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Foods 2023, 12, 4493. https://doi.org/10.3390/foods12244493 www.mdpi.com/journal/foods
Article
In Search of Authenticity Biomarkers in Food Supplements
Containing Sea Buckthorn: A Metabolomics Approach
Ancuța Cristina Raclariu-Manolică 1 and Carmen Socaciu 2,3,*
1 Stejarul Research Centre for Biological Sciences, National Institute of Research and Development for
Biological Sciences, 610004 Piatra Neamț, Romania; ancuta.manolica@incdsb.ro
2 Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine Cluj
Napoca, 400372 Cluj Napoca, Romania
3 BIODIATECH—Research Center for Applied Biotechnology in Diagnosis and Molecular Therapy,
400478 Cluj-Napoca, Romania
* Correspondence: carmen.socaciu@usamvcluj.ro or csocaciu@proplanta.ro
Abstract: Sea buckthorn (Hippophae rhamnoides L.) (SB) is increasingly consumed worldwide as a
food and food supplement. The remarkable richness in biologically active phytochemicals (poly-
phenols, carotenoids, sterols, vitamins) is responsible for its purported nutritional and health-pro-
moting effects. Despite the considerable interest and high market demand for SB-based supple-
ments, a limited number of studies report on the authentication of such commercially available
products. Herein, untargeted metabolomics based on ultra-high-performance liquid chromatog-
raphy coupled with quadrupole-time of flight mass spectrometry (UHPLC-QTOF-ESI+MS) were
able to compare the phytochemical fingerprint of leaves, berries, and various categories of SB-berry
herbal supplements (teas, capsules, tablets, liquids). By untargeted metabolomics, a multivariate
discrimination analysis and a univariate approach (t-test and ANOVA) showed some putative au-
thentication biomarkers for berries, e.g., xylitol, violaxanthin, tryptophan, quinic acid, quercetin-3-
rutinoside. Significant dominant molecules were found for leaves: luteolin-5-glucoside, arginine,
isorhamnetin 3-rutinoside, serotonin, and tocopherol. The univariate analysis showed discrimina-
tions between the different classes of food supplements using similar algorithms. Finally, eight mol-
ecules were selected and considered significant putative authentication biomarkers. Further studies
will be focused on quantitative evaluation.
Keywords: sea buckthorn; Hippophae rhamnoides L.; commercial food supplements; authenticity bi-
omarkers; metabolomics; UHPLC-QTOF-ESI+MS
1. Introduction
Sea buckthorn (SB, Hippophae rhamnoides L. or Elaeagnus rhamnoides (L.) A. Nelson,
Figure 1) is a deciduous, dioecious thorny shrub belonging to the Elaeagnaceae family [1–
4]. Native to regions of Europe and Asia, due to its high adaptability to extreme cold,
drought, saline, and alkaline soils, sea buckthorn grows naturally or is cultivated nowa-
days on millions of hectares worldwide [3–8]. It is a versatile plant with a rich history and
multiple ecological, economic, and therapeutical applications (Supplementary Figure S1)
[7,9–11]. The strong and complex root system with nitrogen-fixing nodules makes SB an
optimal plant for soil and water conservation in eroded areas [12,13], and biodiversity
protection [14]. In the food industry, SB is a valuable ingredient of food items such as
jams, cheese, yogurt, fermented food, juices and other beverages, probiotic foods, or used
as a food additive [10,15–19]. It can also supplement animal diets to improve the produc-
tivity and quality of final products [20–23].
Citation:
Raclariu-Manolică, A.C.;
Socaciu, C. In search of Authenticity
Biomarkers in Food Supplements
Containing Sea Buckthorn: A
Metabolomics Approach.
Foods 2023,
12
, 4493. hps://doi.org/10.3390/
foods12244493
Academic Editor:
Mircea Oroian
Received: 10 November 2023
Revised: 12 December 2023
Accepted: 13 December 2023
Published:
15 December 2023
Copyright:
© 2023 by the authors.
Licensee MDPI, Basel, Swierland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Aribution (CC BY) license
(hps://creativecommons.org/license
s/by/4.0/).
Foods 2023, 12, 4493 2 of 19
Figure 1. Sea buckthorn (Hippophae rhamnoides L. or Elaeagnus rhamnoides (L.) A. Nelson). Branch
with red-orange ripe berries, thorns, and leaves (Photos taken at the Agricultural Research and De-
velopment Station (SCDA) Secuieni, Neamt County, Romania by A.C. Raclariu-Manolică).
The health-promoting properties of SB are aracting by far the most considerable
aention from the research community, producers, and industry [11,24,25], becoming a
common ingredient in a wide range of food supplements available on the markets [17].
Besides the large variability of composition due to its biological (genetic) strain, and geo-
graphical origin, many concerns are related to the authenticity of food supplements de-
clared to contain SB components (berries or leaves). Contamination and adulteration of
food supplements lead to variations in identity, purity, and expected benefits or therapeu-
tic properties of the claimed botanical ingredient [26]. Therefore, finding new analytical
approaches to ensure the quality and authenticity of food supplements is essential to min-
imize the potential risks related to their safe intake and to reach the expected nutritional
and health-promoting effects [27,28].
All parts of sea buckthorn (berries, leaves, stems, shoots, bark, and roots) are used
for their purported exceptional nutritional and health benefits [2,15,24,25,29,30]. The ther-
apeutic activity of SB has been associated with its rich composition of nutritional and bi-
ologically active compounds (about 200) [9,25,31,32], particularly, high quantities of lipo-
philic antioxidants (e.g., carotenoids, tocopherols, phytosterols) and hydrophilic antioxi-
dants (e.g., flavonoids, tannins, phenolic acids, ascorbic acid), among other constituents
[11,32–35]. The small, orange-yellow colored berries, with a sour and astringent taste, are
also rich and valuable ingredients in cosmeceuticals [36–39]. All anatomical parts of the
berry (skin, flesh, endocarp, seed) have an impressive vitamin content, particularly vita-
mins C, A, and E [40–42], minerals [43,44], remarkable amounts of polyphenolic
derivatives (mainly phenolic acids and flavonoids) [45–47], triterpenoids [48], carotenoids
[35,49,50], fay acids [34,44,51], and phytosterols (particularly β–sitosterol) [32,34,52,53].
Consumption of SB berries and derived preparations has been related to health-beneficial
effects on the cardiovascular system (e.g., lipid metabolism, platelet aggregation, and in-
flammation) [54–57], glucose and lipid metabolism [58–61], and associated also with ac-
tivities such as the immunomodulatory [62,63], antioxidant [64,65], antiviral [66,67], pro-
tective and curative effects in different pathologies [11,68–71]. The leaves and the new
tender shoots have a similar chemical profile as berries but with significantly higher
Foods 2023, 12, 4493 3 of 19
amounts of phenolic compounds [17,41,72–75], being a rich source of crude protein (on
average 15%), crude fat, and macro- and microelements [33,42,76–78], being recom-
mended in the production of new pharmaceutical or food ingredients and supplements
[73,79,80]. The leaves have been reported to have anti-inflammatory [81,82], antioxidant
[73,83], immunomodulatory [63], antimicrobial [84,85], anti-platelet and anticoagulant po-
tential [86], as well as other health proprieties [87,88]. Other vegetative parts (e.g., stems,
bark, roots), even if still underutilized, showed therapeutical potential [89–91], e.g., the
root and stem have antioxidant and antimicrobial activity [92,93], while the bark has an-
timetastatic activity [94]. The by-products resulting from berry waste [95] and biomass
(leaves and branches) [96] can be further valorized in the food industry, nutraceuticals,
and cosmetics [97–99].
The phytochemical composition of SB is prone to variability under natural conditions
that may be reflected in a high batch-to-batch variation of the chemical composition, crit-
ically altering the expected therapeutic effects. The chemical content varies among differ-
ent parts of the SB plant [68,100], and in relation to the genotype, sampling location [101–
106], gender (female and male) [89,107], developmental stages, post-harvesting proce-
dures [89,108–110], and the extraction technology [108,109,111], all of these significantly
influence the chemical content of the final preparation [108,109,111]. Furthermore, food
supplements including SB (berries, leaves, lyophilized extracts) may often contain dozens
of ingredients at different levels, making their quality control difficult since the standard
analytical methods lack resolution within complex preparations [26,112].
Advanced analytical approaches, such as high throughput techniques (e.g., high-per-
formance liquid chromatography-mass spectrometry (HPLC-MS), nuclear magnetic reso-
nance (NMR) spectroscopy, or DNA-based methods) coupled with chemometric-guided
approaches have recently aracted considerable aention in the fields of medicinal plants
and derived herbal products [113–119]. The emerging field of plant metabolomics offers
new strategies to determine the highly chemical variable profiles of plant materials [120].
Targeted and untargeted metabolomics strategies using different chromatographic tech-
niques followed by a chemometric approach have been largely applied to document the
metabolomic diversity of SB [75,121]. However, only a limited number of studies have
reported on innovative analytical methodologies applied to authenticate SB commercially
available products. Hurkova et al. [122] used direct analysis in real-time coupled with
high-resolution mass spectrometry (DART-HRMS), ultra-high-performance liquid chro-
matography coupled with high-resolution mass spectrometry (UHPLC-HRMS), and high-
performance liquid chromatography coupled with diode array detector (HPLC-DAD) to
authenticate one SB food supplement (oil-based capsule) purchased at a hypermarket in
the Czech Republic. Covaciu et al. [123] applied Raman spectroscopy, and gas-chroma-
tography equipped with a flame ionization detector (GC-FID), combined with the super-
vised chemometric technique for oil differentiation, and found this suitable approach to
detect possible adulteration of SB oil with sunflower oil. A multilayer perceptron-artificial
neural network (MLP-ANN) was also tested in the same study [123]. Berghian-Grosan
and Magdas [124] proposed a new, cost-effective approach for the control and authenti-
cation of edible oils, based on the rapid processing of Raman spectra using machine learn-
ing algorithms. In our previous studies, we applied ultra-high-performance liquid chro-
matography coupled with quadrupole-time of flight mass spectroscopy, and other tech-
niques like Fourier Transform Infrared spectroscopy or UV-VIS spectroscopy for detect-
ing and profiling phytochemicals in different food products, such as vegetable oils of dif-
ferent origins [125]. Despite the latest analytical advances, the authentication of botanical
food supplements remains a major challenge due to the large diversity of contained ingre-
dients that hinder the accuracy of analytical methods in identifying the targeted species
and detecting the non-targeted species that may occur [126,127].
The objective of this study was to identify specific SB phytochemicals’ fingerprints in
leaves and berries, as well as in various categories of commercialized food supplements
(teas, tablets, capsules, syrups, or oils) to certify their presence, based on untargeted
Foods 2023, 12, 4493 4 of 19
metabolomics procedure using ultra-high-performance liquid chromatography coupled
with quadrupole-time of flight mass spectrometry (UHPLC-QTOF-ESI+MS). These data
generated rapid and useful information on the presence and level of SB ingredients in
different commercial supplements.
2. Materials and Methods
2.1. Samples Analysed
Twenty-three sea buckthorn-based commercial herbal supplements were randomly
purchased from physical and online stores, including twelve herbal teas, three tablets, two
capsules, four syrup/oils, and two dried berries (Table 1). Six genuine SB leaves (L1–L6)
were kindly provided by our collaborators from “Anastasie Fatu” Botanical Garden, Iasi,
Romania, and Agricultural Research and Development Station Secuieni (Secuieni, Neamt
County, Romania). Voucher specimens were deposited at the National Institute of Re-
search and Development for Biological Sciences,” Stejarul” Biological Research Centre (Pi-
atra-Neamt, Romania), and are available on request.
Table 1. Categories of herbal formulations used for scientific analysis, and their collection and anal-
ysis codes. Abbreviations used: T—Tea; Tb—Tablet; C—capsule; S—liquid supplement; B—Berry;
L—Leaves.
Type of Formulation
ID Collection Code/ID Analysis Code
Herbal tea (T)
PC1/T1
PC2/T2
PC3/T3
PC11/T4
PC12/T5
PC13/T6
PC15/T7
PC16/T8
PC17/T9
PC19/T10
PC21/T11
PC23/T12
Tablet (Tb)
PC9/Tb1
PC10/Tb2
PC20/Tb3
Capsule (C)
PC4/C1
PC8/C2
Syrup/Oil (S)
PC6/S1 (oil)
PC7/S2 (hydroalcoholic extract)
PC18/S3 (emulsion)
PC22/S4 (syrup)
Dried Berry (B)
PC5/B1
PC14/B2
Leave
s (L)
ACM1/L1
ACM2/L2
ACM4/L3
ACM5/L4
ACM6/L5
ACM7/L6
2.2. Solvents, Reagents, and Analytical Standards
Foods 2023, 12, 4493 5 of 19
HPLC grade pure solvents (ethanol, acetonitrile, methanol, and tetrahydrofuran
THF) were purchased from Merck (Darmstadt, Germany). Formic acid (99.99%) was pur-
chased from Sigma-Aldrich (St. Louis, MO, USA). Deionized water was produced by a
Milli-Q system (Millipore, Bedford, MA, USA).
2.3. Sample Preparation and Extraction of Phytochemicals
Each sample was finely grounded, and the powders (sieved particles smaller than 20
mesh (1.7 mm)) were subjected first to extraction in ethanol. The same quantity of 1 g from
each powdered sample was suspended in 20 mL ethanol 50%, mixed for 15 min by vortex,
and kept in an ultrasonic bath for 60 min at 50 °C. The suspension was kept for 24 h in the
dark at room temperature, the extract was centrifuged at 12,500 rpm (4 °C) and the super-
natant was collected and filtered through a 0.2 mm nylon filter. The procedure was re-
peated 2 times. To extract the lipophilic molecules after ethanol extraction, the pellet was
mixed two times with 10 mL THF, sonicated in the ultrasonic bath for 3 × 20 min at 50 °C,
left for 24 h in the refrigerator (2 °C), and then centrifuged at 12,500 rpm (4 °C). The THF
extract (supernatant) was filtered through a polytetrafluoroethylene (PTFE) 0.25 mm filter.
Both extracts (duplicated from each sample) were submied to UHPLC-QTOF-ESI+MS
analysis.
2.4. Untargeted Metabolomics Analysis Using UHPLC-QTOF-ESI+MS
The untargeted, metabolomic fingerprints of ethanolic extracts were performed using
ultra-high-performance liquid chromatography coupled with electrospray ionization-
quadrupole-time of flight-mass spectroscopy (UHPLC-QTOF-ESI+MS) on an UltiMate
3000 UHPLC system equipped with a quaternary pump Dionex delivery system (Thermo
Fisher Scientific Inc., Waltham, MA, USA), and mass spectroscopy (MS) detection by a
QqTOF MaXis Impact (Bruker Daltonics GmbH, Bremen, Germany). The metabolites were
separated using a 5 µm Kinetex column (Phenomenex Inc, Torrance, USA) (2.1 × 150 mm)
at 25 °C. The flow rate was set at 0.8 mL · min−1 and the volume of each injected extract
was 10 µL. The mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic
acid in acetonitrile (B). The gradient was 20–40% B (0–5 min), 40–60% B (5–8 min), 60–70%
B (8–10 min), 70–20% B (10–16 min), and 20% B isocratic until 24 min. Several quality con-
trol (QC) samples obtained from each extract group were used to optimize the separations.
The chromatograms were processed using Chromeleon software (Dionex, Thermo Fisher
Scientific Inc., Waltham, MA, USA). The MS parameters were ionization mode positive
ESI+, calibrated with sodium formate, capillary voltage 3500 V, nebulizing gas pressure of
2.8 bar, drying gas flow 12 L/min, drying temperature 300 °C. The resolution of triple-
quadrupole-TOF was 30,000 at m/z = 922. The control of the instrument and the data pro-
cessing were done using the specific softwares TofControl 3.2, HyStar 3.2, and Data Anal-
ysis 4.2 (Bruker Daltonics GmbH, Bremen, Germany).
Data Processing and Statistical Analysis
The Bruker software Compass Data Analysis 4.2 (Bruker Daltonics, GmbH, Bremen,
Germany) was used to process the MS spectra of each component separated by chroma-
tography. The base peak chromatograms (BPC) were obtained from the total ion chroma-
togram and by the algorithm Find Molecular Features (FMF), a bucket matrix was gener-
ated, including the mass-to-charge ratio (m/z) value for [M + 1]+ precursor molecules, the
retention time, the peak intensity, and the signal/noise (S/N) ratio. The initial number of
separated molecules (m/z values) was around 550. The alignment of common molecules
(with the same m/z value) was done by the online software (www.bioinformat-
ica.isa.cnr.it/NEAPOLIS (accessed on 19 September 2023)). A second matrix of the com-
mon molecules found in more than 60% of samples was obtained, having S/N values over
2 and peak intensities over 10,000 units. The resulting data matrix included a few 98 m/z
Foods 2023, 12, 4493 6 of 19
values versus peak intensity and was submied for statistical analysis in the Metaboana-
lyst v5.0 online software for multivariate and univariate (one-way ANOVA) analysis.
The statistical algorithms used to reflect the discrimination between the different
sample groups were the partial least square discriminant analysis (PLSDA), the variable
importance in the projection (VIP) scores, and the correlation heatmaps. The biomarker
analysis included the receiver operating characteristic (ROC) curves and area values un-
der ROC curves (AUC) values which evaluated the sensibility and selectivity of the po-
tential biomarkers. According to the statistical analysis, the candidate molecules for au-
thenticity to be considered putative biomarkers were selected and identified, using the
specialized database FoodDB (hps://foodb.ca/, accessed on 25 September 2023). The mul-
tivariate metabolomic analysis was used to compare the leaves (L1-L6) with dried berries
(B1–B2) to find the most relevant molecules that may discriminate the phytochemicals
specific to leaves versus berries. The data from the univariate one-way ANOVA analysis
was applied to find out the discriminations between the different classes of molecules
found in the food supplement samples that claimed the presence of SB berries in the com-
position. In both cases (the t-test and significance of differences (p-values and post-hoc
Fisher LSD) were calculated.
3. Results
3.1. UHPLC-QTOF-ESI+MS Untargeted Analysis
The untargeted analysis was performed using multivariate and univariate analysis,
and showed possible discriminations between the supplements (groups B, S, C, Tb, and
T) which claimed to contain SB berries as such, or extracts as ingredients in their compo-
sition, at different levels. No clear indication of the concentration or the percentage of SB
herbal components was provided by the product labels. Such analysis aimed to identify
some specific phytochemicals that may indicate at least qualitatively the presence of ber-
ries in FS.
For the metabolomic analysis, based on the MS data (matrix including m/z values
versus peak intensity) 98 molecules were identified according to the described procedure
in Section 2.4. The experimental m/z values were compared with the average m/z values
from FooDB (hps://foodb.ca/, accessed on 25 September 2023). The list of identified phy-
tochemicals is presented in supplementary Table S1. Only molecules having the accuracy
of (theoretical—experimental) m/z values below 20 ppm were considered. For each mole-
cule, the FooDB code was mentioned.
3.1.1. Multivariate Analysis
PLSDA, Fold Change and p-Values
Figure 2 presents the PLSDA score plot which reflects the discrimination between the
SB leaves (L) versus berry (B) composition according to PLSDA analysis (co-variance of
67.3%). Despite the small number of samples, the cross-validation algorithm showed the
highest accuracy, with high R2 values and a significant Q2 value (>0.93) for the third com-
ponent, confirming the good predictability of this model (Supplementary Figure S2). The
VIP score graph (ranging from 1.2–1.5 values), derived from PLSDA analysis, was also
done (data not shown) including the ranking of the molecules that may explain the dis-
crimination between groups L and B. The VIP scores identified the molecules responsible
for the discrimination, either at superior levels in the B group (marked in red) or inferior
in the L group (marked in green).
Foods 2023, 12, 4493 7 of 19
Figure 2. PLSDA score plot showing the discrimination between the groups leaves (code L) and
berries (code B).
The Fold change (FC) and the log2(FC) values, according to the Volcano plot algo-
rithm (shown as Supplementary Figure S3) and the PLSDA/VIP analysis, were useful in
identifying the molecules with increased or decreased levels when comparing the group
L with group B.
Table 2 describes the FC values, log2(FC) combined with the p-values according to
the t-test.
Table 2. Fold change (FC), log 2(FC) values, and p-values according to PLSDA analysis and t-test.
The significance of variation between groups B and L (B > L or B < L) is presented. In Bold are rep-
resented the most significant ones.
B > L
FC
log2(FC)
p-Value
L > B
FC
log2(FC)
p-Value
Quercetin-3-
rutinoside
69.666 6.122 0.0100 Phytoene 0.017 −5.889 0.0012
Stigmasterol
44.887
5.488
0.0103
Acetylspermidine
0.023
−5.442
0.0042
Hydroxy
tryptophan 26.948 4.752 0.0167 DiGlyceride 30:2 0.033 −4.906 0.0182
Biotin amide
26.909
4.75
0.0031
Tocopherol
0.035
−4.834
0.0070
Naringin
21.41
4.42
0.0420
Caffeic acid
0.044
−4.512
0.0450
Lauroyl carnitine
19.186
4.262
0.0046
Serotonin
0.074
−3.75
0.0001
Quinic acid
17.721
4.147
0.0025
Gallic acid
0.079
−3.658
0.0460
Fay acid C20:0
15.023
3.909
0.0450
Sorbitan oleate
0.107
−3.23
0.0001
Fay acid C12:0
13.965
3.804
0.0470
Luteolin-5-glucoside
0.129
−2.959
0.0000
Folic acid
13.405
3.745
0.0001
Hydroxyglutamine
0.141
−2.826
0.0470
Arabinose 13.013 3.702 0.0053
Kaempferol 3-rhamno-
side, 7-glucoside
0.149 −2.744 0.0076
Heptanoyl carnitine
10.675
3.416
0.0017
Fay acid C18:4
0.15
−2.739
0.0018
Quercetin-7-
glucoside
9.976 3.318 0.0470 Fay acid C20:2 0.156 −2.678 0.0039
DG36:0
9.654
3.271
0.0480
Glucuronic acid
0.17
−2.553
0.0068
Foods 2023, 12, 4493 8 of 19
Tryptophan
9.470
3.243
0.0003
Fay acid C18:3
0.277
−1.852
0.0470
Glucitol
9.202
3.202
0.0040
Arginine
0.283
−1.819
0.0002
Xylitol 8.836 3.144 0.0000
Isorhamnetin 3-
rutinoside
0.292 −1.776 0.0002
Violaxanthin
8.11
3.02
0.0000
Luteolin
0.312
−1.679
0.0490
Vanillic acid
6.187
2.629
0.0164
Myristoylcarnitine
0.331
−1.596
0.0041
Glucose
5.89
2.558
0.0154
Ferulic acid
0.335
−1.578
0.0070
These parameters and the sign of the log2(FC) show the top of 20 molecules from
quercetin-3 rutinoside to glucose as being more dominant in berries (positive log2FC val-
ues) and phytoene to ferulic acid being more dominant in leaves (negative log2FC values).
Considering the lowest p-values (<0.0001), in each case, for berries, the putative bi-
omarkers to be considered were xylitol, violaxanthin, folic acid, tryptophan, quinic acid,
quercetin 3 rutinoside. For leaves, significant dominant molecules were luteolin 5-gluco-
side, arginine, isorhamnetin 3-rutinoside, serotonin, and tocopherol. This data was com-
pared also with complementary information given by the heatmap.
Heatmap Plot and Biomarker Analysis
The heatmap plot (Figure 3) illustrates the different clustering of the groups L and B
as well the relationships between molecules (increase or decrease in the groups L and B).
Figure 3. The heatmap showing the clusters of groups of leaves (ACM1, 2, 4, 5, 7) and berries (PC5,
PC6, PC14) considering the mean values for the first 25 molecules selected as most relevant for dis-
crimination.
This represents complementary information and illustrates by colors the levels of the
molecules in the B group (PC5, 6, 14) compared to group L (ACM 1,2,4,5,6,7). We can dis-
tinguish higher levels of quinic and feruloyl quinic acid and xylitol, violaxanthin, folic
acid, tryptophan, and cis retinal to be also of interest for discrimination between leaves
and berries, with significant increases in berries. Considering that all investigated
Foods 2023, 12, 4493 9 of 19
supplements claimed to contain SB berries or extracts of SB berries, the next studies were
focused on these molecules.
According to the biomarker analysis, the highest AUC values (>0.9) for the molecules
to be considered putative biomarkers for berries were found also to be xylitol, violaxan-
thin, folic acid, tryptophan, quercetin-3-rutinoside, and quinic acid.
3.1.2. Univariate One-Way ANOVA Analysis to Evaluate the Discrimination between the
Different Classes of Food Supplements
sPLSDA and Heatmap
The different supplements (teas, tablets, capsules, syrups/oils) were considered for
the one-way ANOVA analysis. The dried berries (group B) were unified in this case with
the liquid samples resulting in a group BS, the same for the groups C and Tb, named CTb.
Therefore, we compared the teas (group T) with groups BS and CTb. Figure 4A shows the
sPLSDA score plot and Figure 4B the loadings plot showing the top 15 molecules
responsible for the discrimination between the 3 groups (BS, CTb, and T). The relative
levels are presented on the right side (red-high; blue-low).
(A)
(B)
Figure 4. (A). sPLSDA score plot shows the discrimination between the groups BS, CTb, and T. (B).
The loadings plot of the top 15 molecules responsible for the discrimination between the 3 groups
(BS, CTb, and T). The relative levels are presented on the right side (red-high; blue-low).
According to Figure 4A, a good discrimination between teas (blue region), CTb group
(green region), and BS group (pink region) was identified. The loadings plot shows varia-
tions among the molecules identified as putative biomarkers for berries: higher levels in
the BS group for miristoylcarnitine, gallocatechin, cis-retinal, riboflavin, violaxanthin,
quinic acid, quercetin-3-rutinoside. This data confirms that some of these molecules can
be considered biomarkers for the berry’s extracts (syrups or SB oil) by multivariate anal-
ysis. Comparatively, the levels of these molecules in groups T or CTb were inferior. Figure
5 illustrates the heatmap data, as complementary information to show the presence of SB
berries in groups T and CTb.
Foods 2023, 12, 4493 10 of 19
Figure 5. The heatmap for the groups BS (berries, syrup/liquids), CTb (capsules, tablets), and T
(teas), considering the mean values for the first 25 molecules selected as most relevant for the dis-
crimination among these groups.
Significant discrimination was also illustrated here, between the groups BS, CTb, and
T. In the BS group, we identified higher levels of violaxanthin, tryptophan, carotene, cat-
echin, feruloylquinic acid, and neoglucobrassicin while in the CTb group, we identified
higher levels of glucose (additive), zeaxanthin, and hydroxytryptophan (possibly as addi-
tives). The group of teas (T) showed especially higher levels of serotonin, gallic acid,
kaempferol 3-rhamnoside, and some unidentified molecules, from the plant mixtures
used in the formulations.
Since this analysis was not satisfactory enough to find the lower levels of SB berries
present in teas and CTb groups, we also evaluated some specific molecules.
3.2. Evaluation of the Selected Putative Biomarkers
Based on the data cumulated from the multivariate and univariate analysis, several
molecules were selected as putative biomarkers for SB berry phytochemicals in such a
diverse cohort of botanical products, as an indication of authenticity. Figure 6 represents
the levels of eight molecules (xylitol, quinic acid, tryptophan, folic acid, quercetin-7-glu-
coside, violaxanthin, quercetin-3-rutinoside, quercetin-3,7-diglucoside), previously se-
lected by multivariate and univariate analysis. The levels were evaluated based on their
peak intensities in the UHPLC-MS analysis.
Foods 2023, 12, 4493 11 of 19
Figure 6. Semiquantitative analysis of phytochemicals specific to SB berries, found in the different
supplements (T—teas; Tb—tablets; C—capsules; S—syrups/oils; B—Dried Berries): the levels of dif-
ferent molecules (xylitol, quinic acid, tryptophan, folic acid, quercetin-7-glucoside, violaxanthin,
quercetin-3-rutinoside, quercetin-3,7-diglucoside) according to their peak intensities in the UHPLC-
QTOF-ESI+MS untargeted analysis.
The comparative evaluation shows that the variability of composition is maintained
but is closer to a more adequate consideration of authenticity. Capsules C1 and C2 showed
significantly lower levels, which may be explained by higher percentages of excipients,
compared to tablets (Tb1–Tb3) which showed a more stable composition. The tea compo-
sition was variable, except for the level of tryptophan which proved to be a major compo-
nent compared to other molecules. Further quantitative evaluation of such molecules will
bring more valuable information for a selection of representative SB biomarkers in herbal
supplements.
4. Discussion
Applying innovative techniques to advance food supplements authentication is
strongly advocated today [27,113,114,116,125,128].
Considering the high market demand for SB-based products, its phytochemistry and
pharmacognosy have stimulated considerable interest, but a limited number of studies on
the quality and authenticity of commercially available food supplements are reported
[122–124]. However, significant progress has been offered in the last years by the method-
ological approaches that combine advanced analytics with multivariate statistics, particu-
larly for SB berries [34,35,37,46,52,53,75].
Metabolomics is an accurate, robust, and time-efficient analytical approach for the
authentication of different molecules in complex botanical products. The emerging field
of plant metabolomics offers new ways to determine the profiles of plant bioactive com-
pounds as such, which are highly variable under the influence of various factors (genetic,
environmental, processing technology), and, on top of this, allow their measurement in
complex commercial botanical products, such as food supplements. The untargeted
metabolomics can offer improved fingerprints and resolution of the authentication pro-
cess of botanical-based foods and food supplements. Comprehensive reviews on inte-
grated analytical approaches and chemometric-guided approaches for profiling and au-
thenticating botanical materials applied to the identification of botanical bioactive com-
pounds and adulteration management were previously published [113,129,130].
0
1000
2000
3000
4000
5000
6000
7000
8000
B1
B2
S1
S2
S3
S4
C1
C2
Tb1
Tb2
Tb3
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
Thousands
Xylitol Quinic acid Tryptophan
Folic acid Quercetin 7-glucoside Violaxanthin
Quercetin 3-rutinoside Quercetin 3,7-diglucoside
Foods 2023, 12, 4493 12 of 19
Authentication is challenging when plant material is powdered or extracted in dif-
ferent solvents, as well as for mixtures consisting of multiple plant species. Moreover,
tracing bioactive phytochemicals claimed on the labels of botanical food supplements is
complicated by the natural variability of the starting raw material which often results in a
significant variation in the composition of the final product. Nevertheless, the deliberate
replacement of bioactive ingredients, their dilution, or the addition of lower-cost ingredi-
ents, is a significant ongoing problem in this sector. Nowadays, the accurate recognition
of phytochemicals within a complex mixture and the identification of specific bioactive
compounds from plant components (leaves, berries) requires the use of orthogonal, fused,
and specific analyses, including multivariate, univariate analysis coupled with chemomet-
rics [113,130].
Our study aimed to demonstrate the added value of the metabolomic approach for
finding key phytochemicals originating from sea buckthorn (leaves or berries) and differ-
ent food supplements including teas, capsules, tablets, syrups, and oils.
Using UHPLC-QTOF-ESI+MS untargeted (multivariate and univariate) analysis in
conjunction with multivariate analysis, using PLSDA score and loadings plots, heatmap,
the Fold change, and t-test, we found that the putative authentication biomarkers (p values
<0.0001) of SB berries are xylitol, violaxanthin, folic acid, tryptophan, quinic acid, querce-
tin-3-rutinoside. For leaves, luteolin-5-glucoside, arginine, isorhamnetin 3-rutinoside, ser-
otonin, and tocopherol were found to be significant dominant molecules. The univariate
analysis aimed to discriminate between the different classes of food supplements (BS,
CTb, and T) using similar algorithms. The sPLSDA plots showed good discrimination be-
tween teas (T), CTb, and BS groups and reflected putative biomarkers for berries (higher
levels in the BS group for miristoylcarnitine, gallocatechin, cis-retinal, riboflavin, violax-
anthin, quinic acid, quercetin-3-rutinoside). The heatmap illustrated the presence of SB
berries in groups T and CTb but at lower levels. In the BS group, we identified higher
levels of violaxanthin, tryptophan, carotene, catechin, feruloylquinic acid, while in the
CTb group, higher levels of glucose (additive), zeaxanthin, and hydroxytryptophan (pos-
sible additives). The group of teas (T) showed especially higher levels of serotonin, gallic
acid, and kaempferol 3-rhamnoside and some unidentified molecules, from the plant mix-
tures used in the formulations.
Since this analysis was not satisfactory enough regarding the lower levels of these
molecules in T and CTb groups, we also considered a semiquantitative evaluation of the
eight selected molecules (xylitol, quinic acid, tryptophan, folic acid, quercetin-7-glucoside,
violaxanthin, quercetin-3-rutinoside, quercetin-3,7-diglucoside) as SB berry biomarkers,
according to their peak intensities in the UHPLC-QTOF-ESI+MS untargeted analysis. The
comparative evaluation shows that the variability of composition is maintained but is
closer to a more adequate consideration of authenticity. Capsules C1 and C2 showed sig-
nificantly lower levels, explained by higher percentages of excipients, while tablets (Tb1-
Tb3) showed a more stable composition. The teas’ composition was variable, except for
the level of tryptophan, found as a major component compared to other molecules. These
molecules can represent a starting point for a further quantitative evaluation of some key
molecules selected here as putative biomarkers of the presence and level of SB berry com-
ponents in botanical food supplements.
A single plant species produces far more metabolites than those produced by most
other organisms [131,132], and, so far, no stand-alone analytical approach has been able
to untangle this diversity [127,131]. Additionally, complex plant-based food supplements
contain numerous plant ingredients, or mixtures of plant and vitamins or mineral ingre-
dients, among others, hindering, even more, the resolution of analytical methods in iden-
tifying the targeted species and detecting the non-targeted species that may occur
[126,127]. Moreover, there is a large body of evidence that unexpected contaminants
and/or adulterants are often present in such herbal matrices [26]. Therefore, orthogonal
testing approaches that include multiple complementary analytical methods are
Foods 2023, 12, 4493 13 of 19
recommended to comprehensively elucidate the ingredients and chemical content of
herbal products [26,120,133,134].
5. Conclusions
The authentication of botanical food supplements based only on specific bioactive
plant phytochemicals remains a major challenge despite the latest advances in analytical
technologies. Even the more advanced analytical methods are not powerful enough to
identify qualitatively, and especially quantitatively, the biomarkers of authenticity for a
specific ingredient, for instance, sea buckthorn. In this study, untargeted metabolomics
based on UHPLC-QTOF-ESI+MS was performed for the identification of the phytochemi-
cal profiling of SB food supplements. This study presented three steps of analytical flow,
from preliminary spectrometric analysis to multivariate and univariate metabolomic fin-
gerprinting, finalized by a semiquantitative evaluation based on the MS peak intensities
of selected phytochemical biomarkers, useful to authenticate food supplements declared
to contain sea buckthorn components (leaves or berries). Finally, there is an urgent need
to apply orthogonal advanced analytical approaches to fully untangle the huge ingredient
and chemical diversity of commercial botanical products.
Supplementary Materials: The following supporting information can be downloaded at
hps://www.mdpi.com/article/10.3390/foods12244493/s1: Figure S1: the main chemical constituents
and applications of sea buckthorn (Hippophae rhamnoides L. or Elaeagnus rhamnoides (L.) A. Nelson;
Figure S2: cross-validation graph showing the accuracy, R2, and Q2 values for the first three com-
ponents, when comparing the composition of sea buckthorn leaves (L) and berries (B); Figure S3:
volcano plot algorithm used to determine log10(p-value) versus log2(FC) values and the
dynamics (increase or decrease) of molecules’ levels between SB leaves and berries; Table
S1: identification of 98 molecules found in sea buckthorn leaves or berries, based on the MS data
[M+H]+ (m/z values). The experimental m/z values were compared with the average m/z values from
the international database FooDB (hps://foodb.ca/, accessed on 25 September 2023). The FooDB
codes were mentioned, considering the accuracy of (theoretical—experimental) m/z values below 20
ppm.
Author Contributions: Conceptualization, A.C.R.-M. and C.S.; methodology, A.C.R.-M. and C.S.;
software, C.S.; validation, A.C.R.-M. and C.S.; formal analysis, A.C.R.-M. and C.S.; investigation,
A.C.R.-M. and C.S.; resources, A.C.R.-M. and C.S.; data curation, A.C.R.-M. and C.S.; writing—orig-
inal draft preparation, A.C.R.-M. and C.S.; writing—review and editing, A.C.R.-M. and C.S.; visual-
ization, C.S. and A.C.R.-M.; supervision, C.S.; project administration, A.C.R.-M.; funding acquisition,
A.C.R.-M. and C.S. All authors have read and agreed to the published version of the manuscript.
Funding: This work was supported by two grants from the Romanian Ministry of Research, Inno-
vation and Digitalization, CNCS-UEFISCDI, project number PN-III-P1-1.1-PD-2019-0522, and grant
number PN-III-P4-PCE-2021-0378 within PNCDI III.
Data Availability Statement: Data is contained within the article or supplementary material.
Acknowledgments: We are grateful to our collaborators from “Anastasie Fatu” Botanical Garden,
Iasi (RO) and Agricultural Research and Development Station Secuieni, Neamt County (RO) for
providing access to samples of sea buckthorn used for scientific analysis. This work is performed
through the Core Program within the National Research, Development, and Innovation Plan 2022–
2027, carried out with the support of MRID, project no. 23020301, and contract no. 7N/2023. This
work was supported by a grant of the Ministry of Research, Innovation and Digitization through
Program 1—Development of the National R&D System, Subprogram 1.2—Institutional Perfor-
mance—Projects for Excellence Financing in RDI, contract no. 2PFE/2021 (for ACRM). This article
acknowledges the support from EU-COST Action LipidNet-PanEuropean Network in Lipidomics
and Epilipidomics CA19105.
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the
design of the study; in the collection, analyses, or interpretation of data; in the writing of the manu-
script; or in the decision to publish the results.
Foods 2023, 12, 4493 14 of 19
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