Artificial Intelligence and Mathematical Modelling of the Drying Kinetics of Pre-treated Whole Apricots

• Published in: Kemija u industriji
• Main Authors: Bousselma, Abla, Abdessemed, Dalila, Tahraoui, Hichem, Amrane, Abdeltif
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2021
• Subjects: apricot; drying kinetics; microwave; models; ANN; ANFIS; marelica; kinetika sušenja; mikrovalna pećnica; modeli; sami
• Online Access: https://hrcak.srce.hr/264637; https://hrcak.srce.hr/file/384257

This study involved monitoring and modelling the drying kinetics of whole apricots pre-treated with solutions of sucrose, NaCl, and sodium bisulphite. The drying was performed in a microwave oven at different power levels (200, 400, and 800 W). Two artificial intelligence models were used for the prediction of drying time (DT) and moisture ratio (MR): artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). On the other hand, the MR prediction was also done with 21 semi-empirical models, one of which we created. The results showed that the drying time decreased with the increase in microwave oven power for the three treatments. The treatment with NaCl was the most suitable for our work. The correlation coefficients of drying time (0.9992) and moisture ratio (0.9997) of ANN were high compared to the ANFIS model, which were 0.9941 and 0.9995, respectively. Among twenty semi-empirical models that were simulated, three models were fitted to our study (Henderson & Papis modified, Henderson & Pabis, and Two Terms). By comparing the three models adapted to our work and the model that we proposed, as well as ANN for MR prediction, it was observed that the model that we created was the most appropriate for describing the drying kinetics of NaCl-treated apricot. This solution opens the prospect of using this potential model to simulate fruit and vegetable drying kinetics in the future. Ovim istraživanjem obuhvaćeno je praćenje i modeliranje kinetike sušenja cjelovitih plodova marelice prethodno obrađenih otopinama saharoze, natrijeva klorida i natrijeva bisulfita. Sušenje je provedeno u mikrovalnoj pećnici pri različitim snagama (200, 400 i 800 W). Za predviđanje vremena sušenja (DT) i omjera vlage (MR) primijenjena su dva modela umjetne inteligencije: umjetna neuronska mreža (ANN) i prilagodljivi sustav neizrazitog zaključivanja zasnovanog na neuronskoj mreži (ANFIS). S druge strane, za predviđanje MR-a upotrijebljeno je 20 postojećih poluempirijskih modela te jedan koji su autori izradili sami. Rezultati su, kod sve tri primijenjene obrade, pokazali redukciju vremena sušenja s povećanjem snage mikrovalne pećnice. Tretman otopinom natrijeva klorida pokazao se najpogodnijim. Koeficijenti korelacije ANN modela za vrijeme sušenja (0,9992) i omjer vlage (0,9997) bili su viši nego kod ANFIS modela (0,9941 i 0,9995). Za dvadeset primijenjenih polu-empirijskih modela, tri modela pokazala su se podudarnim s rezultatima ovog istraživanja (modificirani model Hendersona i Pabisa, model Hendersona i Pabisa te model dvaju pojmova). Uspoređujući tri spomenuta modela i model predložen u ovom radu, kao i predviđanje MR-a ANN-om, uočeno je da je model predložen u radu najprikladniji za opisivanje kinetike sušenja marelice tretirane otopinom natrijeva klorida. Takvi rezultati ukazuju da bi se predloženi model potencijalno mogao ubuduće primjenjivati za simulaciji kinetike sušenja voća i povrća.