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Application of Hybrid Neural Fuzzy System (ANFIS) in Food Processing and Technology

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Abstract

Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic hybrid intelligent system. It combines the human-like reasoning style of fuzzy logic system (FLS) with the learning and computational capabilities of artificial neural networks (ANNs). ANFIS has several applications related to food processing and technology. The first part of this review provides a brief overview and discussion of ANFIS including: the general structure and topology, computational considerations, model development and testing. In the second part, two detailed examples are explained to demonstrate the capabilities of ANFIS in comparison with other modeling methods, followed by a brief but comprehensive discussion of ANFIS applications in different food processing and technology areas. The applications are divided into five main categories: food drying, prediction of food properties, microbial growth and thermal process modeling, applications in food quality control and food rheology. In all applications, the performance of ANFIS is compared to other methods such as ANNs, FLS and multiple regressions when available. It is concluded that, in most applications, ANFIS outperforms other modeling tools such as ANNs, FIS or multiple linear regression. Finally, some application guidelines, advantages and disadvantages of ANFIS are discussed.

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Authors and Affiliations

  1. Department of Chemical Engineering, Jordan University of Science and Technology, PO Box 3030, Irbid, 22110, Jordan

    Majdi Al-Mahasneh & Mohannad Aljarrah

  2. Department of Nutrition and Food Technology, Jordan University of Science and Technology, PO Box 3030, Irbid, 22110, Jordan

    Taha Rababah & Muhammad Alu’datt

Authors
  1. Majdi Al-Mahasneh
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  2. Mohannad Aljarrah
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  3. Taha Rababah
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  4. Muhammad Alu’datt
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Correspondence to Majdi Al-Mahasneh.

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Al-Mahasneh, M., Aljarrah, M., Rababah, T. et al. Application of Hybrid Neural Fuzzy System (ANFIS) in Food Processing and Technology. Food Eng Rev 8, 351–366 (2016). https://doi.org/10.1007/s12393-016-9141-7

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  • DOI: https://doi.org/10.1007/s12393-016-9141-7

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