Comprehensive Evaluation of Mathematical Models Used in the Thin-Layer Cold Dried Foods


KILIÇ A.

FOOD SCIENCE & NUTRITION, cilt.13, sa.7, 2025 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 7
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1002/fsn3.70558
  • Dergi Adı: FOOD SCIENCE & NUTRITION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Food Science & Technology Abstracts, Greenfile, Directory of Open Access Journals
  • Anahtar Kelimeler: cold drying, evaluation, food, mathematical modeling, thin layer
  • Recep Tayyip Erdoğan Üniversitesi Adresli: Evet

Özet

This article focused on the comprehensive evaluation of statistical criteria applied in common mathematical models selected for experimental cold drying data for thin-layer food drying applications. In this context, Mackerel (Trachurus trachurus), known as a functional and sensitive food sample with its bioactive content, was selected as the experimental material for drying applications. For this purpose, four experimental groups (G5MM, G10MM, G15MM, G20MM) with different sample thicknesses (5, 10, 15, 20 mm) at 100 g were dried with 6 m/s air flow at 10 degrees C for 24, 22, 20, and 14 h respectively. Twenty-three common semi-theoretic and empiric mathematical models were applied to the obtained drying values. For the comprehensive evaluation of the models, non-linear regression analysis was performed using 13 different statistical criteria such as r, RSS, SST, SSE, R2, chi 2, RMSE, residuals, RSSE, MBE, EF, SEE, and p. In this context, in the study where the relevant criteria were applied, for G20MM, Newton Lewis, Midilli-K & uuml;& ccedil;& uuml;k, Balbay and & Scedil;ahin, Page, for G15MM, Henderson & Pabis, Logarithmic (Asymptotic), Binomial, Verma et al., Modified Henderson, Simplified Fick diff., Balbay and & Scedil;ahin model were concluded to be the most suitable. In addition, for G10MM, Logarithmic (Asymptotic), Demir et al., Binary, Verma et al., Balbay and & Scedil;ahin, Thompson and Alibas models, and in the G05MM group, Logarithmic (Asymptotic), Demir et al., Binary, Verma et al., Thompson, Balbay-& Scedil;ahin and Alibas models were concluded to be the most suitable. According to the results obtained, it has been revealed that using only r, R2, chi 2 and RMSE equations instead of 13 statistical criteria in the evaluation of mathematical models gives significant and meaningful results.