## 3 Results  
### Metabolite Identification Based on UPLC-Orbitrap-MS  
Principal component analysis (PCA) is a statistical technique used to analyze and differentiate between samples while identifying the variables that significantly influence their similarities and differences (Figure 1). It extracts key patterns from a data matrix, represented through complementary score and loading plots [25]. Both positive and negative ion modes were normalized to the IS signal intensity and subjected to PCA analysis. The secondary metabolites between the three cultivars of R. sativus (RL1-3) were distinguishable according to the PCA and suggested that each group had a relatively distinct overall metabolite composition. To gain deeper insights, overlapping secondary metabolites to each group were identified and analyzed to better understand their composition and significance. The RL1-3 extracts were analyzed using UPLC-Orbitrap-MS, which provided high-resolution mass spectrometry (HRMS) data to identify phytochemicals through molecular information and MS/MS fragmentation patterns [26]. The comprehensive metabolite profiling results are summarized in Table 1, and the identified metabolites were visually represented in Figure 2. This technique allows for the identification of compounds without requiring reference standards, making it particularly valuable for analyzing phytochemicals with low UV absorption or limited commercial availability [24]. To maximize ionization coverage, the extracts were analyzed in both positive and negative modes. Detected phytochemicals were tentatively identified by comparing their mass spectral data and MS2 fragmentation patterns to the Natural Product Atlas Database, maintaining a 5 ppm accuracy tolerance. The presence of phenolics in the methanol extract of the samples was primarily visualized and inspected through TLC analysis, which showed some intense orange-yellow and yellow-green spots, and this indicated the presence of flavonoid glycosides [27]. Additionally, UV/Vis spectra showed the major metabolites from the extracts to be consisted of flavonoids (Supplementary Information, Figures S2-S5). Flavones are characterized by a \(\lambda_{\text{max}}\) band around 340 nm, which is caused by an increased number of hydroxylation, and an increased number of hydroxyl groups causes \(\lambda_{\text{max}}\) to result in higher wavelengths [28]. Metabolite profiling of the methanol extract of _R. sativus_ cultivars was obtained through UPLC-Orbitrap-MS analysis, which consisted of seventeen flavonoids and five phenolics. The combination of the analytical data of their UV spectra, MS spectrum, and the previous literature that isolated these compounds consisted of the structure of flavonoids and flavones. The suggested flavonoid derivatives were further confirmed through the fragmentation pattern observed in the mass spectra. Moreover, the previous literature has reported the presence of mono-, di-, and tri-glycosides of flavonoid derivatives and some  
Figure 1: The principal component analysis (PCA) was performed to compare three _R. sativus_ samples (RL1, 2, and 3) under both positive and negative ion modes. (**A**) Correlation of phytochemical composition with RL1, 2, and 3, and (**B**) relationship of three _R. sativus_ cultivars (RL1\(-\)3). cases of sophorose (two glucoses with \(\beta\)-1,2-linkage) and rutinose (one rhamnose and one glucose with \(\alpha\)-1,6-linkage) as the diglycoside moieties [29; 30; 31]. Consistent with the UV and MS observations, the following metabolites in Table 2 have been tentatively identified. Compound **1** has a pseudomolecular ion at \(m/z\) 287.0551 [M+H]\({}^{+}\), identified as kaempfer based on the fragmentation pattern proposed for flavones (Table 2). Its MS2 spectrum showed fragment ions at \(m/z\) 153.0184 and 121.0285 corresponding to \({}^{1,3}\)A\({}^{+}\) and \({}^{0,2}\)B\({}^{+}\), respectively [32].