## 4 Discussion  
_Multivariate Data Analysis_  
Since two types of seeds were used in this study, i.e., fresh seeds (from 2019) and stored seeds (from 2018), an additional goal was undertaken, namely, to examine the possibility of identifying DSC parameters to distinguish between oils pressed from stored and fresh seeds. In the first stage, before performing any multivariate analysis, the raw data (number of samples \(\times\) number of variables) were standardized in order to obtain scores on the same scale. Principal component analysis was used as the first step of multivariate data analysis, since it enables the differences between objects and the relationship between variables to be visualized. For visual analysis, a two-dimensional projection of samples is usually constructed with the axes (principal components) as the factors. Based on graphical criteria, five PCs were used for the analysis. Each PC is a linear combination of the original responses, and PCs are orthogonal to each other. Figure 2 shows the relationship between variables describing the quality of oils (\(\Sigma\)SFA, total saturated acids; \(\Sigma\)MUFA, total monounsaturated acids; \(\Sigma\)PUFA, total polyunsaturated acids) and all DSC parameters for the melting profiles obtained at three scanning rates (1 \({}^{\circ}\)C/min (Figure 2A,D), 2 \({}^{\circ}\)C/min (Figure 2B,E), and 5 \({}^{\circ}\)C/min (Figure 2C,F)). The first component explains 56.51, 59.09. and 53.08% of the variance, while the second component explains 25.03, 25.46, and 35.3% of the variance for scanning rates of 1, 2, and 5 \({}^{\circ}\)C/min, respectively. In general, the first principal component described by h4, H\({}_{\text{m}}\)4, ZPUFA, T\({}_{\text{m}}\)3, and T\({}_{\text{m}}\)4 separates HP_HLA oils from the others. The highest loadings values of Hm2, h2, and Tm1 were noted in the second principal component, separating HP_HLD and HP_HLC oils from HP_HLB and HP_HLE. Generally, strong negative correlations were found between \(\Sigma\)PUFA and the T\({}_{\text{m}}\)2, T\({}_{\text{m}}\)3, and T\({}_{\text{m}}\)4 parameters, i.e., \(-\)0.92, \(-\)0.95, and \(-\)0.76 at a scanning rate 1 \({}^{\circ}\)C/min and \(-\)0.73, \(-\)0.78, and \(-\)0.78, respectively at a scanning rate 5 \({}^{\circ}\)C/min. Another unsupervised pattern recognition method applied in this study was cluster analysis (CA). The Ward method, known as the minimum variance method with a Euclidean distance between centroids, was applied. Figure 3 shows a dendrogram as the result of the cluster analysis performed, with three clusters obtained for each scanning rate (1 \({}^{\circ}\)C/min (Figure 3A), 2 \({}^{\circ}\)C/min (Figure 3B), and 5 \({}^{\circ}\)C/min (Figure 3C)). The first cluster includes HP_HLA oils, which are definitely different from the others (the longest binding distance); the second cluster includes HP_HLB and HP_HLC oils; and the third cluster includes HLC and HLD oils. The results of the cluster analysis are consistent with the results of the principal component analysis (Figure 2). Thus, principal component analysis, as well as cluster analysis, makes it possible to distinguish a group of lower freshness oils (HP_HLD and HP_HLC) from fresh oils (HP_HLA, HP_HLB, and HP_HLE). This allows for the assumption that it is possible to build a model that discriminates oils according to their freshness. To determine the rules for classifying oil samples, linear discriminant analysis (LDA) was used. LDA is a common method for supervised pattern recognition. LDA identifies the linear functions that best separate the classes by maximizing the between-class variation and minimizing the within-class variation. A data matrix with 45 rows (one for each oil sample) and 12 columns (one for each DSC parameter) [15] was prepared for LDA analysis. The functions were selected using the forward stepwise se-lection (SLDA) method, which chooses the variable with the highest F value and the lowest Wilks' \(\lambda\).