ArticlePDF Available

Multi-Chemical Profiling of Strawberry as a Traceability Tool to Investigate the Effect of Cultivar and Cultivation Conditions

Authors:
  • Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA)

Abstract and Figures

The chemical composition of foods is tightly regulated by multiple genotypic and agronomic factors, which can thus serve as potential descriptors for traceability and authentication purposes. In the present work, we performed a multi-chemical characterization of strawberry fruits from five varieties (Aromas, Camarosa, Diamante, Medina, and Ventana) grown in two cultivation systems (open/closed soilless systems) during two consecutive campaigns with different climatic conditions (rainfall and temperature). For this purpose, we analyzed multiple components closely related to the sensory and health characteristics of strawberry, including sugars, organic acids, phenolic compounds, and essential and non-essential mineral elements, and various complementary statistical approaches were applied for selecting chemical descriptors of cultivar and agronomic conditions. Anthocyanins, phenolic acids, sucrose, and malic acid were found to be the most discriminant variables among cultivars, while climatic conditions and the cultivation system were behind changes in polyphenol contents. These results thus demonstrate the utility of combining multi-chemical profiling approaches with advanced chemometric tools in food traceability research.
Content may be subject to copyright.
foods
Article
Multi-Chemical Profiling of Strawberry as a
Traceability Tool to Investigate the Eect of Cultivar
and Cultivation Conditions
Raúl González-Domínguez 1, 2, * , Ana Sayago 1,2 , Ikram Akhatou 1,2 and
Ángeles Fernández-Recamales 1, 2, *
1Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, 21007 Huelva, Spain;
ana.sayago@dqcm.uhu.es (A.S.); ikram.akhatou@alu.uhu.es (I.A.)
2International Campus of Excellence CeiA3, University of Huelva, 21007 Huelva, Spain
*Correspondence: raul.gonzalez@dqcm.uhu.es (R.G.-D.); recamale@dqcm.uhu.es (Á.F.-R.);
Tel.: +34-959219975 (R.G.-D.); +34-959-219958 (Á.F.-R.)
Received: 23 December 2019; Accepted: 13 January 2020; Published: 16 January 2020


Abstract:
The chemical composition of foods is tightly regulated by multiple genotypic and agronomic
factors, which can thus serve as potential descriptors for traceability and authentication purposes.
In the present work, we performed a multi-chemical characterization of strawberry fruits from five
varieties (Aromas, Camarosa, Diamante, Medina, and Ventana) grown in two cultivation systems
(open/closed soilless systems) during two consecutive campaigns with dierent climatic conditions
(rainfall and temperature). For this purpose, we analyzed multiple components closely related to the
sensory and health characteristics of strawberry, including sugars, organic acids, phenolic compounds,
and essential and non-essential mineral elements, and various complementary statistical approaches
were applied for selecting chemical descriptors of cultivar and agronomic conditions. Anthocyanins,
phenolic acids, sucrose, and malic acid were found to be the most discriminant variables among
cultivars, while climatic conditions and the cultivation system were behind changes in polyphenol
contents. These results thus demonstrate the utility of combining multi-chemical profiling approaches
with advanced chemometric tools in food traceability research.
Keywords:
strawberry; traceability; sugars; organic acids; phenolic compounds; mineral elements;
cultivar; cultivation system
1. Introduction
The composition of foods, in terms of nutrients, bioactive compounds, and other components,
is tightly regulated by multiple factors, such as the genotype, geographical origin, environmental
factors, and agronomic conditions. Therefore, this influences the sensory, nutritional, and nutraceutical
properties of food products, which makes the implementation of quality control strategies mandatory
to ensure their authenticity and traceability. In this vein, it should be noted that food quality and
safety may be influenced by a myriad of factors throughout the entire supply chain, from initial
food production to packaging, processing, and transport, until its final commercialization [
1
]. This
is particularly important for processed foods, which usually require more complex operations and
thus make the implementation of ecient traceability initiatives mandatory. To address these needs,
novel and powerful analytical methods are requested by the food industry to accurately guarantee the
authenticity and traceability of food products.
Strawberry (Fragaria
×
ananassa Duch.) is one of the most commonly consumed berry fruits around
the world and is considered a functional food because of its chemical composition, which rich in essential
and bioactive compounds. Strawberry has been demonstrated to lower post-prandial oxidative stress,
Foods 2020,9, 96; doi:10.3390/foods9010096 www.mdpi.com/journal/foods
Foods 2020,9, 96 2 of 9
hyperglycemia, hyperlipidemia, and inflammation, and its consumption has been associated with
a reduced incidence of cardiovascular diseases (e.g., hypertension), cancer, and other diseases [
2
,
3
].
In this context, this berry fruit has been proposed as a potential ingredient for the production of
nutraceutical products, such as beverages, flours, and powders [
4
]. The main soluble constituents
of strawberry include sugars (e.g., glucose, fructose, and sucrose) and organic acids (e.g., citric and
malic), which influence the final taste and flavor of this fruit [
5
]. Furthermore, strawberry is also a rich
source of numerous bioactive compounds, such as dietary fibers, minerals, vitamins, and phenolic
compounds [
6
,
7
]. In particular, polyphenols are known to elicit multiple biological activities, acting as
natural antioxidants that protect the organism against free radicals [
8
]. Other pro-healthy compounds
found in strawberries are vitamins and minerals, which intervene in a multitude of processes and
chemical reactions inside the cells. For instance, potassium, a major element in strawberry, plays an
important role in protection against cardiovascular diseases [
9
]. According to recent literature, this
characteristic chemical profile of strawberry is largely influenced by multiple factors (e.g., cultivar,
climate, and cultivation conditions) [7,1013], evidencing its potential as a traceability tool.
In this work, we employed a multi-targeted profiling approach to characterize the chemical
composition of strawberry, considering multiple compounds related to sensory and health
characteristics of this berry fruit, including sugars, organic acids, polyphenols, and mineral elements.
This multi-chemical profile was investigated as a potential tool for authentication and traceability
purposes, with the aim of discriminating strawberry varieties grown under dierent climatic and
agronomic conditions. For this purpose, complementary pattern recognition procedures were employed,
including principal component analysis (PCA), linear discriminant analysis (LDA), soft independent
model class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA).
2. Materials and Methods
2.1. Experimental Design and Sampling
Strawberry fruits (Fragaria
×
ananassa Duch.) were collected in two consecutive campaigns
(years 2015 and 2016) from the same experimental plantations located in Huelva (southwest Spain),
at the same commercial ripeness (>75% of the surface showing red color). The first campaign was
characterized by higher total radiation, while in the second one, higher rainfall, and maximum and
minimum temperatures were registered. Five varieties of strawberries, genetically characterized
by the vendor (Aromas, Camarosa, Diamante, Medina, and Ventana) and grown in two soilless
systems (closed and open systems, i.e., with and without recirculation of the nutrient solution,
respectively), were investigated. Plants were grown in a polycarbonate-covered greenhouse using
elevated horizontal troughs filled with coconut fiber as a substrate, and with natural daylight as a
radiation source. The temperature ranged from 25
C during the day to 8
C at night, with relative
humidity held at 75 ±5%.
Several fruits (n=10) were collected for each variety and cultivation system to generate a
representative pooled sample. Immediately after harvesting, fruits were sorted, frozen in situ in a deep
freezer, and shipped to the laboratory in polystyrene punnets. Then, fruits were washed, sepals were
dissected, and pooled fruits (n=10) were gently homogenized by using a kitchen mixer to obtain a
puree (approximately 100–150 mL). Samples were subsequently aliquoted and stored for up to 2 months
at
21
C, until further analysis. For each study condition (i.e., cultivar, campaign, and cultivation
conditions), three replicates (i.e., three pooled and homogenized samples) were prepared.
2.2. Analysis of Sugars and Organic Acids
Sugars and organic acids were analyzed using an Agilent 110 series high-performance liquid
chromatography (HPLC) system coupled to ultraviolet (UV) and refractive index (RI) detectors
(Agilent Technologies, Santa Clara, CA, USA), following the methodology previously described [
7
].
Approximately 1 g of the homogenate was accurately weighed, diluted to 10 mL with ultrapure water
Foods 2020,9, 96 3 of 9
(Millipore, Bedford, Massachusetts, MA, USA), and centrifuged at 10,000 rpm for 10 min (BHG-Hermle
Z 365, Wehingen, Germany). The supernatant was filtered through a 0.45
µ
m PVDF (polyvinylidene
difluoride) filter prior to HPLC analysis.
In a single chromatographic run, three sugars (glucose, fructose, and sucrose) and six organic acids
(oxalic, citric, tartaric, malic, succinic, and lactic) were separated using a Metacarb 87H hydrogen-form
cation-exchange resin-based column (300
×
7.8 mm internal diameter, i.d.) packed with sulfonated
polystyrene. A total of 5 mM of sulfuric acid was delivered in isocratic mode at a 0.5 mL min
1
flow
rate for 15 min, and the injection volume was 20
µ
L. UV detection of organic acids was performed at
210 nm, while sugars were analyzed by using the RI detector. Identifications were accomplished by
comparing retention times (and UV spectra for organic acids) with those of reference standards.
2.3. Analysis of Phenolic Compounds
Homogenized fruits (5.0 g) were dissolved with 25 mL of methanol, sonicated for 30 min,
and then centrifuged at 10,000 rpm for 10 min at 4
C. Supernatants were concentrated by using
a rotary evaporator at 40
C, and the residues were re-dissolved in 3 mL of 50% methanol (v/v).
The concentrated extracts were filtered through 0.45
µ
m PVDF filters, and 20
µ
L was injected into
a reverse phase Ultrabase C18 column (2.5
µ
m, 100 mm
×
4.6 mm i.d.), following the methodology
described elsewhere [
10
]. For the analysis of colorless flavonoids and phenolic acids, elution solvents
were water:methanol:acetic acid (93:5:2, v/v/v) (eluent A) and methanol:acetic acid (98:2, v/v) (eluent B),
which were delivered as follows: 0–29 min, 40% B; 29–34.8 min, 40–60% B; 34.8–37.7 min, 60–75% B;
37.7–40.6 min, 75–100% B; 40.6–46.4 min, 0% B. For anthocyanins, the mobile phase consisted of 10%
(v/v) aqueous formic acid (eluent A) and methanol (eluent B), using the following gradient program:
0–0.70 min, 5% B; 0.70–16.60 min, 5–50% B; 16.60–18.60 min, 50–95% B; 18.60–20.60 min, 95% B. The flow
rate was 0.8 mL min
1
, and the column temperature was set at 20 and 30
C for non-anthocyanin and
anthocyanin compounds, respectively.
The identification of phenolic compounds was achieved by comparing their retention times and
UV spectra with those for commercial standards. For quantification, the following wavelengths were
employed: 260 nm for ellagic acid and derivatives, 280 nm for benzoic acids and flavan-3-ols, 320 nm
for cinnamic acids, 360 nm for flavonols, and 520 nm for anthocyanins.
2.4. Analysis of Mineral Elements
For mineral content analysis, 0.5 g of fruit was placed in a Teflon vessel and digested with 3 mL
of a mixture of nitric and hydrochloric acids, both 1.5 M. Digestion was carried out for 2 min using
a microwave furnace at 250 W. After cooling, the digest was filtered, transferred to a 25 mL flask,
and made-up with ultrapure water.
The major (i.e., Ca, Mg, K, P, and S) and trace (i.e., Al, As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, Ba, Mn, Na,
Sr, V, and Zn) minerals were determined by inductively coupled plasma optical emission spectrometry
(ICP-OES) using a Jobin-Yvon Ultima 2 ICP spectrometer with an ultrasonic nebulizer (U6000 AT+,
Cetac). The instrument was operated at the following conditions: radio frequency, 27 MHz; operating
power, 1200 W; plasma argon flow rate, 2 L min
1
; auxiliary gas flow rate, 2 L min
1
; nebulizer gas
flow rate, 0.02 L min
1
; nebulizer pressure, 1 bar; rinsing time, 35 s; rinsing pump speed, high; transfer
time, 60 s; stabilization time, 20 s; and transfer pump speed, high. ICP Multi Element Standard IV and
VI CertiPur®(Merck) were used to prepare reference solutions.
Foods 2020,9, 96 4 of 9
2.5. Statistical Analysis
One-way analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), and pattern
recognition techniques, including principal component analysis (PCA), linear discriminant analysis
(LDA), soft independent modeling of class analogy (SIMCA), and partial least squares discriminant
analysis (PLS-DA), were carried out to investigate the dierences among strawberry varieties and/or
cultivation systems. All statistical analyses were conducted on Statistica 7.1 (StatSoft Inc., Tulsa,
Oklahoma, OK, USA) and SIMCA-P11.5 (UMetrics AB, Umeå, Sweden).
3. Results and Discussion
3.1. Multi-Chemical Profiling of Strawberry
Mean concentrations for all the analyzed compounds (i.e., sugars, organic acids, polyphenols,
and mineral elements) are listed in Table 1for the five strawberry cultivars investigated. Soluble sugars
identified and quantified in strawberry fruits were fructose, glucose, and sucrose; monosaccharides
were the major species in all varieties, except for “Camarosa”, which showed higher sucrose contents.
The ratio of fructose to glucose content was about the same, regardless of the cultivar, in agreement with
our previous study findings [
10
]. With regards to organic acids, citric acid was the most concentrated
metabolite, followed by malic acid, in consonance with previous studies [
7
,
11
]. In agreement with
results found in the literature, anthocyanins were the predominant polyphenol class in strawberry [
14
],
followed by phenolic acids, with pelargonidin 3-glucoside, pelargonidin 3-rutinoside, and cyanidin
3-glucoside being the three major anthocyanin species [
8
,
15
], which were found at similar levels
to those reported by Crespo et al. [
16
]. The mineral profile was mainly dominated by five major
elements—K, P, Ca, Na, and Mg—with potassium showing the highest concentrations (average
content of 2834.5 mg kg
1
). Phosphorous, calcium magnesium, and sodium were also present in high
concentrations, representing approximately 20% of the total mineral content, while other elements (Fe,
Cu, Zn, and Sr) accounted for less than 1% of the mineral profile. It should be noted that these results
are in line with previous findings [7].
Multivariate analysis of variance (MANOVA) was applied to test the eects of the cultivar and
cultivation system on the chemical profile, and analysis of variance (ANOVA) with a Tukey HSD
post hoc test was used to evaluate the statistical significance of the dierences for each compound
or element measured. The multivariate test showed that both factors have a significant eect on the
content of sugars, organic acids, and polyphenols (p<0.001), but not on the mineral profile (p>0.1
and p>0.5 for the variety and cultivation system, respectively). Univariate results for each variable
are shown in Table 1. “Camarosa” and “Ventana” were found to be the richest cultivars in total sugars
and organic acids. In particular, “Camarosa” strawberries showed the highest content of sucrose and
malic acid. The “Ventana” cultivar presented the richest profile in phenolic acids, mainly dominated
by ellagic acid, while “Camarosa” and “Aromas” varieties showed higher concentrations of total
polyphenols, mainly anthocyanins.
Foods 2020,9, 96 5 of 9
Table 1.
Concentrations (expressed as the mean
±
standard deviation) of sugars (g kg
1
), organic acids
(g kg
1
), phenolic compounds (mg kg
1
), and mineral elements (mg kg
1
) in each strawberry cultivar,
and pvalues obtained by ANOVA.
Compounds Aromas Camarosa Diamante Medina Ventana pValue
sucrose 6.9 ±4.8 14.1 ±2.3 9.2 ±1.7 6.7 ±3.4 10.0 ±3.1 0.0003
glucose 11.9 ±4.4 11.7 ±3.2 12.4 ±3.0 12.3 ±3.9 14.6 ±3.9 0.4836
fructose 11.6 ±4.1 11.0 ±2.8 11.5 ±2.4 11.4 ±3.8 13.3 ±3.4 0.6800
Total Sugars 30.5 ±12.8 36.7 ±6.3 33.1 ±5.7 30.4 ±10.2 37.9 ±8.7 0.3383
ascorbic acid 0.1 ±0.04 0.2 ±0.1 0.2 ±0.02 0.2 ±0.08 0.2 ±0.1 0.4145
citric acid 5.1 ±2.0 6.3 ±0.8 5.3 ±1.1 4.7 ±1.4 5.3 ±0.8 0.1937
tartaric acid 0.08 ±0.08 0.1 ±0.04 0.2 ±0.06 0.07 ±0.09 0.2 ±0.07 0.0959
malic acid 0.5 ±0.1 2.4 ±0.4 0.6 ±0.2 0.5 ±0.1 0.7 ±0.2 0.0871
Total Acids 5.8 ±2.2 8.9 ±2.9 6.2 ±1.3 5.5 ±1.5 6.4 ±0.8 0.0074
pelargonidin derivative 1 0.9 ±0.3 0.8 ±0.2 0.6 ±0.2 0.7 ±0.3 0.7 ±0.4 0.0750
cyanidin 3-glucoside 6.4 ±1.6 4.0 ±0.8 3.0 ±1.1 3.8 ±0.2 1.4 ±0.6 0.0000
pelargonidin 3-glucoside 120.9 ±17.7 117.2 ±29.9 72.4 ±3.3 102.7 ±30.9 86.1 ±22.2 0.0003
pelargonidin 3-rutinoside 7.4 ±1.6 15.8 ±5.1 5.2 ±1.0 6.2 ±0.7 6.7 ±2.5 0.0000
pelargonidin derivative 2 0.7 ±0.2 0.6 ±0.3 0.6 ±0.2 0.7 ±0.09 0.8 ±0.4 0.8180
pelargonidin acetate 3.0 ±0.4 2.3 ±0.7 1.4 ±0.2 2.1 ±0.8 1.1 ±0.4 0.0000
Total Anthocyanins 139.4 ±18.3 140.9 ±35.9 83.3 ±1.9 116.2 ±32.3 96.7 ±25.1 0.0001
p-hydroxybenzoic acid 0.6 ±0.1 1.4 ±0.3 0.8 ±0.9 0.3 ±0.02 0.5 ±0.03 0.0139
caeic acid 0.4 ±0.1 0.6 ±0.2 0.2 ±0.01 0.5 ±0.1 0.9 ±0.2 0.0001
p-coumaric acid 7.8 ±1.7 6.6 ±3.1 4.2 ±2.2 5.8 ±1.3 19.3 ±6.1 0.0000
ferulic acid 0.08 ±0.02 0.2 ±0.07 0.2 ±0.02 0.1 ±0.04 0.4 ±0.08 0.0078
ellagic acid 39.3 ±10.3 35.8 ±10.5 54.3 ±23.8 45.8 ±22.3 63.4 ±25.9 0.1295
Total Phenolic Acids 48.1 ±11.1 44.6 ±13.5 59.8 ±24.5 52.6 ±22.4 84.4 ±36.3 0.0129
quercetin 1.4 ±0.08 1.5 ±0.2 0.9 ±0.2 0.9 ±0.3 0.7 ±0.1 0.0249
Kaempferol O-glucoside 23.0 ±7.2 29.5 ±10.3 18.3 ±6.0 21.2 ±9.5 30.3 ±8.3 0.0262
Total Flavonols 24.4 ±7.2 31.0 ±10.4 19.3 ±6.1 22.2 ±9.9 31.0 ±7.9 0.0237
P 224.3 ±35.6 251.6 ±11.3 196.2 ±29.5 219.2 ±21.8 218.5 ±16.4 0.0025
Ba 0.6 ±0.2 0.5 ±0.06 0.4 ±0.06 0.4 ±0.02 0.4 ±0.04 0.9586
Ca 210.7 ±24.1 244.2 ±35.4 156.4 ±20.1 195.4 ±33.2 235.2 ±28.6 0.9732
Cr 0.1 ±0.02 0.06 ±0.01 0.06 ±0.02 0.05 ±0.01 0.2 ±0.03 0.9932
Cu 4.6 ±1.9 4.8 ±1.1 4.8 ±1.4 4.9 ±1.4 5.4 ±1.3 0.9915
Fe 7.8 ±1.2 7.6 ±1.1 5.8 ±1.6 8.7 ±1.1 7.5 ±1.9 0.9723
K 2843.5 ±287.6 2788.2 ±357.4 2098.1 ±210.2 2844.8 ±351.9 3597.9 ±443.5 0.9263
Mg 226.7 ±26.9 179.3 ±26.0 142.5 ±20.1 167.5 ±23.7 222.6 ±30.7 0.9526
Mn 8.8 ±1.9 6.9 ±1.0 5.9 ±1.9 6.8 ±1.1 9.9 ±1.1 0.9394
Na 189.1 ±28.9 116.0 ±23.7 98.3 ±21.5 88.9 ±17.4 126.2 ±16.1 0.9000
Ni 0.3 ±0.06 0.3 ±0.02 0.3 ±0.07 0.3 ±0.03 0.3 ±0.05 0.9890
Sr 6.0 ±1.0 3.6 ±1.7 3.1 ±1.5 4.8 ±1.8 5.6 ±1.7 0.8802
Zn 3.2 ±0.9 7.5 ±0.8 3.74 ±0.49 3.47 ±0.33 4.26 ±0.30 0.5551
ANOVA, One-way analysis of variance.
3.2. Application of Pattern Recognition Tools for Selecting Chemical Descriptors of Cultivar and
Agronomic Conditions
Several chemometric techniques, including unsupervised and supervised pattern recognition
procedures, were employed to achieve a reliable dierentiation between strawberry samples according
to the cultivar, cultivation system, and/or campaign.
A preliminary data exploration was carried out by principal component analysis (PCA),
using autoscaled data and only considering the principal components (PCs) with eigenvalues greater
than 1. This PCA model allowed 84% of the total variance to be explained with five components. As
shown in the scores plot built using the two first principal components (Figure 1A), a clear separation
was observed along the PC1 among samples collected in the two consecutive campaigns. The first PC
explained 28% of the variance, and was positively related to fructose and tartaric acid, and negatively
associated with pelargonidin 3-glucoside, total flavonoids, and total polyphenols. That is, the content
of anthocyanins and total polyphenols was greater during the second campaign, when rainfall,
and maximum and minimum temperatures were higher, whereas fructose and tartaric acid contents
were more abundant in the first campaign, when total radiation was higher. In this vein, it has previously
been described that the content of many phenolic compounds and the antioxidant capacity increase in
berry fruits as the temperature increases [
17
]. Moreover, a low light intensity and high temperatures
have also been demonstrated to provoke a decreased synthesis of sugars and ascorbic acid [
7
,
11
,
18
].
Foods 2020,9, 96 6 of 9
On the other hand, the plotting of the second and fourth PCs provided a certain dierentiation,
depending on the cultivar (Figure 1B), with “Camarosa” and “Aromas” varieties distributed on the
left side of the projection, and the rest of the samples located on the right side. The most relevant
compounds contributing to this separation were anthocyanins (increased in “Camarosa” and “Aromas”)
and phenolic acids (decreased in “Camarosa” and “Aromas)”, in accordance with the results obtained
by ANOVA.
Foods 2020, 9, x FOR PEER REVIEW 6 of 9
Figure 1. Principal component analysis (PCA) score plots showing the projection of strawberry
samples in the plane defined by the following principal components: (A) PC1 vs. PC2, separation of
samples according to the campaign; (B) PC2 vs. PC4, separation of samples according to the cultivar.
After this preliminary data exploration, several supervised chemometric tools were employed
to build classification models with the aim of assessing the potential of the multi-chemical profile
investigated in this work to authenticate strawberries according to the variety and cultivation
conditions. For this purpose, multiple supervised pattern recognition procedures have recently been
proposed in food research to solve authentication problems for various foods with a high commercial
value, such as strawberry [11,15], olive oil [19–21], or wine [22,23]. In the present study, three
complementary statistical techniques were tested: linear discriminant analysis (LDA), soft
independent modeling of class analogy (SIMCA), and partial least squares discriminant analysis
(PLS-DA).
Linear discriminant analysis (LDA) was first applied to all the study variables, yielding a model
capable of explaining 96% of the total variance with a 95% prediction ability. Applying forward
stepwise analysis, cyanidin 3-glucoside, pelargonidin 3-rutinoside, p-coumaric acid, phosphorous,
malic acid, caffeic acid, and quercetin were identified as the most discriminant variables among
cultivars. As shown in Figure 2A, all samples were correctly classified, with the exception of two
samples of “Medina” cultivar, which were classified as “Diamante”. In line with the results from
PCA, “Aromas” and “Camarosa” cultivars were clearly differentiated from the rest of the samples
along the first root, while the second one described almost complete separation between the other
three cultivars.
Soft independent modeling of class analogy (SIMCA) was subsequently applied to the same data
matrix used in LDA, with the aim of looking for possible overlap among the study groups. Using a
seven-fold cross-validation procedure, 3-PC-based models were obtained explaining 96.4%, 94.5%,
95.0%, 92.5%, and 97.2% of variance for the classes “Aromas”, “Camarosa”, “Diamante”, “Medina”,
and “Ventana”, respectively. These models also provided very good results in terms of their
prediction ability, with 86.7%, 81.5%, 80.8%, 71.3%, and 88.5% correct prediction for the five cultivars.
In this line, representation of the corresponding Coomans plot showed a correct classification of
strawberries according to the variety based on their chemical composition (Figure 2B). However,
SIMCA modeling did not provide suitable results for the classification of strawberry samples
according to agronomic conditions, with samples appearing in the overlapping area from the
Coomans plots (figure not shown).
Figure 1.
Principal component analysis (PCA) score plots showing the projection of strawberry samples
in the plane defined by the following principal components: (
A
) PC1 vs. PC2, separation of samples
according to the campaign; (B) PC2 vs. PC4, separation of samples according to the cultivar.
After this preliminary data exploration, several supervised chemometric tools were employed
to build classification models with the aim of assessing the potential of the multi-chemical profile
investigated in this work to authenticate strawberries according to the variety and cultivation conditions.
For this purpose, multiple supervised pattern recognition procedures have recently been proposed
in food research to solve authentication problems for various foods with a high commercial value,
such as strawberry [
11
,
15
], olive oil [
19
21
], or wine [
22
,
23
]. In the present study, three complementary
statistical techniques were tested: linear discriminant analysis (LDA), soft independent modeling of
class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA).
Linear discriminant analysis (LDA) was first applied to all the study variables, yielding a model
capable of explaining 96% of the total variance with a 95% prediction ability. Applying forward
stepwise analysis, cyanidin 3-glucoside, pelargonidin 3-rutinoside, p-coumaric acid, phosphorous,
malic acid, caeic acid, and quercetin were identified as the most discriminant variables among
cultivars. As shown in Figure 2A, all samples were correctly classified, with the exception of two
samples of “Medina” cultivar, which were classified as “Diamante”. In line with the results from PCA,
“Aromas” and “Camarosa” cultivars were clearly dierentiated from the rest of the samples along the
first root, while the second one described almost complete separation between the other three cultivars.
Soft independent modeling of class analogy (SIMCA) was subsequently applied to the same data
matrix used in LDA, with the aim of looking for possible overlap among the study groups. Using a
seven-fold cross-validation procedure, 3-PC-based models were obtained explaining 96.4%, 94.5%,
95.0%, 92.5%, and 97.2% of variance for the classes “Aromas”, “Camarosa”, “Diamante”, “Medina”,
and “Ventana”, respectively. These models also provided very good results in terms of their prediction
ability, with 86.7%, 81.5%, 80.8%, 71.3%, and 88.5% correct prediction for the five cultivars. In this
line, representation of the corresponding Coomans plot showed a correct classification of strawberries
according to the variety based on their chemical composition (Figure 2B). However, SIMCA modeling
did not provide suitable results for the classification of strawberry samples according to agronomic
conditions, with samples appearing in the overlapping area from the Coomans plots (figure not shown).
Foods 2020,9, 96 7 of 9
Foods 2020, 9, x FOR PEER REVIEW 7 of 9
Figure 2. Results obtained from supervised chemometric modeling. (A) Linear discriminant analysis
(LDA) scores plot showing the distribution of samples in the plane defined by the two first principal
components using the cultivar as the categorical variable; (B) Soft independent model class analogy
(SIMCA) Coomans plots for the classification of strawberry samples according to the cultivar:
“Aromas” vs. ”Camarosa”; (C) Partial least squares discriminant analysis (PLS-DA) scores plot
showing the distribution of samples in the plane defined by the two first principal components using
the cultivar as the categorical variable; (D) PLS-DA scores plot showing the distribution of samples
in the plane defined by the two first principal components using the cultivation system as the
categorical variable.
Finally, partial least squares discriminant analysis (PLS-DA) was also employed as a more
powerful technique for class differentiation and for the selection of the most discriminant variables.
A five-component model was obtained with a good quality of fit (R
2X
= 0.744) and predictive ability
(Q
2
= 0.413) for the classification of strawberry samples according to the cultivar (Figure 2C). The
most important chemical descriptors driving this separation were anthocyanins and phenolic acids,
in line with previous findings from ANOVA and LDA. Interestingly, PLS-DA modeling also enabled
the discrimination of samples grown in the two cultivation systems (i.e., open and closed soilless
systems). The PLS-DA model explained 70.2% of the variance (Figure 2D), with p-hydroxybenzoic
acid, ferulic acid, unknown derivatives of pelargonidin, glucose, pelargonidin acetylglucoside, and
cyanidin 3-glucoside being the most discriminant variables.
4. Conclusions
In this work, we have evaluated the potential of combining multi-chemical profiling and
complementary statistical techniques to investigate the effect of the genotype and cultivation
conditions on the chemical composition of strawberry fruits. The five cultivars investigated showed
clear differences in the content of anthocyanins, phenolic acids, sucrose, and malic acid. On the other
hand, climatic conditions (e.g., rainfall and temperature) were responsible for slight changes in the
polyphenolic profile, with an increased content of anthocyanins and total polyphenols in strawberry
fruits grown under higher rainfall and more extreme temperatures. Similarly, the cultivation
conditions (i.e., open/closed soilless system) also induced minor changes in concentrations of several
anthocyanins and phenolic acids. The present work therefore demonstrates that multi-chemical
profiling can be used to differentiate among strawberry cultivars grown under different agronomic
conditions, thus showing a great applicability for food traceability. In future studies, this approach
could also be tested to search for characteristic patterns associated with the geographical origin,
ripeness status, and other factors related to food production.
Figure 2.
Results obtained from supervised chemometric modeling. (
A
) Linear discriminant analysis
(LDA) scores plot showing the distribution of samples in the plane defined by the two first principal
components using the cultivar as the categorical variable; (
B
) Soft independent model class analogy
(SIMCA) Coomans plots for the classification of strawberry samples according to the cultivar: “Aromas”
vs. ”Camarosa”; (
C
) Partial least squares discriminant analysis (PLS-DA) scores plot showing the
distribution of samples in the plane defined by the two first principal components using the cultivar
as the categorical variable; (
D
) PLS-DA scores plot showing the distribution of samples in the plane
defined by the two first principal components using the cultivation system as the categorical variable.
Finally, partial least squares discriminant analysis (PLS-DA) was also employed as a more
powerful technique for class dierentiation and for the selection of the most discriminant variables.
A five-component model was obtained with a good quality of fit (R
2X
=0.744) and predictive ability
(Q
2
=0.413) for the classification of strawberry samples according to the cultivar (Figure 2C). The most
important chemical descriptors driving this separation were anthocyanins and phenolic acids, in line
with previous findings from ANOVA and LDA. Interestingly, PLS-DA modeling also enabled the
discrimination of samples grown in the two cultivation systems (i.e., open and closed soilless systems).
The PLS-DA model explained 70.2% of the variance (Figure 2D), with p-hydroxybenzoic acid, ferulic
acid, unknown derivatives of pelargonidin, glucose, pelargonidin acetylglucoside, and cyanidin
3-glucoside being the most discriminant variables.
4. Conclusions
In this work, we have evaluated the potential of combining multi-chemical profiling and
complementary statistical techniques to investigate the eect of the genotype and cultivation conditions
on the chemical composition of strawberry fruits. The five cultivars investigated showed clear
dierences in the content of anthocyanins, phenolic acids, sucrose, and malic acid. On the other
hand, climatic conditions (e.g., rainfall and temperature) were responsible for slight changes in the
polyphenolic profile, with an increased content of anthocyanins and total polyphenols in strawberry
fruits grown under higher rainfall and more extreme temperatures. Similarly, the cultivation conditions
(i.e., open/closed soilless system) also induced minor changes in concentrations of several anthocyanins
and phenolic acids. The present work therefore demonstrates that multi-chemical profiling can be used
to dierentiate among strawberry cultivars grown under dierent agronomic conditions, thus showing
a great applicability for food traceability. In future studies, this approach could also be tested to search
for characteristic patterns associated with the geographical origin, ripeness status, and other factors
related to food production.
Foods 2020,9, 96 8 of 9
Author Contributions:
Conceptualization,
Á
.F.-R.; methodology, A.S. and
Á
.F.-R.; software, R.G.-D. and
Á
.F.-R.;
validation, R.G.-D. and
Á
.F.-R.; formal analysis, I.A.; investigation, R.G.-D., A.S., I.A., and
Á
.F.-R.; resources, A.S.
and
Á
.F.-R.; data curation,
Á
.F.-R.; writing—original draft preparation,
Á
.F.-R.; writing—review and editing,
R.G.-D., A.S., I.A., and
Á
.F.-R.; visualization, R.G.-D. and
Á
.F.-R.; supervision,
Á
.F.-R.; project administration,
Á.F.-R. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Aung, M.M.; Chang, Y.S. Traceability in a food supply chain: Safety and quality perspectives. Food Control
2014,39, 172–184. [CrossRef]
2.
Giampieri, F.; Forbes-Hernandez, T.Y.; Gasparrini, M.; Alvarez-Suarez, J.M.; Afrin, S.; Bompadre, S.;
Quiles, J.L.; Mezzetti, B.; Battino, M. Strawberry as a health promoter: An evidence based review. Food Funct.
2015,6, 1386–1398. [CrossRef] [PubMed]
3.
Basu, A.; Nguyen, A.; Betts, N.M.; Lyons, T.J. Strawberry as a functional food: An evidence-based review.
Crit. Rev. Food Sci. Nutr. 2014,54, 790–806. [CrossRef] [PubMed]
4.
Bagchi, D.; Nair, S. Developing New Functional Food and Nutraceutical Products, 1st ed.; Academic Press: San
Diego, CA, USA, 2016.
5.
Sturm, K.; Koron, D.; Stampar, F. The composition of fruit of dierent strawberry varieties depending on
maturity stage. Food Chem. 2003,83, 417–422. [CrossRef]
6.
Giampieri, F.; Tulipani, S.; Alvarez-Suarez, J.M.; Quiles, J.L.; Mezzetti, B.; Battino, M. The strawberry:
Composition, nutritional quality, and impact on human health. Nutrition
2012
,28, 9–19. [CrossRef] [PubMed]
7.
Akhatou, I.; Fern
á
ndez-Recamales, A. Influence of cultivar and culture system on nutritional and organoleptic
quality of strawberry. J. Sci. Food Agric. 2014,94, 866–875. [CrossRef]
8.
Nowicka, A.; Kucharska, A.Z.; Sokoł-Ł˛etowska, A.; Fecka, I. Comparison of polyphenol content and
antioxidant capacity of strawberry fruit from 90 cultivars of Fragaria
×
ananassa Duch. Food Chem.
2019
,270,
32–46. [CrossRef]
9.
Giampieri, F.; Alvarez-Suarez, J.M.; Mazzoni, L.; Romandini, S.; Bompadre, S.; Diamanti, J.; Capocasa, F.;
Mezzetti, B.; Quiles, J.L.; Ferreiro, M.S.; et al. The potential impact of strawberry on human health. Nat. Prod.
Res. 2013,27, 448–455. [CrossRef]
10.
Akhatou, I.; Fern
á
ndez-Recamales, A. Nutritional and nutraceutical quality of strawberries in relation to
harvest time and crop conditions. J. Agric Food Chem. 2014,62, 5749–5760. [CrossRef]
11.
Akhatou, I.; Gonz
á
lez-Dom
í
nguez, R.; Fern
á
ndez-Recamales,
Á
. Investigation of the eect of genotype and
agronomic conditions on metabolomic profiles of selected strawberry cultivars with dierent sensitivity to
environmental stress. Plant Physiol. Biochem. 2016,101, 14–22. [CrossRef]
12.
Wang, S.Y.; Millner, P. Eect of dierent cultural systems on antioxidant capacity, phenolic content, and fruit
quality of strawberries (Fragaria
×
ananassa Duch.). J. Agric. Food Chem.
2009
,57, 9651–9657. [CrossRef]
[PubMed]
13.
Kruger, E.; Josuttis, M.; Nestby, R.; Toldam-Andersen, T.B.; Carlen, C.; Mezzetti, B. Influence of growing
conditions at dierent latitudes of Europe on strawberry growth performance, yield and quality. J. Berry Res.
2012,2, 143–157. [CrossRef]
14.
Aaby, K.; Mazur, S.; Nes, A.; Skrede, G. Phenolic compounds in strawberry (Fragaria
×
ananassa Duch.) fruits:
Composition in 27 cultivars and changes during ripening. Food Chem.
2012
,132, 86–97. [CrossRef] [PubMed]
15.
Akhatou, I.; Sayago, A.; Gonz
á
lez-Dom
í
nguez, R.; Fern
á
ndez-Recamales,
Á
. Application of Targeted
Metabolomics to Investigate Optimum Growing Conditions to Enhance Bioactive Content of Strawberry.
J. Agric. Food Chem. 2017,65, 9559–9567. [CrossRef] [PubMed]
16.
Crespo, P.; Gin
é
Bordonaba, J.; Terry, L.A.; Carlen, C. Characterisation of major taste and health-related
compounds of four strawberry genotypes grown at dierent Swiss production sites. Food Chem.
2010
,122,
16–24. [CrossRef]
17.
Wang, S.Y.; Zheng, W. Eect of plant growth temperature on antioxidant capacity in strawberry. J. Agric.
Food Chem. 2001,49, 4977–4982. [CrossRef]
Foods 2020,9, 96 9 of 9
18.
Pereira, G.E.; Gaudillere, J.-P.; Pieri, P.; Hilbert, G.; Maucourt, M.; Deborde, C.; Moing, A.; Rolin, D.
Microclimate influence on mineral and metabolic profiles of grape berries. J. Agric. Food Chem.
2006
,54,
6765–6775. [CrossRef]
19.
Sayago, A.; Gonz
á
lez-Dom
í
nguez, R.; Beltr
á
n, R.; Fern
á
ndez-Recamales,
Á
. Combination of complementary
data mining methods for geographical characterization of extra virgin olive oils based on mineral composition.
Food Chem. 2018,261, 42–50. [CrossRef]
20.
Sayago, A.; Gonz
á
lez-Dom
í
nguez, R.; Urbano, J.; Fern
á
ndez-Recamales,
Á
. Combination of vintage and
new-fashioned analytical approaches for varietal and geographical traceability of olive oils. LWT
2019
,111,
99–104. [CrossRef]
21.
Gonz
á
lez-Dom
í
nguez, R.; Sayago, A.; Morales, M.T.; Fern
á
ndez-Recamales,
Á
. Assessment of Virgin Olive
Oil Adulteration by a Rapid Luminescent Method. Foods 2019,8, 287. [CrossRef]
22.
Versari, A.; Laurie, V.F.; Ricci, A.; Laghi, L.; Parpinello, G.P. Progress in authentication, typification and
traceability of grapes and wines by chemometric approaches. Food Res. Int. 2014,60, 2–18. [CrossRef]
23.
Gonz
á
lez-Dom
í
nguez, R.; Sayago, A.; Fern
á
ndez-Recamales,
Á
. Metabolomics: An Emerging Tool for Wine
Characterization and the Investigation of Health Benefits. In Engineering Tools in the Beverage Industry. Volume
3: The Science of Beverages, 1st ed.; Grumezescu, A., Holban, A.M., Eds.; Woodhead Publishing: Duxford, UK,
2019; Volume 3, pp. 315–350.
©
2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... In recent years, our research group has made great efforts to develop powerful tools to investigate the influence of variety, geographical origin, and agronomic practices on the chemical composition of different agrifood products, with a special focus on the main crops that are grown in the province of Huelva (southwest Spain). In this respect, we have previously demonstrated the potential of a myriad of chromatographic and spectroscopic methods for the authentication and traceability of strawberry [3][4][5][6], olive oil [7][8][9], Iberian ham [10, 11], and wine-derived products [12,13]. Herein, we describe a novel high-throughput and quantitative metabolomics method for wide-coverage phenolic fingerprinting of diverse food products based on reversed-phase ultrahigh-performance liquid chromatography (RP-UHPLC) coupled with a diode array detector (DAD) and mass spectrometry (MS). ...
... 3. Add 1 mL of the extraction solvent. 4. Vigorously vortex the mixture for 1 min. ...
Chapter
Accurate, robust, and wide-coverage analytical tools are needed in polyphenol research to deal with the high physicochemical complexity of the secondary plant metabolome. In this chapter, a novel method based on reversed-phase ultrahigh-performance liquid chromatography coupled with a diode array detector and mass spectrometry is presented, which enables high-throughput, comprehensive, and quantitative fingerprinting of a broad spectrum of phenolic compounds and related metabolites in different food products. The simplicity, low-cost, and excellent analytical performance of this method would facilitate its implementation in food science for quality control and authenticity purposes.Key wordsPhenolic compoundsPolyphenolsLiquid chromatographySpectroscopyMetabolomicsFood analysis
... In general, the phenolic profiles that were obtained by analyzing methanolic and La:AcNa:W extracts were quite similar regarding the total number of detected polyphenols and related metabolites, although recoveries were slightly higher when using the organic solvent. The major compounds that were detected in these extracts comprised several phenolic acids (mostly chlorogenic acid and other hydroxycinnamic acids) and flavonol derivatives (i.e., quercetin and kaempferol glycosides and aglycones), in line with previous studies conducted on blueberry leaves [42] and other berry fruits [43,44]. On the other hand, the content of these phenolic compounds was in general lower in the extracts derived from the use of choline-based NADES, but as a counterpart the extraction efficacy for anthocyanins was significantly improved, clearly surpassing that provided by the organic solvent. ...
Article
Full-text available
The food industry demands novel green solvents for the extraction of bioactive compounds, particularly from residues of the agrifood industry. Herein, an ultrasound-assisted method has been developed for the environmentally friendly extraction of phenolic compounds from blueberry leaves using natural deep eutectic solvents (NADES). After the screening of multiple NADES, the best extraction efficiencies in terms of total phenol content and antioxidant activity were provided by NADES composed of lactate, sodium acetate, and water (3:1:2), and of choline chloride and oxalic acid (1:1). Using a Box–Behnken experimental design, the optimal extraction conditions were achieved by sonicating for 45 min at 65 °C and using solvent:sample ratios of 15 and 75 (v/w) for the NADES based on lactic acid and choline, respectively. Compared with conventional organic solvents, the use of these NADES composed of lactic acid and choline provided superior performance for the recovery of phenolic compounds (1.6-fold and 2.2-fold greater efficacy, respectively) and antioxidant compounds (1.6-fold and 2.8-fold greater efficacy, respectively). The chromatographic characterization of the extracts obtained under these optimized conditions evidenced that the lactic-based NADES enabled the extraction of a wide range of hydroxycinnamic acids and flavonol derivatives, whereas the choline-based NADES was selective towards the extraction of anthocyanins. These results indicate that the proposed method could represent an excellent green alternative for the recovery of phenolic compounds from plant materials and agrifood wastes, with improved extraction efficacy and/or selectivity compared to that provided by traditional organic solvents.
... Simplicity and rapidity High-throughput analysis Non-destructive nature Low cost Molecular biology methods Specificity and sensitivity Chromatography coupled with spectroscopic or spectrometric detection has been proven to be a suitable tool for determining and quantifying individual components or compound classes in food samples [10]. Liquid chromatography coupled with ultravioletphotodiode array detection (DAD) or mass spectrometry (MS) is routinely employed for the analysis of a wide range of analytes because of its precision, low cost, and versatility [11][12][13]. In this respect, the boom in ultra-high-performance liquid chromatography (UHPLC) has further improved its resolution and sensibility performance, thereby reducing the time needed for accomplishing qualitative and quantitative analyses and consequently enabling the simultaneous determination of different compound classes in a single run [14]. ...
Article
Full-text available
The use of advanced chemometrics tools in food authenticity research is crucial for managing the huge amount of data that is generated by applying state-of-the-art analytical methods such as chromatographic, spectroscopic, and non-targeted fingerprinting approaches. Thus, this review article provides description, classification, and comparison of the most important statistical techniques that are commonly employed in food authentication and traceability, including methods for exploratory data analysis, discrimination, and classification, as well as for regression and prediction. This literature revision is not intended to be exhaustive, but rather to provide a general overview to non-expert readers in the use of chemometrics in food science. Overall, the available literature suggests that the selection of the most appropriate statistical technique is dependent on the characteristics of the data matrix, but combining complementary tools is usually needed for properly handling data complexity. In that way, chemometrics has become a powerful ally in facilitating the detection of frauds and ensuring the authenticity and traceability of foods.
... Also in strawberry, a multi-targeted profiling approach was employed to characterize the global chemical composition in terms of sugars, organic acids, polyphenols and mineral elements, which are related to sensory and health characteristics of this berry fruit (González-Domínguez et al., 2020a). This multi-chemical profile, combined with complementary pattern recognition procedures (PCA, LDA, SIMCA and PLS-DA), was investigated as a potential tool for authentication and traceability purposes, with the aim of discriminating strawberry varieties grown under different climatic and agronomic conditions (Figure 6.3). ...
Article
Numerous pesticides, including fungicides, are applied every year to crop plants. However, such application may affect the plant metabolism and thus crop quality. Strawberry is an economically important crop, but the fruits are highly susceptible, especially to fungal diseases. In the present study, the effects of two fungicides, Cuprozin progress and SWITCH, on the metabolism of two cultivars and the wild strawberry were tested, focusing on primary (amino acids, (in)organic acids, sugars, total phenolics) and specialized metabolites (aroma volatiles), which determine the fruit flavor. The fungicide treatment significantly affected 11 out of 57 metabolites, while 20 of those differed between strawberry types and 27 were affected by the interaction of both factors. Given these modifications in metabolites in response to the treatments, the taste and quality of the strawberries may pronouncedly change when plants are treated with fungicides.
Article
Considering the health-benefits of berry fruits consumption and increased market demands for food authenticity as one of the most important quality assurances, phenolic profiling by high-performance thin layer chromatography and ultra-high-performance liquid chromatography hyphenated with mass spectrometry was combined with multivariate analysis for phytochemical characterization and intercultivar discrimination of cultivated berry seeds. The phenolic profiles of 45 berry seeds from nine genuine Serbian cultivated fruit species (strawberry, raspberry, blackberry, black currant, blueberry, gooseberry, cape gooseberry, chokeberry, and goji berry) revealed a good differentiation according to botanical origin. In order to determine biomarkers responsible for the classification, a total of 103 phenolic compounds were identified, including 53 phenolic acids and their derivatives, 26 flavonoids and 24 glycosides. Biomarkers derived from the phenolic profile of berry seeds proved to be a powerful tool in the authentication of botanical origin, and may be useful in detection of frauds in berry-based seed-containing product.
Article
The origin of fresh eggs is an essential determinant of their quality. This study's main goal was to classify fresh eggs' origins, after the characteristic wavelength from the near-infrared reflectance spectral of the eggs, using a support vector machine (SVM) for classification. To improve classification accuracy, eight categories of eggs were treated as classification targets. A Fourier transform near-infrared (FT-NIR) spectrometer was used to acquire spectral data of 800 eggs. A characteristic wavelength selection approach was presented based on information entropy (CWSABIE). Genetic algorithm-partial least squares (GA-PLS), interval partial least squares (iPLS), and competitive adaptive reweighting sampling (CARS) were compared. Standard normal variable transformation (SNV), Savitzky–Golay filtering, and Centralisation were applied to preprocess spectral data. The results indicate that when using CWSABIE with SNV, the model had the highest accuracy (93.8%) and can be used to classify the data of eggs after 15 days of storage (91.4%). The model show potential for application to online inspection for eggs from different storage days.
Article
Strawberries are one of the most consumed fruits worldwide. Scientific research has revealed unique nutraceutical properties, mainly steaming from their content of polyphenols and fructooligosaccharides (FOS), well known prebiotics. Here, we report the fingerprint (chemical composition) of strawberries as molecular markers of geographical origin, growing region, and genetic background, after analyzing different strawberry varieties grown and collected in Mexico and Canada by thin layer chromatography (TLC), high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD), and Fourier-transform infrared spectroscopy – principal component analysis (FTIR-PCA). The most abundant carbohydrates in all cases were glucose and fructose. The highest levels of sucrose were observed in Canada-grown strawberries, while Mexico-grown varieties presented the highest FOS abundance. There were clear and distinct patterns correlating FOS isomers determined by HPAEC-PAD and geographic origin. FTIR-PCA second derivative spectra also allowed the classification and differentiation of all strawberry varieties grown in both countries. We suggest that carbohydrates fingerprinting could be associated to specific environmental growing conditions, and not only to genetic makeup.
Article
The use of mass spectrometry is currently widespread in polyphenol research because of its sensitivity and selectivity, but its usual high cost, reduced robustness, and nonavailability in many analytical laboratories considerably hinder its routine implementation. Herein, we describe the optimization and validation of a high-throughput, wide-coverage, and robust metabolomics method based on reversed-phase ultra-high-performance liquid chromatography with diode array detection for the identification and quantification of 69 phenolic compounds and related metabolites covering a broad chemical space of the characteristic secondary metabolome of plant foods. The method was satisfactorily validated following the Food and Drug Administration guidelines in terms of linearity (4-5 orders of magnitude), limits of quantification (0.007-3.6 mg L-1), matrix effect (60.5-124.4%), accuracy (63.4-126.7%), intraday precision (0.1-9.6%), interday precision (0.6-13.7%), specificity, and carryover. Then, it was successfully applied to characterize the phenolic fingerprints of diverse food products (i.e., olive oil, red wine, strawberry) and biological samples (i.e., urine), enabling not only the detection of many of the target compounds but also the semi-quantification of other phenolic metabolites tentatively identified based on their characteristic absorption spectra. Therefore, this method represents one step further toward time-efficient and low-cost polyphenol fingerprinting, with suitable applicability in the food industry to ensure food quality, safety, authenticity, and traceability.
Article
Full-text available
The adulteration of virgin olive oil with hazelnut oil is a common fraud in the food industry, which makes mandatory the development of accurate methods to guarantee the authenticity and traceability of virgin olive oil. In this work, we demonstrate the potential of a rapid luminescent method to characterize edible oils and to detect adulterations among them. A regression model based on five luminescent frequencies related to minor oil components was designed and validated, providing excellent performance for the detection of virgin olive oil adulteration.
Article
Full-text available
This work explores the potential of multi-element fingerprinting in combination with advanced data mining strategies to assess the geographical origin of extra virgin olive oil samples. For this purpose, the concentrations of 55 elements were determined in 125 oil samples from multiple Spanish geographic areas. Several unsupervised and supervised multivariate statistical techniques were used to build classification models and investigate the relationship between mineral composition of olive oils and their provenance. Results showed that Spanish extra virgin olive oils exhibit characteristic element profiles, which can be differentiated on the basis of their origin in accordance with three geographical areas: Atlantic coast (Huelva province), Mediterranean coast and inland regions. Furthermore, statistical modelling yielded high sensitivity and specificity, principally when random forest and support vector machines were employed, thus demonstrating the utility of these techniques in food traceability and authenticity research.
Article
Full-text available
A simple, sensitive and rapid assay based on liquid chromatography coupled to tandem mass spectrometry was designed for simultaneous quantitation of secondary metabolites in order to investigate the influence of variety and agronomic conditions on the biosynthesis of bioactive compounds in strawberry. For this purpose, strawberries belonging to three varieties with different sensitivity to environmental conditions ('Camarosa', 'Festival', 'Palomar') were grown in soilless system under multiple agronomic conditions (electrical conductivity, substrate type and coverage). Targeted metabolomic analysis of polyphenolic compounds, combined with advanced chemometric methods based on learning machines, revealed significant differences in multiple bioactives, such as chlorogenic acid, ellagic acid rhamnoside, sanguiin H10, quercetin 3-O-glucuronide, catechin, procyanidin B2, pelargonidin 3-O-glucoside, cyaniding 3-O-glucoside and pelargonidin 3-O-rutinoside, which play a pivotal role in organoleptic properties and beneficial healthy effects of these polyphenol-rich foods.
Article
Full-text available
Since high intake of fruits and vegetables is inversely related to the incidence of several degenerative diseases, the importance of a balanced diet in relation to human health has increased consumer attention worldwide. Strawberries (Fragaria X ananassa, Duch.) are a rich source of a wide variety of nutritive compounds such as sugars, vitamins, and minerals, as well as non-nutritive, bioactive compounds such as flavonoids, anthocyanins and phenolic acids.All these compounds exert a synergistic and cumulative effect in human health promotion and in disease prevention. Strawberry phenolics are indeed able (i) to detoxify free radicals blocking their production, (ii) to modulate the expression of genes involved in metabolism, cell survival and proliferation and antioxidant defense, and (iii) to protect and repair DNA damage.The overall objective of the present review is to update and discuss the key findings, from recent in vivo studies, on the effects of strawberries on human health. Particular attention will be paid to the molecular mechanisms proposed to explain the health effects of polyphenols against the most common diseases related to oxidative stress driven pathologies, such as cancer, cardiovascular diseases, type II diabetes, obesity and neurodegenerative diseases, and inflammation.
Article
Full-text available
Three strawberry varieties cultivated in soilless systems were studied for their content of primary and secondary metabolites in relation to harvest time and crop conditions. The three varieties were chosen based on their sensitivity level to environmental stress: Palomar (very sensitive), Festival (sensitive) and Camarosa (resistant). Throughout the campaign, three samplings were performed: December (extra-early production), January and March (early production). Differences among cultivars and harvest times were observed based on the contents of sugars, organic acids, phenolic compounds and antioxidant capacity. The higher levels for total anthocyanins and flavan-3-ols were found in Camarosa and Festival strawberries, both in January harvest. Palomar variety showed higher total sugar/total organic acids ratio in March harvest. The influence of cultivation practices and environmental conditions was assessed by nested ANOVA and PLS-DA. Differences in the sugar and phenolic content were observed depending upon variety and coverage type. TEAC was most influenced by the substrate type.
Article
There is a great need for having accurate analytical methods able to guarantee the authenticity and traceability of foods, especially for those of high quality and economic value such as extra-virgin olive oil. In the present work, we assessed the potential of combining traditional analytical techniques, based on the characterization of the unsaponifiable fraction, together with novel nuclear magnetic resonance fingerprinting with the aim to investigate the effect of variety and geographical origin on olive oils collected from different locations across the province of Huelva (Spain). Various complementary supervised pattern recognition procedures and machine learning algorithms were then applied to build classification and predictive models. Extra-virgin olive oils were characterized by high concentrations of apparent β-sitosterol (93% of total sterol content), α-tocopherol (representing almost 91% of the total tocopherol fraction), squalene (90% of the total hydrocarbon content), heptacosanol and eicosane (the most abundant aliphatic alcohol and n-alkane, respectively). Furthermore, olive oil classes could be clearly differentiated on the basis of a characteristic chemical pattern, comprising tocopherols, squalene, sterols (campesterol, stigmasterol, β-sitosterol), aliphatic alcohols (heptacosanol, octacosanol)and some nuclear magnetic resonances related to fatty acid chains.
Article
Strawberry fruit is a valuable resource, rich in vitamins and polyphenolic compounds. These compounds have a broad spectrum of biological activity. The aim of this study was to evaluate the qualitative and quantitative composition of polyphenols in strawberry fruit from 90 cultivars of Fragaria × ananassa Duch. from two growing seasons. Eighty of them were analyzed for the first time (to the best of our knowledge). The identification of polyphenolics and other compounds was performed using UPLC-qTOF-MS/MS. Nine compounds were recorded for the first time in mature strawberry fruit. Antioxidant properties were also determined using DPPH and ABTS methods. Statistical analysis of the results was performed using principal component analysis. Tannins, especially ellagitannins with agrimoniin, as well as the total polyphenols, had the greatest influence on antioxidant activity in the ABTS test. Cultivars characterized by a high content of tannins and high antioxidant capacity were selected.
Book
Developing New Functional Food and Nutraceutical Products provides critical information from conceptualization of new products to marketing, aiming to present a solid understanding of the entire process through detailed coverage of key concepts, namely innovation, regulation, manufacturing, quality control, and marketing. Chapters provide insights into market and competitive analysis, product design and development, intellectual property, ingredient sourcing, cost control, and sales and marketing strategies. Examines key considerations in product development Provides a streamlined approach for product development Addresses manufacturing and quality control challenges Includes key lessons for a successful product launch and effective marketing.
Article
Strawberry is one of the most economically important and widely cultivated fruit crops across the world, so that there is a growing need to develop new analytical methodologies for the authentication of variety and origin, as well as the assessment of agricultural and processing practices.In this work, an untargeted metabolomic strategy based on gas chromatography mass spectrometry (GC-MS) combined with multivariate statistical techniques was used for the first time to characterize the primary metabolome of different strawberry cultivars and to study metabolite alterations in response to multipleagronomic conditions. For this purpose, we investigated three varieties of strawberries with different sensitivity to environmental stress (Camarosa, Festival and Palomar), cultivatedin soilless systemsusing various electrical conductivities, types of coverage and substrates. Metabolomic analysis revealed significant alterations in primary metabolites between the three strawberry cultivars grown under different crop conditions, including sugars (fructose, glucose), organic acids (malic acid, citric acid) and amino acids (alanine, threonine, aspartic acid), among others. Therefore, it could be concluded that GC-MS based metabolomics is a suitable tool to differentiate strawberry cultivars and characterize metabolomic changes associated with environmental and agronomic conditions.