FOOD CONTROL: APPLICATION OF RADIAL BASIS NETWORK ANALYSIS (RBN) IN GAZPACHO AND RELATED TOMATO PRODUCTS
Lycopene is one of the most powerful antioxidants present in tomato products due to the presence of conjugated double bonds in their chemical structure that allows capturing reactive oxygen species (ROS). Lycopene has attracted positive attention due to the evidence which suggests an inverse correlation between the consumption of lycopene rich foods and the risk of chronic diseases. Commercial tomato-based products are susceptible to lycopene losses, mainly by oxidation due to its high degree of instauration, being especially sensitive to light, heat, oxygen and pH extremes. Since tomatoes undergo extensive processing conditions and storage before consumption, this study was conducted to evaluate the stability of lycopene in currently available European commercial tomato-based products, as well as to explore a mathematical model, Radial Basis Network Analysis (RBN), to predict lycopene degradation kinetics in tomato-based matrices. Samples include one gazpacho, one brand of whole peeled tomato, two different crushed tomatoes and one tomato sauce with vegetables over storage period. RBN modeling showed a mean prediction error lower than 2.75% and correlation coefficient higher than 0.988, thus RBN approach proposed can be consider as a reliable tool to monitor the stability of lycopene in these tomato products during its shelf life and may be a useful tool for tomato industry.
Fernández-Ruiz, V., Cámara, M., Fernández Redondo, D., Torrecilla, J.S. and Cortes Sánchez Mata, M. (2015). FOOD CONTROL: APPLICATION OF RADIAL BASIS NETWORK ANALYSIS (RBN) IN GAZPACHO AND RELATED TOMATO PRODUCTS. Acta Hortic. 1081, 291-296
lycopene, tomatoes, Artificial Neural Networks, UV-visible