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Journals / Recep Tayyip Erdoğan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2026 / Cilt: 7 Sayı: 1

Determination of the Freeze-Drying Behavior of Fruits in a Rotary-Grid Infrared-Assisted Drying System Using Artificial Neural Networks

Dönen Izgaralı Kızılötesi Destekli Bir Kurutma Sisteminde Meyvelerin Dondurarak Kurutma Davranışının Yapay Sinir Ağları ile Belirlenmesi

Muhammed Emin Topal(RECEP TAYYİP ERDOĞAN ÜNİVERSİTESİ)0000-0002-5237-9003
Birol Şahin(RECEP TAYYİP ERDOĞAN ÜNİVERSİTESİ)0000-0003-1004-4720
Pages
274–293
Publication Date
2026-06-30
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AI Summary & Key Takeaways

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⚡ TL;DR(Too Long; Didn't Read)

In addition, ANN architectures with single, double, and triple hidden layers were compared, and the highest prediction performance was obtained with a two-hidden-layer 9-9 structure, yielding an overall R² of 0.995 for the entire dataset.

📌 3 Key Takeaways

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Objective & Hypothesis:The aim of this study is to characterize the drying behavior of various products (strawberry, mandarin, and apple) using an innovative drying method, namely the rotating-grid infrared-assisted freeze-drying (RG-IRAFD) system, to compare the results with classical freeze-drying, and to evaluate the predictability of the process using both thin-layer mathematical models and artificial neural networks (ANNs).
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Key Findings & Data:In the experiments, samples were sliced to a thickness of 5 mm and frozen at -20 °C.
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Conclusion & Impact:In addition, ANN architectures with single, double, and triple hidden layers were compared, and the highest prediction performance was obtained with a two-hidden-layer 9-9 structure, yielding an overall R² of 0.995 for the entire dataset.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Dönen Izgara · Kızılötesi Isıtıcı · Dondurarak Kurutma

Abstract

The aim of this study is to characterize the drying behavior of various products (strawberry, mandarin, and apple) using an innovative drying method, namely the rotating-grid infrared-assisted freeze-drying (RG-IRAFD) system, to compare the results with classical freeze-drying, and to evaluate the predictability of the process using both thin-layer mathematical models and artificial neural networks (ANNs). The RG-IRAFD system includes a vacuum drying chamber, a cooling unit, a vacuum pump, and infrared heaters, together with a mechanism that enables controlled rotation of the product grid, thereby promoting a more uniform distribution of infrared radiation over the product surface. In the experiments, samples were sliced to a thickness of 5 mm and frozen at -20 °C. RG-IRAFD applications were conducted at two infrared power levels (450 and 900 W); the primary drying stage was performed under vacuum at -5 °C and 5 °C, and the infrared heaters were activated during the secondary stage. Mass changes were monitored at specific intervals to calculate moisture content (MC), drying rate (DR), and the dimensionless moisture ratio (MR). At 450 W, the final moisture contents and total drying times for strawberry, mandarin, and apple were 9.9% (475 min), 9.1% (475 min), and 2.6% (475 min), respectively, whereas at 900 W they were 12.8% (435 min), 10.2% (415 min), and 3.5% (415 min), respectively. In modeling the time-dependent MR data, the Parabolic and Modified Page IV models provided the best fit. In addition, ANN architectures with single, double, and triple hidden layers were compared, and the highest prediction performance was obtained with a two-hidden-layer 9-9 structure, yielding an overall R² of 0.995 for the entire dataset. This value exceeds the best R² achieved by mathematical modeling (0.989), demonstrating that ANNs offer superior predictive capability compared with conventional thin-layer models.

Özet

Bu çalışmanın amacı, yenilikçi bir kurutma yöntemi olan dönen ızgaralı kızılötesi destekli dondurarak kurutma sisteminin (DKDK) çeşitli ürünler (çilek, mandalina ve elma) kullanılarak kurutma davranışını belirlemek, sonuçları klasik dondurarak kurutma ile karşılaştırmak ve sürecin hem ince tabaka matematiksel modeller hem de yapay sinir ağı (YSA) ile tahmin edilebilirliğini değerlendirmektir. DKDK sistemi; vakum altında çalışan bir kurutma kabini, soğutma ünitesi, vakum pompası ve kızılötesi ısıtıcıların yanı sıra ürün ızgarasının kontrollü olarak döndürülmesine olanak tanıyan bir mekanizma içermekte ve kızılötesi ışınımın ürün yüzeylerine daha homojen dağılmasını hedeflemektedir. Deneylerde numuneler 5 mm kalınlığında dilimlenmiş ve -20 °C’de dondurulmuştur. DKDK uygulamaları iki kızılötesi güç seviyesinde (450 ve 900 W) yürütülmüş; ön kurutma vakum altında -5 °C ve 5 °C sıcaklıklarda gerçekleştirilmiş, ikincil aşamada kızılötesi ısıtıcılar devreye alınmıştır. Kütle değişimleri belirli aralıklarla izlenerek nem içeriği (MC), kuruma hızı (DR) ve boyutsuz kütle kaybı oranı (MR) hesaplanmıştır. 450 W’da çilek, mandalina ve elma için nihai nem içerikleri ve toplam kurutma süreleri sırasıyla %9,9 (475 dk), %9,1 (475 dk) ve %2,6 (475 dk) iken; 900 W’da %12,8 (435 dk), %10,2 (415 dk) ve %3,5 (415 dk) olarak elde edilmiştir. Zamana bağlı MR değerlerinin modellemesinde Parabolik ve Modifiye Page IV modelleri en yüksek uyumu göstermiştir. Bunun yanında, tek/çift/üç gizli katmanlı YSA mimarileri karşılaştırılmış ve en yüksek doğruluk çift gizli katmanlı 9-9 yapı ile sağlanarak tüm veri setinde R²=0,995 elde edilmiştir. Bu değer, matematiksel modellemede ulaşılan en yüksek R² değerinin (0,989) üzerinde olup YSA’ların üstün tahmin yeteneğiyle klasik matematiksel modellere kıyasla daha iyi sonuçlar gösterdiğini ortaya koymuştur.

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Cite This Article

Topal, M. E., Şahin, B. (2026). Determination of the Freeze-Drying Behavior of Fruits in a Rotary-Grid Infrared-Assisted Drying System Using Artificial Neural Networks. Recep Tayyip Erdoğan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 7 Sayı: 1, 274–293. https://doi.org/10.53501/rteufemud.1887076

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