## 3 Results
### Principal Components Analysis and Cluster Analysis  
Principal components analysis is a method that is used to reduce a complex dataset into a smaller set of latent variables, known as principal components (PCs). With a large number of spectral bands (input variables), in order to interpret the data in a meaningful way, it is necessary to reduce the number of variables to a few linear combinations of the spectral bands that can be easily explained. The first two PCs captured most of the information and were sufficient to describe the essence of the data in the PCA correlation matrix, as is evident by the steep line that bends quickly and then flattens out. The score plot in Figure 2a,b shows clustered samples with similar values of the input spectral data (i.e., principal components). Three principal components described 69.80% of the total variability; PC1 describes 38.2% and PC2 describes 23.7%, while PC3 describes 7.9% of the total variability. Principal component analysis showed that plant materials originating from the Asteraceae and Lamiaceae families created separate clusters along PC1. Furthermore, based on PC2, Asteraceae samples (samples 4-7) were separated from commercial samples as well as from the Lamiaceae (samples 9-16). Figure 2: **(a)** 3D and **(b)** 2D PCA score plots for all essential oil samples, and **(c)** 2D PCA score plots for concentrated essential oil samples obtained from FLAVEX®. A cluster analysis confirmed that essential oil samples extracted from plants belonging to different botanical families differ significantly in their contents and therefore have unique spectral fingerprints. After filtering and normalization (Standard Normal Variate, SNV), the principal component analysis of 16 samples of concentrated essential oils from FLAVEX(r) clustered samples resulted in a two-component model that explained 69.50% of the total variance. PC1 accounted for 42.7%, while PC2 accounted for 26.8% of the total variance (Figure 2c). Samples were grouped in the same way as before with all samples, into three groups. According to PC1, Lamiaceae samples are clearly separated from the other concentrated essential oils: the latter group sub-divided in two in PC2; one composed of four samples from the Asteraceae family (samples 4-7); and the other comprising frankincense (Burseraceae family), arnica (Asteraceae family) and primrose (Primulaceae family). The first group included four samples from the Asteraceae plant family; the second group included only 9 concentrated oils from the Lamiaceae; while the third group included frankincense, arnica and primrose essential oils that belong to different plant families. The PC loadings plot was used to identify IR bands that are characteristic for the sample groups and have the strongest influence on each group of samples and thus contribute to the unique FTIR profile in the group (Table 2). The bands with the strongest influence on the principal components in the Lamiaceae family group essential oils are bands at \(\sim\)1375 and 1450 cm\({}^{-1}\), a broad band at 3400-3500 cm\({}^{-1}\), in addition to a band at 842 cm\({}^{-1}\) that was shifted to 862 cm\({}^{-1}\) in oregano essential oil (Figure 3a). The vibrational frequencies for linalool and linalool acetate fit correctly in the vinyl group vibration RHC=CH\({}_{2}\) at 1635-1650 cm\({}^{-1}\), but the intensities of these peaks are very low. Linalool is found in the essential oils of over 200 plant species, belonging to different families [24]. Linalool and its ester linalyl acetate are the main constituents in lavender oil. The C=CH\({}_{2}\) in-plane deformation vibration occurs near 1420 cm\({}^{-1}\). Only linalool, linalyl acetate and myrcene show this band, while limonene and \(\beta\)-pinene show no trace of it. However, this band may be hidden under the CH\({}_{2}\) and CH\({}_{3}\) deformation bands near 1450 cm\({}^{-1}\).