Prediction of Methyl Salicylate Effects on Pomegranate Fruit Quality and Chilling Injuries using Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network
الموضوعات :
M. Sayyari
1
(Associate Professor of the Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran.)
F. Salehi
2
(Assistant Professor of the Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran.)
D. Valero
3
(Professor, EPSO, University Miguel Hernández, Ctra. Beniel km. 3.2, 03312 Orihuela, Alicante, Spain.)
الکلمات المفتاحية: Chilling Injury, Fuzzy Inference, Genetic algorithm, Neural Network, Sensitivity analysis,
ملخص المقالة :
Adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithmartificial neural network (GA-ANN) were investigated for predicting methyl salicylate (MeSA) effects on chilling injuries and quality changes of pomegranate fruits during storage. Fruits were treated with MeSA at three concentrations(0, 0.01 and 0.1 mM) and stored under chilling temperature for 84 days. ANFIS and GA-ANN models were used to predict the effect of MeSA application and storage time (0, 14, 28, 42, 56, 70 and 84 days) on chilling injuries, quality parameters and physiological changes of pomegranate during storage. The GA-ANN and ANFIS were fed with 2 inputs of MeSA and time. The developed GAANN, which included 20 hidden neurons, could predict physiological changes and quality parameters of pomegranate fruit (weight loss, pH, titratable acidity, chilling injury index, ion leakage, ethylene, respiration, polyphenols, anthocyanins, total antioxidant activity) with average correlation coefficient of 0.89. The overall agreement between ANFIS predictions and experimental data was also significant (r=0.87).In addition, sensitivity analysis results showed that storage time was the most sensitive factor for prediction of MeSA effects on pomegranate fruit quality attributes during postharvest storage.
Abdulquadri Oluwo, A., Khan, M. & Salami, M. J. E. (2013). Optimized neural network model for a potato storage system. ARPN Journal of Engineering and Applied Sciences 8(6), 449-454.
Arnao, M. B., Cano, A. & Acosta, M. (2001). The hydrophilic and lipophilic contribution to total antioxidant activity. Food Chemistry, 73(2), 239-244.
Bahram-Parvar, M., Salehi, F. & Razavi, S. M. A. (2017). Adaptive neuro-fuzzy inference system (ANFIS) simulation for predicting overall acceptability of ice cream. Engineering in Agriculture, Environment and Food, 10(2), 79-86.
Bahramparvar, M., Salehi, F. & Razavi, S. (2014). Predicting total acceptance of ice cream using artificial neural network. Journal of Food Processing and Preservation, 38(3), 1080–1088.
Cao, S., Zheng, Y., Wang, K., Rui, H. & Tang, S. (2010). Effect of methyl jasmonate on cell wall modification of loquat fruit in relation to chilling injury after harvest. Food Chemistry, 118(3), 641-647.
ElMasry, G., Wang, N. & Vigneault, C. (2009). Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks. Postharvest Biology and Technology, 52(1), 1-8.
FG Areed, F. S., El-Kasassy, M. & Mahmoud, K. (2012). Design of neuro-fuzzy controller for a rotary dryer. International Journal of Computer Applications, 37(5), 34-41.
Fung, R. W., Wang, C. Y., Smith, D. L., Gross, K. C., Tao, Y. & Tian, M. (2006). Characterization of alternative oxidase (AOX) gene expression in response to methyl salicylate and methyl jasmonate pre-treatment and low temperature in tomatoes. Journal of Plant Physiology, 163(10), 1049-1060.
Giménez, M. J., Valverde, J. M., Valero, D., Zapata, P. J., Castillo, S. & Serrano, M. (2016). Postharvest methyl salicylate treatments delay ripening and maintain quality attributes and antioxidant compounds of ‘Early Lory’ sweet cherry. Postharvest Biology and Technology, 117, 102-109.
Gomez-Melendez, D., López-Lambraño, A., Herrera-Ruiz, G., Fuentes, C., Rico-Garcia, E., Olvera-Olvera, C., Alaniz-Lumbrerasc, D., Fernández, T. M. & Verlinden, S. (2011). Fuzzy irrigation greenhouse control system based on a field programmable gate array. African Journal of Agricultural Research, 6(11), 2544-2557.
Gottschalk, K., Nagy, L. & Farkas, I. (2003). Improved climate control for potato stores by fuzzy controllers. Computers and Electronics in Agriculture, 40(1), 127-140.
Han, J., Tian, S. P., Meng, X. H. & Ding, Z. S. (2006). Response of physiologic metabolism and cell structures in mango fruit to exogenous methyl salicylate under low‐temperature stress. Physiologia Plantarum, 128(1), 125-133.
Hayat, S., Ali, B. & Ahmad, A. (2007). Salicylic acid: biosynthesis, metabolism and physiological role in plants. Springer.
Javadikia, P., Dehrouyeh, M. H., Naderloo, L., Rabbani, H. & Lorestani, A. N. (2011). Measuring the weight of egg with image processing and ANFIS model, Swarm, Evolutionary, and Memetic Computing. Springer, pp. 407-416.
Kavdır, I. & Guyer, D. (2004). Comparison of artificial neural networks and statistical classifiers in apple sorting using textural features. Biosystems Engineering, 89(3), 331-344.
Lin, W. C. & Block, G. S. (2009). Neural network modeling to predict shelf life of greenhouse lettuce. Algorithms 2(2), 623-637.
Lu, C., Liao, Z., Jia, H. & Chai, G. (2006). Design of Fuzzy Control System of the Fast Drying Equipment for Chinese Herbs. International Journal of Information Technology, 12(5), 65-72.
Maftoonazad, N., Karimi, Y., Ramaswamy, H. S. & Prasher, S. O. (2011). Artificial neural network modeling of hyperspectral radiometric data for quality changes associated with avocados during storage. Journal of Food Processing and Preservation, 35(4), 432-446.
Mansor, H., Noor, M., Bahari, S., Ahmad, R., Kamil, R., Taip, F. S. & Lutfy, O. F. (2010). Intelligent control of grain drying process using fuzzy logic controller. Journal of Food, Agriculture & Environment, 8(2), 145-149.
May, Z., Nor, N. M. & Jusoff, K., (2011). Optimal operation of chiller system using fuzzy control, Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases. World Scientific and Engineering Academy and Society (WSEAS), pp. 109-115.
Meng, X., Han, J., Wang, Q. & Tian, S. (2009). Changes in physiology and quality of peach fruits treated by methyl jasmonate under low temperature stress. Food Chemistry, 114(3), 1028-1035.
Mirdehghan, S., Rahemi, M., Castillo, S., Martínez-Romero, D., Serrano, M. & Valero, D. (2007). Pre-storage application of polyamines by pressure or immersion improves shelf-life of pomegranate stored at chilling temperature by increasing endogenous polyamine levels. Postharvest Biology and Technology, 44(1), 26-33.
Ramzi, M., Kashaninejad, M., Salehi, F., Sadeghi Mahoonak, A. R. & Ali Razavi, S. M. (2015). Modeling of rheological behavior of honey using genetic algorithm–artificial neural network and adaptive neuro-fuzzy inference system. Food Bioscience, 9, 60-67.
Salehi, F. (2014). Current and future applications for nanofiltration technology in the food processing. Food and Bioproducts Processing, 92(2), 161-177.
Salehi, F., Kashaninejad, M., Najafi, A. & Asadi, F. (2017). Modeling the kinetics of thin-layer drying of button mushroom by hot air using genetic algorithm - artificial neural network. Journal of Food Science Research, 26(3), 457-467.
Salehi, F. & Razavi, S. M. A. (2012). Dynamic modeling of flux and total hydraulic resistance in nanofiltration treatment of regeneration waste brine using artificial neural networks. Desalination and Water Treatment, 41(1-3), 95-104.
Salehi, F. & Razavi, S. M. A. (2016). Modeling of waste brine nanofiltration process using artificial neural network and adaptive neuro-fuzzy inference system. Desalination and Water Treatment, 1-10.
Sayyari, M., Babalar, M., Kalantari, S., Martínez-Romero, D., Guillén, F., Serrano, M. & Valero, D. (2011). Vapour treatments with methyl salicylate or methyl jasmonate alleviated chilling injury and enhanced antioxidant potential during postharvest storage of pomegranates. Food Chemistry, 124(3), 964-970.
Sayyari, M., Babalar, M., Kalantari, S., Serrano, M. & Valero, D. (2009). Effect of salicylic acid treatment on reducing chilling injury in stored pomegranates. Postharvest Biology and Technology, 53(3), 152-154.
Serrano, M., Guillén, F., Martínez-Romero, D., Castillo, S. & Valero, D. (2005). Chemical constituents and antioxidant activity of sweet cherry at different ripening stages. Journal of Agricultural and Food Chemistry, 53(7), 2741-2745.
Tomás-Barberán, F. A., Gil, M. I., Cremin, P., Waterhouse, A.L., Hess-Pierce, B. & Kader, A. A. (2001). HPLC-DAD-ESIMS analysis of phenolic compounds in nectarines, peaches, and plums. Journal of Agricultural and Food Chemistry, 49(10), 4748-4760.
Valero, D. & Serrano, M. (2010). Postharvest biology and technology for preserving fruit quality. CRC press.
Wali, W., Cullen, J., Bennett, S. & Al-Shamma’a, A. (2013). Intelligent PID Controller for Real Time Automation of Microwave Biodiesel Reactor. International Journal of Computer and Information Technology, 2(4), 809-814.
Wang, L., Baldwin, E. A., Plotto, A., Luo, W., Raithore, S., Yu, Z. & Bai, J. (2015). Effect of methyl salicylate and methyl
jasmonate pre-treatment on the volatile profile in tomato fruit subjected to chilling temperature. Postharvest Biology and Technology, 108, 28-38.
