Application of soft-computational models for the prediction of some quality traits of microwave-dried tomato slices

Author:

Hussein Jelili BabatundeORCID,Oke Moruf Olanrewaju,Agboola Fausat Fadeke,Sanusi Mayowa Saheed

Publisher

Springer Science and Business Media LLC

Subject

Computers in Earth Sciences,Statistics, Probability and Uncertainty,General Agricultural and Biological Sciences,General Environmental Science

Reference64 articles.

1. Abano EE, Haile MA, Owusu J, Engmann FN (2013) Microwave-vacuum drying effect on drying kinetics, lycopene and ascorbic acid content of tomato slices. J Stored Prod Postharvest Res 4:11–22

2. Abdalla OA, Elfaki AO, AlMurtadha YM (2014) Optimizing the multilayer feed-forward artificial neural networks architecture and training parameters using genetic algorithm. Int J Comput Appl 96:42–48

3. Alda LM, Gogoasa I, Despina-Maria B, Gergen I, Alda S, Camelia M, Nita L (2009) Lycopene content of tomatoes and tomato products. J Agroaliment Process Technol 15:540–542

4. Alibas I (2012) Microwave drying of strawberry slices and the determination of the some quality parameters. J Agric Mach Sci 8:161–170

5. Al-Sulaiman MA (2011) Prediction of quality indices during drying of okra pods in a domestic microwave oven using artificial neural network model. Afr J Agric Res 6:2680–2691

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