Food Chemistry 188 (2015) 1–7

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

Analytical Methods

Combined chemometric analysis of 1H NMR, 13C NMR and stable isotope data to differentiate organic and conventional milk Sarah Erich a,⇑, Sandra Schill a, Eva Annweiler a, Hans-Ulrich Waiblinger a, Thomas Kuballa b, Dirk W. Lachenmeier b, Yulia B. Monakhova b,c,d,e a

Chemisches und Veterinäruntersuchungsamt (CVUA) Freiburg, Bissierstraße 5, 79114 Freiburg, Germany Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weissenburger Straße 3, 76187 Karlsruhe, Germany Bruker Biospin GmbH, Silberstreifen 4, 76287 Rheinstetten, Germany d Spectral Service AG, Emil-Hoffmann-Straße 33, 50996 Cologne, Germany e Institute of Chemistry, Saratov State University, Astrakhanskaya Street 83, 410012 Saratov, Russia b c

a r t i c l e

i n f o

Article history: Received 11 February 2015 Received in revised form 17 April 2015 Accepted 25 April 2015 Available online 27 April 2015 Chemical compounds: Linolenic acid (PubChem CID: 5280934) Keywords: NMR spectroscopy Organic milk Stable isotope-ratio mass spectrometry (IRMS) Chemometrics Data fusion

a b s t r a c t The increased sales of organically produced food create a strong need for analytical methods, which could authenticate organic and conventional products. Combined chemometric analysis of 1H NMR-, 13C NMR-spectroscopy data, stable-isotope data (IRMS) and a-linolenic acid content (gas chromatography) was used to differentiate organic and conventional milk. In total 85 raw, pasteurized and ultra-heat treated (UHT) milk samples (52 organic and 33 conventional) were collected between August 2013 and May 2014. The carbon isotope ratios of milk protein and milk fat as well as the a-linolenic acid content of these samples were determined. Additionally, the milk fat was analyzed by 1H and 13C NMR spectroscopy. The chemometric analysis of combined data (IRMS, GC, NMR) resulted in more precise authentication of German raw and retail milk with a considerably increased classification rate of 95% compared to 81% for NMR and 90% for IRMS using linear discriminate analysis. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction The demand for organic milk and organic dairy products has been considerably increased over the past decade. In 2013 in Germany the sales quantity of organic milk has increased by 11.3% compared to the previous year (BÖWL, 2014). Consumers expect a higher nutritional value, reduced waste of natural resources or improved animal welfare from organically produced food (BMELV, 2013). Given that consumers are willing to pay higher prices for organic milk and dairy products (BMELV, 2013), the risk of malpractice exists. To prevent and detect fraud, there is a strong need for robust analytical approaches for authentication of organic milk. Production, labeling and certification of organic foods are defined in the Council Regulation (EC) No. 834/2007. Amongst

⇑ Corresponding author. E-mail addresses: [email protected] (S. Erich), [email protected]. de (S. Schill), [email protected] (E. Annweiler), Hans-Ulrich.Waiblinger@ cvuafr.bwl.de (H.-U. Waiblinger), [email protected] (T. Kuballa), Dirk. [email protected] (D.W. Lachenmeier), [email protected] (Y.B. Monakhova). http://dx.doi.org/10.1016/j.foodchem.2015.04.118 0308-8146/Ó 2015 Elsevier Ltd. All rights reserved.

others, detailed rules for feeding and breeding of dairy cattle, cultivation and production of animal food or control are given in the Commission Regulation (EC) No. 889/2008. The guidelines of German organic producer associations such as ‘‘Bioland’’ or ‘‘Demeter’’ are partially more rigorous than the European regulations (Bioland e.V., 2014; Demeter e.V, 2013a, 2013b). Stable isotope-ratio mass spectrometry (IRMS) provides an analytical method to examine the authenticity of organic milk and dairy products. Especially the stable isotope ratio of carbon in milk fat and in the milk protein fraction allows drawing conclusions on the origin and authenticity of milk and dairy products (Bontempo, Lombardi, Paoletti, Ziller, & Camin, 2012; Camin et al., 2012). Furthermore, Molkentin and Giesemann (2007) reported that additional analysis of the a-linolenic acid (C18:3x3) content resulted in significant discrimination between organically and conventionally produced milk. A seasonal variation of both parameters (a-linolenic acid content – and the d13C value of the milk fat) was reported (Molkentin, 2009). The differentiation between organic and conventional milk using the d13C value is based on the fact that the amount of maize in feed is lower in organic farming than in conventional farming. On the other hand, the amount of fresh grass

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and pasture derived feed is increased in organic farming compared to conventional farming. Maize is a C4 plant with d13C values, ranging between 16‰ and 10‰. Grass belongs to the C3 plants, providing d13C-values varying between 32‰ and 24‰. C3- and C4plants are subject to different isotope effects during photosynthetic fixation of carbon dioxide (Metges, Kempe, & Schmidt, 1990). Furthermore, the content of a-linolenic acid in milk fat depends on the feeding of the dairy cattle. It is increased in grass and pasture derived feed, and consequently in organic milk (Ellis et al., 2006; Molkentin & Giesemann, 2007). On the German market so called ‘‘pasture milk’’ is also available. This milk is not produced organically, but the cows are predominately kept on pasture, respectively are fed with pasture derived feed. The term ‘‘pasture milk’’ is not officially defined or protected in the German or European food law, but is basically an advertising term. The dairy industry defined some self-regulation for ‘‘pasture milk’’ were they encourages its farmers to meadow graze their herds for defined hours per day and days per year. The a-linolenic acid content and d13C values of these milk samples are very similar to those of organic milk. Therefore, an additional analytical approach is required for differing between organic and conventionally produced ‘‘pasture milk’’ samples. During the last years NMR spectroscopy has successfully been used for the analysis of various foods as well as their fat extracts and it was shown that the chemical composition and authenticity of different products (e.g., cheese, pine nuts, oils, milk) can be characterized with sufficient accuracy (Brescia et al., 2004; Hu, Furihata, Ito-Ishida, Kaminogawa, & Tanokura, 2004; Köbler et al., 2011; Monakhova, Godelmann, Andlauer, Kuballa, & Lachenmeier, 2013; Monakhova, Kuballa, Leitz, Andlauer, & Lachenmeier, 2012). In many of such studies, the fat fraction of a product is extracted and analyzed by NMR (Köbler et al., 2011; Monakhova et al., 2013). Belloque and Ramos described the application of NMR spectroscopy to milk and dairy products as a useful tool for the authentication of milk (Belloque & Ramos, 1999). In the work of Renou et al. the geographic origin of cow milk was investigated using NMR to determine the proportions of polyunsaturated, monounsaturated and saturated fatty acids in milk fat. Whereas IRMS-analyses were applied to investigate the 18O enrichment of milk water (Renou et al., 2004). The advantages of NMR spectroscopy comprehend detailed spectral information about triglycerides, saturated and unsaturated fatty acids, structure elucidation capabilities, and therefore provides opportunities to trace the geographical and botanical origin of products using chemometric algorithms. For this reason the two complementary methods NMR and IRMS were applied to differentiate between organic, conventional and ‘‘pasture milk’’ samples. The aim of the present study was to establish an analytical approach for authentication of organic raw and retail milk within the context of official food control. We hypothesized that the combination of IRMS, GC and NMR analysis by data fusion algorithms may achieve a more precise authentication of German raw and retail milk than the single methods. 2. Materials and methods 2.1. Samples and chemicals 2.1.1. Samples In total, 85 milk samples (52 organic and 33 conventional, from which 6 were pasture milk) were collected and analyzed between August 2013 and May 2014. Raw milk samples were collected from organic and conventional farms and a dairy in the south of Germany. Additionally, retail milk was collected from Germany. An Austrian and a Swiss milk sample were included. The investigated set of products comprised various milk categories: raw milk, ultra-heat treated (UHT) milk, whole milk, low-fat milk, one goat

milk and one sheep milk. Such variability allowed us to build generalized multivariate models to assess organic milk authenticity. Details of the source and origin of the investigated milk samples are given in the Supplementary Material (S1). 2.1.2. Chemicals Cyclohexane, petroleum ether and diethyl ether were redistilled. All other solvents and reagents used were in pro analysis quality. 2.2. Fat extraction and milk protein isolation The fat fraction was extracted from 50 g milk by homogenization with 30 mL of 2-propanol and 40 mL of cyclohexane three times for 15 s using an Ultra-Turrax disperser, 1327 rcf (UltraTurrax T25, Janke&Kunkel, IKAÒ-Labortechnik, Staufen, Germany). After adding 10 g sea sand, the mixture was centrifuged for 15 min (2700 rcf). The organic phase was separated and dried over sodium sulfate. The solvent was removed by rotary evaporation, water bath temperature 45 °C (Vacuum pump system PC 511, Vacuubrand CVC 2, Wertheim, Germany; Büchi 461 Water Bath and Büchi RE11 Rotavapor, Flawil, Switzerland). Milk protein was prepared via the following procedure: 100 mL milk was pre-defatted by centrifugation for 15 min (2700 rcf) (Hettich, Rotixa/RP, 230V50 Hz, Tuttlingen, Germany). To precipitate the milk protein, 9 mL of 1 N HCl were added. After centrifugation the precipitate was rinsed with 30 mL demineralized water, subsequently with 80 mL acetone and after one more centrifugation step with 40 mL of a mixture of petroleum ether and diethyl ether (2:1, v:v). Each solution was homogenized using an UltraTurrax. The precipitate was dried at 60 °C for 24 h and powdered using a ball mill (Schwingmühle MM301, Retsch GmbH, Haan, Germany). 2.3. Stable isotope analysis 1.4 mg of fat or 0.5 mg of milk protein were weighed into tin capsules, combusted using an Elementar Analyzer (Euro EA 3000, Euro Vectors SpA, Milano, Italy) and analyzed with an Isotope Ratio Mass Spectrometer (IRMS) (Delta Plus XP, Thermo Finnigan, Bremen, Germany) equipped with a ConFlow IV Interface (ThermoFisher Scientific, Bremen, Germany), and an auto sampler (Zero Blank Revolver Autosampler, Blisotec GmbH, Jülich, Germany) controlled by Isodat 3.0 software (Thermo Finnigan, Bremen, Germany). Resulting gases, CO2 and N2, were separated by a GC column and isotope ratios were determined. The 13C/12C isotope ratio is given in ‰ on a d-scale. The value refers to the international reference standard VPDB (Vienna Pee Dee Belemnite).

d ½‰ ¼

Rsample  Rstandard  1000 Rstandard

The standard deviation for IRMS analysis was 60.2‰. Acetanilide, casein and glutamic acid were calibrated as working standards using the international standards (IAEA-CH6, IAEACH7, NBS 22). Samples were analyzed twice and working standards were measured four times to control the stability of the series of measurement. 2.4. Analyzis of a-linolenic acid After transesterification of the extracted milk fat with potassium methoxide, the fatty acid methyl esters (FAME) were measured by gas chromatography and the a-linoleic acid content was calculated as weight percentage of milk fat (g/100 g milk fat). For transesterification, a mixture of 100 mg extracted milk fat

S. Erich et al. / Food Chemistry 188 (2015) 1–7

dissolved in 1 mL n-heptane and 0.5 mL 2 M potassium methylate in methanol was shaken 30 s. After 10 min 0.8 mL 1 N HCl was added using methyl orange as indicator. 0.5 mL of the organic phase was then diluted with 4.5 mL of n-heptane, dried with 1 g sodium sulfate and agitated strongly. 1 lL of the supernatant was injected directly into the GC system (GC FID System, HP 6890 Series, Hewlett Packard, Littleton, USA) using an auto sampler (7683 Series Auto Sampler, Agilent, Santa Clara, USA) The injector temperature was 250 °C (7683 Series Injector, Agilent, Santa Clara, USA). The FAMEs were separated on a 100 m fused silica capillary column (i.d. 0.25 mm), coated with a 0.20 lm film of poly(biscyanopropyl siloxane) (Supelco SP-2560, Sigma Aldrich, St. Louis, USA). An FID was used for detection (260 °C) with hydrogen and synthetic air as combustion gases. Hydrogen was also used as carrier gas at a flow rate of 0.5 mL/min at 70 °C. The oven temperature was started at 70 °C and increased to 170 °C (20 °C/min) with a holding time of 85 min. A second ramp with 2.5 °C/min to 220 °C and a holding time of 30 min was used. 2.5. NMR measurements at 400 MHz NMR measurements were performed under full automation on an AVANCE III 400 MHz spectrometer (Bruker BioSpin GmbH, Rheinstetten, Germany), equipped with a 5 mm SEI probe-head with z-gradient, automated tuning and matching accessory. All acquisition parameters of NMR experiments can be found in previous publications (Köbler et al., 2011; Monakhova et al., 2013). For sample preparation, 200 mg of the milk fat fraction (see Section 2.2) were mixed with 1.0 mL of CDCl3 containing 0.1% tetramethylsilane (TMS). 600 lL of the extract was transferred into an NMR tube for direct measurement. The samples were measured directly after extraction to avoid the increase of peroxide resonances at d 5.80 ppm and at d 6.30 ppm (1H NMR) and at d 130 ppm (13C NMR), which can negatively influence our target multivariate model. All NMR spectra were phased and baseline-corrected. Chemical shifts were referenced to the TMS signal. 2.6. Data preprocessing for chemometrics 1 H NMR spectra were preprocessed by bucketing using AMIX v.3.9.12 (Bruker BioSpin GmbH, Rheinstetten, Germany). Spectral intensities were scaled to total intensity (namely, when each spectrum is set to have unit total intensity by expressing each data point as a fraction of the total spectral integral) and reduced to integrating regions of equal width (0.01 ppm) within the spectral region of d 8.0–0.2 ppm. The signal of chloroform in the region between 7.4 ppm and 7.2 ppm was excluded from the analysis. The final pre-treated data were converted to ASCII files and transferred for multivariate analysis. The full 13C NMR spectra (180–1 ppm, excluding 120–75 ppm region of chloroform signal) were exported using MestReNova software (Mestrelab Reserch, Santiago de Compostela, Spain) and were aligned using interval correlation optimized shifting algorithm (icoshift) in MATLAB 2013b (The Math Works, Natick, MA, USA) (Savorani, Tomasi, & Engelsen, 2010). This algorithm splits a spectral database into ‘‘inter’’ intervals and coshifts each vector left– right to get the maximum correlation toward an average spectrum (Savorani et al., 2010). For combined analysis of data from different analytical techniques the predefined NMR regions: 0.95–0.85 ppm, 2.10– 1.90 ppm and 2.90–2.70 ppm (all 1H NMR) and 174.5–172.5 ppm (13C NMR) were fused with the discrete data of d13C isotope profilings of fat and milk protein and a-linolenic acid contents. In this case all data points in 13C NMR and 1H NMR spectra processed by icoshift algorithm were subjected to multivariate analysis.

3

2.7. Multivariate analysis All calculations were done in MATLAB 2013b (The Math Works, Natick, MA, USA) using SAISIR package for MATLAB (Cordella & Bertrand, 2014). PCA was first applied separately to 1H NMR, 13C NMR and stable isotope data combined with a-linolenic acid content for visualization and as a tool to observe possible differentiation between organic and conventional milk samples. The technique of cross-validation was applied to determine the optimal number of principal components (PCs) required obtaining robust models. Two preprocessing methods (scaling to unit variance and Pareto scaling) as well as no scaling were tested for each data set in order to eliminate the magnitude effect of intensity variations. The following classification methods – linear discriminant analysis (LDA), factorial discriminant analysis (FDA), and partial least squares – discriminant analysis (PLS-DA) – were applied to each of three data sets separately as well as for the combined data. In this study, LDA and FDA were applied to the PCA scores. Furthermore, common component and specific weight analysis (ComDim) has also been utilized for analysis (Qannari, Wakeling, Courcoux, & MacFie, 2000; Qannari, Wakeling, & MacFie, 1995). A number of different methods (mean-centering, weighting, auto-scaling, inverse of the sum of squares, root square scaling, log scaling and second derivatives) were evaluated for block scaling to prevent one block of the data being totally dominant. This is especially important in the case, where NMR data should be fused with completely unrelated discrete data with only three variables (stable isotopes and a-linolenic acid content). The technique of cross-validation was applied to determine the optimal number of latent variables required to obtain robust classification models. During test set validation, cross-validation was applied once again on this reduced training set to check if the optimal number of latent variables is the same. All chemometric models were validated using leave-one-out cross validation as well as test set validation (approximately onefourth of the complete data set: 13 organic milk and 8 conventional milk). To provide a more reliable test set validation, we repeated the training/test splitting ten times and average values are provided throughout.

3. Results and discussion Before chemometric analysis of combined data, the data sets from different analytical techniques should be analyzed using classical and multivariate data analysis to estimate the performance of each method and usefulness of combined data analysis.

3.1. Analysis of stable isotope data combined with the a-linolenic acid content An overview of the stable isotope values and the a-linolenic acid contents of the 85 investigated milk samples is presented in Table 1. Evidently, the values of the two targeted product categories (organic and conventional) are quite different. The mean value of d13C of milk fat (organic) was 30.0 ± 1.07‰ (Min: 28‰, Max: 32.4‰) and 26.0 ± 2.53‰ (conventional; Min: 22.6‰, Max: 28.4‰). The mean value of d13C of milk protein (organic) was 26.2 ± 0.83‰ (Min: 23.8‰, Max: 28‰) and 23.2 ± 1.89‰ (Min: 20.1‰, Max: 26.0‰) for organic and conventional milk, respectively. The average value of the a-linolenic acid content was determined to 1.09 ± 0.21% (Min: 0.7% Max: 1.7%) and 0.68 ± 0.16% (Min: 0.45% Max: 0.85%), respectively (Table 1).

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Table 1 An overview of stable isotope data and a-linolenic acid content. Type of milk

d13CV-PDB fat [‰] mean ± standard deviation

d13CV-PDB milk protein [‰] mean ± STD

a-Linolenic acid in milk fat [%]

Organic (n = 52) Conventional (n = 33) Conventional; without pasture milk (n = 27)

30.0 ± 1.07 26.0 ± 2.53 25.1 ± 1.77

26.2 ± 0.83 23.2 ± 1.89 22.6 ± 1.52

1.09 ± 0.21 0.68 ± 0.16 0.63 ± 0.10

mean ± STD

Several classification methods (LDA, FDA, and PLS-DA) were evaluated for predicting class membership with respect to the milk type from stable isotope data and a-linolenic acid content (Table 2). We obtained 89% and 86–90% correct classification rates for calibration and validation data sets, respectively (both sets included ‘‘pasture milk’’ samples). The evaluation of this first data set verified that stable isotope ratios combined with the a-linolenic acid content can be used as an adequate tool to differentiate organic and conventional milk with high probability. Only ‘‘pasture milk’’, a sub-group of the conventional milk samples, which shows strongly deviating values, represents an exception from the model. 3.2. NMR spectroscopy

Fig. 1. Scatter plot of the PCA scores of stable isotope data (d13C of fat and milk protein) and a-linolenic acid content of milk samples: O – organic milk, C – conventional milk (ellipsoids show 95% confidence interval, mean-centering).

However, d13C values and a-linolenic acid content of conventionally produced ‘‘pasture milk’’ samples were very similar to those of organic milk. In particular, the difference between conventional and organic milk became more evident when these milk samples were excluded from the group of conventional milk samples. Excluding the ‘‘pasture milk’’ samples the two types of milk could be differentiated unambiguously. Moreover, a standard multivariate method – principle component analysis (PCA) – was applied to our data for visualization of the two groups. The scatter plot of the PCA scores of stable isotope data combined with the a-linolenic acid content is shown in Fig. 1. Organic milk samples build a cluster in the region of negative values of PC1, whereas conventional milk samples predominately have positive values along PC1 and negative values along PC3. As expected, the six ‘‘pasture milk’’ samples are found in the organic milk group (these samples are marked on Fig. 1). Table 2 Classification results of different data sets of milk samples (percent of correctly classified samples). Data set

LDA

FDA

Calibrationa Validationb

89 86

89 90

89 90

1

Calibrationa Validationb

98 76

100 81

88 86

13

C NMR

Calibrationa Validationb

100 76

100 81

89 76

Fused data

Calibrationa Validationb

100 91

100 95

89 76

H NMR

a

PLS-DA

d13C of fat, d13C of milk protein and a-linolenic acid content

Leave-out-one cross validation. Independent test set, average correct classification rate for 10 random training/ test splitting is reported. b

In this work NMR technique was used as a complementary method to differentiate organic and conventional milk samples using the fat extract. Due to easy sample preparation and the usage of a standard NMR experiment for fat extracts (Köbler et al., 2011; Monakhova et al., 2013), time effort was minimal. Average 1H and 13 C NMR spectra of organic (n = 52) and conventional (n = 33) milk are shown in Fig. 2. The spectra mainly consist of the signals of saturated and unsaturated fatty acids, triglycerides, peroxides and glycerol. The detailed assignments of 1H NMR and 13C NMR resonances present in milk spectra have been already described (Andreotti, Trivellone, Lamanna, Di Luccia, & Motta, 2000; Brescia et al., 2004; Hu et al., 2004; Hu, Furihata, Kato, & Tanokura, 2007). However, any specific resonances in the NMR spectra of milk fat extracts, which could provide a clear differentiation between organic and conventional milk samples were not detected (Fig. 2). Thus, chemometric methods are needed to interpret the NMR signals and to uncover hidden information in NMR spectra of samples such as the milk production method (conventional or organic). The PCA scores plot of PC3–PC4 visualizes the separation of the two milk groups based on 1H NMR full-spectra information (Fig. 3A). Conventional milk samples are predominately positioned in the region of positive PC3 values and negative PC4 values, whereas the center of organic milk cluster almost coincides with coordinate origin. The same clusters were observed based on the analysis of full 13C NMR spectral data (Fig. 3B). ‘‘Pasture milk’’ samples only were found in the conventional milk cluster irrespective of the applied NMR technique. To show the predictive power of the chemometric methods by classifying NMR spectra of milk samples several data analysis methods (LDA, FDA, and PLS-DA) of the 1H and 13C NMR spectra were evaluated (Table 2). Promising but probably over-optimistic correct classification rates (up to 100%) were obtained for calibration data sets by all methods. Therefore, independent test set validation had to be performed. In this case, training/test set splitting was performed ten times. The correct classification rates for validation data sets were significant and varied between 76% and 86% on average. None of the multivariate methods used outperformed others (Table 2).

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organic milk (Table 2, Figs. 1 and 3). It is obvious, however, that NMR data sets also provide valuable information for the differentiation between organic and conventional milk samples. Loadings plots combined with the manual comparison of NMR spectra revealed some differences in intensities between the two investigated milk categories within the following spectral regions: 0.95–0.85 ppm, 2.10–1.90 ppm and 2.90–2.70 ppm (all 1H NMR) and 174.5–172.5 ppm (13C NMR). These signals belong to the CH3-x1 group, the CH2-D2 group, the bis allyl methylene groups of a-linolenic acid, the carboxyl group of butyric acid and the long-chain fatty acids, respectively (Andreotti et al., 2000; Brescia et al., 2004; Hu et al., 2004, 2007). In all cases, NMR spectra of organic milk are characterized by more intensive signals in the above mentioned regions.

c Organic Conventional

A

b

a

6

5

4

3

2

3.3. Chemometric analysis of combined data

1

ppm

B

Furthermore, the complementary information of both NMR data sets, stable isotope profiling and the a-linolenic acid content were combined. Before performing data fusion, a reasonable set of variables of the NMR data set was selected. In this regard we decided to use the predefined NMR regions, for which differences in intensities between the two groups were observed by manual spectra comparison as well as by multivariate data analysis (see Section 2.7). To avoid a decrease in resolution, all data points within these regions processed by the icoshift algorithm (to eliminate chemical shift variation) were subjected to chemometric analysis. The approach to reduce the number of variables of NMR keeping in mind the small number of discrete isotope variables was previously found to be effective regarding wine analysis (Monakhova et al., 2014). The percentages of correctly classified samples analysing the combined data by LDA, FDA and PLS-DA are summarized in Table 2. The best classification models were observed using the inverse of the sum of squares of the block scaling factor (i.e., after applying the block scaling factor, the total variance of each block equals 1). A definite improvement was obtained when combined data were analyzed by classification algorithms. For example, for the validation data set analyzed by LDA, the correct classification rate was improved from 90% for discrete data, 81% for both 1H and 13C NMR to 95% for the fused data (Table 2). Lower but significant improvement was observed for the PLS-DA method. On the other hand, the FDA model for discrete data was better than that obtained from the fused data (Table 2).

Organic Conventional

d

180

150

40

0

ppm Fig. 2. 1H (A) and 13C NMR (B) average spectra of organic (n = 52) and conventional (n = 33) milk. The following signals are marked: bis allyl methylene groups of linolenic acid linoleic acid (a), CH2-D2 (b), CH3-x1 (c), and carboxyl group of butyric acid and long-chain fatty acids (d). The symbol ‘‘x’’ indicates the position from the methyl group end; the symbol ‘‘D’’ indicates the position from the ester group end.

The finding was that the overall performance of NMR spectroscopy is inferior to stable isotope analysis combined with direct quantification of a-linolenic acid regarding the authentication of

a

b

2

1.5

15

10

1

5

C 2.6%

O

0

A3

3.7%

0.5

−5

O

A4

0

C

−0.5

−10 −1

−15

−1.5

−2 −3

−2.5

−2

−1.5

−1 A3

−0.5 0 5.3%

0.5

1

1.5

2

−20 −15

−10

−5

0 A1

5 4.4%

10

15

20

Fig. 3. Scatter plot of the PCA scores of 1H NMR (A) and 13C NMR (B) spectra of milk samples: O – organic milk, C – conventional milk (ellipsoids show 95% confidence interval, autoscaling).

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Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.foodchem.2015. 04.118. References

Fig. 4. Scatter plot of D1–D2 dimensions after application of ComDim multiblock method to the fused 1H NMR, 13C NMR, stable isotope data (d13C of fat and milk protein) and a-linolenic acid content (n = 85, ellipsoids showed 95% probability).

Furthermore, a specialized multiblock tool – common components and specific weights analysis (ComDim) – was applied demonstrating the efficiency of data fusion (Qannari et al., 1995, 2000). The method consists of determining a common space for several blocks (three in our case), with each block having a specific weighting (‘‘salience’’). The best separation was found in the D1 and D2 plane at the 95% probability level (Fig. 4). All ‘‘pasture milk’’ samples were located correctly in the conventional milk cluster. Several studies described data fusion techniques combining chemical data of different analytical methods (Dearing, Thompson, Rechsteiner, & Marquardt, 2011; Mas, Tauler, & de Juan, 2011; Pere-Trepat & Tauler, 2006; Smilde, van der Werf, Bijlsma, van der Werff-van der Vat, & Jellema, 2005; Vera et al., 2011; Xu, Correa, & Goodacre, 2013). Most of them applied fused chromatographic techniques with a complementary analytical tool (Forshed, Idborg, & Jacobsson, 2007; Mas et al., 2011; Pere-Trepat & Tauler, 2006; Vera et al., 2011). Several studies applied the combination of different vibrational methods (Andreotti et al., 2000; Brescia et al., 2004). To the best of our knowledge, there is only one application of fusion of 1H NMR and stable isotope data so far (Monakhova et al., 2014). In this study significant improvement in comparison with the discrete evaluation by individual methods was obtained for the prediction of the geographical origin and the year of vintage of wine (Monakhova et al., 2014). 4. Conclusion In this study the effective combination of the results from four separate analytical methods is shown: 1H NMR, 13C NMR, stable isotope data and the a-linolenic acid content were combined. The application of data fusion methods improved the authentication of organic milk samples, providing a more efficient differentiation than the models based on single methods. Acknowledgments The authors are grateful to Margit Böhm, Jürgen Geisser, Bernd Siebler, Andreas Probst, Astrid Diehl and Thomas Huber for excellent technical assistance. The research was funded by a grant of the Ministry of Rural Affairs and Consumer Protection BadenWürttemberg, Stuttgart, Germany. YBM received funding in the framework of the state contract 4.1708.2014K of Russian Ministry of Education.

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