Journal of the science of food and agriculture, tập 90, số 11, 2010

Review
Received: 26 October 2009

Revised: 12 May 2010

Accepted: 17 May 2010

Published online in Wiley Interscience: 16 June 2010

(www.interscience.wiley.com) DOI 10.1002/jsfa.4041

Genetic evaluation of dairy cattle using
a simple heritable genetic ground
Josef Pribyl,a Vaclav Rehout,b Jindrich Citekb∗ and Jana Pribylovaa
Abstract
The evaluation of an animal is based on production records, adjusted for environmental effects, which gives a reliable
estimation of its breeding value. Highly reliable daughter yield deviations are used as inputs for genetic marker evaluation.
Genetic variability is explained by particular loci and background polygenes, both of which are described by the genomic
breeding value selection index. Automated genotyping enables the determination of many single-nucleotide polymorphisms
(SNPs) and can increase the reliability of evaluation of young animals (from 0.30 if only the pedigree value is used to 0.60 when
the genomic breeding value is applied). However, the introduction of SNPs requires a mixed model with a large number of
regressors, in turn requiring new algorithms for the best linear unbiased prediction and BayesB. Here, we discuss a method that
uses a genomic relationship matrix to estimate the genomic breeding value of animals directly, without regressors. A one-step
procedure evaluates both genotyped and ungenotyped animals at the same time, and produces one common ranking of all
animals in a whole population. An augmented pedigree–genomic relationship matrix and the removal of prerequisites produce
more accurate evaluations of all connected animals.
c 2010 Society of Chemical Industry
Keywords: genomic breeding value; methods; QTL; SNP; linear model; genomic relationship

INTRODUCTION

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advanced reproductive methods are routinely applied and the BV
of sires is therefore highly reliable; there is a huge global market
for sperm and breeding animals encompassing many companies
and breeder associations; and worldwide workshops on animal
evaluation are frequently organised through publications such as
the Interbull Bulletin.9
The aim of this review is to provide a survey of the procedures
used to evaluate animal production traits using simple heritable
genetic markers. Some basic methodological approaches will be
emphasised, particularly those that connect genomic breeding
value (GEBV) to traditional methodologies.

ANIMAL EVALUATION
An overview of the standard procedures used worldwide in
the genetic evaluation (BV prediction) of production traits in
farm animals, as well as new developments, are continuously
published by ICAR, Interbeef, Interbull, Interstallion and other
international organisations. A number of countries cooperate
in the international evaluation of dairy cattle, which invokes
international inspection of the methods used to estimate BV in
domestic country.9 Here, special attention is paid to the continuous
updating of national and international evaluation procedures.

∗

Correspondence to: Jindrich Citek, Department of Genetics, Faculty of
ˇ e

Agriculture, South Bohemia University, Studentska 13, CZ 370 05 Cesk´
Budˇejovice, Czech Republic. E-mail:

a Institute of Animal Science, CZ 104 01 Praha 10-Uhrineves, Czech Republic
b Department of Genetics, Faculty of Agriculture, South Bohemia University,
Studentska 13, CZ 370 05 Ceske Budejovice, Czech Republic

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c 2010 Society of Chemical Industry

1765

Laboratory techniques and mathematical and statistical methods
for the evaluation of animal breeding values (BV) are undergoing
continuous improvement. Molecular genetic data can be analysed
for associations with production traits.1 However, the relationships
between farm animal production traits and molecular-genetic
information are often measured imprecisely. Many studies of the
relationships between genetic markers and quantitative traits
are methodologically flawed and do not reflect contemporary
breeding practices; sometimes even the basic context of breeding
and farming conditions are not taken into consideration. This
type of research requires very careful experimental design that
considers pedigree structure and generates an adequate quantity
of data using sophisticated mathematical and statistical methods.
The general objective of each evaluation is to explain the
variability of the characteristics studied and determine why
animals or groups of animals differ from one another. Farm
animal productivity is simultaneously influenced by many genetic

and non-genetic factors, and it is practically impossible to
plan a completely balanced experiment. Therefore, sophisticated
statistical procedures must be used.
Recently, the evaluation of animal performance based on
molecular-genetic information has become more widespread.
Dairy cattle populations evaluated for several groups of traits
of moderate and low heritability (production, conformation,
reproduction) using genetic markers have been presented by
several authors2 – 8 as well as in Interbull Bulletin No. 39.9 In pig
and poultry populations, whole-genome scanning and genetic
diversity analysis are quite extensive.10,11 The methodologies used
may be generalised across species, but several facts influence
methodological advancements in relation to dairy cattle: the cost
of genotyping is favourable relative to the price of each animal;


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Generally, mixed linear models of best linear unbiased prediction
(BLUP) in an animal model (AM) are used, and a pedigree of three
or more generations of ancestors is taken into account according
to the model equation:
Y = Xb + Zu + e

(1)

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where Y is the vector of measured performances, X and Z are
known matrices that relate performance to the systematic effects
of the breeding environment and the animals, b and u are the

estimated vectors of fixed systematic environmental effects and
the random effects of an animal (BV) with the additive numerator
relationship matrix (A), and e is the vector of random error.
Using this model equation, a system of normal equations is
constructed in which the unknown constants (b) and (u) are
estimated. These systems of equations are vast, and special
algorithms are required for their solution.12
Most of the variability in any measured production trait is
caused by systematic environmental effects. The influence of
the herd–year–season, or herd–test-day, which identifies a
contemporary group of animals kept under the same conditions,
is usually the most important factor.
Evaluations are generally oriented to the MT-AM (multi-trait
animal model), RR-TDAM (random regression test-day animal
model), AM-maternal, and nonlinear methodologies for survival
(kit) analysis.13 – 23
It is important to find a method of evaluation that minimises
residual error and simultaneously considers all of the effects that
may influence the performance variable being measured. From
a genetic perspective, it is important to ask: What proportion
of variability is explained by the statistical model used? Is this
proportion different in a model that does not account for genetic
effects? Is this model the best (optimal) of all the possibilities
tested? The proportion of variability explained by the statistical
model used (R2 ) and other information criteria for testing the
suitability of the model, such as Akaike’s information criterion
(AIC), Bayes information criterion (BIC), Bayes factor (BF) and the
likelihood ratio test (LRT), are very important in answering these
questions.24 – 28
Molecular-genetic information can be used to improve selection

programmes.29 Animals are evaluated more accurately when their
entire genetic value is partitioned into causal factors and withinfamily genetic components are exploited. The use of moleculargenetic markers in breeding is the inclusion of additional criteria in
the selection indices. These markers increase selection differences
relative to existing traditional breeding programmes by decreasing
the correlation among sib individuals, increasing the accuracy of
animal selection, allowing the utilisation of genetic variability
that is usually included in non-utilisable residuum, and allowing
a shortening of the generation interval (because they may be
analysed in young animals). The use of selection markers is
conditional upon the timely laboratory analysis of the entire
subpopulation subjected to pre-selection (e.g., young bulls) and
rapid application before the determined gene linkages change.
This requires frequent updates of selection indices, as shown
below (Eqn (2)). The consistent application of genomic selection
markedly reduces the cost of a selection programme.3,30
However, data analysis becomes more complicated, the number
of estimated parameters becomes higher, and a modified
information criterion (mBIC) is necessary for the selection of a
suitable model of evaluation.31,32
In order to select individuals for breeding, marker-assisted
selection (MAS) may be applied if several genetic markers are to

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be used. Alternatively, genomic selection utilises high numbers of
markers that densely cover the whole genome.3,33 BV is usually
calculated in two steps. In the first step, the regression coefficients
(v) (substitution effects of the alleles of a considered locus) are

determined in a reference population with known performance
and highly reliable BVs. This reference population usually includes
only a part of the population under selection. From the first step,
quantitative trait loci (QTL) effects are estimated. Subsequently,
BV is determined for all of the young animals in the evaluated
(sub)population by means of a selection index, as described in
Eqn (2).30
The reference population and the evaluated population are
separated by at least one generation. Therefore, the relationships
between markers and QTLs determined in the older generation
may not be fully applicable to the younger evaluated population,
as the QTLs are not fully covered by study markers. Furthermore,
the influences of selection, mutation, immigration of sires used
intensively in artificial insemination, changes in environment, and
the development of the commercial population under selection
can also affect the applicability of QTL data across generations.
Therefore, it is necessary to periodically redetermine u in Eqn (1),
allele frequencies (q) in Eqn (5), their inherence in the genotypes
of individual animals (T), and regression coefficients (v) in (3) so
that the gap between the reference and the evaluated population
will be as small as possible.3,30,34
The GEBV of a given trait is calculated based on known loci and
remaining polygenes according to the selection index:
GEBVj = k1 DGVj + k2 u∗ j

(2)

where GEBVj is the genomic (total) BV for an individual (j)
determined based on the genomic information at the locus (i)
and remaining polygenic effect. DGVj is the direct genetic value,

calculated as the sum of BVs for a particular loci:
DGVj =

j Tij vij

(3)

where Tij (with regard to Eqn (9)) is the ith element in the jth row
of the known incidence matrix correlating the genetic effects of
particular alleles to the observed individual, vij is the vector of
genetic marker effects, u∗ j represents the BV calculated based on
the remaining polygenes, and k1 and k2 are the weights of the
information sources in the index.35
If the GEBV is calculated for young animals without their own
production records, u∗ j represents only information about their
parents. In cases where a high density of genetic markers is
available, the u∗ j in Eqn (2) is frequently omitted.

GENETICALLY CONDITIONED VARIABILITY
OF PERFORMANCE
Reliably determined population-genetic parameters are a precondition for genetic evaluation. We are usually interested in
phenotype variability (σ 2 P ), which can be separated into genetic
additive (σ 2 A ), genetic dominant (σ 2 D ), genetic epistatic (σ 2 I ), and
unpredictable residual (σ 2 E ) components, plus covariance caused
by genotype/environment interaction (2σGE ).36 It is generally assumed that the genotype/environment interaction is negligible.
Therefore:
σ 2P = σ 2G + σ 2E = σ 2A + σ 2D + σ 2I + σ 2E

(4)

The additive effects that are accumulated over successive
generations of selection are used in breeding. Nevertheless, other

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genetic effects are also reflected in performance, and if they are
omitted the results of the evaluation may be distorted.
One locus
Some genes may have a direct impact on quantitative production
traits, and therefore efforts are made to utilise them directly in
breeding. These candidate genes (or quantitative trait loci (QTL), if
describing markers) explain a portion of the genetic variability in
the trait being considered.
At two alleles in a locus, the portion of the additive genetic
variance conditioned by one gene (i) can be approximated by a
binomial distribution:36
σ 2 Ai = 2qi (1 − qi )v i 2

(5)

where qi is the frequency of the studied allele at locus i and vi is
the additive substitution effect of alleles at locus i.
The portion of the variance caused by a dominant allele at a

given locus is
(6)
σ 2 Di = (2qi (1 − qi )di )2
where di is the dominance effect in locus i. Often, it is assumed
that di = 0.
In the case of genes with major effects on the trait being
studied, the analysis is slightly easier because animals carrying a
desirable allele frequently exceed the normal variable range for the
measured production trait. This is obvious from the distribution
function of the estimated BV of the evaluated trait (additional
peaks, outliers), which indicates that a special genetic effect is
occurring and should be included as a separate factor in the
model.37
Several loci
Correlations may exist between the genes in question. Therefore,
the variability of an observed trait that is explained by several genes
depends on the variability caused by each gene and combinations
thereof (λ).38 The additive covariance between two loci can be
expressed as
cov(Ai , Ai ) = (1 − 2λ)2 (σAi σAi )

(7)

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Polygenic effects: the ‘infinitesimal model’ (pol)
A large number of unknown genes are assumed to affect the
majority of production traits, and their overall influence on
performance and its variability is the object of interest. In general,
the components of variance are currently determined as in Eqn (1),

by REML methods or by applying the Bayesian approach using
the Gibbs sampling method.12 These methods require the analysis
and adjustment of input datasets so that specific components of
variance (for example, within families, between families, caused
by different effects of genes) can be estimated.19,21
Joint effects of particular loci with remaining polygenes
The overall influence of genetic effects on the observed production
trait is expressed by the coefficient of heritability (h2 = σ 2 A /σ 2 P ).
The specific roles of the genes which exert these effects generally
remain unknown.
The total additive genetic variability is the sum of the known
loci according to Eqn (5), adjusted for mutual linkages (7) and
‘residual’ additive genetic variability caused by the remaining
polygene σ 2 Apol :
σ 2A =

2
j σ Ai

+

j

j;

cov(Ai , Ai ) + σ 2 Apol

(8)

Hence both single-locus effects and the remaining polygenic

effects of the ‘genetic background’ should be considered
simultaneously.7,42 – 45

EXPERIMENTAL DESIGN FOR THE EVALUATION
OF GENETIC MARKERS
The objective is to estimate the genetic contribution to specific
production traits. However, the experimental design should ensure
the reliable estimation of all factors that influence performance.
The power of the evaluation of data depends on the structure
and the size of the experiment, and the minimum number of
observations required to achieve adequate predictive power can
be calculated.46 Large datasets spanning progeny from many sires
are usually necessary.2,3,30,43 Generally, thousands of animals are
included in any one experiment.
Laboratory analyses are expensive, and therefore the decision
of which animals from which generation should be genotyped
should be made carefully, to achieve the highest possible reliability
with the lowest possible cost. Several methods based on the
relationship matrices between animals have been developed for
this purpose.49
Both the screening of allele frequencies and the evaluation of
their relationship to production traits require a pedigree analysis.
Sires, especially those imported from other populations for artificial
insemination, can dramatically change the frequencies of alleles
in a herd or an entire population in a short period of time.
There are differences in the methodologies used to evaluate
F1/F2 generation-designed experiments in which extremely

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The theory of selection indices is used to determine the shares of
several loci in the total genotype.39,40
Genes interact with one another, and any gene may have
pleiotropic effects. These interactions are mostly unknown and
may be quite extensive. This implies genetic epistatic variability
(σ 2 I ) based on two or more interactions among all loci studied.
It is expected that multi-generational, similarly oriented ongoing
selection in commercial breeds will lead to the stabilisation of
favourable genetic combinations. The fixation of desirable alleles
could also occur at a number of loci in an improved breed. However,
breeding conditions change constantly, and combinations of
genes are disturbed by selection, mutation and by the immigration
of sires from other populations. Therefore, inter-gene interactions
within some families may be expressed differently for a certain
period before the gene linkages are again stabilised. This can be
exploited in selection.
When studying the influence of a selected gene on performance,
the effects of nearby (linked) genes will also be included; thus the
result does not correspond only to the studied gene or to the
studied marker–QTL relationship. Therefore, when a low number

of sparsely located markers is analysed, the effects of any given
marker are frequently overestimated.33 The effects calculated for
each genetic parameter strongly depend on the number and
density of genetic parameters included in a simultaneous analysis.
One locus can also have an epistatic effect on several traits, which

may be either positively or negatively correlated. It is therefore
necessary to distinguish between pleiotropic and closely linked
QTL effects.41


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different breeds are crossed38,50 and studies involving stably
selected commercial populations, where alleles are expected to
be in favourable interactions. The second case, which is connected
with the continuous improvement of already productive breeds,
is generally of greater interest to breeders.
DD + GDD
Sib animals, belonging to the same families, generally have similar
performance capabilities. They share both the observed genes
and background polygenes. It is crucial to determine whether
performance is influenced by the studied locus or by other
polygenes.
Study designs that incorporate data from multiple generations
have been developed for the analysis of small numbers of markers.
These daughter design (DD) and granddaughter design (GDD)
analyses allow estimation of the effects of the studied loci within
families, i.e., within groups of sib animals with a similar genetic
background.38,51 In this type of analysis, the initial generations of
sires (i.e., parents or grandparents) must be heterozygous at the
studied locus. In this way, each initial animal gives rise to two
genetically different groups of progeny with respect to the alleles
studied.
In the proposed GDD experiment, only generations of ancestors
without their own performance measurements can be genotyped.
Their performance scores are assigned by means of progeny

testing from a large set of non-genotyped progeny. This
considerably decreases the number of individuals that must be
genotyped despite achieving a high reliability of evaluation. The
total number of animals required for the experiment is relative
to the proportion of genetic variability influenced by the locus,
allele frequencies, and the level of recombination between QTL
and the marker. However, only the additive effects of genes can
be estimated in this design. The GDD and general pedigree design
analysis of QTL in dairy cattle have been compared in simulation
studies.52
Design with a large number of markers (SNP)
An increased number of markers introduces more complexity. The
size of the reference population of sires with highly reliable BV
estimates is particularly important.2,3,43 Larger numbers are better,
and several thousands of sires are desirable.

A MODEL FOR ESTIMATING GENETIC EFFECTS
The principle of evaluation consists in the separation of the
effects of purely heritable loci from the effects of other genetic
background.43,47,48,53 The BLUP method and similar approaches
are the best procedures that can be used to adjust measured
BV values. Consistent with Eqns (1) and (2), the evaluation can be
formally expressed by a modified mixed linear model:
Y = Xb + Z[u∗ + Tv] + e

(9)

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where T is the known matrix of the experiment design that links

an animal to the genetic effects of particular alleles. Each row
may include columns according to particular loci and several
genetic effects of each locus with values for additive effects
(tAi ¤ <1, 0, −1 >), dominance effects (tDi ¤ <0, 1 >), twolocus epistatic interactions between loci (tIii = tAi tAi );32 u∗ is the
estimated vector of the random ‘residual’ polygenic effects for
each animal (i.e., the partial BV after the effects of the studied loci

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are excluded) with the additive relationship matrix; and v is the
estimated vector of the effects of genetic markers. This may also
comprise several loci and encompass additive, dominance and
epistatic effects.
Genetic markers (v) can be considered to have fixed32,53 or
random effects. In the latter case, either the diagonal genetic
matrix alone, Iσ 2 Ai , is considered54 for each random effect (i), or
the complete covariance structure and its relationship with the
identity by descent matrix (IBD), IBDσ 2 Ai , is taken into account.
IBD describes the probable positional relationships between each
marker/QTL pair and the probability of inheriting the paternal
or maternal QTL allele. The construction of IBD depends on
whether linkage analysis (LA), linkage disequilibrium (LD) or a
combination of both methods (LDLA) was used to determine
linkage status.8 In this context, several teams have developed
algorithms for the construction of an IBD matrix.41,44,55 They have
also derived genotype values for non-genotyped sib animals
whose performance data may be then used to identify candidate
genes.

DATA FOR EVALUATION
Several types of data describing performance can be used for
these evaluations. Either direct performance records or adjusted
values may be used. For the second approach, data are adjusted
for non-genetic noise as precisely as possible, when BV with high
reliability is estimated in large populations. This yields adjusted
(pseudo) values for BV, yield deviation (YD) or daughter yield
deviation (DYD) that may be used in further analyses.
Direct individual performance
If genetic parameters (markers) are determined directly in animals
from their performance records, the evaluated trait (Y) according
to Eqn (9) is their recorded performance.
Given the pedigree structure and design of the experiment, it
is possible to estimate additive, dominance and epistatic genetic
effects, all of which could be included in v. However, in practice
relatively few performance values are known for each animal.
Therefore the values of vectors u∗ , v and other effects b in
Eqn (9) can be estimated only with considerable error.56,57
Breeding value
Animals with highly reliable BV are used for evaluation (usually
sires whose value has been proven by progeny testing). In BV
analysis, the effects of selected loci on major traits associated with
milk performance are determined58 – 60 according to the following
model:
(10)
uˆ = Tv + u∗
where uˆ is the vector of BV determined by a routine method
BLUP-AM according to Eqn (1) based on all polygenes.
The BV of an animal summarises the data on performance

deviations of the contemporaries of all sib animals. The expected
BV of the progeny (uO ) is related to the BV of sires (uS ) and mothers
(uM ) and to the random Mendelian sampling of parental gametes
(MS).
(11)
uO = 0.5 [uS + uM ] + MS
One half of the additive genetic variability of (uO ) is caused by
MS. Therefore the result of Eqn (10) significantly depends on the
volume and sources of information that contributed to the BV. A
reliable input BV, which can be achieved only for animals with a

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large set of progeny, is a condition for a correct evaluation. This,
however, implies that it is possible to evaluate only the additive
genetic component.
We must take into account the fact that BV represents a
random effect (regressed value) and its value directly depends
on the reliability of estimation (r2 ). The variability of BV (σ 2 u ) is
therefore higher at higher reliabilities, as shown by the following
relationship:
(12)
r2 = σ 2 u /σ 2 A

This demonstrates that for BV estimates with low, unbalanced
reliabilities the animal rank may change and the results of markers
analysis are not very reliable.
Daughter yield deviations
DYD computed from Eqn (1) are used in most routine evaluations
of markers.45 Initially, yield deviations (YD) adjusted for all
non-genetic effects are determined according to the following
equation:61
YD = Y − Xb
(13)
where YD is the vector of yield deviations. The average deviations
of sires’ daughters (DYD) is then determined and adjusted for 0.5
BV of their mothers:
DYD = Z S [YD − 0.5Z M uM ]N−1

(14)

METHODS FOR EVALUATING GENETIC
MARKERS
When only a small number of genes is studied, it is not
possible to evaluate the experiment correctly without splitting
the genotype influence into the part played by singular observed
genes and the part played by the other (residual) polygenic
‘genetic background’.7,33,64,65 On the other hand, single observed
genes also contribute to the additive effects of all genes, and
the polygenic effect (u) is the sum of these additive effects.
Therefore, it is not easy to distinguish between the influence of
the polygenic ‘genetic background’ and the effects of individual
genes; in these cases the effect of the individual observed genes
is frequently reduced.66 Therefore careful experimental design,

particularly with respect to the size of the experiment, is necessary
to estimate the effects of genetic markers.
Several connected questions must be asked in any evaluation of
genetic markers: (A) What proportion of the genetic variability of
the evaluated trait is explained by the studied genetic factors?
(B) What is the genetic correlation between the influence of
the factors studied and the influence of the ‘remaining’ genetic
background on the evaluated trait? (C) Do the results from a model
that considers only polygenic effects and a model that includes
both QTL and remaining polygenic effects differ from each other?
(D) What is the influence of each allele? (Note that it does not
make sense to answer (D) without first answering (A).) (E) Do the
studied genetic factors have similar effects in all groups of related
animals?
A small number of QTLs
When a small number of loci are evaluated, QTLs are often used
to represent fixed effects of the genotype in a linear model,
for example in GLM/SAS.67 – 69 The evaluation model also reflects
systematic effects of the breeding environment or of groups of
animals according to their relationships. With this method, it is
not possible to wholly avoid the influence of correlated loci, and
the effects of individual loci are therefore usually significantly
overestimated.33 The model can be improved by including a
parameter for the random effects of the parents of genotyped
animals.56,57
The effect of the studied locus depends on the genetic background of the animal and could differ between populations.43,65
The BLUP, REML and Bayesian analysis methods incorporate common fixed effects for particular loci and ‘residual’ random effects of
remaining polygenes to provide more exact results.7,43,45 Another
approach for obtaining more exact results is also to use particular
loci as random effects with IBD to account for their variability.8,55

The weight (w) for weighted analysis, which is the inverse of DYD
variance, corresponds to the value of EDC. The exact derivation
of the weight factor for special situations has been described
previously.61,62
DYD has been used in several GDD studies; one evaluated 39
markers in a set of 4993 sires and another evaluated 263 markers
in a set of 872 sires.7,63
As in the evaluation of BV, only the additive genetic component
can be determined by DYD. If the number of progeny per sire is
large then they prevail in his BV, r2 is high and balanced, and the
sire’s MS is almost completely contained both in BV and in DYD.
The correlation between BV and DYD is in this case high, and the
results of evaluation for genetic markers on the basis of BV and
DYD are similar.32

A large number of SNPs
Production traits depend on a large number of mutually linked,
interacting genes that may be distributed across the entire
genome. Currently, it is possible to sequence tens to hundreds of
thousands of single-nucleotide polymorphisms (SNPs) for many
individual animals, densely covering the entire genome. A multiple
regression analysis of all SNP markers describes their relationships
to the production trait in question. Thus this analysis can be used
to find the DGV and GEBV according to Eqns (3) and (2). Because
a large number of SNPs is considered, there is less emphasis on
the quantitative relationships between individual markers and the
relevant QTL; instead, the overall relationship to the production
trait in question is important.

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1769

where ZS is the known matrix that relates daughter performance to
the sire; ZM is the known matrix that relates daughter performance
to the mother; uM is the vector of mothers’ BV; and N is the diagonal
matrix that describes the number of daughters per sire.
The values of DYD are independent of the reliability of sires BV
estimates, and therefore are more comparable between sires with
different reliabilities of BV estimation. In agreement with Eqn (11),
DYD comprise 0.5 BV of a sire, including MS and random error. The
alternative of DYD is de-regressed BV.3
Performances adjusted in this way are evaluated by weighted
analysis according to (9), where the vector Y is substituted by the
vector DYD, and vector b may encompass additional fixed effects.
DYD values are the means for n daughters of sires. Taking
into account the number of contemporaries connected to each
daughter in DYD, and the structure of the entire dataset, we
can generate the effective daughter contribution (EDC), which is
determined based on the reliabilities of the estimation of sires’ BVs
(r2 ):
EDC = (r2 /(1 − r2 ))((4 − h2 )/h2 )
(15)

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The high density of markers also allows the generalisation of
effects. Relationships no longer need to be calculated individually
within particular families and the effects of alleles are assumed to
be consistent across all families for simplification.33
While the breeding values of young dairy cattle can be predicted
with a reliability of about 30% by pedigree value on the basis of
polygenes, an increase in reliability (to 50–70%) can be expected
when large number of SNPs are evaluated.2,3,33
Computational strategies used to evaluate SNP data
Generally, techniques based on the BLUP and BayesB methods
are used to evaluate large numbers of SNPs.33 Depending on
the total number of SNPs sequenced, it is usually necessary to
calculate many genetic regression relationships between a given
production trait and the studied alleles.54,70 These relationships
may be formally solved according to Eqn (9). Compared to a
general AM calculated on the basis of polygenes only, the size
of the vector DYD is relatively smaller (thousands of sires) but
the size of the vector v is large (tens of thousands of regression
coefficients). When there is a high density of SNPs across the
entire genome, the term u∗ is often omitted from the solution
and only SNPs are used (vector v).43 In practice, however, it is
expected that even when a high density of markers is obtained
some QTLs will not be covered and the polygene effect is therefore
still considered.3 Only additive effects are evaluated due to the
large number of SNPs; the inclusion of non-additive effects would
increase the number of effects in the model enormously. After
simplification, the computation model can be expressed as
DYD = Xb + Tv + e

(16)

where Xb describes the total mean and fixed effects included in
this step.
Often, the majority of SNPs do not have any information content.
If the relationships between the markers and QTLs are already
known, it is possible to reduce the number of regressors in the
model, which will simplify the solution and also reduce the cost of
laboratory analyses.71
Because of the large number of independent variables, these
systems of equations (16) are poorly conditioned and cannot
always be solved. Therefore, the systems of equations and
algorithms of solutions must be rearranged. For example, ridge
regression may be applied, which means that SNPs are treated as
random effects. At the same time, numerical values are added to
the diagonal of the matrix of the system to ensure the solubility of
the equations.34 The added values are the inverse of the genetic
variabilities of each SNP. These values are not usually known for
many genetic parameters, so other simplifications must be used
when constant components of variance are required for all SNPs.33
The sum of components across all loci yields the total additivegenetic variability of the studied trait σ 2 A . Matrix T in Eqn (16)
has f rows corresponding to the number of evaluated sires with
known daughter performance. If only additive gene effects are
considered, matrix T has m columns corresponding to the number
of SNP markers considered (m > f ). Therefore, a system of the
matrix size m × m at least is solved, and tens of thousands of
regression coefficients are estimated.54

J Pribyl et al.

which describes deviations of allelic frequencies in the basic ‘nonselected’ population. The ith column of Q contains the deviation
of the frequency of the second allele in locus i from the expected
value (0.5) multiplied by two Qi = 2(qi − 0.5).72 The dimensions
of matrix Q correspond to those of matrix T. The matrix G has the
form
(17)
G = ([T − Q][T − Q] )/(2 qi (1 − qi ))
which is analogous to the generally used numerator relationship
matrix (A) in Eqns (1) and (9). Its dimensions are f × f , where
the diagonal indicates the number of homozygous loci in the
evaluated animal and the elements off the diagonal indicate the
numbers of alleles shared by sib animals.
The diagonal residual covariance matrix Rσ 2 E of dimensions
f × f is then constructed. This matrix corresponds to the residual
effect (e) in Eqn (16). Relative to Eqn (15), the elements on diagonal
R are connected with the reliabilities of BV estimates for particular
sires, but only on the basis of their progeny from which DYD were
computed (excluding other sources of information):
Rjj = (1 − r2 jP )/r2 jP

where r2 jP is the partial reliability of the sire’s BV based on his
progeny.
Based on the theory of selection indices, it is then possible
to determine the direct genetic value of sires with known
daughter performance (DGVS )72 by adapting 2DYD for the vector
of observations:42
DGVS = G[G + Rk]−1 2DYD

1770

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(19)

where k = σ 2 E /σ 2 A .
The genomic covariance matrix between proven sires (S) and
young unevaluated animals (O) is
C = ([TO − QO ][TS − QS ] )/(2 qi (1 − qi ))

(20)

where TO and TS are the known matrices assigning particular loci
to the young animals and proven sires and QO and QS are Q
matrices that have been modified according to the number of
young animals and proven sires included.
The predicted direct genetic value for young animals (DGVO ) is a
genomic regression based on proven animals with already known
BV:
(21)
DGVO = CG−1 DGVS
The solution by means of the selection index according to
Eqns (17)–(21) is identical to the preceding solutions in Eqns (16)
and (3) but the dimensions of the matrices are substantially smaller,
corresponding to the numbers of genotyped animals (f ).72,73 The
estimation of genetic regression coefficients according to the
particular loci (v) may also be omitted. Hence the solution is
simplified and does not require iterative methods.72 Therefore,
a direct determination of DGV estimate reliability is feasible. For
sires with known daughter performance (S), reliability estimates
correspond to the diagonal elements of the term:

G[G + Rk]−1 G

(22)

For young animals without known performance (O), reliability
estimates correspond to the diagonal elements of the term
C[G + Rk]−1 C

DIRECT ESTIMATION OF GEBV
Based on T, a genomic relationships matrix (G) can be
determined.72,73 This requires the introduction of the matrix Q,

(18)

(23)

A similar solution can also be obtained by the weighted analysis
of a linear model as in Eqn (1) with DYD substituted for input data

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Evaluation of animals by simple heritable markers

www.soci.org

and weighted according to Eqn (18). In this case, A is substituted
by G and Xb covers only the general mean.72,73

A one-step approach
The process of evaluation described above has several disadvantages, namely that it is influenced by the input parameters used
in a multi-step procedure. Inaccuracies in these parameters may
bias the evaluation. It is also difficult to compare genotyped and
ungenotyped animals evaluated by different procedures. This may
be overcome by incorporating all parts of the evaluation into a
one-step procedure.
From Eqn (19), it follows that molecular-genetic information
is collected in G. The additive numerator relationship matrix
(A) is probability based and deviates from expected values
due to random Mendelian sampling.74 The realised genomic
relationship matrix (G) should therefore be more precise and
lead to more precise selection.73 A single-step evaluation using
original measured performances (Y) as input has been proposed,
in which the pedigree-based numerator relationship matrix (A)
covering all evaluated animals is augmented by a contribution
from (G) with genotyped animals.75
A matrix H has been derived, which is substituted for the
usual matrix (A) in Eqn (1).76 Further, a computational procedure
has been developed for the solution of animal models directly
from the accumulated measured data of all genotyped and
non-genotyped animals in large commercial populations.6,75 The
essential component of the system of equations constructed
according to Eqn (1) is the inverse of the relationship matrix, in
this case:
0
0
(24)
H−1 = A−1 +
0 λ(G−1 − A−1

22 )
where H is the pedigree–genomic relationship matrix, λ is a scaling
factor and A22 is a block of A that corresponds to the genotyped
animals.
This one-step procedure eliminates several assumptions that
must be made for multi-step procedures. It is less biased and allows
the evaluation of large commercial populations even when only
some individuals in the population are genotyped. This improves
evaluation accuracy both for genotyped and ungenotyped animals
and generates a single common rank for all animals. This model
further enables the use of multi-trait AM and models with different
complexities, which are now common in animal evaluations.6

CONCLUSIONS

J Sci Food Agric 2010; 90: 1765–1773

ACKNOWLEDGEMENTS
This work was supported by the Ministry of Agriculture of the Czech
Republic (MZe 0002701404) and by the Ministry of Education of
the Czech Republic (MSM6007665806). We gratefully acknowledge
the helpful comments of anonymous reviewers.

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common rank for all genotyped and ungenotyped animals in the
population.
The methods described here have significant practical importance in animal breeding.


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Research Article
Received: 26 May 2009

Revised: 25 March 2010

Accepted: 29 March 2010

Published online in Wiley Interscience: 22 June 2010

(www.interscience.wiley.com) DOI 10.1002/jsfa.3998

Root colonisation by the arbuscular
mycorrhizal fungus Glomus intraradices alters
the quality of strawberry fruits (Fragaria ×
ananassa Duch.) at different nitrogen levels
Vilma Castellanos-Morales,a∗ Javier Villegas,b Silvia Wendelin,c
a
´
Horst Vierheilig,d Reinhard Ederc and Raul
´ Cardenas-Navarro
Abstract
BACKGROUND: Arbuscular mycorrhizal fungi (AMF) increase the uptake of minerals from the soil, thus improving the growth
of the host plant. Nitrogen (N) is a main mineral element for plant growth, as it is an essential component of numerous plant

compounds affecting fruit quality. The availability of N to plants also affects the AMF–plant interaction, which suggests that the
quality of fruits could be affected by both factors. The objective of this study was to evaluate the influence of three N treatments
(3, 6 and 18 mmol L−1 ) in combination with inoculation with the AMF Glomus intraradices on the quality of strawberry fruits.
The effects of each factor and their interaction were analysed.
RESULTS: Nitrogen treatment significantly modified the concentrations of minerals and some phenolic compounds, while
mycorrhization significantly affected some colour parameters and the concentrations of most phenolic compounds. Significant
differences between fruits of mycorrhizal and non-mycorrhizal plants were found for the majority of phenolic compounds and
for some minerals in plants treated with 6 mmol L−1 N. The respective values of fruits of mycorrhizal plants were higher.
CONCLUSION: Nitrogen application modified the effect of mycorrhization on strawberry fruit quality.
c 2010 Society of Chemical Industry
Keywords: mycorrhizal; nitrogen; strawberry; quality; fruit

INTRODUCTION

1774

Most land plants benefit from their interaction with symbiotic
soil-borne fungi known as arbuscular mycorrhizal fungi (AMF).
In this symbiosis the AMF receives carbon from the plant, while
the fungus takes up nutrients with its extraradical mycelium and
provides them to the host plant.1
The uptake of nitrogen (N) by the extraradical mycelium has
been shown before and this N is available to the host plant,2,3
so the AMF improves the N status of the host.4,5 Nevertheless, it
also has been reported that the N availability in the soil affects the
dynamic of plant–AMF association.4,6
Nitrogen is an essential element for plant growth. Owing
to its role in the synthesis of proteins, nucleic acids, various
coenzymes and many products of secondary plant metabolism,7
it is important for strawberry fruit quality. It has been shown

that a leaf N concentration below 19 g kg−1 (deficiency) causes
chlorosis of strawberry leaves, thus decreasing the leaf area, fruit
size and anthocyanin concentration,8 whereas an excess of foliar N
(∼40 g kg−1 ) promotes vegetative growth, delays fruit maturation
and causes a loss of firmness in fruits, thus reducing quality.9,10
Strawberry quality and consumer preference for strawberry
fruits are determined by parameters such as size, firmness, levels
of soluble sugars and acid concentration, the last of which affects
the aromatic compounds that impart flavour and aroma.11,12

J Sci Food Agric 2010; 90: 1774–1782

Strawberry fruits possess antioxidant activity owing to their high
content of anthocyanins, flavonoids, phenolic acids and other
compounds.13
Recent data suggest that mycorrhization not only has a positive
effect on various plant growth parameters but can also affect
the quality of crop products. For example, root colonisation by
different AMF enhances the essential oil concentration in a number
of plants from different plant families such as oregano (Origanum
vulgare),14 basil (Ocimum basilicum L.),15,16 menthol mint (Mentha

∗

Correspondence to: Vilma Castellanos-Morales, Estaci´on Experimental del
Zaid´ın, Prof. Albareda 1, Apdo 419, E-18008 Granada, Spain.
E-mail: vilma c

a Instituto de Investigaciones Agropecuarias y Forestales, Universidad
Michoacana de San Nicol´as de Hidalgo, Km 9.5, Carretera Morelia-Zinap´ecuaro,

CP 58880, Tar´ımbaro, Michoac´an, Mexico
b Instituto de Investigaciones Qu´ımico-Biol´ogicas, Universidad Michoacana de
San Nicol´as de Hidalgo, CP 58000, Ciudad Universitaria, Morelia, Michoac´an,
Mexico
c Federal College and Research Institute for Viticulture and Pomology,
Wienerstrasse 74, A-3400 Klosterneuburg, Austria
d Estaci´on Experimental del Zaid´ın (CSIC), E-18008 Granada, Spain

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c 2010 Society of Chemical Industry


Effects of AMF and N level on quality of strawberry fruits
arvensis)17 and coriander (Coriandrumsativum L.).18 In others plants
such as alfalfa (Medicago sativa L.),19 – 21 barrel medic (Medicago
truncatula),22 red clover (Trifolium pratense)23 and soybean (Glycine
max L.),24 increases in flavonoid levels after mycorrhization have
been reported.
There are several reports on strawberry plants concerning
inoculation with AMF and its effects on plant growth. It has been
shown that AMF root colonisation stimulates plant growth,25
modifies the production of runners,26 enhances photosynthesis27
and increases the number of fruits.28 However, to the best
of our knowledge, there are currently no data on how AMF
root colonisation in combination with different N levels affects
strawberry fruit quality parameters such as colour, soluble sugars,
acids, minerals and phenolic compounds.

MATERIALS AND METHODS

J Sci Food Agric 2010; 90: 1774–1782

time the fruits were separated into two equal batches. One batch
was used for the determination of fruit fresh weight, diameter,
length and Brix grade (total solids). The last measurement was
done at 25 ◦ C using a refractometer (ATAGO CO., LTD) (N-1α). The
other batch was frozen in liquid nitrogen and stored at −20 ◦ C.
Prior to chemical and colour analyses, these samples were ground
to a fine powder (Retsch MM200 mill, Thomas Scientific, New
Jersey, United States) in liquid N2 and then freeze-dried.
Titratable acidity is a measure of organic acids in a sample and
is determined by adding enough alkali of known molarity to the
sample to neutralise all acids present. For the measurement of
titratable acidity, 0.1 g of freeze-dried fruit was mixed with 5 mL
of distilled water and shaken, then 0.05 mol L−1 NaOH was added
up to a pH of 8.1. The results are expressed as % citric acid.
For macro- and micro-nutrient determination, 10 mL of distilled
water was added to 0.2 g of ground sample. The mixture was
sonicated (FS30H, Fisher Scientific, Pittsburgh, United State)
and then centrifuged (2744 × g , 10 min). The supernatant was
filtered through a 0.45 µm membrane (Millipore, Thebarton, South
Australia). For macronutrient measurement, 9 mL of 0.5 mol L−1
HCl and 200 µL of lanthanum oxide were added to 1 mL of the
filtrate. For micronutrient determination, 200 µL of concentrated
HCl was added to 9 mL of the filtrate. All samples were shaken on
a vortex for 5 min and their mineral contents were quantifed
by atomic absorption (Solar 939, ATI Unicam, Basingstoke,
U.K).
Soluble sugars were extracted by the method of Gomez

et al.,32 with some modifications. All extractions were carried
out at 4 ◦ C. Briefly, 4 mL of methanol/water (1 : 1 v/v) and 1 mL
of chloroform were added to 15 mg of lyophilised sample. The
mixture was shaken on a vortex for 2 min and then on a horizontal
agitator (Libline 4638, Melrose Park, Illinois) at medium speed
for 30 min. After centrifugation (1585 × g , 30 min), two liquid
phases separated by the plant powder were obtained. A 2.8 mL
volume of the methanol/water supernatant was recovered and
dried in a vacuum evaporator (Labconco 7810000 Speed-Vac,
Kansas City, Missouri). The resulting pellet was stored at −20 ◦ C
overnight. Next day it was redissolved in 2 mL of distilled water
by shaking on a vortex for 20 min. The aqueous extract was then
poured into a tube with 0.015 g of polyvinylpyrrolidone (Sigma
P6755) to remove residual phenols by crosslinking. After shaking
on a vortex for 20 min, the tube was centrifuged (1585 × g ,
90 min). The supernatant was recovered using a 1 mL insulin
syringe and stored at −20 ◦ C for the direct measurement of
glucose and the indirect measurement of fructose and sucrose
by the enzymatic method33 with a photometer (Multiskan
Ascent 354, Thermolabsystem, Finlandia imported by Labtech,
Mexico) at 340 nm, using a calibration curve in the range
0–0.2 g L−1 glucose (Baker 1916-01, Xalostoc, Edo. M´exico). To
verify the correct measurement of soluble sugars, controls of
fructose (Sigma F0127) and sucrose (Sigma S7903) were used.
Before measurement the extract was diluted with distilled water
(1 : 20 v/v).
Total phenols and the anthocyanins cyanidin-3-glucoside and
pelargonidin-3-glucoside were extracted by the method of
Markakis,34 with some modifications. Briefly, 5 mL of methanol/HCl
(1 : 5 v/v) was added to 0.1 g of lyophilised sample. The mixture

was sonicated for 300 s and then centrifuged (2744 × g , 10 min).
The supernatant was filtered (0.45 µm membrane, Millipore) and
the filtrate obtained was used for the measurement of total
phenol and anthocyanin concentrations. Colour parameters and
the absorbance at 500 nm were also measured in the same filtrate.

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1775

The experiment was conducted in a ‘shade’-type greenhouse
with 30% shade at the Instituto de Investigaciones Agropecuarias
y Forestales (IIAF), Universidad Michoacana de San Nicol´as de
Hidalgo (UMSNH), Morelia, Michoac´an, Mexico. Maximum and
minimum temperatures in the greenhouse varied between 28 and
32 ◦ C and between 8 and 18 ◦ C respectively.
Plants of the strawberry cultivar ‘Aromas’ were used that had
previously been grown in a sterilised (95 ◦ C water/steam, 40 min)
substrate of coconut fibre/perlite (1 : 3 v/v) under greenhouse
conditions. Before the experiment was established, the absence of
AMF in the roots was verified by the ink and vinegar technique,29
modifying the duration of immersion in KOH and ink/vinegar
solution (7 and 5 min respectively). Before planting, roots were
disinfected by submerging them for 20 s in 20 g L−1 sodium
hypochlorite solution and rinsing them in water.
The inoculum was prepared with spores of Glomus intraradices
cultivated in liquid medium (3.5 × 106 spores L−1 , 90% viability;
Premier Tech Biotechnologies Company, Quebec, Canada), which

was diluted with fitagel (Sigma P-8169, Saint Louis, MO, USA)
solution at 50 g L−1 to obtain a final concentration of about 5×104
spores L−1 . The viability of spores was determined according to
the method of An and Hendrix.30
Eighteen days after setting up the experiment, each plant
received 2 mL of inoculum applied directly to the recently formed
roots. One month later, after staining,29 the percentage of root
colonisation was determined by the gridline intersect method.31
The experiment was organised as a full factorial, completely
randomised design with two factors: inoculation (two levels:
mycorrhizal and non-mycorrhizal plants) and N concentration
in the nutrient solution (three levels: 3, 6 and 18 mmol L−1 ).
The six treatments were replicated four times, producing 24 experimental units with ten plants each. Every second day, all plants
were irrigated up to substrate saturation. Nitrogen was supplied
as NO−
3 and the cation/anion ratio was kept constant by varying
−1
the concentration of SO2−
4 . When N was below 18 mmol L , the
+
cation concentrations were maintained as follows: K , 3; Ca2+ , 3.5;
Mg2+ , 1.5 mmol L−1 . They were increased in the 18 mmol L−1 N
treatment: K+ , 6.5; Ca2+ , 7.5; Mg2+ , 3.25 mmol L−1 . In all nutrient
solutions the concentration of phosphorus (P) was 0.3 mmol L−1 .
The other nutrients in the solutions were: H3 BO3 , 20; CuSO4 · 5H2 O,
0.5; Fe-EDTA (Ethylenediaminetetraacetic acid iron (III) sodium
salt), 15; MnSO4 ·H2 O, 12; (NH4 )6 Mo7 O24 · 4H2 O, 0.05; ZnSO4 · 7H2 O,
3 µmol L−1 . The pH was adjusted to 5.5 at every application date.
Mature fruits of each experimental unit were collected between
140 and 160 days after setting up the experiment. At sampling

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1776

Total phenol concentration was quantified by the Folin–
Ciocalteu method,35 with minor modifications. The volumes of
sample, Folin–Ciocalteu’s phenol reagent and sodium carbonate
were reduced to one-tenth of those used in the original method,
giving a final volume of 20 mL. The measurement was made
at 765 nm (Spectrophotometer – Cintra 10e, GBC, Dandenong,
Victoria, Australia), using a linear calibration curve of caffeic acid
(0–250 mg L−1 ) to calculate the total phenol concentration.
Strawberry fruit colour was determined by measuring the
absorbance at 500 nm36 with a spectrophotometer (Shanghai,
Analytical Instrument LTD, China) (HP-8452A, Cheadle Heath,
Stockport Cheshire, UK). Additionally, colour was measured using
a photometer (Licor-2000, DR Lange, Dusseldorf, Germany) in
terms of L∗ , a∗ and b∗ values, where L∗ defines lightness (from
white = 100 to black = 0), a∗ defines red/greenness (from −60
to +60) and b∗ defines blue/yellowness (from −60 to +60). From
the a∗ and b∗ values the following colour parameters were also
calculated: colour evolution (a∗ /b∗ ), shade (tan−1 (b∗ /a∗ ), ranging
from 0◦ (red) to 90◦ (yellow) to 270◦ (blue)) and chromaticity
(C ∗ = (a2 + b2 )1/2 , indicating the vividness of colour and ranging
from 0 (discoloured) to 60 (powerful)).
Phenolic acids and flavonols were extracted by acid hydrolysis.37

Briefly, 7.5 mL of 5.33 g L−1 ascorbic acid solution, 12.5 mL of
methanol (liquid chromatography/mass spectrometry grade) and
5 mL of 6 mol L−1 HCl were added to 0.25 g of sample. The mixture
was sonicated for 2 min, the air in the mixture was replaced with
gaseous N2 (1–1.5 min) and the mixture was shaken on a horizontal
agitator (35 ◦ C) for 16 h. The cold sample was filtered (0.45 µm
membrane, Millipore), concentrated in a rotavapor (35 ◦ C) and
redissolved in 1 mL of methanol. This solution was filtered (0.45 µm
membrane, Millipore) and 10 µL of the filtrate was used for the
measurement of phenolic acids and flavonols.
Phenolic compounds (anthocyanins, phenolic acids and
flavonols) were quantified by reverse phase high-performance
liquid chromatography (RP-HPLC)38 using an Agilent 1090 Aminoquant HPLC system (Waldbrot, Germany). Each 10 µL sample was
injected for separation on two narrow-bore HP-ODS Hypersil RP18 columns (Shandon, U.K) (5 µm, 200 mm × 2.1 mm and 5 µm,
100 mm × 2.1 mm) linked in series. A linear gradient of 5 g L−1
formic acid (pH 2.3) and methanol at a flow rate of 0.2 mL min−1
was used. The column temperature was 40 ◦ C and detection was
achieved at 320 nm for all compounds. The standards used and the
concentration ranges of their calibration curves were as follows:
callistephin (Extrasynthese 0907S, Lyon, France), 1–200 mg L−1 ;
kuromanin (Extrasynthese 0915S), 1–200 mg L−1 ; gallic acid
monohydrate (Roth 7300, Karlsruhe, Germany), 10.9–545 mg L−1 ;
p-coumaric acid (Roth 9908), 26.2–1308 mg L−1 p-coumaric acid;
ferulic acid (Roth 9936), 9.8–490 mg L−1 ; ellagic acid (Sigma
E2250), 8.1–81.4 mg L−1 ; quercetin dehydrate (Extrasynthese
1135S), 8.7–436 mg L−1 ; kaempferol (Fluka 60010, Saint Louis,
MO, USA), 8.5–426 mg L−1 ; catechin (Roth 6200), 9.2–460 mg L−1 .
The results are presented as means of four replicates (each
replicate consists of fruits from all plants in one experimental
unit). Statistical analyses were performed using SYSTAT for

´ 9.01, Cranes
Windows, Version 9.01 (Systat Software version
Software International, LTD). The effects of each single factor
(N concentration and inoculation) and their interaction (N
concentration × inoculation) were evaluated using two-way
analysis of variance (ANOVA). Multiple comparisons were made
using Tukey’s test. Differences at P < 0.05 were considered
significant.

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V Castellanos-Morales et al.

Table 1. Fresh weight, diameter and length of fruits of strawberry
plants inoculated with Glomus intraradices and fertilised with different
nitrogen concentrations in irrigation water
Factor

Fresh weight (g per fruit)

Diameter (cm)

Length (cm)

−1

Nitrogen concentration (mmol L )
3
14.19a
2.98a

6
13.14a
2.93a
18
13.36a
2.93a
Inoculation
M
13.39a
2.93a
NM
13.73a
2.96a
Interaction (nitrogen concentration × inoculation)
3×M
14.44a
3.01a
3 × NM
13.94a
2.96a
6×M
12.47a
2.88a
6 × NM
13.80a
2.98a
18 × M
13.27a
2.90a
18 × NM

13.46a
2.96a

3.30a
3.22a
3.26a
3.22a
3.26a
3.23a
3.27a
3.19a
3.25a
3.27a
3.25a

Each value represents the mean of four replicates. Two-way ANOVA was
applied for each parameter; when statistical differences were found,
a Tukey test (P < 0.05) was conducted independently for nitrogen
concentration (3, 6 and 18 mmol L−1 ), inoculation (mycorrhizal (M) and
non-mycorrhizal (NM)) and nitrogen concentration × inoculation (3 ×
M, 3 × NM, 6 × M, 6 × NM, 18 × M and 18 × NM). For each factor,
means with the same letter in a column do not differ significantly.

RESULTS
Tables 1–5 show the results for the effects of the two factors and
their interaction on the variables evaluated. At the end of the
experiment the extent of AMF colonisation ranged from 65 to
80%. None of the treatments affected the fresh weight, diameter
and length of fruits (Table 1).
In terms of colour, different N concentrations resulted in statistically significant effects only on fruit lightness and absorbance

at 500 nm (Table 2). Lightness was significantly higher and absorbance was significantly lower in fruits of plants fertilised with
3 mmol L−1 N than in fruits of plants treated with 6 mmol L−1
N, but both values did not differ from those in fruits of plants
fertilised with 18 mmol L−1 N. Mycorrhization resulted in statistically significant effects on all colour parameters except colour
evolution and shade. Fruits of mycorrhizal plants showed a 2.0%
increase in lightness and 14.3, 12.9, 13.9 and 21.2% decreases
in red/greenness, blue/yellowness, chromaticity and absorbance
respectively compared with fruits of non-mycorrhizal plants. Increasing N concentration in the irrigation solution did not lead
to statistically significant differences in colour parameters between fruits within each mycorrhizal treatment. Nor were there
significant differences between fruits of mycorrhizal and nonmycorrhizal plants fertilised with the same N concentration
(Table 2).
Titratable acidity, glucose, fructose and Brix grade were lowest
in fruits of plants fertilised with 3 mmol L−1 N (Table 3). Their
titratable acidity, glucose and fructose values were significantly
lower than those in fruits of plants treated with 6 mmol L−1 N,
while their Brix grade was significantly lower than that in fruits of
plants treated with 18 mmol L−1 N. Mycorrhization modified only
fructose concentration, with fruits of mycorrhizal plants containing
8.5% less fructose than those of non-mycorrhizal plants. When the
applied N was increased, a significant difference in titratable acidity
between mycorrhizal and non-mycorrhizal plants treated with the

c 2010 Society of Chemical Industry

J Sci Food Agric 2010; 90: 1774–1782


Effects of AMF and N level on quality of strawberry fruits

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Table 2. Lightness (L∗ ), red/greenness (a∗ ), blue/yellowness (b∗ ), colour evolution (a∗ /b∗ ), shade (tan−1 (b∗ /a∗ )), chromaticity (C ∗ = (a2 + b2 )1/2 )
and absorbance at 500 nm of fruits of strawberry plants inoculated with Glomus intraradices and fertilised with different nitrogen concentrations in
irrigation water
Factor

Lightness

Red/greenness

Blue/yellowness

Colour evolution

Shade

Chromaticity

Absorbance

17.16a
19.79a
18.18a

1.62a
1.57a
1.63a

31.77a
32.47a

31.67a

32.59a
36.85a
34.67a

0.54b
0.64a
0.57ab

17.26b
19.49a

1.59a
1.62a

32.21a
31.72a

32.44b
36.95a

0.52b
0.63a

16.52a
17.80a
18.68a
20.90a
16.58a

19.78a

1.57a
1.66a
1.57a
1.56a
1.63a
1.62a

32.47a
31.07a
32.56a
32.38a
31.59a
31.72a

30.80a
34.38a
34.85a
38.85a
31.69a
37.65a

0.48b
0.59ab
0.60ab
0.68a
0.50b
0.63ab

L−1 )

Nitrogen concentration (mmol
3
84.93a
27.69a
6
83.31b
31.07a
18
84.31a
29.50a
Inoculation
M
84.99a
27.45b
NM
83.37b
31.38a
Interaction (nitrogen concentration × inoculation)
3×M
85.72a
25.98a
3 × NM
84.14ab
29.40a
6×M
83.80ab
29.40a
6 × NM

82.82b
32.74a
18 × M
85.46ac
26.98a
18 × NM
83.16bc
32.02a

Each value represents the mean of four replicates. Two-way ANOVA was applied for each parameter; when statistical differences were found, a Tukey
test (P < 0.05) was conducted independently for nitrogen concentration (3, 6 and 18 mmol L−1 ), inoculation (mycorrhizal (M) and non-mycorrhizal
(NM)) and nitrogen concentration × inoculation (3 × M, 3 × NM, 6 × M, 6 × NM, 18 × M and 18 × NM). For each factor, means with different letters
in a column differ significantly.

Table 3. Titritable acidity, soluble sugar concentrations and Brix
grade of fruits of strawberry plants inoculated with Glomus intraradices
and fertilised with different nitrogen concentrations in irrigation water

Factor

Titratable
acidity (%
citric acid)

Soluble sugars (g kg−1 DM)
Glucose

Fructose

Sucrose

Brix grade

L−1 )

Nitrogen concentration (mmol
3
1.28b
136.63b 148.96b
94.16a
6
1.36a
153.21a 167.54a
62.68a
18
1.30b
140.50ab 149.35b
83.07a
Inoculation
M
1.30a
139.85a 148.99b
80.45a
NM
1.33a
147.04a 161.58a
79.45a
Interaction (nitrogen concentration × inoculation)
3×M
1.21c

132.02c 141.96c
113.10a
3 × NM
1.35ab
141.24bc 155.96bc
75.21a
6×M
1.38a
146.34bc 158.85bc
45.83a
6 × NM
1.34ab
160.07ab 176.24ab
79.54a
18 × M
1.31ab
141.20bc 146.16c
82.43a
18 × NM
1.28bc
139.81bc 152.54bc
83.71a

4.93b
5.39ab
6.09a
5.96a
5.99a
5.05bc
4.81c

5.30abc
5.48abc
6.17a
6.00ab

Each value represents the mean of four replicates. Two-way ANOVA was
applied for each parameter; when statistical differences were found,
a Tukey test (P < 0.05) was conducted independently for nitrogen
concentration (3, 6 and 18 mmol L−1 ), inoculation (mycorrhizal (M) and
non-mycorrhizal (NM)) and nitrogen concentration × inoculation (3 ×
M, 3 × NM, 6 × M, 6 × NM, 18 × M and 18 × NM). For each factor,
means with different letters in a column differ significantly.

J Sci Food Agric 2010; 90: 1774–1782

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1777

same N concentration was observed only in the treatment with
3 mmol L−1 N.
Some nutrient concentrations were significantly different between fruits of plants treated with 3 and 18 mmol L−1 N (Table 4).
Fruits from the treatment with 3 mmol L−1 N contained 9.4, 13.3,
61.0 and 48.0% more K, Mg, Fe and Zn respectively and 11.3% less

Ca than fruits of plants fertilised with 18 mmol L−1 N. The Mn concentration in fruits of plants fertilised with 3 mmol L−1 N was significantly higher than that in fruits of plants treated with 6 mmol L−1
N. Fruits of mycorrhizal plants had higher K and Cu concentrations
but lower Mn concentration than fruits of non-mycorrhizal plants.

Mycorrhization significantly modified the Ca, Mg, Fe, Cu, Zn and Mn
concentrations in fruits when the N applied was changed from 3
to 18 mmol L−1 , and the K concentration in fruits when N changed
from 3 to 6 mmol L−1 . With the exception of Ca, the concentrations
of all elements studied were higher in fruits of plants fertilised with
3 mmol L−1 N. Significant differences between fruits of mycorrhizal
and non-mycorrhizal plants of the same N treatment were found
for Cu, Zn and Mn concentrations. Fruits of mycorrhizal plants had
38.0 and 39.3% more Cu and Zn respectively in the 6 mmol L−1
N treatment and 39.6% less Mn in the 18 mmol L−1 N treatment
than their non-mycorrhizal counterparts.
Nitrogen treatment significantly affected the concentrations
of total phenols, gallic acid, ferulic acid, ellagic acid, cyanidin3-glucoside, quercetin and kaempferol in fruits (Table 5). Fruits
of plants fertilised with 3 mmol L−1 N had 20.5, 31.2 and 11.4%
lower concentrations of total phenols, gallic acid and cyanidin3-glucoside respectively and 21.0, 50.0 and 61.5% higher concentrations of ellagic acid, quercetin and kaempferol respectively
than fruits of plants treated with 18 mmol L−1 N. Fruits of plants
fertilised with 6 mmol L−1 N had a significantly higher concentration of ferulic acid than fruits of plants treated with 3
and 18 mmol L−1 N. Mycorrhization significantly modified the
concentrations of all phenolic compounds except pelargonidin3-glucoside and catechin. Fruits of mycorrhizal plants had 20.0,
15.0, 50.0 and 28.6% higher concentrations of p-coumaric acid,
cyanidin-3-glucoside, quercetin and kaempferol respectively and
29.0, 50.0 and 11.0% lower concentrations of gallic acid, ferulic
acid and ellagic acid respectively than fruits of non-mycorrhizal
plants.
Fruits of mycorrhizal plants fertilised with 6 mmol L−1 N had
a lower gallic acid concentration than fruits of mycorrhizal plants


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V Castellanos-Morales et al.

Table 4. Macro- and micronutrient concentrations of fruits of strawberry plants inoculated with Glomus intraradices and fertilised with different
nitrogen concentrations in irrigation water
Macronutrients (g kg−1 DM)
Factor

K

Na

Nitrogen concentration (mmol L−1 )
3
190.94a
2.09a
6
174.63b
1.69a
18
174.55b
2.24a
Inoculation
M
185.53a
1.93a
NM
177.38b
2.09a
Interaction (nitrogen concentration × inoculation)
3×M

194.90a
1.20a
3 × NM
185.49ab
2.19a
6×M
178.33b
1.49a
6 × NM
170.40b
1.89a
18 × M
183.38ab
2.30a
18 × NM
175.72b
2.19a

Micronutrients (mg kg−1 DM)

Ca

Mg

Cu

Fe

Zn

Mn

15.09b
16.25ab
16.79a

15.04a
14.03ab
13.28b

2.5a
2.5a
2.7a

6.6a
4.6b
4.1b

11.1a
10.1a
7.5b

9.9a
7.1b
9.3a

15.86a
16.24a

13.75a

14.49a

2.8a
2.3b

4.8a
5.4a

9.8a
9.3a

7.7b
9.9a

14.14b
16.05ab
16.98a
15.52ab
16.44a
17.15a

14.65a
15.43a
14.03ac
14.04ac
12.56bc
13.99ac

3.1a
1.8b

2.9ad
2.1be
2.5cde
2.9ac

6.3ab
6.9a
5.2abc
4.0bc
2.9c
5.3abc

10.7abc
11.5ab
11.7a
8.4bcd
7.0d
8.0cd

9.2b
10.6ab
6.8c
7.4c
7.0c
11.6a

Each value represents the mean of four replicates. Two-way ANOVA was applied for each parameter; when statistical differences were found, a Tukey
test (P < 0.05) was conducted independently for nitrogen concentration (3, 6 and 18 mmol L−1 ), inoculation (mycorrhizal (M) and non-mycorrhizal
(NM)) and nitrogen concentration × inoculation (3 × M, 3 × NM, 6 × M, 6 × NM, 18 × M and 18 × NM). For each factor, means with different letters
in a column differ significantly.

Table 5. Total phenol and phenolic compound concentrations of fruits of strawberry plants inoculated with Glomus intraradices and fertilised with
different nitrogen concentrations in irrigation water
Phenolic compounds (mg kg−1 DM)
Flavonoids
Anthocyaninsa

Phenolic acids
Factor

Total phenols (g kg−1 DM)

Gallic

Nitrogen concentration (mmol L−1 )
3
541.63b
11b
6
503.32b
10b
18
682.04a
16a
Inoculation
M
571.31a
10b
NM
580.01a

14a
Interaction (nitrogen concentration × inoculation)
3×M
590.38ab
11bc
3 × NM
492.87b
11bc
6×M
506.74b
6c
6 × NM
499.89b
14ab
18 × M
616.79ab
13ab
18 × NM
747.29a
18a

Flavonols

p-Coumaric

Ferulic

Ellagic

Cya-3-glu

Pel-3-glu

Quercetin

Kaempferol

Catechin

99a
96a
100a

1b
2a
1b

753a
699ab
622b

248b
275a
280a

3370a
3710a
3545a

3a

2b
2b

21a
15b
13b

249a
219a
368a

107a
89b

1b
2a

681b
765a

287a
249b

3692a
3391a

3a
2b

18a

14b

322a
236a

109ab
89ab
113a
78b
99ab
101ab

1b
1b
1b
2a
1b
1b

633bc
873a
595c
803ab
626bc
618c

258bc
238b
314a
236b

288ac
272ab

3564a
3176a
4198a
3222a
3314a
3776a

4a
3ab
3ab
2c
3ac
2bc

21a
21a
20a
10b
13ab
12ab

324a
174a
233a
206a
408a
328a

Each value represents the mean of four replicates. Two-way ANOVA was applied for each parameter; when statistical differences were found, a Tukey
test (P < 0.05) was conducted independently for nitrogen concentration (3, 6 and 18 mmol L−1 ), inoculation (mycorrhizal (M) and non-mycorrhizal
(NM)) and nitrogen concentration × inoculation (3 × M, 3 × NM, 6 × M, 6 × NM, 18 × M and 18 × NM). For each factor, means with different letters
in a column differ significantly.
a Cya-3-glu, cyanidin-3-glucoside; Pel-3-glu, pelargonidin-3-glucoside.

1778

treated with 18 mmol L−1 N and a higher cyanidin-3-glucoside
concentration than fruits of mycorrhizal plants treated with
3 mmol L−1 N, the difference being significant in both cases.
Significant differences between fruits of mycorrhizal and nonmycorrhizal plants of the same N treatment were found for

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all phenolic compounds except pelargonidin-3-glucoside and
catechin. Fruits of mycorrhizal plants had higher p-coumaric acid,
cyanidin-3-glucoside, quercetin and kaempferol concentrations
and lower gallic acid, ferulic acid and ellagic acid concentrations
than fruits of non-mycorrhizal plants when fertilised with

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Effects of AMF and N level on quality of strawberry fruits
6 mmol L−1 N, and a lower ellagic acid concentration when
fertilised with 3 mmol L−1 N (Table 5).

DISCUSSION

J Sci Food Agric 2010; 90: 1774–1782

be explained by the enhanced foliar area of these plants (35.9
and 25.3% higher than that of plants in the 3 and 18 mmol L−1 N
treatments respectively) when fructification started.
Fruits of mycorrhizal plants had a lower fructose concentration
than fruits of non-mycorrhizal plants, indicating that mycorrhization reduced the accumulation of this carbon compound in the
fruits. This could be explained by the fact that AMF act as carbon
sinks (4–20% of the total carbon fixed by the plant).49
Around 4% of the dry matter of plants comprises mineral
elements, which, owing to their role in enzymatic reactions
essential for fruit development and its cold conservation, are
important for fruit quality.50 In this study we observed that different
N levels modified the concentrations of some minerals in the fruits.
Fruits of plants fertilised with 3 mmol L−1 N showed higher K, Mg,
Fe and Zn levels than fruits of plants treated with 18 mmol L−1
N. These results suggest that the roots of plants fertilised with
3 mmol L−1 N took up higher amounts of these minerals.
It has been shown previously that a low availability of N in the
soil affects root growth. Tolley-Henry and Raper51 suggested that
under conditions of low N availability the roots have priority to
N compared with other plant organs and therefore root growth
is promoted. Rufty et al.52 demonstrated that a low availability of
N in the soil increases the amount of photosynthates addressed
to the roots, thus being available for enhanced root growth. In
our experiment, root dry weight and volume were also measured
(data not shown). The root dry weight of plants fertilised with 3

and 18 mmol L−1 N was 2.0 and 1.7 g per plant respectively, while
the root volume of these plants was 22.0 and 14.8 cm3 per plant
respectively. These data suggest that a higher soil volume was
explored by plants fertilised with 3 mmol L−1 N compared with
plants treated with 18 mmol L−1 N, which could explain the higher
K, Mg, Fe and Zn levels in fruits of plants of the 3 mmol L−1 N
treatment.
To date, no adequate data are available on the effect
of mycorrhization on macro- and micronutrients in fruits.
In our experiment, mycorrhization significantly modified the
concentrations of K, Cu and Mn, with fruits of mycorrhizal plants
having higher K and Cu levels and a lower Mn level. Although
mycorrhizal root colonisation frequently increases macro- and
micronutrient accumulation in the leaves and stalks of plants,53,54
Liu et al.55 found lower Cu, Zn, Mn and Fe concentrations in the
shoots of mycorrhizal corn plants. The inconsistent results on
nutrient acquisition by mycorrhizal plants have been attributed to
changes in the rhizosphere due to increased N levels in the soil,
which affect mycorrhizal development.56
Fruits of mycorrhizal plants fertilised with 3 mmol L−1 N had
higher concentrations of those minerals than fruits of mycorrhizal
plants treated with 18 mmol L−1 N. Similar results of lower, equal
or higher acquisition of macro- and/or micronutrients dependent
on the level of mineral fertilisation have been reported in lettuce
inoculated with Glomus mosseae.4 The lack of a beneficial effect of
mycorrhization in terms of mineral acquisition in our 18 mmol L−1
N treatment could be attributed to a negative effect of this N
concentration on the extraradical mycelium development of the
AMF. The suppressive effect of high N levels on the formation
of extraradical mycelium has been described previously and has

been linked with reduced nutrient acquisition in mycorrhizal
plants.57 Fruits of mycorrhizal plants fertilised with 6 mmol L−1
N had significantly higher Cu and Zn concentrations than fruits
of non-mycorrhizal plants fertilised with the same N level. This
indicates a positive effect of mycorrhization and N treatment on

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1779

In fruit production, parameters such as fruit fresh weight, diameter
and length are important for fruit quality. In a study of the effect of
N application on peaches, Crisosto et al.39 observed that different
N levels did not affect the size of peach fruits,39 indicating that the
N levels tested were not a determining factor for this parameter.
In our study, neither N treatment nor mycorrhization had an effect
on these parameters of strawberry fruits.
Colour is another important determinant of fruit quality. Shade
and chromaticity are two parameters used to quantify purity,
while red intensity is used for the description of colour.40,41 The
values of these variables determined in the present study are
within the ranges observed previously in strawberry fruits.42 In
our experiment, some colour parameters were modified by N
treatment. Fruits of plants fertilised with 6 mmol L−1 N had lower
lightness and higher absorbance at 500 nm than fruits of plants
treated with 3 mmol L−1 N.
Changes in chromaticity due to mycorrhization have been reported previously in Capsicum annuum L. by Mena-Violante et al.,43
who found that fruits of mycorrhizal plants had a lower chromaticity than fruits of non-mycorrhizal plants. Interestingly, our study

showed a similar effect of mycorrhization on chromaticity, with
a lower chromaticity being found in fruits of mycorrhizal plants
than in fruits of non-mycorrhizal plants. These data suggest that
the effect of mycorrhization on chromaticity is a general one and
not fruit-specific. It has been proposed that the colour of strawberry fruits is closely linked with the synthesis and/or expression
of pelargonidin-3-glucoside and cyanidin-3-glucoside, two principal anthocyanins.44 In our context, this means that the colour
changes we observed as a result of mycorrhization are possibly
due to changes in the levels of these two anthocyanins.
The flavour of strawberry fruits is determined by the balance
of sugars and acids.12 Glucose, fructose and sucrose are the most
important sugars for the sensory quality of strawberry fruits,
representing 99% of the total carbohydrate content.45 Moreover,
citric acid and malic acid are the most important acids in strawberry
fruits.46 Besides their impact on flavour, acids are important
because they affect the gelling properties of pectin. Brix grade
is a composite parameter reflecting sugars, acids, salts and others
compounds soluble in water and is measured as the total soluble
solids present in the fruit.
In our study, titratable acidity and Brix grade varied between
1.21 and 1.38% and between 4.81 and 6.17 respectively in all
treatments. These values are wthin the ranges reported by PerkinsVeazie and Collins47 for titratable acidity (0.5–1.87%) and Brix
grade (5–12). Glucose and fructose concentrations were higher
than sucrose concentration in all cases and the fructose/glucose
ratio was about 1 : 1, in agreement with values reported previously
for strawberry fruits.42
The level of applied N had a significant effect on titratable
acidity, glucose and fructose concentrations and Brix grade. Fruits
of plants fertilised with 6 mmol L−1 N were more acidic and their
glucose and fructose concentrations were higher in comparison
with fruits from other treatments, without significant differences

in Brix grade. These data indicate that fruits from the 6 mmol L−1
N treatment had the best quality according to Mitcham.48 In our
experiment, foliar area was also measured (data not shown). The
higher production of sugars in the 6 mmol L−1 N treatment could

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V Castellanos-Morales et al.

the acquisition of Cu and Zn by the roots and their translocation
to the fruits.
Our results on the effect of N fertilisation on phenolic
compounds in strawberry fruits show that from the 3 mmol L−1
N treatment to the 18 mmol L−1 N treatment the concentrations
of ellagic acid, quercetin and kaempferol decreased while the
concentrations of total phenols, cyanidin-3-glucoside and gallic
acid increased.
A decrease in quercetin and kaempferol concentrations at high
N levels has been reported in tomato fruits,58 while an increase
in ellagic acid concentration at low N levels has been found in
strawberry fruits.59 In addition, Keller and Hrazdina60 reported that
the N concentration in the soil had different effects on the total
phenol concentration in grapes. The application of high N levels
led to low accumulation of flavonols, whereas the proportion of
anthocyanins was similar to that at low N levels. Our results could
be explained by the effect that N has on the biosynthetic pathways
of phenolic compounds. Phenylalanine ammonia-lyase (PAL) is the

principal enzyme of the phenylpropanoid pathway.61 This enzyme
catalyses the transformation of the amino acid L-phenylalanine by
deamination to trans-cinnamic acid, which is the first product
necessary for the synthesis of phenolic compounds. Interestingly,
it has been reported that at low N levels the enzymatic activity of
PAL is increased, liberating N for the amino acid metabolism, and
whereas the carbon products are diverted via 4-coumaroyl-CoA
into the flavonoid biosynthetic pathway.62 In our study, this could
be an explanation for the increase in concentrations of some
flavonoids (cyanidin-3-glucoside, kaempferol and quercetin) in
fruits of plants fertilised with 3 mmol L−1 N.
Mycorrhization modified the levels of most phenolic compounds. The cyanidin-3-glucoside, p-coumaric acid, quercetin and
kaempferol concentrations were higher and the gallic acid, ferulic acid and ellagic acid concentrations were lower in fruits of
mycorrhizal plants than in fruits of non-mycorrhizal plants. To our
knowledge, there are no data on the effect of AMF on phenolic
compound accumulation in fruits. However, there are reports on
changes in the levels of p-coumaric acid and ferulic acid in the roots
of mycorrhizal onion plants,63 changes in the levels of biochanin A,
formononetin, genistein and daidzein in the roots of mycorrhizal
alfalfa (M. sativa L.)19 – 21 and barrel medic (M. truncatula)22 and
changes in the level of glyceoline in mycorrhizal soybean (G. max
L.).24 Most recently, it has been shown that through mycorrhization the levels of phenols can also be altered in plant shoots.23 Our
results extend these observations, showing that mycorrhization
can induce changes in phenolic compound levels even in fruits.
An increase in applied N modified the concentrations of
some phenolic compounds between fruits of mycorrhizal and
non-mycorrhizal plants. Differences were determined in fruits
of plants fertilised with 6 mmol L−1 N. These results indicate
that N fertilisation modifies the response of the strawberry
plant to the AMF G. intraradices. This could be attributed to

changes in the rhizosphere due to N levels in the soil, which
affect mycorrhizal development56 and thus the acquisition of
other nutrients necessary for the production of phenols. To our
knowledge, we have provided the first evidence that, depending
on the N level applied, the accumulation of phenolic compounds
is altered in fruits of mycorrhizal strawberry plants.

parameters. Moreover, fruits of mycorrhizal plants had higher K
and Cu concentrations and showed greater accumulation of most
phenolic compounds. The results indicate that the 3 mmol L−1
N treatment had a positive effect on the accumulation of some
minerals in strawberry fruits, and fruits of mycorrhizal plants had
significantly higher phenolic compound, Cu and Zn concentrations
than fruits of non-mycorrhizal plants when they were treated with
6 mmol L−1 N. In recent years, much interest has focused on the
intake of phenolic compounds from the human diet and the
health benefits due to their antioxidant nature. It is therefore of
interest to produce crops rich in flavonols without the need for
genetic modification. Although previous studies have identified
a link between nutrient deficiency and phenolic compound
accumulation in plant tissue, the present study provides evidence
that mycorrhization and N application in strawberry plants can be
one strategy for increasing phenolic compound concentrations in
the fruits. In addition, up-regulation of the flavonoid biosynthetic
pathway in strawberry fruits may afford protection against
pathogen attack or light-induced damage. Further studies are
required to test this theory.

CONCLUSION

1780

Mycorrhization did not modify the weight, diameter or length
of strawberry fruits but had a negative effect on most colour

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ACKNOWLEDGEMENTS
The authors are grateful to ‘Fondos Mixtos CONACyT – Gobierno
del Estado de Michoac´an’, Mexico for support of project 12268
‘Optimization of nitrogen and water in the strawberry crop
(Fragaria × ananassa Duch.) by the use of arbuscular mycorrhizal
fungi’ and to CONACYT for provision of a PhD grant to Vilma
Castellanos. Moreover, we thank Dr Philippe Lobit, Sandra and
´
Silvia Velasco Lopez,
Flor Lorena Reyes S´anchez and Alejandrino
´
Lopez
Hern´andez from UMSNH, Morelia, Michoac´an, Mexico and
Veronica Schober, Monika Marek and Karin Korntheuer from the
Chemistry Laboratory of the Federal College and Research Institute
for Viticulture and Pomology, Klosterneuburg, Austria for their
support.

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1782
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c 2010 Society of Chemical Industry

J Sci Food Agric 2010; 90: 1774–1782


Research Article
Received: 28 September 2009

Revised: 6 April 2010

Accepted: 9 April 2010

Published online in Wiley Interscience: 25 May 2010

(www.interscience.wiley.com) DOI 10.1002/jsfa.4012

Effect of different light transmittance paper
bags on fruit quality and antioxidant capacity
in loquat

Hong-xia Xu, Jun-wei Chen∗ and Ming Xie
Abstract
BACKGROUND: Bagging has been widely used to improve the commercial value of fruit. The purpose of this study was to
evaluate the effects of different light transmittance paper bags on the quality and antioxidant capacity of loquat fruit. Two
loquat cultivars, Baiyu and Ninghaibai (Eriobotrya japonica Lindl.), were used for materials. One-layered white paper bags
(OWPB) with ∼50% light transmittance and two-layered paper bags with a black inner layer and a grey outer layer (TGDPB)
with ∼0% light transmittance were used as treatments and unbagged fruits were used as the control (CK) in this experiment.
Fruit quality was determined by physicochemical characteristics, the quantity of sugar, total phenolic, flavonoid, carotenoid
and vitamin C. The antioxidant capacities of the methanol extracted from the pulp were tested using three different assays.
RESULTS: The results showed that bagging decreased the weight of fruit but promoted the appearance of loquat fruits. The
total sugar content in the fruit bagged with OWPB was higher than in controls and in fruit bagged with TGDPB. The total
phenolic and flavonoid contents were decreased by both bagging treatments, with the lowest occurring in the fruit bagged
with TGDPB. Bagging also decreased the total antioxidant capacity of the fruit pulp, which was again lower in TGDPB-treated
fruits than in those bagged using OWPB. Correlation analysis showed a linear relationship between total antioxidant capacity
and the content of total phenolic and flavonoid.
CONCLUSION: The results showed that different light transmittance bags had different effects on fruit quality and antioxidant
capacity. In particular, bags with low light transmittance (TGDPB) decreased the inner quality and total antioxidant capacity of
loquat fruit. All results indicated that bagging with OWPB was more suitable for maintaining the quality of the loquat fruit than
bagging with TGDPB.
c 2010 Society of Chemical Industry
Keywords: antioxidant capacity; bagging; Eriobotrya japonica Lindl; flavonoid content; loquat; total phenolic content

INTRODUCTION

J Sci Food Agric 2010; 90: 1783–1788

In addition to sugars and organic acids, loquat fruits also contain
diverse nutrient and non-nutrient molecules, such as phenolics
(especially the flavonoids), vitamin C, and β-carotene, many of
which have antioxidant properties. These compounds exert a

range of biological effects including antibacterial, antiviral, antiinflammatory, antithrombotic and vasodilatory actions.9,10 They
also have pronounced antioxidant and free-radical-scavenging
activities.11 – 13 However, most of the studies on bagging fruit
focus on the appearance and general qualities of the fruit and
studies of the effects of bagging on antioxidant compounds and
antioxidant capacity of fruits are rare. Therefore, this study was
carried out to examine the effect of different light transmittance
paper bags on fruit quality and antioxidant capacity in two loquat
cultivars.

∗

Correspondence to: Jun-wei Chen, Institute of Horticulture, Zhejiang Academy
of Agricultural Sciences, Hangzhou, Zhejiang, 310021, China.
E-mail:
Institute of Horticulture, Zhejiang Academy of Agricultural Sciences, Hangzhou,
Zhejiang, 310021, China

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1783

Loquat (Eriobotrya japonica Lindl.) is widely cultivated in subtropical regions of Asia and other continents. Ripe loquat fruits are
spherical or oval in shape, orange/yellow or white in colour and
have a soft and juicy flesh. During their growth and maturation,
they are susceptible to insect pests, birds, diseases and mechanical
damage, which reduce their commercial value. Bagging, a physical protection technique commonly applied to many fruits, not
only improves fruit visual quality,1 – 4 by promoting fruit coloration

and reducing the incidences of fruit cracking and russet, but can
also change the microenvironment of fruit development, which
has multiple effects on the inner quality of fruits. Sugars and organic acids are the major determinants of fruit taste and flavour.
However, varied results have been obtained from experiments on
the effects of bagging on the sugar and organic acid contents of
fruits. Chundawat et al.5 showed that bagging generally reduces
the sugar content of fruit, whereas Hussein et al.6 reported that
bagging significantly increased the total sugar content. Huang
et al.7 reported that bagging treatments did not affect the total
soluble sugar content, but decreased the organic acid contents of
fruit. Kim et al.8 showed that titratable acids tended to increase
after bagging with yellow paper of low light transmittance.


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MATERIALS AND METHODS
Standards and chemicals
ABTS [2,2-azino-bis (3-ethylbenzthiozoline-6-sulfonic acid)],
DPPH (the 1,1-diphenyl-2- picrylhydrazyl radical), TPTZ
[2,4,6-tri(2-pyridyl)-s-triazine],
Trolox
(6-hydroxy-2,5,7,8tetramethylchroman-2-carboxylic acid), DPIP (phenolindo2,6- dichlorophenol), rutin, β-carotene, HPLC-grade sucrose,
glucose, fructose and sorbitol were all purchased from SigmaAldrich (Shanghai, China). All reagents were of analytical grade
unless indicated otherwise.
Plant material
Two loquat cultivars, Baiyu and Ninghaibai (Eriobotrya japonica
Lindl.), grown in a commercial orchard in Qingpu District, Shanghai,
China were used to test the bagging treatment. Approximately
100 fruitlets that were similar in appearance and size and which

received sunlight uniformly were randomly selected for each
bagging treatment. Approximately 100 unbagged fruitlets that
were also similar in appearance and size were tagged as controls
(CK). Bagging treatments were conducted after fruit thinning in
early April. At maturity, 50 fruits of each treatment were used for
fruit quality and antioxidant capacity analyses. Fruit maturity and
ripeness were assessed based on fruit firmness and skin colour.
Two types of bag were used in this study: (1) 25 cm × 36 cm onelayered white paper bags (OWPB) with ∼50% light transmittance;
(2) 25 cm × 36 cm two-layered paper bags with a black inner layer
and a grey outer layer (TGDPB) with ∼0% light transmittance. All
bags were coated with wax and supplied by Shangyu Jiali Paper
Bag Product Co., Ltd. (Ningbo, China).
Physicochemical characteristics analyses
The fruit mass of 30 loquats from each bagging treatment was
measured by using an electronic balance (0–210 g ± 0.001 g;
model C-600-SX; Cobos, Barcelona, Spain). These fruits were
randomly divided into five groups (replicates) with six fruits in each
group. The fruits were then manually peeled, cut into small pieces
and juiced together. The soluble solids concentration (SSC) was
measured in the filtered juice by using a hand-held refractometer
(Atago, Tokyo, Japan) and calibrated with distilled water. The
juice was also analysed for titratable acidity (TA) by titration
with 0.01 mol L−1 NaOH, using phenolphthalein as an indicator.
Twenty fruits of each treatment were selected for surface colour
determination using a chromameter (ADCI-60-C, Beijing, China)
calibrated with a manufacturer-supplied white calibration plate.
Results were expressed as lightness (L∗ ), redness (a∗ ), yellowness
(b∗ ) and hue angle (hab = tan−1 [(b∗ )(a∗ )−1 ]). The colour reading
was taken fourth at the equatorial region of each fruit and averaged
to give a value for each fruit. After surface colour determination,

the fruits were manually peeled, cut into small pieces and the
composite fruit samples ranging from 2 to 10 g were weighed and
frozen in liquid nitrogen, then stored at −80 ◦ C until analysis.

1784

Determination of sugar content
The quantity and types of sugar were determined as described
by Chen et al.14 Soluble sugars were extracted by grinding 5 g
frozen fruit in five volumes (w/v) of methanol : chloroform : water
as 12 : 5:3 (v/v). Extracts were centrifuged at 5000 ×g for 5 min. The
extraction was performed three times. Water and chloroform were
then added to bring the final methanol : chloroform : water ratio
to 10 : 6:5 and the chloroform layer was removed. The remaining
aqueous–alcohol phase was adjusted to pH 7.0 using 0.1 mol L−1

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H-X Xu, J-W Chen, M Xie
NaOH, then dried in a vacuum and redissolved with distilled
water. The sugar in water solution was analysed by using HPLC
(Waters 1525; Waters, Milford, Massachusetts, USA). The column
temperature was 90 ◦ C and 80% acetonitrile was used as an elution
at a flow rate of 1 mL min−1 . Fructose, glucose, sucrose and sorbitol
were identified and quantified by comparing the retention and
integrated peak areas of external standards.
Total carotenoid content analysis
The total carotenoid content was determined as described by
Reyes et al.15 Carotenoids were extracted from 2 g frozen fruit by
homogenising with 25 mL of acetone : ethanol (1 : 1) containing

200 mg L−1 butylated hydroxytoluene (BHT). The homogenate
was filtered through a Whatman no. 4 filter, washed with the solvent (∼60 mL) and diluted to 100 mL using the extraction solvent.
Extracts were transferred to a plastic container to which 50 mL
hexane was added. The container was then shaken and allowed
to stand for 15 min after which 25 mL of nanopure water was
added. The container was shaken again and the contents were allowed to separate for 30 min. The spectrophotometer was blanked
with hexane and absorbance of the samples in 1-cm quartz cuvettes was measured at 470 nm. Carotenoid was quantified as
β-carotene using a standard curve for this compound
(1–4 µg mL−1 ). Results were expressed as µg β-carotene equivalent g−1 fresh weight.
Vitamin C content analysis
The vitamin C content of the fruit extracts was determined
by the 2,6-dichloroindophenol titrimetric method.16 Briefly, the
samples were mixed with 40 mL of buffer (1 g L−1 oxalic acid plus
4 g L−1 anhydrous sodium acetate) and were titrated against
the dye solution containing 295 mg L−1 DPIP (phenolindo2,6-dichlorophenol) and 100 mg L−1 sodium bicarbonate. The
standard curve was generated with concentrations of 0.2, 0.4,
0.6, 0.8 and 1 mg of standard L-ascorbic acid (AnalaR; BDH,
Buffalo, New York, USA). The ascorbic acid content in the samples
was determined from the standard curve and the results were
expressed as µg ascorbic acid equivalent g−1 fresh weight.
Extracts for phenolic and antioxidant capacity measurement
To analyse the total phenolic and antioxidant activity, fruit extracts
in methanol were prepared using the method of Swain and
Hillis,17 with some modifications. A 10 g sample of fruit were
homogenised in 25 mL absolute methanol using a Waring blender.
The homogenates were kept at 4 ◦ C for 12 h and then centrifuged
at 15 000 × g for 20 min. The supernatants were collected, and
extraction of the residue was repeated using the same conditions.
The two supernatants of methanol were combined and divided
into two equal aliquots and then stored at −20 ◦ C until analysis.

The first supernatant was used for the quantitative analysis of
phenolic compounds and the second was used to determine the
antioxidant activity.
Total phenolic content analysis
The Folin–Ciocalteu reagent assay18 was used to determine the
total phenolic content. A 0.1 mL sample aliquot was mixed with
5 mL of 0.2 mol L−1 Folin–Ciocalteu reagent. The solution was
allowed to stand at 25 ◦ C for 5 min before adding 4 mL of 15%
(w/v) sodium carbonate solution in distilled water. The absorbance
at 765 nm was read after the initial mixing and then for up to 90 min

c 2010 Society of Chemical Industry

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Effect of bagging on loquat fruit quality and antioxidant capacity
until it reached a plateau. Gallic acid was used as a standard for
the calibration curve. Results were expressed as µg gallic acid
equivalent g−1 fresh weight.
Total flavonoid content analysis
The flavonoid content was measured using a colorimetric assay
developed by Jia et al.19 Plant extract (2.0 mL) or standard solutions
of rutin (Sigma) were added to a 10 mL volumetric flask. Distilled
water was added to make a volume of 5 mL. At zero time, 0.3 mL
of 5% w/v NaNO2 was added to the flask. After 5 min, 0.6 mL of
10% w/v AlCl3 was added and after 6 min, 2 mL of 1 mol L−1 NaOH
was added to the flask, followed by 2.1 mL distilled water. The
absorbance was read at 510 nm against the blank (water) and the
flavonoid content was expressed as µg rutin equivalent g−1 fresh

weight.
Antioxidant capacity determinations
Free radical scavenging activity on DPPH
The free radical scavenging activity of the extracts, based on
the scavenging activity of the stable 1,1-diphenyl-2-picrylhydrazyl
(DPPH) free radical, was determined by the method described by
Braca et al.20 Plant extract (0.1 mL) was added to 3 mL of a 0.004%
MeOH solution of DPPH. Absorbance at 517 nm was determined
after 30 min, and the percentage inhibition activity was calculated
from [(A0 − A1 )/A0 ] × 100, where A0 is the absorbance of the
control, and A1 is the absorbance of the extract/standard. Results
were expressed as µmol Trolox equivalent g−1 fresh weight.
Antioxidant activity using the ABTS assay
The ABTS• scavenging ability of extracts was determined according
to the method described by Re et al.21 ABTS• was generated by
reacting an ABTS aqueous solution (7 mmol L−1 ) with K2 S2 O8
(2.45 mmol L−1 , final concentration) in the dark for 16 h and
adjusting the absorbance at 734 nm to 0.700 with ethanol. A
0.2 mL aliquot of appropriate dilution of the extract was added
to 2.0 mL ABTS• solution and the absorbance was measured
at 734 nm after 15 min. Results were expressed as µmol Trolox
equivalent g−1 fresh weight.

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Ferric reducing/antioxidant power assay
The FRAP assay was used as described by Benzie and Strain22
with some modifications. The stock solutions included 300 mmol
acetate buffer (3.1 g C2 H3 NaO2 ·3H2 O and 16 mL C2 H4 O2 ), pH 3.6;
10 mmol TPTZ (2,4,6-tripyridyl-s-triazine) solution in 40 mmol HCl,
and 20 mmol FeCl3 ·6H2 O solution. The fresh working solution was

prepared by mixing 25 mL acetate buffer, 2.5 mL TPTZ solution,
and 2.5 mL FeCl3 ·6H2 O solution and then warmed at 37 ◦ C before
use 150 µL of fruit extracts or methanol (for the reagent blank) was
reacted with 2850 µL of the FRAP solution at 37 ◦ C for 30 min in the
dark (in a water bath). Readings of the coloured product (ferrous
tripyridyltriazine complex) were then taken at 593 nm. Results
were expressed as µmol Trolox equivalent g−1 fresh weight.
Statistical analysis
The significance of the results and statistical differences were
analysed using SYSTAT version 10.0 (SPSS, Chicago, IL, USA).
Analysis of variance (ANOVA) of the data was performed to
compare mean values for each variable under different cultivars.
The least significant difference test (LSD) was used to determine the
differences between means at a 5% significance level. Correlation
coefficients of DPPH, TEAC and FRAP with respect to total phenolic,
total flavonoid, total carotenoid and vitamin C contents were
evaluated.

RESULTS AND DISCUSSION
The effects of bagging on the physicochemical characteristics
of loquat fruit
Bagging is already known to affect the size and weight of
pomegranate,6,23 apple24 and banana.25 In this study, all bagging
treatments decreased the weight of loquat fruit compared with
controls (Table 1). And, fruits treated with TGDPB were smaller than
that treated with OWPB. The total soluble solids remained constant
and titratable acid decreased in Baiyu fruits treated with OWPB,
whereas total soluble solid significantly decreased and titratable
acid markedly increased in Baiyu fruits treated with TGDPB as
compared with Baiyu controls. However, there was little effect on

the total soluble solids in Ninghaibai fruits bagged with either

Table 1. Effects of bagging on the physicochemical characteristics of loquat fruit
Surface colour (n = 20)
Treatment

Fruit mass (g) (n = 30)

SSC (%) (n = 5)

TA (%) (n = 5)

L∗

Baiyu

CK
OWPB
TGDPB
F-value
LSD0.05

6.3a

28.1 ±
24.2 ± 5.0b
24.1 ± 6.2b
4.35∗
3.20

1.1a

13.1 ±
13.3 ± 0.9a
11.1 ± 1.6b
13.07∗∗∗
0.69

0.00b

0.39 ±
0.30 ± 0.03c
0.55 ± 0.88a
56.73∗∗∗
0.05

1.1b

65.1 ±
65.5 ± 0.9b
71.0 ± 1.8a
150.36∗∗∗
0.73

Ninghaibai

CK
OWPB
TGDPB
F-value

LSD0.05

29.4 ± 4.5a
27.3 ± 5.4a
24.1 ± 4.0b
6.97∗∗
2.70

14.2 ± 1.0a
14.1 ± 1.5a
14.6 ± 1.4a
0.32
1.03

0.23 ± 0.04c
0.32 ± 0.02a
0.26 ± 0.18b
29.67∗∗∗
0.02

65.6 ± 1.7b
68.7 ± 1.6a
69.4 ± 2.0a
32.25∗∗∗
0.97

Cultivar

a∗

b∗

hab

14.9 ±
14.7 ± 1.3a
10.3 ± 1.9b
65.79∗∗∗
0.87

1.8b

44.0 ±
45.2 ± 1.3a
44.7 ± 1.8ab
3.14∗
0.97

71.3 ± 1.6b
71.9 ± 1.6b
77.1 ± 2.5a
61.62∗∗∗
1.09

14.4 ± 1.2a
10.6 ± 3.4b
8.4 ± 1.9c
34.73∗∗∗
1.40

42.8 ± 1.8c
45.7 ± 3.0b
47.2 ± 2.4a
18.66∗∗∗
1.41

71.4 ± 1.8c
77.1 ± 3.8b
80.0 ± 2.2a
53.76∗∗∗
1.62

1.3a

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1785

Values are expressed as means ± SD. Means within the same cultivar followed by the same superscript letter are not significantly different at P = 0.05.
∗ Significant at P = 0.05.
∗∗ significant at P = 0.01.
∗∗∗ significant at P = 0.001.
CK, control (unbagged); OWPB, one-layered white paper bags; TGDPB, two-layered paper bags with a black inner layer and a grey outer layer.


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H-X Xu, J-W Chen, M Xie

Table 2. Effects of bagging on sugar content in loquat fruit (mg g−1 )
Cultivar

Treatment

Sucrose

Glucose

Fructose

Sorbitol

Total sugar

Baiyu

CK
OWPB
TGDPB
F-value
LSD0.05

2.43 ± 0.04b
3.00 ± 0.04a
2.27 ± 0.10c
91.85∗∗∗

0.14

29.0 ± 1.47b
36.7 ± 0.10a
25.4 ± 1.34c
76.17∗∗∗
2.29

42.1 ± 2.14b
47.6 ± 0.40a
42.3 ± 2.42b
8.19∗
3.76

2.26 ± 0.09a
2.59 ± 0.24a
1.77 ± 0.10b
19.77∗∗
0.32

75.8 ± 3.71b
89.9 ± 0.53a
71.8 ± 3.90b
27.87∗∗∗
6.24

Ninghaibai

CK
OWPB

TGDPB
F-value
LSD0.05

1.59 ± 0.08b
2.26 ± 0.10a
2.57 ± 0.34a
17.03∗
0.42

39.7 ± 2.57a
40.8 ± 0.99a
35.1 ± 1.52b
8.07∗
3.63

51.7 ± 3.32a
52.8 ± 1.67a
45.6 ± 0.82b
9.27∗
4.39

3.42 ± 0.56a
2.40 ± 0.10b
2.15 ± 0.19b
11.50∗∗
0.69

96.4 ± 6.30a
98.2 ± 2.31a

85.5 ± 2.01b
8.70∗
8.08

Values are expressed as means ± SD of three replications. Means within the same cultivar followed by the same superscript letters are not significantly
different at P = 0.05.
∗ Significant at P = 0.05.
∗∗ significant at P = 0.01.
∗∗∗
significant at P = 0.001.
CK, control (unbagged); OWPB, one-layered white paper bags; TGDPB, two-layered paper bags with a black inner layer and a grey outer layer.

OWPB or TGDPB, although the titratable acid content increased
significantly compared with Ninghaibai controls.
Surface colour is an important marketable (consumer acceptance) quality attribute and is a measure of L∗ , a∗ , b∗ and hab .
Table 1 shows that bagging improved fruit surface lightness as
L∗ was higher in the bagged than in the control fruits of both
Baiyu and Ninghaibai. Fruit treated with TGDPB had the highest
lightness values. Reduced light is known to promote the degradation of existing chlorophyll and inhibits carotenoid synthesis in
fruit peel,26 and this resulted in unbagged fruits having a lower
hab and a more red than yellow hue (i.e. were more orange in
colour compared with bagged fruits). In addition, fruits treated
with TGDPB had higher hab than did those treated with OWPB.
The effects of bagging on sugar content
Sugar content is considered to be an important quality characteristic of fresh fruit. However, bagging with different materials
can exert different effects on the composition of soluble sugars.
For example, Padmavathamma and Hulamani23 found that total
sugars varied significantly with bag colour, whereas Yang et al.27
observed that bagging tended to reduce sugar content slightly,
although the sugar content was not significantly affected by bag

type. Table 2 indicates that the effects of bagging type on sugar
content varied between cultivars. OWPB treatment increased the
sucrose, glucose, fructose and sorbitol content and significantly
increased the content of total sugar in Baiyu fruit. However, OWPB
treatment increased the sucrose content, did not affect the glucose, fructose and total sugar content but decreased the sorbitol
content in Ninghaibai fruit. Total sugar contents in Baiyu and Ninghaibai after TGDPB treatment were reduced by 5.6% and 11.3%,
respectively, as compared with the controls.

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The effects of bagging on antioxidant compounds
and antioxidant capacity
Numerous studies have shown that fruit and vegetables are
sources of diverse nutrient and non-nutrient molecules, many of
which have antioxidant properties. The present study determined
the antioxidant capacities of loquat fruit and analysed fruit extracts
for compounds (total phenolic, flavonoid, carotenoid and vitamin

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C) that might contribute to the antioxidant activity. Table 3 shows
that the total phenolic and flavonoid contents decreased after
bagging treatment. Following OWPB and TGDPB treatment, the
total phenolic content of Baiyu fruit was reduced by 9.5% and
45.6%, respectively, and that of Ninghaibai fruit was reduced by
5.0% and 26%, respectively. This indicates that bagging influences
the metabolism of phenolic compounds, of which the flavonoids
are the dominant family. The pattern of variation in flavonoid
content was similar to that observed for total phenolic, with
maximum levels occurring in unbagged Baiyu (28.2 ± 4.4 µg g−1 )

and Ninghaibai (51.0 ± 6.4 µg g−1 ) fruits. The flavonoid content
was also lower in TGDPB-treated fruits than in OWPB-treated fruit.
Carotenoids and vitamin C are also the antioxidant compounds
in loquat. The study found that the carotenoid and vitamin C
contents increased after bagging fruit with OWBP, but decreased
in fruit bagged with TGDBP. Our other experiments have also
observed that the carotenoid and vitamin C content varies
significantly with bag type; however, both decreased markedly
when light was excluded during the maturation of loquat as
compared with that of control fruit (data not shown).
Three independent methods, the DPPH, TEAC and FRAP assays,
were used to compare the antioxidant capacity of fruit extracts.
The results presented in Table 3 show that the antioxidant
potential of loquat fruit extracts was significantly affected by
light transmittance. The highest antioxidant potential in both of
Baiyu and Ninghaibai loquat fruits was under full sunlight and
lowest under bagging with TGDPB.
Table 4 indicates that the total phenolic content and antioxidant
capacity are well correlated (DPPH, r = 0.64; TEAC, r = 0.77;
FRAP, r = 0.90). Fruits with the highest phenolic content
(unbagged fruits of Baiyu and Ninghaibai) had the highest
antioxidant potentials whereas fruit extracts characterised by
low total phenolic levels exhibited a poor antioxidant capacity.
Numerous studies have reported similar linear relationships
between antioxidant activities and phenolic content.28 – 30 A
good correlation was also observed between total flavonoid and
antioxidant capacity (DPPH, r = 0.84; TEAC, r = 0.87; FRAP, r =
0.99) (Table 4). Flavonoids are low-molecular-weight polyphenolic
compounds that are widely distributed in fruit and vegetables,31
and many have been shown to have antioxidant32 and anticancer

c 2010 Society of Chemical Industry

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Effect of bagging on loquat fruit quality and antioxidant capacity

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Table 3. Changes in total phenolic, flavonoid, carotenoid and vitamin C content and antioxidant activities as assessed by DPPH, TEAC and FRAP
assays of loquat fruit after bagging

Carotenoid (µg
β-carotene g−1 FW)

Vitamin C
(µg ascorbic
acid g−1
fresh weigh)

DPPH (µmol
Trolox g−1 FW)

TEAC (µmol
Trolox g−1 FW)

FRAP (µmol
Trolox g−1 FW)

28.2 ± 4.4a
21.6 ± 2.7b
16.1 ± 1.4c
39.88∗
2.77

35.7 ± 4.1b
48.3 ± 3.1a
27.4 ± 0.7c
63.90∗
3.62

13.6 ± 0.9b
15.7 ± 0.6a
11.5 ± 0.7c
68.70∗
0.75

1.85 ± 0.10a
1.54 ± 0.10b
1.22 ± 0.06c
143.55∗
0.08

1.61 ± 0.15a
1.53 ± 0.12a
0.98 ± 0.04b
75.64∗
0.12

2.21 ± 0.09a
1.96 ± 0.04b
1.65 ± 0.09c
110.54∗
0.07

51.0 ± 6.4a
43.5 ± 3.7b
30.3 ± 1.4c
71.08∗
3.54

23.4 ± 0.3b
30.8 ± 0.3a
24.5 ± 0.1b
15.58∗
3.03

16.8 ± 1.5b
18.3 ± 0.5a
15.4 ± 1.9c
9.78∗
1.37

3.23 ± 0.12a
2.08 ± 0.06b
1.59 ± 0.10c
746.79∗
0.09

2.46 ± 0.17a
1.83 ± 0.05b
1.62 ± 0.13c
111.49∗
0.12

3.6 ± 0.16a
3.03 ± 0.11b
2.32 ± 0.10c
268.37∗
0.11

Treatment

Total phenolic
(µg gallic acid
g−1 FW)

Flavonoid (µg
rutin g−1 FW)

Baiyu

CK
OWPB
TGDPB
F-value
LSD0.05

220.0 ± 26.9a

199.2 ± 30.0a
119.7 ± 17.5b
59.60∗
21.73

Ninghaibai

CK
OWPB
TGDPB
F-value
LSD0.05

450.7 ± 31.1a
427.9 ± 24.7a
333.5 ± 20.7b
61.79∗
23.05

Cultivar

Values are expressed as means ± SD of five replications. Means within the same cultivar followed by the same superscript letter are not significantly
different at P = 0.05.
∗ Values are significant at P = 0.001.
CK, control (unbagged); FW, fresh weight; OWPB, one-layered white paper bags; TGDPB, two-layered paper bags with a black inner layer and a grey
outer layer.

Table 4. Correlation coefficients of DPPH, TEAC and FRAP with
respect to total phenolic, flavonoid, carotenoid and vitamin C content
of loquat fruit

Assay

Total phenolic

Flavonoid

Carotenoid

Vitamin C

DPPH
TEAC
FRAP

0.64
0.77∗
0.90∗∗

0.84∗
0.87∗
0.99∗∗

0.14
0.07
0.20

0.35
0.56
0.60

∗

Significant at P = 0.05; ∗∗ significant at P = 0.01; (n = 6).

J Sci Food Agric 2010; 90: 1783–1788

Bagging experiments using two cultivars of loquat showed that
bagging could improve fruit commercial value by improving
fruit visual quality, however, the light transmittance levels of
the bags significantly affected fruit inner quality. Bags with low
light transmittance (TGDPB) significantly increased the content of
titratable acid but decreased the fruit weight and the total sugar,
phenolic, flavonoid, carotenoid and vitamin C contents as well
as the antioxidant capacity of the fruit. However, bagging with
OWPB had less influence on fruit quality and antioxidant capacity.
Therefore, using a bag with appropriate light transmittance is
necessary to maintain fruit quality and antioxidant capacity, and
OWPB was more suitable for loquat bagging than was TGDPB.

ACKNOWLEDGEMENTS
This work was supported by Natural Science Foundation of
Zhejiang Province in China (Y307577 and Y306128) and Important
Item of Science and Technology Department of Zhejiang Province
in China (2005C22062).

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CONCLUSIONS


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