Design of Fuzzy System for Sensory Evaluation of Dried Apple Slices Using Infrared Radiation

Document Type : Research Paper


1 Ph.D. Graduated of Food Materials & Processing Design Engineering, Gorgan University of Agricultural Sciences & Natural Resources

2 Associate professor, Department of Food Materials & Processing Design Engineering, Gorgan University of Agricultural Sciences & Natural Resources

3 Professor, Department of Food Materials & Processing Design Engineering, Gorgan University of Agricultural Sciences & Natural Resources


In food industry, the use of quality monitoring and evaluating systems which increase the production efficiency and desirability of product is increasing. Fuzzy logic has provided an appropriate tool in the design of the decision maker systems based on human experience. In this study, a fuzzy system was designed for sensory evaluation of apple slices during drying using infrared radiation. For this purpose, the slices of apple were prepared in three thicknesses of 5 (Thin), 9 (Moderate) and 13 mm (Thick). Intermittent radiation operation was performed at three constant temperatures of 70 (Low), 75 (Moderate) and 80 °C (High) to achieve a moisture level of 15 (Low), 20 (Moderate) and 25 % kg/kg, wb (High). Evaluating the sensory attributes including color, aroma, flavor, texture and overall acceptability was performed by ten trained panelists using linguistic and hedonic method. Similarity analysis between sensory properties in terms of importance and statistical analysis for considering the impact of process conditions on the desirability were also performed. Finally, the fuzzy model has been set. The results showed that, color and texture are of the same importance with Pearson correlation coefficient (PCC) equal to 0.981 in the sensory evaluation. Sensory qualities of apple slices were better in thin slices, low temperature and moderate humidity. Fuzzy model with mean absolute percentage error (MAPE) equal to 14.54 %, had a good prediction about average evaluated scores.


Main Subjects

Acevedo, N. C., Briones, V., Buera, P., & Aguilera, J. M. (2008). Microstructure affects the rate of chemical, physical and color changes during storage of dried apple discs. Journal of Food Engineering, 85(2), 222-231.
AOAC. (2000). Official methods of analysis. 17th ed., Association of Official Analytical Chemists, Washington, DC, Unites States.
Barrett, D. M., Beaulieu, J. C., & Shewfelt, R. (2010). Color, flavor, texture, and nutritional quality of fresh-cut fruits and vegetables: desirable levels, instrumental and sensory measurement, and the effects of processing. Critical reviews in food science and nutrition, 50(5), 369-389.
Birle, S., Hussein, M., & Becker, T. (2013). Fuzzy logic control and soft sensing applications in food and beverage processes. Food Control, 29(1), 254-269.
Brown, R., Rothwell, T., & Davidson, V. (2001). A fuzzy controller for infrared roasting of cereal grain. Canadian Biosystems Engineering, 43, 3.9-3.16.
Chatterjee, D., Bhattacharjee, P., & Bhattacharyya, N. (2014). Development of methodology for assessment of shelf-life of fried potato wedges using electronic noses: Sensor screening by fuzzy logic analysis. Journal of Food Engineering, 133, 23-29.
Chaturvedi, D. K. (2009). Modeling and simulation of systems using MATLAB and Simulink: CRC Press.
Chen, H.-W., Wang, Z.-C., Kuo, S.-Y., & Chou, Y.-H. (2015). A Novel Method for Stock Forecasting based on Fuzzy Time Series Combined with the Longest Common/Repeated Sub-sequence. arXiv preprint arXiv:1506.06366.
Christensen, R. H. B. (2015). Statistical methodology for sensory discrimination tests and its implementation in sens R.
Debjani, C., Das, S., & Das, H. (2013). Aggregation of sensory data using fuzzy logic for sensory quality evaluation of food. Journal of food science and technology, 50(6), 1088-1096.
Haug, M. T., King, E. S., Heymann, H., & Crisosto, C. H. (2013). Sensory Profiles for Dried Fig (Ficus carica L.) Cultivars Commercially Grown and Processed in California. Journal of Food Science, 78(8), S1273-S1281.
Kilimann, K., Hartmann, C., Delgado, A., Vogel, R., & Gänzle, M. (2005). A fuzzy logic-based model for the multistage high-pressure inactivation of Lactococcus lactis ssp. cremoris MG 1363. International Journal of Food Microbiology, 98(1), 89-105.
Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic (Vol. 4): Prentice hall New Jersey.
Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1-27.
Kupongsak, S., & Tan, J. (2006). Application of fuzzy set and neural network techniques in determining food process control set points. Fuzzy Sets and Systems, 157(9), 1169-1178.
Lao, S., Choy, K. L., Ho, G. T., Yam, R. C., Tsim, Y., & Poon, T. (2012). Achieving quality assurance functionality in the food industry using a hybrid case-based reasoning and fuzzy logic approach. Expert Systems with Applications, 39(5), 5251-5261.
Lazim, M., & Suriani, M. (2009). Sensory evaluation of the selected coffee products using fuzzy approach. World Academy of Science, Engineering and TechnologyTechnol, 50, 717-720.
Liu, Y., Zhu, W., Luo, L., Li, X., & Yu, H. (2014). A mathematical model for vacuum far-infrared drying of potato slices. Drying Technology, 32(2), 180-189.
Mukhopadhyay, S., Majumdar, G., Goswami, T., & Mishra, H. (2013). Fuzzy logic (similarity analysis) approach for sensory evaluation of chhana podo. LWT-Food Science and Technology, 53(1), 204-210.
Nowak, D., & Lewicki, P. P. (2005). Quality of infrared dried apple slices. Drying Technology, 23(4), 831-846.
Odetunji, O. A., & Kehinde, O. O. (2005). Computer simulation of fuzzy control system for gari fermentation plant. Journal of Food Engineering, 68(2), 197-207.
Perrot, N., Ioannou, I., Allais, I., Curt, C., Hossenlopp, J., & Trystram, G. (2006). Fuzzy concepts applied to food product quality control: A review. Fuzzy Sets and Systems, 157(9), 1145-1154.
Rywotycki, R. (2002). The effect of fat temperature on heat energy consumption during frying of food. Journal of Food Engineering, 54(3), 257-261.
Seth, K., Sharma, A., & Seth, A. (2009). Component Selection Efforts Estimation–a Fuzzy Logic Based Approach. International Journal of Computer Science and Security,(IJCSS), 3(3), 210-215.
Singh, K., Mishra, A., & Mishra, H. (2012). Fuzzy analysis of sensory attributes of bread prepared from millet-based composite flours. LWT-Food Science and Technology, 48(2), 276-282.
Sivanandam, S., Sumathi, S., & Deepa, S. (2007). Introduction to fuzzy logic using MATLAB (Vol. 1): Springer.
Tsekouras, G., Sarimveis, H., Raptis, C., & Bafas, G. (2002). A fuzzy logic approach for the classification of product qualitative characteristics. Computers & chemical engineering, 26(3), 429-438.
Xie, G., Xiong, R., & Church, I. (1998). Comparison of kinetics, neural network and fuzzy logic in modelling texture changes of dry peas in long time cooking. LWT-Food Science and Technology, 31(7-8), 639-647.
Zhu, Y., Pan, Z., McHugh, T. H., & Barrett, D. M. (2010). Processing and quality characteristics of apple slices processed under simultaneous infrared dry-blanching and dehydration with intermittent heating. Journal of Food Engineering, 97(1), 8-16.