Radial basis network analysis to estimate lycopene degradation kinetics in tomato-based products

Food Research International 49 (2012) 453–458

Contents lists available at SciVerse ScienceDirect

Food Research International journal homepage: www.elsevier.com/locate/foodres

Radial basis network analysis to estimate lycopene degradation kinetics in tomato-based products Montaña Cámara a,⁎, Virginia Fernández-Ruiz a, Delia Fernández Redondo a, Ma Cortes Sánchez-Mata a, José S. Torrecilla b a b

Departamento de Nutrición y Bromatología II, Facultad de Farmacia, Universidad Complutense de Madrid, Avenida Complutense s/n, 28040, Madrid, Spain Departamento de Ingeniería Química, Facultad de Ciencias Químicas, Spain

a r t i c l e

i n f o

Article history: Received 6 April 2012 Accepted 17 July 2012 Keywords: Stability Lycopene Tomato products UV–vis spectroscopy Radial basis network

a b s t r a c t This study aims to evaluate the stability of lycopene content in current European commercial tomato-based products over a 12 month storage period during its commercial shelf life; and to explore a mathematical model to predict the lycopene degradation kinetics in tomato-based products. A total of 10 commercial tomato-based products rich in lycopene: juices (4 different brands), tomato sauces (3) and ketchups (3), were considered for analysis. Tomato products were stored at room temperature, avoiding direct light or heat. Lycopene degradation kinetics in tomato-based products included in this study was modeled using a radial basis network (RBN) obtaining a mean prediction error lower than 2.62% and a correlation coefficient higher than 0.983, thus RBN mathematical approach proposed can be considered as a reliable tool to monitor the stability of lycopene in tomato products (juices, sauces and ketchups) during its shelf life and may be a useful tool for tomato industry. © 2012 Elsevier Ltd. All rights reserved.

1. Introduction Tomato and tomato products have an enormous economical relevance worldwide. Its importance lies in its highly appreciated sensory properties, being able to integrate into diverse food preparations, either cooked or raw; and also, in its nutritional properties, being an important source of bioactive and antioxidant compounds, as lycopene (among others). Lycopene, the carotenoid responsible for the red color of tomatoes, is a lipophilic compound with high antioxidant activity by scavenging oxygen radicals which reduce oxidative stress in the organism (Maiani et al., 2009; Rao, 2006). This antioxidant activity results in a protective effect against cardiovascular disease, hypertension, atherosclerosis, cancer and diabetes among others (Etminan, Takkouche, & Caamano-Isorna, 2004; Kohlmeier et al., 1997; Kong et al., 2010; Ried & Fakler, 2011), which is why its presence in the diet is considered of great interest. Fresh tomatoes and its processed products are the main contributors to the total lycopene intake in the diet. The concentration of lycopene in tomato-based products depends on the original raw material and technological applied and can ranged between 0.85 and 94.0 mg/100 g (Cámara, Matallana, Sánchez-Mata, Lillo, & Labra, 2003; Maiani et al., 2009). In addition, lycopene bioavailability of tomato-based products is higher than lycopene of raw tomatoes, probably due to the release of

⁎ Corresponding author. Tel./fax: +34 91 394 17 99. E-mail address: [email protected] (M. Cámara). 0963-9969/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodres.2012.07.030

lycopene from cellular matrix and isomerization to its cis-isomeric form caused by the heating process (Agarwal, Shen, AgarwaL, & Rao, 2001). Both consumers and producers want to know the nutritive and bioactive compounds present in food and how these compounds are influenced by industrial processing (Capanoglu, Beekwilder, Boyacioglu, de Vos, & Hall, 2010). Lycopene degradation in commercial tomato-based products not only affects the attractive color, but also the nutritional (Ré, Bramley, & Rice-Evans, 2002) and bioactive or functional value of the final product. There have been some studies reporting data of light and heat carotenoid stability (Agarwal et al., 2001; Tonucci et al., 1995) and lycopene stability in tomato powder and extracts (Goula, Adamopoulos, Chatzitakis, & Nikas, 2006; Lambelet, Richelle, Bortlik, Franceschi, & Giori, 2009). Most of the published studies related to the stability of lycopene are focused on the effect of processing (Akanbia & Oludemia, 2004; Colle et al., 2010; Kessy, Zhang, & Zhang, 2011; Lavelli & Torresani, 2011; Sharma & Le Maguer, 1996) however it is important to address lycopene level characterization in current tomato commercial products as well as the control of its potential degradation during its shelf life storage, at room temperature and avoiding direct light or heat, according to the labeling instructions. The European Regulation (EC) No. 1925/2006 of the European Parliament and of the Council of 20 December 2006, establish that in order to have nutritional or health claims on labeling, the total amount of nutrients and other bioactive compounds should be given on the label of the food products. Therefore since the stability of lycopene is critical for health benefits, it is essential to asses this

454

M. Cámara et al. / Food Research International 49 (2012) 453–458

content in current commercial tomato products. Most of commercial tomato-based products have a quite long shelf life even up to 12 months which make them susceptible to lycopene losses, mainly by oxidation due to its high degree of instauration, being especially sensitive to light, heat, oxygen and pH extremes (Mayeaux, Xu, King, & Prinyawiwatkul, 2006). For all that reasons there is a growing need for cost-effective, rapid and reliable monitoring methods for the determination of bioactive compounds, as lycopene in food products, which can be easily applied in any laboratories for routine analysis. In this sense and related to mathematical models, non-linear algorithms have been applied to solve the spectroscopy interference effects of β-carotene and lycopene (Torrecilla, Cámara, Fernández-Ruiz, Piera, & Caceres, 2008); to predict lycopene and β-carotene concentrations in food samples, combined with a simple and fast technique, such as UV–vis spectroscopy (Cámara, Torrecilla, Caceres, Sánchez-Mata, & Fernández-Ruiz, 2010); and also to estimate lycopene content on tomato fruits from color parameters (L*, a* and b*) using Minolta or Hunter equipments in fresh tomato fruits (Fernández-Ruiz, Torrecilla, Cámara, Sánchez Mata, & Shoemaker, 2010). Non lineal models could be useful to predict lycopene degradation kinetics in different commercial tomato matrices, being a useful tool for tomato industry. This study aims to evaluate the stability of lycopene content in current European commercial tomato-based products over a 12 month storage period during its commercial shelf life; and to explore a mathematical model to predict the lycopene degradation kinetics in tomato-based products. These results may be of great interest for both, consumers and food industries. 2. Materials and methods 2.1. Standards Standards of all-trans-lycopene used in this work were supplied by Sigma–Aldrich–Fluka (St. Louis, MO), with a purity≥90%. Standard curve of lycopene was performed in preparing individual working standard solutions by dilution in hexane in the range of 0.4–3.2 μg mL−1. The curve obtained was, y=0.3409+0.0095, r2 =0.999. The standard purity was checked by calculating the concentration of the standard solution using the extinction coefficient (AOAC, 2005; Zechmeister, LeRosen, Schroeder, Polgar, & Pauling, 1943). 2.2. Food samples A total of 10 commercial tomato products rich in lycopene: juices (4 brands), tomato sauces (3 brands) and ketchups (3 brands), were considered for analysis (Table 1). Nine units of each commercial tomato-based product were purchased in local markets. According to the instructions on the label, all products were stored at room temperature of 22 °C, avoiding direct light or heat. Lycopene analysis was performed periodically, based on the expired date of each product:

Table 1 Expired data, type of packaging and storage conditions of tomato brands analyzed. Tomato Sample Industrial products codes codes

Expired date

Types of packaging

Storage conditions

Juice

16/Feb./2009 26/Dec./2008 25/April/2009 12/Feb./2010 14/Nov./2009 Jan./2011 March/2009 28/Nov./2008 March/09 01/March/09

Glass Glass Cardboard Glass Cardboard Glass Cardboard Plastic Plastic Plastic

22 22 22 22 22 22 22 22 22 22

Sauce

Ketchup

TJ1 TJ2 TJ3 TJ4 TS1 TS2 TS3 K1 K2 K3

C320:39 ZT-18:40 L03:59 L22 L05 LO31823:18 l73040292 R18:16. L7332BP099 LOT117816:52 33470919TK1

°C °C °C °C °C °C °C °C °C °C

eight analyses were preformed in each batch of tomato-based product during the whole period of 12 months, giving a total number of 80 lycopene analysis. Analyses were performed in triplicate. 2.3. Analytical methods Determination of soluble solids at 20 °C was performed by a direct measurement of refraction index, and expressed as °Brix value, or percentage of sucrose at 20 °C (AOAC, 2005). The analytical procedure has been previously optimized and validated to assure the highest extraction yield (Cámara et al., 2010; Olives, Camara, Sanchez Mata, Fernandez Ruiz, & Lopez Saenz de Tejada, 2006; Torrecilla et al., 2008). Samples were extracted in a mixture of hexane/ acetone/ethanol (50:25:25 v/v/v). Lycopene extraction was performed using ultrasound (P Selecta Ultrasons-H) assisted extraction process (10 min at 22 °C). Samples were allowed to stand for 1 h and a water aliquot was added to separate the upper hexane layer which was separated and evaluated by spectrophotometric analysis at 502. Every sample was prepared in triplicate, and then, each one was monitored three times. To avoid the effect of external factors on carotenoid degradation, all the laboratory materials used were protected from light and oxygen, during the manipulations. In all tomato products, a minimum of three independent lycopene extractions and three replicate measurements of spectroscopic absorbance was performed. For spectrophotometer calibration and the identification and quantification of lycopene in tomato samples, standard solutions of known amounts of lycopene in n-hexane (Merk, Darmstadt, Germany) were prepared every day. Absorption spectra were measured in hexane extracts at 400–600 nm wavelength in a Pharmacia Ultrospec 4000 UV–visible spectrophotometer. The three lycopene characteristic maximum of absorbance (444, 470 and 502 nm) were identified in the extracts, which allowed to confirm the identity of lycopene in the samples (Fig. 1). For quantification absorbance was measured at 502 nm using quartz cells of path length 1 cm. Data acquisition and spectrometric evaluation were performed using PESSW software, version 1.2. 2.4. Radial basis network model The radial basis network (RBN) model consists of three layers: the input, hidden radial basis and output linear, Fig. 2. The input layer has no calculation power and serves as an input distributor to the hidden radial basis layer. The input to the hidden radial basis neuron is the vector distance between its weight vector (self-adjustable parameter of the net), w, and the input vector, p, multiplied by the bias (the two last layers have biases, Fig. 2). The transfer function of hidden radial basis neurons is a Gaussian function, Eq. (1). The radial basis function has a maximum of 1 when its input is 0. As the distance between w and p decreases, the output increases. The bias allows the sensitivity of the radial basis neuron to be adjusted. The operation of the output layer is a linear combination of the radial basis units according to Eq. (2) (Demuth, Beale, & Hagan, 2007).

Gj ðxÞ ¼

yk ðxÞ ¼

1

ð1Þ

2

ex

nh X

wjk ⋅Gj ðxÞ þ wk :

ð2Þ

j

In Eqs. (1) and (2), yk (estimated lycopene concentration, vide infra) is the kth output unit for the input vector x, nh is the number of hidden radial basis neurons, wjk is the weight between the jth hidden and the kth output neurons, Gj is the notation for the output of the jth radial basis neuron, and wk is the bias.

M. Cámara et al. / Food Research International 49 (2012) 453–458

0,6

(a) 1

0,5

Absorbance

455

0,4 0,3

Lycopene standard

0,2 0,1 0 400

Σ

p 450

500

550

600

xk

yk

Wavelength (nm) 0,5 0,45

(b)

Absorbance

0,4 0,3

Output linear layer

1

0,25 0,2 0,15

Hidden radial basis layer

0,1 0,05 0 400

450

500

550

600

Wavelength (nm) 0,5

0,3

0,2

0,1

0 400

Fig. 2. Scheme of calculation of radial basis network (□ bias node) (Fernández-Ruiz et al., 2010).

2.5. Learning, verification and validation samples

(c)

0,4

Absorbance

Wjk

Input layer

0,35

450

500

550

600

Wavelength (nm) Fig 1. Absorbance profiles for tomato juices (a) (···· 1.518 μg mL−1; ― 0.718 μg mL−1; — 0.656 μg mL−1), tomato sauces (b) (···· 1.157 μg mL−1; ― 1.138 μg mL−1; — 1.033 μg mL−1) and ketchups (c) (···· 1.130 μg mL−1; ― 0.620 μg mL −1; — 0.500 μg mL−1).

As the spread constant (SC) is the only parameter of the RBN which can be optimized, it was optimized by testing different spread constant values between 0.001 and 10 (Demuth et al., 2007). The response variables were the mean prediction error (MPE, %), Eq. (3), correlation coefficient (R2) (predicted vs. experimental values).

Every data set of the learning and verification samples is composed of two parameters (storage time of food and its respective Brix degree values) and their respective lycopene concentration. The learning and verification samples are composed of 240 data sets, which were distributed in 72 for sauce, 72 for ketchup and 96 for tomato juice. The only difference between the verification and learning samples is that the latter is composed of 80% (192 datasets) of data and the former of the remaining 20%. Taking into account that every datum of the verification sample should be interpolated within learning range, the data were randomly distributed between both samples (Guidance, 2007). In order to carry out an external validation process of the optimized RBN model a new database has been made presenting the same aforementioned format and composed of five new data sets, which corresponded to 3 samples of tomato sauce and 2 of tomato juice (Guidance, 2007). The data sets of the validation sample ranged in the same interval of input values (Brix degree and lycopene content) as the learning and verification samples. In order to guarantee the reliability of the classifications carried out by the RBN model, the applicability domain has been evaluated selecting the compounds with cross-validated standardized residuals greater than three standard deviation values (Gramatica, 2007; Gramatica, Giani, & Papa, 2007). No response outlier was found in this evaluation applied to learning, verification and validation samples. 3. Results and discussion

MPE ¼

1 jr −yn j ∑ n 100: N n rn ⋅

ð3Þ

In Eq. (3), N, yn, and rn, are the number of observations, neural network model estimation and real value, respectively. The design was analyzed taking into account that the estimations should be carried out with the need to achieve the least MPE with the highest values of correlation coefficient. The RBN model used in this work was designed using Matlab version 7.01.24704 (R14) (Demuth et al., 2007).

3.1. Lycopene degradation kinetics in tomato-based products Initial lycopene content of tomato products considered in this study (juices, sauces and ketchups) presents a wide range of values due to the different technological processes applied which also induce differences in total solids content. Tomato juices showed the initial lowest lycopene values (from 13 to 20 mg/100 g) and ketchups the highest (from 15 to 25 mg/100 g). Lycopene values may also vary within the same type of product considering the different commercial brands. In this study,

456

M. Cámara et al. / Food Research International 49 (2012) 453–458

Table 2 Lycopene content (average values) in different brands of tomato products. For each type of product different letters mean significant statistical differences (ANOVA Tukey test, p b 0.05). Tomato products Juice Code of tomato brands Lycopene (mg/100g)

Sauce

TJ1

TJ2

TJ3

TJ4

TS1

TS2

TS3

K1

K2

K3

13.16 ± 0.568a

16.72 ± 0.114b

20.10 ± 1.019c

12.56 ± 0.479a

20.25 ± 0.710a

20.67 ± 0.288a

19.75 ± 0.593a

15.37 ± 0.177a

21.95 ± 0.266b

24.60 ± 0.825b

major differences were observed between ketchup brands (pb 0.05) clearly differentiating K1 from the other two K2 and K3 ketchups, with no differences on tomato sauces, Table 2. Regarding lycopene degradation kinetics, all commercial tomatobased tomato products considered in this study showed a decrease in lycopene content over the 12 month storage period, wherein tomato

mg lycopene/100g

25 20 15 TS1

10

TS2 5

TS3

0 0

1

2

3

4

7

10

12

Time (months)

mg lycopene/100g

25 20 15 TJ1

10

TJ2 5

TJ3 TJ4

0 0

1

2

3

4

7

10

12

Time (months) 25

20

mg lycopene/100g

Ketchup

K1

15

K2 K3

10

5

juices and tomato sauces demonstrate more stability compared with tomato ketchup (Figs. 3 and 4). Tomato juices and tomato sauces showed a quite stable behavior until the fifth month in which a remarkable lycopene decrease was found. As an example in tomato juices, TJ3 decreases from 20.10 to 9.90 mg/100 g and in tomato sauces as TS1 from 20.25 to 12.87 mg/100 g. This aforementioned lycopene degradation kinetics was very similar in ketchups, K2 and K3 with decreased values from 21.95 and 24.60 to 10.88 and 11.79 mg/100 g, respectively. During the last months of storage a quite stable behavior, with a slight decrease, was observed. Therefore, it is important to stress that lycopene decrease is especially significant in the case of ketchups, up to 93%, while tomato juices and sauces showed a decrease of about 40% (that means more than 50% of initial content is retained). Ketchup processing needs a more intense thermal treatment than other tomato products such as tomato juice. This treatment usually induces isomerization of all-trans-lycopene to cis-isomers, which has been shown to be less stable than the naturally occurring all-translycopene (Kong et al., 2010; Nguyen & Schwartz, 1999). This could explain that during the shelf life of ketchup, lycopene tends to be more easily degraded than in other tomato products. García-Alonso et al. (2009) reported changes in antioxidant compounds during the shelf life of commercial tomato juices showing the influence of the different packaging systems applied. In our study among the different types of tomato juice analyzed, significant differences (p b 0.05) in terms of their lycopene content, were found, with no clear influence of the packaging system, showing that tomato juices TJ1 and TJ2 have the highest content (both packaged in glass), and TJ3 (cardboard packaging) and TJ4 (glass packaging) have the lowest levels of lycopene. Although no specific recommendation for lycopene daily intake has been established, Mackinnon, Rao, and Rao (2009) reported that levels of lycopene in the range of 6–60 mg per day have beneficial effects against chronic diseases. In addition, Rao (2006) considers that a daily intake of 7–8 mg of lycopene is enough to show its antioxidant capacity and to prevent chronic diseases. Regarding the lycopene content present in samples considered in this study, the minimum lycopene content (7–8 mg), to show its antioxidant capacity, is present in one serving (250 mL) of tomato juice, 100 g of any of the tomato sauces and 3 servings (10 mL) of ketchups analyzed. And it is important to note that despite the decrease on lycopene content, even at the end of the shelf life of the commercial products studied, about 50% of the daily intake recommended lycopene content (7–8 mg) would be covered with a reasonable serving. Only in the case of K1, levels of lycopene are no longer significant (less than 5 mg/100 g) after 5 months of storage, emphasizing that the consumption of this product is made in very small portions, and thus will not cover lycopene levels established by Rao (2006). 3.2. Radial basis network optimization

0 0

1

2

3

4

7

10

12

Time (months) Fig. 3. Lycopene stability of juices, tomato sauces and ketchup (tomato juices: TJ1,TJ2, TJ3, TJ4; tomato sauces: TS1, TS2, TS3; and ketchups: K1, K2, K3).

Lycopene degradation kinetics in tomato-based products included in this study was mathematical modeling using a radial basis network. The added value of RBN model in comparison with other linear models is that RBN is able to calculate the concentration of chemicals with lower

M. Cámara et al. / Food Research International 49 (2012) 453–458

457

100% 90% 80% 70% 60%

% Losses

50%

% Retention

40% 30% 20% 10% 0% TJ1 TJ2 TJ3 TJ4

TS1 TS2 TS3

K1

K2

K3

Fig. 4. Retention and losses at the end of the shelf life of lycopene of juices, tomato sauces and ketchup brands (tomato juices: TJ1,TJ2, TJ3, TJ4; tomato sauces: TS1, TS2, TS3; and ketchups: K1, K2, K3). Table 3 Lycopene estimated by RBN model in commercial ketchup products. Product

Storage time (weeks)

Real lycopene (mcg/mLa)

Estimated lycopene RBN model (mcg/mLa)

K1 K1 K1 K1 K2 K2 K2 K2 K3 K3 K3 K3

4 12 36 48 4 12 36 48 4 24 36 48

0.709 0.278 0.080 0.029 0.872 0.571 0.387 0.308 0.802 0.757 0.437 0.395

0.564 0.274 0.066 0.034 0.874 0.577 0.438 0.318 0.815 0.758 0.456 0.427

a

Acknowledgments

Values of lycopene concentration in hexane extracts.

estimation error. In addition, this type of models can be optimized only using input–output data without any physicochemical knowledge of the process. Using Brix degree and the storage time of foods, the concentration of lycopene concentration was estimated using a RBN which consists of two input neurons (Brix degree and the storage time of foods) and one output neuron to estimate the concentration of lycopene at the time and Brix degree input into the RBN model (Table 3). In the radial basis network optimization, the spread constant value was optimized. The aforementioned design was analyzed taking into account that the estimations should be carried out with the lowest MPE and the highest R2 values possible. The optimized spread constant value to the RBN model is 0.0375, and the adequate statistical results taken during the verification sample lead us to think that this tools is suitable to estimate the stability of lycopene in food samples (MPE and R 2 are less than 2.62% and higher than 0.983, respectively). In order to validate the RBN proposed a new validation sample (vide supra) has been input into the model. The main results are shown in Table 4. As the results obtained with the validation sample (MPE b 1.2% and R2 > 0.99) are even better than those obtained with the verification

Table 4 Lycopene concentration estimations by RBN model (SC = 0.0375) of validation samples (tomato sauces and juices). Sample

Input values Storage time Brix (weeks) degree

Tomato sauces 48 48 60 Tomato juices 64 80

Real lycopene Estimated lycopene MPE (mg/100 g) (mg/100 g) (%)

15.767 9.761 13.733 11.699 13.733 11.507 4.700 5.809 4.700 5.217

sample, this model is adequate to estimate lycopene degradation kinetics in tomato-based products. To conclude, analytical results obtained in this study are a key feature as a first step to assess the lycopene health claim in the label, as food labeling recent regulations, require a guaranteed content of nutrients and other bioactive compounds in food products during their shelf life. In addition, the radial basis network analysis proposed is a reliable tool to estimate the lycopene degradation kinetics in tomato-based products with a MPE and R 2 less than 2.62% and higher than 0.983, respectively. This model is a simple method for predicting lycopene concentration in tomato products (juices, sauces and ketchups) during its shelf life and may be a useful tool for tomato industry with the only requirement of software Matlab version 7.01.24704 (R14) or similar.

9.713 11.788 11.376 5.835 5.058

0.49 0.76 1.13 0.44 3.04

The authors are grateful to a UCM-OTRI project no. 26-2008/09 sponsored by AGRUCON-La Mesa del Tomate and ASOZUMOS; and to the Spanish “Comunidad de Madrid” for the financial support of project S2009/PPQ-1545. References Agarwal, A., Shen, H., AgarwaL, S., & Rao, A. V. (2001). Lycopene content of tomato products: Its stability, bioavailability and in vivo antioxidant properties. Journal of Medicinal Food, 4, 10–15. Akanbia, C. T., & Oludemia, F. O. (2004). Effect of processing and packaging on the lycopene content of tomato products. International Journal of Food Properties, 7, 139–152. AOAC (2005). Official methods of analysis Association of Official Analytical Chemists (18th ed.) . Gaithersburg, MD: AOAC International. Cámara, M., Matallana, M. C., Sánchez-Mata, M. C., Lillo, R., & Labra, E. (2003). Lycopene and hydrometilfurfural (HMF) evaluation in tomato products. Acta Horticulturae, 613, 365–371. Cámara, M., Torrecilla, J. S., Caceres, J. O., Sánchez-Mata, M. C., & Fernández-Ruiz, V. (2010). Neural network analysis of spectroscopic data of lycopene and β-carotene content in food samples compared to HPLC-UV–vis. Journal of Agricultural and Food Chemistry, 58, 72–75. Capanoglu, E., Beekwilder, J., Boyacioglu, D., de Vos, R. C., & Hall, R. D. (2010). The effect of industrial food processing on potentially health-beneficial tomato antioxidants. Critical Reviews in Food Science and Nutrition, 50, 919–930. Colle, I. J. P., Lemmens, L., Tolesa, G. N., De Van Buggenhout, S., Vleeschouwer, K., Van Loey, A. M., et al. (2010). Lycopene degradation and isomerization kinetics during thermal processing of an olive oil/tomato emulsion. Journal of Agricultural and Food Chemistry, 58, 12784–12789. Demuth, H., Beale, M., & Hagan, M. (2007). Neural network toolbox for use with MATLAB user's guide. version 5. Ninth printing revised for version 5.1 (release 2007b). Etminan, M., Takkouche, B., & Caamano-Isorna, F. (2004). The role of tomato products and lycopene in the prevention of prostate cancer: a meta-analysis of observational studies. Cancer Epidemiology, Biomarkers & Prevention, 13, 340–345. Fernández-Ruiz, V., Torrecilla, J. S., Cámara, M., Sánchez Mata, M. C., & Shoemaker, C. (2010). Radial basis network analysis of color parameters to estimate lycopene content on tomato fruits. Talanta, 83, 9–13. García-Alonso, F. J., Bravo, S., Casas, J., Pérez-Conesa, D., Jacob, K., & Periago, M. J. (2009). Changes in antioxidant compounds during the shelf life of commercial tomato juices in different packaging materials. Journal of Agricultural and Food Chemistry, 57, 6815–6822. Goula, A., Adamopoulos, K., Chatzitakis, P., & Nikas, V. (2006). Prediction of lycopene degradation during a drying process of tomato pulp. Journal of Food Engineering, 74, 37–46.

458

M. Cámara et al. / Food Research International 49 (2012) 453–458

Gramatica, P. (2007). Principles of QSAR models validation: Internal and external. QSAR and Combinatorial Science, 26, 694–701. Gramatica, P., Giani, E., & Papa, E. (2007). Statistical external validation and consensus modeling: A QSPR case study for Koc prediction. Journal of Molecular Graphics, 25, 755–766. Guidance Document on the Validation of (Quantitative) Structure Activity Relationship (2007) [(Q)SAR] Models, No. 69, OECD, Series on Testing and Assessment, Organisation of Economic Cooperation and Development, Paris, France. Kessy, H. H., Zhang, H., & Zhang, L. (2011). A study on thermal stability of lycopene in tomato in water and oil food systems using response surface methodology. Journal of Food Science and Technology, 46, 209–215. Kohlmeier, L., Kark, J. D., Gomez-Gracia, E., Martin, B. C., Steck, S. E., Kardinal, A. F. M., et al. (1997). Lycopene and myocardial infarction risk in the EURAMIC study. American Journal of Epidemiology, 146, 618–626. Kong, K. W., Khoo, H. E., Prasad, K. N., Ismail, A., Tan, C. P., & Rajab, N. F. (2010). Revealing the power of the natural red pigment lycopene. Molecules, 15, 959–987. Lambelet, P., Richelle, M., Bortlik, K., Franceschi, F., & Giori, A. M. (2009). Improving the stability of lycopene Z-isomers in isomerised tomato extracts. Food Chemistry, 112, 156–161. Lavelli, V., & Torresani, M. C. (2011). Modelling the stability of lycopene-rich by-products of tomato processing. Food Chemistry, 125, 529–535. Mackinnon, E. S., Rao, A. V., & Rao, L. G. (2009). Lycopene intake by Canadian women is variable, similar among different ages, but greater than that reported for women in other countries. Journal of Medicinal Food, 12(4), 829–835. Maiani, G., Periago Castón, M. J., Catasta, G., Toti, E., Goñi Cambrodón, I., Bysted, A., et al. (2009). Actual knowledge on food sources, intakes, stability and bioavailability and their protective role in humans. Molecular Nutrition & Food Research, 53, S194–S218.

Mayeaux, M., Xu, Z., King, J. M., & Prinyawiwatkul, W. (2006). Effects of cooking conditions on the lycopene content in tomatoes. Journal of Food Science, 71, 461–464. Nguyen, M. L., & Schwartz, S. J. (1999). Lycopene: chemical and biological properties. Food Technology, 53(2), 38–44. Olives, A. I., Camara, M., Sanchez Mata, M. C., Fernandez Ruiz, V., & Lopez Saenz de Tejada, M. (2006). Application of a UV–vis detection-HPLC method for a rapid determination of lycopene and betacarotene in vegetables. Food Chemistry, 95, 328–336. Rao, V. (2006). Tomatoes, lycopene and human health. Barcelona: Caledonian Press. Ré, R., Bramley, P. M., & Rice-Evans, C. (2002). Effects of food processing on flavonoids and lycopene status in a Mediterranean tomato variety. Free Radical Research, 36, 803–810. Ried, K., & Fakler, P. (2011). Protective effect of lycopene on serum cholesterol and blood pressure: Meta-analyses of intervention trials. Maturitas, 68, 299–310. Sharma, S. K., & Le Maguer, M. (1996). Kinetics of lycopene degradation in tomato pulp solids under different processing and storage conditions. Food Research International, 29, 309–315. Tonucci, L. H., Holden, J. M., Beecher, G. R., Khachik, F., Davis, C. S., & Mulokozi, G. (1995). Carotenoid content of thermally processed tomato-based food products. Journal of Agricultural and Food Chemistry, 43, 579–586. Torrecilla, J. S., Cámara, M., Fernández-Ruiz, V., Piera, G., & Caceres, J. O. (2008). Solving the spectroscopy interference effects of β-carotene and lycopene by neural networks. Journal of Agricultural and Food Chemistry, 56, 6261–6266. Zechmeister, L., LeRosen, A. L., Schroeder, W. A., Polgar, A., & Pauling, L. (1943). Spectral characteristics and configuration of some stereoisomeric carotenoids including prolycopene and pro-β-carotene. Journal of the American Chemical Society, 65, 1940–1950.