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Lycopene Estimation in Tomato LinesUsing Infrared Absorbance and TomatoAnalyzerDilip R. Panthee a , Penelope Perkins-Veazie b , Dan Randall c & AllanF. Brown ba Department of Horticultural Science , North Carolina StateUniversity, Mountain Horticultural Crops Research and ExtensionCenter , Mills River, North Carolinab Department of Horticultural Science , North Carolina StateUniversity, Plants for Human Health Institute , Kannapolis, NorthCarolinac Verivide Ltd. , Charlotte , North CarolinaPublished online: 02 May 2013.

To cite this article: Dilip R. Panthee , Penelope Perkins-Veazie , Dan Randall & Allan F. Brown (2013)Lycopene Estimation in Tomato Lines Using Infrared Absorbance and Tomato Analyzer, InternationalJournal of Vegetable Science, 19:3, 240-255, DOI: 10.1080/19315260.2012.715324

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International Journal of Vegetable Science, 19:240–255, 2013Copyright © Taylor & Francis Group, LLCISSN: 1931-5260 print / 1931-5279 onlineDOI: 10.1080/19315260.2012.715324

Lycopene Estimationin Tomato Lines UsingInfrared Absorbanceand Tomato Analyzer

Dilip R. Panthee,1 Penelope Perkins-Veazie,2 Dan Randall,3

and Allan F. Brown2

1Department of Horticultural Science, North Carolina State University, MountainHorticultural Crops Research and Extension Center, Mills River, North Carolina2Department of Horticultural Science, North Carolina State University, Plants forHuman Health Institute, Kannapolis, North Carolina3Verivide Ltd., Charlotte, North Carolina

The color of red tomatoes (Solanum lycopersicum L.) is mostly from the carotenoidpigment lycopene, which is of interest to consumers and the tomato industry becauseof its purported protective effects against diabetes, cardiovascular events, and somecancers. Lycopene content was measured in at least 179 tomato lines with pink, red,and dark red fruit derived from a diverse genetic background to determine the levelof variation for lycopene and to develop prediction models. Two methods (TomatoAnalyzer or DigiEye) for quantifying total lycopene and to develop prediction modelswere tested on tomato fruit to find a high throughput lycopene measurement systemsuitable for screening hundreds of lines in a breeding program. The tomato lycopenecontent ranged from 28 to 133 mg•kg−1 of tomato sample, indicating a wide variationin the set of tomato lines. Using this variation, lycopene prediction models were devel-oped. Though a single equation could not be developed using data from the DigiEyeor Tomato Analyzer to predict lycopene content of tomato fruit, individual equationswithin color groups proved useful in predicting lycopene content (r = 0.77, P < 0.05).

Current address for Dan Randall: Shaw Industries Inc., 200 Industrial Blvd.,Bainbridge, GA 39817.This research was supported in part by grants from the North Carolina Departmentof Agriculture, Specialty Crop Block Grant Program, and the North Carolina TomatoGrowers Association. The technical help of Candice Anderson, Phillip Sanders, and Dr.Guoying Ma is highly appreciated.Address correspondence to Dilip R. Panthee, Department of Horticultural Science,North Carolina State University, Mountain Horticultural Crops Research andExtension Center, MHCREC, 455 Research Dr., Mills River, NC 28759. E-mail:Dilip_panthee@ncsu.edu

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Lycopene Estimation in Tomato 241

Our data indicate that rapid analysis of tomato fruit, kept relatively intact, can be doneto accurately predict lycopene content in a wide range of fruit colors.

Keywords Breeding for health benefits, Lycopene, Multicolored, Solanum lycoper-sicum, Tomato.

The average human diet includes ingestion of about 40 carotenoids (Levinet al., 2006). Tomato (Solanum lycopersicum L.) fruit contain up to 20 ofthese, including lycopene, α-carotene, β-carotene, lutein, zeaxanthin, andβ-cryptoxanthin. Carotenoids are large molecule compounds that can be pre-cursors for vitamin and antioxidants (Dorais et al., 2008). In red-fruited ripetomato, trans-lycopene makes up more than 80% of the total carotenoids.Lycopene is a fat-soluble unsaturated hydrocarbon that can be an end prod-uct or a substrate for biosynthesis of other carotenoids, such as lutein andβ-carotene (Clinton, 1998). In addition to its antioxidant properties, lycopeneis reported to have anticancer properties (Campbell et al., 2004, 2006) and tohelp prevent cardiovascular diseases and diabetes (Kun et al., 2006).

In humans, fresh tomatoes yield less available lycopene than do cookedtomatoes (Stahl and Sies, 1992), most likely due to proteins attached to chro-moplasts that must be inactivated by heat to release lycopene. However,human plasma can hold high amounts of lycopene (Ross et al., 2011; Shi andLe Maguer, 2000). Increasing the lycopene content in fresh market tomatoescould offset decreased availability of lycopene. Some researchers have exploredthe genetic variability of lycopene content in tomato for use of potential lines inbreeding programs. Lenucci et al. (2006) found differences in lycopene among14 cherry and four high-pigment tomato cultivars. Among S. lycopersicum(L.) genotypes, cherry tomatoes were found to have as high as 91.9 mg•kg−1

of lycopene (Kuti and Konuru, 2005). Hanson et al. (2004) reported higheramounts of lycopene in three S. pimpinellifolium (L.) compared to 50 lines ofS. lycopersicum. In contrast, large sample sizes of tomatoes from five cultivarshad no difference in lycopene content (Jordan, 2007). These studies indicatethat genetic variation does exist for lycopene content among tomatoes, butresults can depend on the number of genotypes and lines sampled.

The preferred method for determining both carotenoid profile and amountsof cis- and trans-lycopene is extraction of carotenoids using organic solvents fol-lowed by analysis with high-performance liquid chromatography (HPLC). Thedrawbacks to this method are the high cost of the instrument and of analysesand the slow turnaround time, making it impractical for breeding programswhere hundreds and thousands of fruit must be evaluated. Spectrophotometricanalysis, using visible light and organic solvent extraction of carotenoids, isless expensive and faster. However, spectrophotometric analysis can distin-guish samples only for total lycopene rather than its components such as cis-or trans-lycopene. Additionally, β-carotene, present in relatively high amounts

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242 D. R. Panthee et al.

with the ogc gene, has spectra that overlap with lycopene. This can cause a10% to 20% overestimation of lycopene (Fish et al., 2002).

Reflectance or absorbance of light at a particular spectrum is widely usedin agricultural research, as a biochemical compound has the unique prop-erty of absorbing or reflecting radiation of a specific wavelength (Delwicheet al., 2007; Guthrie and Walsh, 1997; Rubio-Diaz et al., 2010). On the basisof these characteristics, concentration of a compound can be determined.It has been reported that intensity of red color is positively correlated withlycopene content (Odriozola-Serrano et al., 2009). Any instrument that canmeasure reflectance or absorbance of tomato may be used as a means of quicklyevaluating lycopene content.

Nondestructive measurement of color in tomatoes and estimation oflycopene from colorimeter values has been attempted using reflectance col-orimeters. This system gives a good correlation of L∗, a∗, or hue whencomparing stages of ripeness from green to red (Dsouza et al., 1992; Ruiz et al.,2005), Using colorimeters to estimate lycopene by Commission Internationalede l’Eclairage (CIE) or absorbance has been more successful using tomatopurees (Hyman et al., 2004), where chromaticity correlates reasonably wellwith total lycopene content (r > 0.90). However, that study only used redtomatoes.

Scanning colorimeters equipped with xenon light enable transmittance oflight through a tomato homogenate, and lycopene absorbs light at 560 nm.Plotting the absorbance values at 560 nm against absorbance values forlycopene obtained from organic solvent extraction, a slope is calculated thatcan be used to estimate total lycopene from a simple puree of tomato (Daviset al., 2003). When the same purees are used for scanning colorimetry,spectrophotometric analysis, and HPLC, trans-lycopene values showed agree-ment with HPLC analysis within a 5% level of variation. The drawback tousing HPLC analysis is that it is destructive and sample preparation takesa significant amount of time. Though use of a tomato puree without furtherorganic solvent extraction for total lycopene estimation is simple and accu-rate, a simpler method that will keep fruit as intact as possible is desired.Scanning of cut tomatoes, using a regular scanner and predicting color param-eters with specially adapted software, has proven to be a useful means ofdetermining fruit color (Darrigues et al., 2008). Additionally, a technique uti-lizing a mounted camera to digitize tomato fruit in two dimensions is available(DigiEye, VeriVide Limited, Leicester, UK). Both of these methods offer themeans to quickly evaluate fresh fruit, if they provide an accurate estimate oftotal lycopene.

The first objective of this study was to determine the level of varia-tion for lycopene content in tomato lines of different fruit colors rangingfrom pink to red and dark red derived from a wide genetic background.A second objective was to develop prediction models for lycopene content

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Lycopene Estimation in Tomato 243

in cut fruit to test the effectiveness of CIE color parameters collected fromthe Tomato Analyzer (TA) software (ver. 2.2; http://www.oardc.ohio-state.edu/vanderknaap/tomato_analyzer.htm; Darrigues et al., 2008) and DigiEye(DE) instrument in order to develop a rapid lycopene screening of tomato linesof fresh fruit for use in breeding programs.

MATERIALS AND METHODS

Plant MaterialsA wide range of plant materials included in the study were lines

derived from Hawaii 7998 (S. lycopersicum), LA 3707 (S. pimpinellifolium),large grape, lines with crimson (ogc) gene, lines derived from LA 1033 (S.habrachaites L.), lines derived from LA 716 (S. pennellii L.), red cherries,deep red lines, pink tomatoes, and regular grape, plum, and large-fruitedround tomatoes. A total of 179 lines were grown with two replicates at theMountain Horticultural Crops Research and Extension Center, Mills River,North Carolina, from June through October in 2009 and 2010. Seed wereplanted in 72-cell flats (56 × 28 cm2) in potting mix in the first week of Mayand transplants, at about 6 weeks from seed, were planted by hand in the field.Six plants of each line were planted with 45-cm plant-to-plant and 150-cm row-to-row spacing. The soil was a clay loam and the natural daylight photoperiodwas about 14/10 h with 25–30◦C high and 14–16◦C low temperatures.

Color Capturing With DigiEye and Tomato AnalyzerFully ripe fruit (USDA stage 6) were harvested from the middle four plants

from each line. Fruit were taken to the horticulture laboratory immediatelyafter harvest and rinsed with tap water to remove debris and then air dried.A minimum of six fruit per breeding line, free of injury, were sliced in half lon-gitudinally (from stem to blossom end) with a sharp knife. The cut fruits wereput on the tray of the DE facing up, with a black background. Precautionswere taken so that no fruit was tilted. The DE door was closed so that it wascompletely dark. Because the camera was mounted on the top of the DE, thedistance was constant for all samples. An image of each sample was capturedand saved as .jpeg files, which were used for further analysis. Color parameterswere estimated and expressed using definitions of the CIE, which are consid-ered the industry standard (CIE, 1995). The CIE defines L∗ as the indicator ofthe lightness of the surface, a∗ as the indicator of a red–green surface, and b∗

as the indicator of a yellow–blue surface. Chroma is the intensity of the surfacecolor, and hue is a combination of basic colors (red, blue, and yellow). Analysisof sliced fruit for color pigment CIE values and reflectance (%) was performedusing a DE instrument. The DE uses the principle of reflectance of visible light

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244 D. R. Panthee et al.

by the object in the range of 400–700 nm wavelength to determine color. Eachlateral section was measured for color using the DigiPix feature by selection ofdominant color pixels within 50 �E units from selection. Measurements fromeach fruit were averaged for the plot to yield CIE L∗, a∗, b∗, chroma, hue, andreflectance percentage for the plot. For optimum color measurement, the illu-minating system was calibrated to daylight D65 daily. The DE cube providesdiffused illumination to minimize gloss and specular aberrations. Spectral cal-ibration of the digital single-lens reflex camera provides absolute CIE values.White point and uniformity calibration of the optical system and color pixelswere selected on the basis of �E from the dominant color of the pericarp for cal-ibration. Areas in the pericarp were selected by DigiPix software and markedas black. The software can exclude seed, stem, locular cavities, and lighter ornonpigmented vascular tissue. Any black-marked tissue or area selected by theinstrument was used to estimate the CIE parameters by the software; thoseexcluded remained white. The pixels selected in the instrument were convertedto CIE colorimetric values in D65 illuminant and 10◦ observer condition char-acteristic of diffuse daylight. The reference for light condition corresponds toan observer looking at a white background in diffuse daylight (CIE, 1995).Spectral value (reflectance percentage) curves were synthesized and availablefor further calculations as K/S (absorbance) or strength (%). Calculation ofdarker red pigment was as a percentage of total slice.

The same images from the DE in 2009 were used in the TA software(Darrigues et al., 2008) to obtain color parameters CIE L∗, a∗, b∗, hue, andchroma estimates. However, in 2010, images were taken from cut fruits usinga flatbed scanner (Canon Image Scanner CanoScan 8800F Canon, Melville,NY). The scanner and fruit were covered with a cardboard box with a blackbackground to avoid effects of ambient light. Detailed instructions on takingimages of tomato fruits were described by Rodríguez et al. (2010). Images of atleast six fruit per line were taken and saved as .jpeg files. The TA was used toestimate the color parameters L∗, a∗, b∗, hue, and chroma of the image. Averagedata of all fruit per scan were entered into an Excel file for further analysis.

Lycopene, Total Soluble Solids, and pH DeterminationFollowing the above measurements, about 250–400 g of tomato (one-half

slice per fruit) was placed in plastic ziplock bags and held at −80◦C untilanalyzed for lycopene, pH, and total soluble solids (TSS). Tomato slices werethawed and pureed using a homogenizer (Polytron PT10-35-GT with 20 mmPTDA Generator, Kinematica, Bohemia, NY). Diluted purees (1:4 w/w water)were mixed in 50-mL centrifuge tubes and then 20 mL was poured into a10-cm cuvette. Absorbance at 350–760 nm was measured using a HunterLab Ultra Scan Pro equipped with diffuse/8 xenon illumination (HunterAssociates, Reston, VA). Lycopene was calculated by subtracting the peak

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Lycopene Estimation in Tomato 245

area at 750 nm (background light scatter) from that at 560 nm (the peakabsorbance wavelength for total lycopene). Total lycopene was calculated usingthe system of Davis et al. (2003). Lycopene values were verified using HPLCand spectrophotometer analyses according to Perkins-Veazie et al. (2006; datanot shown). Percentage TSS was measured by placing a 0.5-mL sample ofpuree onto the sensor of a digital refractometer (model 32a, Atago, Westbury,NY). Puree pH was measured using combination of electrode and pH meter(Accumet AB15, Fisher Scientific, Pittsburgh, PA) calibrated to pH 4.0, 7.0,and 10.0.

Data AnalysisGenotypic differences among genotypes were determined using SAS

(ver. 9.2, SAS Inc., Cary, NC) software. A general linear method with geno-type as fixed and year as random effects was used. Data were transformedto Log10 to normalize distribution and the ratio between a∗ to b∗ was deter-mined. All data were transformed to the power of 2, 3, and 4 before performingregression analysis of lycopene on color parameters. Data were analyzed forcorrelation and regression using SAS software. PROC CORR was used to deter-mine Pearson’s correlation coefficients among lycopene content and CIE colorparameters. Linear, polynomial, and multiple regression analyses of lycopeneon data generated from DE and TA including L∗, a∗, b∗, hue (H∗), and chroma(C∗), and transformed data were performed with PROC REG in SAS to developprediction equations.

RESULTS

Tomato lines differed for TSS and lycopene but not pH (Table 1). A Gaussiandistribution was found in the tomato lines for lycopene content, with somelines containing a very high lycopene content (Figure 1A). The tomato line081-12(x)-1 was highest for lycopene (133 mg•kg−1) followed by NC 860-1A(2008) and NC 07310(2007)-14-1 (103 mg•kg−1). The lines NC 051(x)-66-6 andNC3 Grape had 101 and 100 mg•kg−1 lycopene, respectively. Total soluble

Table 1: Descriptive statistics for lycopene and other fruit quality traits in tomato linesderived from a wide genetic background.

Variable Mean Minimum Maximum LSD(0.05)a

pH 4.3 4.0 4.6 0.9Total soluble solids (TSS) (%) 5.3 2.7 7.9 2.7Lycopene (mg•kg−1) 64.3 27.9 133.1 10.7

aLSD(0.05) = Least significant difference at 5% probability level; the LSD values were used toseparate means of genotypes at 5% probability levels.

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246 D. R. Panthee et al.

Figure 1: Frequency distribution of tomato lines derived from multiple genetic backgrounds for(A) lycopene content (mg•kg−1) and (B) TSS (%).

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Lycopene Estimation in Tomato 247

solids presented a wide variation among tomato lines, ranging from 2.7% to7.9% with a normal distribution of lines (Figure 1B). The wide variation forlycopene and TSS found in the tomato lines from a wide genetic backgroundprovides an opportunity for using some of these lines as parents in futurebreeding programs to improve lycopene and TSS contents in hybrid tomatoes.Most high-lycopene and high-TSS lines were grape types as determined byclustering of lines (Figure 2).

Correlation AnalysisA positive correlation occurred between TSS and lycopene content

(Table 2). Soluble solids, including organic acids, sugars, and water-solublepolysaccharides, can be utilized in glycolosis and for many physiological pro-cesses, including synthesis of carotenoids. No significant correlation occurredbetween pH and other attributes. This finding contradicts those of Vogel et al.(2010) in tomato mutants. Correlations between CIE color parameters L∗,a∗, b∗, H∗, and C∗ data obtained from the DE and TA system and lycopenewere determined. The correlation between DE and TA color parameters wasstrongly positive (Table 2). This indicated that both methods estimate similarcolor parameters. This analysis was on the basis of all fruit, including thosewith different colors. When separate analyses were performed on the basis offruit color, correlations were improved, indicating that prediction models maybe more accurate if used on the basis of color group.

Prediction Models for Different Colored TomatoesAttempts were made to develop a single prediction model using the color

parameters estimated by DE and TA to predict level of lycopene in all coloredtomatoes. However, the coefficient of determination was very small (R2 = 0.18),indicating that the model was not adequate to predict lycopene level. An empir-ical assessment of the correlation between L∗, b∗, and lycopene revealed thata certain segment of the population showed moderate correlation with L∗ andb∗. By applying equal weights to L∗ and b∗, the following equation improvedcorrelation to 0.67, which has R2 = 45.9% (Figure 3).

Lycopene_General = (50 − L∗)0.268

+ (34 − b∗)0.143

[1]

This still was not a sufficient correlation to develop prediction models, somodel development based on fruit colors was attempted because correlationcoefficients among lycopene and color parameters based on grouping tomatoesby colors was better. Tomato colors in this study were red, dark red (with acrimson gene that makes the tomato dark red), and pink.

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248 D. R. Panthee et al.

Figure 2: Clustering of tomato lines derived from multiple genetic backgrounds for (A)lycopene content (mg•kg−1) and (B) TSS (%).

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Tab

le2:

Co

rre

latio

nc

oe

ffic

ien

tb

etw

ee

nC

IEc

olo

rp

ara

me

ters

de

rive

dfr

om

Dig

iEye

an

dTo

ma

toA

na

lyze

ra

nd

lyc

op

en

ein

tom

ato

line

sd

eriv

ed

fro

ma

wid

eg

en

etic

ba

ckg

rou

nd

.

Varia

ble

TA_L

∗TA

_a∗

TA_b

∗TA

_H∗

TA_C

∗Ly

co

pe

neD

E_L∗

DE_

a∗

DE_

b∗

DE_

H∗

DE_

C∗

pH

TA_a

∗−0

.81∗∗

∗TA

_b∗

0.81

∗∗∗

−0.6

8∗∗∗

TA_H

∗0.

87∗∗

∗−0

.97∗∗

∗0.

79∗∗

∗TA

_C∗

0.44

∗∗∗

−0.0

8ns

0.77

∗∗∗

0.26

∗∗∗

Lyc

op

en

e−0

.34∗∗

∗0.

24∗∗

∗−0

.07

−0.2

0∗∗∗

0.17

∗∗∗

DE_

L∗0.

98∗∗

∗−0

.92∗∗

∗0.

87∗∗

∗0.

95∗∗

∗0.

58∗∗

∗−0

.41∗∗

∗D

E_a

∗−0

.75∗∗

∗0.

92∗∗

∗−0

.61∗∗

∗−0

.88∗∗

∗−0

.20∗∗

∗−0

.08n

s−0

.75∗∗

∗D

E_b

∗0.

87∗∗

∗−0

.82∗∗

∗0.

98∗∗

∗0.

88∗∗

∗0.

83∗∗

∗−0

.21∗∗

∗0.

91∗∗

∗−0

.60∗∗

∗D

E_H

∗0.

91∗∗

∗−0

.98∗∗

∗0.

85∗∗

∗0.

99∗∗

∗0.

51∗∗

∗−0

.11n

s0.

93∗∗

∗−0

.92∗∗

∗0.

86∗∗

∗D

E_C

∗0.

64∗∗

∗−0

.45∗∗

∗0.

81∗∗

∗0.

550.

90∗∗

∗−0

.23n

s0.

68∗∗

∗−0

.13∗

0.87

∗∗∗

0.50

∗∗∗

pH

0.00

−0.0

3ns

0.10

ns

0.07

0.06

ns

−0.0

1ns

−0.0

1ns

−0.0

6ns

0.07

ns

0.09

ns

0.00

TSS

−0.0

5ns

0.01

ns

−0.0

5ns

−0.0

3−0

.04n

s0.

18∗∗

∗−0

.25∗∗

∗−0

.21∗∗

∗−0

.15∗

0.01

ns

−0.3

1∗∗∗

0.01

ns

ns,

∗ ,∗∗

∗ No

nsig

nifi

ca

nt

ors

ign

ific

an

ta

t0.

05a

nd

0.00

1le

vels

of

pro

ba

bili

ty.

249

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250 D. R. Panthee et al.

Lycopene = (50-L*)/0.268+(34-b*)/0.143 R2

= 45.9%

Figure 3: Scatter plot of observed and predicted lycopene content using equations devel-oped from the DigiEye instrument.

The overall approach of model development was from simple to com-plex; that is, we started with simple linear regression and then moved on topolynomial regression and multiple regression until an adequate model wasfound. First, we regressed the lycopene on untransformed original data, Log10,and the ratio between a∗ and b∗ followed by transformation to the power of 2,3 and 4 as well as multiple regression.

Models for Red TomatoesA linear regression model did not fit well in describing lycopene level in red

tomatoes by any color parameters. As a result, a polynomial regression with 2,3, and 4 degrees of power was performed. For red tomatoes, color parametersobtained from the TA could explain 38% (R2 value) of the total variation inlycopene content, which had a correlation coefficient (r) = 0.62.

Lycopene_Red = 2410.9 − 22.9L∗ − 28.1a∗ − 22.9b∗ − 99.6H∗ − 56.9(

a∗

b∗

)

+ 2.9H∗2 + 0.7C∗2 − 51(

a∗

b∗

)2

[2]

Though this equation was not better than Eq. [1], using the color parametersfrom the DE changed the prediction equation and improved the R2 value to49.4%.

Lycopene_Red = 784.2 − 92.4b∗ − L∗2 + 2b∗2 + 2.7C∗2 + 0.3L∗3 + 0.01a∗3

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Lycopene Estimation in Tomato 251

− 0.08C∗3 − 48.1(

a∗

b∗

)3

[3]

Models for Dark Red Tomatoes With Crimson GeneThe presence of the crimson gene (ogc) gives tomato fruit a dark red color

and the flower and fruit gel a shiny orange color. Using lines of segregation bycolor, a similar function as in Eq. [1] where the L∗:b∗ parameters estimated byDE were weighted 3:1 resulted in Eq. [4]. Correlation coefficient for Eq. [4] withfruit in this group was 0.75 (R2 = 56.3%), with pericarp color characterized asdark red.

Lycopene_Dark red = 5.6(50 − L∗) + 3.5(34 − b∗) [4]

When color parameters generated by the TA software were used to develop theprediction equation, it resulted in Eq. [5].

Lycopene_Dark red = 7180.73 − 16510.9(

a∗

b∗

)+ 12725

(a∗

b∗

)2

− 3255.56(

a∗

b∗

)3

[5]

Observed and predicted values were in close agreement, with an R2 = 56.9%.

Models for Pink TomatoesPink tomatoes differ in breeding programs, because they have an uncolored

peel but may have lycopene levels similar to red tomatoes. Multiple predic-tion equations were found to be adequate to estimate lycopene level in pinktomato. One of the best prediction equations (Eq. [6]) explained almost 99% ofthe variation, with a coefficient of variation of R2 = 98.8%.

Lycopene_Pink = −680.249 + 85.3525a∗ − 3.26548a∗2 + 0.04141a∗3 [6]

This prediction equation was found to be equally useful to predict lycopeneon the basis of color parameters estimated by the DE or TA (Figure 4). An addi-tional prediction equation for pink tomatoes using color parameter data fromthe TA with R2 > 98% was

Lycopene_Pink = −682.5 − 3L∗ + 48.3a∗ + 23.2b∗ + 16H∗ − 46.4C∗ [7]

With the color parameters data from the DE, the equation for pink tomato was

Lycopene_Pink = −485.8 + 15.6L∗ − 1.3a∗ + 5.3b∗ − 8H∗ [8]

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A

Lycopene=-485.8+15.6L*-1.3a*+5.3b*-8H* R2=98.0%

B

Lycopene=-682.5-3L*+48.3a*+23.2b*+16H*-46.4C* R2=98.1%

Figure 4: Comparison of prediction of lycopene in pink tomato on the basis of color parame-ters estimated by (A) DigiEye and (B) Tomato Analyzer.

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Lycopene Estimation in Tomato 253

The three equations were found to have R2 ≥ 98%, indicating that colorparameters estimated either from the DE or TA can be used in more than oneway to predict lycopene content in pink tomato (Figure 4).

DISCUSSION

Variation in Lycopene Content Measured by ColorimetryMethodsThere was a reasonably good amount of variation of lycopene content in

this study, which was similar to the results of other studies (Hanson et al.,2004; Hyman et al., 2004). This addressed our first objective to screen tomatolines for total lycopene content. The information obtained from this studywill provide the basis for future breeding to improve total lycopene con-tent in tomato. In contrast to Thompson et al. (2000), who reported thatthe crimson gene increased lycopene content by 51% to 74%, there was nohigh lycopene content in large-fruited tomato lines with the crimson gene.Because most high-lycopene lines in our study were grape tomatoes, a goodgenetic source for lycopene content in large-fruited tomatoes is still needed.Transferring the gene(s) from grape tomato to large-fruited and plum toma-toes will be a challenge in order to maintain the fruit size while introgressingthe lycopene-conferring genes. We expected that the lines derived from red-fruited wild relatives, particularly red-fruited S. pimpinellifolium, would havea high lycopene content. Though one of the lines, ‘Richters wild tomato’,was a high-lycopene line, others did not consistently show high levels oflycopene.

Potential of Using DigiEye and Tomato Analyzer to PredictLycopene Content in TomatoA single equation did not predict lycopene content in different colored

tomatoes using the TA software or the DE method. However, by separatingcolor groups of tomatoes and then using tailored equations, DE and TA can beemployed to predict lycopene content, which may be useful in a breeding pro-gram where minimally destructive methods are useful. A prediction equationfor pink-colored tomatoes was developed with R2 > 98%. Though predictionmodels for red tomatoes have been developed in other studies (Fernandez-Ruiz et al., 2010; Hyman et al., 2004; Nikbakht et al., 2011; Torrecilla et al.,2011), to our knowledge, this is the first report of such models for predictinglycopene from pink tomatoes. We developed a system that allows high through-put of tomato breeding material to rapidly estimate total lycopene content. Thetotal lycopene content from an extensive number of lines derived from a widegenetic background and covering a range of colors was evaluated. Information

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on lycopene content obtained from this study should help with development ofa breeding program to improve lycopene content in tomato.

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