Centella asiatica (GK) and Withania somnifera (AS) seeds were supplied by Botanik Sämereien (Switzerland).

Bacopa monnieri (BR) juvenile plants were obtained from Hellwig (gardening center, Germany).

Breeding conditions in the green house were kept stable at 20˚C - 24˚C with humidity of 40% - 60%.

The LED-based illumination system (LED Company, Estonia) provided a light wavelength distribution of 380 - 780 nm. Defined light conditions were achieved for 24 hours: daylight from 8:00 - 19:59 and darkness from 20:00 - 7:59 [10] [23].

Capsules of BR were filled with a mixture from leaves and stems powder, in case of AS with root powder, which were purchased from the Himalaya Drug company, Bangalore, India.

GK was acquired in form of capsules, filled with leaf powder, from SHAG Psoriasis EX, Berlin, Germany.

For the investigations, the powder material interior of capsules was removed for usage. The nutraceuticals were stored at 4˚C, but 30 minutes before experimental application, samples were taken out of the fridge, to equilibrate and handle them at room temperature.

SFS extracts were prepared with maceration with mortar and pestle (3 g plant material with 30 ml solvent) and were directly used after [10] [23].

For Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid Chromatography-Mass Spectrometry (LC-MS) measurements two different kinds of plant extractions were utilized:

For GC-MS the SEs were needed to be prepared free from water. Due to the huge sample amount the evaporation was carried out by water bath evaporation which was achieved by placing 50 ml vessel with sample via slow spinning system into an 80˚C water bath [10] [23]. Over the duration of two hours, the entire solvent was evaporated and the dried remains were dissolved in methanol for further measurements. (For such sample amounts, vacuum drying can be considered inefficient and would have taken a long time.)

The Chromatographic separation was performed by use of an Agilent 1290 UPLC (Agilent Technologies, Santa Clara, CA) system equipped with an Agilent RRHD SB-Aq (2.1 × 150 mm, 1.8 µm) column (AGI859700914) and an Agilent SB-Aq (2.1 mm, 1.8 µm) guard column (AGI821725936). The mobile phase A contained an aqueous solution of 0.1% formic acid, and the mobile phase B an acetonitrile solution of 1% MilliQ water and 0.1% formic acid. A solvent gradient of 95% A 0 - 2 min, 95% - 0% A 2 - 22 min, 0% A 22 - 25 min, 0% - 95% A 25 - 26 min, 95% A 26 - 30 min were used. The flow rate was 0.3 ml/min. The analytical column was maintained at 40˚C whilst the autosampler was maintained at 4˚C. 5 µl of the sample was injected for each run.

Mass spectrometry was performed by using a G6540A QTOF (Agilent Technologies, Santa Clara, CA), a quadrupole and orthogonal acceleration time-of-flight tandem mass spectrometer equipped with an Agilent Jet Stream Ion Source (AJS). The scan range was set between 50 and 1000 m/z. Electrospray capillary voltages (negative and positive) were adjusted to 4500 V, nozzle voltage to 1000 V, fragmentor to 175 V, skimmer 1 to 65 V and OctopoleRFPeak to 750 V. Gas temperatures were set to 325˚C at 8.5 l/min, sheath gas to 325˚C at 8 l/min and the nebulizer to 45 psig. The mass spectrometer was operated in the extended dynamic range mode (1700 m/z) with an acquisition rate of 1 Hz. Data acquisition was performed by using the Mass Hunter LC/MS Data Acquisition Workstation Software (Build 6.01.6172 SP1), the data analysis by the Mass Hunter Qualitative Analysis Workstation Software (Build 7.0.7024.0) and the Mass Hunter Mass Profiler (8.0.136), all from Agilent Technologies, Santa Clara, CA. Requested peak picking was carried out with an ion intensity threshold over 2000 counts, an unbiased isotope model and a charge state of 1. Identification was performed using the Mass Hunter ID Browser (Agilent Technologies, Santa Clara, CA) applying the Metlin AM PCD library with a mass tolerance of 5 ppm [29].

All applied chemicals were obtained from Sigma Aldrich and industrial gases were provided by AS Linde Gas, Estonia.

The GC-MS analysis was performed utilizing an Agilent 5973N system with a 6890N MS detector. As a column, a 30 m Phenomenex ZB-5MSi with 0.25 mm diameter and 0.25 mm film thickness was used. For GC-MS detection, an electron ionization system with ionization energy of 70 eV was applied. Helium gas (99.99%; AS Linde Gas, Estonia) was used as a carrier gas at a constant flow rate of 1.3 ml/min. Injection and mass transfer line temperature were set to 250 and 230˚C respectively. The oven temperature was programmed for ramping from 70˚C to 150˚C with a heating rate of 5˚C/min, pursuing to 250˚C with a rate of 5˚C/min, held there for two minutes and finally increased to 320˚C with a heating rate of 10˚C/min and then held for 3 min. 1 μL of the sample was inserted with the splitless injector mode by a mass scan of m/z 50 - 700. The relative percentage of each extract constituent was expressed as a percentage with peak area normalization. Interpretation of the mass spectrum of the plant extracts was conducted using the database of the National Institute of Standard and Technology (NIST11) library [30].

The evaporated SE samples for the GC-MS measurements were dissolved in 5 ml methanol before usage. Hereby, 1 ml of this methanol solution was filtrated through a micro-filter.

All applied chemicals were obtained from Sigma Aldrich.

To obtain 3D fluorescence matrices of the samples the SFS technique (Laser Diagnostic Instruments AS, Estonia) was used by applying variable exitation and emission wavelengths. Utilizing color patterns, a 2D representation was extracted, where the colors represent the fluorescence intensities to quantify the substances/solvents. A qualitative identification was achieved since various chemical compounds have characteristic fluorescence spectral patterns [6] [31] - [37].

The PCA were used to preprocess the SFS data with the program named “StandardScaler” (StSc). Hereby the StSc assumes the SFS produced data. Those data are normally distributed within each feature and scaled them in a way, that the distribution is centered around 0, with a standard deviation of 1. Nevertheless, the mean and standard deviation are calculated for the feature and its scale based on: xi-mean(x)/stdev(x) [38].

Furthermore allows the PCA to transform the high dimensional into low dimensional data for the best representation of entire data.

AS extracts and nutraceuticals affirmed to be complex mixtures consisting of many substances. Different measurements with LC-MS, GC-MS, and SFS were carried out. Hereby, with LC-MS and GC-MS around 10 k possible compounds were denoted, from which around 6 k were identified with 80% - 100% Q Score. The final list of all identified substances without duplicates was found to be around 2 k (see supporting information), from which a selection of expected compounds, i.e. withaferin A, were identified and are listed in Table 1 [10] [23] [38] - [60]. In addition, approximately 4 k unknown possible ingredients remain unresolved.

The SFS data were analyzed via Principal Component Analysis (PCA), see Figure 1. The score SFS plots show differences between samples of spectral regions which were made visible in the loading plots, see Figure 2. In the PCA plot of AS, differently prepared samples distribute into separated data clusters in Figure 1. The PCA is based on the variation of the AS samples related to two spectral components (Figure 2): with maximum variance of the first main component (80.22%) and with a smaller variance for the second main component (−15.47%), all other main components are less presented within percentages (less than 2%; third component have a variance of 1.97% and the forth main component a variance of −1.24%). In this plot, we observe that AS powder forms very distinct clusters which are related to AS dissolved in 40/50% ethanol, powder and a wider distribution related to the rest of samples. Hereby, the SFS spectra provide intensities, if both—Emission and Exitation—are present. Those Emission and Exitation data of all compared samples were the basis for the PCA measurements.

GC-MS and LC-MS data of the chemical compounds found in Withania somnifera are selected: expected compounds based on the literature [23] [39] - [60] are provided in the left columnand extracted compounds which are selected accordingly the limit of the retention peaks in respect to the highest abundance and on the Q Score of 100% (threshold of 20%). The highlighted compounds are the most probable expected ones listed in the first column.

BR data were investigated and analyzed as described above (AS data) with LC-MS, GC-MS, and SFS. Hereby, we found several proteins, amino acids, vitamins and most of the expected compounds which are listed in Table 2 [10] [23] [57] [58] [61] [62] [63], as betulinic acid, alkaloids, and phenols, even brassinolide. However, we could not find the most expected ingredients: bacosides.

The PCA score plots (Figure 3 and Figure 4) show that the most important main component 1 variance of BR samples is 89.80% and the second component variance lay by −9.61%. However, the third and forth main compounds component variance was less than 1% (component 3: 0.27% and component 4: −0.08%). Hereby, dissolved and macerated BR constitutes a single PCA cluster. Notably, there is a certain difference between ethanol (EtOH) and the other samples (Figure 3).

Selected GC-MS and LC-MS data of the chemical compounds found in Bacopa monnieri extracts are listed: expected compounds which are based on literature [23] [57] [58] [61] [62] [63] in the left column and extracted ingredients in the right column. The compounds are limited related to the highest abundance of the retention peak area with a Q Score of 100% and a threshold of 30%, i.e. peptides, proteins, and hormones were discarded (which based on typical plant metabolites and may be found in the most living plants).

GK LC-MS and GC-MS data are listed in Table 3 [10] [23] [57] [58] (limited to the main abundance peak areas with a Q Score of 100%; with a threshold around 30%) show more compounds than expected: about 5.000 out of approximately 10.000 components and fragments were identified based on a Q Score of 80% - 100%. Hereby, approximate thousand compounds were specified. Otherwise, several thousand peaks were analyzed but yet not be known. The PCA and score plot of the SFS measurements (Figure 5 and Figure 6) show that between all GK samples (Figure 5) exists the highest considerable differences of 64.13% for the first component and only—18.08% of variance for the second component.

GC-MS and LC-MS data of the chemical ingredients of the medical plant Centella asiatica are compiled. The expected substances (based on the previous literature [23] [57] [58]) were listed in the 1st column. Most important and unexpected compounds are marked in the 2nd column (unexpected, like stearaldehyde; stearic acid; stearidonic acid; stearolic acid). Hereby were the compounds limited related to the highest retention peak area with a Q Score of 100% and some components were discarded, i.e. peptides, proteins, and hormones, which are only essential for plant survival and may be not relevant for any kind of treatment. Nevertheless the threshold approximatelyof 20% - 30% was set.