Morpho-Physiological Responses and Secondary Metabolites Modulation by Preharvest Factors of Three Hydroponically Grown Genovese Basil Cultivars

Abstract

Sweet basil (Ocimum basilicum L.) is an economically important leafy vegetable especially in Mediterranean countries. In Italian gastronomy, the large elliptical leaves of the Genovese type are mostly used for the well-known pesto sauce, and almost all (>90%) professional production is for the food industry. The growing demand for fresh leaves with standardized technological and sensory characteristics has prompted basil producers to adopt advanced cultivation methods such as the floating raft system (FRS). The aim of this study was to evaluate the productive, qualitative, and physiological performance of three Genovese basil cultivars (“Aroma 2,” “Eleonora,” and “Italiano Classico”) in two successive harvests and at two densities (159 and 317 plants m–2). Caffeic, chicoric, rosmarinic, and ferulic acid were determined through the high-performance liquid chromatography (HPLC) system, whereas the extraction and quantification of the volatile organic compounds (VOCs) were performed by solid-phase microextraction (SPME) and gas chromatography coupled to a mass spectrometer (GC/MS). “Aroma 2” showed the highest fresh yield and photosynthetic rate together with the lowest nitrate content. For all the tested cultivars, the higher density, while reducing the number of leaves per plant, resulted in higher fresh and dry production per unit area, without altering the aroma profile. Successive harvests resulted in a significant increase in both the yield (37.5%) and the total phenolic acids (75.1%) and favored Eucalyptol and 1-octen-3-ol accumulation (+25.9 and +15.1%, respectively). The here presented comprehensive and multifactorial assessment of the productive and qualitative response of basil provides evidence of the positive effects (from biomass to specialized metabolites) that can be obtained from the management of the pre-harvest factors in soilless cultivation. In addition, it also highlights the role and constraints of the genetic factor in the observed response. We also discuss the implications of our work considering the impact for the food processing industry. Future research may explore the phenolic acids accumulation as a possible fortification means to extend the pesto sauce shelf life, reducing the need of added antioxidants and thermal processing.

Full text

fpls-12-671026 April 20, 2021 Time: 16:1 # 1
ORIGINAL RESEARCH
published: 26 April 2021
doi: 10.3389/fpls.2021.671026
Edited by:
Rita Maggini,
University of Pisa, Italy
Reviewed by:
Hardeep Singh,
Oklahoma State University,
United States
Hamzah Saleem,
Huazhong Agricultural University,
China
*Correspondence:
Youssef Rouphael
youssef.rouphael@unina.it
Specialty section:
This article was submitted to
Crop and Product Physiology,
a section of the journal
Frontiers in Plant Science
Received: 22 February 2021
Accepted: 22 March 2021
Published: 26 April 2021
Citation:
Ciriello M, Formisano L,
El-Nakhel C, Corrado G, Pannico A,
De Pascale S and Rouphael Y (2021)
Morpho-Physiological Responses
and Secondary Metabolites
Modulation by Preharvest Factors
of Three Hydroponically Grown
Genovese Basil Cultivars.
Front. Plant Sci. 12:671026.
doi: 10.3389/fpls.2021.671026
Morpho-Physiological Responses
and Secondary Metabolites
Modulation by Preharvest Factors of
Three Hydroponically Grown
Genovese Basil Cultivars
Michele Ciriello, Luigi Formisano, Christophe El-Nakhel, Giandomenico Corrado,
Antonio Pannico, Stefania De Pascale and Youssef Rouphael*
Department of Agricultural Sciences, University of Naples Federico II, Naples, Italy
Sweet basil (Ocimum basilicum L.) is an economically important leafy vegetable
especially in Mediterranean countries. In Italian gastronomy, the large elliptical leaves
of the Genovese type are mostly used for the well-known pesto sauce, and almost
all (>90%) professional production is for the food industry. The growing demand for
fresh leaves with standardized technological and sensory characteristics has prompted
basil producers to adopt advanced cultivation methods such as the floating raft
system (FRS). The aim of this study was to evaluate the productive, qualitative, and
physiological performance of three Genovese basil cultivars (“Aroma 2,” “Eleonora,”
and “Italiano Classico”) in two successive harvests and at two densities (159 and 317
plants m−2). Caffeic, chicoric, rosmarinic, and ferulic acid were determined through the
high-performance liquid chromatography (HPLC) system, whereas the extraction and
quantification of the volatile organic compounds (VOCs) were performed by solid-phase
microextraction (SPME) and gas chromatography coupled to a mass spectrometer
(GC/MS). “Aroma 2” showed the highest fresh yield and photosynthetic rate together
with the lowest nitrate content. For all the tested cultivars, the higher density, while
reducing the number of leaves per plant, resulted in higher fresh and dry production
per unit area, without altering the aroma profile. Successive harvests resulted in a
significant increase in both the yield (37.5%) and the total phenolic acids (75.1%) and
favored Eucalyptol and 1-octen-3-ol accumulation (+25.9 and +15.1%, respectively).
The here presented comprehensive and multifactorial assessment of the productive and
qualitative response of basil provides evidence of the positive effects (from biomass to
specialized metabolites) that can be obtained from the management of the pre-harvest
factors in soilless cultivation. In addition, it also highlights the role and constraints of the
genetic factor in the observed response. We also discuss the implications of our work
considering the impact for the food processing industry. Future research may explore
the phenolic acids accumulation as a possible fortification means to extend the pesto
sauce shelf life, reducing the need of added antioxidants and thermal processing.
Keywords: Ocimum basilicum L., floating raft system, cut, specialized metabolites, phenolic acids, volatile
compounds
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Ciriello et al. Preharvest Factors Modulate Basil Quality
INTRODUCTION
Sweet basil (Ocimum basilicum L.) is an annual herbaceous
species of the Lamiaceae family considered among the most
popular Mediterranean aromatic and edible herbs (Shahrajabian
et al., 2020). The genetic and morphological variability of
the Ocimum genus has led to the classification of over
60 species (Filip, 2017), which differ in growth habits, leaf
morphology, pigmentation, and aromatic content (Makri and
Kintzios, 2008). Furthermore, the recent intense plant breeding
has made taxonomic classification more challenging by fixing
morphological natural variation in a number of different
horticultural types (Dudai and Belanger, 2016). Basil has also
historically been used in folk medicine as a soothing agent for
stomach and intestinal discomforts. Nowadays, O. basilicum is
used for its distinctive aroma in the food processing, cosmetic,
and pharmaceutical industries (Barátová et al., 2015). In Italian
cuisine, freshly picked leaves are a popular food garnish (e.g., the
real pizza Margherita, Caprese salad). Specifically, the “Basilico
Genovese,” which has obtained the European Union (EU)
Protected Designation of Origin label (EU Reg. 611/2010), is the
central ingredient of the famous green sauce worldwide known
as “pesto” (Salvadeo et al., 2007;Kiferle et al., 2011). Over the
last decades, the total area used for the cultivation of Genovese
basil in Italy has increased by over 66%, with a 25% increase in
the protected environment (Italian National Institute of Statistics
(ISTAT), 2019), driven mainly by the growing demand of the food
industry (Morano et al., 2017).
In aromatic plants, composition of the essential oil is a
relevant qualitative feature, which can influence consumer choice
(Dudai and Belanger, 2016). In sweet basil, most of the aromatic
molecules are stored in trichomes and belong to (mono-)terpenes
and phenylpropanoids (Marotti et al., 1996). Among the latter,
linalool and methyl chavicol characterize the fine aroma of this
herb (Makri and Kintzios, 2008;Bekhradi et al., 2015;Filip, 2017).
Nowadays, consumer’s choice is increasingly oriented toward
high-quality foods with nutritional properties (Sgherri et al.,
2010;Morano et al., 2017). Recently, there has been a strong
interest in the biochemical characterization of minor species
that could represent a relevant source of antioxidants beneficial
to human health (Ahmed et al., 2019). Basil’s high antioxidant
capacity is mainly attributable to rosmarinic acid, a characteristic
metabolite of several medicinal plants along with other phenolic
acids (e.g., caffeic, chicoric, and ferulic acids) (Petersen and
Simmonds, 2003;Makri and Kintzios, 2008;Lee and Scagel, 2009;
Salachas et al., 2015). The phenolic composition and the aromatic
bouquet of basil are also strongly affected by the genetic factor
and its interaction with the environment, including agronomic
practices (Makri and Kintzios, 2008;Sgherri et al., 2010;Luz et al.,
2014;Pinto et al., 2019).
The necessity to meet the growing demands of the processing
industry for a clean, crunchy, uniform, tasty, and aromatic
product represents a challenge for producers considering the
strong effect of year-to-year variability for aromatic plants. This
challenge has led the scientific community and growers to focus
on alternative growing methods with controlled environmental
and nutrient conditions such as hydroponics (Maggini et al.,
2014;Salachas et al., 2015). These systems can guarantee higher
yields, improve nutritional quality, reduce the incidence of
pests and pathogens (Pardossi et al., 2006;Kiferle et al., 2013;
Maboko and Du Plooy, 2013;Walters and Currey, 2015), allow
the seasonal adjustment of production, and shorten production
cycle (Hassanpouraghdam et al., 2010). Among hydroponic
techniques, the floating raft system (FRS) is well-suited to the
large-scale cultivation of relatively small medicinal and aromatic
plants such as basil, due to simplicity of management and cost-
effectiveness (Miceli et al., 2003;Valenzano et al., 2003;Pardossi
et al., 2006;Maggini et al., 2010). Hydroponics also represents
a useful method to produce leafy vegetables with a low nitrate
content due to the possibility of constant monitoring of the
nutrient solution (Miceli et al., 2003). The reduction of nitrates
has become an important quality prerogative for the production
and marketability of leafy vegetables (Orsini and De Pascale,
2007). The European Commission (EC) regulations n. 1881/2006
and 1258/2011 did not set threshold for nitrate pertaining the
Lamiaceae. However, sweet basil can accumulate nitrate at levels
higher than those permitted by the EC legislation [5,000 mg
kg−1of fresh weight (fw)], thus entering the hyper accumulative
species (Colla et al., 2018).
In basil, different pre-harvest factors can be manipulated to
improve yield and quality, leading to the conclusion that pre-
harvest factors should be simultaneously analyzed to uncover
their translational value and significant interactions in cultivation
(Chen et al., 2004;Raimondi et al., 2006;Nicoletto et al., 2013;
Corrado et al., 2020a). For instance, plant density plays a key
role in shaping growth and development of different plant organs
(Maboko and Du Plooy, 2013;Morano et al., 2017). Likewise,
in the ordinary cultivation of basil, plants are cut more than
once during the crop cycle, with harvests having a cut-specific
leaf quality profile (Nicoletto et al., 2013;Corrado et al., 2020a).
To the authors’ knowledge, the scientific literature has mainly
focused on the manipulation of the nutrient solution to vary
the qualitative and quantitative characteristics of basil in soilless
systems, while evidence regarding the impact of plant density
and cut, and their interaction with the genotypes, is very scarce.
To fill this gap in crop science, a fully factorial experiment was
conducted in hydroponics with the aim of evaluating (i) the
adaptability of three Genovese basil cultivars, (ii) the impact
of two densities, and (iii) the influence of two cuts on yield
and quality attributes, in order to characterize and standardize
production during spring season. The specific and significant
morpho-physiological, phytochemicals, and aroma variations
revealed the strong impact of the analyzed factors and the
complexity of their interaction, whose implications are of interest
also for the production of basil for the food industry.
MATERIALS AND METHODS
Plant Material, Experimental Design, and
Harvest
The experiment was conducted at the pilot farm “Torre Lama”
(Department of Agricultural Sciences, University of Naples
Federico II) located in Bellizzi (SA, Italy; latitude 43◦10’ N,
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Ciriello et al. Preharvest Factors Modulate Basil Quality
longitude 14◦58’ E, altitude 60 m a.s.l.) in a glass greenhouse
with passive ventilation (10 m wide, 30 m long, 3 and 4.5
m high at the eaves and ridge, respectively) from April 11
to May 13, 2019. The mean air temperature was 25◦C (min:
15◦C; max: 32◦C), while relative humidity was 55% during day
and 79% during night. Fifteen days after sowing, seedlings of
three Genovese basil (O. basilicum L. var. basilicum) cultivars
“Eleonora” (Enza Zaden, Enkhuizen, Netherlands), “Aroma 2”
(Fenix, Belpasso, Italy), and “Italiano Classico” (La Semiorto,
Sarno, Italy) were grown in a FRS. The nutrient solution (NS)
was a modified Hoagland formulation prepared with reverse
osmosis water and the following nutrients: 14 mM N-NO3−,
1.75 mM S, 1.5 mM P, 3.0 mM K, 4.5 mM Ca, 1.5 mM Mg,
1.0 mM NH4+, 15 µM Fe, 9 µM Mn, 0.3 µM Cu, 1.6 µM
Zn, 20 µM B, and 0.3 µM Mo. As recommended by Singh and
Dunn (2016), the electrical conductivity (EC) of the NS was
2.0 ±0.1 dS m−1. The pH was monitored daily and maintained
at 6.0 ±0.3 using a portable pH/EC/TDS/Temperature Meter
HI991301 with HI1288 probe (Hanna instruments, Woonsocket,
RI, United States). The instrument was calibrated according to
the manufacturer’s recommendations with calibration solutions
(two-point calibration at pH 4.01 and 7.01; EC: 1-point
calibration at 12.88 dS m−1). The experimental design was
full factorial, with three factors: cultivar (CV) with three levels
(“Aroma 2,” “Eleonora,” and “Italiano Classico”), density (D)
with two levels (DHigh and DLow), and cut (CT) with two
levels (first, CT1 and second, CT2). Each experimental unit
consisted of a single plastic tank filled with 35 L of NS,
containing a 54-hole polystyrene tray (52 ×32 ×6 cm; upper
hole diameter: 4.5; bottom hole diameter: 3 cm; volume: 0.06
L) and an immersion pump Aquaball 60 (Eheim, Stuttgart,
Germany) to maintain a constant dissolved oxygen level above
the threshold limit of 6 mg L−1. The planting densities were
317 plant (pl) m−2(54 plants/tray; DHigh) and 159 pl m−2(27
plants/tray; DLow) (Figure 1). During the trial, basil plants were
harvested twice [18 days, CT1, and 32 days after transplanting
(DAT), CT2], when they reached the phenological phase of
pre-flowering, leaving two internodes at CT1. Soon after CT1,
FIGURE 1 | Fresh biomasses of Genovese basil plants at the end of the first harvest at different densities. (A,B) Illustrative pictures of Genovese basil cv. “Aroma 2”
at DHigh and DLow densities. (C,D) Illustrative pictures of Genovese basil cv. “Eleonora” at DHigh and DLow densities. (E,F) Illustrative pictures of Genovese basil cv.
“Italiano Classico” at DHigh and DLow densities.
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Ciriello et al. Preharvest Factors Modulate Basil Quality
the NS was replaced to guarantee the same initial mineral
nutrient conditions.
Yield, Growth, and Analysis Sampling
From each experimental unit (54 pl for DHigh and 27 pl for DLow),
15 basil plants (observational unit) were sampled at each cut,
separated into leaves, side branches, and stems, that were weighed
and counted. Stem diameter, total fw, and leaf-to-stem ratio were
recorded. A subsample of the plant was stored in paper bags and
dried in a forced-air oven at 70◦C until constant weight (72 h) to
determine the dry weight (dw). Dry matter content was calculated
as follows: dw/fw ×100. A sample of plants was collected and
immediately frozen in liquid nitrogen and stored at −80◦C before
being freeze-dried for further qualitative analysis (i.e., phenolics
and volatiles determination). For mineral determination, the dry
plant material was milled and sieved with an MF10.1 Wiley
laboratory mill equipped with an MF0.5 sieve (IKA, Staufen im
Breisgau, Baden-Württemberg, Germany).
CIELAB Leaf Colorimetry and Soil Plant
Analysis Development (SPAD) Index
Ten colorimetric coordinates were recorded on 10 representative
leaves of each experimental unit at each harvest date, using a
Chroma Meter Minolta CR-300 (Minolta Co. Ltd, Osaka, Japan)
calibrated with a correspondent Minolta standard. The color
spaces were expressed with L∗,a∗, and b∗values, hue angle,
and chroma, as described by the International Commission of
Illumination (CIE) where L∗is degree of lightness (100) to
darkness (0), a∗is degree of greenness (−) to redness (+), and
b∗is degree of blueness (−) to yellowness (+).
Chroma and hue angle were calculated based on the following
equations:
Chroma = [(a∗)2+(b∗)2]0.5
Hue angle =tan−1b∗
a∗
Chroma is the “colorfulness” quantitative attribute, the degree
of visual difference from neutral gray of the same lightness.
A higher color intensity perceived by humans is indicated by
high chroma values. The hue angle describes the qualitative color
attribute in the relative amounts of redness and yellowness (i.e.,
the difference of certain color in reference to the gray color with
the same lightness).
At 17 and 31 DAT, the SPAD index measurements as indicator
of greenness were performed on 20 young fully expanded leaves
of 10 representative plants per experimental unit using a portable
chlorophyll meter SPAD-502 (Minolta Co. Ltd, Osaka, Japan), as
described by Singh et al. (2019).
Leaf Gas Exchange and Chlorophyll
Fluorescence
At 17 and 31 DAT, between 11:00 and 13:00, gas exchange and
chlorophyll fluorescence emission measurements were carried
out. The measurements were performed on young fully expanded
basil leaves, avoiding the central rib, using nine plants per
experimental unit. The net carbon dioxide (CO2), assimilation
rate (ACO2), transpiration rate (E), and stomatal resistance (rs)
were determined through a portable gas exchange analyzer
(LCA 4; ADC BioScientific Ltd., Hoddesdon, United Kingdom),
equipped with a broad-leaf chamber (window cuvette area of 6.25
cm2). The CO2concentration, photosynthetically active radiation
(PAR), as well as relative humidity (RH), were set to ambient
values (365 ±5 ppm, 700 ±50 µmol photons m−2s−1, 55 ±5%,
respectively) and the airflow rate was set to 400 ml s−1. The
instantaneous water use efficiency (WUEi) was calculated as
ACO2/E.
On the same day of leaf gas exchange measurements (17 and
31 DAT), a portable fluorometer Fv/FmMeter (Opti-Sciences Inc.,
Hudson, United States) was used for chlorophyll fluorescence
determination. Chlorophyll fluorescence was performed on the
leaves of nine plants per experimental unit after their dark
adaptation (for at least 10 min) by leaf clips. According to
Kitajima and Butler (1975), the maximum quantum efficiency
of Photosystem II (PSII) Fv/Fmwas calculated as (Fm-
F0)/Fm, where F0and Fmwere the ground fluorescence signal
and the maximal fluorescence intensities in the dark-adapted
state, respectively.
Mineral Determination
The ion chromatography system ICS 3000 (Thermo Scientific
Dionex, Sunnyvale, California, United States) was used to
determine the cationic (K+, Ca2+, and Mg2+) and anionic
(NO3−and PO43−) profile of basil, following the protocol
described by Rouphael et al. (2017). For the determination of the
cations, the IonPac CG12A guard column (4 ×250 mm) and
the IonPac CS12A analytical column (4 ×250 mm) were used,
whereas the IonPac AG11-HC guard column (4 ×50 mm) and
the IonPac AS11-HC analytical column (4 ×250 mm) were used
for anion determination. The ion concentrations of the tested
samples were calculated based on the standard curves of cations
and anions. All chemicals were purchased from Sigma-Aldrich
(Milan, Italy). The detected minerals were expressed in g kg−1
dw, except for nitrate that was expressed in mg kg−1fw by taking
into consideration the dry matter percentage of each sample.
Phenolics Determination
Phenolic extracts for high-performance liquid chromatography
(HPLC) analysis were obtained following the method described
by Ciriello et al. (2021), with some modifications. Briefly, 100 mg
of freeze-dried basil samples was added to 2 ml of 70% aqueous
methanol (v/v). The mixture was thoroughly mixed for 1 min
(Vortex Classic stirrer; Velp Scientifica, Usmate Velate, Monza
Brianza, Italy), sonicated for 20 min (Q500 ultrasonic sonicator;
Qsonica, Newtown, Connecticut, United States), stirred by
tilting shaker for 10 min (SSL4 see-saw rocker; Cole-Parmer,
Vernon Hills, Illinois, United States), centrifuged at 6,800 rpm
for 10 min (R10M, Remi Elektrotechnik Limited, Mumbai,
India), and finally filtered through a 0.45-µm Teflon membrane
(Phenomenex, Torrance, CA, United States). The supernatant
was pipetted into a vial and analyzed by HPLC to quantify the
following phenolic acids: caffeic, rosmarinic, chicoric, and ferulic
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acids. The chromatographic separation of phenolic acids in the
extract was performed on an Agilent Technologies 1100 Series
HPLC system (Palo Alto, CA, United States) equipped with a
degasser (G4225A), a quaternary pump (G13111A), and a diode
matrix detector (G1315B) using a 20-µl sample injection loop.
A reversed-phase Kinetex C18 100-Å column (5 µm particle size,
150 ×4.6 mm; Phenomenex, Torrance, California, United States)
was used. The eluents were 0.1% (v/v) trichloroacetic acid
in water (eluent A) and acetonitrile (eluent B). The gradient
schedule was 0–50% B in 50 min at a constant flow rate of
1 ml min−1. Identification was made by comparing the retention
times with those of commercially available standards. Calibration
curves were built using seven concentration levels for each
standard (0.15, 0.5, 1, 10, 20, 50, and 100 mg L−1). The detection
of each of the phenolic acids was performed at 280 nm and
illustrated in Supplementary Figure 1. All HPLC grade reagents
and solvents were purchased from Sigma Aldrich (Milan, Italy).
Volatiles Determination
The extraction and quantification of volatile organic compounds
(VOCs) was performed by solid-phase microextraction (SPME)
and gas chromatography coupled to a mass spectrometer
(GC/MS) following the protocol described by Ciriello et al.
(2021). Briefly, 500 mg of fresh frozen basil was transferred
into a 20-ml glass headspace vial with a Teflon septum
screw cap (Supelco, Bellefonte, Pennsylvania, United States)
and stirred for 10 min at 30◦C (ARE magnetic stirrer; Velp
Scientifica, Usmate Velate, Monza, Italy) to promote the VOCs’
migration into the headspace. A 1-cm-long and 50/30-µm-
thick divinylbenzene/carboxane/polydimethylsiloxane SPME
fiber (Supelco, Bellefonte, Pennsylvania, United States) was
introduced into the vials for VOC adsorption. The SPME
fiber was introduced into the split-splitless injector of GC
6890N coupled to MS 5973N (Agilent, Santa Clara, California,
United States), where thermal desorption of the analytes was
performed at 250◦C for 10 min. The VOCs were separated on a
30 m ×0.250 mm capillary column coated with a 0.25-µm 5%
diphenyl/95% dimethylpolysiloxane film (Supelco, Bellefonte,
Pennsylvania, United States). A splitless injection was used
for the samples. The temperature was maintained at 50◦C
for 2 min and increased from 50 to 150◦C to 10◦C/min and
from 150 to 280◦C to 15◦C/min. The injection source and ion
source temperatures were 250 and 230◦C, respectively. Helium
(99.999%) was used as the carrier gas at a 1 ml min−1flow
rate. The mass spectrometer was set to 70 eV. The compounds
were identified using the National Institute of Standards and
Technology (NIST) Atomic Spectra Database version 1.6
(U.S. Department of Commerce, Gaithersburg, Maryland,
United States) and verified by retention indexes.
Statistical Analysis
The experiment consisted of a randomized block design with
three factors: Cultivar-CV, Cut-CT, and density-D. A two-way
analysis of variance (ANOVA) was implemented to assess the
significance of the effects and interaction between the factor
pairs: CV ×D, D ×CT, and CV ×CT. One-way ANOVA
was used to compare the mean effect of CV, while CT and
D were compared according to the Student’s t-test. Statistical
significance was determined at p<0.05 level using Duncan’s
Multiple Range Test (DRMT) for CV ×D, D ×CT, and
CV ×CT interactions and for CV factor. All data are presented
as mean ±standard error. All statistical analyses were performed
using IBM SPSS 20 (Armonk, NY, United States) package for
Microsoft Windows 10. Principal component analysis (PCA) was
performed as described by Kassambara (2017).
RESULTS
Morphological Traits and Production
Response
The cultivar factor had a highly significant main effect on all the
measured biometric variables, which were also strongly affected
by the cut (Table 1). The lower density (DLow) resulted in a
significant increase in the number of leaves, stem diameter,
and number of nodes. On the other hand, the higher density
(DHigh) led to higher fresh yield and dry biomass. The cut
significantly influenced all biometric variables and, differently
from the cultivar factors, there was a significant interaction effect
with the density for all (but dry matter percentage) biometric
variables (Table 1). For instance, a specific density ×cut
interaction was observed for dry biomass and leaves/stem ratio,
while leaf number and fresh yield were also affected by the
cultivar ×density (CV ×D) interaction. When the density
was reduced, the leaf number increased (38.5%) while fresh
yield decreased (24.1%) in all tested cultivars. Fresh yield and
leaves/stem ratio were the most sensitive parameters because
the three-way interaction was highly significantly. Overall, as
opposed to stem diameter and leaf-to-stem ratio, the CT1
resulted in a decrease in leaf number and dry biomass for both
densities. Specifically, the most significant increase in the leaf
number and nodes per plant was at DLow ×CT2, which recorded
the lowest stem diameter value (0.43 cm) (Table 1).
SPAD Index and Color Leaf
Measurement
Significant differences were not observed among cultivars for
the principal CIELAB colorimetric parameters, as opposed to
SPAD index values, which were higher in “Aroma 2” and lower
in “Italiano Classico” (Table 2). Both Lightness (L∗) and SPAD
index showed significant variations in relation to density. DLow
density resulted in a decrease in L∗(2.7%), in contrast to
the SPAD index (+4.3%). The cut significantly influenced b∗,
Chroma, and SPAD index that were reduced at CT1, in contrast
to a∗that showed an opposite trend. Significant differences were
found in the interactions (CV ×D, D ×CT, and CV ×CT)
between the considered factors under investigation exclusively
for SPAD index. The latter parameter increased from higher to
lower density and from the first to the second cut, respectively,
for CV ×D and CV ×CT. Specifically, the highest SPAD values
were shown for DLow ×CT2 (41.96) and Aroma 2 ×CT2 (43.34)
(Table 2). The data indicated that the colorimetric indexes of the
cultivars are fixed, as the varieties have been selected to adhere to
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Ciriello et al. Preharvest Factors Modulate Basil Quality
the Genovese type standard, and little altered by the density and
factors interactions.
Physiological and Biochemical
Performance
The net CO2assimilation rate (ACO2) and the maximum
quantum efficiency of open Photosystem II (Fv/Fm) were both
affected by the cultivar (Table 3). The density choice did not
affect the gas exchange parameters nor the instantaneous WUEi,
but the higher density reduced Fv/Fm. On the other hand,
the cut significantly affected all physiological measurements
performed, except for WUEi. Specifically, plants harvested at
CT1 showed an increase of transpiration (E) (17.2%) compared
with CT2 and, conversely, stomatal resistance (rs) decreased
by 24.5%. All physiological parameters were affected by the
interaction between cultivar and density, revealing a robust
cultivar-dependent response to the densities under investigation
(Table 3). Except for Fv/Fm, where the lowest value was obtained
at CT2 with density DHigh, the density ×cut combination showed
no difference for the physiological parameters. With respect
to CV ×CT, ACO2and Fv/Fmshowed significant differences.
Particularly, “Eleonora,” and “Aroma 2” recorded the highest
ACO2values at CT1, while “Eleonora” ×CT2 showed the lowest
Fv/Fmvalue.
Minerals Accumulation
The effects on the mineral composition and nitrate content
due to the cultivar, density, and cut are presented in Table 4.
TABLE 1 | Leaf number, stem diameter, node number, fresh yield, dry biomass, leaf-to-stem ratio, and dry matter of Genovese basil cultivars Eleonora, Aroma 2, and
Italiano Classico in light of density and cut treatments.
Source of variance Leaf number Stem diameter Node number Fresh yield Dry biomass Leaf-to-stem ratio Dry matter
(no. plant−1) (cm) (no. plant−1) (kg m−2) (kg m−2) (%)
Cultivar (CV)
Eleonora 36.02 ±3.65 b 0.44 ±0.03 ab 3.10 ±0.11 b 3.74 ±0.19 c 0.43 ±0.03 c 1.54 ±0.03 b 11.42 ±0.73 b
Aroma 2 43.28 ±5.11 a 0.42 ±0.02 b 3.43 ±0.12 a 4.57 ±0.35 a 0.62 ±0.08 a 1.48 ±0.05 c 13.13 ±0.82 a
Italiano Classico 30.54 ±3.40 c 0.46 ±0.02 a 2.72 ±0.10 c 4.40 ±0.41 b 0.54 ±0.06 b 1.74 ±0.09 a 11.90 ±0.38 b
*** ** *** *** *** *** ***
Density (D)
DHigh 30.70 ±2.39 0.41 ±0.02 2.88 ±0.09 4.81 ±0.22 0.60 ±0.05 1.59 ±0.04 12.38 ±0.58
DLow 42.52 ±3.93 0.47 ±0.01 3.28 ±0.12 3.66 ±0.26 0.46 ±0.05 1.58 ±0.07 11.92 ±0.55
t-test ** ** ** ** * ns ns
Cut (CT)
CT1 25.28 ±1.07 0.50 ±0.01 2.92 ±0.09 3.57 ±0.22 0.36 ±0.02 1.75 ±0.05 10.14 ±0.19
CT2 47.94 ±2.99 0.39 ±0.01 3.25 ±0.12 4.91 ±0.24 0.70 ±0.04 1.42 ±0.03 14.16 ±0.37
t-test *** *** * *** *** *** ***
CV ×D
Eleonora ×DHigh 28.63 ±2.20 b 0.41 ±0.04 2.91 ±0.14 4.15 ±0.24 abc 0.48 ±0.02 1.57 ±0.05 12.03 ±1.28
Aroma 2 ×DHigh 37.37 ±5.12 ab 0.39 ±0.03 3.19 ±0.07 5.27 ±0.31 a 0.70 ±0.10 1.46 ±0.04 12.93 ±1.20
Italiano Classico ×DHigh 26.10 ±3.61 b 0.42 ±0.03 2.56 ±0.10 5.03 ±0.47 ab 0.62 ±0.08 1.75 ±0.06 12.19 ±0.49
Eleonora ×DLow 43.40 ±5.67 ab 0.47 ±0.04 3.30 ±0.13 3.34 ±0.18 c 0.37 ±0.04 1.50 ±0.04 10.81 ±0.76
Aroma 2 ×DLow 49.18 ±8.64 a 0.46 ±0.02 3.67 ±0.20 3.88 ±0.51 bc 0.55 ±0.12 1.49 ±0.10 13.34 ±1.22
Italiano Classico ×DLow 34.98 ±5.48 ab 0.49 ±0.01 2.88 ±0.15 3.77 ±0.59 bc 0.46 ±0.09 1.74 ±0.17 11.61 ±0.60
** ns ns *** ns ns ns
D×CT
DHigh ×CT1 22.63 ±1.21 c 0.48 ±0.01 b 2.83 ±0.14 b 4.41 ±0.12 b 0.45 ±0.01 c 1.70 ±0.05 a 10.20 ±0.30
DLow ×CT1 27.92 ±1.31 c 0.51 ±0.01 a 3.00 ±0.13 b 2.72 ±0.07 c 0.27 ±0.01 d 1.80 ±0.08 a 10.09 ±0.24
DHigh ×CT2 38.77 ±2.54 b 0.34 ±0.01 d 2.93 ±0.10 b 5.21 ±0.40 a 0.76 ±0.06 a 1.48 ±0.04 b 14.56 ±0.37
DLow ×CT2 57.12 ±3.24 a 0.43 ±0.01 c 3.57 ±0.15 a 4.60 ±0.23 ab 0.64 ±0.05 b 1.36 ±0.02 b 13.75 ±0.63
*** ** ** *** * *** ns
CV ×CT
Eleonora ×CT1 27.35 ±1.64 c 0.53 ±0.01 a 3.10 ±0.15 b 3.82 ±0.39 b 0.35 ±0.04 d 1.63 ±0.03 b 9.23 ±0.14 d
Aroma 2 ×CT1 27.98 ±0.90 c 0.47 ±0.01 bc 3.17 ±0.07 b 3.67 ±0.41 b 0.38 ±0.04 d 1.63 ±0.04 b 10.50 ±0.16 c
Italiano Classico ×CT1 20.50 ±1.18 c 0.49 ±0.01 ab 2.48 ±0.07 c 3.21 ±0.34 b 0.35 ±0.04 d 1.99 ±0.06 a 10.71 ±0.23 c
Eleonora ×CT2 44.68 ±5.10 b 0.36 ±0.02 e 3.11 ±0.16 b 3.67 ±0.05 b 0.50 ±0.02 c 1.44 ±0.01 c 13.62 ±0.64 b
Aroma 2 ×CT2 58.57 ±4.52 a 0.38 ±0.02 de 3.69 ±0.19 a 5.47 ±0.23 a 0.86 ±0.04 a 1.32 ±0.02 d 15.76 ±0.41 a
Italiano Classico ×CT2 40.58 ±3.04 b 0.42 ±0.03 cd 2.96 ±0.13 b 5.58 ±0.22 a 0.73 ±0.03 b 1.49 ±0.06 c 13.09 ±0.11 b
*** *** * *** *** *** ***
ns, *, **, ***, non-significant or significant at p ≤0.05, 0.01, and 0.001, respectively. Different letters within each column indicate significant differences according to
Duncan’s multiple-range test (p = 0.05). Density and cut factors are compared according to Student’s t-test. All data are expressed as mean ±standard error, n = 3.
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Ciriello et al. Preharvest Factors Modulate Basil Quality
Basil cultivars affected both nitrate and assayed minerals,
except for sodium. “Aroma 2” showed a lower average of
nitrate (-33%) compared with the other cultivars. The lowest
P and Ca content were obtained in “Eleonora” while K
concentration was lower in “Italiano Classico.” Neither nitrate
nor mineral composition was influenced by the density. By
contrast, CT2 significantly decreased the nitrate, P, K, Ca,
and Mg concentrations. Concerning the interaction between
the factors under investigation, the values of nitrate and
Mg were influenced by the cultivar and density. In contrast,
K values were affected by the interaction between density
and cut, with the lowest value obtained in DLow ×CT2
(31.13 g kg−1dw). The CV ×CT interaction affected
Ca content, where the minimum value was obtained in
“Eleonora” ×CT1 (0.75 g kg−1dw). However, in response to
the interactions between the studied factors, P did not show
substantial changes.
Quantification of Phenolic Acids
Total phenolic acids were affected by the factors under
investigation and their interactions (Table 5). Rosmarinic acid
was the most prevalent compound, followed by chicoric, caffeic,
and ferulic acids. “Italiano Classico” showed the highest content
of rosmarinic (144.0 µg g−1dw) and chicoric acids (74.49 µg g−1
dw) with an overall higher accumulation of 44.2% (on average)
in total phenolic acids, compared to the other two cultivars. The
density influenced the content of the most abundant phenolic
acids (rosmarinic and chicoric acids), as well as the total phenolic
acids content. Except for rosmarinic acid, the cut impacted all the
phenolic profile. In addition, the interaction between cultivar and
TABLE 2 | Soil Plant Analysis Development Index (SPAD index), CIELAB color space parameters, chroma, and hue angle of Genovese basil cultivars Eleonora, Aroma 2,
and Italiano Classico in light of density and cut treatments.
Source of variance SPAD index L*a*b* Chroma Hue angle
Cultivar (CV)
Eleonora 39.82 ±0.53 b 41.61 ±0.35 −7.06 ±0.56 14.84 ±1.22 16.44 ±1.33 115.60 ±0.33
Aroma 2 41.62 ±0.60 a 41.74 ±0.32 −7.38 ±0.50 15.29 ±1.19 16.98 ±1.29 116.07 ±0.36
Italiano Classico 38.20 ±0.21 c 41.91 ±0.62 −7.35 ±0.44 16.32 ±1.07 18.01 ±1.14 116.99 ±1.90
*** ns ns ns ns ns
Density (D)
DHigh 39.05 ±0.40 42.32 ±0.30 −7.32 ±0.40 15.62 ±0.95 17.25 ±1.03 115.33 ±0.27
DLow 40.71 ±0.54 41.19 ±0.37 −7.21 ±0.41 15.35 ±0.94 17.03 ±1.02 117.11 ±1.24
t-test * * ns ns ns ns
Cut (CT)
CT1 38.88 ±0.27 41.82 ±0.24 −8.62 ±0.24 18.81 ±0.42 20.76 ±0.43 116.31 ±1.27
CT2 40.88 ±0.58 41.69 ±0.46 −5.91 ±0.24 12.16 ±0.56 13.53 ±0.61 116.14 ±0.29
t-test ** ns *** *** *** ns
CV ×D
Eleonora ×DHigh 38.53 ±0.25 b 42.02 ±0.32 −6.74 ±0.69 14.28 ±1.46 15.80 ±1.61 115.35 ±0.51
Aroma 2 ×DHigh 40.84 ±0.68 a 41.84 ±0.45 −7.35 ±0.77 15.34 ±1.75 17.01 ±1.91 115.80 ±0.32
Italiano Classico ×DHigh 37.79 ±0.23 b 43.09 ±0.64 −7.87 ±0.69 17.23 ±1.77 18.95 ±1.89 114.85 ±0.55
Eleonora ×DLow 41.11 ±0.72 a 41.20 ±0.59 −7.38 ±0.93 15.39 ±2.06 17.08 ±2.26 115.86 ±0.44
Aroma 2 ×DLow 42.39 ±0.93 a 41.64 ±0.50 −7.41 ±0.73 15.24 ±1.78 16.95 ±1.92 116.35 ±0.65
Italiano Classico ×DLow 38.61 ±0.26 b 40.73 ±0.86 −6.84 ±0.54 15.42 ±1.25 17.07 ±1.33 119.12 ±3.71
** ns ns ns ns ns
D×CT
DHigh ×CT1 38.31 ±0.31 b 42.21 ±0.27 −8.80 ±0.21 19.06 ±0.61 21.00 ±0.64 114.86 ±0.30
DLow ×CT1 39.45 ±0.36 b 41.43 ±0.36 −8.45 ±0.43 18.55 ±0.59 20.51 ±0.61 117.75 ±2.50
DHigh ×CT2 39.79 ±0.66 b 42.43 ±0.55 −5.84 ±0.32 12.17 ±0.71 13.51 ±0.77 115.80 ±0.41
DLow ×CT2 41.96 ±0.84 a 40.95 ±0.67 −5.97 ±0.38 12.15 ±0.92 13.55 ±0.99 116.47 ±0.39
* ns ns ns ns ns
CV ×CT
Eleonora ×CT1 38.94 ±0.39 cd 41.51 ±0.39 −8.77 ±0.34 18.34 ±0.83 20.34 ±0.89 115.62 ±0.24
Aroma 2 ×CT1 39.90 ±0.30 bc 41.60 ±0.50 −8.81 ±0.14 18.66 ±0.47 20.64 ±0.48 115.37 ±0.39
Italiano Classico ×CT1 37.81 ±0.24 d 42.34 ±0.29 −8.29 ±0.64 19.41 ±0.85 21.29 ±0.88 117.93 ±3.93
Eleonora ×CT2 40.70 ±0.89 b 41.71 ±0.61 −5.35 ±0.31 11.33 ±0.95 12.54 ±0.98 115.59 ±0.65
Aroma 2 ×CT2 43.34 ±0.54 a 41.88 ±0.45 −5.95 ±0.52 11.91 ±1.21 13.32 ±1.32 116.78 ±0.46
Italiano Classico ×CT2 38.59 ±0.26 cd 41.48 ±1.25 −6.42 ±0.32 13.24 ±0.70 14.72 ±0.77 116.04 ±0.28
*** ns ns ns ns ns
ns, *, **, ***, non-significant or significant at p ≤0.05, 0.01, and 0.001, respectively. Different letters within each column indicate significant differences according to
Duncan’s multiple-range test (p = 0.05). Density and cut factors are compared according to Student’s t-test. All data are expressed as mean ±standard error, n = 3.
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density affected rosmarinic, chicoric, and caffeic acids including
total phenolic acids. Moreover, for all cultivars, DLow density led
to an increase of rosmarinic, chicoric, and total phenolic acids by
58.3, 84.2, and 55.2%, respectively. In addition, the concentration
of all phenolic acids and their sum (total phenolic acids) was
affected by the density ×cut interaction with DLow ×CT2
combination resulting in their highest accumulation. Lastly, the
phenolic profile was strongly affected by CV ×CT, increasing
from the first to the second cut for all the studied cultivars.
Volatile Profile Estimation
The percentages of the major volatile compounds are shown in
Table 6. Linalool was the most prevalent compound, followed
by eucalyptol, eugenol, α-bergamotene, 1-octen-3-ol, and β-cis-
ocimene. Except for eucalyptol, all volatile compounds detected
were affected significantly by the cultivar. “Eleonora” recorded
the highest concentration of 1-octen-3-ol and α-bergamotene
but the lowest linalool concentration; instead, “Italiano Classico”
showed the lowest β-cis-ocimene value while “Aroma 2” showed
the lowest eugenol percentage. The density only influenced
the β-cis-ocimene content, with the highest value recorded
in DLow. Conversely, all volatile compounds, except β-cis-
ocimene, were affected by the cut. In contrast to linalool,
eugenol, and α-bergamotene, the highest percentage values of
eucalyptol and 1-octen-3-ol were obtained at the second cut. 1-
octen-3-ol, β-cis-ocimene, and linalool buildup were influenced
exclusively by the interaction between cultivar and density, with
TABLE 3 | Net photosynthesis (ACO2), stomatal resistance (rs), transpiration (E), instantaneous water use efficiency (WUEi), and chlorophyll fluorescence of Genovese
basil cultivars Eleonora, Aroma 2, and Italiano Classico in light of density and cut treatments.
Source of variance ACO2rsEWUEi Fluorescence Fv/Fm
(µmol CO2m−2s−1) (m2s−1mol−1) (mol H2O m−2s−1) (µmol CO2mol−1H2O)
Cultivar (CV)
Eleonora 17.99 ±0.58 b 7.44 ±0.63 3.71 ±0.19 4.97 ±0.26 0.79 ±0.01 b
Aroma 2 18.74 ±0.45 a 6.06 ±0.51 4.02 ±0.21 4.79 ±0.25 0.80 ±0.00 a
Italiano Classico 17.60 ±0.25 b 5.86 ±0.86 4.15 ±0.26 4.44 ±0.29 0.78 ±0.01 b
*** ns ns ns ***
Density (D)
DHigh 18.52 ±0.35 6.13 ±0.58 4.05 ±0.17 4.70 ±0.20 0.78 ±0.01
DLow 17.70 ±0.38 6.77 ±0.56 3.87 ±0.20 4.76 ±0.25 0.80 ±0.00
t-test ns ns ns ns *
Cut (CT)
CT1 19.05 ±0.34 5.55 ±0.25 4.28 ±0.15 4.54 ±0.16 0.80 ±0.00
CT2 17.17 ±0.25 7.35 ±0.71 3.65 ±0.19 4.93 ±0.26 0.78 ±0.01
t-test *** * ** ns **
CV ×D
Eleonora ×DHigh 19.30 ±0.62 a 6.81 ±0.27 ab 3.75 ±0.08 ab 5.15 ±0.12 ab 0.77 ±0.01 b
Aroma 2 ×DHigh 18.71 ±0.63 a 4.77 ±0.26 b 4.55 ±0.12 a 4.11 ±0.12 bc 0.80 ±0.01 a
Italiano Classico ×DHigh 17.56 ±0.36 ab 6.80 ±1.65 ab 3.86 ±0.45 ab 4.83 ±0.50 abc 0.77 ±0.01 b
Eleonora ×DLow 16.69 ±0.64 b 8.07 ±1.24 a 3.68 ±0.39 ab 4.79 ±0.52 abc 0.80 ±0.01 a
Aroma 2 ×DLow 18.76 ±0.70 a 7.34 ±0.64 ab 3.49 ±0.26 b 5.47 ±0.29 a 0.81 ±0.00 a
Italiano Classico ×DLow 17.65 ±0.37 ab 4.91 ±0.43 b 4.44 ±0.26 a 4.04 ±0.24 c 0.80 ±0.00 a
*** * ** *** *
D×CT
DHigh ×CT1 19.43 ±0.50 5.50 ±0.39 4.33 ±0.23 4.58 ±0.26 0.80 ±0.01 a
DLow ×CT1 18.68 ±0.47 5.60 ±0.34 4.23 ±0.19 4.49 ±0.22 0.81 ±0.00 a
DHigh ×CT2 17.61 ±0.23 6.75 ±1.08 3.77 ±0.23 4.81 ±0.30 0.77 ±0.01 b
DLow ×CT2 16.73 ±0.39 7.95 ±0.94 3.52 ±0.31 5.04 ±0.44 0.80 ±0.00 a
ns ns ns ns *
CV ×CT
Eleonora ×CT1 19.31 ±0.62 a 6.03 ±0.47 4.07 ±0.20 4.82 ±0.31 0.81 ±0.00 a
Aroma 2 ×CT1 20.16 ±0.17 a 5.41 ±0.45 4.38 ±0.17 4.64 ±0.19 0.81 ±0.00 a
Italiano Classico ×CT1 17.69 ±0.39 b 5.21 ±0.38 4.40 ±0.37 4.15 ±0.30 0.78 ±0.01 b
Eleonora ×CT2 16.68 ±0.62 b 8.85 ±0.86 3.36 ±0.27 5.12 ±0.44 0.76 ±0.01 c
Aroma 2 ×CT2 17.31 ±0.21 b 6.70 ±0.88 3.67 ±0.34 4.94 ±0.49 0.80 ±0.00 ab
Italiano Classico ×CT2 17.52 ±0.34 b 6.50 ±1.72 3.90 ±0.38 4.73 ±0.49 0.78 ±0.01 b
*** ns ns ns ***
ns, *, **, ***, non-significant or significant at p ≤0.05, 0.01, and 0.001, respectively. Different letters within each column indicate significant differences according to
Duncan’s multiple-range test (p = 0.05). Density and cut factors are compared according to Student’s t-test. All data are expressed as mean ±standard error, n = 3.
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Ciriello et al. Preharvest Factors Modulate Basil Quality
the latter exhibiting the lowest value in “Eleonora” ×DLow
(36.1%). The interaction between the density and cut showed
significant variations for eucalyptol, linalool, and α-bergamotene.
Specifically, eucalyptol content was higher in DHigh ×CT2
(31.1%). Interaction between cultivar and cut resulted in
differences exclusively for eucalyptol and α-bergamotene content,
with the latter showing the maximum value in “Eleonora” ×CT1.
Principal Component Analysis
A PCA was conducted for all the agronomical and
physicochemical composition parameters assessed in this
study, which were shaped by the investigated factors and their
significant interactions. The first two components accounted
for 61.8% of the total variance (Supplementary Figure 2).
The two-dimensional component plot uncovered an internal
structure of the data consistent with the experimental factors
(Figure 2). Samples were separated coherently along the PC1
based on the density, with all DLow samples (respectively, DHigh)
in the positive (resp, negative) PC1 plot area. Considering the
prominent contribution of the first component (45.8% of total
variance), the density factor associated with the largest linearly
projected variance in the measured basil traits. Moreover,
samples were much more distributed at the lower planting
density, indicating that the total common variance of the basil
traits is restrained when plants grow tighter. Considering the cut,
there was good separation along the PC2 for nearly all samples.
The clustering of the samples according to the cultivar indicated
that the genotype-dependent effect on the measured traits does
not vary strongly depending on the conditions, and it is inferior
to the other pre-harvest factors, as the three varieties consistently
TABLE 4 | Nitrate and mineral content of Genovese basil cultivars Eleonora, Aroma 2, and Italiano Classico in light of density and cut treatments.
Source of variance Nitrate P K Ca Mg
(mg kg−1fw) (g kg−1dw) (g kg−1dw) (g kg−1dw) (g kg−1dw)
Cultivar (CV)
Eleonora 3,590 ±273 a 3.39 ±0.37 b 41.67 ±2.29 a 8.34 ±0.29 b 2.51 ±0.08 b
Aroma 2 2,332 ±238 b 4.05 ±0.42 a 39.31 ±2.37 ab 9.92 ±0.32 a 2.83 ±0.07 a
Italiano Classico 3,418 ±234 a 3.96 ±0.43 a 37.28 ±1.65 c 9.37 ±0.44 a 2.45 ±0.13 b
*** *** ** *** ***
Density (D)
DHigh 2,872 ±189 3.73 ±0.35 39.84 ±1.20 9.00 ±0.34 2.66 ±0.07
DLow 3,354 ±272 3.87 ±0.32 39.00 ±2.19 9.42 ±0.30 2.54 ±0.10
t-test ns ns ns ns ns
Cut (CT)
CT 1 3,785 ±174 5.12 ±0.11 45.30 ±1.05 10.08 ±0.29 2.78 ±0.07
CT 2 2,442 ±182 2.48 ±0.09 33.54 ±1.04 8.34 ±0.21 2.41 ±0.08
t-test *** *** *** *** ***
CV ×D
Eleonora ×DHigh 3,156 ±410 ab 3.31 ±0.57 40.13 ±2.69 7.76 ±0.27 2.40 ±0.06 bc
Aroma 2 ×DHigh 2,339 ±100 b 4.08 ±0.62 40.65 ±1.26 9.98 ±0.57 2.93 ±0.09 a
Italiano Classico ×DHigh 3,122 ±317 ab 3.79 ±0.68 38.73 ±2.31 9.25 ±0.54 2.64 ±0.12 abc
Eleonora ×DLow 4,025 ±289 a 3.47 ±0.52 43.20 ±3.87 8.91 ±0.40 2.63 ±0.13 abc
Aroma 2 ×DLow 2,324 ±489 b 4.02 ±0.62 37.97 ±4.74 9.85 ±0.36 2.72 ±0.08 ab
Italiano Classico ×DLow 3,714 ±323 a 4.13 ±0.60 35.84 ±2.42 9.48 ±0.74 2.27 ±0.21 c
** ns ns ns **
D×CT
DHigh ×CT1 3,445 ±240 5.10 ±0.16 43.73 ±0.84 a 9.88 ±0.49 2.78 ±0.11
DLow ×CT1 4,125 ±207 5.13 ±0.16 46.87 ±1.84 a 10.27 ±0.32 2.78 ±0.09
DHigh ×CT2 2,300 ±110 2.35 ±0.11 35.94 ±1.26 b 8.11 ±0.26 2.53 ±0.08
DLow ×CT2 2,584 ±352 2.61 ±0.13 31.13 ±1.24 c 8.56 ±0.32 2.30 ±0.13
ns ns *** ns ns
CV ×CT
Eleonora ×CT1 4,279 ±174 4.58 ±0.10 48.31 ±1.95 8.83 ±0.43 bc 2.65 ±0.12
Aroma 2 ×CT1 2,953 ±224 5.42 ±0.13 45.81 ±1.36 10.65 ±0.39 a 2.94 ±0.11
Italiano Classico ×CT1 4,123 ±142 5.35 ±0.13 41.79 ±1.16 10.74 ±0.23 a 2.74 ±0.11
Eleonora ×CT2 2,902 ±328 2.20 ±0.12 35.02 ±1.28 7.84 ±0.30 d 2.37 ±0.05
Aroma 2 ×CT2 1,710 ±211 2.68 ±0.06 32.82 ±2.47 9.18 ±0.30 b 2.71 ±0.05
Italiano Classico ×CT2 2,714 ±146 2.57 ±0.18 32.77 ±1.60 8.00 ±0.20 cd 2.16 ±0.16
ns ns ns * ns
ns, *, **, ***, non-significant or significant at p ≤0.05, 0.01, and 0.001, respectively. Different letters within each column indicate significant differences according to
Duncan’s multiple-range test (p = 0.05). Density and cut factors are compared according to Student’s t-test. All data are expressed as mean ±standard error, n = 3.
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Ciriello et al. Preharvest Factors Modulate Basil Quality
clustered according to the level of the other two factors (cut and
density). It should be added that PCA orthogonally transforms
data, and the grouping of the cultivars may also be interpreted
considering a possible non-linear genotypic-dependent response
to the cut and density of the different varieties. Overall, the
multivariate analysis indicated that most of the variance can be
explained considering the two growing conditions, and that, at
higher density, the variability of the measured traits due to the
genotype and cut factors is less extensive.
DISCUSSION
The FRS is a valuable tool to deseasonalize, anticipate, and
improve basil plants’ productivity, useful also to understand
plant response to the combined action of different pre-harvest
factors on various classes of basil traits. The biometric parameters
were the most affected, followed by polyphenols, considering
the relative presence of three-way interactions. From an applied
perspective, it is noteworthy that the fresh biomass per area
was affected by each factor and all their interactions. Among
the yield components, the number of leaves was the highly
sensitive parameter to the various factors and interaction. Also,
total polyphenols were highly affected by all the factors and this
is reasonable considering their inducible accumulation and, as
indicated by our data, that distinct major polyphenols of sweet
basil vary differently according to the pre-harvest factors.
Our data showed an improved production performance of the
tested cultivars, both in fresh yield and in advance production,
achieving yields about twofold higher than those obtained by
TABLE 5 | Phenolic acids and total polyphenols of Genovese basil cultivars Eleonora, Aroma 2, and Italiano Classico in light of density and cut treatments.
Source of variance Caffeic acid Chicoric acid Rosmarinic acid Ferulic acid Total phenolic acids
(µg g−1dw) (µg g−1dw) (µg g−1dw) (µg g−1dw) (µg g−1dw)
Cultivar (CV)
Eleonora 40.94 ±3.55 b 56.59 ±8.21 c 46.75 ±3.49 c 4.63 ±0.72 a 145.5 ±11.9 c
Aroma 2 55.69 ±8.77 a 67.35 ±12.0 b 111.9 ±16.6 b 4.88 ±0.64 a 237.9 ±35.5 b
Italiano Classico 55.51 ±1.70 a 74.49 ±19.5 a 144.0 ±12.6 a 3.24 ±0.38 b 276.4 ±32.3 a
*** *** *** *** ***
Density (D)
DHigh 46.71 ±3.21 46.55 ±6.09 78.15 ±9.62 3.83 ±0.44 172.4 ±12.8
DLow 54.72 ±5.77 85.74 ±13.4 123.7 ±15.3 4.53 ±0.52 267.5 ±31.3
t-test ns * * ns **
Cut (CT)
CT1 39.76 ±2.77 35.28 ±2.45 85.10 ±10.4 2.67 ±0.13 159.8 ±12.9
CT2 61.66 ±4.87 97.01 ±12.0 116.7 ±15.7 5.50 ±0.44 279.8 ±28.7
t-test *** *** ns *** ***
CV ×D
Eleonora ×DHigh 48.68 ±5.16 bc 47.27 ±2.21 b 38.89 ±4.09 b 3.91 ±0.40 135.3 ±10.7 c
Aroma 2 ×DHigh 36.21 ±6.20 c 59.49 ±17.3 b 72.24 ±5.70 b 5.09 ±0.80 173.0 ±29.8 c
Italiano Classico ×DHigh 55.23 ±1.56 b 32.91 ±1.91 b 123.3 ±13.1 a 2.54 ±0.13 212.1 ±10.7 bc
Eleonora ×DLow 33.21 ±2.20 c 65.92 ±16.0 ab 54.61 ±3.47 b 4.98 ±1.06 158.7 ±21.7 c
Aroma 2 ×DLow 75.17 ±12.2 a 75.22 ±17.8 ab 151.6 ±23.5 a 4.67 ±1.06 306.7 ±53.5 ab
Italiano Classico ×DLow 55.78 ±3.19 b 116.1 ±31.4 a 164.7 ±19.0 a 3.95 ±0.65 340.5 ±53.5 a
*** *** *** ns ***
D×CT
DHigh ×CT1 37.46 ±4.40 c 33.17 ±4.24 c 79.64 ±18.2 b 2.91 ±0.25 c 152.2 ±20.8 b
DLow ×CT1 42.07 ±3.43 bc 37.40 ±2.51 bc 90.56 ±11.2 b 2.51 ±0.11 c 171.3 ±16.6 b
DHigh ×CT2 55.95 ±1.71 ab 59.94 ±9.73 b 76.67 ±7.87 b 4.44 ±0.64 b 195.8 ±12.0 b
DLow ×CT2 67.37 ±9.47 a 134.1 ±13.3 a 156.8 ±24.3 a 6.55 ±0.36 a 363.8 ±40.3 a
** *** *** *** ***
CV ×CT
Eleonora ×CT1 33.05 ±2.53 c 39.56 ±4.34 b 39.87 ±4.89 b 2.69 ±0.21 b 113.8 ±4.46 c
Aroma 2 ×CT1 35.28 ±5.74 c 28.44 ±3.49 b 79.83 ±8.88 b 2.88 ±0.26 b 144.1 ±18.5 bc
Italiano Classico ×CT1 50.96 ±0.70 bc 37.85 ±3.96 b 135.6 ±9.12 a 2.46 ±0.14 b 225.4 ±7.82 b
Eleonora ×CT2 48.84 ±4.90 bc 73.63 ±12.7 ab 53.63 ±3.27 b 5.59 ±0.81 a 180.2 ±11.2 bc
Aroma 2 ×CT2 76.10 ±11.8 a 106.3 ±4.59 a 144.0 ±26.9 a 6.88 ±0.35 a 331.8 ±43.1 a
Italiano Classico ×CT2 60.05 ±1.98 ab 111.1 ±33.5 a 152.5 ±24.3 a 4.03 ±0.61 b 327.7 ±59.0 a
*** *** *** *** ***
ns, *, **, ***, non-significant or significant at p ≤0.05, 0.01, and 0.001, respectively. Different letters within each column indicate significant differences according to
Duncan’s multiple-range test (p = 0.05). Density and cut factors are compared according to Student’s t-test. All data are expressed as mean ±standard error, n = 3.
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Nicoletto et al. (2013) in the open field. Regardless of plant
density and cuts, “Aroma 2” exhibited a better adaptability
to the FRS, ensuring higher fresh yield and dry biomass per
square meter, which can be ascribed to a better photosynthetic
performance and a higher number of leaves and nodes per
plant. On the contrary, a recent comparative study illustrated
for the same cultivars grown in the autumn–winter season a
diametrically opposite production response, indicating a high
impact of the environmental factors (Ciriello et al., 2020).
Apart from plant material, both the cut and density affected
yield and yield-related parameters. Similar to Zheljazkov et al.
(2008) and Puccinelli et al. (2021), a linear increase in fresh yield,
dry biomass, number of leaves, and nodes per plant were marked
from the first to the second cut. As suggested by Zheljazkov et al.
(2008), the increase in production could be due to a well-formed
root system at the second cut that facilitates a faster regrowth of
the epigeal part. Moreover, the suppression of apical dominance
would have stimulated lateral buds’ emission, which led to an
increase in the number of nodes and leaves per plant, and
consequently to a decrease in the leaf-to-stem ratio (Tekalign and
Hammes, 2005). Other studies on herbaceous crop suggested that
the cut may increase cytokinin concentration, hence stimulating
cell division and regulating the leaf primordia emission (Le Bris,
2017;Skalák et al., 2019).
Prior to the second harvest, gas exchange measurements
showed a decrease of plants’ main physiological parameters,
such as transpiration rate, net CO2fixation, and increased
stomatal resistance compared to CT1. These results could be
TABLE 6 | Most abundant volatile compounds of Genovese basil cultivars Eleonora, Aroma 2, and Italiano Classico in light of density and cut treatments.
Source of variance 1-Octen-3-ol Eucalyptol β-cis-Ocimene Linalool Eugenol α-Bergamotene
(%) (%) (%) (%) (%) (%)
Cultivar (CV)
Eleonora 4.03 ±0.05 a 25.72 ±1.45 3.09 ±0.15 a 38.49 ±1.01 b 4.59 ±0.22 a 5.17 ±0.56 a
Aroma 2 2.86 ±0.11 c 25.71 ±1.83 2.97 ±0.30 a 44.56 ±0.87 a 3.92 ±0.25 b 3.13 ±0.35 b
Italiano Classico 3.30 ±0.13 b 25.58 ±0.63 2.36 ±0.29 b 44.84 ±0.94 a 4.51 ±0.29 a 2.97 ±0.16 b
*** ns *** *** ** ***
Density (D)
DHigh 3.47 ±0.13 26.90 ±1.31 2.19 ±0.17 43.32 ±0.74 4.17 ±0.23 3.51 ±0.47
DLow 3.32 ±0.15 24.44 ±0.80 3.42 ±0.15 41.94 ±1.24 4.51 ±0.20 4.01 ±0.29
t-test ns ns *** ns ns ns
Cut (CT)
CT1 3.17 ±0.15 22.73 ±0.75 2.84 ±0.17 44.14 ±1.03 5.03 ±0.13 4.55 ±0.43
CT2 3.62 ±0.11 28.61 ±0.97 2.77 ±0.26 41.13 ±0.91 3.65 ±0.14 2.97 ±0.25
t-test * *** ns * *** **
CV ×D
Eleonora ×DHigh 3.99 ±0.09 a 26.40 ±2.92 2.81 ±0.15 bc 40.88 ±1.15 b 4.55 ±0.34 5.22 ±1.06
Aroma 2 ×DHigh 2.91 ±0.17 c 27.67 ±2.84 2.19 ±0.34 cd 45.98 ±1.10 a 3.57 ±0.35 2.66 ±0.47
Italiano Classico ×DHigh 3.51 ±0.13 b 26.63 ±0.81 1.57 ±0.10 d 43.11 ±0.70 ab 4.40 ±0.41 2.63 ±0.18
Eleonora ×DLow 4.07 ±0.05 a 25.05 ±0.73 3.37 ±0.21 ab 36.11 ±0.94 c 4.63 ±0.31 5.11 ±0.51
Aroma 2 ×DLow 2.81 ±0.16 c 23.75 ±2.26 3.75 ±0.21 a 43.14 ±1.15 ab 4.27 ±0.32 3.60 ±0.47
Italiano Classico ×DLow 3.09 ±0.20 c 24.52 ±0.81 3.14 ±0.33 ab 46.58 ±1.48 a 4.62 ±0.44 3.31 ±0.18
* ns *** *** ns ns
D×CT
DHigh ×CT1 3.28 ±0.19 22.70 ±1.09 c 2.46 ±0.19 b 44.59 ±1.06 4.88 ±0.18 4.63 ±0.76 a
DLow ×CT1 3.05 ±0.24 22.76 ±1.10 c 3.22 ±0.23 a 43.69 ±1.83 5.18 ±0.20 4.47 ±0.45 a
DHigh ×CT2 3.66 ±0.16 31.10 ±1.30 a 1.92 ±0.26 b 42.06 ±0.92 3.46 ±0.24 2.39 ±0.24 b
DLow ×CT2 3.59 ±0.16 26.11 ±0.88 b 3.62 ±0.18 a 40.20 ±1.58 3.83 ±0.14 3.55 ±0.34 ab
ns *** *** ns ns **
CV ×CT
Eleonora ×CT1 3.92 ±0.06 21.92 ±0.89 cd 3.25 ±0.26 40.17 ±1.34 5.20 ±0.07 6.67 ±0.49 a
Aroma 2 ×CT1 2.52 ±0.04 20.17 ±0.82 d 3.17 ±0.18 46.23 ±1.29 4.63 ±0.17 4.05 ±0.36 b
Italiano Classico ×CT1 3.06 ±0.17 26.11 ±0.77 b 2.10 ±0.20 46.01 ±1.62 5.27 ±0.32 2.92 ±0.13 bc
Eleonora ×CT2 4.14 ±0.06 29.52 ±1.64 a 2.93 ±0.13 36.82 ±1.26 3.98 ±0.24 3.67 ±0.50 b
Aroma 2 ×CT2 3.20 ±0.09 31.25 ±1.33 a 2.77 ±0.60 42.90 ±0.76 3.21 ±0.21 2.21 ±0.24 c
Italiano Classico ×CT2 3.53 ±0.15 25.04 ±1.03 bc 2.61 ±0.55 43.67 ±0.84 3.75 ±0.19 3.02 ±0.31 bc
ns *** ns ns ns ***
ns, *, **, ***, non-significant or significant at p ≤0.05, 0.01, and 0.001, respectively. Different letters within each column indicate significant differences according to
Duncan’s multiple-range test (p = 0.05). Density and cut factors are compared according to Student’s t-test. All data are expressed as mean ±standard error, n = 3.
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Ciriello et al. Preharvest Factors Modulate Basil Quality
FIGURE 2 | Principal component analysis (PCA) of the basil response. Symbol shape indicates the growing density (DLow: circle; DHigh: square). Each condition is
colored according to the variety (“Eleonora”: blue; “Italiano Classico”: gray; “Aroma 2”: gold). For each cut, the plot displays the symbol empty (CT1) or filled (CT2).
The color and symbol legends are reported on the right side.
attributed to the combined effects of cut and tissues lignification
of plant nearing the end of their life cycle. Scientific evidence
demonstrated a direct relationship between the end of life cycle
and the reduction in the photosynthetic activity, attributed to
a degradation of RuBisCO activity and the alteration of redox
processes involving the electron transport chain (Krieger-Liszkay
et al., 2019). The minimal reduction in net CO2assimilation rate,
transpiration, and Fv/Fmratio would confirm the onset of leaf
senescence processes in the plants at the second cut. The observed
phenomenon was also confirmed by the increase in dry matter,
due to the progressive lignification of plant tissues (Corrado
et al., 2020b). Noteworthy, for the industrial processing of pesto,
the dry matter content is a crucial technological parameter.
An excessive fibrousness would extend the processing duration,
thus causing oxidation with a decrease in the quality of the
final product (pesto blackening) (Nicoletto et al., 2013). Another
crucial industrial requirement is basil leaves’ color, which drives
consumer choice (León et al., 2006). Colorimetric parameters
were not affected by genotype, like the results obtained in a recent
open field trial wherein the same cultivars were compared for
production and quality (Ciriello et al., 2021). However, the cut
resulted in a reduction in perceived color intensity (Chroma),
attributable to both a∗and b∗variations, probably due to the
lower nitrate content in basil leaves (Fallovo et al., 2009).
On the other hand, density choice did not affect food
processing key parameters such as dry matter and leaf-to-stem
ratio, in contrast to the observations of Miceli et al. (2003), which
reported an increase in dry matter with density growth. This
result can be attributed to the different plant material and the
different densities that were almost double (226 and 593 plants
m−2) compared to those tested (159 and 317 plants m−2) in
the current study. However, the double density (DHigh) in our
experiment led to an increased fresh yield and dry shoot biomass
for all assayed cultivars, as supported by the results reported in
the reviewed literature (Miceli et al., 2003;Maboko and Du Plooy,
2013;Mahlangu et al., 2020). Nonetheless, the increased fresh
yield and dry biomass at the higher density is due to the higher
number of plants per unit area (Maboko and Du Plooy, 2013), as
highlighted by the lower number of leaves and nodes per plant.
It should be added that in hydroponics, neighboring plants little
compete for below−ground resources (water and nutrients). The
reduction in the number of nodes is probably caused by the lower
light capture of the canopy because the resources competition
increases with the distance decrease (Postma et al., 2020). An
interesting study by Ballaré and Pierik (2017) revealed that plants
grown at high densities, due to a reduced ratio between red and
far-red light (R:FR) in the canopy, reduce the diameter of the
stem, corroborating our findings.
Our results showed a significant cultivar-dependent response
for mineral accumulation, in agreement with the findings of
Licina et al. (2014), who compared the mineral composition of
different basil genotypes. The positive lower nitrate accumulation
recorded in “Aroma 2” emphasizes the genotype’s key role in
accumulating this potentially risky dietary compound for human
health (Colla et al., 2018). This may be connected to a different
expression of genes involved in nitrate transport, as shown in
lettuce (Razgallah et al., 2017) and/or a higher nitrate reductase
activity (Luo et al., 2006). Magnesium is a central cation of
the chlorophyll molecule and involved in RuBisCO activation,
promoting CO2assimilation (Karthika et al., 2018). The higher
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Ciriello et al. Preharvest Factors Modulate Basil Quality
magnesium content in “Aroma 2” is reflected in the higher SPAD
and net CO2assimilation values, which resulted in higher fresh
yield. In contrast to the density effect, successive cuts resulted in
a decrease in all analyzed minerals. However, the overall mineral
profile reduction was associated with a significant increase in
dry matter (about twice as much) from the first to the second
cut. This would explain the decrease in minerals as an effect
of dilution and not directly attributable to cut-induced distress
(Jarrell and Beverly, 1981).
Besides synthesizing primary compounds for growth and
development, plants produce a wide range of specialized
metabolites, such as phenolics, which act as passive defense
barriers (Trivellini et al., 2016). Their biosynthesis is strongly
affected by genotype and environmental stressors (Rouphael
et al., 2012). As outlined in our investigation, phenolic acids
were strongly influenced by genotype. “Aroma 2” and “Italiano
Classico” phenolic profiles had a higher concentration of
rosmarinic acid (a compound found to be the more predominant
in basil), in contrast to “Eleonora” that accumulated more
chicoric acid. A recent study performed in an FRS provided
comparable results, highlighting a significant cultivar-dependent
response to chicoric and rosmarinic acid accumulation using
the same cultivars of Genovese basil (Ciriello et al., 2020).
Rosmarinic acid accumulation was higher than the one obtained
by Sgherri et al. (2010) in a soilless experiment, but well
below the values of Javanmardi et al. (2002) in the open
field. These discrepancies can be ascribed to the different
growing conditions, extraction and determination methods,
and various plant material adopted by each author (Filip,
2017). A study carried out by Kwee and Niemeyer (2011)
revealed in the spice basil (O. basilicum ×O. americanum)
a lower content of chicoric acid compared to our findings.
In contrast, Thai basil (O. basilicum var. thyrsiflorum) had
a higher chicoric acid content, underlining the impact of
genotype on biosynthesis and accumulation of phenolic acids.
Concerning the total phenolic acid content, this study showed
values about fourfold lower than those obtained by the same
cultivars in an open field experiment (Ciriello et al., 2021).
The higher values obtained in the open field may be imputable
to pedoclimatic conditions, less favorable than those in the
soilless system, leading to an oxidative stress that fostered
phenolic acids accumulation as a defense mechanism (Sgherri
et al., 2010). Furthermore, continuous exposure of field-grown
plants to UV radiation can prompt higher phenylalanine
ammonia-lyase (PAL) activity resulting in increased phenolic
acid accumulation (Neocleous and Ntatsi, 2018;Loconsole
and Santamaria, 2021). Additionally, specialized metabolite
biosynthesis is also influenced by perceived solar radiation,
varying with seasonality and planting density. Therefore, the rise
of total phenolic acids with the lowest density (DLow) could be
due to a lower shading of the plants. Apart from having a positive
effect on primary metabolism, light is a critical parameter for
producing carbon compounds in plants such as phenolic acids
(Kumar et al., 2013). Similarly, the accumulation of phenolic
acids is stimulated by stress factors that cause the evolution
of “reactive oxygen species (ROS)” in plant tissues (Naikoo
et al., 2019). Like other biotic and abiotic stresses, the cut
led to a linear increase in the total phenolic acid content in
sweet basil, as confirmed by Nicoletto et al. (2013) and Ciriello
et al. (2021). The increase in total phenolic acids in response
to cut suggests that this agronomic practice might promote
PAL activity; in addition, better production performance at the
second harvest might have led to an increased allocation of
photosynthates to the shikimic acid pathway (Shaw et al., 1998;
Crozier et al., 2007).
Basil is also endowed with aromatic molecules belonging to
different chemical groups (i.e., monoterpenes, sesquiterpenes,
and phenylpropanoids), whose composition confers the
characteristic aroma and taste of the plant (Salvadeo et al., 2007).
The tested cultivars showed either the absence of undesirable
aromatic compounds (e.g., estragole, thymol, and carvacrol) or
a predominance (more than 60%) of oxygenated monoterpenes
such as linalool and eucalyptol, typical volatiles of Genovese
cultivars used for pesto sauce production (Salvadeo et al.,
2007). Variations in volatiles composition among cultivars
were attributable to the different percentage content of minor
aromatic compounds, mainly related to different genotypes’
intrinsic characteristics (Ibrahim et al., 2006). The higher
concentration of 1-octen-3-ol and α-bergamotene in “Eleonora”
and the lower of β-cis-Ocimene in “Italiano Classico” are traits
fixed by the genotype. Recent experiments carried out under
different conditions and growth systems with the same cultivars
showed an increased accumulation of the abovementioned
minor compounds, which contribute to enrich and diversify
the aromatic bouquet of basil (Ciriello et al., 2020, 2021). In
dill (Anethum graveolens L.) plants grown in the open field,
the employment of high densities resulted in significantly
increased amounts of major aroma compounds due to the
root competition for water and nutrients (El-Zaeddi et al.,
2017). However, in our experiment, independently from the
cultivar, the density choice did not induce significant variations
in eucalyptol and linalool values. On the other hand, the
aroma profile of basil changed in response to successive cuts.
In agreement with Ciriello et al. (2021), the cut significantly
impacted the expression of the major volatiles (eucalyptol and
linalool), thus confirming the strict link between the volatiles’
biosynthesis and stressors. However, concerning the results of
several open field trials, the second cut reduced the linalool
content (Zheljazkov et al., 2008;Tsasi et al., 2017;Ciriello
et al., 2021). This difference could be attributed either to using
different growing systems (open field vs. FRS) or the different
climatic conditions that characterized the experiments (Luz
et al., 2014). In contrast to linalool content, eucalyptol increased
significantly at the second cut; probably, the cut induced a
better expression of the enzyme 1,8-cineole synthase, which
converts geranyl pyrophosphate (GPP) to eucalyptol, at the
expense of the enzyme linalool synthase (LIS), which catalyzes
the GPP-Linalool reaction (Chang et al., 2007). Apart from
the factors under investigation, the cut caused a decrease in
eugenol as observed in an open field study on basil (Tsasi
et al., 2017). Similarly, research on sorrel (Rumex acetosa L.)
showed a significant reduction of sesquiterpenes concentration,
evidenced by the reduced α-bergamotene at the second cut
(Ceccanti et al., 2020).
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Ciriello et al. Preharvest Factors Modulate Basil Quality
CONCLUSION
The increased demand of the food industry for fresh basil with
standardized technological and aromatic attributes has fostered
the diffusion of hydroponics. Among the tested cultivars, “Aroma
2” ensured the best production performance, the lowest nitrate
content, and the highest dry matter percentage. The latter, as well
as the aromatic profile, were not affected by the density, whereas
the yield was increased with the highest density. Successive cuts,
ordinarily performed for basil production, also increased the
yield per area and favored the accumulation of phenolic acids
(+75.1%), without modifying linalool content, though triggering
eucalyptol (+25.9%) and 1-octen-3-ol (+15.1%) accumulation.
Our work provides useful information on the productive and
qualitative response of the main basil cultivars used for the
food industry. The observed wide-ranging responsiveness also
suggests that an assessment under different climatic conditions
(e.g., autumn cycle) will be a useful complement to manage the
year-round production of Genovese leaves for the food industry.
Finally, future research may also explore the here described
impact of the cut on the phenolic acids’ accumulation as a possible
fortification means to extend the pesto sauce shelf life, reducing
the need of added antioxidants and thermal processing.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
AUTHOR CONTRIBUTIONS
YR: conceptualization and project administration. MC and
LF: methodology, validation, formal analysis, investigation, and
writing—original draft preparation. AP: software. YR and SDP:
resources. MC, LF, and AP: data curation. MC, LF, CE-N, GC, and
YR: writing—review and editing. GC and YR: visualization. GC,
SDP, and YR: supervision. SDP: funding acquisition. All authors
contributed to the article and approved the submitted version.
FUNDING
This research was conducted in the framework of the Ph.D.
sponsored by the Italian Ministry of Education (PON research
and innovation).
ACKNOWLEDGMENTS
We are grateful to Annunziata Lanni for her technical and
moral support and editing the manuscript. We would like to
acknowledge Alessandra Aiello, Lucia De Luca, and Giampaolo
Raimondi for their technical assistance in the field trial and
Raffaele Romano and Fabiana Pizzolongo for providing the access
to HPLC and GC/MS facilities and analysis.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpls.2021.
671026/full#supplementary-material
Supplementary Figure 1 | Chromatograms of phenolic acids in Genovese basil
extract by HPLC at density D2 with separation of caffeic acid (1), ferulic acid (2),
chicoric acid (3), and rosmarinic acid (4). (A,B) Aroma 2 at first and second cut.
(C,D) Eleonora at first and second cut. (E,F) Italiano Classico at
first and second cut.
Supplementary Figure 2 | Scree plot of the eigenvalues of the
principal components.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2021 Ciriello, Formisano, El-Nakhel, Corrado, Pannico, De Pascale and
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