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Modeling quality changes in heat-processed orange juice: a comparative study of artificial neural network and multiple linear regression approaches

Yıl 2025, Cilt: 29 Sayı: 2, 237 - 254, 16.06.2025

Asiye Akyıldız , Tüba Şimşek Mertoğlu , Nuray İnan Çınkır , Erdal Ağçam

https://doi.org/10.29050/harranziraat.1572534

Öz

The purpose of the study was to assess the prediction ability of multiple linear regression (MLR) and artificial neural network (ANN) models for the browning index, total carotenoid content, 5-hydroxymethylfurfural (HMF) and ascorbic acid of orange juice during storage after heat processing. For ANN models, the effect of neuron number of the hidden layer, epoch number, training algorithms and transfer functions are investigated using the trial-error method for selecting best design ANN models. The different methods (stepwise and enter) in multiple linear regression models were performed for detecting impact of independent variables on dependent variables. The performance of ANN and MLR models was determined through unseen data by means of statistical analysis. Regarding performance indices of ANN models for test data, overall R and R2 were recorded as follows: 0.92 and 0.84 (browning index), 0.99 and 0.98 (HMF), 0.92 and 0.86 (ascorbic acid), 0.97 and 0.94 (total carotenoid content), respectively. R and R2 values of MLR models for test data were 0.79 and 0.68 (browning index), 0.94 and 0.88 (HMF), 0.92 and 0.85 (ascorbic acid), 0.93 and 0.90 (total carotenoid content), respectively. Both models provided accurate predictions. However, the superior predictive power of ANN models is that they can learn directly from examples without calculating the parameters using statistical techniques. The results revealed that ANN models showed greater potential with high R and R2 value, and the lowest error values when compared with the MLR model, but both ANN and MLR had almost same performance for prediction of ascorbic acid.

Anahtar Kelimeler

Artificial neural network multiple linear regression orange juice color modelling

Proje Numarası

ZF2012YL3

Kaynakça

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Isıl işlem uygulanmış portakal suyunda kalite değişimlerinin modellenmesi: yapay sinir ağı ve çoklu doğrusal regresyon yaklaşımlarının karşılaştırmalı bir çalışması

Yıl 2025, Cilt: 29 Sayı: 2, 237 - 254, 16.06.2025

Asiye Akyıldız , Tüba Şimşek Mertoğlu , Nuray İnan Çınkır , Erdal Ağçam

https://doi.org/10.29050/harranziraat.1572534

Öz

Çalışmanın amacı, ısıl işlemden sonra depolama sırasında portakal suyunun esmerleşme indeksi, 5-hidroksimetilfurfural (HMF), askorbik asit ve toplam karotenoid içeriği için çoklu doğrusal regresyon (MLR) ve yapay sinir ağı (ANN) modellerinin tahmin yeteneğini değerlendirmektir. En iyi tasarım YSA modellerini seçmek için gizli katmandaki nöron sayısının, epok sayısının, eğitim algoritmalarının ve transfer fonksiyonlarının etkisi deneme-yanılma yöntemi kullanılarak araştırılmıştır. Tüm bağımsız değişkenlerin bağımlı değişkenler üzerindeki etkisini tespit etmek için çoklu doğrusal regresyon modellerindeki farklı yöntemler (stepwise ve enter) uygulanmıştır. ANN ve MLR modellerinin performansı istatistiksel analiz yoluyla test verileri kullanılarak değerlendirilmiştir. Test verileri için ANN modellerinin performans endekslerine ilişkin olarak, genel R ve R2 sırasıyla; 0,92 ve 0,84 (esmerleşme indeksi), 0,99 ve 0,98 (HMF), 0,92 ve 0,86 (askorbik asit), 0,97 ve 0,94 (toplam karotenoid içeriği) belirlenmiştir. Test verileri için MLR modellerinin R ve R2 değerleri sırasıyla 0,79 ve 0,68 (esmerleşme indeksi), 0,94 ve 0,88 (HMF), 0,92 ve 0,85 (askorbik asit), 0,93 ve 0,90 (toplam karotenoid içeriği) olarak tespit edilmiştir. YSA modellerinin üstün tahmin gücü, istatistiksel teknikler kullanarak parametreleri hesaplamaya gerek kalmadan doğrudan örneklerden öğrenebilmeleridir. Sonuçlar, ANN modelinin yüksek R ve R2 değeri, ve düşük hata değerleri ile MLR modellerine kıyasla daha büyük potansiyel göstermiştir. Askorbik asit için hem ANN hem de MLR'nin tahminleme performansı neredeyse aynı düzeyde olduğu belirlenmiştir.

Anahtar Kelimeler

Yapay sinir ağı çoklu doğrusal regresyon portakal suyu renk modelleme

Proje Numarası

ZF2012YL3

Kaynakça

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Toplam 78 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Meyve-Sebze Teknolojisi
Bölüm Araştırma Makaleleri
Yazarlar

Asiye Akyıldız 0000-0001-5584-0849

Tüba Şimşek Mertoğlu 0000-0002-9694-211X

Nuray İnan Çınkır 0000-0002-8878-6794

Erdal Ağçam 0000-0002-2677-2020

Proje Numarası ZF2012YL3
Erken Görünüm Tarihi 11 Haziran 2025
Yayımlanma Tarihi 16 Haziran 2025
Gönderilme Tarihi 23 Ekim 2024
Kabul Tarihi 19 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 29 Sayı: 2

Kaynak Göster

APA Akyıldız, A., Şimşek Mertoğlu, T., İnan Çınkır, N., Ağçam, E. (2025). Modeling quality changes in heat-processed orange juice: a comparative study of artificial neural network and multiple linear regression approaches. Harran Tarım Ve Gıda Bilimleri Dergisi, 29(2), 237-254. https://doi.org/10.29050/harranziraat.1572534
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