Chemometric approach to characterization of the selected grape seed oils based on their fatty acids composition and FTIR spectroscopy

Fatty acid compounds of the selected grape seed oils

The fatty acid composition of the oils extracted from eight grape cultivars and 2 years of harvesting is shown in Table 1 linoleic (70.10–71.55%), oleic (15.33–17.28%), and palmitic (6.84–8.18%) acids were the predominant fatty acids in oils, consistent with previously reported data^8,20. The differences between the selected acids compared to varieties and vintages are given in %-age units.

Chemometric analysis of fatty acid compounds and physical parameters of selected grape seed oils

Correlation analysis

Analysis of correlations between unsaturation and the physical parameters was the first step in characterizing selected grape seed oils. Therefore, the fatty acids were grouped into saturated fatty acid (SFA), monosaturated fatty acid (MUFA), and polyunsaturated fatty acid (PUFA). The relationship between the number of unsaturation and the physical parameters was obtained by analyzing correlations between concentration of SFA, MUFA, PUFA, and the values of physical parameters: mass density and apparent viscosity. Pearson’s correlation coefficients were presented in Table 2. A high value of modulus of coefficients between two considered variables explains the direction of their relation.

The SFA concentration in analyzed oils has significant correlations with MUFA (|R| = 0.77) and PC2 (|R| = 0.55), but small values of correlations coefficients with other variables (|R| < 0.5). A high correlation relations were observed between MUFA and PUFA (|R| = 0.71), MUFA and µ (|R| = 0.5), MUFA and PC2 (|R| = 0.61). The difference between concentrations of SFA and MUFA in analyzed oils is lower than the difference between SFA and PUFA. These relations are shown in correlation analysis. PC1 and PC3 present lower values of correlation coefficients when compared to PC2.

PCA

In the first approach for characterization of grape seed oils the PCA was applied to analyze seven common fatty acids, three groups of fatty acids and two physical parameters to obtain a linear estimate of dimensionality. Based on the Kaiser criterion in PCA, three components having eigenvalue higher than 1 were determined. The first three main components explained 88.66% of the total variance, and two components explained above 70% of it. Therefore, to simplify the description, we consider only the first two components in the following. The first component, PC1, explained 51.06%, the second PC2—19.2%, and the last, PC3-18.4%. The highest values of PC1 are for Pinot Gris 2015, Donfelder 2015, Paláva 2015, and Zweigeltrebe 2017. For PC2 dominant values are oils: Riesling 2015, Hibernal 2017, and Sauvignon 2017. According to the loadings the highest contribution of the fatty acid of PC1 take C18:2n6c and PUFA, while the mass density and C16:0 were for PC2.

k-Means for two PCs

The second step involved the selection of initial values for the means in the mixture model. This was done by applying the k-Means method for normalized principal components, i.e., for reduced data set. As initial values the centers (or the means) of the clusters were taken. The sum of squared errors (SSE) suggests that the five clusters are an optimal choice. The clustering result is presented in Fig. 1.

GMM for clustering

Next, the values for the parameters in GMM based on the number of clusters obtained with k-Means clustering were calculated. Five Gaussian components using Bayesian Information Criterion (BIC) were chosen in order to estimate the optimal model for Gaussians (Fig. 2), resulting in ‘diag’ (covariance matrix is diagonal) optimal Gaussian model for five components. The values of parameters from the fit are presented in Table 3, and the split into clusters is presented in Fig. 3.

FT-IR spectroscopy analysis

The ATR-FTIR spectra for selected oil samples obtained from grape seeds of the selected cultivars harvested in the respective experimental years depending on the cultivar are presented in Fig. 4. The cultivars selected for the FTIR study included: Dornfelder 2015, Hibernal 2017, Neuburger 2017, Pálava 2015, Pinot Gris 2015, Riesling 2015, Sauvignon 2017, Tramin 2015, Zweigeltrebe 2015, and Zweigeltrebe 2017. For better convenience of analysis, discussion, and comparison of the respective samples studied, the spectra were normalized at the maximum of 1745/cm. The samples were placed on a ZnSe crystal and studied under N₂ atmosphere (see “Materials and methods” section for details). Table 4 presents all the characteristic bands present in the oil samples selected for the study (from the first and second measurement years) from the aforementioned selected cultivars, and a correlation of the functional group vibrations with the corresponding bands (with a detailed literature review).

It is worth noticing that all the infrared spectra (ATR-FTIR) of the selected oil samples, both in the first and the second year of the experiment, revealed highly intensive and distinct bands that could be correlated with specific functional groups vibrations originating from ingredients typically present in food products. A vast majority of edible plant fats, potential oily materials, are composed primarily of various fractions of triglycerides, differentiated mainly by the degree of unsaturation and the length of their respective hydrocarbon chains^21,22. In many publications, the authors were able to match the particular bands present in the spectra of both animal and plant oils^{21,22,23,24,25,26,27,28,29,30,31} to specific vibrations of molecules or groups thereof. However, the majority of the literature available pertains to FTIR analyses of specific plants (e.g., rape) and animal oils, while only a few such studies have been carried out on the types of samples discussed in this work. Furthermore, a precise assignment of bands to a specific functional group is often problematic. Table 4 presents a detailed analysis of characteristic band frequencies with the most important widening observed in the oil spectra, and the correlations with their respective functional groups (including a review of relevant literature data^{21,22,28,29,30,31}. Also, a subscript was used to account for the intensity of bands of the typical spectra in the infrared region. It is noteworthy that identifying stretching vibrations is significantly easier in this type of biological sample, especially when compared to deformation vibrations, which are often overlapped.

In the general characteristics of the selected oil samples spectra, vibrations of the methylene group located within the spectral range from 1350 to 1165/cm were observed^21,22. In the case of our samples, these bands represented the stretching vibrations originating from the –C–H group bound to the –CH3 group (usually approx. 1350–60/cm, in our samples approx. 1348/cm) as well as deformation vibrations of the same group (present at approx. 1160/cm, in our case—1157/cm). It is noteworthy that the stretching vibrations of the (C–O) ester bond composed of two combined asymmetric vibrations are, in this case, vibrations of the C–C(=O)–O and O–C–C groups³¹. In the former case, the intensity of vibration is significantly higher³⁰. The bands are present in the region from 1300 (as C–C(=O)–O, in our case approx. 1271/cm, as enhancement of the band with the maximum at approx. 1238/cm) and at approx. 1000/cm (in our case approx. 1027/cm for this group). In turn, the bands associated with saturated esters such as: C–C(=O)–O are found between 1240 and 1160/cm (in the case of the grapeseed oils samples selected for the study at approx. 1238/cm), while in the case of unsaturated, the vibrations usually emerge at lower frequencies²¹. At the same time, however, the O–C–O band often associated with primary alcohols is observed in the region from 1090 to 1020/cm (for the functional groups analyzed in our study, it was at approx. 1027/cm). In the case of secondary alcohols, the band usually emerges with the maximum at approx. 1100/cm (in our study approx. 1099/cm). Both types of esters described above are present in triglyceride molecules. However, authors often associate the band mentioned above (at approx. 1238/cm) exclusively with the out-of-plane bending vibrations of the methylene group³². The subsequent two bands presented in Table 4 (and in Fig. 4) have the maxima at approx. 1421 and 1315/cm, respectively (band widening, see Fig. 4, both for samples from the first and second measurement year). The first of said groups of vibrations (with the maximum at approx. 1421/cm) may originate from the vibrations of methyl groups in the aliphatic chains of the selected oil samples^21,32. The second group of bands (i.e., the band widening) with the maximum at approx. 1315/cm (in all analyzed samples) was observed simultaneously with weak bands with maxima at approx. 965 and 905/cm. The 905/cm band present in all oil samples is associated with the stretching vibrations of cis-substituted olefinic groups²¹ and may also be associated with vibrations of the vinyl group.

The selected samples of grapeseed oil obtained in the two experimental years produced largely similar infrared spectra, but it should be noted that depending on the cultivar, certain differences were nonetheless observed that seem to be relatively characteristic and easily identifiable. Firstly, the studies revealed noticeably significant differences in terms of the respective bands’ intensity (not represented as the band levels were equalized at the peak related to the vibrations of the carbonyl group C=O to facilitate easier interpretation of the results), which seems to be related to the differences between the respective cultivars.

Another very characteristic region of vibrations contained bands with the maximum at approx. 1745/cm characteristic of stretching vibrations of the C=O carbonyl group²¹ in esters. Apart from the band characteristic for vibrations of the carbonyl group, on the lower wavenumber side there was also an enhancement with the maximum at approx. 1709/cm (distinctly less intensive in samples from, e.g., the Pinot Gris 2015 cultivar), which also corresponded to vibrations of the carbonyl group but occurred in the acid groups of the oil samples selected for the study^21,23,30. The next band, with the maximum at 1652/cm corresponded to the stretching vibrations of the –C=C– group (from the cis-transformation)^21,28. A characteristic region also contains vibrations with the maximum at 1462/cm originating from the deformation vibrations of the –C–H groups in –CH2 and –CH3 (bending vibrations). One should also mention vibrations in the region from 900 to 650/cm which represent characteristic deformation vibrations associated with the –HC=CH– groups (cis-conformation, out of plane) as well as the rocking vibrations of said groups ((–(CH2)n– and –HC=CH– (cis–))^21,28.

As we proceed to vibrations in higher wavenumber regions, one should also mention the very significant stretching vibrations =C–H (trans-transformation) with the maximum at approx. 3066/cm (Table 4—very low intensity) originating from vibrations of the triglyceride fraction^21,33 (in Fig. 4 with very low intensity—primarily in the Zweigeltrebe 2015 cultivar). In turn, the stretching vibrations of =C–H in the cis-configuration were observed as very characteristic and intensive vibrations with the maximum at approx. 3011/cm (Fig. 4 and Table 4). The vibrations with the maximum at approx. 2934, 2863/cm originate from the stretching –C–H vibrations in the –CH3, CH2 groups belonging to triglyceride aliphatic groups^{21,22,23,24,25,26,27,28,29}.

It should also be noted that the spectra of the analyzed oil samples produced from the seeds of various grape cultivars (and from different years of the experiment) (Fig. 4) reveal noticeable differences in the shape of bands in the region from 1770 to 1660/cm. For most of the analyzed samples, one can clearly observe a slight band enhancement at 1745/cm (corresponding to the vibrations of the C=O, as already discussed above) on the lower wavenumber side, with a clear maximum at approx. 1709/cm³⁴, which can also be correlated with forming a hydrogen bond between the C=O⋯H–O– groups (more intensive in the first year for the Pinot Gris 2015 group). Simultaneously with the emergence of the band at 1709/cm, we can observe a distinct change in the intensity of bands at approx. 1150–1070, 721/cm²⁸, which can also be correlated to the stretching vibrations of C–O and C–C groups (described above). The bands, given the possibly decreasing affinity of the associated molecules with the formation of the C=O⋯H–O–H hydrogen bond, may suggest a slight increase in intensity thereof.

The spectral changes seem to correlate very well with the changes in the fatty acid profile presented in Table 1 and discussed in the first part of this section. Apart from the visible differences in the bands with the maxima at approx. 1710–1715, one should also emphasize the possibly most important observation, i.e., the emergence of a very clearly visible band with the maximum at approx. 840/cm (Fig. 4, Table 4) that may originate from the stretching vibrations on bonds existing between various acid fractions in the analyzed samples.

Chemometric analysis of FTIR spectra of selected grape seed oils

PCA

According to the previously adopted procedure, firstly, the PCA method was applied to approximate the dimensionality of spectra data in a linear manner. Based on the Kaiser criterion in PCA three components having eigenvalue higher than 1 were determined. The first three main components explained 98.46% of the total variance, and two components explained above 95.18% of it. Therefore, we proceed further with our analysis using the first two components. The first component, PC1, explained 81.1%, the second PC2—14.09%, and the third, PC3, 3.27%. The highest values of PC1 takes for Zweigeltrebe 2017 and Neuburger 2017. In the case of PC2 dominant values are oils: Riesling 2015, Hibernal 2017, and Sauvignon 2017, which is a similar result like in analysis from fatty acid profile. According to the loadings, the highest contribution of FTIR spectra of PC1 take the vibration of w(–HC=CH–, trans-) out-of-plane deformation from the range 700-1500/cm, while for PC2 the vibration of (–C=O_vst) in esters located in the region from 1600 to 2000/cm.

k-Means for two PCs

Next, based on the SSE criterion, five components were selected as the optimal choice for k-Means clustering for two normalized PCs reduced FTIR data. In this way we distinguish five clusters. The first one contains the oils Hibernal 2017, Riesling 2015, and Sauvignon 2017. The second one constitutes the next three types of oils, namely Dornfelder 2015, Pálava 2015 and Tramin 2015. The third contains Neuburger 2017 and Pinot Gris 2015 oils. The last two clusters are unit sets of oils Zweigeltrebe 2015 and Zweigeltrebe 2017, respectively. The clustering result is presented in Fig. 5.

GMM for clustering

The parameters were extracted in GMM based on the number of clusters obtained with k-Means clustering. Estimation of the optimal model for Gaussians by five Gaussian components using BIC is presented in Fig. 6. Following the BIC criterion, the optimal Gaussian model for five components is ‘full’ (full covariance matrix). The values of the parameters from the fit are presented in Table 5 and the split into clusters is presented in Fig. 7.