Journal of Food Composition and Analysis 110 (2022) 104559
Contents lists available at ScienceDirect
Journal of Food Composition and Analysis
journal homepage: www.elsevier.com/locate/jfca
Application of ED-XRF spectra for determination of macroelements in
edible seaweeds
Jelena Milinovic a, b, *, Filipe M.J. Figueiredo b, João Paulo Noronha a, José Sardinha b, c
a
LAQV/REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
Centro de Recursos Naturais e Ambiente (CERENA), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
c
Departamento de Engenharia e Ciências Nucleares (DECN), Instituto Superior Técnico, Universidade de Lisboa, Campus Tecnológico e Nuclear, Estrada Nacional 10,
2695-066 Bobadela, Portugal
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Edible seaweeds
Macroelements
Overlapping peaks
ED-XRF spectra vs. ICP-OES
Multivariate regression
CVPLS
NIPALS algorithm
RMSE
Method robustness
The application of an increased number of edible seaweeds in gastronomy requires accurate, rapid, and nondestructive mineral evaluation. In this work, energy dispersive X-ray fluorescence (ED-XRF) spectra were used
to determine Ca, K, and P in edible green, red, and brown seaweeds from the Portuguese coasts. ED-XRF raw
spectra of seaweed samples were correlated against results of conventional inductively coupled plasma-optical
emission spectrometry (ICP-OES), by the full cross-validation partial least-square (CVPLS) regression method,
with nonlinear iterative partial least squares (NIPALS) algorithm. The standard reference material (Thallus
laminariae) was used for the validation of CVPLS. Statistical parameters, correlation coefficients (R2), root mean
square errors (RMSE), and bias, were used for the assessment of the CVPLS method. For selected minerals, R2 was
high (0.88–0.95), while RMSE values (< 1.02) and bias (0) were low, thus indicating a viable potential for their
routine analysis by ED-XRF. These findings highlighted the quality of ED-XRF spectral data, which can be used
for an efficient non-destructive determination of macroelements in edible seaweeds on a large food industrial
scale.
1. Introduction
Edible seaweeds (marine algae) have been used as a food source for
centuries (Chapman and Chapman, 1980; McHugh, 2003). Due to their
valuable nutrition composition, currently, around 85% of the global
seaweed industry is used as food products for direct human consumption
(MacArtain et al., 2007; FAO, 2018). Among other nutrients, such as
carbohydrates, fibers, etc., green, red, and brown seaweeds contain a
large fraction of minerals, that can be present from 10% to 50% (w/w,
referred to dry weight, dw) (Jensen, 1993). Sodium and potassium
chlorides are dominant minerals, whereas more valuable Ca and P are
present in lower contents in most of the seaweed species (Paiva et al.,
2014; Rodrigues et al., 2015; Fleurence, 2016; Milinovic et al., 2021a).
Based on their mineral composition, seaweeds can be selectively used to
improve the quality of different food products. Thus, the biomass of
some seaweeds or seaweed-derived products (e.g., Himanthalia elongata,
Porphyra umbilicalis, Undaria pinnatifida) have been added to improve
mineral profiles in some snacks, meat products, and/or drinks
(López-López et al., 2010; Gupta and Abu-Ghannam, 2011; Gullón et al.,
2020). Therefore, it is essential to know the exact levels of minerals in
seaweeds, for their successful application in the development of func­
tional foods.
The analysis of macroelements in food matrices usually implies timeconsuming wet digestion sample pretreatments before analysis by con­
ventional techniques, such as atomic absorption spectrometry (AAS) or
inductively coupled plasma-optical emission spectroscopy (ICP-OES)
(Varma et al., 2011; Malea et al., 2015; Rubio et al., 2017). Direct
analysis of macroelements with minimal sample preparation is an
alternative that would overcome the time-consuming limitation of
sample preparation before conventional analysis. In this regard, energy
dispersive X-ray fluorescence (ED-XRF) analysis can be used for the
rapid cost-effective analysis of elements. This robust technique is rapid,
non-destructive, and with minimized contamination risk and waste
generation (Bosco, 2013). A small amount of sample enables fast
* Corresponding author at: LAQV/REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica,
Portugal.
E-mail addresses: j.milinovic@fct.unl.pt (J. Milinovic), filipe.figueiredo@tecnico.ulisboa.pt (F.M.J. Figueiredo), jpnoronha@fct.unl.pt (J.P. Noronha), jose.
sardinha@tecnico.ulisboa.pt (J. Sardinha).
https://doi.org/10.1016/j.jfca.2022.104559
Received 31 December 2021; Received in revised form 26 March 2022; Accepted 30 March 2022
Available online 2 April 2022
0889-1575/© 2022 Elsevier Inc. All rights reserved.
J. Milinovic et al.
Journal of Food Composition and Analysis 110 (2022) 104559
Fig. 1. Location of seaweed samples collected along the central western Portuguese coasts, Cape Mondego and Óbidos Lagoon.
high sample output, and the capability of simultaneous multi-element
analysis (McComb et al., 2014).
Although XRF has been used for the rapid multi-element food anal­
ysis, e.g., in vegetal foodstuffs (Gallardo et al., 2016) or infant milk
powder (Herreros-Chavez et al., 2019), it has not been sufficiently
addressed and studied in the direct elemental analysis of seaweeds (Bull
et al., 2016; Brito et al., 2017; Turner et al., 2017). Rapid ED-XRF
analysis of Ca, K, and Mg in seaweeds collected in Brazil, was cali­
brated by linear regression using ICP-OES results (Brito et al., 2017). For
the accuracy of ED-XRF analysis, certified reference materials (CRMs) of
plant origin were used and the recoveries ranged from 85% to 89% (for
Ca) to 127–129% (for K). The accuracy of these results, calculated by the
software algorithm, was affected by overlapping peaks of Ca and K
(Bosco, 2013; Brito et al., 2017). This type of interference is the main
disadvantage of ED-XRF analysis, caused by the phenomena of absorp­
tion of the emission signal by the other elements present in the matrix.
To avoid these interferences and to improve the accuracy of results,
mathematical corrections using different chemometrics tools, including
multivariate regression analysis, can be applied to spectra. As a form of
predictive modeling technique, the partial least square (PLS) regression
with a developed algorithm can be run on ED-XRF spectra, which may
serve for the determination of elements (Engelen et al., 2004; Nengsih
et al., 2019).
The objective of this research was to apply ED-XRF spectra for the
determination of Ca, K, and P concentrations in edible seaweeds. In this
context, integral ED-XRF spectral data were correlated with the inde­
pendently acquired (ICP-OES) data by PLS regression. To the best of our
knowledge, these data are scarce in the literature and hence, do not
provide a basis for comparison.
Table 1
Seaweed species collected at the central western Portuguese coast.
Seaweed species
Traditional name
Sampling
season
Sampling site
Chlorophytae (green)
Codium tomentosum
Spongeweed
autumn, 2019
Ulva rigida
Sea lettuce
autumn, 2019
Ulva sp
Sea lettuce
autumn, 2019
Rhodophytae (red)
Gracilaria gracilis
Cape
Mondego
Cape
Mondego
Óbidos
Lagoon
Slender wart weed
spring, 2019
Osmundea pinnatifida
Pepper Dulse
spring, 2019
Porphyra sp
Nori, Laver
summer, 2019
Phaeophyceae
(brown)
Saccorhiza polyschides
Furbelows
summer, 2019
Undaria pinnatifida
Wakame
summer, 2019
Cape
Mondego
Cape
Mondego
Cape
Mondego
Cape
Mondego
Cape
Mondego
Table 2
Concentrations of Ca, K, and P (%, w/w, dw) in edible seaweeds determined by
ICP-OES.
Seaweed phylum
Species
Chlorophyta (green)
Codium tomentosum
Ulva rigida
Ulva sp
Rhodophyta (red)
Gracilaria gracilis
Osmundea pinnatifida
Porphyra sp
Phaeophyceae (brown)
Saccorhiza polyschides
Undaria pinnatifida
Ca
% (w/w, dw)
K
P
0.94
0.69
3.06
5.88
2.02
1.52
0.29
0.25
0.46
0.17
0.55
0.20
10.60
5.31
2.90
0.29
0.28
0.60
1.40
0.97
8.46
7.64
0.32
0.68
2. Materials and methods
All chemical reagents were of analytical grade, purchased from
commercial suppliers, and used as received. The certified standard stock
solutions of macroelements were purchased from Merck Inorganic
Ventures. Working solutions were prepared by diluting the stock solu­
tion with ultrapure Milli-Q water (18 MΩ cm− 1). Dry brown seaweed
species Thallus laminariae (kelp) used as the standard reference material
(SRM 3232), was purchased from the National Institute of Standards &
Technology (NIST, Gaithersburg, MD, USA).
acquisition of the ED-XRF spectrum, which describes the patterns of
elements present, according to characteristic energies emitted by X-rays.
The X-ray energies for each element are specific and increase with the
atomic number of elements. The main advantages of ED-XRF analysis
are high accuracy and precision, sensitivity (sub-ppm levels to %, w/w),
2.1. Seaweed samples
Seaweeds were collected from the central western Portuguese coasts
- Cape Mondego (40º11′ N, 08º54′ W), and the Óbidos lagoon (39º25′ N,
2
J. Milinovic et al.
Journal of Food Composition and Analysis 110 (2022) 104559
Fig. 2. ED-XRF spectrum of SRM 3232. Peaks corresponding to P, K, and Ca are labelled.
09º13′ W) (Fig. 1).
In total, eight seaweed species were sampled, of which three green
(Codium tomentosum, Ulva rigida, Ulva sp), three red (Gracilaria gracilis,
Osmundea pinnatifida, Porphyra sp), and two brown (Saccorhiza poly­
schides, Undaria pinnatifida) (Table 1).
All species were collected in 2019, from spring (red species Gracilaira
gracilis and Osmundea pinnatifida) to early autumn (green species),
following the best ecological practices and favor weather conditions
(Milinovic et al., 2020; Milinovic et al., 2021b). During sampling in
spring and late summer/early autumn seasons, the physico-chemical
parameters of seawater did not change significantly, i.e., the tempera­
ture was 16 ◦ C, pH 7.6 and 8.0, and salinity 31–35 ng L− 1 and
34–36 ng L− 1, respectively (Milinovic et al., 2021b, 2021c).
Seaweed species were chosen based on their abundance at the sam­
pling sites, i.e., available in sufficient biomass to perform further ana­
lyses. Seaweeds were collected at the fully developed adult phase and
their identification was performed using the morphological character­
istics and by systematical comparison with the MACOI Portuguese
database (Pereira, 2009, 2012).
Collected seaweed samples were washed with filtered seawater and
their transportation was performed by rapid shipment to the laboratory
facilities. All samples were washed thoroughly with distilled water to
further remove salt, sand grains, and epiphytes, and then freeze-dried
(CoolSafe 9 L, LaboGene™, Denmark) under conditions explained
elsewhere (Milinovic et al., 2020). Freeze-dried samples were crushed
and milled with a blender, and the grains were sifted through the lab­
oratory stainless steel sieve (R 20 cm, 120 mesh, 0.125 mm aperture).
Representative samples of the fine fraction were kept in labeled poly­
propylene bags, sealed with a vacuum sealer, and stored in the freezer
(at − 18 ◦ C) before analysis.
HNO3 (v/v, 1:10) to avoid matrix interferences due to salinity. The
digestion method and operational conditions of the analysis, as well as
quality control, have been explained in detail elsewhere (Milinovic
et al., 2021a, 2021b, 2021c). The determined concentrations of target
elements expressed in % (w/w) refers to dry weight (dw) of seaweeds.
The NIST certified values for the concentrations of Ca, K, and P in
SRM 3232, were determined also by ICP-OES analysis, consistent with
the Joint Committee for Guides in Metrology (JCGM, 2008; Yu et al.,
2018; NIST, 2019). The SRM 3232, contains Ca, K, and P in respective
concentrations: 1.23 ± 0.01%, 7.6 ± 0.1%, and 0.46 ± 0.01% (w/w,
referred to dw).
2.3. ED-XRF spectral analysis of seaweed samples
A portable ED-XRF analyzer (S1 Titan series 600, Bruker, Illinois,
USA) equipped with an Rh X-ray source, was used for the elemental
analysis of homogenized fine fraction seaweed samples. A representative
portion of the sample (thickness = 10–15 mm) was placed in a poly­
ethylene cup (using Mylar film as sample support), positioned centrally
above the detector window (active surface of 20 mm2) of the ED-XRF
analyzer. The GeoMining calibration mode was selected for the anal­
ysis of macroelements via three-phase measurements, each 30 s. The
voltage and current excitations used in the three phases were respec­
tively: 30 kV and 23.20 µA, 50 kV and 25.30 µA, and 15 kV and
22.35 µA. Spectra in the third phase of measurement (run at the lowest
potential and energy), enable the analysis of the light elements,
including Ca, K, and P. The K lines were used since they were the only
ones observed for P and K, while for Ca the L line could be detected but
was characterized by very low energy (0.34 keV).
The raw spectral data were computed by Bruker’s Instrument Tools
(v. 1.6.0.110). To obtain quantitative data, integral spectra were
exported by Bruker Artax software (v. 8.0.0.476).
2.2. ICP-OES analysis of macroelements in seaweeds
2.4. ED-XRF spectra vs. ICP-OES: application of multivariate regression
analysis
Seaweed samples were analyzed for total Ca, K, and P concentrations
by inductively coupled plasma-optical emission spectroscopy (ICP-OES,
Horiba Jobin Yvon Activa M). Briefly, approximately 200 mg of the
homogenized lyophilized samples were placed in a Teflon vessel, with
added 2 mL of 2% HNO3. The solution in the closed vessel was heated in
a microwave (CEM Mars 5) according to the following program: within
the first 10 min, the temperature has been increased to 180 ◦ C and
remained for the next 10 min. After cooling, 0.5 mL of 30% H2O2 was
added to the solution, following a new digestion cycle, as previously
described. Then, the sample was evaporated to 0.5 mL and Milli-Q water
was added to the final volume of 25 mL. All samples were diluted in 2%
The X-ray energy raw spectral data along with ICP-OES results were
exported to Unscrambler™ to perform multivariate regression analysis.
The multivariate modeling was based on full cross-validation partial
least square (CVPLS) regression, applied to dependent (ED-XRF spectral
data) and independent (ICP-OES results) datasets (Martens and Dard­
enne, 1998). The nonlinear iterative partial least squares (NIPALS) al­
gorithm was selected for CVPLS with Hotelling’s T2 outlier estimation at
a 95% level of confidence (p < 0.05). The NIPALS algorithm was
3
J. Milinovic et al.
Journal of Food Composition and Analysis 110 (2022) 104559
Fig. 3. ED-XRF spectra of edible seaweeds: A - green spp.; B - red spp.; C - brown spp.
selected as the preferentially used algorithm that enables the model to
be fitted on smaller datasets (Stott et al., 2017; Nengsih et al., 2019).
The seven ED-XRF measurements of the SRM 3232 and each seaweed
sample were used for the full cross-validation PLS. Given the small size
of the available dataset, each replicate analysis (of the SRM 3232 and
eight seaweed samples) was considered individually (n = 63). The crossvalidation used is a well-established model implemented in the
Unscrambler™ program, based on a systematic resampling of the
available data (Martens and Dardenne, 1998). This version of full model
cross-validation differs from local component cross-validation, which
only cross-validates each component locally, and doesn’t attempt to
estimate the ‘true’ prediction error both for the calibration and valida­
tion sets. In the model input, the maximum number of components used
was 7, thus assuring a more reliable CVPLS analysis. The number of
4
J. Milinovic et al.
Journal of Food Composition and Analysis 110 (2022) 104559
Fig. 4. Original plots of CVPLS model for predicted Ca content (%) in seaweed samples by ED-XRF vs. ICP-OES results. Seven latent variables (factor - 7) were used to
maximize the covariance between the predicted and independent dataset. The inserted squares contain statistical parameters of calibration (blue) and validation
(red) which quantitatively explain the goodness of the model. Slope and offset (intercept) define the linear relationship between two variables of the regression line.
High values of correlation, Pearson (R2), and correlation coefficient (R-square), together with low values of RMSE, SE, and bias describe a strong prediction of Ca.
latent variables (LV) was computed but was not used as a critical
parameter for the fitness of the method, since it has less influence
(Martins et al., 2010). The statistical parameters used to explain the
goodness of fit of the quantitative multivariate PLS analysis were: cor­
relation coefficient (R2), root mean square error (RMSE), and bias for
both calibration and validation datasets. The robustness of the method
was estimated by deliberate variations after removing some datasets
(seaweed sample Ulva sp) and checking the parameters of the goodness
of fit and occurrence of possible outliers in the results, at a significant
level (p < 0.05).
respectively. The concentration of P in all samples was present at the
same order of magnitude as Ca, in a narrow range, from 0.25% (Ulva
rigida) to 0.68% (Undaria pinnatifida). Ca and P concentrations were
positively correlated (R2 = 0.90), after the exclusion of Porphyra sp. and
Saccorhiza polyschides, which confirms the adequate ratio of both mac­
ronutrients (Ca/P > 1) in the analyzed seaweeds. In a proper human
diet, a portion of about 1.0 g (dw) per day of seaweed (e.g., Codium
tomentosum, Undaria pinnatifida) will provide an optimal intake of both
macronutrients, vital for bone health and reduced risk of osteoporosis
(Lee and Cho, 2015). Similar results were reported for the same edible
species of seaweeds collected at different coastlines worldwide
(Rupérez, 2002; Domínguez-Gonzalez et al., 2005; Flores et al., 2015;
Rubio et al., 2017). Nutritional aspects of the results and the importance
of selected species in gastronomy were discussed in detail elsewhere
(Milinovic et al., 2021a).
Concentrations of Ca, K, and P in the standard reference material seaweed Kelp powder (SRM 3232) - were in the same range as in the
studied seaweed samples, which assured quality control purposes by the
selected SRM (Yu et al., 2018; Linsinger and Botha, 2019). In some other
works, the reference materials used were based on plant leaves (apple,
3. Results and discussion
3.1. ICP-OES concentrations of macroelements in seaweeds
Concentrations of Ca, K, and P determined by ICP-OES analysis of
eight seaweed species originating from the Western Portuguese coast are
presented in Table 2. Concentrations of Ca and K varied one order of
magnitude among the samples, i.e., from 0.17% (Gracilaria gracilis) to
3.06% (Ulva sp) and from 1.52% (Ulva sp) to 10.60% (Gracilaria gracilis),
5
J. Milinovic et al.
Journal of Food Composition and Analysis 110 (2022) 104559
Fig. 5. Original plots of CVPLS model for predicted K content (%) in seaweed samples by ED-XRF vs. ICP-OES results. See Fig. 4 for detailed explanations of
the parameters.
peach, and/or spinach), which may not be suitable for external valida­
tion of methods due to their different chemical composition compared to
seaweeds (Collins et al., 2014; Brito et al., 2017).
The SRM 3232 used for the quality assurance of the in-house ED-XRF
analysis showed that the recoveries for selected elements (after seven
replicate measurements) were 112 ± 1% for Ca, 96 ± 4% for P, and 119
± 2% for K. Although precision was acceptable, the accuracy of results
for Ca and K was higher, as expected because of their overlapping peaks
(Fig. 2) when pre-installed factory calibration software is used
(Gallhofer and Lottermoser, 2018).
Representative ED-XRF spectra with labeled macroelements of three
sets of samples containing green, red, and brown seaweed species,
respectively, are shown in Fig. 3. As can be seen, the three sets of
samples, collected from the central western Portuguese coasts showed
similar ED-XRF patterns. Low peaks of P (at 2.01 keV) were registered in
all samples at low intensities, whereas K and Ca (at 3.31 and 3.59 keV;
3.69 and 4.01 keV, respectively), were registered at higher intensities.
Among red species, the main peak for K showed the highest intensity in
Gracilaria gracilis (Fig. 2 B) and this corresponds to the results deter­
mined by ICP-OES (Table 2), which is also visible in Fig. 3 C, for Sac­
corhiza polyschides and Undaria pinnatifida.
Direct determination of macroelements by ED-XRF showed poor
accuracy and linear regression for ED-XRF vs. ICP-OES results had a
wide interval of R2 values, ranging from 0.35 (Ca) to 0.81 (P) (Table 1S).
In the case of Ca, very low regression can be explained by extrapolation
3.2. ED-XRF spectral analysis of seaweeds
In the selected GeoMining mode of ED-XRF, the limit of detection
(LOD) for the analyzed macroelements ranged from 0.0035% to
0.0053% (w/w, dw). The upper limit of calibration concentrations for
the analysis of P, K, and Ca, were 13.2%, 18.4%, and 31.0% (w/w, dw)
respectively, thus providing direct sample analysis (without dilution).
An ED-XRF spectrum of SRM 3232 with the main features for P, K,
and Ca, is shown in Fig. 2. The small peak at 2.01 keV corresponds to P,
while the most intense peak at 3.31 keV (Kα line) and several times
lower, following an asymmetric peak at 3.59 keV (Kβ lines) are associ­
ated with K. A broad small peak at 3.69 keV corresponds to Ca (Kα line)
and it overlapped with K. Another Ca peak (Kβ line) was observed at
4.01 keV. The peak typical for Mg (Kα at 1.25 keV) was not visible,
which was restricted by the calibration package of the selected mode
used. For the other two macroelements, S (Kα at 2.31 keV) and Cl (Kα at
2.62 keV), peaks were visible, but their concentrations were not deter­
mined by ICP-OES, and hence, they were not considered.
6
J. Milinovic et al.
Journal of Food Composition and Analysis 110 (2022) 104559
Fig. 6. Original plots of CVPLS model for predicted P content (%) in seaweed samples by ED-XRF vs. ICP-OES results. See Fig. 4 for detailed explanations of
the parameters.
associated with overlapping peaks (Fig. 3). To avoid this problem, and to
minimize erroneous software quantification due to chemical in­
terferences, ED-XRF spectral results were treated by chemometrics tools.
In this context, the multivariate regression analysis was applied to EDXRF spectral results in order to achieve a more accurate quantification
of major elements in seaweed matrices.
the linear regression equations of calibration, all three macroelements
showed higher correlation coefficients after the proposed multivariate
regression model (Table 1S). For the calibration of Ca and P, 7 latent
variables were necessary to maximize the relationship between depen­
dent (ED-XRF spectra) and independent variables (ICP-OES results). For
the calibration datasets all three macroelements, showed high correla­
tion coefficients in the range from 0.88 (K) to 0.95 (P). RMSE and the
associated standard errors (SE), commonly used as uncertainty measure
of a model, were low (< 1.02), while bias was equal to zero. The vali­
dation datasets showed similar values (Figs. 4–6, Table 1S), thus con­
firming the goodness of fit in comparison with scarce data available in
the literature.
The robustness of the applied CVPLS method, tested after removing
data of a sample from the Óbidos lagoon (Ulva sp), confirmed the
goodness of fit. As an example, Fig. 7 shows original plots of the CVPLS
model for predicted K content after excluding results for Ulva sp.
(n = 56). Compared to the entire sample size (Fig. 5), the statistical
parameters in the reduced sample size were improved, i.e., higher R2
(0.95) and lower RMSE (0.58) values (Fig. 7). Nevertheless, a lower
sample size, affected the number of latent variables increasing it to the
maximum number (7). Similar results were confirmed for Ca (R2 = 0.95,
3.3. Quantitative statistics: prediction of macroelements in seaweed
samples
The full cross-validation as superior multivariate modeling was used
in the quantitative statistics, i.e., prediction model, thus providing cor­
rect true apparent prediction error (Martens and Dardenne, 1998). In
this model, all available results were used for calibration and full
cross-validation, and not removing some results from an already limited
set of available samples to an independent validation dataset. Setting
aside some of the results can reduce the predictive performance of the
calibrated models and give an uncertain, systematically over-optimistic
assessment of the predictive performance of models.
The values of statistical parameters of the CVPLS model for pre­
dicting Ca, K, and P are shown in Figs. 4–6 and Table 1S. Compared to
7
J. Milinovic et al.
Journal of Food Composition and Analysis 110 (2022) 104559
Fig. 7. Original plots of CVPLS model for predicted K content (%) in seaweeds, after exclusion of Ulva sp. (n = 56) by ED-XRF vs. ICP-OES results. See Fig. 4 for
detailed explanations of the parameters.
by statistically significant parameters (R2, RMSE, and bias) with smaller
datasets (n = 56–63) having a limited set of samples available. The
proposed method will be extended including more edible species
(depending on their availability), as well as elements, towards scaling up
to routine day-to-day analysis for food industrial applications.
RMSE = 0.09), while no differences in model parameters were observed
for P (R2 = 0.95, RMSE = 0.03). This can be explained by the differences
in the range of concentrations of the three macroelements in selected
samples (Table 2).
To summarize, results demonstrated a significantly good correlation
between ED-XRF spectra (dependent variables) and ICP-OES results
(independent variables) for predicted macroelements in edible seaweeds
after applied CVPLS multivariate analysis. The relatively high R2 values,
low RMSE and bias provided an efficient and robust model to predict the
concentrations of individual major mineral components in representa­
tive edible seaweeds.
Funding
This work was supported by the project “MAR-01.03.01-FEAMP0016-Alga4Food” which is funded by the European Maritime and Fish­
eries Fund and co-funded by the Operational program MAR2020 in the
field of Sustainable development of Aquaculture in the domains of
Innovation, Advice and Productive Investment - Innovation and
Knowledge Action.
F. Figueiredo and J. Milinovic gratefully acknowledge research fel­
lowships from Portuguese Agência Nacional de Inovação (ANI) and
Mar2020 program (Portugal), respectively. J. Sardinha acknowledges
the support from Mar2020 (Portugal) through project MAR-02.01.01FEAMP-0039.
4. Conclusions
Non-destructive, rapid, and economically acceptable ED-XRF spec­
tral analysis can serve for the quantification of macroelements (Ca, K,
and P) in edible seaweeds, by using established multivariate regression
analysis. The proposed full cross-validated PLS regression model can be
used as a pattern for the evaluation of major mineral components in
edible seaweeds based on their rapid ED-XRF spectral analysis. The
goodness of fit, as well as the robustness of the method, were confirmed
8
J. Milinovic et al.
Journal of Food Composition and Analysis 110 (2022) 104559
CRediT authorship contribution statement
Metrology. Available at: 〈https://www.bipm.org./utils/common/documents/jcgm/
JCGM_100_2008_E.pdf〉 (Accessed 07 December, 2021).
Jensen, A., 1993. Present and future needs for algae and algal products. Hydrobiologia
260–261, 15–23.
Lee, A.W., Cho, S.S., 2015. Association between phosphorous intake and bone health in
the NHANES population. Nutr. J. 14, 28.
Linsinger, T., Botha, A., 2019. Principles for the characterisation and the value
assignment of the candidate reference material in the new ISO Guide 35:2017.
Accredit. Qual. Assur. 24, 157–161.
López-López, I., Cofrades, S., Yakan, A., Solas, M.T., Jiménez-Colmenero, F., 2010.
Frozen storage characteristics of low-salt and low-fat beef patties as affected by
Wakame addition and replacing pork backfat with olive oil-in-water emulsion. Food
Res. Int. 43, 1244–1254.
MacArtain, P., Gill, C.I.R., Brooks, M., Campbell, R., Rowland, I.R., 2007. Nutritional
value of edible seaweeds. Nutr. Rev. 65, 535–543.
Malea, P., Chatziapostolou, A., Kevrekidis, T., 2015. Trace element seasonality in marine
macroalgae of different functional-form groups. Mar. Environ. Res. 103, 18–26.
Martens, H.A., Dardenne, P., 1998. Validation and verification of regression in small data
sets. Chemometr. Intell. Lab. Syst. 44, 99–121.
Martins, J.P.A., Teófilo, R.F., Ferreira, M.C., 2010. Computational performance and
cross-validation error precision of five PLS algorithms using designed and real data
sets. J. Chemometr. 24, 320–332.
McComb, J.Q., Rogers, C., Han, F.X., Tchounwou, P.B., 2014. Rapid screening of heavy
metals and trace elements in environmental samples using portable x-ray
fluorescence spectrometer, a comparative study. Water Air Soil Pollut. 225, 2169.
McHugh, D.J. ,2003. A guide to the seaweed industry. Fisheries Technical Paper 441 (pp.
73–90). Rome, Italy: Food and Agriculture Organization of the United Nations.
Milinovic, J., Campos, B., Mata, P., Diniz, M., Noronha, J.P., 2020. Umami free amino
acids in edible green, red, and brown seaweeds from the Portuguese seashore.
J. Appl. Phycol. 32, 3331–3339.
Milinovic, J., Fernando, A.L., Campos, B., Leite, B., Mata, P., Diniz, M., Sardinha, J.,
Noronha, J.P., 2021a. Nutritional benefits of edible macroalgae from the central
Portuguese coast: inclusion of low-calorie ‘sea-vegetables’ in human diet. Int. J.
Environ. Sci. Nat. Resour. 28, 556249.
Milinovic, J., Rodrigues, C., Diniz, M., Noronha, J.P., 2021b. Determination of total
iodine content in edible seaweeds: application of inductively coupled plasma-atomic
emission spectroscopy. Algal Res. 53, 102149.
Milinovic, J., Vale, C., Botelho, M.J., Pereira, E., Sardinha, J., Murton, B.J., Noronha, J.
P., 2021c. Selective incorporation of rare earth elements by seaweeds from Cape
Mondego, western Portuguese coast. Sci. Total Environ. 795, 148860.
Nengsih, T.A., Bertrand, F., Maumy-Bertrand, M., Meyer, N., 2019. Determining the
number of components in PLS regression on incomplete data set. Stat. App. Genet.
Mol. Biol. 18, 20180059.
NIST ,2019. Certificate of Analysis Standard Reference Material® 3232 Kelp powder
(Thallus laminariae). National Institute of Standards and Technology. Available at:
〈https://www-s.nist.gov/srmors/certificates/3232.pdf〉 (Accessed 07 December,
2021).
Paiva, L., Lima, E., Patarra, R.F., Neto, A.I., Baptista, J., 2014. Edible Azorean
macroalgae as source of rich nutrients with impact on human health. Food Chem.
100, 1331–1336.
Pereira, L., 2009. Guia ilustrado das macroalgas: conhecer e reconhecer algumas espécies
da flora portuguesa. Tipografia Lousanense, Lda, Lousã, Portugal (Ed.). Available at:
https://doi.org/10.14195/978–989-26–0397-1 (Accessed 09 February, 2021).
Pereira, L. , 2012. MACOI - Portuguese Seaweeds website, IMAR-CMA, University of
Coimbra (Ed.). Available at: http://macoi.ci.uc.pt (Accessed 09 February 2021).
Rodrigues, D., Freitas, A.C., Pereria, L., Rocha-Santos, A.P., Vasconcelos, M.W.,
Roriz, M., Rodriguez-Alcalá, L.M., Gomes, A.M.P., Duarte, A.C., 2015. Chemical
composition of red, brown and green macroalgae from Buarcos bay in Central West
Coast of Portugal. Food Chem. 183, 197–207.
Rubio, C., Napoleone, G., Luis-González, G., Gutiérrez, A.J., González-Weller, D.,
Hardisson, A., Revert, C., 2017. Metals in edible seaweed. Chemosphere 173,
572–579.
Rupérez, P., 2002. Mineral content of edible marine seaweeds. Food Chem. 79, 23–26.
Stott, A.E., Kanna, S., Mandic, D.P., Pike, W.T. ,2017. An online NIPALS algorithm for
partial least squares, in: Proceedings of the IEEE International Conference on
Acoustics, Speech, and Signal Processing (pp. 4177–4181). New Orleans, Louisiana,
USA.
Turner, A., Poon, H., Taylor, A., Brown, M.T., 2017. In situ determination of trace
elements in Fucus spp. by field-portable-XRF. Sci. Total Environ. 593–594, 227–235.
Varma, R., Turner, A., Brown, M.T., 2011. Bioaccumulation of metals by Fucus ceranoides
in estuaries of South West England. Mar. Pollut. Bull. 62, 2557–2562.
Yu, L.L., Browning, J.F., Burdette, C.Q., Caceres, G.C., Chieh, K.D., Davis, W.C.,
Kassim, B.L., Long, S.E., Murphy, K.E., Oflaz, R., Paul, R.L., Sharpless, K.E., Wood, L.
J., Yen, J.H., Zeisler, R., 2018. Development of a kelp powder (Thallus laminariae)
standard reference material. Anal. Bioanal. Chem. 410, 1265–1278.
J. Milinovic conceptualized and designed the complete study,
interpreted data, drafted and revised the manuscript. F.M.J. Figueiredo
performed the experimental part of the analysis. J.P. Noronha, J. Sar­
dinha provided the study materials, obtained funding, revised and
confirmed the final approval of the manuscript.
Conflict of interest
The authors declare that there are no conflicts of interest.
Acknowledgments
The authors are thankful to J.M.A. de Sousa for constructive and
valuable comments, to improve the manuscript until its final version.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.jfca.2022.104559.
References
Bosco, G.L., 2013. Development and application of portable, hand-held x-ray
fluorescence spectrometers. Trends Anal Chem. 45, 121e134.
Brito, G.B., Teixeira, L.S.G., Korn, M.G.A., 2017. Direct analysis of marine macroalgae for
determination of macro minerals by energy dispersive x-ray fluorescence.
Microchem. J. 134, 35–40.
Bull, A., Brown, M.T., Turner, A., 2016. Novel use of field-portable-XRF for the direct
analysis of trace elements in marine macroalgae. Environ. Pollut. 1–6.
Chapman, V.J., Chapman, D.J., 1980. Sea vegetables (Algae as food for man). Seaweeds
and Their Uses. Chapman & Hall, London, England, pp. 62–97.
Collins, G.S., A. de Groot, J.A., Dutton, S., Omar, O., Shanyinde, M., Tajar, A.,
Voysey, M., Wharton, R., Yu, L.M., Moons, K.G., Altman, D.G., 2014. External
validation of multivariable prediction models: a systematic review of methodological
conduct and reporting. BMC Med. Res. Methodol. 14, 40.
Domínguez-Gonzalez, R., Moreda-Piñeiro, A., Bermejo-Barrera, A., Bermejo-Barrera, P.,
2005. Application of ultrasound-assisted acid leaching procedures for major and
trace elements determination in edible seaweed by inductively coupled plasmaoptical emission spectrometry. Talanta 66, 937–942.
Engelen, S., Hubert, M., Branden, K.V., Verboven, S., 2004. Robust PCR and PLSR: a
comparative study. Stat. Indus. Technol. 105–117.
FAO, 2018. The global status of seaweed production, trade and utilization. Globefish
Research Program. Food and Agriculture Organization of the United Nations, Rome,
Italy.
Fleurence, J., 2016. Seaweeds as food. In: Fleurence, J., Levine, I. (Eds.), Seaweed in
Health and Disease Prevention. Elsevier Science, Kansas City, Missouri, pp. 149–167.
Flores, S.R.L., Dobbs, J., Dunn, M.A., 2015. Mineral nutrient content and iron
bioavailability in common and Hawaiian seaweeds assessed by an in vitro digestion/
Caco-2 cell model. J. Food Compos. Anal. 43, 185–193.
Gallardo, H., Queralt, I., Tapias, J., Guerra, M., Carvalho, M.L., Marguí, E., 2016.
Possibilities of low-power X-ray fluorescence spectrometry methods for rapid
multielemental analysis and imaging of vegetal foodstuffs. J. Food Compo. Anal. 50,
1–9.
Gallhofer, D., Lottermoser, B.G., 2018. The influence of spectral interferences on critical
element determination with portable X-ray fluorescence (pXRF). Minerals 8, 320.
Gullón, P., Astray, G., Gullón, B., Franco, D., Campagnol, P.C.B., Lorenzo, J.M., 2020.
Inclusion of seaweeds as healthy approach to formulate new low-salt meat products.
Curr. Opin. Food Sci. 40, 20–25.
Gupta, S., Abu-Ghannam, N., 2011. Recent developments in the application of seaweeds
or seaweed extracts as a means for enhancing the safety and quality attributes of
foods. Innov. Food Sci. Emerg. Technol. 12, 600–609.
Herreros-Chavez, L., Morales-Rubio, A., Cervera, M.L., 2019. Green methodology for
quality control of elemental content of infant milk powder. LWT Food Sci. Technol.
111, 484–489.
JCGM ,2008. Evaluation of measurement data - Guide to the expression of uncertainty in
measurement (GUM 1995with minor corrections). Joint Committee for Guides in
9