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Modeling Physicochemical Characteristics of Apple Using Adaptive Neuro-Fuzzy Inference System Publisher



Tahani B1 ; Beheshti B1 ; Heidarisoltanabadi M2 ; Hekmatian E3, 4
Authors
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Authors Affiliations
  1. 1. Biosystems Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
  2. 2. Agricultural Engineering Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran
  3. 3. Department of Oral and Maxillofacial Radiology Department, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
  4. 4. Member of Dental Implants Research Center, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Journal of Food Measurement and Characterization Published:2025


Abstract

Apple is considered a major crop in the Iranian agriculture market. Cold storage allows for the preservation of this fruit for an extended period. However, the qualitative characteristics of apples can be affected by their internal changes during storage. Therefore, we must predict these changes and provide suitable storage conditions to maintain the nutritional and economic values of this crop. This study analyzed the physicochemical characteristics of Golden Delicious apples under storage at 0 °C and 4 °C for 0, 45, 90, and 135 days. The examined physicochemical characteristics were pH, firmness, density, soluble solids (SS), and moisture. The adaptive neuro-fuzzy inference system (ANFIS) was then employed to predict the physicochemical characteristics of apples based on color space components (L*a*b*), CT (Computed Tomography) number and storage temperature and duration. The results of implementing and comparing different ANFIS models indicated that R2, RMSE, MAPE, and EF in the best models of prediction were 0.954, 1.793, 3.580, and 0.910 for firmness, 0.965, 0.085, 1.565, and 0.931 for PH, 0.970, 0.026, 2.422, and 0.940 for density, 0.960, 0.309, 1.349, and 0.921 for SS, and 0.980, 0.0005, 0.448, and 0.960 for moisture, respectively. As per the results, we can accurately and roughly predict the physicochemical characteristics of apples under cold storage to assess quality during storage. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
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