## 3 Results
### Statistical Approach Using FAMEs Profile  
As in the case of Raman spectra chemometric processing, the same statistical approach was applied in the case of FAME profiles. By applying LDA using the matrix given by the concentrations measured in each oil sample, for the simultaneous differentiation of the six varieties, 100% in initial and 95.7% in cross-validation procedure were obtained (Figure 4). This discrimination was based on the following predictors: elaidic acid, \(\gamma\)-linolenic acid, _cis_-10-heptadecenoic acid, stearic acid, linoleic acid, pentadecanoic acid, behenic acid, and palmitic acid. In the cross-validation procedure, one sea buckthorn sample was assigned to the walnut group. This result is in good agreement with the classification obtained using Raman spectra, where, among other samples, the same oil sample was also assigned to the walnut group. Among the organic compounds that were found as discriminant markers, the maximum mean values were measured in sunflower oils (linoleic and behenic acid), sea buckthorn (pentadecanoic and palmitic acid), and sesame (elaidic and stearic acids), while \(\gamma\)-linolenic acid presented maximum mean values in hemp samples, and _cis_-10-heptadecenoic acid had maximum mean values in linseed samples. As in the case of Raman spectra, for a better highlighting of each oil's features, a comparison between each type of oil and the rest of the samples was performed. In the case of sunflower oil, the percent for initial classification was 95.7%, while for cross-validation a percentage of 87% was reached. The fact that the value for initial classification was different from 100% might be explained by the fact that sunflower oil samples which were analyzed in this study were obtained through different procedures (cold pressed and refined), which might have conducted to different FAMEs profile. Moreover, this GC-FID method offers highly accurate results in terms of FAME quantification, compared to Raman spectroscopy. The obtained classification model is based only on two acids, behenic and _cis_-11,14-eicosadenoic acid. In the case of walnut classification, the percent obtained for initial classification was 100%, while in the cross-validation step 95.7% was reached, due to one sunflower sample, which was overlapped on the walnut group. The most representative compounds for this classification are the following acids: arachidonic, linoleic, behenic, erucic, and oleic. In the case of sea buckthorn, the same percent for initial and cross-validation was obtained, namely 95.7%, the same sample which was misclassified in Raman classification being misclassified here too. Palmitoleic acid is the only significant marker obtained in this case. For the other three varieties (sesame, linseed, and hemp) very clear discriminations were obtained (100% for both classifications-initial and cross-validation). The most representative markers used in every model were as follows: for sesame, palmitic acid, pentadecanoic acid, stearic acid, behenic acid, lauric acid, and erucic acid; for linseed, heneicosanoic acid, \(\gamma\)-linolenic acid, and caproic acid; while for hemp, \(\gamma\)-linolenic acid. The obtained classification using MLP-ANN is summarized in Table 2 presented below. It can be observed that the percent obtained for the training set was 100%, while the percent obtained for the testing set was 75%, due to one misclassified sample from the sunflower group. These values suggest that the estimated model will be able to accurately classify the oil samples between the two predefined categories. In Figure 5, the box-plot of pseudo-probabilities is presented. For the dependent variable (1-sunflower, 2-all), the chart displays box-plots that classify the predicted pseudo-probabilities taken into consideration in the whole dataset. The correct predictions are those above 0.5 for each box plot. For the sunflower group, all values are above 0.5, which means that the predicted pseudo probability is very high for this category.