Journal of Food Composition and Analysis 23 (2010) 640–647
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Journal of Food Composition and Analysis
journal homepage: www.elsevier.com/locate/jfca
Original Article
Study on the compositional differences between transgenic and
non-transgenic papaya (Carica papaya L.)
Zhe Jiao a, Jianchao Deng a, Gongke Li a,*, Zhuomin Zhang a, Zongwei Cai b
a
b
School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China
Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 28 March 2009
Received in revised form 22 February 2010
Accepted 22 March 2010
Transgenic papaya (Carica papaya L.) was produced with the introduction of replicase (rep) gene for
resistance to papaya ringspot virus (PRSV). In order to investigate the potential unintended
compositional changes in transgenic papaya, profiles of volatile organic compounds (VOCs), sugar/
polyals, organic acids, carotenoids and alkaloids in transgenic and non-transgenic papaya were obtained
respectively by HPLC, GC–MS and LC–MS, and compared mutually by multivariate statistical methods,
including principal component analysis (PCA) and similarity analysis method. Results showed that the
composition in transgenic papayas exhibited great similarity to non-transgenic counterparts for
measured components. The contents of important nutrients of b-carotene and vitamin C and two natural
toxicants, including benzyl isothiocyanate (BITC) and carpaine, were compared by analysis of variance
(ANOVA). The results also showed that content was similar between transgenic papayas and nontransgenic counterparts for these components. The variation of composition in papaya caused by genetic
effect was slight during two harvesting times during our work. It is hoped that this study could provide
some reference value for a safety evaluation of transgenic papaya from the compositional point of view,
and could also propose a method for discrimination of transgenic food from non-transgenic
counterparts.
ß 2010 Elsevier Inc. All rights reserved.
Keywords:
Transgenic papaya
Principal component analysis
Similarity analysis
Analysis of variance
Genetically modified organism
GMO
GM plant
Safety assessment of r-DNA food
Horticulture and biodiversity
Food analysis
Food composition
1. Introduction
Papaya (Carica papaya L.) is a native fruit of tropical America,
but it is currently disseminated throughout the tropics. It is a very
popular fruit with consumers for its high content of sugar, vitamin
C and carotenoids as well as for its pleasant aromatic odor (Bari
et al., 2006). Papaya produces special compounds during the
growing process that are thought to play an important role in its
defense against pests and herbivores, such as benzyl isothiocryanate (BITC) and carpaine. These so-called natural toxicants might
have an adverse effect on human nutrition, but are generally
considered safe because they occur only in low levels, especially in
mature papayas (Roberts et al., 2008).
Papayas suffer from a serious disease caused by papaya ringspot
virus (PRSV), a major limitation to papaya production worldwide.
This virus induces plant stunting and drastically reduces the yield
and quality of fruits. Efforts to develop PRSV-resistant transgenic
papaya were initiated in 1985. The first commercialized transgenic
papayas genetically modified with coating protein were SunUp and
Rainbow in Hawaii. One PRSV-resistant papaya currently under
* Corresponding author. Tel.: +86 20 84035156; fax: +86 20 84112245.
E-mail address: [email protected] (G. Li).
0889-1575/$ – see front matter ß 2010 Elsevier Inc. All rights reserved.
doi:10.1016/j.jfca.2010.03.004
development in China was generated by cloning of the papaya
PRSV replicase (rep) gene, and then introducing the rep gene into
papaya. It has been shown to exhibit resistance to PRSV in field
trials (Chen et al., 2001).
The safety of transgenic food has always been a public concern
with respect to its potential risks to people’s health and
environment (König et al., 2004). The safety evaluation for GM
(genetically modified) food was based on the compositional
comparison of transgenic food with their traditionally cultivated
counterparts. This was termed the ‘‘Principle of Substantial
Equivalence’’, which was predominantly formalized by the
Organization for Economic Cooperation and Development (OECD)
in 1993 and further elaborated in 2000 by the Food and Agriculture
Organization of the United Nations (FAO) and the World Health
Organization (WHO). The compositions that should be studied
included the important nutrients as well as the antinutrients or
natural toxicants existing in food. Some comparative studies have
been reported for transgenic potato, papayas and rice. These
studies presented the mean and standard error of contents of every
determined component; after one-to-one comparison, good
correspondence was found between transgenic food and the
non-transgenic counterparts (Li et al., 2008a; Roberts et al., 2008;
Rogan et al., 2000). However, the reported comparison method was
considered biased for focusing on target compounds, which could
Z. Jiao et al. / Journal of Food Composition and Analysis 23 (2010) 640–647
not systematically evaluate the overall compositional differences
(Cellini et al., 2004).
In order to increase the possibility of detecting unintended
compositional changes, profiling analysis was considered a useful
alternative. The technologies commonly exploited for profiling
analysis depended on the type of compounds to be analyzed.
Several techniques including gas chromatography–mass spectrometry (GC–MS) (Roessner et al., 2001) or high performance
liquid chromatography–mass spectrometry (LC–MS) (Le Gall et al.,
2003) have often been employed for analyzing the suitable
compounds. Moreover, 1H nuclear magnetic resonance (NMR)
(Baker et al., 2006) or near-infrared reflectance spectrometry (NIR)
were also applied to rapidly screen the overall compositional
fingerprint (Xie et al., 2007). The profiling analysis should provide
the overall compositional characteristics. Thus, the suitable dataprocessing methods should be used for interpreting the entire
profile characteristics and distilling important bio-markers.
Chemometric strategies such as PCA, PLS–DA and HCA have been
applied to the study of large natural variability in the chemical
composition of foodstuffs (Jiao et al., 2010).
In the present work, the nutrient composition including sugars,
organic acids, carotenoids and natural toxicants carpaine and BITC
in transgenic papayas expressing rep gene were studied and
compared to their non-transgenic counterparts. First, the compositional profiles of volatile organic compounds (VOCs), sugar/
polyals, organic acids, carotenoids and alkaloids were obtained by
GC–MS, HPLC, LC–MS and so on, then the similarity of these
compositions in transgenic papaya and its non-transgenic
counterpart were evaluated by the similarity-analysis method
and PCA as well as analysis of variance (ANOVA). It was hoped that
the study would provide a reference value for the safety evaluation
of the transgenic papaya expressing rep gene.
2. Materials and methods
2.1. Materials and reagents
Two papaya cultivars were transformed with rep gene:
cv.MeiZhongHong (MZH), red flesh, and cv.SuiZhongHong (SZH),
yellow flesh. The transgenic papayas were grown alongside their
non-transgenic counterparts. A total of 20 non-transgenic and 20
transgenic fruits were harvested from both papaya cultivars (10
fruits were obtained in May and 10 in September); 80 samples
were prepared in total. The fruits were carefully examined for
uniformity in size and maturity. Average-sized fruits (usually
between 200 and 250 g of fresh weight per fruit) were cut in half
longitudinally and each half cut longitudinally again. All samples
(except for VOCs analysis) were frozen in liquid-nitrogen, because
it is necessary to rapidly quench enzymatic activity in the fruit. A
pre-fractionation step was adopted in which samples were divided
into aliquots, with subsequent parallel measurements for selective
analysis of different compositions in each aliquot in order to
optimize analysis and facilitate the detection of even minor
changes in a structurally diverse composition data set.
Standards of b-carotene, sugars and polyals and flavor constituents were purchased from Sigma-Aldrich (St. Louis, MO), standards
of organic acids were purchased from Tianjin Chemical Reagent Co.
(Tianjin, China), and methoxylamine hydrochloride and N-(tertbutyldimethylsilyl)-N-methyl-trifluoroacetamide (BSTFA) were
purchased from Thermo Fisher Scientific (Pittsburgh, PA). All
chemicals were of standard reagent grade unless otherwise stated.
2.2. PCR analysis
Total DNA was extracted from the leaves of putative transgenic
papaya plants using the cetyltriethylammonium bromide method.
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The reaction mixture for PCR was prepared in a PCR reaction tube.
The reaction volume of 25 mL contained 50 ng of genomic DNA,
20 mmol deoxyribonucleoside triphosphate, 1.5 mmol magnesium
chlorides, 0.6 mmol of forward and reverse primers, and 1.0 units
of AmpliTaq Gold (Applied Biosystems, Foster City, CA). The
reactions were buffered with PCR buffer II (Applied Biosystems)
and amplified in a Thermal Cycler (Biometra, Germany) according
to the following PCR step-cycle program: preincubation at 94 8C for
2 min, denaturation at 94 8C for 2 min, annealing at 50 8C for 1 min
and extension at 72 8C for 1 min. The cycle was repeated 40 times
followed by a final extension at 72 8C for 10 min. The amplified
products were analyzed by electrophoresis with 1.0% agarose gel
and transferred to UVP gel document system (Bio-Rad, USA) for
visualization.
2.3. VOCs analysis
The VOCs were analyzed by headspace solid-phase microextraction (HSSPME) coupled to GC–MS. Five commercial SPME fiber
coatings, 100 mm polydimethylsiloxane (PDMS), 65 mm polydimethylsiloxane-divinylbenzene (PDMS/DVB), 65 mm carbowaxdivnylbenzene (CW/DVB), 85 mm polyacrylate and 75 mm carboxen-polydimethylsiloxane (CAR/PDMS) were tested for extraction, and 65 mm polydimethylsiloxane-divinylbenzene (PDMS/
DVB) was shown to exhibit a better extraction efficiency for
volatiles in papaya. A 3.0 g amount of papaya pulp was
hermetically sealed in a 15 mL screw top amber vial with a
polypropylene hole cap and PTFE/silicone septa (Supelco, Bellefonte, PA, USA) and equilibrated for 50 min. Then, the SPME device
was inserted into the sealed vial by manually penetrating the
septum and the fiber was exposed to the plant material headspace
for 50 min. After sampling, the SPME was immediately inserted
into the GC injector and the fiber was thermally desorbed. A
desorption time of 3 min at 250 8C was used in splitless mode.
Before sampling, each fiber was reconditioned for 5 min in the GC
injector port at 250 8C.
Volatile compositions were determined by a Hewlett-Packard
(HP) 6890 gas chromatograph with a HP 5973 mass detector. A
desorption time of 3 min at 250 8C was used in splitless mode.
Before sampling, each fiber was reconditioned for 5 min in the GC
injector port at 250 8C. Ultra-pure helium (purity >99.999%,
constant flow: 1 mL/min, Xicheng Air Product Ltd. Co., China)
served as carrier gas with the purge flow of 80 mL/min. For
separations, a 60 m length 0.32 mm I.D. 1.8 mm film thickness
HP-VOC column (Agilent Scientific, USA) was used. The oven was
programmed at the following rates. The initial temperature of the
column was 60 8C followed by a ramp of 10 8C/min to 200 8C (5 min
hold), a second ramp of 10 8C/min to 260 8C (4 min hold), and
finally ramped to 270 8C with a post-run for 3 min. The parameters
of HP5973 mass detector were: ion mass/charge ratio, 20–550 m/z;
quadrupole temperature, 150 8C; EI source temperature, 230 8C;
interface temperature, 280 8C; electron multiplier voltage,
2518 ev; scan model, 2.57 scans/s.
2.4. Sugar/polyals analysis
Approximately 2.0 g of frozen, ground and homogenized pulp
was weighed, methanol of 35 mL was added immediately and
vortexed for 10 s to halt biological activity and minimize
degradation, then 4 mL of pure water was added followed by
extraction in microwave accelerated solvent extraction system
(CEM, USA) at 70 8C for 15 min. In order to separate polar and nonpolar compositions, 40 mL of chloroform was added to the mixture
and shaken vigorously. The supernatant layer was made up to
50 mL, and 100 mL was removed into the sample vial. After being
dried over a nitrogen flow, the derivative reaction was completed
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Z. Jiao et al. / Journal of Food Composition and Analysis 23 (2010) 640–647
with 40 mL of 20 mg/mL methoxyamine hydrochloride in imidazole at 37 8C for 40 min, followed by a 30 min treatment with 60 mL
NO-Bis(trimethylsilyl)trifluoro acetamide (BSTFA) for trimethylsilylation. A sample volume of 1 mL was injected into GC–MS for
analysis. Compositions contained in the lower phase were not
analyzed in this study.
The chromatographic separation was carried out on a GC–MSQP2010 system (Shimadzu, Kyoto, Japan) with a DB-5 coated fused
silica capillary column (30 m 0.32 mm, 0.25 mm film thickness)
(J&W Scientific, Folsom, CA). Then 1 mL of the derivative sample
was injected into GC–MS using split mode (50:1). Ultra-pure
helium (constant flow, 1.5 mL/min) served as carrier gas with the
purge flow of 3 mL/min. The oven was programmed at the
following rates. The initial temperature of the column was 70 8C
followed by a ramp of 8 8C/min to 160 8C (3 min hold), a second
ramp of 3 8C/min to 260 8C (5 min hold), and finally a ramp to
280 8C at 10 8C/min. Mass conditions were as follows: electron
impact ionization (EI); interface temperature, 250 8C; ion source
temperature, 200 8C; the detector voltage, 1 kV; solvent delay,
3 min. All data were obtained by collecting the full-scan mass
spectra within the scan range of 50–600 amu.
column (250 mm 4.6 mm, I.D. 5 mm, Dikma) and detected by a
UV detector (280 nm and 254 nm). The mobile phase consisted of
eluent A (acetonitrile) and eluent B (ammonium acetate, 0.05 mol/
L, pH 8.0). The column was eluted with a linear gradient: 12% of
solvent A (v/v) (0–5 min), 12–23% of solvent A (5–25 min), 23–30%
of solvent A (25–35 min), 30% solvent A (35–55 min) and then 30–
100% of solvent A (55–70 min). The mass spectra were obtained
with positively charged ion electrospray mode using the following
tune parameters: capillary voltage was set to 5000 V, the
temperature of drying nitrogen was maintained at 350 8C with
flow of 10 L/min, and electrospray needle voltage was 50 psi.
2.8. Validation for analytical methods
The precision for obtaining the profiles of VOCs, sugars, organic
acids, carotenoids and alkaloids was examined. Five reduplicated
analyses of the same sample solution were performed to evaluate
injection precision. The analysis precision was confirmed by five
different work solutions extracted from the same papaya plant.
The accuracy of analytical method, achieved by recovery test, was
conducted by spiking the samples with a known concentration of
these compounds.
2.5. Organic acids analysis
2.9. Statistical analysis
Approximately 2.0 g of frozen, ground and homogenized pulp
was weighed and extracted with 30 ml pure water for 20 min with
magnetic stirring. The mixture was centrifuged at 15 000 rpm for
10 min. The supernatant was recovered and made up to 50 mL.
Analysis of organic acids and carotenoids were both carried out on
LC-2010C system (Shimadzu). For organic acids, a Diamonsil C18
column (250 mm 4.6 mm, I.D. 5 mm, Dikma, Beijing, China) was
eluted with a non-linear gradient of 97% eluent A (potassium
dihydrogen phosphate, 0.05 mol/L, pH 3.0) and 3% eluent B
(acetonitrile) (v/v) over 25 min at a flow rate of 0.8 mL/min.
Data analysis was performed by original software ‘‘Chromatographic data-processing system’’ based on Matlab 6.5 (Mathworks, Natick, MA, USA). Data transformation in our study involved
centering, scaling to unit variance and log centering. The software
was specially coded for analyzing a series of chromatographic data
and evaluating the similarities of different chromatograms by PCA
and the similarity analysis (Zhang et al., 2005). The differences of
target compounds were assessed using analysis of variance
(ANOVA) by Statistical Package for Social Sciences (SPSS) version
13.0 (USA). Means and standard deviations were used throughout.
2.6. Carotenoid analysis
3. Results and discussion
Approximately 2.0 g of frozen, ground and homogenized pulp
was weighed and carotenoids were extracted by adding 30 mL of
ethanol and acetone (v/v = 1:2) containing 0.1% butylated hydroxytoluene as anti-oxidants (w/v), mixing by vortex for 20 s and
extracted for 15 min; after centrifugation (12 000 rpm) the
supernatant was recovered and residues following reduction were
redissolved and made up to 10 mL. Carotenoids were determined
using Inertsil ODS-P column (150 mm 4.6 mm, I.D. 5 mm, Dikma)
with a non-linear gradient of 90% eluent A (acetonitrile) and 10%
eluent B (methanol). The analysis time was 50 min and detection
was done at 450 nm.
3.1. Polymerase chain reaction (PCR) for identification of rep gene
The production and characterization of the transgenic lines
included in this study have been described previously (Ruan et al.,
2004). PCR experiment was conducted to validate whether the rep
gene has been integrated into the papaya genome and inherited
steadily in the transgenic papaya. As shown in Fig. 1, an
[(Fig._1)TD$IG]amplification fragment of 373 base pair (bp) was specifically
2.7. Alkaloids analysis
Extraction of alkaloids from papaya leaves was as follows. The
dried leaves were milled to fine powder with particle size of
0.38 mm (40 meshes). 20 g were extracted in microwave oven
(Sineo Microwave Chemistry Technology Company, Shanghai,
China) with a solution of 89% ethanol, 10% water and 1% acetic acid
(v/v/v). The resulting extracts were filtrated and then condensed to
give a dark green tar in rotor evaporator (Shenke Instrument,
Shanghai, China). Then 5 mL of acetic acid and 50 mL of pure water
was added and this mixture was extracted with ether to remove all
acid-insoluble material. The aqueous solution was then made basic
with 10 g of potassium carbonate and extracted with chloroform to
obtain the bases. The solution was dried, concentrated and finally
made up in 10 mL methanol solution.
The alkaloids were determined on Agilient 1100/MSD Trap XCT
HPLC–MS system. Separation was achieved on a Diamonsil C18
Fig. 1. PCR analysis for rep genes from transgenic and non-transgenic papaya. (A1)
Lane 1, DL2000 DNA Marker; lane 2, negative control; lanes 3–7, cv.MZH transgenic
papayas. (A2) Lane 1, DL2000 DNA Marker; lane 2, positive control; lanes 3–7,
cv.MZH non-transgenic non-transgenic. (B1) Lane 1, DL2000 DNA Marker; lane 2,
negative control; lanes 3–7, cv.SZH transgenic papayas. (B2) Lane 1, DL2000 DNA
Marker; lane 2, positive control; lanes 3–7, cv.SZH non-transgenic.
Z. Jiao et al. / Journal of Food Composition and Analysis 23 (2010) 640–647
detected in transgenic papaya, whereas no such amplification
fragment was yielded by PCR performed with the DNA extracted
from non-transgenic materials.
3.2. Compositional profiles of transgenic papayas and the nontransgenic counterparts
[(Fig._2)TD$IG]
3.2.1. Characteristics of VOCs
The profiles of VOCs from papaya samples showed that 14
volatiles were identified from cv.MZH and 15 volatiles identified
from cv.SZH. The detected compounds consisted of 6 main groups
according to their diverse functional groups, namely esters,
alkenes, alcohols, alkanes, aldehydes and other organic compounds. From the semi-quantitative results of volatile compounds
determined by GC–MS, the volatile compositions showed great
similarity between the transgenic and non-transgenic papayas
picked during the two harvesting times, although the contents of
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some volatile compositions were found to have changed at
different harvesting times. 3,7-Dimethyl-1,6-octadien-3-ol, BITC,
b-myrcene and 3-carene were the characteristic compounds
identified in transgenic cv.MZH and the non-transformed counterparts, while 3,7-dimethyl-1,6-octadien-3-ol, BITC, butanoic acid
methyl ester, benzoic methyl ester and methyl salicylate were
characteristic compounds in the transgenic cv.SZH and the nontransformed counterparts. In both cultivars, there were no
significant compositional differences between transgenic and
non-transgenic papayas.
3.2.2. Sugars and polyals
The profiles of sugars and polyals showed that papayas
contained 7 kinds of sugars, namely fructose, sorbose, galactose,
xylose, mannose, ribose and b-D-gluctopyranose; 3 kinds of
polyals, namely glycerol, inositol and glucitol; and gluconic acid
(Fig. 2A and B). b-D-Gluctopyranose, D-fructose, glucitol and
Fig. 2. The GC–MS profiles of sugar, polyals in papaya. (A1) cv.MZH transgenic. (A2) cv.MZH non-transgenic. (B1), cv.SZH transgenic. (B2), cv.SZH non-transgenic. 1. Glycerol,
2. 2-keto-D-gluconic acid, 3. D-fructose, 4. D-ribose, 5. sorbose, 6. b-D-gluctopyranose, 7. xylose, 8. galactose, 9. glucitol, 10. mannose, 11. inositol.
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Z. Jiao et al. / Journal of Food Composition and Analysis 23 (2010) 640–647
inositol were the primary compounds accounting for about 70% in
sugar composition, which was similar in both papaya cultivars.
Whether in cv.MZH or cv.SZH, the varieties of sugars and their
content distribution in the transgenic papaya were similar to those
found in non-transgenic counterparts at two harvesting times, but
an increase of certain sugar levels was observed in September
compared to May.
3.2.3. Organic acids and carotenoids
The profiles of organic acids and carotenoids were obtained by
HPLC. There were 8 kinds of organic acids identified, among which
vitamin C exhibited the highest concentration. b-Carotene, bcryptoxanthin, zeaxanthin and violaxanthin were the typical
carotenoids in papaya. Some organic acids and carotenoids varied
greatly in May and September and seemed non-comparable
between two cultivars, while obvious differences were not
observed between transgenic and non-transgenic papayas.
The HPLC profiles of alkaloids in transgenic and non-transgenic
papaya were obtained. Choline, pseudocarpaine, dehydrocarpaine I
and II and carpaine were reported as the main alkaloids in papaya
leaves, among which carpaine exhibited a wide range of biological
activities but if injected in overdose amounts it can lead to cardiac
arrest (Bienz et al., 2002). The alkaloids profiles also showed great
similarity in transgenic papaya and non-transgenic counterparts
within two cultivars. Although the alkaloid compositions also
changed at different harvesting times, the composition in
transgenic papayas from different harvesting times was consistently similar to the non-transgenic counterparts.
3.3. Validation of analytical methods
The reproducibility and repeatability for profiling analysis were
based on calculating the relative standard deviations (RSDs) of the
relative retention time (the ratio of peak retention time of sample
constituents to the reference component) and the relative peak
area (the ratio of peak area of sample constituents to the reference
component). The RSDs of relative retention times and relative peak
areas for replicated injection were less than 0.2% and 3% (n = 5).
The RSDs of relative retention times and the relative peak areas for
sample replicates were less than 0.2% and 7.0%, respectively. These
results indicated that the analytical methods for obtaining
compositional profiles of papaya were reliable and applicable.
Relative retention time of peaks and relative peak areas were
obtained based on the following selective components. For volatile
compositions, BITC was an important bioactive compound in
papaya responsible for the anthelmintic activity and accounted for
a high fraction among all the identified components. Therefore, it
was adopted as the reference substance. D-Fructose was chosen as
the reference substance in all identified sugars and polyals for its
high content in both papaya cultivars. Vitamin C, b-carotene were
selected as reference substances for the profiles of organic acids
and carotenoids as they were abundant nutrient components in
papaya; carpaine was a special alkaloid extracted from papaya
with a particular biological activity, and it was selected as
reference substance for the alkaloids profile.
3.4. Similarity analysis
After obtaining the compositional profiles for papaya, it was
necessary to interpret all of the profile characteristics in statistics.
Similarity analyses for profiles of VOCs, sugar/polyals, organic
acids, carotenoids and alkaloids were conducted by calculating
correlative the coefficient of original chromatographic data
between the transgenic and non-transgenic papayas. First, the
means of chromatograms for each of the two groups was
calculated, and then the similarity was analyzed through
Table 1
The similarity between transgenic papayas and non-transgenic papayas by
correlation coefficient analysis.
Compositions
Cultivar
May
September
Vocs
MZH
SZH
0.955
0.959
0.942
0.950
Sugars
MZH
SZH
0.976
0.971
0.966
0.957
Organic acids
MZH
SZH
0.956
0.967
0.903
0.925
Carotenoids
MZH
SZH
0.956
0.948
0.972
0.926
Alkaloids
MZH
SZH
0.982
0.987
0.972
0.963
comparison in terms of correlative coefficient. As seen in
Table 1, the correlation coefficients for chromatographic profiles
in transgenic papaya and non-transgenic papayas ranged from
0.903 (organic acids) to 0.987 (alkaloids), which means that the
compositions in transgenic papaya were similar to non-transgenic
papayas. A conclusion could be drawn that these compositions did
not change distinctly after genetic modification at either harvest
time (May or September). In order to distill the potential
compositions contributed to the difference of entire profile
characteristics, principal component analysis (PCA) was carried
out; we report data in the following section that facilitated the
discrimination between transgenic and non-transgenic papayas
from two different harvesting times.
3.5. Principal component analysis (PCA)
The profiles of VOCs, sugar/polyal, organic acids, carotenoids
and alkaloids from transgenic papayas and non-transgenic
counterparts harvested at two different times were analyzed by
PCA. PCA is an unsupervised clustering method (in that it does not
require any knowledge of the data set) that attempts to reduce the
dimensionality of multivariate data while preserving most of the
variance therein. If an unsupervised algorithm clusters samples
close together, then they can be objectively considered to be
similar, and if classes cannot easily be discriminated by supervised
methods, then they are objectively similar (Catchpole et al., 2005).
When PCA was performed on the compositional profiles of
different papayas, data-points from transgenic papayas and their
non-transgenic counterparts formed a tight cluster, but papayas
from different harvesting times were separated. The results were
consistent in analyzing the profiles of VOCs, sugar/polyals, organic
acids, carotenoids and alkaloids. As seen in Fig. 3, when the
compositional profiles in cv.MZH were subject to PCA, an obvious
separation was achieved by combining principal component 1
(PC1) with principal component 2 (PC2), with the first two
principal components cumulatively accounting for everything
above 60% of the variance. There was no clear separation between
non-transgenic and transgenic samples from either harvesting
time, although groups derived from two different harvest times
were separated. The results were similar in analyzing the profiles
of cv.SZH. It indicated that the compositional differences caused by
transgenic effect were less than seasonal variation.
Using the PCS approach, it was possible to find the discriminatory compounds that contributed most to the separation of distinct
clusters, which stood up for the seasonal variation. It showed that
three volatile terpenes including b-myrcene, limonene, 3-carene
contributed most to the seasonal variation in transgenic and nontransgenic cv.MZH, which was in agreement with previous studies
(Pino and Queris, 2008). It should be pointed out that terpene
[(Fig._3)TD$IG]
Z. Jiao et al. / Journal of Food Composition and Analysis 23 (2010) 640–647
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Fig. 3. Principal components analysis (PCA) scores plot for PC1 and PC2 from the PCA model of the total data set of cv.MZH coloured in 4 different ways: (A) VOCs; (B) sugars;
(C) organic acids; (D) carotenoids; (E) alkaloids. (&) Transgenic in May, ([TD$INLE] ) non-transgenic in May, (~) transgenic in September, (*) non-transgenic in September.
Z. Jiao et al. / Journal of Food Composition and Analysis 23 (2010) 640–647
646
Table 2
Content of target compounds in papayas.
Compound
cv.MZH
Content (mg/100 g) (mean SD, n = 10)
Oxalic acid
Succinic acid
Malic acid
Vitamin C
b-Carotene
BITC
Carpaineb
Referencea
cv.SZH
Content (mg/100 g) (mean SD, n = 10)
Content (mg/100 g)
T1
T2
C1
C2
T1
T2
C1
C2
12.72 1.32
13.44 0.97
32.79 2.01
57.26 4.81
0.34 0.01
0.07 0.02
1.00 0.15
7.75 0.76
10.23 0.85
27.54 1.33
63.67 4.13
0.38 0.01
0.03 0.01
0.65 0.05
12.57 0.83
13.20 1.05
33.61 2.58
56.58 4.34
0.33 0.01
0.06 0.01
0.89 0.09
7.59 0.75
9.95 0.84
26.25 1.29
61.10 4.97
0.39 0.01
0.02 0.01
0.59 0.08
6.41 0.64
25.36 1.81
16.14 1.12
40.36 4.92
0.12 0.01
0.12 0.03
1.00 0.14
3.91 0.20
20.64 0.77
13.47 1.00
47.33 3.40
0.15 0.02
0.06 0.01
0.63 0.04
6.28 0.54
26.51 1.43
15.87 1.15
39.83 4.05
0.11 0.01
0.11 0.01
0.92 0.11
3.82 0.63
21.58 0.99
12.18 1.07
46.21 4.33
0.14 0.01
0.05 0.01
0.58 0.04
4.53–17.021,2
37.911
11.271
29.4–1222,3,4
0.097–0.413
Not available
Not available
a
1: Cano et al. (1994); 2: Roberts et al. (2008); 3: Wall (2006); 4: Bari et al. (2006).
Carpaine content was relative by assuming the largest peak area as 1.0, and the remaining were achieved by comparing their own area to the largest one. T1, transgenic
papayas in May; T2, transgenic papayas in September; C1, control (non-transgenic) papayas in May; C2, control papayas in September.
b
content decreased along with the ripening process. In transgenic
and non-transgenic cv.SZH, the ester compounds such as butanoic
acid methyl ester or hexanoic acid methyl ester increased
significantly in September. The papayas were grown in a
subtropical climate, and the summer lasted from April to
September, with enduring high temperatures. Studies have shown
that the volatile compositions in both transgenic and nontransgenic papaya change significantly under increasing temperature (Almora et al., 2004). The effect of temperature on the
composition of other compounds was also observed: b-Dgluctopyranose, fructose and galactose contents increased by
the month of September, which was consistent with an earlier
study (Li et al., 2008b). The PCA results also showed that vitamin C,
malic acid and succinic acid contributed significantly to seasonal
variation among the profile of organic acids. b-carotene and
carpaine also contributed most to the carotenoids and alkaloid
content variation at different harvesting times by PCA. Although
composition varied at different harvest times, there was still great
similarity between transgenic and non-transgenic papayas.
3.6. ANOVA for compositional differences
To validate the PCA results, certain compounds that possessed
particular biological activity or nutrition value were selected for
quantitative analysis; the differences were evaluated by ANOVA.
Four organic acids, b-carotene, BITC and carpaine were selected.
The accuracy and precision of the quantitative method of analysis
was evaluated. Recoveries of the compositions analyzed performed
by spiking the samples before extraction were satisfactory,
between 91.3% and 108.4%. The precision of analysis was
confirmed by five different work solutions extracted from the
same papaya plant, for which the RSDs were <7.5%. The
quantitative results are listed in Table 2. This content is consistent
with the reported value (Cano et al., 1994; Roberts et al., 2008;
Wall, 2006; Bari et al., 2006). Comparison for the carpaine content
was made by assuming the largest peak area as 1.0, and the
remaining peaks were achieved by comparing their own peak area
to the largest one (this was done because a carpaine standard was
not available in the market). Mass spectrum of carpaine (m/
z = 479) was consistent with published reports (Bennett et al.,
2004).
Analysis of variance (ANOVA) was used to determine whether
the difference was significant. The calculation involved two
cultivars each and included 10 transgenic samples and 10 nontransgenic samples analyzed at both of the harvesting times.
Difference was not considered significant when it was below the
critical values (F0.05 = 4.41). The results showed that the calculated
F values of vitamin C, b-carotene, BITC and carpaine between
transgenic papaya and the non-transgenic counterparts were
lower than the critical F values in May and September, also in two
papaya cultivars. The results indicated that levels of vitamin C, bcarotene, BITC and carpaine content in transgenic papayas were
not significantly different compared to the non-transgenic
samples.
However, when the contents of vitamin C, b-carotene, BITC and
carpaine were determined at two harvesting times and subject to
ANOVA, most of them were higher than critical value, which meant
that significant difference existed. On average, in transgenic and
non-transgenic papayas, vitamin C content increased by 13%, malic
acid decreased by 20%, succinic acid decreased by 22%, and oxalic
acid decreased by 38% in September compared to May. The content
of b-carotene increased on average by 17%. Overall, the compositional changes were as expected and comparable to those reported
previously (Bari et al., 2006). BITC decreased at about 56% with
increasing temperature, which was in agreement with earlier
reports (Almora et al., 2004). The remarkably decreased content of
carpaine (36%) in September compared to May explained the
occurrence of food poisoning for pigs feeding on papayas,
especially when the fruits are not mature, as they contain higher
amount of carpaine compared to later mature stage (Wang, 2003).
Although seasonal differences affected both transgenic and nontransgenic papayas, their composition was similar at different
harvesting times.
From the discussion above, the results suggested that both
nutrient composition and toxicants in transgenic papaya showed
great similarity with their traditional counterparts. Although
seasonal variations did occur, the compositional differences were
found to be slight when fruit from the two different harvesting times
were compared. The two papaya cultivars with genetic modification
bore great similarity to their non-transgenic counterparts.
4. Conclusion
Profiling analysis was a useful tool for obtaining an overview of
compositional changes in papaya as a result of genetic modification. The profiles of VOCs, sugars, organic acids, carotenoids and
alkaloids in papaya demonstrated great similarity by PCA and
correlation coefficient analysis. The target compounds of vitamin
C, b-carotene, BITC and carpaine showed no significant difference
by ANOVA. However, papayas harvested across different time
periods showed a higher degree of compositional variability. In
addition, papaya species of different cultivars were found to be
highly variable in composition and non-comparable to each other.
It was recognized that the transgenic and non-transgenic papaya
showed great similarity in all of the comparisons. Further study to
complement the present work will aim at a high-throughput
detection of composition in order to avoid biases against certain
compound classes in profiling analysis.
Z. Jiao et al. / Journal of Food Composition and Analysis 23 (2010) 640–647
Acknowledgements
The authors are grateful to Dr. Li Huaping for kindly providing
GM papayas and laboratory assistance for PCR identification. We
also would like to thank the National Natural Science Foundation of
China No. 20575081.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.jfca.2010.03.004.
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