Performance evaluation of GSA, SOS, ABC and ANN algorithms on linear and quadratic modelling of eggplant drying kinetic

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OZDEN S., KILIÇ F.

FOOD SCIENCE AND TECHNOLOGY, cilt.40, sa.3, ss.635-643, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 40 Sayı: 3
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1590/fst.12719
  • Dergi Adı: FOOD SCIENCE AND TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, Food Science & Technology Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Sayfa Sayıları: ss.635-643
  • Anahtar Kelimeler: food drying, eggplant drying, optimization, modeling, SYMBIOTIC ORGANISMS SEARCH, BEE COLONY ABC, OPTIMIZATION
  • Gazi Üniversitesi Adresli: Evet

Özet

In this study, kinetics of eggplant drying was modeled in the laboratory-scaled Food Drying Oven (FDO) with resistance heater was designed and manufactured. The temperature, energy consumption and drying time of FDO were recorded by keeping the temperature of at different temperatures as 40, 50 and 60 degrees C. These saved values were chosen as the input parameters of the modeL The weight value of the eggplant was taken as the output parameter. Linear and quadratic equations were developed for modeling and constant coefficients of these equations were estimated with Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), symbiotic organisms search (SOS) algorithms. In addition, the performances of these models were compared with the model developed with ANN in terms of performance and time. The results show that the lowest error of the developed linear and quadratic equations was obtained with SOS algorithm. The MSE metric results of ANN were fifty times higher than the performance of SOS algorithm, and the SOS algorithm reached best value three times faster than the ANN.