Document Type : Original Article


1 Department of Food Science and Technology, Roudehen Branch, Islamic Azad University, Roudehen, Iran

2 Department of Food Science and Technology, Sabzevar Branch, Islamic Azad University, Sabzevar, Iran

3 Department of Food Safety and Hygiene, Science and Research Branch, Islamic Azad University, Tehran, Iran

4 Department of Food Science and Technology, Tuyserkan Faculty of Engineering & Natural Resources, Bu-Ali Sina University, Hamedan, Iran


The physical properties of almond kernel are necessary for the proper design of equipment for transporting, drying, processing, sorting, grading, and storage this crop. In this study, different models of ANNs with different activation functions were used to forecast surface area, volume, mass, and kernel density of almond. The results showed that multilayer perceptron network with tanh-tanh activation function as a goodness activation function can be estimated surface area, volume, mass, and kernel density with R2 value 0.983, 0.986, 0.981, and 0.982, respectively. Furthermore, the physical properties were fitted by regression relationships, the result showed linear regression method can be predicted surface area, volume, mass and kernel density with R2 value 0.979, 0.961, 0.945, and 0.791, respectively. Generally, the result showed neural network model had high ability to forecast the physical properties of almond than the linear regression method.


  1. 1. Abdallah A, Ahumada MH, Gradziel TM. Oil content and fatty acid composition of almond kernels from different genotypes and California production regions. Journal of the American Society for Horticultural Science. 1998;123:1029-33.
  2. 2. Kotwaliwale N, Brusewitz GH, Weckler PR. Physical characteristics of pecan components: effect of cultivar and relative humidity. American Society of Agricultural Engineers. 2004;47(1):227-31.
  3. 3. Aremu AK, Fowowe SO. Development and performance evaluation of a manually operated plantain-slicing machine. Proceedings of the Nigerian Institution of Agricultural Engineers. 2000;22:30-5.
  4. 4. Taser OF, Altuntas E, Ozgoz E. Physical properties of Hungarian and Common Vetch seeds. Journal of Applied Science. 2005;5(2):323-6.
  5. 5. Asoegwu SN, Maduike JO. Some physical properties and cracking energy of Irvingea gabonensis (ogbono) nuts. Proceeding of the Nigerian Institution of Agricultural Engineers. 1999;21:131-7.
  6. 6. Alabadan BA. Physical properties of selected biomaterials as related to their postharvest handling. Proceeding of the Nigerian Society of Agricultural Engineers. 1996;18:328-31.
  7. 7. Coskuner Y, Karababa E. Physical properties of coriander seeds (Coriandrum sativum L.). Food Engineering. 2007;80(2):408-16.
  8. 8. Loghavi M, Souri S, Khorsandi F. Physical and mechanical properties of almond (Prunus dulcis l. cv. 7Shahrood). American Society of Agricultural and Biological Engineers. 2011;doi:10.13031/2013.37425.
  9. 9. Altuntas E, Gercekcioglu R, Kaya C. Selected mechanical and geometric properties of different almond cultivars. International Journal of Food Properties. 2010;13: 282-93.
  10. 10. Aydin C. Some engineering properties of peanut and kernel. Food Engineering. 2007;79:810-6.
  11. 11. Turkan A, Polat R, Atay U. Comparison of mechanical properties of some selected almond cultivars with hard and soft shell under compression loading. Food Engineering. 2007;30:773-89.
  12. 12. Aydin C. Physical Properties of Almond Nut and Kernel. Food Engineering. 2003;60:315-20.
  13. 13. Viswanathan R, Palanisamy PT, Gothandapani L, Sreenarayanan VV. Some physical properties of Green Gram. Journal of  Agricultural Engineering. 1996;63:19-26.
  14. 14. Latrille E, Corrieu G, Thibault J. pH prediction and final fermentation time determination in lactic acid batch fermentations. Escape 2. Computers & Chemical Engineering. 1993;17:423-8.
  15. 15. Erenturk S, Erenturk K. Comparison of genetic algorithm and neural network approaches for the drying process of carrot. Food Engineering. 2007;78:905-12.
  16. 16. Martynenko AI, Yang SX. Biologically inspired neural computation for ginseng drying rate. Biosystems Engineering. 2006;95(3):385-96.
  17. 17. Hernandez-Perez JA, Garcia-Alvarado MA, Trystram G, Heyd B. Neural networks for heat and mass transfer prediction during drying of cassava and mango. Innovative Food Science and Emerging Technologies. 2004;5:57-64.
  18. 18. Janjai S, Intawee P, Tohsing K, Mahayothee B, Bala BK. Neural network modeling of sorption isotherms of longan (Dimocarpus longan Lour.). Computers and Electronics in Agriculture. 2009;66:209-14.
  19. 19. Tavakolipour H, Mokhtarian M, Kalbasi-Ashtari A. Intelligent monitoring of zucchini drying process based on fuzzy expert engine and ANN. Journal of Food Process Engineering. 2014;37:474-81.
  20. 20. Tavakolipour H, Mokhtarian M. Neural network approaches for prediction of pistachio drying kinetics. International Journal of Food Engineering. 2012;8:3,doi:10.1515/1556-3758.2481.
  21. 21. AOAC. Official methods of analysis, Association of Official Analytical Chemists, Washington, DC. 1990.
  22. 22. Dehspande SD, Bal S, Ojha TP. Physical properties of soybean. Journal of Agricultural Engineering Research. 1993;56:89-98.
  23. 23. Aydin C. Physical properties of hazel nuts. Biosystems Engineering. 2002;82(3):297-303.
  24. 24. Mohsenin NN. Physical properties of plants and animal materials, Gordon and Breach Science Publishers, NW, New York, 1980.
  25. 25. Jain RK, Bal S. Properties of pearl millet. Journal of Agricultural Engineering Research. 1997;66:85-91.
  26. 26. Demir F, Dogan H, Ozcan M, Haciseferogullari H. Nutritional and physical properties of hackberry (Celtis australis L.). Food Engineering. 2002;54:241-7.
  27. 27. Wu CH, McLarty JW. Neural Networks and Genome Informatics. Elsevier Publishing Co. USA, 2000.