Design of a machine (federated) learning based generalized model for predicting drying kinetics of foods

Erenturk K., ERENTÜRK S.

CYTA - Journal of Food, cilt.23, sa.1, 2025 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23 Sayı: 1
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1080/19476337.2025.2533463
  • Dergi Adı: CYTA - Journal of Food
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, BIOSIS, CAB Abstracts, Compendex, Food Science & Technology Abstracts, INSPEC, Veterinary Science Database, Directory of Open Access Journals, DIALNET
  • Anahtar Kelimeler: drying kinetics of foods, estimation, federated learning, Food drying, machine learning
  • Atatürk Üniversitesi Adresli: Evet

Özet

Different from the previously published modeling techniques, the federated learning (FL) approach provides a global modeling tool for obtaining a global model for food drying processes. The main aim of this work is to design a trained federated model for modeling drying processes of different foods. Drying data for carrot, Echinacea Angustifolia, eggplant and mushroom have been used to train FL model to overcome the estimation challenges of different food drying processes using a single and food kind independent architecture. To validate the trained FL model, apple and strawberry drying data have been used. Obtained final model has been proven to ability of modeling different types of foods with higher accuracy and flexibility for future applications. Obtained FL model has proven its ability to estimate the drying characteristics of apple and strawberry that have not been used during training process with a higher accuracy R2 value of 0.9864.