Comparative Machine Learning Modeling of Infrared Drying Kinetics in Cactus Fruit (Opuntia ficus-indica) Slices

Abstract

The objective of this study was to model the infrared drying kinetics of cactus fruit (Opuntia ficus-indica) slices using advanced machine learning (ML) approaches. Drying experiments were conducted at a constant temperature of 70 °C using slice thicknesses of 2, 5, and 8 mm. Approximately 200 experimental data points describing the temporal evolution of moisture ratio (MR) were obtained. In previous analyses, the Midilli–Küçük model was identified as the most suitable semi-empirical thin-layer model for this dataset. In the present study, the same experimental data were re-evaluated using nonlinear ML algorithms to further improve predictive accuracy. Support vector machines (SVM), artificial neural networks (ANN), random forest (RF), and linear regression (LR) were employed. Drying time and slice thickness were used as input variables, while moisture ratio was defined as the output variable. Model performance was evaluated using a rigorous 10-fold cross-validation procedure. The results indicated that the SVM model achieved the highest prediction accuracy, with a coefficient of determination of R² ≈ 0.9998 and a root mean square error of approximately 0.005, followed closely by the ANN model (R² ≈ 0.9990). In contrast, the linear regression model failed to adequately capture the nonlinear characteristics of the drying process. Overall, the findings demonstrate that SVM and ANN provide robust and accurate alternatives to conventional empirical thin-layer models for predicting infrared drying kinetics of cactus fruit.

Keywords

• Artificial neural networks
• infrared drying
• machine learning
• moisture ratio
• Opuntia ficus-indica
• support vector machines

Kaktüs Meyvesi (Opuntia ficus-indica) Dilimlerinin Kızılötesi Kurutma Kinetiğinin Karşılaştırmalı Makine Öğrenmesi ile Modellenmesi

Abstract

Bu çalışmanın amacı, kaktüs meyvesi (Opuntia ficus-indica) dilimlerinin kızılötesi kurutma kinetiğini ileri makine öğrenmesi (ML) yaklaşımları kullanarak modellemektir. Kurutma deneyleri 70 °C sabit sıcaklıkta ve 2, 5 ve 8 mm dilim kalınlıkları kullanılarak gerçekleştirilmiştir. Nem oranının (MR) zamana bağlı değişimini tanımlayan yaklaşık 200 deneysel veri noktası elde edilmiştir. Önceki analizlerde, bu veri seti için en uygun yarı ampirik ince tabaka modelinin Midilli-Küçük modeli olduğu belirlenmiştir. Bu çalışmada, tahmin doğruluğunu daha da artırmak amacıyla aynı deneysel veriler doğrusal olmayan makine öğrenmesi algoritmaları kullanılarak yeniden değerlendirilmiştir. Analizlerde destek vektör makineleri (SVM), yapay sinir ağları (YSA), rastgele orman (RF) ve doğrusal regresyon (LR) algoritmaları kullanılmıştır. Kurutma süresi ve dilim kalınlığı girdi değişkenleri, nem oranı ise çıktı değişkeni olarak tanımlanmıştır. Model performansı, titiz bir 10-katlı çapraz doğrulama prosedürü ile test edilmiştir. Sonuçlar, SVM modelinin R² ≈ 0.9998 belirtme katsayısı ve yaklaşık 0.005 kök ortalama kare hatası ile en yüksek tahmin doğruluğuna ulaştığını, bunu YSA modelinin (R² ≈ 0.9990) yakından takip ettiğini göstermiştir. Buna karşılık, doğrusal regresyon modeli kurutma işleminin doğrusal olmayan özelliklerini yeterince yakalayamamıştır. Genel olarak bulgular, kaktüs meyvesinin kızılötesi kurutma kinetiğinin tahmin edilmesinde SVM ve YSA'nın geleneksel ampirik ince tabaka modellerine güçlü ve doğru alternatifler sunduğunu göstermektedir.

Keywords

• Yapay sinir ağları
• kızılötesi kurutma
• makine öğrenmesi
• nem oranı
• Opuntia ficus-indica
• destek vektör makineleri

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