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Changes in olive oil VOCs induced by water status and light
environment in canopies of Olea europaea L. trees
Journal: Journal of the Science of Food and Agriculture Manuscript ID: JSFA-14-1685.R1
Wiley - Manuscript type: Research Article Date Submitted by the Author: n/a
Complete List of Authors: Benelli, Giovanni; University of Pisa, Agriculture, Food and Environment Caruso, Giovanni; University of Pisa, Agriculture, Food and Environment Giunti, Giulia; University of Pisa, Agriculture, Food and Environment Cuzzola, Angela; University of Pisa, Agriculture, Food and Environment Saba, Alessandro; University of Pisa, Department of Surgical, Medical, Molecular and Critical Area Pathology
Raffaelli, Andrea; National Research Council, Institute of Clinical Physiology Gucci, Riccardo; University of Pisa, Agriculture, Food and Environment Key Words: canopy position, GC-MS, olive oil quality, photosynthetic active radiation,
VOCs, water deficit
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Changes in olive oil VOCs induced by water status and light environment in canopies of 1
Olea europaea L. trees 2
3
Running Title: Changes in olive oil VOCs induces by abiotic stresses
4 5
Giovanni Benelli1§, Giovanni Caruso1, Giulia Giunti1, Angela Cuzzola1, Alessandro Saba2, 6
Andrea Raffaelli3, Riccardo Gucci1* 7
8
1 Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, I-9
56124, Pisa, Italy
10
2 Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Via 11
Paradisa, 2, I-56124 Pisa, Italy
12
3 CNR – Institute of Clinical Physiology, Via Moruzzi, 1, I-56126 Pisa, Italy 13
14
Correspondence:
15 §
Tel.: +39-0502216141. Fax: +39-0502216087. E-mail address: g.benelli@sssup.it;
16
benelli.giovanni@gmail.com
17
* Tel.: +390502216138. Fax: +390502216147. E-mail: riccardo.gucci@unipi.it
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Abstract22 23
BACKGROUND: Light and water are major factors in fruit development and quality. In this
24
study, the effect of water and light in Olea europaea trees on volatile organic compounds
25
(VOCs) in olive oil was studied over two years. Mature fruits were harvested from three zones of
26
the canopy with different light exposure (64%, 42%, and 30% of incident light) of trees subjected
27
to full, deficit, or complementary irrigation. VOCs were determined by SPME GC-MS and
28
analysed by principal component analysis followed by discriminant analysis to partition
29
treatment effects.
30
RESULTS: Fruit fresh weight and mesocarp oil content decreased in zones where intercepted
31
light was less. Low light levels significantly slowed down fruit maturation, whereas conditions of
32
water deficit accelerated the maturation process. The presence of cyclosativene and
α-33
muurulene was associated with water deficit, nonanal, valencene with full irrigation;
α-34
muurulene, (E)-2-hexanal were related to low light conditions, while trans-β-ocimene,
α-35
copaene, (Z)-2-penten-1-ol, hexanal and nonanal to well exposed zones. The year strongly
36
affected the VOC profile of olive oil.
37
CONCLUSION: This is the first report on qualitative changes in VOCs induced by light
38
environment and/or water status. This information is valuable to better understand the role of
39
environmental factors on VOO sensory quality.
40 41
Keywords: canopy position; GC-MS; olive oil quality; photosynthetic active radiation; VOCs; 42 water deficit 43 44 45 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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INTRODUCTION46 47
Light and water are major factors in fruit development and quality. Plant canopies intercept light
48
and convert it into chemicals bonds and energy via photosynthesis. Besides geographical and
49
climatic factors the amount of light intercepted by tree canopies depends on species, growth,
50
orchard design and management 1,2. The light environment is not uniform within the canopy as it
51
depends on the spatial variability determined by orchard design (e.g. row orientation, planting
52
distance), tree architecture (e.g. height, training system), and leaf development 3. The
53
distribution of light within the tree canopy is important for fruit production since it can affect
54
reproductive processes and fruit qualitative attributes 4-6. Shading usually decreases weight,
55
colour, soluble sugar and secondary metabolite concentrations in the fruit of many perennial
56
species of economic importance, including olive 4, 7-11.
57
Water availability influences virtually all aspects of tree performance including fruit
58
development and quality 12,13. In fruit trees and vines it has been shown that supplying water to
59
fully compensate for water losses does not necessarily lead to optimal fruit quality, and that
60
periods of water deficit can improve fruit quality depending on the timing of stress imposition
12-61
15. The sensory quality of pome and stone fruits is actually enhanced by water deficit, which
62
increases sugar content, the sugar-acid ratio and chemical compounds responsible for flavour
63
and aroma 13.
64
In recent years changes in the quality of virgin olive oil (VOO) induced by soil water
65
availability have been reported 16-20. For instance, most studies showed a negative correlation
66
between concentrations of phenols, ortho-diphenols, secoiridoids and the volume of water
67
applied, whereasthe irrigation regime had negligible effects on free acidity, peroxide value, fatty
68
acid composition, and concentrations of lignans of VOO 16-18, 21. Among phenolic compounds,
69
secoiridoids have the highest antioxidant power 22. Secoiridoids derivatives of oleuropein and
70
dimethyloleuropein, such as 3,4-DHPEA-EDA and 3,4-DHPEA-EA, contribute to the sensory
71
properties of VOO as they are mainly responsible for VOO bitterness 23, whereas ligstroside
72
derivatives, like p-HPEA-EDA, are strongly correlated with both bitter and pungent sensory
73 notes 23,24. 74 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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While the effects of water availability on phenolic concentrations of VOOs have been
75
elucidated, results from the few studies investigating the effect of tree water status on changes
76
of the VOCs profile of VOOs were quite variable 17,18,21. Olive oil VOCs include over 100
77
molecules produced during the oil mechanical extraction process via the lipoxygenase pathway
78
and represent the main class of compounds responsible for sensory notes such as fruity, cut
79
grass, and floral flavours 25. The concentration of VOCs is mainly dependent on the cultivar and
80
the ripening stage 25, 26, but some recent studies indicate that soil water availability is also
81
important 18,26,27. Gomez-Rico et al.27 reported that major volatile compounds concentrations
82
were higher in oils produced under irrigated conditions. Servili et al.18 showed that the tree
83
water status had a marked effect on C6-saturated and unsaturated aldehydes, alcohols, and
84
esters of VOO, despite the variability due to the growing seasons. Caruso et al.21 recently
85
showed that VOCs seemed to be more consistently influenced by the year than soil water
86
availability. With regard to the effect of light on the composition of the volatile fraction of VOO,
87
we are not aware of any published study.
88
In this research, we investigated the individual and combined effects of water and light
89
conditions in canopies of olive trees on the presence of VOCs in VOO over two consecutive
90
years. We used automatized Solid Phase Micro-Extraction (SPME) and two GC-MS techniques
91
(electron ionization and chemical ionization techniques) to determine VOCs. In order to partition
92
the effects due to light environment, water shortage and year of cultivation, data were analyzed
93
by principal component analysis (PCA) followed by discriminant analysis.
94 95
EXPERIMENTAL 96
97
Plant material, radiation interception and tree water status 98
99
Experiments were conducted in a high-density (513 trees ha-1) olive (cv. Frantoio) orchard
100
planted in a sandy-loam at Venturina (43° 01’ N; 10° 36’ E), Italy, in 2008 and 2009. Climatic
101
conditions and orchard management practices during the two years were previously reported 20.
102
In brief, annual precipitation was 1107 and 771 mm in 2008 and 2009, respectively, while
103
reference evapotranspiration, calculated according to the Penman–Monteith equation, was 993
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and 1101 mm in those two respective years. During the summer, the average mean
105
temperature and rainfall were similar (23.1 and 23.3 °C, 74 mm and 87 mm in 2008 and 2009,
106
respectively). The mean maximum temperature reached 27.2 (August), and 26.7 °C (July) in
107
2008 and 2009, respectively.
108
Localized irrigation was managed to achieve three distinct conditions of tree water
109
status during the periods 2 July - 10 October in 2008 and 1 July - 9 October in 2009 20. In 2008,
110
the volumes of water supplied were 3633, 1744 and 229 L per tree for full, deficit and
111
complementary irrigated trees, respectively. In 2009, volumes were 4168, 971 and 89 L per tree
112
for the same respective treatments. Trees subjected to controlled deficit conditions (DI,
113
hereafter) received about half the volume distributed to fully-irrigated trees (FI, receiving 100%
114
of evapotranspiration) in 2008, whereas in 2009, due to rains during the irrigation period, the
115
water applied was 23% of FI trees. However, when effective precipitation was considered the
116
amount of water received by DI trees during the 2009 irrigation period was 46% of FI trees. The
117
third group of trees (CI, hereafter) experienced almost rainfed conditions and received three
118
complementary irrigations corresponding to 2-6% of the water of FI trees. Tree water status was
119
determined by measuring the pre-dawn leaf water potential (PLWP) at 7-10 day intervals during
120
the irrigation period in both years. The dates of PLWP measurement in 2008 and 2009 and
121
protocols were previously reported 20.
122
Three, fully-productive trees of approximately 3.5 m height were selected for each
123
irrigation treatment, for a total of nine trees. Three volumes of 1 m3 each in different zones of
124
the canopy were identified when trees were in bloom and tagged as follows (Figure 1):
125
i) Top (T), at a height of about 3 m, representative of conditions of maximum
126
irradiance level received by foliage and fruits;
127
ii) Low-South side, (L-S), located in the lower part of the South side of the canopy, at
128
1.5-2 m above ground;
129
iii) Low-North side, (L-N), located in the lower part of the North side of the canopy, at
130
1.5-2 m above ground;
131
Measurements of photosynthetically active radiation (PAR) were made at regular
132
intervals from dawn until sunset on clear days with a LI-COR Line Quantum Sensor (LI-191 SB,
133
Licor, Lincoln, USA) in 2008 and a Sun Scan System (SS1, Delta-T Devices Ltd, Cambridge,
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UK) in 2009. Each linear sensor of 1 m was held horizontally and two measurements were
135
taken in North-South and East-West directions with the center of the sensor positioned in the
136
center of an imaginary one-meter cube volume (Figure 1). The North-South and East-West
137
readings were averaged and divided by above canopy PAR readings to calculate percentage of
138
available PAR as a measure of light distribution for the three canopy positions of each tree.
139 140
Fruit harvest, oil extraction and analysis 141
142
Fruits were harvested on 21 and 19 October (20 and 21 weeks after full bloom, respectively) in
143
2008 and 2009, respectively. Fully-irrigated, DI and CI trees yielded 18869+7453, 14232+628
144
and 11192+2226 g per treein 2008, respectively, and 23130+9155, 10931+1177 and
145
8430+2205 g per tree in 2009 (values are means+standard deviations of three trees for each
146
irrigation treatment), equivalent to 9680±3823, 7301±835 and 5741±1142 kg ha-1 in 2008, 147
respectively, and 11866±4696, 5608±909 and 4325±1131 kg ha-1 in 2009. At harvest 50 fruits
148
were sampled from each canopy position to measure average fruit fresh weight. The same fruits
149
were also used to determine the maturation index (MI) according to standard methodology,
150
whereby the skin and flesh colours were scored according to a 0 to 7 scale 20. The oil content of 151
the fruit mesocarp of five fruits for each canopy position was measured by nuclear magnetic 152
resonance using an Oxford MQC-23 analyzer (Oxford Analytical Instruments Ltd., Oxford, UK) 153
20
.
154
Oil was extracted using an Abencor system (MC2, Ingenieria y Systemas, Sevilla,
155
Spain) within 24 h from fruit harvest. The olive fruit samples (1.5-2 kg) were only harvested from
156
the canopy volumes where irradiance had been measured, then washed with tap water,
157
crushed with a hammer crusher (radius 47.2 mm with a sieve of 5.0 mm hole diameter) at 3000
158
rpm, and the paste mixed in a thermobeater at 28 °C for 30 min; the malaxed paste was
159
centrifuged at 3500 1370 g rpm (radius 100 mm) for 3 min and the oil separated after 8 min by
160
decantation in a glass cylinder. Oils were stored in the dark at 14 °C until analyzed for VOCs.
161 162
GC-MS analysis of the volatile fraction on the olive oil samples 163 164 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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The volatile fraction of the olive oil samples was analyzed by GC-MS, both with Electron
165
Ionization (EI) and Chemical Ionization (CI) techniques, as reported below. The analyses were
166
performed on a Saturn 2000 Ion Trap mass spectrometer (Varian Inc., Palo Alto CA, USA)
167
interfaced to a Varian 3800 gas chromatograph with a Varian 1079 temperature-programmable
168
injector. The volatile components were sampled using an automatized Solid Phase
Micro-169
Extraction (SPME) technique by a CTC Combipal Autosampler. Two grams of olive oil were
170
placed in a 10 mL autosampler vial for the SPME sample preparation and GC injection. The
171
SPME sample preparation programme included pre-incubation of the vial at 40°C for 20 min,
172
exposition of the SPME fiber for 30 min placing the fiber at 10 mm from the bottom of the vial.
173
The SPME syringe was introduced into the 1079 injector equipped with a 0.8 mm SPME liner.
174
The injection was made keeping the injector at 230°C, isothermal, operating in splitless mode
175
for the first 3 min and with a split ratio 50 for the remaining time.
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A Varian VA-5MS GC Column (Poly-95%-dimethiy-5%-diphenylsiloxane 30 m x 0.25
177
mm, 0.25 mm film thickness) was used. The column oven temperature was programmed at 60
178
°C (0 min), 240 °C (3 °C min-1, 0 min), 280°C (30 °C min-1, 1 min) and helium was used as the
179
carrier gas at a constant flow rate of 1 mL min-1. Total runtime was 63 min. Conditions for the EI
180
MS analysis were: trap temperature: 200 °C; manifold temperature: 80 °C; transfer line
181
temperature: 250 °C; axial modulation voltage: 3.2 volts. Mass range acquisition: 30-400 Th,
182
ionisation control automatic, emission current: 10 µA, AGC target: 15,000 counts. Conditions for
183
the CI MS analysis were: trap temperature: 200 °C; manifold temperature: 80 °C; transfer line
184
temperature: 250 °C; axial modulation voltage: 3.2 volts. Mass range acquisition: 60-400 Th;
185
reagent gas: isobutane; CI storage level: 25.0 m/z; ejection amplitude: 7.4 m/z; background
186
mass: 65 m/z; maximum ionization time: 2000 µsec; maximum reaction time: 60 msec; AGC
187
target: 7500 counts.
188 189
Identification of olive oil VOCs 190
191
Constituent identifications were based on comparison of retention times with those of standards.
192
This implied comparing their Linear Retention Indexes with the series of n-hydrocarbons and
193
using computer matching against commercial (NIST 2008 and ADAMS) and homemade library
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mass spectra (built up from pure substances and components of known oils and mass spectra
195
literature data) 28,29. Moreover, molecular weights of all identified substances were confirmed by
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chemical ionization mass spectrometry, using isobutane as the reagent gas.
197
An absolute quantification of the different substances examined was not carried out, as
198
this would have required stable isotopically marked internal standards, one for each component
199
to be quantified, but we performed a relative quantitative determination by comparing the peak
200
area intensity. Such approach was possible since all the samples were acquired in the same
201
batch using a new and pre-conditioned SPME fibre. Its reliability was checked by injecting the
202
same sample several times along the whole analytical batch, obtaining comparable peak area
203
values for the same components.
204 205
Experimental design and statistical analysis 206
207
Nine trees (three per irrigation treatment) were selected similar in size, productivity and location
208
within the orchard. The effect of canopy zone and irrigation was determined by analysis of
209
variance, using a one factor randomized complete block design with zone as the fixed factor
210
and irrigation level as the randomized factor within each year. Means of irrigation treatments
211
and canopy position were separated by least significant differences (LSD) (α = 0.05) after
212
analysis of variance using MSTAT software (Michigan State University, East Lansing, USA).
213
For each compound and chemical class, the area integration report was transformed
214
into log values, before statistical analysis. The normal distribution of data was checked using
215
Shapiro–Wilk test. Data were processed using a General Linear Model (GLM) (JMP SAS
216
Institute Inc., Cary NC, USA) with three factors, irrigation, light and year: yj = µ + Ij +Lj + Yj + Ij*Lj 217
+ Ij*Yj + Lj*Yj + Ij*Lj*Yj + ej, in which yj is the observation, µ is the overall mean, Ij the irrigation (j 218
= 1-3), Lj the light (j = 1-3), Yj the year (j = 1-2), Ij*Lj the interaction irrigation*light, Ij*Yj the 219
interaction irrigation*year, Lj*Yj the interaction light*year, Ij*Lj*Yj the interaction 220
irrigation*light*year and ej the residual error. Averages were separated by Tukey-Kramer HSD 221
test (α = 0.05).
222
For each compound, PCA was performed on normalized data sets with JMP software. PCA
223
was developed to calculate linear combinations of the original data by extracting eigenvalues
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and eigenvectors of the correlation matrix of volatiles’ areas. Two-dimensional PCA score plots
225
were created from the data. The PC1 was the axis, which contained the largest possible amount
226
of information and PC2 was perpendicular to PC1. The principal components were orthogonal
227
and linear combinations of the original variables. PCA score plots were used to determine
228
whether olive oil volatiles from different treatments could be grouped into different classes.
229
Then, multi-factorial analysis (MFA) was performed. The two common factors related to the
230
main aspects of VOCs’ production were extracted from the data by JMPusing a maximum
231
likelihood estimation procedure and a VARIMAX orthogonal rotation technique. The scores for
232
common factors for each sample were calculated as described by Macciotta et al.30. To
233
evaluate relationships between the two common factors Factor 1 and Factor 2 and the abiotic
234
factors known to influence the volatiles’ production, factor scores were analyzed by a mixed
235
linear model with irrigation (FI, DI, CI), light (T, L-N, L-S) and year (2008 and 2009) as fixed
236
factors. Means were compared using the LSD test (α = 0.05). Simple correlations were
237
determined between selected response variables.
238
Discriminant analysis, performed with JMP, was used to analyze data in a similar manner to
239
PCA. However, unlike PCA, discriminant analysis reduces data redundancy while discriminating
240
power is preserved in the first several canonical discriminant functions. The ratio (Wilks’s
241
lambda) between the generalized within-category dispersion and total dispersion was
242
considered 31. For better visualization, the canonical scores were plotted in discriminant space.
243
Discriminant analysis was performed using different VOCs as a set of independent variables.
244 245
RESULTS 246
247
Tree water status, light environment and fruit characteristics 248
249
Climatic (air temperature, precipitations, evapotranspiration) and experimental (irrigation
250
regimes and canopy positions) conditions during fruit development were similar in both growing
251
seasons. Tree water status, expressed as the integrated value of measured PLWP over the
252
period from 3-5 until 20-21 weeks after full bloom, ranged between -0.88 and -0.86 (Full), -1.37
253
and -1.50 (Deficit), and -1.83 and -2.08 (Stress) MPa and, hence, it was consistent in both
254 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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years. The amount of the average daily PAR intercepted at each canopy position was also
255
similar in 2008 and 2009.Since we measured similar light levels in trees with different water
256
status, the intercepted PAR data, expressed as percentage of PAR above canopy, were
257
summarized only for the different canopy positions: in 2008 values were 67, 46 and 33% for T,
258
L-S and L-N zones, respectively, and similar values were measured in 2009 (61, 38 and 27%
259
for T, L-S and L-N, respectively).
260
Both canopy zone and water status affected fruit fresh weight (FW). Since there was no
261
significant interaction between the two treatments, the results could be analyzed as separate
262
factors (Table 1). The position within the canopy, and consequently the level of daily PAR
263
interception, influenced fruit characteristics in both years. Fruit FW was significantly lower in
264
zones where intercepted light was less. The difference in FW between fruits from L-N and Top
265
zones ranged between 22 and 31% in 2008 and 2009, whereas differences between FI and CI
266
were comprised between 10 and 16%. Water deficit also decreased fruit FW, but only when
267
trees were severely stressed (range -1.83-2.08 MPa). On the other hand, in both years there
268
were no differences in fruit FW between FI and DI trees (Table 1).
269
With regard to the maturation index, a significant interaction was found between the
270
effect of water status and light level, as well as a significant effect of both factors (Table 2). Low
271
light levels significantly slowed down the development of colour and fruit maturation, whereas
272
conditions of water deficit led to an acceleration of the maturation process, so those fruits turned
273
from green to dark earlier.The oil content in the mesocarp increased as the level of interception 274
increased. Fruit in the top part of the canopy (Top) had 105 and 107% (dry weight) of the oil of 275
the fruits located in the L-N position in 2008 and 2009, respectively (Table 3). 276
277
Qualitative and quantitative changes in the production of olive oil VOCs 278
279
Table 4 reports 29 VOCs identified by SPME of olive oils extracted from fruits exposed to
280
different levels of light and water status. GLM procedure evidenced that the presence of
281
cyclosativene (F = 200.949, d.f. = 2, P < 0.001) and α-muurulene (F = 81.786, d.f. = 2, P <
282
0.001) was strictly related to water deficit (Supporting Information File 1 –Table 1) since both
283
compounds were absent when trees were fully irrigated. Nonanal (F = 8.391, d.f. = 2, P = 0.001)
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and valencene (F = 5.441, d.f. = 2, P = 0.009) exhibited an opposite trend as they increased in
285
oils from fully irrigated olive trees.
286
Light conditions also influenced the VOCs profile. The presence of (E)-2-hexenal was
287
lower in oils obtained from fruits sampled from the top of the tree canopy with respect to those
288
from the lower sides (F = 5.745, d.f. = 2, P = 0.007), while α-farnesene was less in oils produced
289
from the L-N canopy position (F = 22.964, d.f. = 2, P < 0.001). Furthermore, the year markedly
290
affected the VOCs profile. Several compounds were lower in 2009 than in 2008
[(Z)-2-penten-1-291
ol (F = 21.376, d.f. = 1, P < 0.001), (E)-2-hexenal (F= 232.602, d.f. = 1, P < 0.001),
6-methyl-5-292
hepten-2-one (F = 77.416, d.f. = 1, P < 0.001), trans-β-ocimene (F = 26.213, d.f. = 1, P <
293
0.001), undecane (F = 113.618, d.f. = 1, P < 0.001), methyl salicylate (F = 124.412, d.f. = 1, P <
294
0.001), cyclosativene (F = 159.597, d.f. = 1, P < 0.001), α-farnesene (F = 52.165, d.f. = 1, P <
295
0.001)].
296
Concerning the chemical classes of VOCs, the canopy zone determined relative
297
changes in sesquiterpenes (F = 11.778, d.f. = 2, P = 0.001) whereas water deficit did not
298
(Supporting Information File 1 –Table 2). The growing season affected the production of
non-299
terpene hydrocarbons (F = 280.650, d.f. = 1, P < 0.001), non-terpene alcohols (F = 125.360, d.f.
300
= 1, P < 0.001), non-terpene aldehydes (F = 45.334, d.f. = 1, P < 0.001), monoterpenes (F =
301
49.414, d.f. = 1, P < 0.001) and sesquiterpenes (F = 309.007, d.f. = 1, P < 0.001).
302
Furthermore, PCA followed by discriminant analysis allowed a more precise partition of
303
treatment effects on the VOO emission of VOCs. The Kaiser’s coefficient was calculated on a
304
dedicated matrix (Supporting Information File 2 – Table 1), thus reducing the original number
305
of VOCs. The Kaiser’s coefficient was 0.79. VOCs were classified in two principal components,
306
1 and 2 (eigenvalue 1 = 67.115; eigenvalue 2 = 27.965), explaining the 44.743% and 18.643%
307
of variation, respectively (cumulated percentage = 63.387%). Eigenvectors of the single VOCs
308
were provided in Supporting Information File 3 – Table 1. Rotated factor patterns were
309
provided in Supporting Information File 3 – Table 2 (the rotated factors with an eigenvector of
310
at least ± 0.6 were marked in bold and considered for the following analysis). After rotation,
311
Factor 1 explained 44.496% of variation and Factor 2 explained 18.890 of variation (cumulated
312 percentage = 63.387%). 313 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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Table 5 reported sources of variation having a significant effect on Factor 1 and 2.
314
Water deficit and year of cultivation were the major sources of variation affecting Factor 1 and
315
their interaction was significant. On the other hand, there was no significant role of light and its
316
interaction with Factor 1. Factor 2 was affected also by canopy position and its interaction with
317
water and year (Table 5). On this basis, we candidated “Water deficit+Year” as label for Factor
318
1 and “Canopy position” as label for Factor 2. Least square means tables on sources of
319
variations of Factor 1 (i.e. Water shortage+Year) and Factor 2 (i.e. Canopy position) were
320
provided in Supporting Information File 3 – Table 3. Concerning Factor 1, we observed that
321
the least squares means diminished from negative to positive values, when water and light
322
became more abundant. On the contrary, with regard to Factor 2, the least square means
323
raised from negative to positive values, when water and light availability increased.
324
Full details on discriminant analysis results were provided for the three sources of
325
variation (water deficit, canopy zone and year of cultivation) as Supporting Information. In brief,
326
(i) outputs of Wilks' Lambda, Pillai's Trace, Hotelling-Lawley and Roy's Max Root tests were
327
provided in Supporting Information File 3 – Table 4, (ii) number of misclassified, percent of
328
misclassified and -2LogLikelihood were given in Supporting Information File 3 – Table 5; (iii)
329
eigenvalues, percentages and canonical correlation values were given in Supporting
330
Information File 3 – Table 6. 331
Discriminant analysis highlighted that the production of some VOCs was strongly
332
affected by water deficit and canopy position. According to GLM results, α-copaene, 3-octanone
333
and cyclosativene were present in VOOs from almost rainfed trees (Figure 2A), whereas
6-334
methyl, 5-hepten-2-one, α-muurulene, (E)-2-hexanal, α-farnesene and undecane were found in
335
VOOs from DI trees and methyl salicylate in ones from FI trees. Canopy position was able to
336
affect the VOCs profile of VOO (Figure 2B). The presence of α-muurulene and (E)-2-hexanal
337
was related to Low-North zone, while undecane, limonene, 3-octenone, 6-methyl,
5-hepten-2-338
one, cyclosativene and α-farnesene seemed to be related to Low-South position. Top-tree light
339
conditions were related to the production of trans-β-ocimene, α-copaene, (Z)-2-penten-1-ol,
340
hexanal and nonanal. The year of cultivation significantly affected the olive oil VOCs production
341
(Figure 2C). We noted the production of 3-octenone, α-muurulene, hexanal and cyclosativene
342
was significantly affected by 2008, while the other VOCs were closely related to 2009.
343 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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The differences in VOCs composition induced by water deficit, canopy position and year
344
of cultivation can be visually appreciated by the location of circles and lines reported in Figure
345
2. Each circle enclosed the region in close proximity of the centroid (indicated by a small cross) 346
and encompassed 50% of the samples classified into one of the discriminated groups. Lines
347
referred to variables considered in the model and their arrangement indicated how they were
348
related with the discriminated groups. For instance, a line oriented towards a group indicated
349
that the variable it represented was the component allowing discriminating that group from
350 others. 351 352 DISCUSSION 353 354
Both canopy position and water deficit affected size and colour of olive fruits. Fruit fresh weights
355
at the top of the canopy were greater than in the L-S or L-N positions, where intercepted light
356
levels were 42 and 33% of incident PAR (average of two years), respectively. Fruit dry weight
357
has been shown to increase linearly in olive canopies of a high density orchard up to about 40%
358
of PAR, a threshold beyond which it became insensitive to light levels 11, similarly to the
359
response described for apple fruits 32.The 64% level of incident PAR (average of both years)
360
intercepted at the Top position was far above the reported threshold for maximum fruit weight
361
and colour development in olive and apple 11,32, confirming that olive fruits located in well-lit
362
parts of the canopy were heavier than those grown in shaded portions of the canopy 33. There
363
are no data available about thresholds for fruit colour development in olive trees. In our study on
364
“Frantoio”, a cultivar characterized by a slow progression of exocarp colour development, there
365
was a highly significant effect of light on maturation index, but the limited number of light levels
366
we considered did not allow us to identify a clear response pattern or threshold. Connor and 367
Gómez-del-Campo34 reported that in a hedgerow olive orchards (cv. Arbequina) the fruit 368
maturation index increased by increasing the row spacing and the daily incident radiation on 369
canopy walls. The significant interaction between light environment and water status found in
370
our study seems to indicate that the colour change response to light was at least partially
371
altered by tree water relations which, in turn, may have affected gas exchange and carbon
372
partitioning to the fruit. It should be noted that in our experiments the range of pre-dawn leaf
373 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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water potential integrated over the whole irrigation period was wide (but not extreme) and
374
typical of trees grown either under non-limiting water conditions, mild water deficit or severe
375
water deficit 20,35. Regarding the mesocarp oil content, fruits growing at the top of the canopy 376
produced more oil (% dw) than fruits from the lower parts. Similar results were found in a study 377
conducted on sixteen-year-old olive trees of cvs Picual and Arbequina where the oil content in 378
fruit from the top part of the canopy was 105 and 103% (dry weight) of those located in the 379
lower part for Picual and Arbequina, respectively 36. 380
Several classes of VOCs were identified by SPME GC-MS: aldehydes, alcohols, esters,
381
monoterpenes, sesquiterpens, alkane and alkene hydrocarbons. Their presence and
382
abundance in VOO had been previously related to cultivar and geographical origin 37-39. In this
383
work, using multivariate statistical data analysis, we showed that VOCs composition was
384
markedly affected by the light environment at different canopy positions and by the irrigation
385
regime experienced by trees. In particular, α-copaene, 3-octanone and cyclosativene were
386
present in oils from almost rainfed trees, whereas 6-methyl-5-hepten-2-one, α-muurulene,
(E)-2-387
hexanal, α-farnesene and undecane in those from deficit-irrigated trees, and methyl salicylate
388
from fully-irrigated trees. Although these results need to be confirmed for other cultivars or
389
longer periods, they complement the currently available information on changes in the
390
composition of the volatile fraction induced by different conditions of soil water availability
391
17,18,20,27. Servili et al.18 reported that the concentration of hexanal, (E)-2-hexenal,
(E)-2-hexen-1-392
ol, (Z)-2-hexen-1-ol and 1-hexen-3-ol in cv. Leccino was significantly increased by irrigation.
393
Gomez-Rico et al.17found an inverse relationship between hexanal, (E)-2-hexenal, hexan-1-ol
394
and the water stress integral in cultivars Cornicabra and Morisca. Overall, previous results and
395
those from our study show that the irrigation regime not only markedly changed the VOCs
396
concentrations 17,18,21,27, but also modified the VOC composition by evoking some exclusive
397
molecules with sensory impact. Differences in tree water status may also contribute to explain
398
some of the changes in VOCs measured in oils of different geographical origin 37,39because soil
399
water availability varies depending on climate and soil characteristics. Since the concentration
400
of olive oil VOCs of three cultivars responded differently to the level of water supplied26, we
401
cannot rule out that the exclusive presence of some VOCs under conditions of water deficit is
402
cultivar dependent too.
403 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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The light environment within the canopy altered the VOCs profile of olive oil. The
404
compounds α-muurulene and (E)-2-hexanal were found only in Low-North position, while
405
undecane, limonene, 3-octenone, 6-methyl, 5-hepten-2-one, cyclosativene and α-farnesene
406
were correlated with the Low-South location. Hexanal, nonanal, trans-β-ocimene, α-copaene,
407
and (Z)-2-penten-1-ol were present in oils from fruits growing at the top of the canopy. These
408
compounds have also been identified in VOOs from other cultivars too 38,39. Both shading and
409
well irrigated conditions delayed fruit maturation (expressed as colour change), confirming
410
previous results 20,40 and therefore we cannot exclude that differences in fruit maturity may have
411
confounded or amplified the effect of individual treatments 27. It should be noted that the
412
growing season also exerted a strong impact on the VOCs profile of olive oil, as also found in
413
previous studies 17,18,21. For instance, the presence of 3-octenone, α-muurulene, hexanal and
414
cyclosativene was evident in 2008, whereas other VOCs were related to VOOs produced in
415
2009.
416
Most volatile molecules we detected have a sensory impact and, therefore, play a key
417
role in oil quality. The current definition of oil quality includes sensory notes strictly related to
418
VOCs concentrations 25. In fact, not only the presence of fruity attributes and the absence of
419
defects, assessed by sensory analysis, is indispensable for olive oils to be classified as
extra-420
virgin according to the European regulations 2568/1991 and 1989/2003, but high quality VOOs
421
are usually rich in VOCs, such as linear unsaturated and saturated aldehydes, alcohols, esters,
422
and hydrocarbons 25. These substances are produced starting from polyunsaturated fatty acids
423
(e.g. linoleic acid, linolenic acid) by several enzymatic steps included in the lipoxygenase
424
pathway once fruits are crushed during processing 25,41. Monoterpenes and sesquiterpenes are
425
terpenoids synthesized via the mevalonic acid pathway, isopentenyl pyro phosphate and
426
farnesyl pyro phosphate being their respective precursors 42. Many terpenoids play an essential
427
role in plant metabolism and an important ecological role in insect-plant interactions and
428
defence against pathogens 43. All the above listed compounds have been related to mechanical
429
wounding or abiotic stresses 44.
430
Olive oil production involves mechanical extraction and VOCs develop as a result of the
431
breakdown of fruit integrity. When fruits are crushed in the mill to produce olive oil, the key
432
enzymes, lypoxygenase and hydroperoxide lyase, use lipids as substrates and generate a
433 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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cascade series of events that eventually lead to VOCs formation. So, the production of VOO
434
can be assimilated to the wounding response of plants 44. In this respect, the changes in VOCs
435
composition in olive oil are likely occurring in the fruit in response to wounding or abiotic stress.
436
Interestingly, abiotic stresses induce and/or enhance emissions of a wide range of VOCs in
437
many plant species and organs 45. The main emphasis in quantitative VOCs studies has been
438
on constitutive emissions of isoprene and specific monoterpene species that are present only in
439
certain plant species 42-44. Further research on VOCs emitted by olive fruits exposed to abiotic
440
stress (e.g. water shortage and/or canopy shading) is required. A special focus is needed on
441
changes in VOCs emission evoked by abiotic stress, since they may affect foraging behaviour
442
of insect pests (e.g. the olive fruit fly) and their natural enemies 46.
443 444
ACKNOWLEDGEMENTS 445
446
We would like to thank two anonymous reviewers for commenting an earlier version of our
447
manuscript. We are grateful to Pierluigi Cioni for discussion about the interpretation of mass
448
spectra and Marcello Mele and Giuseppe Conte for their comments on data analysis. Research
449
supported by UNAPROL-Italy projects Reg. UE no. 2080/2005 and no. 867/2008. Funder had
450
no role in study design, data collection and analysis, decision to publish, or preparation of the
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parasitoid Psyttalia concolor (Szépligeti) (Hymenoptera: Braconidae)? Chemoecology
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positions where light interception was measured using linear quantum sensors.JSFA@wiley.com 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
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performed using different VOCs as a set of independent variables. Each circle enclosed the region in proximity of the centroid. It encompassed 50% of the samples classified into one of the discriminated groups. Lines referred to variables considered in the model. Their arrangement indicated how they were related with the discriminated groups. A line oriented towards a group indicated that the variable it represented was the component allowing discriminating that group from others. FI = full irrigation; DI = deficit irrigation; CI = complementary irrigation, centroid = small cross.
(A) -8 -7 -6 -5 -4 C a n o n ic a l2 C I D I F I (Z)-2-Penten-1-ol 1-Octene Hexanal (E)-2-Hexenal 3-octanone 5-Hepten-2-one, 6methyl-Limonene trans-ß-Ocimene Undecane Nonanal Methyl salicilate Cyclosativene a Copaene a-muurulene a Farnesene -2 0 2 4 6 Canonical1 CI DI FI Irrigation JSFA@wiley.com 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
For Peer Review
(B) -3 -2 -1 0 1 2 C a n o n ic a l2 L o w N L o w S T o p (Z)-2-Penten-1-ol 1-Octene Hexanal (E)-2-Hexenal 3-octanone 5-Hepten-2-one, 6methyl-Limonene trans-ß-Ocimene Undecane Nonanal Cyclosativene a Copaene a-muurulene a Farnesene -7 -6 -5 -4 -3 -2 -1 0 Canonical1 Low N Low S Top Light JSFA@wiley.com 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55For Peer Review
-22 -21 -20 -19 -18 C a n o n ic a l2 2 0 0 8 2 0 0 9 (Z)-2-Penten-1-ol 1-Octene Hexanal (E)-2-Hexenal 3-octanone 5-Hepten-2-one, 6methyl-Limonene trans-ß-Ocimene Undecane Nonanal Methyl salicilate Cyclosativene a Copaene a-muurulene a Farnesene 23 24 25 26 27 28 29 30 31 32 33 34 35 Canonical1 2008 2009 Year JSFA@wiley.com 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55For Peer Review
Table 1. Fresh weight of fruits from three different canopy zones of olive trees (cv. Frantoio) grown under full,
deficit or complementary irrigation determined at 20 and 21 weeks after full bloom in 2008 and 2009, respectively. Values are means of nine replicates (n=9). Different letters indicate least significant differences (LSD) between irrigation treatment and between canopy positions after analysis of variance within each year (P < 0.05). The asterisk indicates significant differences at P < 0.0001.
Treatment
Fruit fresh weight (g)
2008 2009 Irrigation (I) Full 1.9 a 2.5 a Deficit 2.0 a 2.4 ab Complementary 1.6 b 2.2 b Canopy zone (CZ) Top 2.1 a 2.8 a Low-South 1.8 b 2.6 b Low- North 1.6 c 2.0 c Significance I 0.001 0.015 CZ 0.000 (*) 0.001 I x CZ n.s. n.s. 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
For Peer Review
Table 2. Maturation index (MI) of fruits from three different canopy zones (CZ) of olive trees (cv. Frantoio) grown
under full, deficit or severe stress irrigation determined at 20 and 21 weeks after full bloom in 2008 and 2009, respectively. Values are means of three replicates (n=3). Different letters indicate least significant differences (LSD) between irrigation treatment (I) and between canopy zones (CZ) after analysis of variance within each year (P ≤ 0.05). Asterisks indicate significant differences at P < 0.0001.
Treatment Maturation index
Irrigation (I) Canopy zone (CZ) 2008 2009
Full Top 2.70 b 2.53 b Full Low-South 1.37 d 1.90 d Full Low -North 1.07 e 1.32 e Deficit Top 3.32 a 3.66 a Deficit Low -South 2.00 c 2.38 bc Deficit Low -North 1.28 de 1.60 de Complementary Top 3.57 a 3.91 a Complementary Low -South 2.20 c 2.48 b Complementary Low -North 1.33 de 1.99 cd
LSD (0.05) 0.270 0.439 Significance I 0.000 (*) 0.000 (*) CZ 0.000 (*) 0.000 (*) I x CZ 0.009 0.045 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
For Peer Review
Table 3. Oil content in mesocarp (% dw) of fruits from three different canopy zones of olive trees (cv. Frantoio)
grown under full, deficit or complementary irrigation determined at 20 and 21 weeks after full bloom in 2008 and 2009, respectively. Values are means of nine replicates (n=9). Different letters indicate least significant differences (LSD) between irrigation treatment and between canopy positions after analysis of variance within each year (P < 0.05).Data were transformed by arcsine transformation prior to ANOVA The asterisk indicates significant differences at P < 0.0001.
Treatment
Oil content in mesocarp (% dw)
2008 2009 Irrigation (I) Full 67.7 ab 68.4 Deficit 67.1 b 69.6 Complementary 69.4 a 69.2 Canopy zone (CZ) Top 70.2 a 71.1 a Low-South 67.2 b 69.8 a Low- North 66.9 b 66.3 b Significance I 0.090 n.s. CZ 0.002 0.000(*) I x CZ n.s. n.s. 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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and water status. Linear ritention index (LRI) from NIST 2011.
CAS number LRI Compound
625-31-0 639 4-Penten-2-ol 123-51-3 706 Isopentyl alcohol 1576-95-0 743 (Z)-2-Penten-1-ol 111-66-0 785 1-Octene 66-25-1 769 Hexanal 6728-26-3 822.4 (E)-2-Hexenal 106-68-3 963 3-octanone 111-27-3 852 Hexanol 110-93-0 958 6methyl-5-Hepten-2-one 123-35-3 979 β-myrcene 3681-71-8 987 (Z)-3-Hexen-1-ol, acetate 142-92-7 990 Hexyl acetate 99-87-6 1011 para-Cymene 138-86-3 1020 Limonene 470-82-6 1023 Eucalyptol 3338-55-4 1024 cis-β-Ocimene 3779-61-1 1034 trans-β-Ocimene 111-87-5 1054 Octanol 1120-21-4 180.4 Undecane 124-19-6 1081 Nonanal 95452-08-7 1071 2-ethenyl-1,1-dimethyl-3methylene-cyclohexane 119-36-8 1176 Methyl salicilate 112-40-3 209.9 Dodecane 22469-52-9 1394 Cyclosativene 3856-25-5 1397 α-Copaene 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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13474-59-4 1430 trans-α-bergamotene 4630-07-3 1515 Valencene 31983-22-9 1490 α-Muurulene 502-61-4 1499 α-Farnesene 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57For Peer Review
Table 5. Principal component analysis of olive oil VOCs showing sources of variation with a significant effect on
Factor 1 and Factor 2. Significant differences are reported in italics.
Factor 1, effect tests
Source DF Sum of Squares F Ratio P value
Irrigation 2 3.500.270 190.008 <.0001 Year 1 42.888.027 4.656.249 <.0001 Irrigation*Year 2 1.804.735 97.968 0.0004 Canopy zone 2 0.159958 0.8683 0.4283 Irrigation*Canopy zone 4 0.486789 13.212 0.2807 Year*Canopy zone 2 0.178429 0.9686 0.3893 Irrigation*Year*Canopy zone 4 0.665886 18.073 0.1488
Factor 2, effect tests
Source DF Sum of Squares F Ratio P value
Irrigation 2 30.675.545 727.445 <.0001
Year 1 4.693.526 222.606 <.0001
Irrigation*Year 2 1.402.530 33.260 0.0473
Canopy zone 2 3.663.290 86.872 0.0008
Irrigation* Canopy zone 4 0.189196 0.2243 0.9230 Year* Canopy zone 2 0.722131 17.125 0.1948 Irrigation*Year* Canopy zone 4 4.063.380 48.180 0.0032
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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SUPPORTING INFORMATION FILE 1Changes in olive oil VOCs induced by water status and light environment in canopies of Olea europaea L. trees
Giovanni Benelli1§, Giovanni Caruso1, Giulia Giunti1, Angela Cuzzola1, Alessandro Saba2, Andrea Raffaelli3, Riccardo Gucci1*
1
Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, I-56124, Pisa, Italy
2
Department of Surgical, Medical, Molecular and Critical Area Pathology, Via Paradisa, 2, I-56124 Pisa, Italy
3
CNR – Institute of Clinical Physiology, Via Moruzzi, 1, I-56126 Pisa, Italy
Correspondence:
§ Tel.: +39-0502216141. Fax: +39-0502216087. E-mail address: g.benelli@sssup.it; benelli.giovanni@gmail.com
* Tel: +390502216138. Fax: +390502216147. E-mail address: riccardo.gucci@unipi.it
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Supporting Information File 1 – Table 1. Differences evoked by water shortage and light environment in abundance olive oil VOCs
(GLM, P < 0.05). JSFA@wiley.com 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
For Peer Review
JSFA@wiley.com 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55For Peer Review
JSFA@wiley.com 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55For Peer Review
JSFA@wiley.com 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55For Peer Review
Supporting Information File 1 – Table 2. Differences evoked by water shortage and light environment in abundance of chemical classes of olive oil VOCs (GLM, P < 0.05). JSFA@wiley.com 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
