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foods
Article
Multi-Chemical Profiling of Strawberry as a
Traceability Tool to Investigate the Effect 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 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.
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 efficient 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) [7,10–13], 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 different 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 differences among strawberry varieties and/or
cultivation systems. All statistical analyses were conducted on Statistica 7.1 (StatSoft Inc., Tulsa,
Oklahoma, OK, USA) and SIMCA-P™11.5 (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 effects 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 differences for each compound
or element measured. The multivariate test showed that both factors have a significant effect 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
caffeic 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 differentiation 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 differentiation,
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, 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).
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 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.
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.
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2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
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