voltage of 600 V and a pressure of 2.09 ± 0.01 mbar were maintained in the reaction chamber. The headspace was drawn from the sample flask at a rate of 15 ml/min which was led through a heated transfer line into the high sensitivity PTR-MS for on-line analysis. Data were collected for the mass range m/z 20–149 using a dwell time of 0.2 s mass1. The instrument was operated at a standard E/N (ratio of electric field strength across the drift tube, E, to buffer gas density, N) of 138 Td (1Td = 1017 cm2 V molecule1). Inlet and drift chamber temperatures were 60°C. Each sample was analysed for at least 5 full mass scans. The headspace concentrations of the compounds during the cycles #2, #3 and #4 were calculated as described by Hansel et al. (1995) and background and mass discrimination corrections were applied. Headspace concentrations were subsequently averaged over the three mass scans for further statistical analysis. In preliminary experiments some of the samples were analysed for seven cycles: the results did not show consistent changes in headspace concentrations (especially no decrease) after the first cycle. Therefore, cycles #2, #3 and #4 were selected for calculations. Milk fat, coconut oil, palm oil and palm kernel oil samples were analysed in duplicate. Most animal fat samples were samples from individual animals, lack of sample material did not allow a sufficient number of PTR-MS analysis on the same samples to carry out PLS-DA analyses. Therefore, for the PTR-MS analysis, only milk fat and the vegetable oils were considered for statistical analysis.
0.5 0.5 1.1 1.6 0.1 0.2 0.1 0.6 0.2 0.0 0.1 0.0 0.0 0.0 1.4 3.3 12.6 18.9 1.8 10.5 2.6 0.6 0.6 0.1 0.1 0.0 0.0 0.0 0.6 1.2 2.1 1.7 0.1 0.7 0.1 21.0 31.6 35.2 36.1 7.0 40.7 16.1 1.6 1.7 0.0 0.0 0.0 0.0 0.0 0.4 0.5 0.2 0.3 0.1 0.1 0.0 10.6 24.0 17.6 5.5 3.1 4.8 2.6 1.9 2.4 1.6 4.8 0.0 0.2 0.0
c
a
b
Coefficient of variance (variance between sample means, not between replicates). Mean value of 14 fractionated and 8 unfractionated samples. Mean value of eight samples.
30.4 26.2 27.4 23.9 10.2 48.1 9.0 1.0 0.4 0.1 0.1 0.0 0.0 0.0 1.0 0.5 0.0 0.4 0.0 0.0 0.0 11.5 5.1 1.8 2.5 18.4 1.1 16.5 3.9 0.5 0.3 3.6 49.4 0.2 51.3 3.1 0.1 0.1 0.2 6.6 0.0 3.8 1.4 0.1 0.1 0.1 8.6 0.1 4.2 2.4 0.0 0.0 0.0 0.7 0.0 0.3 Milk fat Cow fatc Pig fatc Poultry fatc Coconut oilc Palm oilc Palm kernel oilc
C18:3 C18:2conj C18:2 C18:1c2 C18:1c1 C18:1 C18:t2 C18:1t1 C16:1 C16:0 C15:0 C14:1 C14:0 C12:0
The FAME compositions of the fats and oils characterized by 18 FAs are presented in Table 1. The FAME compositions of the cow fat (CV = 44%), pig fat (CV = 43%) and poultry fat samples (CV=34%) varied most across samples (CV based on variance between sample means, not between replicates). This is not surprising as these samples originated from different single animals. The FAME data were subsequently subjected to PLS-DA. The best prediction rates appeared to be obtained with auto-scaling of the variables and no scaling of the units. The prediction success rates in single cross-validation runs varied from 74% (1 component) to 100% (100%: 4 and 8 components). It was therefore decided to run the 100 repeated cross-validations for 4 components to gain insight into the stability of the results. On average 1.4% of the fats were misclassified per replication. Fig. 1 shows box plots of the distribution of the 100 Ci-values obtained for each sample (for the definition of C, see the paragraph on ‘Statistical Analysis’). The figure reveals that some samples are occasionally misclassified. Cow fat 8 is misclassified most often (column 38 in
C10:0
3.1. FAME
C8:0
3. Results and discussion
C6:0
(where pit = pim when the classification is correct, and pit = pim2 when the classification is incorrect) has a value between 0 and 0.5 when sample i is incorrectly classified, and between 0.5 and 1 when it is correctly classified. The larger the difference between pm and pm2, the closer the value of C will be to either 1 or 0. If the difference between pim and pim2 is small, C will be near 0.5. Therefore, values near 0 or 1 indicate high confidence, whereas values around 0.5 indicate lower confidence in prediction results. The box plots of Ci (one box plot for each sample) were composed as a summary of the results of the 100 repeated cross validations.
C18:0
pit pim þ Pim2
Table 1 Absolute fatty acid composition of animal and vegetable fats/oils [g fatty acid/100 g fat].
Ci ¼
CVa [%]
2.2.4. Statistical analysis PLS-DA models (Barker & Rayens, 2003) were estimated to predict the identity of the samples using either the FAME, TAG or VOC data. The analyses were carried out in Matlab using the PLS toolbox (Wise et al., 2006). PLS-DA performs a dimension reduction on the predictor variables. The dimensions (components) extracted are composed such that they exhibit maximal correlation with Y (class membership, e.g. milk fat, poultry fat, palm oil, etc.). After estimation of the classification model, its performance was evaluated by means of 10-fold cross-validation: 10% of the samples were randomly removed from the data set, and a model built with the remaining samples was used to classify these left out samples. The procedure was repeated ten times to obtain predictions for all samples. The number of components that is extracted is an important parameter in a PLS-DA model. Therefore, models with 1–8 components were investigated to select the most appropriate (optimal) number of components. Various standardization methods (on variables and on units) were investigated to improve prediction rates, i.e. mean-centering, auto-scaling, and log transformation. After selecting the model, the 10-fold cross validation (which is a random process) was repeated 100 times to get insight into the repeatability of the classification results. The output of the 100 replications is summarized as follows. PLS-DA outputs, for each sample i, a posterior probability pi (pi1, pi2,. . ..pik) for membership in each of the k classes. The sample is assigned to the class with the highest posterior probability. Let pim be the probability for this class, and let pim2 be the probability for the class with the second-largest posterior probability. Then
20 44 43 34 14 15 18
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b
950
951
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Fig. 1. Results for 100 replications of the random cross validation of the predicted classification of fat and oil samples by their absolute fatty acid compositions into identity classes (e.g. cow fat, palm oil, etc.) using PLS-DA models. For each of the samples, a box shows the location of the .25 and .75 quartiles of the quantity C (see text), the dotted lines (whiskers) are the whiskers; they have length 1.5 * the inter-quartile range (or shorter, if there are no more observations), and the crosses are outliers, lying outside the whiskers. The data have been sorted so that the 22 milk fat samples are displayed first (column 1–22), followed by 8 coconut oil (column 23–30), 8 cow fat (columns 31–38), 8 palm kernel oil (column 39–46), 8 palm oil (columns 47–54), 8 pig fat (columns 55–62), and 8 poultry fat samples (columns 63–70).
Table 2 Number and (percentages) of predicted classification of oils and fats into product classes by their absolute fatty acid composition using a four component PLS-DA model. The correctly classified samples in bolda,b. Sample
Milk fat Cow fat Pig fat Poultry fat Coconut oil Palm oil Palm kernel oil a b
PLS-DA classification
Total correctly classified (%)
Milk fat
Cow fat
Pig fat
Poultry fat
Coconut oil
Palm oil
Palm kernel oil
22 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
0 7 0 0 0 0 0
0 0 8 0 0 0 0
0 1 0 8 0 0 0
0 0 0 0 8 0 0
0 0 0 0 0 8 0
0 0 0 0 0 0 8
(0%) (87.5%) (0%) (0%) (0%) (0%) (0%)
(0%) (0%) (100%) (0%) (0%) (0%) (0%)
(0%) (12.5%) (0%) (100%) (0%) (0%) (0%)
(0%) (0%) (0%) (0%) (100%) (0%) (0%)
(0%) (0%) (0%) (0%) (0%) (100%) (0%)
(0%) (0%) (0%) (0%) (0%) (0%) (100%)
100 87.5 100 100 100 100 100
69 out of 70 Samples were correctly classified (98.6%). Variables standardized by means of auto-scaling, no standardization on units applied.
the plot): 22 times out of 100 this sample is incorrectly classified as milk fat. However, in most cases the confidence in the incorrect classification is not very high (Ci closer to 0.5 than to 0). Four of the pig fat samples are occasionally misclassified as cow fats1 (columns 55–62). Four more cow fat samples (columns 31–35) are sometimes incorrectly classified as pig fat. However, for these samples, the probabilities for the correct and incorrect class are almost equal, resulting in Ci -values of near 0.5. Therefore, these misclassifications are hardly visible in the box plot. Finally, one coconut fat sample (column 30) is occasionally misclassified with higher confidence, either as cow or pig fat. More detailed results are now given for one particular cross validation run. For this run, the numbers of samples predicted into the various product groups are listed in Table 2. A scores plot of the first three dimensions of the model is shown in Fig. 2. In this 1 The box plot shows whether a sample is misclassified or not, it does not show into which incorrect class a misclassified sample is placed. This information is available, but due to space considerations, it is impossible to present it in a table. It was therefore decided to mention it in the text, where appropriate.
run, only one cow fat sample was misclassified as poultry fat. But the posterior probability for this classification is not very high (0.13). As the second largest posterior probability is 0.05, this incorrect classification would result in a Ci-value of 0.05/0.18 = 0.28. The scores plot (Fig. 2) shows that the misclassified cow fat is positioned more or less halfway between the cow fat and the poultry fat samples. A similar approach was adopted for the relative FAME data. With 4 components extracted, on average 1% of the classifications was incorrect. 3.2. TAG The TAG compositions of the fats and oils were determined; they were characterized by 17 triglycerides (Table 3). The animal fats (CV = 21–40%) showed a more varied TAG composition than the vegetable oils (CV = 3–10%; CV considering variance between sample means only). TAG composition of the cow fat was generally within the ranges specified by Precht (1992), who developed a method for detection of foreign fat in milk fat. The compositions
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Fig. 2. Scores plot of the first three dimensions of PLS-DA on the absolute fatty acid composition data of seven oils and fats.
Table 3 Absolute triacylglycerol composition of animal and vegetable fats/oils [g triacylglycerol/100g fat].
b
Milk fat Cow fatc Pig fatc Poultry fatc Coconut oilc Palm oilc Palm kernel oilc a b c
Chol.
C26
C28
C30
C32
C34
C36
C38
C40
C42
C44
C46
C48
C50
C52
C54
C56
CVa (%)
0.4 0.1 0.1 0.1 0.2 0.0 0.1
0.3 0.0 0.0 0.0 0.7 0.0 0.9
0.6 0.0 0.0 0.0 1.0 0.0 0.7
1.3 0.0 0.0 0.0 3.0 0.0 1.3
2.7 0.0 0.0 0.0 11.6 0.1 6.2
6.3 0.0 0.0 0.0 15.2 1.1 8.0
11.0 0.2 0.1 0.2 17.9 4.2 20.2
12.7 0.2 0.0 0.2 16.2 2.4 15.5
9.8 0.0 0.0 0.5 10.4 0.1 9.2
6.9 0.2 0.0 1.2 7.7 0.0 8.9
6.5 1.0 0.2 2.3 4.6 0.1 6.7
7.3 3.7 0.6 5.8 2.7 0.6 5.4
9.0 11.8 2.9 9.9 2.5 7.3 6.5
10.8 23.3 15.6 20.5 2.1 38.0 2.9
9.6 38.1 62.6 36.1 1.8 39.6 3.0
4.6 24.7 20.8 26.2 1.0 12.0 3.3
0.3 0.8 1.6 1.2 0.0 0.6 0.1
21 28 25 40 9 10 3
Coefficient of variance (variance between sample means, not between replicates). Mean value of 14 fractionated and 8 unfractionated samples. Mean value of eight samples
of coconut oil and pig fat in the present study showed generally higher concentrations of the longer chain TAGs than those reported by Precht (1992). For the TAG data too, auto-scaling of the variables and no scaling of the units appeared to give the best results. The optimal number of components was larger here than it was for the FAME data before. The cross-validation procedure was repeated 100 times for a solution with 6 components, giving a confidence box plot (Fig. 3). It appeared that all cow fat samples (columns 31–38) are occasionally classified as poultry fat samples, and all poultry fat samples (columns 63–70) sometimes as cow fat samples (see Footnote 1). Apparently, separating these two types of fat by their triglyceride compositions is a challenging task. A similar approach was adopted for the relative TAG data. Here, with 6 components, on average 10 % of the samples was incorrectly classified. Without exception these were poultry and cow fats being confounded. 3.3. Combined FAME and TAG analysis The FAME and TAG datasets were combined for further improvement of the identity prediction of the fats and oils. Optimal results regarding identity prediction were obtained with a six com-
ponent PLS-DA model with auto-scaling of the variables and no scaling of the units. The confidence plot of this combined six component model is presented in Fig. 4. The box plot shows that all confidence levels are between 0.5 and 1.0 which indicates that the combined model classified 100% of the samples correctly. Combination of the two datasets resulted further in higher confidence in the predictions in general than for the single data sets: most values are close to 1. A similar approach was adopted for the combined relative data. They resulted on average in a correct classification of 99 % of the samples. 3.4. VOC Proton transfer reaction-mass spectrometry (PTR-MS) is a promising technique for analysis of volatile compounds and has been used to investigate different issues in food science. For instance the effect of technological conditions on the VOC profiles of orange juices has been studied (Biasioli et al., 2003). Pollien and co-workers used PTR-MS for on-line monitoring of acrylamide (Pollien, Lindinger, Yeretzian, & Blank, 2003). Correlation with sensory data (Biasioli et al., 2006) and real-time in-nose analysis (Fransnelli, van Ruth, Kriukova, & Hummel, 2005) are other
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Fig. 3. Results for 100 replications of the random cross validation of the predicted classification of fat and oil samples by their absolute triacylglycerol compositions into identity classes (e.g. cow fat, palm oil, etc.) using PLS-DA models. For each of the samples, a box shows the location of the .25 and .75 quartiles of the quantity C (see text), the dotted lines (whiskers) are the whiskers; they have length 1.5 * the inter-quartile range (or shorter, if there are no more observations), and the crosses are outliers, lying outside the whiskers. The data have been sorted so that the 22 milk fat samples are displayed first (column 1–22), followed by 8 coconut oil (column 23–30), 8 cow fat (columns 31–38), 8 palm kernel oil (column 39–46), 8 palm oil (columns 47–54), 8 pig fat (columns 55–62), and 8 poultry fat samples (columns 63–70).
Fig. 4. Results for 100 replications of the random cross validation of the predicted classification of fat and oil samples by their combined absolute fatty acid and triacylglycerol compositions into identity classes (e.g. cow fat, palm oil, etc.) using PLS-DA models. For each of the samples, a box shows the location of the .25 and .75 quartiles of the quantity C (see text), the dotted lines (whiskers) are the whiskers; they have length 1.5 * the inter-quartile range (or shorter, if there are no more observations), and the crosses are outliers, lying outside the whiskers. The data have been sorted so that the 22 milk fat samples are displayed first (column 1–22), followed by 8 coconut oil (column 23–30), 8 cow fat (columns 31–38), 8 palm kernel oil (column 39–46), 8 palm oil (columns 47–54), 8 pig fat (columns 55–62), and 8 poultry fat samples (columns 63–70).
important applications. Proton transfer reactions are used to induce chemical ionisation of the vapours to be analysed. The sample gas is continuously introduced into a drift tube, where it is mixed with H3O+ ions formed in a hollow cathode ion source. Volatile compounds that have proton affinities higher than water
(>166.58 kcal/mol) are ionised by proton transfer from H3O+, mass analysed in a quadrupole mass spectrometer and eventually detected as ion counts/s (cps) by a secondary electron multiplier. The outcome is a mass resolved fingerprint of the total volatile profile of a sample. PTR-MS is interesting for this fingerprinting
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units are presented in Table 4. The milk fats, coconut oils and palm oils were correctly classified except for one coconut oil. Palm kernel oils showed poor classification results, with only 50% of the samples being correctly classified. Misclassified palm kernel samples were placed into all product groups, therefore, it seems that the palm kernel oils were not characterized by specific VOCs, but showed great overlap with the VOCs of the other product groups. Overall, 89% of the samples were classified correctly using PTR-MS data. Therefore, classification was reasonably successful, but rates were not as high as for the FAME and TAG analysis. However, PTR-MS analysis has the advantage of being very rapid compared with the other analysis techniques, which would make it a potential technique for screening of particular fats and oils.
Unfractionated milk fat 100000
Concentration [ppbv]
10000
1000
100
10
1 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Mass [m/z] Fig. 5. Mean fingerprint mass spectrum of the volatile organic compounds in the headspace of regular milk fat obtained by proton transfer reaction mass spectrometry.
Table 4 Number and (percentages) of predicted classification of oils and fats into product classes by their volatile organic compound compositions using a three component PLS-DA model. The correctly classified samples in bold a,b. Sample
Milk fat Coconut oil Palm oil Palm kernel Oil
PLS-DA classification
Total correctly classified (%)
Milk fat
Coconut oil
Palm oil
Palm kernel oil
22 (100%) 0 (0%) 0 (0%) 1 (12.5%)
0 7 0 2
0 0 8 1
0 1 0 4
(0%) (87.5%) (0%) (25%)
(0%) (0%) (100%) (12.5%)
(0%) (12.5%) (0%) (50%)
100 87.5 100 50
a
41 out of 46 Samples were correctly classified (89.1%). Variables standardized by means of auto-scaling, no standardization on units applied. b
approach as (i) it requires no pre-treatment of the sample, (ii) it allows rapid measurements (typically <1 min for a complete mass spectrum), and (iii) the technique is extremely sensitive (ppt level). The PTR-MS data of the VOCs covered a range of 129 masses (data not shown). An example of the mean fingerprint mass spectrum of the regular milk fats is presented in Fig. 5. Results for one cross-validation run while applying a three component PLS-DA model with auto-scaling of the variables and no scaling of the
Table 5 Overview of correct classifications of fats and oils by their fatty acid, triacylglycerol or volatile organic compound composition using PLS-DA. Techniques
Number of components in PLS-DA
Correct classifications (%)
Fatty acid composition, absolute data Fatty acid composition, absolute date Fatty acid composition, relative data Triacylglycerol composition, absolute data Triacylglycerol composition, relative data Combined Fatty acid and triacylglycerol composition, absolute data Combined fatty acid and triacylglycerol composition, relative data Volatile organic compound composition measured by proton transfer reaction mass spectrometry
4 6 4 6 6 5
99 97 99 96 90 99
6
100
3
89
4. Conclusions For identity prediction all techniques used were reasonably successful in classifying the various animal fats and vegetable oils (89–100%, Table 5). The identity of the samples was most successfully predicted using the combined absolute FAME and TAG data set (i.e. for the present dataset 100%). The combined FAME-TAG dataset has the additional advantage that a more robust prediction is obtained as the confidence in the predictions is generally higher than for the other datasets. Alternatively, if a certain prediction rate (e.g. 95%) is required, fewer PLS components are sufficient to obtain this success rate. PTR-MS analysis which results in a volatile fingerprint was least successful but still resulted in 89% correct classifications with the additional advantage of the speed of the technique. The technique gave promising results, but further studies are required to confirm its use as a good alternative for FA and TAG analyses. References Araghipour, N., Colineau, J., Koot, A., Akkermans, W., Moreno Rojas, J. M., Beauchamp, J., et al. (2007). Geographical origin classification of olive oils by PTR-MS. Food Chemistry, 108, 374–383. Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17, 166–173. Biasioli, F., Gasperi, F., Aprea, E., Colato, L., Boscaini, E., & Märk, T. D. (2003). Fingerprinting mass spectrometry by PTR-MS: Heat treatment vs. pressure treatment of red orange juice, a case study. International Journal of Mass Spectrometry, 223–224, 343–353. Biasioli, F., Gasperi, F., Aprea, E., Endrezzi, I., Framondino, V., Marini, F., et al. (2006). Correlation of PTR-MS spectral fingerprints with sensory characterisation of flavour and odour profile of ‘Trentingrana’ cheese. Food Quality and Preference, 17, 63–75. Buchgraber, M., Ulberth, F., Emons, H., & Anklam, E. (2004). Triacylglycerol profiling by using chromatographic techniques. European Journal of Lipid Science and Technology, 106, 621–648. Cosio, M. S., Ballabio, D., Benedetti, S., & Gigliotti, C. (2006). Geographical origin and authentication of extra virgin olive oils by an electronic nose in combination with artificial neural networks. Analytica Chimica Acta, 567, 202–210. Cserháti, T., Forgács, E., Deyl, Z., & Miksik, I. (2005). Chromatography in authenticity and trace ability tests of vegetable oils and dairy products: A review. Biomedical Chromatography, 19, 183–190. Fransnelli, J., van Ruth, S. M., Kriukova, I., & Hummel, T. (2005). Intranasal concentrations of orally administered flavours. Chemical Senses, 30, 572–582. Hai, Z., & Wang, J. (2006). Detection of adulteration in camellia seed oil and sesame oil using an electronic nose. European Journal of Lipid Science and Technology, 108, 116–224. Hansel, A., Jordan, A., Holzinger, R., Prazeller, P., Vogel, W., & Lindinger, W. (1995). Proton transfer reaction mass spectrometry: On-line trace gas analysis at the ppb level. International Journal of Mass Spectrometry and Ion Processes, 149(150), 609–619. Högnadóttir, A., & Rouseff, R. L. (2003). Identification of aroma active compounds in orange essential oil using gas chromatography-olfactometry and gas chromatography-mass spectrometry. Journal of Chromatography A, 998, 201–211. Kamm, W., Dionisi, F., Hischenhuber, C., & Engel, K.-H. (2001). Authenticity assessment of fats and oils. Food Reviews International, 17(3), 249–290.
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