3.5.4. Multivariate Data Analysis

Principal component analysis (PCA) is a multidimensional scale analysis that enables transformation of the variables into new ones, called principal components. The role of principal components is to explain the maximum amount of variance with the fewest number of components. PCA was performed for the quantitative and qualitative matrix containing 12 samples and 25 phenolic compounds. The PCA was applied for the quantitative correlation matrix using unit vector normalization and to the binary matrix for the qualitative analysis. Quatrimax rotation was performed and the bi-plots were selected for the visualization of the results.

Additionally, the Ward's hierarchical clustering method with Euclidean distances as measures of dissimilarity were applied.

#### **4. Conclusions**

In this paper, qualitative and quantitative analyses were combined together for the integrated characterisation and comparative analysis of the polyphenolic profile of *Medicago sativa L*. and *Trifolium pratense L.* sprouts in different germination stages. A variable data independent acquisition (vDIA) approach was used, which improved both selectivity and sensitivity for the fragment ions. This was beneficial for screening performance and identification capabilities.

By comparing MS/MS fragmentation patterns of reference compounds and the systematic identification strategy, a total of 59 polyphenolic compounds including isoflavones, flavones, flavonones, flenolic acids, and flavonols were identified in the alfalfa and red clover sprout extracts. A quantitative

determination method had been validated and applied for the quantification of 30 compounds. Three extraction methods were optimised and compared.

The 29 phenolic compounds that have been identified in sprout extracts are: isoflavones with estrogeninc action as biochanin A, coumestrol, prunetin, isoflavones as irilone, pratensein, pseudobaptigenin, flavone as tricin, chriosoeriol, and phenolic acid as ethyl gallate. Glucosides of apigenin, kaempherol, and coumestrol or isorhamnetine were also identified. For both plant species, sprouts in the third and fourth germination days were found to contain higher quantities of biologically active isoflavones as genistin, daidzein, formononetin, glycitein, apigenin, hesperetin, quercetin, ferulic acid, and *p*-coumaric acid.

The method presented in this paper has been demonstrated as an effective pathway for analysing the bioactive compounds in a complex sample from a natural resource as sprouts of alfalfa and red clover. This study also demonstrated the feasibility and advantage of the *v*DIA strategy on untargeted screening. The development of advanced methods for analysis of individual, biologically-active compounds will enable future understanding of their mechanisms of action on human organisms.

Despite the well-known medicinal properties of *M. sativa* and wide consumption of alfalfa sprouts, only a few reports on biological activity of single compounds have been published. This study provides an important scientific basis for further study on clinical application and functional food of alfalfa and red clover sprouts.

**Supplementary Materials:** The following are available online. Figure S1. Influence of different procedures on the extraction yield of the main active compounds. Results are presented as mean (*n* = 3) values ± STDev. Superscripts with different letters indicate significant differences (*p* < 0.05). Figure S2. Total ions current TIC and the extracted chromatograms of the main identified compounds in alfalfa extract on the third day of germination (the chromatograms were extracted from TIC using a 5 ppm mass accuracy window, negative ion mode, full scan, base peak in the range 150–1000 *m*/*z*). Figure S3. Influence of different procedures on the extraction yield of the main active compounds. Results are presented as mean (*n* = 3) values ± STDev. Superscripts with different letters indicate significant differences (*p* < 0.05). Figure S4. Extracted ion chromatogram for *m*/*z* 299.05 in alfalfa sprout on the third day of germination (**A**) and MS-MS spectra of the diagnostic ions of chryosoeriol (**B**), tectorigenin (**C** and **D**), and pratensein (**E**). Figure S5: Results of the calitative screening: variation of the compounds in the samples (alfalfa and red clover sprout samples coded as flow: RCV-red clover, ALF-alfalfa, s-seeds, day 1-first day of germination, day 2-s day of germination, day 3-third day of germination, day 4-fourth day of germination, day 5-fith day of germination). Table S1: UHPLC-MS/MS method validation parameters.

**Author Contributions:** Conceptualization, E.R.C., and C.L.C. Methodology, C.L.C., M.L., and E.-I.G. Software, D.B. Validation, E.R.C., C.L.C., and G.-V.B. Formal analysis, C.E.G. Writing—original draft preparation, E.R.C. and C.L.C. Writing— E.R.C. and C.L.C. Visualization, all authors. Supervision, R.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was not funded by a research project.

**Acknowledgments:** The authors thank the Romanian Center for Modeling in Recirculating Aquaculture Systems for the infrastructure provided by the project POS 2.2.1, No 622/11.03.2014.

**Conflicts of Interest:** The authors declare no conflict of interest.
