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Non-Invasive Phenotyping of Postharvest Quality Traits
in Tomato and Strawberry
MSc Minor Thesis
Orianne Gudenschwager
Registration number: 800328288110
MSc Plant Biotechnology
Supervisors: Dr. Arnaud Bovy
Dr. Yury Tikunov
i
ABSTRACT
Tomato and strawberry breeders are facing the challenge to select for varieties with
postharvest quality traits that meet consumer expectations. Quality traits as soluble solids
content (SSC), titratable acidity (TA), firmness, color and aroma play a major role in the
acceptability of tomato and strawberry consumers. The phenotypic analyses of these traits
are mostly destructive, requiring intensive labor work and time. Therefore, it is essential to
develop a reliable non-invasive method that allows estimating simultaneously most of these
postharvest quality traits. In this study, non-invasive techniques as NIR, hyperspectral
imaging, VIS-NIR transmission, VideometerLab, chlorophyll fluorescence and PTR-MS were
used to predict simultaneously SSC, acidity, firmness, pigments content and aroma volatiles
of different tomato and strawberry genotypes during postharvest at commercial conditions.
For this, data obtained through invasive and non-invasive phenotyping were subjected to
multivariate regression analysis using the OmicsFusion WUR platform to find the best
predictors for each quality trait tested. In general, poor relations were found between the
predictors and the different quality traits determined in tomato. Tomato SSC and firmness
were those that were predicted best, R2 0.41 and 0.63, respectively. The predictions of
tomato traits were hampered by a too low number of samples and the low variation in
genotypes and over storage period. Better predictions were found for strawberry, showing
R2 0.82, 0.63 and 0.65 for SSC, acidity and volatile esters, respectively. These good
predictions were achieved with an improved experimental design that included a larger
number of samples and more variation between genotypes than for tomato trial.
Hyperspectral imaging and VIS-NIR transmission were the techniques that best predicted
postharvest quality traits in tomato. NIR was the technique that achieved the best
predictions for strawberry traits, even though hyperspectral data analysis is still ongoing and
could also bring good predictions. These techniques together with an improved experiment
setting represent a great potential for further development of a platform able to assist
breeders in screening a diverse collection of tomato and strawberry varieties for postharvest
quality traits.
TABLE OF CONTENTS
Abstract ................................................................................................................................... i
1. Introduction ................................................................................................................... 1
2. Material and Methods .................................................................................................... 5
2.1. Fruit samples .......................................................................................................... 5
2.2. Non-invasive phenotyping ....................................................................................... 6
2.2.1. Chlorophyll fluorescence ................................................................................. 6
2.2.2. Hyperspectral imaging ..................................................................................... 6
2.2.3. Multispectral imaging ..................................................................................... 7
2.2.4. NIR .................................................................................................................. 7
2.2.5. VIS-NIR Transmission ....................................................................................... 7
2.2.6. Pigment Analyzer ............................................................................................ 7
2.2.7. PTR-MS ............................................................................................................ 8
2.2.8. AFS acoustic .................................................................................................... 8
2.3. Invasive phenotyping ................................................................................................. 8
2.3.1. Firmness .......................................................................................................... 8
2.3.2. SSC and acidity ................................................................................................ 9
2.3.3. Carotenoids ..................................................................................................... 9
2.3.4. Anthocyanins .................................................................................................. 10
2.4. Statistical analysis ..................................................................................................... 10
3. Results ........................................................................................................................... 11
3.1. Tomato Phenotyping .............................................................................................. 11
3.1.1. Destructive measurements ............................................................................. 11
3.1.2. Non-destructive measurements ..................................................................... 13
3.1.2.1. Chlorophyll fluorescence .................................................................... 13
3.1.2.2. NIR, hyperspectral imaging and VIS-NIR transmission .......................... 15
3.1.2.3. PTR-MS ............................................................................................... 16
3.1.3. OmicsFusion analysis ...................................................................................... 18
3.2. Strawberry Phenotyping ......................................................................................... 20
3.2.1. Destructive measurements ............................................................................. 20
3.2.2. Non-destructive measurements ..................................................................... 22
3.2.2.1. Chlorophyll fluorescence .................................................................... 22
3.2.2.2. Pigment analyzer ................................................................................. 23
3.2.2.3. NIR, hyperspectral imaging and VIS-NIR transmission .......................... 24
3.2.2.4. PTR-MS ............................................................................................... 25
3.2.3. OmicsFusion analysis ...................................................................................... 26
4. Discussion ......................................................................................................................... 28
5. Conclusions ....................................................................................................................... 33
6. Recommendations ............................................................................................................ 34
7. References ....................................................................................................................... 35
Appendix 1 ........................................................................................................................... 39
Appendix 2 ........................................................................................................................... 40
Appendix 3 ........................................................................................................................... 45
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1. INTRODUCTION
Currently, the fresh fruit market is expected to provide good-quality produce year-round, which
demands a continuous effort from breeders, growers and handlers to maintain the required
quality standards of fruits between harvest and consumption. The quality of fresh fruit is
related to many attributes, such as appearance, texture, colour and flavour, being this last one
relying on a combination of taste and aroma (Beckles, 2012). Efforts to provide fresh fruits of
high quality over a long period of time are made primarily in terms of appearance and texture.
This often results in fruits with poor flavor quality, which is currently the main complaint of
consumers. As a result, breeding programs that have primarily focused on improving yield,
disease resistance, fruit firmness and shelf life, nowadays, have the challenge to additionally
address flavor in order to ultimately improve fruit quality upon reaching the consumer (Farneti
et al., 2012). Responding to this need, breeding companies are developing modern approaches
to select fruit varieties that are able to retain their flavor during postharvest.
Tomato (Solanum lycopersicum) and strawberry (Fragaria x ananassa Duch.) are important
commodities in the fresh fruit market worldwide. Among the quality traits of these fruits,
soluble solids content (SSC), titratable acidity (TA), firmness, color and aroma play a major role
in consumer acceptability (Flores et al., 2009; Sturm et al., 2003). These traits are primarily
influenced by the genotype and the maturity stage at harvest (Clément et al., 2008).
Postharvest conditions can also affect these quality parameters, especially firmness and aroma.
Thus, all these traits are key parameters in assessing and grading quality of tomato and
strawberry fruits at harvest and during their postharvest.
Traditional methods to assess quality traits are mostly destructive, time consuming, and require
labor-intensive work. Hence, among a fruit batch, a limited amount of samples can be analyzed,
and the averaged levels of quality traits obtained are not representative of those in the
individual fruits (Dong and Guo, 2015; Fan et al., 2015). Moreover, destructive methods do not
allow to perform a reliable study on the dynamics of changes in quality parameters over time,
since a different fruit has to be monitored and often the interfruit variability is large (Nicolaï et
al., 2014). Therefore, it is essential to develop a non-invasive method that could assist the
breeders to determine quality traits efficiently with the ultimate aim to select for flavor that
meets consumer expectations.
During the past decades, several technologies have been developed in order to determine
quality parameters in fruits non-destructively. Among these technologies, considerable
research has been done in optical-based methods that use spectroscopy to predict fruit quality.
Near infrared (NIR) spectroscopy has been applied extensively in nondestructive measurement
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of quality traits in fruits and vegetables (Nicolaï et al., 2007). In NIR spectroscopy, the fruit is
irradiated in a spot with NIR radiation (780-2500 nm) and this radiation may be reflected,
absorbed or transmitted, depending on its interaction with molecular groups associated with
quality traits. These groups include C–H in sugars and acids, and the O–H group in water.
Moreover, fruit microstructures possess light scattering properties, which could be related to
firmness (Alves de Oliveira et al., 2014). The variation of the intensity of the reflected light
forms a spectrum, that represents overtones of the fundamental bands absorbed in the NIR
region, which result from vibrational and rotational transitions (Nicolaï et al., 2007). Therefore,
spectral changes are observed when NIR measurements are performed in different fruit
samples, because different scattering and absorption processes take place. In VIS-NIR (400-
1000 nm) region, components as sugars, pigments and water are sensitive to specific
wavelengths and therefore VIS-NIR has also been used to predict quality traits. NIR and VIS-NIR
can be used in reflectance, transmittance or interactance mode, and each of them has been
used to predict quality parameters such as SSC and TA in fruits. However, reflectance mode is
considered easier to operate because the light reflected is relatively high and most of the time
no contact with the fruit is required (Alves de Oliveira et al., 2014).
Technologies that extend the potential of NIR and VIS-NIR are the multispectral and
hyperspectral reflection imaging. These techniques combine spectroscopic and imaging systems
to collect at the same time spectral and spatial information. The data is collected as a 3D data
cube formed by two dimensional spatial information and one dimensional spectral information
at each pixel (Dong and Guo, 2015). Therefore, the information of each pixel results in one
spectrum, and the average of all these spectrums allows to evaluate quality traits through the
entire exposed area of the fruit. The difference between multispectral and hyperspectral
systems is that the first acquires spectral images at few discrete wavelengths, while
hyperspectral does it at several wavelengths (Mollazade et al., 2014).
There are successful attempts of nondestructive measurements described in the literature that
use some of the optical-based technologies previously mentioned to predict quality attributes
in tomato and strawberry. A review of the research that has been done so far in these fruits is
shown in Table 1.
Other optical-based technologies have been developed to determine other traits that are
directly or indirectly related to fruit quality. One of them is chlorophyll fluorescence (CF), which
is used for monitoring the photosynthetic system in planta providing information about the
physiological state of the plant. The principle of CF is that light energy absorbed by chlorophyll
molecules can be used in three processes: to drive photosynthesis, it can be released as heat or
it can be released as fluorescence, which is around 2% of the total light absorbed (Maxwell and
Johnson, 2000). These 3 processes occur in competition, so if there is any change in the
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efficiency of one process, it will affect the efficiency of the other two (Maxwell and Johnson,
2000). Thus, by measuring the yield of CF, it is possible to obtain information about changes in
the photosynthesis efficiency. During fruit ripening and senescence, chlorophyll is degraded and
therefore CF is altered. This effect could be correlated with variations in ripening-associated
traits like skin color and fruit firmness, and therefore CF could potentially be applied to predict
these traits. In the literature, CF has been applied mainly during postharvest to study the effect
of treatments on fruit physiology (DeEll et al., 1995; Lechaudel et al., 2010) and some studies
have involved the use of CF to monitor color evolution (Kolb et al., 2006; Solovchenko et al.,
2005) and degree of ripeness in fruits (Song et al., 1997); however, to my knowledge, there are
not studies performed to find a relation between CF and quality traits in tomato and
strawberry.
Table 1. Literature review on quality traits prediction by optical-based technologies for tomato and
strawberry fruits.
Fruit Technology Mode Wavelength range Quality trait Accuracy (R2) References
(nm)
Tomato VIS-NIR Reflectance 400-1500 Firmness 0.75 Clément et al., 2008
SSC 0.10
TA 0.36
VIS-NIR Reflectance 400-2500 SSC 0.63-0.82 Flores et al., 2009
TA 0.41-0.71
VIS-NIR Reflectance 350-2500 SSC 0.90 Shao et al., 2007
pH 0.83
Firmness 0.81-0.83
VIS-NIR Transmittance 305-1150 SSC 0.91 Khuriyati and Matsuoaka, 2004
VIS-NIR Interactance 400-1100 SSC 0.92 Slaughter et al., 1996
NIR Reflectance 1000-2500 SSC 0.52 Alves de Oliveira et al., 2014
TA 0.51
NIR Interactance 1100-2500 Firmness 0.95 Sirisomboon et al., 2012
SSC 0.80
Multispectral imaging Reflectance 400-1000 Firmness 0.95 Mollazade et al., 2015
SSC 0.74
TA 0.81
Strawberry VIS-NIR Reflectance 325-1075 pH 0.86 Shao and He, 2008
NIR Reflectance 1600-2400 Firmness 0.48 Sánchez et al., 2012
SSC 0.69
TA 0.65
pH 0.40
Multispectral imaging Reflectance 405-970 Firmness 0.94 Liu et al., 2014
SSC 0.83
Hyperspectral imaging Reflectance 650-1100 Firmness 0.79 Tallada et al., 2006
Hyperspectral imaging Reflectance 400-1000 SSC 0.80 ElMasry et al., 2007
pH 0.94
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Color has also a strong influence on the acceptability of tomato and strawberry consumers.
During ripening, the fruit color change due to chlorophyll breakdown and the accumulation of
carotenoids and anthocyanins in tomato and strawberry, respectively (Clément et al., 2008). In
fully ripe tomato, lycopene is the major carotenoid present representing 75-83% of the total
pigment content (Clément et al., 2008). The anthocyanin pelargonidin-3-glucoside is the main
contributor to the red color in strawberries and constitutes 85-95% of the total pigment
content (Lopes-Da-Silva et al., 2002). The chlorophyll, carotenoids and anthocyanins content
can also be determined by non-destructive means based on spectral measurements. There is a
hand-held spectrophotometer, the pigment analyzer that has shown high prediction accuracy
on monitoring pigments in fruits. This device is able to determine the chlorophyll content
trough the Normalized Difference Vegetation Index (NDVI), and carotenoids and anthocyanins
through the Normalized Anthocyanin Index (NAI). These indexes are calculated from the visible
and near-infrared light reflected by the fruit (Sadar et al., 2013).
An acoustic-based technology has been developed to determine firmness as a non-destructive
alternative to the destructive Magness–Taylor texture analyzer. AFS acoustic is a mechanical
method that measures the acoustic responses to vibrations and impacts. A small hammer with
a high stiffness is used to excite a broad range of frequencies in the fruit, and then a
microphone records the vibration which is associated with a certain resonance frequency
(Nicolaï et al., 2014). This frequency is integrated in a formula by software and the data is
collected as firmness values. This technology has been widely used to predict firmness in fruits
as tomatoes (Ketelaere et al., 2006) and peaches (Diezma-Iglesias et al., 2006) and the industry is
already using this technology for on-line analysis.
As mentioned before aroma plays a primary role in consumer acceptability especially for
strawberry consumers. A non-destructive alternative to the classical destructive GC analysis is
the Proton-Transfer-Reaction Mass Spectrometry (PTR-MS). This technology enables
simultaneous real-time detection, monitoring and quantification of volatile organic compounds
(VOCs). The principle of PTR-MS is based on an ionization process that can be written as:
H3O+ + R → RH+ + H2O
In this reaction protonated water (H3O+) reacts with the volatile (R), transferring a proton to the
volatile molecule, resulting in a protonated/ionized molecule (RH+) and water. Then the mass
detector coupled to the PTR provides a signal whose intensity is proportional to the
concentration of RH+. The volatile showing a certain signal can be identified through its mass.
Altogether, there are several non-invasive technologies available to determine quality traits in
fruits and many of them have shown a good prediction for the tested traits; however, there are
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only few successful studies, if at all, that involve the simultaneous analysis by non-invasive
technologies of several quality traits in tomatoes and strawberries.
Therefore, the main purpose of this research project was to find non-invasive method(s) that
could predict quality traits such as firmness, SSC, acidity, pigment content and aroma in tomato
and strawberry with the ultimate aim to provide breeders with a reliable non-invasive toolbox
for simultaneous phenotyping of these quality traits. To attain this purpose, the following sub-
objectives were set:
- Study the evolution of quality traits for different fruit genotypes during postharvest
storage through invasive and non-invasive phenotyping.
- Find the best predictors for each measured variable/trait by establishing a relation
between the invasive and non-invasive data through OmicsFusion, a multivariate data
analysis technique developed by WUR.
2. MATERIAL AND METHODS
2.1 Fruit samples
Tomato assay. Tomato fruits from five greenhouse-grown genotypes that differ in terms of
quality traits were obtained from ENZA Zaden at commercial harvest stage. The genotypes
corresponded to cocktail varieties named “Anaisa”, “Anaclaire”, “Brioso”, “Campari” and
“Camarque”. On arrival at the laboratory, fruits were immediately stored at 18°C and 80%
relative humidity for 15 days to simulate commercial postharvest conditions. During storage, 9
fruits free of visual defects were selected from each genotype at day 1, 4, 8, 11, and 15 for
further analysis.
Strawberry assay. Strawberry fruits from five genotypes that differ in terms of quality traits
were obtained from Fresh Forward at commercial harvest stage. The genotypes corresponded
to 1101 (Elsanta), 1102 (Lambada), 1113, 1121 and 1127 lines. On arrival at the laboratory,
fruits were immediately stored at 15°C and 80% relative humidity for 3 days, followed by 5 days
of storage at 18°C and 80% relative humidity to simulate commercial postharvest conditions.
During storage, 15 fruits free of visual defects were selected from each genotype at day 1, 4, 5,
6, 7 and 8 for further analysis.
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On each sampling day and prior to non-invasive phenotyping, tomato and strawberry fruits
were removed from storage until they reached room temperature. Green calyxes were not
removed from the fruit, neither during storage nor during non-invasive phenotyping to avoid
alteration of the ripening process.
2.2 Non-invasive phenotyping
2.2.1 Chlorophyll fluorescence
CF measurements were performed with the CropReporterTM consisting of a CCD camera that
captures images at different combinations of the excitation light and with 6 different optical
filters. The measurement was based on the direct fluorescence method to analyse the
induction or Kautsky curve of the fluorescence intensity over time (Strasser et al., 2000).
Tomato and strawberry fruits were placed in a custom made rack with the green calix down and
most of the fruit surface facing the camera. Images were obtained during the induction curve
process from F0 (all reaction centres open) until Fm (all reaction centres closed). The
CropReporter software exported the following parameters: starting value of the fluorescence
(F0), the saturation value (Fm), maximum efficiency of Photosystem II (Fv/Fm=(Fm-Fo)/Fm), Red,
Green, Blue, Chlorophyll index, and Anthocyanin index. Only Fm, Fv/Fm and Chlorophyll index
were used for further data analysis.
2.2.2 Hyperspectral imaging
This technology was used for phenotyping tomatoes only. The hyperspectral imaging station
consisted of a halogen illumination system, a spectrograph and a CCD camera. Reflection
images of tomatoes placed in the rack as described in CF measurements, were taken in the
wavelength range 400-1000 nm and the exposure time was adjusted at 150 ms. The stepper
table was set at 0.5mm stepsize and 500 steps to cover a distance of 250mm. Before fruit
scanning, white and dark reference images were taken. The first one was acquired from a white
Teflon plate with 99% reflectance, and the dark reference image was obtained with the camera
lens covered with its cap. The three-dimensional spatial and spectral data obtained from fruit
scans was corrected with the white and dark reference and then subjected to several
pretreatments for further data analysis. The construction of the 3D data cube and pretreatment
steps were performed by Gerrit Polder.
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2.2.3 Multispectral imaging
The VideometerLab device, a multispectral imaging method, was used for strawberries
phenotyping as an alternative to the hyperspectral system used in tomatoes. Prior to image
capture, the device was calibrated with respect to color, geometry and self-illumination. Then,
reflection images were acquired from individual fruits placed on a sample holder with the calyx
down in the wavelength region 375-970 nm. The three-dimensional cube contained spatial
information and spectral data from 19 wavelengths. The construction of the 3D data cube and
pretreatment steps were performed by Gerrit Polder.
2.2.4 NIR
Reflectance spectra of tomatoes and strawberries were acquired with a Perkin-Elmer Spectrum
One NTS spectrophotometer in the wavelength region 700-2500 nm. Fruits were placed
individually on the lens for spot measurements. For tomatoes, 3 spot measurements were
performed at different locations across the fruit equator, while 2 different spots were
measured in strawberries. At the beginning of the assay and after 20 spot measurements, a
white reference was measured for calibration purposes. Each spectrum obtained from each
fruit spot was the average of 5 scans.
2.2.5 VIS-NIR Transmission
For the measurement of transmitted light through tomato and strawberry fruits, an optic fiber
connected to a portable spectrometer was used in the wavelength range 400-1000nm. The fruit
was vertically oriented with the calix down, the top was irradiated with a LED light and the light
transmitted through the fruit was detected by the optic fiber from 3 different spots located at
the equator. Prior to measurements, a white reference spectrum was acquired from a Teflon
plate and a dark reference spectrum was obtained by covering the optic fiber. The raw data was
corrected with the white and dark references.
2.2.6 Pigment Analyzer
The pigment content of strawberries was determined by the CP Pigment Analyzer PA1101
device at two locations at the fruit equator. The chlorophyll and anthocyanins content were
determined by the NDVI and NAI indexes displayed by the device, respectively.
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2.2.7 PTR-MS
Head space volatiles were determined in intact strawberries and in frozen powder of tomatoes
through PTR-MS. For strawberry analysis, 5 fresh fruits well identified were placed in a sealed
glass vessel (500 mL) connected to the PTR-MS and the releasing of VOCs was monitored during
10 min. While 5 g of frozen powder from each tomato pool were placed in a 20 ml glass vessel
connected to the PTR-MS and incubated for 4 min at 30°C. The release of VOCs from tomatoes
was monitored during 1 min. The PTR-MS system was decontaminated between measurements
by flushing clean air into the system. The data collected was composed of the mass value of the
detected ions that correspond to the volatile molecular mass plus the mass of a single proton,
and the intensity of the signal expressed as ppb.
2.2.8 AFS acoustic
Strawberry and tomato firmness were determined non-destructively by an acoustic firmness
sensor (AFS, Aweta). Each fruit was placed on the device fruit holder and the firmness was
determined at 4 different places at the fruit equator. Firmness was expressed as Hz2·g2/3.
2.3 Invasive phenotyping
Immediately after non-invasive measurements, firmness was determined by a destructive
method on each sampling day. Once firmness measurements finished, the fruit samples were
pooled. Two pools of tomato fruits were made consisting of 5 and 4 fruits randomly chosen.
While for strawberries, 3 pools of 5 fruits each well identified were prepared. After pooling,
strawberry and tomato samples were frozen and ground for further destructive analysis.
Fruit firmness, SSC, acidity, anthocyanins and carotenoids content were determined by
reference methods as follows.
2.3.1 Firmness
Tomato firmness was determined by compression with a Magness-Taylor Texture Analyzer. A
cylindrical metal plate of 75 mm diameter was used to compress the fruit 5 mm at two
positions in opposite sides of the equatorial zone. The texture analyzer setting used is shown in
appendix 1a. After each fruit measurement, the maximum compression force was obtained and
expressed as kg·m/s2.
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Strawberry firmness was determined by puncture test using the Magness-Taylor Texture
Analyzer. A flat 4 mm probe was used to penetrate the fruit a distance of 10 mm. The puncture
was made only in one spot located in the zone of the largest diameter. The texture analyzer
setting used is shown appendix 1b. The maximum puncture force was obtained for each fruit
and expressed as kg·m/s2.
2.3.2 SSC and acidity
SSC and acidity were determined simultaneously with a pocket Brix-Acidity Meter (Tomato)
PAL-BX|ACID3 ATAGO. Around 0.5 g frozen powder of pooled strawberry and tomato fruits
were transferred to 2ml Eppendorf tubes and thawed on ice. Tubes were centrifuged briefly
and 200 ul of the supernatant were used for SSC and acidity measurements according to the
Brix-Acidity Meter supplier’s instructions. SSC and acidity were expressed as °Brix and citric acid
(mg/L), respectively.
2.3.3 Carotenoids
Carotenoids extraction and analyses were performed by other members of the team involved in
this project. The protocol used was as follows: 25 mg freeze dried tomato samples placed in a
10 ml glass tube were extracted with 4.6 ml chloroform/methanol (1:1) containing 0.1% (w/v)
butylated hydroxytoluene (BHT) and 0.003 mg/ml of internal standard Sudan I. After vortex, the
extracts were standing on ice for 30 min with vortexing every 15 min. Then the tubes were
centrifuged at 1100 g for 15 min. 1000 ul of supernatants were transferred to 2ml Eppendorf
tubes and dried in speedvac around 1.5 hr. The pellet was resuspended in 217.5 ul ethanol 96%
including 0.1% (w/v) BHT. After vortex, tubes were sonicated for 5 min following centrifugation
for 10 min at max speed. The samples were filtered through 0.2 µm nylon syringe filters into
glass inserts in ambar vials. Carotenoids analysis was carried out by HPLC equipped with a UV-
VIS detector. 5 µl samples were injected into YMC C30 column (4.6mm× 250 mm, 5 µm particle
size). Calibration curves were made with the standard stock solutions for pigments
quantification. Lycopene, β-carotene and lutein content was expressed as the pigment amount
relative to the internal standard.
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2.3.4 Anthocyanins
Anthocyanins content was determined in strawberry samples from day 1 and 8 to confirm if
there is any change in anthocyanins content during storage. The extraction and analyses were
performed by others members of the team involved in this project, and the protocol used was
as follows: 0.5 g frozen powder placed in a 10 ml glass tube was extracted with 1.5 ml methanol
containing 1% (w/v) formic acid. After vortex, the extracts were standing on ice for 30 min.
Then the tubes were sonicated for 15 min following centrifugation at 2500 rpm for 10 min. The
samples were filtered through 0.2 µm PTFE syringe filters into 1.5 ml vials. Samples were
analyzed by LC-MS. Anthocyanin content was expressed as the amount relative to the internal
standard.
2.4 Statistical analysis
A one-way analysis of variance was performed for quality parameters determined by reference
methods and by non-destructive methods (CF and pigment analyzer parameters) to test for
significant differences between genotypes at each day of storage, and for significant differences
for each genotype over the storage period. Means were compared using LSD test at P = 0.05. All
data was analyzed using SPSS for Windows, version 22 (SPSS Inc., Chicago, II.) statistical
software. The result of the one-way analysis of variance and LSD tests for tomato and
strawberry trials are shown in Appendix 1 and 2, respectively.
The data obtained by non-invasive and invasive phenotyping was submitted as predictors and
responses, respectively, to the OmicsFusion web application (www.plantbreeding.wur.nl/
omicsFusion2/). Through this platform, the data was subjected to multivariate regression
analysis using different modern techniques: principal component regression (PCR), partial least
squares (PLS), lasso regression, elastic net regression, sparse PLS regression (SPLS), and random
forest regression. The OmicsFusion analysis included a tenfold cross-validation procedure
where the data set was split into two groups, the training and the testing data, which
corresponded to 90% and 10% of the samples, respectively. Once the analysis finished, a list
was obtained where the predictors were ranked according to the mean rank over all the
methods. The top list contained the most important predictors.
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3. RESULTS
3.1 Tomato Phenotyping
3.1.1 Destructive measurements
SSC, acidity and firmness were determined by reference methods in tomato genotypes. Figure
1 shows the evolution of these quality parameters during storage. Anaisa was found to have a
significantly higher SSC at 1, 11 and 15 days of storage than other tomato genotypes tested.
Anaisa tomatoes had SSC ranging between 5.9 and 6.8 °Brix, which minimum value was reached
at day 8 and then it increased significantly until reaching the maximum °Brix at the last day of
storage. Significant differences in SSC between the other genotypes were only found during the
first day of storage, in which Anaclaire tomatoes showed the lowest SSC (5.5 °Brix). After 4 days
of storage, there were no significant differences in SSC between these genotypes and their SSC
levels remained more or less constant during the whole period under storage.
Figure 1. Evolution of SSC, acidity, firmness determined by compression (FC) and by acoustic method (FA) in tomato
genotypes during storage for a period of 15 days. Values are means ± standard deviation. n=2 for SSC and acidity;
n=9 for Fc and FA.
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Acidity was expressed as citric acid equivalents (mg/L). Citric acid and malic acid are the main
organic acids present in tomatoes (Beckles, 2012). 1 day stored tomatoes showed significant
differences in acidity between genotypes, among them Anaclaire and Anaisa had the highest
acidity values. Thereafter, there were no significant differences in this quality parameter
between genotypes. The evolution in acidity of Anaclaire and Anaisa over storage time is not
clear due to the large variation (standard deviation) observed in the data set (n=2) mostly at 1
and 4 days of storage. Camarque tomatoes showed significant changes in acidity over storage
time, showing the minimum and maximum acidity values at day 1 and 15, respectively.
Tomato firmness was determined destructively by a compression method. For most genotypes,
the maximum force determined by compressing the fruits 5 mm did not change significantly
after 15 days of storage. Firmness values of 15 days stored tomatoes were almost the same
compared to the values observed during the first day of storage. The only genotype that
showed a significant decrease in firmness after 15 days of storage was Campari. The evolution
of firmness shown by this method was not expected, since after 15 days of storage the tomato
genotypes got clearly softer than 1 day after arrival. Looking at the firmness evolution
determined by the acoustic method, there was a sharp and significant decrease in firmness
during the first 8 days of storage for all genotypes tested. Although this is a non-invasive
method, it showed more reliable firmness values than the destructive one. Therefore, firmness
values obtained by acoustic measurements were used as the reference values in further
multivariate regression analysis.
Another quality parameter determined by a destructive method was carotenoids content.
Figure 2 shows the evolution during storage of the content of the main carotenoids identified in
tomatoes: lycopene, β-carotene and lutein. There were no significant differences between
genotypes in any of the carotenoids content determined. Regarding variation during storage,
Anaisa showed a significant decrease in β-carotene content at 4 and 11 days of storage. The
highest β-carotene level reached at day 8 was recovered during the last day of storage. A
significant effect of storage was also observed on lycopene content in Anaclaire tomatoes,
which showed a significant increase in lycopene at day 4 and then decreased at day 11 of
storage. At the final storage day, lycopene level was recovered reaching the value observed at 4
days of storage. Overall, there was no clear trend of variation during storage and this is mainly
due to the large standard deviation values observed in carotenoids content mostly in Brioso,
Campari and Camarque tomatoes (Appendix 2, Table 8b, 9b and 10b). This could have masked
any effect of genotype and storage on carotenoids content.
13
Figure 2. Evolution of carotenoids β-carotene, lycopene and lutein in tomato genotypes during storage for a period
of 15 days. Values are means ± standard deviation for n=2.
3.1.2 Non-destructive measurements
3.1.2.1 Chlorophyll fluorescence
Chlorophyll fluorescence analysis was used to determine photosynthesis efficiency (Fv/Fm) and
maximum fluorescence (Fm) parameters. Moreover, CropReporterTM was also used to perform
spectral analysis on tomatoes to determine their chlorophyll content. The evolution of these 3
quality parameters during storage is shown in Figure 3. Photosynthesis efficiency was
significantly lower in Anaisa than other tomato genotypes tested during the whole storage
period. The other genotypes showed no significant differences in photosynthesis efficiency,
with the exception of Campari that had significantly lower Fv/Fm levels than Anaclaire, Brioso
14
and Camarque between 4 and 11 days of storage. Photosynthesis efficiency decreased
gradually and significantly from 4 days of storage onwards, with exception of Brioso that
showed a slight trend to decrease. Maximum fluorescence also decreased significantly during
storage reaching the minimum value at the last day in all genotypes tested. Regarding
differences between genotypes, Fm levels were only significantly lower in Anaisa tomatoes at 4
days of storage compared to other genotypes. Chlorophyll degradation during storage was
significant for most genotypes reaching the minimum values at the last day of storage. As seen
for photosynthesis efficiency, Anaisa tomatoes showed the lowest chlorophyll content during
storage.
Figure 3. Evolution of photosynthesis efficiency (Fv/Fm), maximum fluorescence (Fm) and chlorophyll content in
tomato genotypes during storage for a period of 15 days. Values are means ± standard deviation for n=9.
15
3.1.2.2 NIR, hyperspectral imaging and VIS-NIR transmission
A substantial amount of spectral data was obtained from optical-based techniques such as NIR,
hyperspectral imaging and VIS-NIR transmission. Figure 4 shows the averaged spectrums
acquired with these techniques on tomato genotypes at the first day of storage.
Figure 4. Average spectra obtained by NIR, hyperspectral imaging and VIS-NIR transmission analysis on tomato
genotypes stored 1 day at 18°C.
NIR raw data was converted to absorbance (log (1/Reflectance)) to visualize more clearly the
absorption bands. Tomato contains around 90% water and therefore its NIR spectrum is clearly
dominated by water absorption bands at 760, 970, 1170 and 1450 nm, which are overtones of
the O-H bonds (Alves de Oliveira et al., 2014; Clément et al., 2008). There is a fourth water
16
absorption band at 1930 nm that correspond to stretch and deformation of the O–H bonds
(Sirisomboon et al., 2012). The described water absorption bands at 970, 1170 and 1450 nm are
probably overlapping the peaks that should appear at 980, 1190 and 1500 nm, resulting in
broader bands. These last peaks correspond to O-H overtone from carbohydrates, C–H
overtones of sugars (fructose, sucrose, and glucose) and O-H overtones related to organic acids,
respectively (Bobelyn et al., 2010, Alves de Oliveira et al., 2014).
The spectrum generated from raw hyperspectral imaging data in the VIS-NIR region shows an
absorbance band at 680 nm that correspond to chlorophyll absorption (Clément et al., 2008).
The small absorption bands observed at 750 and 850 nm could correspond to overtones of
water bonds and overtones of C-H group, respectively (Fan et al., 2015). The chlorophyll
absorption band is also observed in the spectrum obtained by VIS-NIR transmission, but in this
case this band provides information about the absorption process that takes place in the whole
fruit and not only in the surface. There are also some small absorption bands around 750 and
850 nm that could correspond to the absorption groups mentioned before.
Overall, there are several absorption bands in the spectrums obtained by the different
techniques used that could provide information about the chemical composition and physical
properties of the fruits tested. Moreover, it can be observed that the different tomato
genotypes possess quite similar spectrums with bands largely dominated by water absorption,
which makes compulsory to use sophisticated multivariate regression techniques to extract key
wavelengths and data closely related to the quality parameters that are under study.
3.1.2.3 PTR-MS
The volatiles released from the different tomato genotypes were monitored on-line by PTR-MS.
Because of technical issues, the analyses were made on frozen powder samples, which mean
that the method was not strictly non-invasive. This possibly influenced the release of some
volatiles that in intact tomatoes could have not been released as easily in presence of a strong
barrier as tomato skin is.
Several signals were detected through PTR-MS analysis and many of them were isomeric forms
of others. The evolution during storage of the concentration of the main VOCs identified in
tomato genotypes is shown in Figure 5. There were interesting patterns observed, such as the
sharp decrease in the release of the key tomato volatile benzyl alcohol/benzaldehyde during
the first 4 days of storage for most genotypes. The gradual increase of acetone/propanal could
also indicate an effect of storage on the release of these volatiles. For the purpose of this
research, it was interesting to find out if any of the non-destructive techniques besides PTR-MS
were able to predict any of these volatiles that play a key role in aroma and tomato quality.
17
Therefore, and taking advantage of the larger variation observed over time from volatiles data
compared to the other parameters determined by destructive methods, all the data obtained
by PTR-MS was used as predictors as well as responses in further multivariate regression
analysis.
Figure 5. Evolution of some VOCs in tomato genotypes during storage for a period of 15 days. Values are means ±
standard deviation for n=2.
18
3.1.3 OmicsFusion analysis
All the data obtained from invasive and non-invasive measurements were subjected to
multivariate regression analysis using the OmicsFusion platform. SSC, acidity, firmness,
carotenoids content and each m/z detected by PTR-MS data were submitted as responses, and
all the data obtained by CF and spectral measurements at each wavelength were submitted as
predictors. A second round of analysis was performed where PTR-MS data was used as
predictors.
Table 2. The best three predictors (non-invasive methods) for quality parameters in tomatoes
determined by OmicsFusion analysis.
Quality parameter
NI Method R2NI Method R2
NI Method R2
SSC Hyperspectral 0.41 CF - Fv/Fm 0.24 Hyperspectral 0.25
λ 931.7 λ 915.6
Acidity PTR-MS 0.18 PTR-MS 0.08 PTR-MS 0.06
m/z 45 m/z 125 m/z 69
Firmness (Acoustic) Hyperspectral 0.63 VNIR Transmission 0.61 Hyperspectral 0.61
λ 557.7 λ 855.5 λ 582.2
β-Carotene Hyperspectral 0.18 Hyperspectral 0.20 Hyperspectral 0.20
λ 631.5 λ 634.6 λ 637.7
Lutein VNIR Transmission 0.11 Hyperspectral 0.10 NIR 0.12
λ 402.6 λ 934.9 λ 739.3
Lycopene NIR 0.09 NIR 0.09 NIR 0..09
λ 749.8 λ 2258.4 λ 2251.4
Benzyl alcohol, VNIR Transmission 0.36 NIR 0.47 CF - Fm 0.45
Benzaldehyde λ 403.4 λ 2498.1
n-Hexanol, NIR 0.38 NIR 0.38 NIR 0.37
1-Penten-3-one λ 1308.1 λ 1302.9 λ 1260.9
2-Isobutylthiazole VNIR Transmission 0.27 Hyperspectral 0.32 VNIR Transmission 0.19
λ 405.4 λ 539.4 λ 422.3
Ethanol VNIR Transmission 0.29 VNIR Transmission 0.17 VNIR Transmission 0.28
λ 510.5 λ 869.1 λ 471.3
Acetone, Propanal VNIR Transmission 0.26 VNIR Transmission 0.31 VNIR Transmission 0.26
λ 510.5 λ 405.4 λ 404.3
NI, Non-invasive. R2, linear regression coefficient determined on the raw data.
1ST
Predictor 2nd
Predictor 3rd
Predictor
19
Table 2 summarizes the results obtained by OmicsFusion analysis, in which the predictors were
ranked from the first to the third best. For each predictor, information about the non-invasive
method as specific wavelength, m/z and CF parameter is listed together with the regression
coefficient (R2) obtained from linear regressions made on the raw data.
Hyperspectral imaging at 931.7 nm was found to be the best predictor for SSC in tomatoes and
the R2 0.41 suggests that SSC is partially explained by this method at that particular wavelength.
It seems that the region close to this wavelength possess more key features that could explain
SSC, since the third best predictor also involves a wavelength in this region. Photosynthesis
efficiency was ranked as the second best predictor, however the R2 0.24 indicates that this
parameter is loosely related to SSC. A poor relation was also found between the three best
predictors and acidity. The first predictor, the volatile acetaldehyde (m/z 45) had a R2 0.18, and
the other predictors showed even lower R2 values for acidity. Firmness determined by the
acoustic method and its three predictors showed the highest R2 values (~0.6) among all the
quality parameters analyzed. The two wavelengths from hyperspectral imaging that best
predicted firmness were close to each other in the VIS region. While the key wavelength from
VNIR transmission that correspond to the second best predictor, is in the NIR region. There
were poor relations (R2 0.09-0.20) between the content of the different carotenoids and the
wavelengths from the optical-based techniques found as the best predictors.
Among all the compounds detected by PTR-MS, only few were found to be correlating with
non-invasive predictors. The volatiles listed as quality parameters in Table 2 were those
compounds that showed the best relation, based on R2 values, with their predictors.
Wavelengths in the VIS region (403-510 nm) from VNIR transmission were ranked as the best
predictors for benzyl alcohol/benzaldehyde, 2-isobutylthiazole, ethanol and acetone/propanal.
NIR (2498.1 nm) and maximum fluorescence were also found to be related to benzyl
alcohol/benzaldehyde, showing even higher R2 values (0.45-0.47) than the first predictor (R2
0.36). Hyperspectral imaging at 539.4 nm ranked as the second best predictor for 2-
isobutylthiazole showed also higher R2 value (0.32) than the first predictor. For n-hexanol/1-
penten-3-one, wavelengths close to each other (1260-1302 nm) in the NIR region were found as
the best predictors.
20
3.2 Strawberry Phenotyping
3.2.1 Destructive measurements
The evolution of SSC, acidity and firmness determined by reference methods during storage of
strawberry genotypes is shown in Figure 6. The genotype 1102 showed a significantly higher
SSC than other strawberry genotypes during the whole storage period. There were no
significant changes of SSC in 1102 fruits over storage time, in which SSC ranged between 10.5
and 11.3 °Brix. Regarding the other genotypes, 1101 and 1113 showed similar SSC during the
storage period. Only at day 5, SSC of 1101 was significantly higher than 1113. SSC of 1121 and
1127 also were quite similar during the first 6 days of storage and thereafter 1121 showed a
significantly lower SSC than 1127. There was a significant variation in °Brix from genotypes
1101, 1113, 1121 and 1127 over time, showing significantly lower values at day 8 compared to
the beginning of the storage. 1121 showed the largest decrease in SSC (1.6 °Brix), while 1101,
1113, and 1127 decreased 0.8, 1, and 0.6 °Brix, respectively, during storage.
Figure 6. Evolution of SSC, acidity and firmness by puncture (FP) in strawberry genotypes during storage for a
period of 8 days. Values are means ± standard deviation. n=3 for SSC and acidity; n=15 for Fp.
21
Acidity was expressed as citric acid content (mg/L), the main organic acid present in
strawberries. 1102 genotype had the highest significant acidity values during the storage period
(4.7-5.2 mg/L), while 1121 showed the lowest significant values for this quality parameter (2.5-
3.3 mg/L). The genotypes 1101, 1113 and 1127 had similar acidity levels over storage time,
showing significant differences only at day 7 when 1113 reached its minimum acidity during
storage. Acidity of 1102 strawberries did not change significantly over storage time, while other
genotypes showed significantly lower acidity values after 8 days compared to the beginning of
the storage period.
Strawberry firmness determined by puncture showed significant changes over storage time,
however, only 1113 fruits got softer especially after 6 days of storage. Other genotypes showed
similar or even higher firmness values at 8 days than at the beginning of the storage. Moreover,
all the genotypes showed an increase, significant or not, in firmness at 4 days of storage.
Although the reliability of this data was doubtful, the data generated by the puncture method
was used nevertheless for further multivariate regression analysis.
Figure 7. Content of the main anthocyanins quantified by LC-MS in strawberry genotypes at day 1 and 8 of storage.
Values are means ± standard deviation for n=3.
22
The content of individual anthocyanins was determined by LC-MS in extracts from strawberries
picked at day 1 and 8 to see the effect of storage on anthocyanin content (Figure 7). The major
anthocyanins identified were pelargonidin 3-O-glucoside, pelargonidin 3-rutinoside and a
malonyl conjugate of the first one, pelargonidin 3-O-(6-O-malonyl-β-D-glucoside). There were
no significant differences in the content of any of the anthocyanins identified between the first
and last day of storage. There were some significant differences between genotypes in
anthocyanins content. 1102 and 1127 possessed a significantly higher content of pelargonidin
3-O-glucoside than other genotypes tested. 1102 and 1113 showed the highest significant
content of pelargonidin 3-rutinoside. For pelargonidin 3-O-(6-O-malonyl-β-D-glucoside), 1102
had again the highest significant content followed by 1101.
3.2.2 Non-destructive measurements
3.2.2.1 Chlorophyll fluorescence
Figure 8. Evolution of photosynthesis efficiency (Fv/Fm), maximum fluorescence (Fm) and chlorophyll content in
strawberry genotypes during storage for a period of 8 days. Values are means ± standard deviation for n=15.
23
The evolution of CF parameters during storage is shown in Figure 8. The photosynthesis
efficiency of all genotypes decreased significantly over time. After 5 days of storage, this
parameter decreased at a higher rate in 1101, showing the lowest significant values of
photosynthesis efficiency among genotypes. The other genotypes showed similar levels of this
parameter, although 1102 showed the highest photosynthesis efficiency among genotypes
during last two days of storage. There was also a decrease in maximum fluorescence over time,
although the decreasing rate was lower than for (Fv/Fm). 1101 showed the highest significant Fm
values among genotypes over the whole storage period, followed by 1113. The lowest values
over most of the storage period were observed in 1121 and 1127.
The spectral determination of chlorophyll content showed that chlorophyll was degraded
significantly over time in 1101 and 1113 genotypes. Regarding differences between genotypes,
1127 and 1101 had the largest differences in chlorophyll content between each other over
time.
3.2.2.2 Pigment analyzer
Figure 9. Evolution of anthocyanins (NAI) and chlorophyll (NDVI) content determined with the pigment analyzer on
strawberry genotypes during storage for a period of 8 days. Values are means ± standard deviation for n=15.
Chlorophyll and total anthocyanins content were estimated during storage with the pigment
analyzer (Figure 9). A small but still significant decrease in anthocyanin content was observed in
1113 and 1121 fruits. NAI did not change uniformly in most genotypes during storage;
therefore, there were not clear differences observed between genotypes. Because of the small
24
variation observed in anthocyanins content over time, which was previously confirmed by
destructive analysis of individual anthocyanins (Figure 7), it was decided to do not perform the
determination of individual anthocyanins in samples from day 4 to 7, and therefore the data of
individual anthocyanins content was not included in the multivariate regression analysis.
Chlorophyll content measurements by pigment analyzer are comparable to the determinations
performed with the CropReporterTM, since both technologies use spectral analysis to estimate
chlorophyll content in fruits. And this was the case in this experiment, where the evolution of
chlorophyll content determined by the pigment analyzer (Figure 7) is quite similar to the one
determined by the CropReporterTM (Figure 8). There were small differences, but probably this is
because the pigment analyzer performs spot measurements, while with the CropReporterTM the
whole fruit surface exposed to the camera is included in the analysis.
3.2.2.3 NIR, hyperspectral imaging and VIS-NIR transmission
Figure 10 shows the averaged spectrums acquired with NIR, VideometerLab and VIS-NIR
transmission on strawberry genotypes at 4 days of storage. NIR raw data was converted to
absorbance (log (1/Reflectance)) to visualize more clearly the absorption bands of strawberry
fruits.
Like tomato, strawberry contains around 90% water and therefore its NIR spectrum is also
dominated by the overtones of the O-H bonds of water which absorbed at 760, 970, 1170, 1450
and 1930 nm. These absorption bands, as in tomato, are probably also overlapping those peaks
that correspond to C-H and O-H from functional groups related to quality parameters.
The spectrum in the VIS-NIR region generated with raw data from VideometerLab
measurements shows spectral information at only 19 wavelengths, therefore the spectrum is
not continuous along the VIS-NIR region. However, it is possible to observe the absorption
intensity for some key wavelengths at, for instance, 680 nm that is related to chlorophyll
absorption.
During VIS-NIR transmission measurements of strawberries there was a large amount of light
leaking between the light source and the fruit, which could have entered directly to the optical
fiber and therefore the fiber processed light energy that was not transmitted through the fruit.
The strawberry spectrum showed a different pattern than the one generated by tomatoes,
especially the chlorophyll absorption band at 680 nm, and the intensity of the light transmitted
after 750 nm that dropped continuously until 950 nm. Although the data obtained by VIS-NIR
transmission is not completely reliable, the data generated by this method was used
nevertheless for further multivariate regression analysis.
25
Figure 10. Spectra obtained by NIR, multispectral imaging (VideometerLab) and VIS-NIR transmission analysis on
strawberry genotypes stored 4 days at 15°C.
3.2.2.4 PTR-MS
PTR-MS was used to monitor online the volatiles released from intact strawberries. Several
signals were detected through PTR-MS analysis, many of them were isomeric forms of others
and some others showed patterns with low variation between genotypes and over time (data
not shown). Figure 11 shows the evolution during storage of some VOCs that revealed
interesting patterns during storage and also that were identified as key volatiles in strawberry
aroma. It can be observed that the release of pentanal, acetaldehyde and esters increased
during storage particularly in 1102 genotype, which was the one that had the highest SSC and
acidity values.
26
Because of the large variation seen for some volatiles during storage and also the necessity to
determine key volatiles influencing strawberry flavor with non-destructive techniques, the PTR-
MS generated data was subjected to OmicsFusion analysis as predictors as well as responses.
Figure 11. Evolution of some VOCs in strawberry genotypes during storage for a period of 8 days. Values are means
± standard deviation for n=3.
3.2.3 OmicsFusion analysis
The procedure of OmicsFusion analysis was basically the same as the one described for tomato.
Table 3 summarizes the results obtained for strawberries, in which the predictors were ranked
from the first to the third best. SSC was found to be best predicted by NIR at 1836.6 nm and
with a high R2 value (0.82). The other two predictors also correspond to NIR and at very close
wavelengths to the first predictor, suggesting that the NIR region around 1830 nm possess key
features that could explain to a large extent the SSC values observed in the different genotypes.
Interestingly, wavelengths (1841-1848 nm) close to this region were also ranked as the best
predictors for acidity with a relatively high R2 (0.62-0.63). Firmness was loosely related to the
predictors found, compounds determined by PTR-MS, showing very low R2 values (0.15-0.22).
27
Volatile esters were the best predicted according to the R2 values found (0.65). The three
predictors correspond to NIR wavelengths very close to each other (1994, 1995 and 2015 nm).
Pentanal was found to be best predicted by NIR at 714.8 nm and the R2 0.42 suggests that this
volatile is partially explained by this wavelength. The third best predictor also involves a
wavelength in this region (776 nm) showing a similar R2 (0.43). Maximum fluorescence was
ranked as the second best predictor but with a lower R2 (0.37 nm). Acetaldehyde volatile was
found to be best predicted by NIR at wavelengths very close to each other (725, 728, 711 nm)
with R2 ranging between 0.45 and 0.49, suggesting that this region could partially explain the
variation observed for this volatile.
Table 3. The best three predictors (non-invasive methods) for quality parameters in strawberries
determined by OmicsFusion analysis.
Quality parameter
NI Method R2NI Method R2
NI Method R2
SSC NIR 0.82 NIR 0.82 NIR 0.82
λ 1836.6 λ 1834.9 λ 1831.4
Acidity NIR 0.63 NIR 0.62 NIR 0.62
λ 1848.9 λ 1841.9 λ 1847.1
Firmness (Puncture) PTR-MS 0.22 PTR-MS 0.15 PTR-MS 0.18
m/z 37 m/z 114 m/z 38
Pentanal NIR 0.42 CF - Fm 0.37 NIR 0.43
λ 714.8 λ 776.1
Esters NIR 0.65 NIR 0.65 NIR 0.65
λ 1994.1 λ 1995.9 λ 2015.1
Acetaldehyde NIR 0.47 NIR 0.49 NIR 0.45
λ 725.3 λ 728.8 λ 711.3
NI, Non-invasive. R2, linear regression coefficient determined on the raw data.
1ST
Predictor 2nd
Predictor 3rd
Predictor
28
4. DISCUSSION
In this study, a combination of different non-invasive analysis techniques was used to estimate
simultaneously several quality traits in different tomato and strawberry genotypes. A large
amount of data was generated from non-invasive phenotyping, particularly from the spectral
measurements. This data together with the one obtained from invasive phenotyping, were
subjected to multivariate regression analysis in order to find the best predictors among all the
parameters measured through non-invasive methods. To find an accurate predictor with good
discrimination power for the quality traits tested, a relatively high variation is needed in these
traits.
For the tomato trial, fruits of five commercial cultivars belonging to the same segment of
cocktail varieties were used. All five genotypes possessed high quality and therefore low
variation in quality traits. This low variation was observed in SSC, acidity and firmness between
genotypes and/or during storage. For SSC (Figure 1), only one genotype (Anaisa) was
significantly different from the others, and only this genotype showed some variation during
storage, decreasing 0.6 °Brix until day 8 and increasing 0.9 °Brix at 15 days of storage. The
decrease in Anaisa SSC could be explained by the use of soluble solids, mainly sugars, as a
substrate for respiration during storage, and the following SSC increase could result from the
counteracting effect of dehydration during the last 7 days of storage. Even though dehydration
was not determined during this experiment, dehydration symptoms were clearly observed
during the last days of the storage period. This effect of dehydration could also explain the
increase in acidity, around 20 and 30% for Brioso and Camarque genotypes, respectively, when
comparing the first and last day of storage (Figure 1). For other genotypes, the evolution of
acidity was not clear due to the large variation between samples of the same genotype/day
(replicates), leading to no significant changes during storage and no clear differences between
genotypes. There are studies reporting no changes in SSC and acidity (Javanmardi and Kubota,
2006; Kagan-Zur and Mizrahi, 1993), as well as others reporting a decrease in these quality
parameters during storage (12.5 °C) (de León-Sánchez et al., 2009; Maul et al., 2000). The effect
of storage has been suggested to be cultivar dependent, being some cultivars more susceptible
than others to changes in these quality parameters during storage at room and low
temperatures (de León-Sánchez et al., 2009).
Overall, the low variation of the data obtained from reference methods for SSC and acidity in
tomatoes could have partly affected the results obtained from multivariate regression analysis,
from which poor relations were found between °Brix and its best predictor (R2 0.41), and even
worse for acidity (R2 0.18) (Table 2). Some studies using optical-based techniques in reflectance
mode have developed prediction models for quality traits in several tomato varieties and have
reported good predictions for SSC and acidity based in the coefficient of determination
29
obtained (R2 for regression between predicted and observed values) (Table 1). The success of
these approaches is due to the individual analysis of a large number of samples with high
variability. Flores et al. (2009) applied VIS-NIR reflection and reference methods to analyze
individually 180 tomato fruits at commercial maturity ranging between 2.5 and 9 °Brix
(coefficient of variation, CV: 24%) and 0.41-0.71 % citric acid (CV 31%), which led to good
predictions of SSC (R2 0.82) and acidity (R2 0.71). Using the same technique, Shao et al. (2007)
estimated SSC (R2 0.82) in 98 individual samples at pink-light red maturity ranging between 2.02
and 4.8 °Brix (CV 11%). In contrast, even though in our experiment 225 fruits were analyzed
non-destructively, only 50 samples with low variability, 5.4-6.8 °Brix (CV 6.4%) and 0.56-0.79 %
citric acid (CV 12%), were analyzed by destructive methods (2 pools per each genotype/day
combination). Therefore, the data from non-destructive phenotyping had to be averaged to
match the amount of data generated by destructive methods and consequently only 50
samples were included in the multivariate regression analysis. Moreover, during pooling the
tomato fruits were chosen randomly, and therefore, the fruits samples included in each
averaged non-invasive data did not necessarily correspond to the same fruits included in the
pool for destructive analysis. Consequently, the low variation, the not optimal pooling method
and the low number of data samples were the main factors affecting the prediction of SSC and
acidity in this experiment. The positive effect of an individual analysis of a large number of
samples on the prediction accuracy was clearly seen in the estimation of firmness in tomatoes.
OmicsFusion analysis was run with the 225 data samples (reference and non-invasive methods)
to predict firmness in tomatoes, resulting in relatively good relations between firmness
measured by the acoustic method and its best predictors (R2 0.61-0.63) (Table 2). Even though
the decrease in firmness during the first 8 days of storage added some variation, there were not
large differences in firmness between genotypes (Figure 1) and therefore the total data
variation was not particularly large (CV 12.7%). Shao et al. (2007) reported a good prediction for
firmness determined by compression (R2 0.81) and by puncture (R2 0.83) using firmness data
with a large variation (CV 52-71%). Thus, the prediction of firmness still could be improved
adding more variability to this quality trait, by using, for instance, more genotypes that differ
largely in firmness.
Regarding carotenoids content prediction in tomatoes, poor relations were found between the
carotenoids quantified destructively and their best predictors (R2 0.09-0.20) (Table 2). Besides
the same issues that negatively affected the prediction of SSC and acidity, carotenoids
prediction may have also been affected by the large variability observed within replicates for
some genotypes. There were some modifications made to carotenoids extraction protocol
routinely used in the laboratory that could have affected the recovery of the quantified
carotenoids and therefore resulting in large variability between biological replicates.
30
There were found predictors that partially estimated the concentration of some masses (m/z)
detected by PTR-MS. These masses were identified as important aroma volatiles in tomato
(Table 2), although their presence and concentration have to be confirmed destructively by gas
chromatography (GC). Putative volatiles as benzyl alcohol/benzaldehyde and 1-hexanol/1-
penten-3-one were the best predicted among all volatiles identified, R2 0.45 and 0.38,
respectively. Benzaldehyde and 1-penten-3-one have been described as important tomato
flavor contributors, providing almond-roasty and green sweet notes, respectively (Selli et al.,
2014). 2-Isobutylthiazole, described as a tomato flavor enhancer, was also partially predicted
(R2 0.32). Interestingly, this compound and benzaldehyde were best predicted by similar
wavelengths with VNIR transmission, suggesting that these compounds may share similar
precursors that absorb in this region of the spectrum. Ethanol and acetone/propanal were also
partially estimated, showing weaker predictions than for the other compounds. These
compounds together with other volatiles have been found to represent a large fraction of the
total volatile composition in tomatoes, and even though they have a low impact on tomato
aroma because of their high odor thresholds, they could also influence indirectly tomato flavor
by interacting with other compounds (Farneti et al., 2012). Overall, tomato volatiles prediction
could also be improved with an optimized experimental design that, for instance, include a
reference method (GC) to analyze tomato samples individually from genotypes that possess
large variations in this trait.
An improved experimental setup was used for strawberry trial, in which the number of samples
and the variation between genotypes were increased compared to the tomato trial. These
factors influenced greatly SSC and acidity prediction in strawberries. An accurate estimation of
SSC was found for its predictors (R2 0.82) (Table 3), which could have been achieved partly by
the large variation observed in °Brix, ranging between 6.1 and 11.3 °Brix (CV 16%). This
variation was mainly due to differences in °Brix between 1101 and the other genotypes, since
no large changes were observed during storage. Some genotypes decreased more than 1 °Brix
during storage, and although this difference can be perceived by a consumer, it does not
provide the variation needed for developing a predictor for this trait. Liu et al. (2014) found also
good estimations for SSC (R2 0.83) through predictions models. This team used multispectral
imaging to phenotype strawberries with °Brix ranging between 5 and 11.4 and with the same
variation as the strawberries used in our experiment (CV 16%).
The prediction of acidity was also improved in strawberry with respect to tomatoes, showing R2
0.63 with its best predictor (Table 3). However, a strong relation was found between acidity
and SSC values determined by destructive methods (R2 0.68), and in addition, both traits were
predicted by similar wavelengths with NIR. Thus, the high prediction achieved for acidity might
be influenced to a large extent by the absorption processes of sugars that constitute the total
soluble solids. This could be confirmed by determining individual sugars and organic acids, and
31
perform predictions for these compounds. In some studies, changes have been found in
individual sugars that determine fruit flavor while SSC remains unchanged (Beckles, 2012).
Therefore, the analysis of individual sugars and organic acids and their further prediction would
also provide a better estimation of taste of strawberries as well as tomatoes.
The determination of firmness by a destructive method failed in strawberries as it was the case
for tomatoes as well. This was confirmed by the multivariate analysis, from which no good
predictors were found for strawberry firmness. A high prediction (R2 0.94) for this trait was
found by Liu et al. (2014) using multispectral analysis on unripe and ripe strawberries. The
puncture method that this author used contrast with our experiment in terms of probe
diameter (6 mm) and the penetration depth (7 mm). Regarding this last parameter, in this
experiment strawberries were punctured 10 mm, which distance was beyond the cortical
region where the tissue breakdown occurs, and therefore in genotypes that possess small
fruits, the probe might have reached the core or next to it resulting in readings that were not in
agreement with the firmness of the surface layer. This could explain the firmness pattern
observed from genotype 1113 that had considerably bigger fruits than other genotypes,
showing a gradual decrease in firmness from 5 days onwards, while the small genotypes
firmness remained constant during storage (Figure 6).
The optimized experimental setup used for strawberry could also have improved the prediction
of strawberry volatiles. Relatively good predictions were achieved for some masses detected by
PTR-MS that were identified as important strawberry volatiles as esters (R2 0.65), acetaldehyde
(R2 0.49) and pentanal (R2 0.43) (Table 3). Even though the presence and concentration of the
specific esters have to be confirmed by GC, esters in general are the main contributor of
strawberry aroma, providing fruity, caramel sweet notes (Schwieterman et al., 2014). These
compounds showed the highest concentration in the genotype that had the highest SSC during
storage (1102) (Figure 11), and therefore these volatiles might be playing a major role in the
perception of sweetness for this genotype. Pentanal and acetaldehyde were best predicted by
wavelengths located close to each other in the spectrum (around 714-725 nm) with NIR,
suggesting that these compounds may share similar precursors that absorb in this region of the
spectrum. Acetaldehyde is the major product of anaerobic respiration; however this volatile has
also been found to accumulate in air-stored fruits as a result of physiological breakdown in
overripe strawberries (Larsen and Watkins, 1995). This could explain the increase of this volatile
mainly in 1102 genotype during the last 2 days of storage when most of the genotypes looked
overripe (Figure 11). Pentanal has been identified as a minor aldehyde in strawberries showing
a negative correlation to flavor intensity (Schwieterman et al., 2014); however this volatile
could also be associated to other compounds that determine strawberry flavor.
32
Overall, good predictions were found for strawberry traits, and for tomato there are some
promising predictors whose discrimination power can be improved with an optimized
experimental design. For development of a non-invasive platform able to phenotype
simultaneously quality traits in these fruits, further studies could be carried out with an
improved setting and using those technologies that performed best in these trials. The non-
invasive phenotyping was quite intensive in terms of lab-work and also in the continuous
handling of the fruit samples that somehow could have affected some traits and their
predictions. Therefore, the use of a limited number of technologies for next studies could help
to avoid these issues. Few technologies were found to be promising for the prediction of quality
traits in tomatoes and strawberries. For tomato SSC and firmness, hyperspectral imaging was
the technology that performed best. VIS-NIR transmission also predicted firmness relatively
well, and interestingly, this technology showed the best predictions for most volatiles. Taking
into account that tomato volatiles analysis was performed in frozen and homogenized powder,
the prediction performance of VIS-NIR transmission could be explained by the ability of this
technology to take information from the internal flesh of tomatoes that is highly
heterogeneous, and therefore including information of volatiles precursors that might be
differently distributed in the flesh. On the other hand, NIR was the technology that provided
the best predictions for the different quality traits tested in strawberries. However,
hyperspectral analysis, that has not been included in this project because the data analysis is
still ongoing, could also provide good predictions for strawberry traits. NIR relies on spot
measurements while hyperspectral imaging performs the analysis all over the fruit surface
exposed to the camera. Thus, hyperspectral imaging could be more accurate when quality traits
are differently distributed over the fruit surface, and this could have been the case for tomato
traits predictions.
Among the technologies that did not provide good predictors were VideometerLab and
chlorophyll fluorescence. VideometerLab, a multispectral analysis, acquires spectral images at
only 19 wavelengths, and some compounds related to strawberry traits could have
absorbed/reflected light in a wavelength that is not included in the range of this technology,
resulting in poor prediction performance as it was found for strawberries. Also for strawberries,
VIS-NIR transmission was not adequate for the analysis, mainly because of the irregular shape
of this fruit that was not suitable for the determination with the equipment setup used.
Parameters of chlorophyll fluorescence were found to predict some of the traits in tomato and
strawberry, but in general this technology performed poorly as a predictor of quality traits.
Interesting patterns were observed for the different parameters determined with this
technology, where clear differences between genotypes and also differences in the evolution
during storage were observed (Figure 3 and 8). However, these patterns might be related to
cellular processes that are not necessarily linked to the quality traits under study. The pigment
analyzer was another technology that was included in the non-invasive phenotyping; however
33
pigments as carotenoids were not predicted by this technology mainly because they were not
properly determined by a destructive method. Yet, the prediction of pigments content could
also be predicted by hyperspectral imaging and transmission since these technologies possess
the wavelengths included in the spectral measurement of the pigment analyzer.
Finally, one key step in developing an accurate non-invasive method is the determination of
optimal wavelengths that reduce the spectral dimensionality by eliminating those that has no
discrimination power. The predictions found in this project correspond to individual
wavelengths from the different technologies. More discriminating wavelengths can be found by
other powerful data mining tools and can be all added to a prediction model that would carry
the maximum spectral information explaining the quality trait. Thus, the predictions found in
this experiment can be significantly improved by the selection and execution of a robust
multivariate regression model able to include the most important wavelengths associated to
quality traits.
5. CONCLUSIONS
In this study, several non-invasive methods were evaluated in estimating postharvest quality
traits in different tomato and strawberry genotypes. Good predictions were found for SSC,
acidity and some volatiles in strawberry, while tomato quality predictions were hampered by
the low variation in the sample set and the low amount of data. These issues were mainly
originating from the low differences in quality traits between tomato genotypes that belong to
the same segment, and the destructive measurement of quality traits in few samples pools and
not in individual fruits. Another issue faced during this project was the reference method used
for the determination of firmness in tomato and strawberry fruits. The selected methods did
not provide reliable measurements for this trait and therefore they could not be linked to the
data obtained by non-invasive methods. Therefore, there is still room for improvement in terms
of experimental setup that would for sure increase the discrimination power of the most
promising predictors found. Among the non-invasive technologies used, hyperspectral imaging,
and VIS-NIR transmission are the most promising in estimating postharvest quality traits in
tomato. NIR and potentially hyperspectral imaging are the technologies that could predict best
these quality traits in strawberry fruits. Overall, this project found interesting predictors for
quality traits and highlighted the potential of some of the technologies used that could be
included in further works required to develop a robust non-invasive platform for phenotyping
postharvest quality traits in tomato and strawberry fruits.
34
6. RECOMMENDATIONS
Further studies in developing a robust platform for non-invasive phenotyping of postharvest
quality traits should include the following elements:
- Design an experimental setup that includes a large variation in postharvest quality traits,
for instance, by selecting a large collection of diverse genotypes that are interesting for
breeders to select for.
- Use a large amount of samples for the calibration and the validation set. The samples
used for calibration should not be used for validation in order to avoid overfitting.
- Perform destructive and non-destructive determinations in individual fruits, in which
each non-invasive data is linked to the invasive one.
- Find a proper reference/destructive method for the determination of firmness in
individual tomatoes and strawberries. Use literature review and/or experts consultancy
to define the most appropriate method and also the most appropriate textural
components determining firmness in these fruits. Perform a pilot experiment to check
the performance of the reference method.
- Use a reliable reference/destructive method for the determination of acidity in
individual tomato and strawberry fruits. The detection method of the devise used in this
study is largely affected if some debris remains in the juice after centrifugation.
- Determine individual sugars, organic acids, pigments and volatiles concentration by
reference/destructive methods in individual fruits if it is possible.
- Use few non-invasive technologies for phenotyping in order to not affect quality traits
due to the continuous/exhaustive handling.
- Develop a proper prediction model using robust data mining tools to select those
features/wavelengths that possess high discrimination power for screening postharvest
quality traits within a large and diverse collection of tomato and strawberry genotypes.
35
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39
Appendix 1.
a. Tomato firmness. Texture analyzer setup.
b. Strawberry firmness. Texture analyzer setup.
Trigger threshold 100 gram
Forward soeed 30 mm/sec
Reverse speed 40 mm/sec
Measure speed 6 mm/sec
Measure distance 5 mm
Reverse increment 20 mm
Calibration weight 2500 gram
Max. Distance 140 mm
Max. Load 15000 gram
Step Ratio 24695.77
Mass Ratio 823.57934
Frequency 147456
Trigger threshold 100 gram
Forward soeed 30 mm/sec
Reverse speed 40 mm/sec
Measure speed 5 mm/sec
Measure distance 10 mm
Reverse increment 20 mm
Calibration weight 2500 gram
Max. Distance 140 mm
Max. Load 15000 gram
Step Ratio 24695.77
Mass Ratio 823.57934
Frequency 147456
40
Appendix 2. Results of one way ANOVA and LSD test for genotype and storage day factors on
quality traits determined in tomatoes.
Table 1a.
Table 1b.
Table 2a.
Table 2b.
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and SSC variable Means ± Standard deviation **
Variable at F -value Pr > F* Anaclaire Anaisa Brioso Campari Camarque
Day 1 28.92 0.001 5.5 ± 0.1 c 6.6 ± 0.2 a 5.7 ± 0.1 bc 5.8 ± 0.1 b 5.8 ± 0.1 b
Day 4 5.489 0.045 5.8 ± 0.1 b 6.4 ± 0.4 a 5.6 ± 0.0 b 6.1 ± 0.1 ab 5.7 ± 0.1 b
Day 8 4.713 0.060 5.4 ± 0.1 b 5.9 ± 0.1 a 5.4 ± 0.1 b 5.7 ± 0.1 ab 5.7 ± 0.1 ab
Day 11 10.572 0.012 5.5 ± 0.1 b 6.0 ± 0.0 a 5.4 ± 0.0 b 5.5 ± 0.1 b 5.5 ± 0.2 b
Day 15 9.845 0.014 5.6 ± 0.1 b 6.8 ± 0.2 a 5.7 ± 0.2 b 5.7 ± 0.3 b 5.9 ± 0.1 b
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and SSC variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 8 Day 11 Day 15
Anaclaire 3.689 0.092 5.5 ± 0.1 b 5.8 ± 0.1 a 5.4 ± 0.1 b 5.5 ± 0.1 b 5.6 ± 0.1 ab
Anaisa 5.434 0.046 6.6 ± 0.2 ab 6.4 ± 0.4 abc 5.9 ± 0.1 c 6.0 ± 0.0 bc 6.8 ± 0.2 a
Brioso 2.433 0.178 5.7 ± 0.1 a 5.6 ± 0.0 a 5.4 ± 0.1 a 5.4 ± 0.0 a 5.7 ± 0.2 a
Campari 3.914 0.083 5.8 ± 0.1 ab 6.1 ± 0.1 a 5.7 ± 0.1 ab 5.5 ± 0.1 b 5.7 ± 0.3 ab
Camarque 2.375 0.184 5.8 ± 0.1 ab 5.7 ± 0.1 ab 5.7 ± 0.1 ab 5.5 ± 0.2 b 5.9 ± 0.1 a
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and acidity variable Means ± Standard deviation **
Variable at F -value Pr > F* Anaclaire Anaisa Brioso Campari Camarque
Day 1 9.242 0.016 7.9 ± 1.0 a 7.9 ± 0.6 a 5.7 ± 0.3 b 6.3 ± 0.4 b 5.8 ± 0.0 b
Day 4 2.904 0.137 7.9 ± 1.2 a 6.8 ± 1.1 ab 5.6 ± 0.2 b 6.7 ± 0.1 ab 6.6 ± 0.0 ab
Day 8 5.139 0.051 7.3 ± 0.3 a 7.5 ± 0.8 a 5.8 ± 0.6 b 7.4 ± 0.2 a 6.3 ± 0.2 ab
Day 11 3.574 0.097 6.9 ± 0.2 ab 6.5 ± 0.4 ab 6.1 ± 0.2 b 7.1 ± 0.7 a 6.0 ± 0.0 b
Day 15 2.654 0.157 7.5 ± 0.1 ab 6.9 ± 0.1 ab 6.8 ± 0.1 b 7.2 ± 0.8 ab 7.8 ± 0.1 a
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and acidity variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 8 Day 11 Day 15
Anaclaire 0.752 0.597 7.9 ± 1.0 a 7.9 ± 1.2 a 7.3 ± 0.3 a 6.9 ± 0.2 a 7.5 ± 0.1 a
Anaisa 1.492 0.331 7.9 ± 0.6 a 6.8 ± 1.1 a 7.5 ± 0.8 a 6.5 ± 0.4 a 6.9 ± 0.1 a
Brioso 4.234 0.073 5.7 ± 0.3 b 5.6 ± 0.2 b 5.8 ± 0.6 b 6.1 ± 0.2 ab 6.8 ± 0.1 a
Campari 1.362 0.365 6.3 ± 0.4 a 6.7 ± 0.1 a 7.4 ± 0.2 a 7.1 ± 0.7 a 7.2 ± 0.8 a
Camarque 91.372 < 0.001 5.8 ± 0.0 d 6.6 ± 0.2 b 6.3 ± 0.2 c 6.0 ± 0.0 cd 7.8 ± 0.1 a
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
41
Table 3a.
Table 3b.
Table 4a.
Table 4b.
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and fimness (compression) variable Means ± Standard deviation **
Variable at F -value Pr > F* Anaclaire Anaisa Brioso Campari Camarque
Day 1 8.173 < 0.001 2.2 ± 0.2 a 1.8 ± 0.1 c 2.0 ± 0.3 b 2.2 ± 0.1 a 2.1 ± 0.1 ab
Day 4 11.333 < 0.001 2.2 ± 0.2 a 1.8 ± 0.2 b 2.2 ± 0.1 a 2.2 ± 0.2 a 2.3 ± 0.2 a
Day 8 12.652 < 0.001 2.2 ± 0.1 ab 1.9 ± 0.2 d 2.1 ± 0.1 bc 2.3 ± 0.1 a 2.0 ± 0.1 cd
Day 11 10.473 < 0.001 2.1 ± 0.1 a 1.9 ± 0.1 b 2.1 ± 0.1 a 2.2 ± 0.1 a 2.2 ± 0.2 a
Day 15 9.026 < 0.001 2.2 ± 0.1 a 1.8 ± 0.1 c 2.0 ± 0.2 b 2.0 ± 0.1 b 2.0 ± 0.1 ab
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and fimness (compression) variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 8 Day 11 Day 15
Anaclaire 0.280 0.889 2.2 ± 0.2 a 2.2 ± 0.2 a 2.2 ± 0.1 a 2.1 ± 0.1 a 2.2 ± 0.1 a
Anaisa 0.653 0.628 1.8 ± 0.1 a 1.8 ± 0.2 a 1.9 ± 0.2 a 1.9 ± 0.1 a 1.8 ± 0.1 a
Brioso 3.291 0.02 2.0 ± 0.3 bc 2.2 ± 0.1 a 2.1 ± 0.1 ab 2.1 ± 0.1 abc 2.0 ± 0.2 c
Campari 5.609 0.001 2.2 ± 0.1 a 2.2 ± 0.2 a 2.3 ± 0.1 a 2.2 ± 0.1 a 2.0 ± 0.1 b
Camarque 4.607 0.004 2.1 ± 0.1 bc 2.3 ± 0.2 a 2.0 ± 0.1 c 2.2 ± 0.2 ab 2.0 ± 0.1 c
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and fimness (acoustic) variable Means ± Standard deviation **
Variable at F -value Pr > F* Anaclaire Anaisa Brioso Campari Camarque
Day 1 4.125 0.007 2.7 ± 0.2 b 2.6 ± 0.3 b 2.8 ± 0.4 ab 3.0 ± 0.3 a 2.6 ± 0.2 b
Day 4 1.954 0.120 2.3 ± 0.4 b 2.3 ± 0.1 b 2.6 ± 0.2 a 2.3 ± 0.2 b 2.3 ± 0.2 b
Day 8 4.105 0.007 2.2 ± 0.2 ab 2.0 ± 0.1 c 2.2 ± 0.2 ab 2.3 ± 0.1 a 2.0 ± 0.1 bc
Day 11 2.054 0.105 2.1 ± 0.3 a 2.0 ± 0.1 b 2.1 ± 0.1 ab 2.0 ± 0.2 b 2.1 ± 0.1 ab
Day 15 0.999 0.420 2.1 ± 0.2 a 2.0 ± 0.2 a 2.1 ± 0.3 a 2.2 ± 0.1 a 2.1 ± 0.3 a
* p -Value, level of significance α
** Means in the same row with the same letter are not s ignificantly di fferent
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and fimness (acoustic) variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 8 Day 11 Day 15
Anaclaire 5.041 0.002 2.7 ± 0.2 a 2.3 ± 0.4 b 2.2 ± 0.2 b 2.1 ± 0.3 b 2.1 ± 0.2 b
Anaisa 21.511 < 0.001 2.6 ± 0.3 a 2.3 ± 0.1 b 2.0 ± 0.1 c 2.0 ± 0.1 c 2.0 ± 0.2 c
Brioso 11.560 < 0.001 2.8 ± 0.4 a 2.6 ± 0.2 a 2.2 ± 0.2 b 2.1 ± 0.1 b 2.1 ± 0.3 b
Campari 31.082 < 0.001 3.0 ± 0.3 a 2.3 ± 0.2 b 2.3 ± 0.1 b 2.0 ± 0.2 c 2.2 ± 0.1 b
Camarque 10.625 < 0.001 2.6 ± 0.2 a 2.3 ± 0.2 b 2.0 ± 0.1 c 2.1 ± 0.3 c 2.1 ± 0.3 bc
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
42
Table 5a.
Table 5b.
Table 6a.
Table 6b.
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and photosynthesis efficiency (Fv/Fm) variable Means ± Standard deviation **
Variable at F -value Pr > F* Anaclaire Anaisa Brioso Campari Camarque
Day 1 20.737 < 0.001 0.644 ± 0.01 a 0.537 ± 0.02 c 0.632 ± 0.04 ab 0.608 ± 0.04 b 0.638 ± 0.02 a
Day 4 15.381 < 0.001 0.626 ± 0.02 a 0.567 ± 0.02 c 0.641 ± 0.03 a 0.597 ± 0.03 b 0.643 ± 0.02 a
Day 8 12.188 < 0.001 0.598 ± 0.02 a 0.526 ± 0.03 c 0.595 ± 0.04 a 0.565 ± 0.04 b 0.599 ± 0.01 a
Day 11 19.050 < 0.001 0.581 ± 0.02 a 0.490 ± 0.04 c 0.605 ± 0.03 a 0.551 ± 0.03 b 0.588 ± 0.03 a
Day 15 10.487 < 0.001 0.546 ± 0.02 ab 0.461 ± 0.04 c 0.567 ± 0.05 a 0.522 ± 0.05 b 0.552 ± 0.02 ab
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and photosynthesis efficiency (Fv/Fm) variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 8 Day 11 Day 15
Anaclaire 44.555 < 0.001 0.644 ± 0.01 a 0.626 ± 0.02 b 0.598 ± 0.02 c 0.581 ± 0.02 c 0.546 ± 0.02 d
Anaisa 17.267 < 0.001 0.537 ± 0.02 ab 0.567 ± 0.02 a 0.526 ± 0.03 b 0.490 ± 0.04 c 0.461 ± 0.04 d
Brioso 5.300 0.002 0.632 ± 0.04 ab 0.641 ± 0.03 a 0.595 ± 0.04 bc 0.605 ± 0.03 ab 0.567 ± 0.05 c
Campari 6.149 0.001 0.608 ± 0.04 a 0.597 ± 0.03 ab 0.565 ± 0.04 bc 0.551 ± 0.03 cd 0.522 ± 0.05 d
Camarque 28.122 < 0.001 0.638 ± 0.02 a 0.643 ± 0.02 a 0.599 ± 0.01 b 0.588 ± 0.03 b 0.552 ± 0.02 c
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and maximum fluorescence (Fm) variable Means ± Standard deviation **
Variable at F -value Pr > F* Anaclaire Anaisa Brioso Campari Camarque
Day 1 4.941 0.002 6198 ± 890 a 4582 ± 399 c 5369 ± 1148 b 5690 ± 843 ab 5185 ± 528 bc
Day 4 1.978 0.116 4875 ± 499 ab 4751 ± 723 ab 5348 ± 1014 ab 4637 ± 716 b 5396 ± 578 a
Day 8 1.534 0.211 4606 ± 349 a 4211 ± 869 ab 4029 ± 665 b 3985 ± 529 b 4277 ± 405 ab
Day 11 2.579 0.052 4284 ± 502 a 3542 ± 735 b 4232 ± 444 a 3835 ± 621 ab 4218 ± 832 a
Day 15 1.595 0.194 3850 ± 402 a 3254 ± 457 b 3799 ± 764 ab 3464 ± 626 ab 3523 ± 460 ab
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and maximum fluorescence (Fm) variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 8 Day 11 Day 15
Anaclaire 22.014 < 0.001 6198 ± 890 a 4875 ± 499 b 4606 ± 349 bc 4284 ± 502 cd 3850 ± 402 d
Anaisa 8.854 < 0.001 4582 ± 399 a 4751 ± 723 a 4211 ± 869 a 3542 ± 735 b 3254 ± 457 b
Brioso 7.122 < 0.001 5369 ± 1148 a 5348 ± 1014 a 4029 ± 665 b 4232 ± 444 b 3799 ± 764 b
Campari 12.374 < 0.001 5690 ± 843 a 4637 ± 716 b 3985 ± 529 bc 3835 ± 621 c 3464 ± 626 c
Camarque 15.414 < 0.001 5185 ± 528 a 5396 ± 578 a 4277 ± 405 b 4218 ± 832 b 3523 ± 460 c
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
43
Table 7a.
Table 7b.
Table 8a.
Table 8b.
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and chlorophyll index variable Means ± Standard deviation **
Variable at F -value Pr > F* Anaclaire Anaisa Brioso Campari Camarque
Day 1 7.619 < 0.001 0.113 ± 0.02 a 0.065 ± 0.01 c 0.091 ± 0.03 b 0.086 ± 0.02 b 0.095 ± 0.01 ab
Day 4 5.565 0.001 0.095 ± 0.01 a 0.069 ± 0.02 c 0.090 ± 0.03 ab 0.075 ± 0.02 bc 0.103 ± 0.01 a
Day 8 8.413 < 0.001 0.087 ± 0.01 a 0.061 ± 0.01 b 0.069 ± 0.02 b 0.065 ± 0.02 b 0.093 ± 0.01 a
Day 11 15.794 < 0.001 0.091 ± 0.01 a 0.048 ± 0.01 c 0.079 ± 0.02 a 0.062 ± 0.01 b 0.085 ± 0.01 a
Day 15 8.811 < 0.001 0.078 ± 0.01 a 0.047 ± 0.01 c 0.065 ± 0.02 ab 0.060 ± 0.01 b 0.077 ± 0.01 a
* p -Value, level of significance α
** Means in the same row with the same letter are not s ignificantly di fferent
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and chlorophyll index variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 8 Day 11 Day 15
Anaclaire 10.098 < 0.001 0.113 ± 0.02 a 0.095 ± 0.01 b 0.087 ± 0.01 bc 0.091 ± 0.01 b 0.078 ± 0.01 c
Anaisa 5.600 0.001 0.065 ± 0.01 a 0.069 ± 0.02 a 0.061 ± 0.01 a 0.048 ± 0.01 b 0.047 ± 0.01 b
Brioso 2.408 0.065 0.091 ± 0.03 a 0.090 ± 0.03 a 0.069 ± 0.02 ab 0.079 ± 0.02 ab 0.065 ± 0.02 b
Campari 3.152 0.024 0.086 ± 0.02 a 0.075 ± 0.02 ab 0.065 ± 0.02 b 0.062 ± 0.01 b 0.060 ± 0.01 b
Camarque 7.500 < 0.001 0.095 ± 0.01 ab 0.103 ± 0.01 a 0.093 ± 0.01 ab 0.085 ± 0.01 bc 0.077 ± 0.01 c
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and β-carotene content variable Means ± Standard deviation **
Variable at F -value Pr > F* Anaclaire Anaisa Brioso Campari Camarque
Day 1 3.164 0.119 0.36 ± 0.04 a 0.58 ± 0.00 a 0.41 ± 0.01 a 0.47 ± 0.13 a 0.51 ± 0.05 a
Day 4 1.175 0.422 0.45 ± 0.03 a 0.47 ± 0.01 a 0.57 ± 0.13 a 0.54 ± 0.04 a 0.43 ± 0.11 a
Day 8 1.269 0.392 0.47 ± 0.22 a 0.63 ± 0.03 a 0.69 ± 0.18 a 0.57 ± 0.03 a 0.44 ± 0.02 a
Day 11 0.994 0.488 0.48 ± 0.09 a 0.46 ± 0.05 a 0.68 ± 0.17 a 0.46 ± 0.02 a 0.41 ± 0.21 a
Day 15 0.453 0.769 0.54 ± 0.00 a 0.63 ± 0.08 a 0.60 ± 0.12 a 0.57 ± 0.11 a 0.54 ± 0.03 a
* p -Value, level of significance α
** Means in the same row with the same letter are not s ignificantly di fferent
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and β-carotene content variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 8 Day 11 Day 15
Anaclaire 0.733 0.607 0.36 ± 0.04 a 0.45 ± 0.03 a 0.47 ± 0.22 a 0.48 ± 0.09 a 0.54 ± 0.00 a
Anaisa 7.377 0.025 0.58 ± 0.00 a 0.47 ± 0.01 b 0.63 ± 0.03 a 0.46 ± 0.05 b 0.63 ± 0.08 a
Brioso 1.736 0.278 0.41 ± 0.01 a 0.57 ± 0.13 a 0.69 ± 0.18 a 0.68 ± 0.17 a 0.60 ± 0.12 a
Campari 0.844 0.553 0.47 ± 0.13 a 0.54 ± 0.04 a 0.57 ± 0.03 a 0.46 ± 0.02 a 0.57 ± 0.11 a
Camarque 0.536 0.717 0.51 ± 0.05 a 0.43 ± 0.11 a 0.44 ± 0.02 a 0.41 ± 0.21 a 0.54 ± 0.03 a
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
44
Table 9a.
Table 9b.
Table 10a.
Table 10b.
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and lycopene content variable Means ± Standard deviation **
Variable at F -value Pr > F* Anaclaire Anaisa Brioso Campari Camarque
Day 1 0.314 0.858 0.16 ± 0.01 a 0.15 ± 0.02 a 0.26 ± 0.18 a 0.21 ± 0.10 a 0.18 ± 0.07 a
Day 4 3.253 0.114 0.31 ± 0.11 a 0.28 ± 0.02 a 0.30 ± 0.01 a 0.22 ± 0.04 a 0.18 ± 0.01 a
Day 8 0.762 0.592 0.36 ± 0.08 a 0.32 ± 0.11 a 0.21 ± 0.18 a 0.20 ± 0.07 a 0.20 ± 0.09 a
Day 11 1.035 0.472 0.14 ± 0.03 a 0.24 ± 0.09 a 0.27 ± 0.13 a 0.18 ± 0.04 a 0.29 ± 0.14 a
Day 15 0.598 0.680 0.31 ± 0.03 a 0.28 ± 0.08 a 0.22 ± 0.04 a 0.20 ± 0.05 a 0.28 ± 0.15 a
* p -Value, level of significance α
** Means in the same row with the same letter are not s ignificantly di fferent
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and lycopene content variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 8 Day 11 Day 15
Anaclaire 7.456 0.025 0.16 ± 0.01 b 0.31 ± 0.11 a 0.36 ± 0.08 a 0.14 ± 0.03 b 0.31 ± 0.03 a
Anaisa 2.182 0.207 0.15 ± 0.02 a 0.28 ± 0.02 a 0.32 ± 0.11 a 0.24 ± 0.09 a 0.28 ± 0.08 a
Brioso 0.243 0.902 0.26 ± 0.18 a 0.30 ± 0.01 a 0.21 ± 0.18 a 0.27 ± 0.13 a 0.22 ± 0.04 a
Campari 0.104 0.976 0.21 ± 0.10 a 0.22 ± 0.04 a 0.20 ± 0.07 a 0.18 ± 0.04 a 0.20 ± 0.05 a
Camarque 0.498 0.740 0.18 ± 0.07 a 0.18 ± 0.01 a 0.20 ± 0.09 a 0.29 ± 0.14 a 0.28 ± 0.15 a
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and lutein content variable Means ± Standard deviation **
Variable at F -value Pr > F* Anaclaire Anaisa Brioso Campari Camarque
Day 1 0.846 0.552 0.046 ± 0.009 a 0.023 ± 0.007 a 0.032 ± 0.000 a 0.047 ± 0.031 a 0.032 ± 0.017 a
Day 4 0.658 0.647 0.028 ± 0.011 a 0.030 ± 0.003 a 0.034 ± 0.020 a 0.047 ± 0.016 a 0.043 ± 0.012 a
Day 8 0.535 0.718 0.032 ± 0.000 a 0.025 ± 0.001 a 0.041 ± 0.021 a 0.033 ± 0.006 a 0.036 ± 0.014 a
Day 11 0.486 0.748 0.032 ± 0.006 a 0.023 ± 0.023 a 0.053 ± 0.027 a 0.042 ± 0.012 a 0.039 ± 0.032 a
Day 15 0.951 0.505 0.038 ± 0.004 a 0.030 ± 0.006 a 0.031 ± 0.002 a 0.033 ± 0.006 a 0.035 ± 0.004 a
* p -Value, level of significance α
** Means in the same row with the same letter are not s ignificantly di fferent
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and lutein content variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 8 Day 11 Day 15
Anaclaire 0.644 0.655 0.046 ± 0.009 a 0.028 ± 0.011 a 0.032 ± 0.000 a 0.032 ± 0.006 a 0.038 ± 0.004 a
Anaisa 0.517 0.729 0.023 ± 0.007 a 0.030 ± 0.003 a 0.025 ± 0.001 a 0.023 ± 0.023 a 0.030 ± 0.006 a
Brioso 0.403 0.800 0.032 ± 0.000 a 0.034 ± 0.020 a 0.041 ± 0.021 a 0.053 ± 0.027 a 0.031 ± 0.002 a
Campari 0.281 0.878 0.047 ± 0.031 a 0.047 ± 0.016 a 0.033 ± 0.006 a 0.042 ± 0.012 a 0.033 ± 0.006 a
Camarque 0.117 0.971 0.032 ± 0.017 a 0.043 ± 0.012 a 0.036 ± 0.014 a 0.039 ± 0.032 a 0.035 ± 0.004 a
* p -Value, level of significance α
** Means in the same row with the same letter are not significantly different
45
Appendix 3. Results of one way ANOVA and LSD test for genotype and storage day factors on
quality traits determined in strawberries.
Table 1a.
Table 1b.
Table 2a.
Table 2b.
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and SSC variable Means ± Standard deviation **
Variable at F -value Pr > F* 1101 1102 1113 1121 1127
Day 1 47.526 < 0.001 8.9 ± 0.4 b 11.3 ± 0.4 a 9.0 ± 0.4 b 7.7 ± 0.3 c 8.2 ± 0.2 c
Day 4 28.15 < 0.001 8.7 ± 0.3 b 10.7 ± 0.4 a 8.5 ± 0.2 b 8.3 ± 0.6 bc 7.8 ± 0.1 c
Day 5 66.888 < 0.001 8.7 ± 0.2 b 10.5 ± 0.1 a 8.2 ± 0.1 c 7.6 ± 0.3 d 7.5 ± 0.4 d
Day 6 41.645 < 0.001 8.1 ± 0.4 bc 10.7 ± 0.6 a 8.6 ± 0.3 b 7.1 ± 0.3 d 7.8 ± 0.3 c
Day 7 46.439 < 0.001 8.1 ± 0.4 bc 11.2 ± 0.7 a 8.6 ± 0.3 b 7.2 ± 0.3 d 7.8 ± 0.1 c
Day 8 31.498 < 0.001 8.1 ± 0.6 b 10.7 ± 1.0 a 8.0 ± 0.2 b 6.1 ± 0.5 c 7.6 ± 0.2 b
* p -Value, level of significance α
** Means in the same row with the same letter are not s ignificantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and SSC variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 5 Day 6 Day 7 Day 8
1101 3.076 0.051 8.9 ± 0.4 a 8.7 ± 0.3 ab 8.7 ± 0.2 ab 8.1 ± 0.4 b 8.1 ± 0.4 b 8.1 ± 0.6 b
1102 0.767 0.591 11.3 ± 0.4 a 10.7 ± 0.4 a 10.5 ± 0.1 a 10.7 ± 0.6 a 11.2 ± 0.7 a 10.7 ± 1.0 a
1113 6.204 0.005 9.0 ± 0.4 a 8.5 ± 0.2 b 8.2 ± 0.1 bc 8.6 ± 0.3 ab 8.6 ± 0.3 ab 8.0 ± 0.2 c
1121 13.119 < 0.001 7.7 ± 0.3 ab 8.3 ± 0.6 a 7.6 ± 0.3 ab 7.1 ± 0.3 b 7.2 ± 0.3 b 6.1 ± 0.5 c
1127 3.355 0.040 8.2 ± 0.2 a 7.8 ± 0.1 ab 7.5 ± 0.4 b 7.8 ± 0.3 b 7.8 ± 0.1 ab 7.6 ± 0.2 b
* p -Value, level of s ignificance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and acidity variable Means ± Standard deviation **
Variable at F -value Pr > F* 1101 1102 1113 1121 1127
Day 1 20.957 < 0.001 4.1 ± 0.3 b 4.9 ± 0.3 a 4.0 ± 0.3 b 3.2 ± 0.2 c 4.0 ± 0.2 b
Day 4 11.59 0.001 4.0 ± 0.1 b 5.2 ± 0.4 a 4.5 ± 0.5 ab 3.3 ± 0.4 c 4.3 ± 0.2 b
Day 5 29.606 < 0.001 4.1 ± 0.1 b 5.0 ± 0.5 a 4.1 ± 0.2 b 3.0 ± 0.2 c 4.2 ± 0.2 b
Day 6 61.664 < 0.001 3.6 ± 0.1 b 4.7 ± 0.3 a 3.8 ± 0.2 b 2.8 ± 0.1 c 3.7 ± 0.1 b
Day 7 39.491 < 0.001 3.9 ± 0.2 bc 4.8 ± 0.3 a 3.4 ± 0.2 c 2.5 ± 0.3 d 3.9 ± 0.2 b
Day 8 52.662 < 0.001 3.5 ± 0.3 b 4.9 ± 0.2 a 3.5 ± 0.2 b 2.7 ± 0.2 c 3.4 ± 0.1 b
* p -Value, level of significance α
** Means in the same row with the same letter are not s ignificantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and acidity variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 5 Day 6 Day 7 Day 8
1101 6.453 0.004 4.1 ± 0.3 a 4.0 ± 0.1 a 4.1 ± 0.1 a 3.6 ± 0.1 bc 3.9 ± 0.2 ab 3.5 ± 0.3 c
1102 0.61 0.694 4.9 ± 0.3 a 5.2 ± 0.4 a 5.0 ± 0.5 a 4.7 ± 0.3 a 4.8 ± 0.3 a 4.9 ± 0.2 a
1113 7.995 0.002 4.0 ± 0.3 b 4.5 ± 0.5 a 4.1 ± 0.2 ab 3.8 ± 0.2 bc 3.4 ± 0.2 c 3.5 ± 0.2 c
1121 4.773 0.012 3.2 ± 0.2 ab 3.3 ± 0.4 a 3.0 ± 0.2 abc 2.8 ± 0.1 bcd 2.5 ± 0.3 d 2.7 ± 0.2 cd
1127 18.427 < 0.001 4.0 ± 0.2 bc 4.3 ± 0.2 a 4.2 ± 0.2 ab 3.7 ± 0.1 d 3.9 ± 0.2 cd 3.4 ± 0.1 e
* p -Value, level of s ignificance α
** Means in the same row with the same letter are not significantly different
46
Table 3a.
Table 3b.
Table 4a.
Table 4b.
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and fimness (puncture) variable Means ± Standard deviation **
Variable at F -value Pr > F* 1101 1102 1113 1121 1127
Day 1 28.938 < 0.001 0.49 ± 0.10 b 0.36 ± 0.05 c 0.64 ± 0.09 a 0.59 ± 0.10 a 0.47 ± 0.06 b
Day 4 30.762 < 0.001 0.58 ± 0.07 c 0.44 ± 0.10 d 0.79 ± 0.11 a 0.69 ± 0.10 b 0.59 ± 0.07 c
Day 5 29.007 < 0.001 0.59 ± 0.08 b 0.45 ± 0.11 c 0.82 ± 0.09 a 0.66 ± 0.09 b 0.62 ± 0.09 b
Day 6 27.276 < 0.001 0.54 ± 0.09 c 0.40 ± 0.06 d 0.74 ± 0.14 a 0.67 ± 0.13 ab 0.59 ± 0.09 bc
Day 7 8.712 < 0.001 0.55 ± 0.10 ab 0.40 ± 0.12 c 0.48 ± 0.14 bc 0.67 ± 0.20 a 0.50 ± 0.07 b
Day 8 8.883 < 0.001 0.47 ± 0.14 bc 0.43 ± 0.07 b 0.44 ± 0.13 c 0.78 ± 0.21 a 0.54 ± 0.10 b
* p -Value, level of significance α
** Means in the same row with the same letter are not s ignificantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and fimness (puncture) variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 5 Day 6 Day 7 Day 8
1101 3.779 0.004 0.49 ± 0.10 bc 0.58 ± 0.07 a 0.59 ± 0.08 a 0.54 ± 0.09 ab 0.55 ± 0.10 ab 0.47 ± 0.14 c
1102 4.402 0.001 0.36 ± 0.05 b 0.44 ± 0.10 ab 0.45 ± 0.11 a 0.40 ± 0.06 ab 0.40 ± 0.12 ab 0.43 ± 0.07 c
1113 29.674 < 0.001 0.64 ± 0.09 b 0.79 ± 0.11 a 0.82 ± 0.09 a 0.74 ± 0.14 ab 0.48 ± 0.14 c 0.44 ± 0.13 c
1121 2.533 0.035 0.59 ± 0.10 b 0.69 ± 0.10 a 0.66 ± 0.09 ab 0.67 ± 0.13 ab 0.67 ± 0.20 b 0.78 ± 0.21 a
1127 7.797 < 0.001 0.47 ± 0.06 d 0.59 ± 0.07 ab 0.62 ± 0.09 a 0.59 ± 0.09 ab 0.50 ± 0.07 cd 0.54 ± 0.10 bc
* p -Value, level of s ignificance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and photosynthesis efficiency (Fv/Fm) variable Means ± Standard deviation **
Variable at F -value Pr > F* 1101 1102 1113 1121 1127
Day 1 6.291 < 0.001 0.408 ± 0.09 c 0.507 ± 0.04 a 0.477 ± 0.05 a 0.491 ± 0.05 ab 0.455 ± 0.05 b
Day 4 9.529 < 0.001 0.340 ± 0.06 c 0.431 ± 0.05 a 0.395 ± 0.05 ab 0.437 ± 0.05 a 0.364 ± 0.05 bc
Day 5 25.555 < 0.001 0.238 ± 0.06 d 0.380 ± 0.03 a 0.332 ± 0.04 b 0.356 ± 0.03 ab 0.292 ± 0.04 c
Day 6 53.909 < 0.001 0.138 ± 0.04 d 0.320 ± 0.04 a 0.253 ± 0.03 b 0.292 ± 0.03 a 0.214 ± 0.05 c
Day 7 112.115 < 0.001 0.082 ± 0.01 e 0.264 ± 0.04 a 0.162 ± 0.04 c 0.210 ± 0.03 b 0.143 ± 0.03 d
Day 8 167.652 < 0.001 0.057 ± 0.01 e 0.229 ± 0.03 a 0.128 ± 0.02 c 0.158 ± 0.03 b 0.121 ± 0.01 d
* p -Value, level of s ignificance α
** Means in the same row with the same letter are not s ignificantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and photosynthesis efficiency (Fv/Fm) variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 5 Day 6 Day 7 Day 8
1101 186.346 < 0.001 0.408 ± 0.09 a 0.340 ± 0.06 b 0.238 ± 0.06 c 0.138 ± 0.04 d 0.082 ± 0.01 e 0.057 ± 0.01 f
1102 107.939 < 0.001 0.507 ± 0.04 a 0.431 ± 0.05 b 0.380 ± 0.03 c 0.320 ± 0.04 d 0.264 ± 0.04 e 0.229 ± 0.03 f
1113 196.461 < 0.001 0.477 ± 0.05 a 0.395 ± 0.05 b 0.332 ± 0.04 c 0.253 ± 0.03 d 0.162 ± 0.04 e 0.128 ± 0.02 f
1121 189.593 < 0.001 0.491 ± 0.05 a 0.437 ± 0.05 b 0.356 ± 0.03 c 0.292 ± 0.03 d 0.210 ± 0.03 e 0.158 ± 0.03 f
1127 172.958 < 0.001 0.455 ± 0.05 a 0.364 ± 0.05 b 0.292 ± 0.04 c 0.214 ± 0.05 d 0.143 ± 0.03 e 0.121 ± 0.01 f
* p -Value, level of s ignificance α
** Means in the same row with the same letter are not significantly di fferent
47
Table 5a.
Table 5b.
Table 6a.
Table 6b.
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and maximum fluorescence (Fm) variable Means ± Standard deviation **
Variable at F -value Pr > F* 1101 1102 1113 1121 1127
Day 1 32.793 < 0.001 7458 ± 1816 a 4630 ± 913 c 5669 ± 1089 b 3638 ± 965 d 3344 ± 629 d
Day 4 61.111 < 0.001 8356 ± 1514 a 4490 ± 1034 c 5735 ± 1346 b 3279 ± 673 d 2994 ± 560 d
Day 5 63.08 < 0.001 6826 ± 1754 a 3475 ± 577 c 5417 ± 1180 b 2711 ± 503 d 2535 ± 292 d
Day 6 79.159 < 0.001 6517 ± 1412 a 2968 ± 531 c 4871 ± 968 b 2455 ± 503 d 2230 ± 320 d
Day 7 78.504 < 0.001 5820 ± 1501 a 2673 ± 499 c 4004 ± 589 b 2026 ± 457 d 1991 ± 282 d
Day 8 129.715 < 0.001 5754 ± 1266 a 1977 ± 372 c 3529 ± 694 b 1761 ± 317 c 1746 ± 177 c
* p -Value, level of s ignificance α
** Means in the same row with the same letter are not s ignificantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and maximum fluorescence (Fm) variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 5 Day 6 Day 7 Day 8
1101 5.207 < 0.001 7458 ± 1816 ab 8356 ± 1514 a 6826 ± 1754 bc 6517 ± 1412 bc 5820 ± 1501 c 5754 ± 1266 c
1102 43.726 < 0.001 4630 ± 913 a 4490 ± 1034 a 3475 ± 577 b 2968 ± 531 c 2673 ± 499 c 1977 ± 372 d
1113 14.112 < 0.001 5669 ± 1089 a 5735 ± 1346 a 5417 ± 1180 ab 4871 ± 968 b 4004 ± 589 c 3529 ± 694 c
1121 27.391 < 0.001 3638 ± 965 a 3279 ± 673 a 2711 ± 503 b 2455 ± 503 b 2026 ± 457 c 1761 ± 317 c
1127 37.400 < 0.001 3344 ± 629 a 2994 ± 560 a 2535 ± 292 b 2230 ± 320 c 1991 ± 282 d 1746 ± 177 e
* p -Value, level of s ignificance α
** Means in the same row with the same letter are not significantly di fferent
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and chlorophyll index variable Means ± Standard deviation **
Variable at F -value Pr > F* 1101 1102 1113 1121 1127
Day 1 8.173 < 0.001 0.152 ± 0.03 a 0.144 ± 0.02 a 0.146 ± 0.03 a 0.125 ± 0.02 b 0.116 ± 0.01 b
Day 4 12.837 < 0.001 0.161 ± 0.02 a 0.137 ± 0.02 bc 0.140 ± 0.03 b 0.123 ± 0.02 c 0.108 ± 0.01 d
Day 5 11.211 < 0.001 0.146 ± 0.03 a 0.128 ± 0.02 b 0.127 ± 0.02 b 0.119 ± 0.02 d 0.102 ± 0.01 c
Day 6 5.082 0.001 0.133 ± 0.02 a 0.139 ± 0.02 a 0.117 ± 0.02 b 0.123 ± 0.02 ab 0.112 ± 0.01 b
Day 7 15.979 < 0.001 0.133 ± 0.02 a 0.135 ± 0.02 a 0.099 ± 0.01 b 0.135 ± 0.02 a 0.108 ± 0.02 b
Day 8 19.247 < 0.001 0.130 ± 0.01 b 0.149 ± 0.02 a 0.102 ± 0.01 c 0.127 ± 0.02 b 0.112 ± 0.01 c
* p -Value, level of significance α
** Means in the same row with the same letter are not s ignificantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and chlorophyll index variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 5 Day 6 Day 7 Day 8
1101 5.224 < 0.001 0.152 ± 0.03 a 0.161 ± 0.02 a 0.146 ± 0.03 ab 0.133 ± 0.02 bc 0.133 ± 0.02 bc 0.130 ± 0.01 c
1102 2.155 0.067 0.144 ± 0.02 a 0.137 ± 0.02 ab 0.128 ± 0.02 b 0.139 ± 0.02 ab 0.135 ± 0.02 ab 0.149 ± 0.02 a
1113 13.204 < 0.001 0.146 ± 0.03 a 0.140 ± 0.03 ab 0.127 ± 0.02 bc 0.117 ± 0.02 c 0.099 ± 0.01 d 0.102 ± 0.01 d
1121 1.008 0.418 0.125 ± 0.02 ab 0.123 ± 0.02 ab 0.119 ± 0.02 b 0.123 ± 0.02 ab 0.135 ± 0.02 a 0.127 ± 0.02 ab
1127 2.175 0.065 0.116 ± 0.01 a 0.108 ± 0.01 ab 0.102 ± 0.01 b 0.112 ± 0.01 a 0.108 ± 0.02 ab 0.112 ± 0.01 a
* p -Value, level of s ignificance α
** Means in the same row with the same letter are not significantly different
48
Table 7a.
Table 7b.
Table 8a.
Table 8b.
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and NDVI variable Means ± Standard deviation **
Variable at F -value Pr > F* 1101 1102 1113 1121 1127
Day 1 6.020 < 0.001 -0.23 ± 0.09 a -0.26 ± 0.06 ab -0.30 ± 0.10 b -0.36 ± 0.04 c -0.28 ± 0.11 ab
Day 4 7.520 < 0.001 -0.21 ± 0.06 a -0.26 ± 0.11 ab -0.32 ± 0.09 c -0.34 ± 0.06 c -0.3 ± 0.03 bc
Day 5 2.722 0.036 -0.26 ± 0.08 a -0.28 ± 0.10 ab -0.34 ± 0.07 c -0.33 ± 0.08 bc -0.31 ± 0.05 abc
Day 6 12.356 < 0.001 -0.26 ± 0.06 a -0.27 ± 0.06 ab -0.39 ± 0.06 d -0.31 ± 0.06 bc -0.32 ± 0.05 c
Day 7 13.709 < 0.001 -0.29 ± 0.05 a -0.32 ± 0.06 ab -0.42 ± 0.04 c -0.31 ± 0.04 a -0.35 ± 0.07 b
Day 8 15.958 < 0.001 -0.29 ± 0.06 a -0.25 ± 0.08 a -0.41 ± 0.04 c -0.34 ± 0.07 b -0.34 ± 0.06 b
* p -Value, level of significance α
** Means in the same row with the same letter are not s ignificantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and NDVI variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 5 Day 6 Day 7 Day 8
1101 3.251 0.01 -0.23 ± 0.09 ab -0.21 ± 0.06 a -0.26 ± 0.08 bc -0.26 ± 0.06 bc -0.29 ± 0.05 c -0.29 ± 0.06 c
1102 1.778 0.126 -0.26 ± 0.06 a -0.26 ± 0.11 a -0.28 ± 0.10 ab -0.27 ± 0.06 a -0.32 ± 0.06 b -0.25 ± 0.08 a
1113 8.477 < 0.001 -0.30 ± 0.10 a -0.32 ± 0.09a -0.34 ± 0.07a -0.39 ± 0.06 b -0.42 ± 0.04 b -0.41 ± 0.04b
1121 1.68 0.148 -0.36 ± 0.04 b -0.34 ± 0.06 ab -0.33 ± 0.08 ab -0.31 ± 0.06 a -0.31 ± 0.04 a -0.34 ± 0.07 ab
1127 2.566 0.033 -0.28 ± 0.11a -0.3 ± 0.03ab -0.31 ± 0.05 ab -0.32 ± 0.05 abc -0.35 ± 0.07 c -0.34 ± 0.06 bc
* p -Value, level of s ignificance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor LSD test, α = 5%
and NAI variable Means ± Standard deviation **
Variable at F -value Pr > F* 1101 1102 1113 1121 1127
Day 1 4.225 0.004 0.78 ± 0.02 b 0.78 ± 0.02 b 0.80 ± 0.01 a 0.81 ± 0.02 a 0.80 ± 0.02 ab
Day 4 3.084 0.021 0.81 ± 0.02 a 0.80 ± 0.02 ab 0.80 ± 0.01 ab 0.81 ± 0.02 a 0.79 ± 0.02 b
Day 5 0.632 0.641 0.79 ± 0.02 a 0.79 ± 0.02 a 0.80 ± 0.02 a 0.80 ± 0.04 a 0.79 ± 0.03 a
Day 6 1.626 0.177 0.80 ± 0.02 ab 0.80 ± 0.01 ab 0.79 ± 0.02 a 0.80 ± 0.02 ab 0.80 ± 0.02 ab
Day 7 3.804 0.007 0.79 ± 0.02 ab 0.80 ± 0.02 ab 0.78 ± 0.02 b 0.81 ± 0.02 a 0.78 ± 0.02 b
Day 8 1.187 0.324 0.79 ± 0.01 a 0.79 ± 0.02 a 0.78 ± 0.02 a 0.78 ± 0.03 a 0.79 ± 0.02 a
* p -Value, level of s ignificance α
** Means in the same row with the same letter are not significantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor LSD test, α = 5%
and NAI variable Means ± Standard deviation **
Variable in F -value Pr > F* Day 1 Day 4 Day 5 Day 6 Day 7 Day 8
1101 2.402 0.044 0.78 ± 0.02 c 0.81 ± 0.02 a 0.79 ± 0.02 abc 0.80 ± 0.02 ab 0.79 ± 0.02 abc 0.79 ± 0.01 bc
1102 1.523 0.191 0.78 ± 0.02 b 0.80 ± 0.02 a 0.79 ± 0.02 ab 0.80 ± 0.01 ab 0.80 ± 0.02 ab 0.79 ± 0.02 b
1113 5.785 < 0.001 0.80 ± 0.01 a 0.80 ± 0.01 ab 0.80 ± 0.02 ab 0.79 ± 0.02 bc 0.78 ± 0.02 c 0.78 ± 0.02 c
1121 3.661 0.005 0.81 ± 0.02 a 0.81 ± 0.02 a 0.80 ± 0.04 a 0.80 ± 0.02 a 0.81 ± 0.02 a 0.78 ± 0.03 b
1127 1.542 0.186 0.80 ± 0.02 ab 0.79 ± 0.02 ab 0.79 ± 0.03 ab 0.80 ± 0.02 a 0.78 ± 0.02 b 0.79 ± 0.02 ab
* p -Value, level of s ignificance α
** Means in the same row with the same letter are not significantly different
49
Table 9a.
Table 9b.
ANOVA Pairwise comparison
F -statistics and p -value for genotype factor and individuals LSD test, α = 5% **
anthocyanins content as variables
Variable F -value Pr > F* 1101 1102 1113 1121 1127
Pelargonidin 3-O -glucoside 8.757 < 0.001 b a b b a
Pelargonidin 3-rutinoside 7.125 0.001 b a a b b
pelargonidin 3-O-(6-O-malonyl-β-D-glucoside) 42.867 < 0.001 b a c d c
* p -Value, level of s ignificance α
** Same letter in the same row, means are not s ignificantly different
ANOVA Pairwise comparison
F -statistics and p -value for storage day factor and individuals LSD test, α = 5%
anthocyanins content as variables
Variable F -value Pr > F* Day 1 Day 8
Pelargonidin 3-O -glucoside 0.335 0.569 a a
Pelargonidin 3-rutinoside 1.775 0.198 a a
pelargonidin 3-O-(6-O-malonyl-β-D-glucoside) 0.644 0.432 a a
* p -Value, level of significance α
** Same letter in the same row, means are not significantly different