Radial basis network analysis of color parameters to estimate lycopene content on tomato fruits |
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Authors: | Fernández-Ruiz Virginia Torrecilla José S Cámara Montaña Mata Ma Cortes Sánchez Shoemaker Charles |
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Affiliation: | a Departamento de Nutrición y Bromatología II, Facultad de Farmacia, Universidad Complutense de Madrid, 28040 Madrid, Spain b Departamento de Ingeniería Química, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain c Food Science and Technology Department, University of California, 95616 Davis, CA, USA |
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Abstract: | With the purpose of estimating the lycopene concentration in tomato food samples, in an non-destructive way, several types of linear models of color parameters have been tested using individual values of L*, a* and b* values, (a*/b*), (a * 2/b * 2) and chroma parameters from tomato juice and fresh tomato fruits obtained with two different apparatus (Minolta CR-200b triestimulus colorimeter and HunterLab LabScan XE). Lycopene concentrations of fresh tomato and tomato juice (used as an input) were analyzed by UV-Vis spectroscopy. For all linear methods applied, the best one to estimate the lycopene concentration in tomato was the L*, a* and b* values of tomato juice measured with Hunter colorimeters (adjusted correlation coefficient, and mean prediction error, MPE < 6.59%). Four different RBEF models were designed firstly using three color parameters (L*, a* and b*) designated as “Lab case”, and secondly individually by the (a*/b*), (a * 2/b * 2) and chroma parameters. The lycopene concentration estimations were carried out with the lowest MPE and highest values possible. In order to test the reliability of the non-linear models, external validation process was also performed. From the testing of the all non-linear models applied, the RBEF Lab case model was the best to estimate lycopene content from color parameters (L*, a* and b*) using Minolta or Hunter equipments (MPE lower than 0.009 and higher than 0.997). This was a simple non-destructive method for predicting lycopene concentration in tomato fruits and tomato juice, which was reproducible and accurate enough to substitute chemical extraction determinations, and may be a useful tool for tomato industry. |
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Keywords: | Neural network Color Lycopene Tomato S. lycopersicum |
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