• Mladenka Pestorić University of Novi Sad, Institute of Food Technology
  • Jasna Mastilović University of Novi Sad, Institute of Food Technology
  • Žarko Kevrešan University of Novi Sad, Institute of Food Technology, Novi Sad, Serbia
  • Lato Pezo University of Belgrade, Institute of General and Physical Chemistry
  • Miona Belović University of Novi Sad, Institute of Food Technology
  • Svetlana Glogovac Institute of Field and Vegetable Crops, Laboratory for Biotechnology, Novi Sad, Serbia
  • Dubravka Škrobot University of Novi Sad, Institute of Food Technology, Novi Sad, Serbia
  • Nebojša Ilić University of Novi Sad, Institute of Food Technology, Novi Sad, Serbia
  • Adam Takač Institute of Field and Vegetable Crops, Novi Sad, Serbia
Keywords: fresh tomato quality, sensory evaluation, physicochemical properties, artificial neural network model


Sensory analysis is the best mean to precisely describe the eating quality of fresh foods. However, it is expensive and time-consuming method which cannot be used for measuring quality properties in real time. The aim of this paper was to contribute to the study of the relationship between sensory and instrumental data, and to define a proper model for predicting sensory properties of fresh tomato through the determination of the physicochemical properties. Principal Component Analysis (PCA) was applied to the experimental data to characterize and differentiate among the observed genotypes, explaining 73.52% of the total variance, using the first three principal components. Artificial neural network (ANN) model was used for the prediction of sensory properties based on the results obtained by basic chemical and instrumental determinations. The developed ANN model predicts the sensory properties with high adequacy, with the overall coefficient of determination of 0.859.

Author Biography

Miona Belović, University of Novi Sad, Institute of Food Technology
Naučni saradnik


AOAC International (2000). Official methods of analysis (17th ed.). Arlington, VA, USA: Association of Official Analytical Chemists.

Arias, R., Lee, T. C., Logendra, L., & Janes, H. (2000). Correlation of lycopene measured by HPLC with the L*, a*, b* color readings of a hydroponic tomato and the relationship of maturity with color and lycopene content. Journal of Agricultural and Food Chemistry, 48, 1697-1702.

Bahramparvar, M., Salehi, F., & Razavi, S. M. (2014). Predicting total acceptance of ice cream using artificial neural network. Journal of Food Processing and Preservation, 38 (3),1080-1088.

Baldwin, E. A., Scott, J. W., Shewmaker, C. K., & Schuch, W. (2000). Flavor trivia and tomato aroma: biochemistry and possible mechanisms for control of important aroma components. HortScience, 35, 1013-1021.

Basheer, L. A., & Hajmeer, M. (2000). Artificial neural networks: Fundamentals, computing, design and application. Journal of Microbiological Methods, 43(1), 3-31.

Batu, A. (2004). Determination of acceptable firmness and colour values of tomatoes. Journal of Food Engineering, 61(3), 471-475.

Cancilla, J. C., Wang, S. C., Díaz-Rodríguez, P., Matute, G., Cancilla, J. D., Flynn, D., & Torrecilla, J. S. (2014). Linking chemical parameters to sensory panel results through neural networks to distinguish olive oil quality. Journal of Agricultural and Food Chemistry, 62(44), 10661-10665.

Canene-Adams, K., Campbell, J. K., Zaripheh, S., Jeffery, E. H., & Erdman, J. W. (2005). The tomato as a functional food. Journal of Nutrition, 135, 1226-1230.

Carbonell, L., Izquierdo, L., Carbonell, I., & Costell, E. (2008). Segmentation of food consumers according to their correlations with sensory attributes projected on preference spaces. Food Quality and Preference, 19, 71-78.

Corollaro, M. L., Endrizzi, I., Bertolini, A., Aprea, E., Demattè, M. L., Costa, F., .Biasioli, F., & Gasperi, F. (2013). Sensory profiling of apple: Methodological aspects, cultivar characterisation and postharvest changes. Postharvest Biology and Technology, 77, 111-120.

Geeson, J. D., Browne, K. M., Maddison, K., Shepherd, J., & Guarald, F. (1985). Modified atmosphere packaging to extend the shelf life of tomatoes. Journal of Food Technology, 20, 339–349.

Hu, X. & Weng, Q. (2009). Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113, 2089–2102.

ISO. (2012). Sensory analysis — General guidelines for the selection, training and monitoring of selected assessors and expert sensory assessors. ISO 8586. Geneva, Switzerland: International Organization for Standardization.

ISO. (2014). Methodology – Guidelines for monitoring the performance of a quantitative sensory panel. ISO 11132. Geneva, Switzerland: International Organization for Standardization.

ISO. (2007). Sensory analysis – general guidance for the design of test rooms, AMENDMENT 1. ISO 8589. Geneva, Switzerland: International Organization for Standardization.

Kaiser, H. F. & Rice, J. (1974). Little Jiffy, Mark IV. Educational and Psychological Measurement, 34(1), 111-117.

Kyriacou, M. C., & Rouphael, Y. (2018). Towards a new definition of quality for fresh fruits and vegetables. Scientia Horticulturae, 234, 463-469.

Lenucci, M. S., Cadinu, D., Taurino, M., Piro, G., & Dalessandro, G. (2006). Antioxidant composition in cherry and high-pigment tomato cultivars. Journal of Agricultural and Food Chemistry, 54, 2606-2613.

Maul, F., Sargent, S. A., Sims, C. A., Baldwin, E. A., Balaban, M. O., & Huber, D. J. (2000). Tomato flavor and aroma quality as affected by storage temperature. Journal of Food Science, 65, 1228-1237.

Oraguzie, N., Alspach, P., Volz, R., Whitworth, C., Ranatunga, C., Weskett, R., & Harker, R. (2009). Postharvest assessment of fruit quality parameters in apple using both instruments and an expert panel. Postharvest Biology and Technology, 52, 279-287.

Otto, M. (1999). Chemometrics Statistics and Computer Application in Analytical Chemistry. Wiley-VCH: Weinheim, Germany.

Pezo, L. L., Ćurčić, B. Lj., Filipović, V. S., Nićetin, M. R., Koprivica, G. B., Mišljenović, N. M., & Lević, Lj. B. (2013). Artificial neural network model of pork meat cubes osmotic dehydratation. Hemijska industrija, 67, 465-475.

Pinela, J., Barros, L., Carvalho, A. M., & Ferreira, I. C. (2012). Nutritional composition and antioxidant activity of four tomato (Lycopersicon esculentum L.) farmer’ varieties in North eastern Portugal homegardens. Food and Chemical Toxicology, 50, 829-834.

Pinheiro, J., Alegria, C., Abreu, M., Gonçalves, E. M., & Silva, C. L. (2013). Kinetics of changes in the physical quality parameters of fresh tomato fruits (Solanum lycopersicum, cv .‘Zinac’) during storage. Journal of Food Engineering, 114(3), 338-345.

Rouphael, Y., Kyriacou, M. C., Petropoulos, S. A., De Pascale, S., & Colla, G. (2018). Improving vegetable quality in controlled environments. Scientia Horticulturae, 234, 275-289.

Ruiz, J. J., Alonso, A., García-Martínez, S., Valero, M., Blasco, P., & Ruiz-Bevia, F. (2005). Quantitative analysis of flavour volatiles detects differences among closely related traditional cultivars of tomato. Journal of the Science of Food and Agriculture, 85, 54-60.

Sabio, E., Lozano, M., Montero de Espinosa, V., Mendes, R. L., Pereira, A. P., Palavra, A. F., & Coelho, J. A. (2003). Lycopene and β-carotene extraction from tomato processing waste using supercritical CO2. Industrial & Engineering Chemistry Research, 42, 6641-6646.

Singh, R. R. B., Ruhil, A. P., Jain, D. K., Patel, A. A., & Patil, G. R. (2009). Prediction of sensory quality of UHT milk – a comparison of kinetic and neural network approaches. Journal of Food Engineering, 92(2), 146-151.

Slimestad, R. & Verheul, M. (2009). Review of flavonoids and other phenolics from fruits of different tomato (Lycopersicon esculentum Mill.) cultivars. Journal of the Science of Food and Agriculture, 89, 1255-1270.

Turan, D., Capanoglu, E., & Altay, F. (2015). Investigating the effect of roasting on functional properties of defatted hazelnut flour by response surface methodology (RSM). LWT-Food Science and Technology, 63, 758-765.

USDA. (1991). United States Standards for grades of fresh tomatoes. United States Department of Agriculture, Agricultural Marketing Service.

Zaborowicz, M., Boniecki, P., Koszela, K., Przybył, J., Mazur, R., Kujawa, S., & Pilarski, K. (2013). Use of artificial neural networks in the identification and classification of tomatoes. In Proceedings of the Fifth International Conference on Digital Image Processing (ICDIP 2013) (Vol. 8878, p. 88782R). Beijing, China.

Original research paper