Influence of agronomic variables on quality of tomato fruits

doi:10.4236/as.2011.24054

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Vol.2, No.4, 424-431 (2011)

doi:10.4236/as.2011.24054

Agricultural Scienc es

Influence of agronomic variables on quality of tomato

fruits

Marcos Hernández Suárez1, Eladia Peña Méndez2, Beatriz Rodríguez Galdón3,

Elena Rodríguez Rodríguez2, Carlos Díaz Romero2*

1Agriculture Technology National Centre “Extremadura” CTAEX, Villafranco del Guadiana, Badajoz, Spain;

2Analytical Chemistry, Nutrition and Food Science Department, La Laguna University, La Laguna, Spain;

*Corresponding Aut hor: cdiaz@ull.es

3Biochemistry and Molecular Biology Department, Extremadura University, Badajoz, Spain.

Received 4 August 2011; revised 18 September 2011; accepted 11 October 2011.

ABSTRACT

In order to study interactions between agro-

nomic variables and chemical composition that

determine the quality of tomato fr uit s, a group of

statistical techniques were applied: discriminant

analysis (DA), cluster analysis (CA) and prince-

pal component analysis (PCA) combined with

ANOVA. The results of DA when characterizing

the agronomic parameters were successful, es-

pecially when the collection date was used as a

factor for classification. CA showed the impor-

tance of the chemical variables related to the

metabolic relationships, w hile the principal com-

ponent analysis and ANOV A provide information

on the interaction between variables related to

the production and chemical composition of

tomatoes. The combined use of PCA and ANOVA

is a suitable tool for studying the complex in-

teractions between agronomy and chemical

composition. Collection date was the main ag-

ronomic parameter effected the chemical com-

position, while variety and production system

had a minor effect. The application of PCA-

ANOVA showed that the taste of tomato de-

pends on three factors: sugars (glucose and

fructose), acidity (citric, malic and ascobirc

acids), an d mine ral s (Na and Mg). F or the tom a-

toes with same maturity degree, the taste de-

pends on interaction of date collection and

system production.

Keywords: Tomato; Chemical Composition;

Agronomy; Multivariate Analysis

1. INTRODUCTION

The tomato (Solanum lycopersicon) is not only one of

the world’s most important vegetables, but it is also the

most widely used as well as being a versatile vegetable

crop. They are consumed fresh and are also used to

manufacture a wide range of processed products. These

are some of the reasons why the scientific community

has recently become interested in the tomato [1]. It is an

excellent source of the following nutrients and second-

dary metabolites which are important for human health:

minerals, vitamins C and E, β-carotene, lycopene, fla-

vonoids, organic acids, phenolics and chloroph yll [2-4].

In the case of plant foods, fruit ripening is a geneti-

cally programmed process culminating in changes in

color, texture, flavor, and chemical compositions [5]. As

a result, the chemical composition of the tomato fruit

depends on factors such as cultivar, maturity and the

environmental conditions in which they are grown [6-8].

Foodstuffs are a physico-chemical complex matrix of

several interacting factors. A complete understanding of

the complex interactions between environment, metabo-

lism, and chemical composition of crops would require

the input of information fro m a multidiscip lin ary team of

scientists and the use of tools to in terpret those relations,

by example multivariate analysis [9].

In previous reports [10-13], the chemical composition

of the tomato has been widely described according to

several agronomic variables such as variety, date of col-

lection, cultivation area and production system. The aim

of this work is to characterize and unravel the relation-

ship between agronomy and chemical composition of

tomatoes by using the combination of several chemom-

etric tools.

2. MATERIALS AND METHODS

2.1. Samples

One hundred and sixty seven samples, belonging to

five cultivars of tomatoes (Dorothy, Boludo, Thomas,

Dominique, Dunkan), were provided by ACETO (Aso-

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425425

ciación Provincial de Cosecheros Exportadores de To-

mates de Tenerife) and other companies from different

farms located in the southern and western regions of the

island of Tenerife (Spain). Four samples (1 kg of weight)

of each of the five cultivars were collected during dif-

ferent periods. Agronomic parameters such as variety,

production system, date of collection and cultivation

area were considered (Tab le 1). Additional information

relating to the tomato samples can be found in previous

research [10-13].

2.2. Analytical Methods

Three tomatoes selected from each sample were

hand-rinsed with ultrapure water, shaken to remove any

excess water and gently blotted with a paper towel. The

tomatoes were then mixed and homogenised to a homo-

geneous puree using a model T-25 Basic Turmix (Ika-

Werke, Staufen, Germany). The puree was stored in a

polyethylene tube at –80˚C. Several sub-samples were

taken in duplicate from this puree to measure the differ-

ent parameters.

Moisture content was determined by drying the sam-

ples to a constant weight at 105˚C according to AOAC

[14]. Ash content was measured by calcinations at 550˚C

to a constant weight, according to AOAC [14]. Nitrogen

content was determined according to the Kjeldahl

method and nitrogen value was multiplied by 6.25 as

conversion factor [14]. Total fibre was determined ac-

cording to the method proposed by Prosky et al. [15].

Ascorbic acid was determined by the 2,6-dichlorophe-

nol-indophenol titration procedure [14]. The acidity was

determined by means of titration with NaOH 0.1 mol/L

until pH 8.1, expressing the results in grams of anhy-

drous citric acid per 100 g. The pH was determined by

potenciometric measurement at T = 20˚C with a pH-

meter [14]. The content of total phenolics was deter-

mined according to the Folin-Ciocalteu method [16].

Lycopene concentration was determined spectropho-

tometrically [17].

The mineral content was determined by atomic ab-

sorption spectrophotometry previous to nitric digestion

[11], except for phosphorous which was measured by a

colorimetric method, using the Vanadate-Molybdate re-

agent [18]. The analytical HPLC methods were used to

measure the contents of sugars were proposed by Li et al.

[19] and modified by Hernández et al. [12]. The analyti-

cal method used to measure the content of organic acids

was proposed by Hernández et al. [13]. Hydroxycin-

namic acids were determined according the methos pro-

posed by Martínez-Valverde et al. [20] and modified by

Hernández et al. [10].

2.3. Statistical Analysis

All the statistics were performed by means of the

SPSS version 17.0 software for Windows (SPSS Inc.

Chicago, IL). Each quantitative variable was standard-

ized according to a typical z-standarization.

A linear discriminant analysis (LDA) was applied on

chemical composition to classify the tomato samples

according to the agronomical parameters such as variety,

production system, date of collection and the cultivation

area. The variables used for the classification of tomato

samples Stepwise LDA was applied using Wilk’s lambda

and F-statistic as the selection criterion for the quantita-

tive variables, thereby enhancing the discrimination be-

tween established groups.

Cluster analysis (CA) is one of the most useful chemo-

metric tools for studying the classification tendency of

the samples. Moreover, CA was applied to find out the

underlying relationships between the chemical parame-

ters used in this study. Among several clustering algo-

rithms, Ward’s method was selected as the linkage method

using Euclidean distance as the measure of similarity.

Table 1. Distribution of the tomato samples analyzed according to cultivar, cultivation method, sampling period, and region of pro-

duction.

Production system Date of collection Cultivation area*

Variety Total

Intensive Organic HydroponicOct 04-Nov 04Dec 04-Jan 05Feb 05-Mar 05Apr 05-Jun 05 WestSouth

Dorothy 50 25 14 11 14 16 12 8 16 9

Boludo 46 28 14 4 12 12 11 11 15 13

Dominique 19 10 9 0 4 8 5 2 0 0

Thomas 25 16 9 0 8 8 4 5 0 0

Dunkan 27 4 12 11 2 10 9 6 0 0

Overall 167 83 58 26 40 54 41 32 31 22

*Only in intensive cultivation.

M. H. Suárez et al. / Agricultural Sciences 2 (2011) 424- 431

426

Principal component analysis (PCA) was applied to

explore the complex relationships/connections between

the parameters regarding the agronomical uses and the

chemical composition. One way ANOVA was performed

to investigate which agronomical parameters influence

each principal component (PC) and clarify the results

obtained.

3. RESULTS AND DIS CUSSION

Some scientists suggest that multivariate analysis

should be performed with those independent variables

without a clear relationship between them [21]. In a pre-

vious papers [10-13], we reported that pH, acidity and

ash depend on other analyzed variables, organic acids

and minerals respectively. Besides, in this moisture

showed an inverse relationship with the rest of the vari-

able studied due to the fact that an increase in water

content reduces the concentration of other parameters

[10-13]; these facts are consistent with data from other

authors [22]. Therefore, the variables moisture, ash, pH,

and acidity were not consid ered in this paper.

3.1. Characterization of Agronomic

Variables by LDA

When the tomato variety is used as the criterion for

classification, a very low classification, 32.3% (27.5%

after cross-validation) was obtained and the selected

variables in this stepwise-LDA were Fe, glucose and

ferulic acid. This percentage of classification suggests

that the chemical parameters analyzed were not good

enough to characterize the varieties. Alth ough the results

appear to contradict other studies that report a change in

chemical composition with the tomato variety [7,8,20],

in our case the variety has a limited influence on the

chemical composition. This could be attributed to dif-

ferences in the ripening stage of tomatoes belonging to

different varieties, although, in this work, tomato sam-

ples did not show significant difference between each

other regarding the maturity index [12].

Tomatoes undergo a wide range of biosynthetic as well

as degradative reactions that markedly affect the final

chemical composition of the fruit during ripening [23].

These changes are highly coordinated and modified by

genetic and environmental factors. This aspect is impor-

tant because the consumer chooses the tomatoes for their

appearance (color, size, shape, freedom from physio-

logical disorders, and decay), firmness, texture, dry mat-

ter, and organoleptic (flavor) and health properties [24].

In the case of the date of collection as th e criterion for

classification, a high percentage of classification (91.6%,

89.8% after cross-validation) was observed when select-

ing the following variables: glucose, lycopene, and py-

ruvic, malic, citric, fumaric, and p-c ou maric acids, Na, K,

Mg, P, and Ca. Figure 1 presents the classification of the

tomato samples on the two firsts discriminant functions.

This percentage of classification suggests that the physi-

co-chemical matrix of data selected can be used to char-

acterize the tomatoes according to the date of collection

satisfactorily (Figure 1). An analysis of the results

shows the relationship of the variables selected in the

stepwise-LDA with temperature. The synthesis of glu-

cose (photosynthesis) and organic acids (pyruvic, malic,

citric and fumaric) of the Krebs cycle are regulated by

the temperature [25], and lycopene synthesis is com-

pletely inhibited at 32˚C and temperatures higher than

30˚C - 35˚C notably reduced the lycopene content [26].

Adverse environmental conditions can generate reactive

oxygen species (ROS) in cherry tomato fruits, which

attack all types of biomolecules, causing several altera-

tions in the fruit [26-28]. This could explain why lyco-

pene and p-coumaric acids, both antioxidants, were se-

lected to characterize the date of collection.

As regards the minerals, the deficiency of Ca in the

tissues can cause physiological disorders. Besides which,

salinization also limits the absorption of Ca. Both proc-

esses involve a regulation of ion concentrations (Ca, Na

and K), especially in the summer [29,30].

When the production system was the criterion of clas-

sification, a low percentage of classification, 69.3%

(63.9% after cross-validation) was obtained after select-

ing the following variables: glucose, lycopene, P, Na,

Mg, Mn, and total fibre. Therefore, no clear relationship

Figure 1. Scores plot of tomato samples according to the date

of collection on the space of two first discriminant functions.

M. H. Suárez et al. / Agricultural Sciences 2 (2011) 424- 431

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between the quantitative variables with the production

system was found. Few studies have shown that there are

no differences in the physico-chemical and sensory qual-

ity of conventional tomatoes grown in soil or in rock-

wool slabs (a kind of hydroponic culture) [31]. However,

it has been shown that organic foods seem to have higher

levels of vitamin C, certain essential minerals (Ca, Mg,

Fe) and phytochemicals such as lycopene in tomatoes,

polyphenols in potatoes or flavonols in apples [31-33 ].

Organic tomatoes contained more salicylic acid but

less vitamin C and lycopene when compared to crops

grown using conventional and organic methods [31,34,

35]. Organic fruits had a slightly higher protein content

than conventionally cultivated fruits, perhaps because

the plants were grown und er stressful conditions [36,3 7].

Cultivation area was considered as the fourth factor

for classification. Only intensively cultivated Dorothy

and Boludo varieties were considered in this case. The

following variables: pyruvic acid, ascorbic acid, total

fibre, Na, Ca, Mg, and Mn were selected and gave a high

percentage of classification, 92.3% (90.8% after cross-

validation). The good results suggest that when the pro-

duction system is homogeneous, the differences between

tomatoes varieties grown in different areas could mainly

be due to the mineral composition of soils as well as to

climatic factors. Low levels of Ca in soil may influence

the content of total fibre in tomatoes [38]. The presence

of ascorbic acid in the selected variables could be ex-

plained by two reasons: firstly because Na increases

ascorbic acid in the tomato fruit [36], and secondly be-

cause light exposure is favourable to vitamin C accumu-

lation [26,39]. Metabolic relationships could explain the

presence of Mg, Mn and pyruvic acid. Mg active en-

zymes such as phosphoenol pyruvate carboxylase and

Mn are involved in photosynthesis [25].

3.2. Characterization of Chemical

Composition by CA

Figure 2 presents the dendrogram obtained using

Ward’s linkage method (Euclidean distance). Five main

groups were considered: A, B, C, D and E, where there

are several subgroups withi n each group.

Group A included two subgroups, sugars (fructose and

glucose) and certain organic acids (malic, citric,and fu-

maric acids). These variables are associated with or-

ganoleptic properties which are mainly attrib uted to their

aroma compounds, sugars, and organic acid contents

[31]. Glucose and fructose account for about 95% of the

total sugars in the tomato whereas sucrose is detected in

trace amounts [40,41].

The rest of groups are linked to the nu tritional quality.

Worthington [31] defined nutritional quality o f a fruit for

the content in minerals, vitamins, and bioactive com-

pounds such as carotenoid and flavonoid contents.

Group B included chemical parameters essential for

good plant development. A first subgroup: oxalic and

pyruvic acids; and a second subgroup with protein and

Fe. Pyruvic and oxalic acids are precursors of the Krebs

cycle and Fe is the main micronutrient, often linked to

proteins or enzymes [25].

Figure 2. Dendrogram obtained by cluster analysis of variables using Ward’s method of linkage.

M. H. Suárez et al. / Agricultural Sciences 2 (2011) 424- 431

428

The several subgroups of parameters included within

group C have clear relationships between them. The

availability of Mg depends on K concentration and both

minerals are involved in the synthesis of carotenoid

compounds [42]. Salt enrichment (Na) in the nutrient

solution of plants is known to increase the ascorbic acid

content, which adds acidic taste to the fruit [43]. As re-

gards phenolic compounds, although genetic control is

the main factor in determining their accumulation in

vegetable foods, external factors may also have a sig-

nificant effect on this. In many plant species the flavonol

content may be enhanced in response to elevated light

levels, in particular to increased UV-B radiation and the

level of P. Tomato plants grown under high light accu-

mulate a higher soluble phenol content (rutin and chloro-

genic acid) than low-light plants [44-47].

Chemical parameters related with structural function

appear in group D. Ca plays an essential role in this

process by preserving the structural and functional in-

tegrity of plant membranes, stabilizing cell wall struc-

tures and regulating ion transport [48,49].

Group E consisted of the main antioxidants, such as

hydroxycinnamic acids and lycopene, and of Cu. The

data available on the effect of mineral nutrients on the

antioxidant compounds of tomato are either scarce or not

very reliable or appl icabl e [26 ] .

3.3. Characterization of Agronomic

Variables and Chemical

Composition by PCA

After performing PCA analysis, 71.3% of the variance

could be explained by eight main components having

eigenvalues higher than 1. Table 2 shows the first six

PCs for the characterization of tomatoes. The chemical

variables had low loads on PC7 and PC8, and therefore,

they are not shown. The last column of Tab l e 2 shows

the result of one way ANOVA on each PC. Figure 3

shows the loading p lot of the considered variables in the

PCA projected on the plane of PC1 vs PC2.

Table 2. Results of PCA without rotation. Total variance explained: 71.3%.

PC % variance Main quantitative variables included Agronomic parameters that influences in the PC§

PC1 16.4 Glucose, fructose, malic acid, citric acid, ascorbic acid, Na, Mg* Date of collection, production system, cultivation area†

PC2 13.3 Lycopene, oxalic acid, protein, Zn, Fe, Cu Date of collection, cultivation area†

PC3 11.1 p-Coumaric acid, caffeic acid, ferulic acid, Ca, Mn, P Date of collection, production system, cultivation area†

PC4 9.1 Total fibre, pyruvic acid, chlorogenic acid Production system

PC5 7.1 K, Mg* Date of collection, production system

PC6 5.8 Phenolic compounds, fumaric acid Variety

*The PCA included the Mg in two PCs, 1 and 5, with similar load; §ANOVA on each PC (P < 0.05); †Only for the intensive Dorothy and Boludo varieties.

Figure 3. Loading plot of Principal Components Analysis (PCA) projected on

the space of PC1 vs PC2.

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PC1 explains 16.4% of the variance and is formed by

the chemical variables that define the organoleptic prop-

erties of the tomato (Table 2): sugars (glucose and fruc-

tose), the main organic acids (malic, citric and ascorbic

acids) and the minerals Na and Mg together (Mg is also

included in PC5). Date of collection, production system

and the cultivation area are the agronomic variables

which influence this PC. As has already been mentioned,

the organic acids are related with organoleptic properties

and they contribute to increasing ascorbic acid as well as

adding acidic taste to the fruit [36].

PC2 explains 13.3% of the variance and groups the

following variables together: lycopene, oxalic acid, pro-

tein, Zn, Fe and Cu. This PC contains variables directly

influenced by date of collection and the cultivation area

and suggests a metabolic relationship that depends on

the environmental conditions. The synthesis of lycopene

is influenced by temperature [26]. Therefore, the synthe-

sis of this carotenoid starts at temperatures above 16˚C,

is optimal at 22˚C - 25˚C and decreases from 30˚C [26].

Furthermore, the accumulation of lycopene blocks radia-

tion with values greater than 650 W/m2 thereby affecting

the homogeneous red color of the fruit [23]. With respect

to the proteins, plants grown under stressful conditions

may provide a storage form of nitrogen that is re-utilized

when the stress is over, the protein may also be synthe-

sized in response to salt stress, such as happens in crop

fertilization [48,49]. The presence of Zn, Fe and Cu in

this PC may be due to a metal-protein association, such

as the synthesis of isoenzymes of superoxide dismutase

linked to Zn and Cu which is a vegetable defense me-

chanism [25].

Hydroxycinnamic acids (p-coumaric, caffeic and fer-

ulic acids) and the minerals Ca and P and the trace ele-

ment, Mn, are associated to PC3. This PC could be asso-

ciated to antioxidant activity and to some minerals. Plants

synthesize antioxidant compounds such as the hydroxyl-

cinnamic acid (p-coumaric, caffeic and ferulic acids)

which frequently occur in foods as simple esters with

quinic acid or glucose, as well as flavonoids and phenol-

lic compounds to prevent oxidation reactions [50]. Fla-

vonoids and pheno lic acids are componen ts der ived fr om

the route of the pentose phosphate, which is involved in

maintaining and providing antioxidant defense functions

and they may accumulate because of a deficiency of N, P

and Fe [51]. The presence of P in this PC could be ex-

plained by the fact that an appropriate level of P is cru-

cial not only for normal growth and development of the

plant, but also for the synthesis of various secondary

metabolites [47]. Mn participates in the electron trans-

port of photosynthesis and Ca is a structural element of

the membrane through which the exchange of electrons

takes place [25].

PC4 groups the following variables together: total fi-

bre, pyruvic acid and chlorogenic acid. The percentage

of variance explained was 9.1% and the production sys-

tem was the agronomic variable associated to this PC.

PC5 explains 7.1% of variance, the variables associated

were K and Mg; which depend on date of collection and

production system. PC6 is the only PC that depends on

variety. It groups phenolic compounds and fumaric acid

together and it explains only 5.8% of the variance.

Each of the remaining PCs should be formed by at

least 4 variables in order to interpret their meaning cor-

rectly [21]. An intuitive interpretation could be taken

from reviews in the literature. Thus, PC5 is formed by K

and Mg which are related with the synthesis of carote-

noids and pigments. The application of K fertilizers,

especially lycopene, can increase the carotenoid conten ts

in tomatoes and Mg is a component of chlorophyll [26,

52]. Note that PC6 is the only principal component

where variety is the variable with the greatest load.

Some authors have used the metabolic compounds asso-

ciated to the second metabolites, as a quick classification

of samples according to their origin or biological prove-

nance [53].

4. CONCLUSIONS

Multivariate statistical analysis is a suitable tool for

the evaluation of extensive data tables but in some cases

it is essential the combination of several techniques to

obtain good results. The application of PCA-ANOVA

was an effective tool to analyze the chemical composi-

tion of tomato and to know the agronomic variables that

influence the composition. For the same maturity, the

organoleptic quality of tomato expressed as the sugar

content, organic acids and mineral content (Na) only

depends on interaction of production system and date of

collection.

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LEGEND

Cou: p-coumaric acid, Cu: copper, Lyc: lycopene, Zn:

zinc, Mn: manganese; Fer: ferulic acid, Caf: caffeic acid,

P: phosphorus, Pyr: pyruvic acid, Oxa: oxalic acid, Pro:

protein, Fe: iron, Fum: fumaric acid, Cit: citric acid, Fru:

fructose, Glu: glucose, Mal: malic acid, AA: ascorbic

acid, TF: total fibre, Phe: phenolic compounds, Mg:

magnesium, Na: sodium, Chlo, chlorogenic acid, K: po-

tassium, Ca: calcium