Modeling Drying Properties of Pistachio Nuts, Squash and Cantaloupe Seeds under Fixed and Fluidized Bed Using Data-Driven Models and Artificial Neural Networks
Abstract
This paper presents the application of feed forward and cascade forward neural networks to model the non-linear behavior of pistachio nut, squash and cantaloupe seeds during drying process. The performance of the feed forward and cascade forward ANNs was compared with those of nonlinear and linear regression models using statistical indices, namely mean square error (
References
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Articles in the same Issue
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- Synthesis of Carboxymethyl Flaxseed Gum and Study of Nonlinear Rheological Properties of Its Solutions
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- Studies on the Physicochemical and Processing Properties of Tremella fuciformis Powder
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Articles in the same Issue
- Articles
- Comparisons of Processing Stability and Antioxidant Activity of the Silkworm Pupae Protein Hydrolysates by Spray-dry and Freeze-dry
- Synthesis of Carboxymethyl Flaxseed Gum and Study of Nonlinear Rheological Properties of Its Solutions
- Influence of Freezing–Thawing Cycle on Water Dynamics of Turbot Flesh Assessed by Low-Field Nuclear Magnetic Resonance and Magnetic Resonance Imaging
- Studies on the Physicochemical and Processing Properties of Tremella fuciformis Powder
- Tempering-Drying Simulation and Experimental Analysis of Corn Kernel
- Modeling Drying Properties of Pistachio Nuts, Squash and Cantaloupe Seeds under Fixed and Fluidized Bed Using Data-Driven Models and Artificial Neural Networks
- Rheological, Antioxidative, and Sensory Properties of Chinese Alkaline Noodle Prepared with Regular and Whole Wheat Flour
- Production of Thermal-Resistant Cornstarch-Alginate Beads by Dripping Agglomeration
- Effects of Pig Skin and Coconut Powder Mixture on Gelling and Rheological Properties of Composite Gel Prepared with Squid Myofibrillar Protein and Lard
- Microencapsulation of Bioactive Compounds from Hibiscus Calyces Using Different Encapsulating Materials