PRECISION GROWTH MODELING OF ESCHERICHIA COLI IN SPARSE DATA SCENARIOS: A MULTI-STAGE LEARNING APPROACH

Öz

This study investigates the prediction of Escherichia coli growth in environments where experimental data are limited, by integrating mathematical curve fitting with machine learning regression models. Two hybrid frameworks are developed: Fourier Series Curve Fitting combined with Gaussian Process Regression (FSCF-GPR), and Gaussian Curve Fitting integrated with Support Vector Machine Regression (GCF-SVMR). The raw dataset, initially composed of only 10 experimental measurements, was expanded to 114 data points through mathematical smoothing, providing a richer basis for model training. Model performance was assessed using Root Mean Square Error (RMSE), Mean Squared Error (MSE), Coefficient of Determination (R²), and Mean Absolute Error (MAE). Results demonstrate that the FSCF-GPR framework achieved outstanding predictive accuracy with an R² of 0.9999, while GCF-SVMR also showed strong performance with an R² of 0.9934. These findings highlight that data augmentation via curve fitting can substantially enhance the accuracy and robustness of machine learning approaches in microbiological growth prediction under data-scarce conditions.

Anahtar Kelimeler

• Microbial growth modeling
• Data augmentation
• Machine learning
• Regression model

Etik Beyan

No conflict of interest was declared by the authors.

SEYREK VERİ SENARYOLARINDA ESCHERICHIA COLI'NİN HASSAS BÜYÜME MODELLEMESİ: ÇOK AŞAMALI BİR ÖĞRENME YAKLAŞIMI

Öz

Bu çalışma, matematiksel eğri uydurma ile makine öğrenimi regresyon modellerini entegre ederek, deneysel verilerin sınırlı olduğu ortamlarda Escherichia coli büyümesinin tahminini araştırmaktadır. İki hibrit çerçeve geliştirilmiştir: Gauss Süreci Regresyonu ile birleştirilmiş Fourier Serisi Eğri Uydurma (FSCF-GPR) ve Destek Vektör Makinesi Regresyonu ile entegre edilmiş Gauss Eğri Uydurma (GCF-SVMR). Başlangıçta sadece 10 deneysel ölçümden oluşan ham veri kümesi, matematiksel düzeltme yoluyla 114 veri noktasına genişletilerek model eğitimi için daha zengin bir temel sağlanmıştır. Model performansı, Kök Ortalama Kare Hatası (RMSE), Ortalama Kare Hatası (MSE), Belirleme Katsayısı (R²) ve Ortalama Mutlak Hata (MAE) kullanılarak değerlendirilmiştir. Sonuçlar, FSCF-GPR çerçevesinin 0,9999 R² ile olağanüstü bir tahmin doğruluğu elde ettiğini, GCF-SVMR'nin ise 0,9934 R² ile güçlü bir performans gösterdiğini ortaya koymaktadır. Bu bulgular, eğri uydurma yoluyla veri artırmanın, veri kıtlığı koşullarında mikrobiyolojik büyüme tahmininde makine öğrenimi yaklaşımlarının doğruluğunu ve sağlamlığını önemli ölçüde artırabileceğini vurgulamaktadır.

Anahtar Kelimeler

• Mikrobiyal büyüme modellemesi
• Veri artırma
• Makine öğrenmesi
• Regresyon model

Kaynakça

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