Image-Based Predictive Model to Optimize Drying Endpoints in the Chili Pepper Drying Process DOI
Dasong Yu,

Aekyeung Moon

Published: Sept. 11, 2024

Language: Английский

Machine Learning Approaches for Binary Classification of Sorghum (Sorghum bicolor L.) Seeds from Image Color Features DOI
Beyza Çiftçi, Necati Çetin, Seda Günaydın

et al.

Journal of Food Composition and Analysis, Journal Year: 2025, Volume and Issue: 140, P. 107208 - 107208

Published: Jan. 8, 2025

Language: Английский

Citations

3
Comparison performance of the CNN-based deep learning models for the distinguishing ultrasound pretreated and microwave dried jujube fruits DOI
Banu Ulu, Seda Günaydın, Necati Çetin

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117047 - 117047

Published: Feb. 1, 2025

Language: Английский

Citations

2
Drying Time, Energy and Exergy Efficiency Prediction of Corn (Zea mays L.) at a Convective-Infrared-Rotary Dryer: Approach by an Artificial Neural Network DOI Creative Commons
Yousef Abbaspour‐Gilandeh,

Safoura Zadhossein,

Mohammad Kaveh

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(3), P. 696 - 696

Published: Feb. 3, 2025

Energy consumption in the drying industry has made an energy-intensive operation. In this study, time, quality properties (color, shrinkage, water activity and rehydration ratio), specific energy (S.E.C), thermal, exergy efficiency of corn using a hybrid dryer convective-infrared-rotary (CV-IR-D) were analyzed. addition, parameters predicted artificial neural network (ANN) technique. The experiments conducted at three rotary rotation speeds 4, 8 12 rpm, temperatures 45, 55 65 °C, infrared power 0.25, 0.5 0.75 kW. By increasing temperature, speed, S.E.C decreased while Deff, energy, thermal increased. highest values ratio redness (a*) lowest brightness (L*), yellowness (b*) color changes (ΔE) obtained kW, air temperature °C speed rpm. range S.E.C, during process was 5.05–28.15 MJ/kg, 3.26–29.29%, 5.5–32.33% 21.22–55.35%. prediction results ANNs showed that R for data 0.9938, 0.9906, 0.9965, 0.9874 0.9893, respectively, indicating successful prediction.

Language: Английский

Citations

1
Formulation and analysis of probiotic Bacillus licheniformis MCC 2514 infused osmo dried carrot DOI

Rohith HS,

Ishrat Jahaan Peerzade,

A. S. Chauhan

et al.

Journal of Food Science and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: March 7, 2025

Language: Английский

Citations

0
Model comparison and hyperparameter optimization for visible and near-infrared (Vis-NIR) spectral classification of dehydrated banana slices DOI
Mehmet Akif Buzpinar,

Seda Gunaydin,

Erhan Kavuncuoğlu

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127858 - 127858

Published: April 1, 2025

Language: Английский

Citations

0
A novel approach for modelling and predicting the drying kinetics of couscous grains using artificial neural networks DOI
Fouad Ait Hmazi, Hamza Bagar,

Abdellah Madani

et al.

Journal of Food Composition and Analysis, Journal Year: 2024, Volume and Issue: 132, P. 106301 - 106301

Published: May 6, 2024

Language: Английский

Citations

0
Machine Learning Modeling of Anchovy Waste Treatment Using Solar Drying DOI

Najjar Mohammed,

Zakaria Tagnamas, Younes Bahammou

et al.

Heat Transfer, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 3, 2024

ABSTRACT This study aims to valorize coproducts from the anchovy processing chain by obtaining compounds of interest through implementation environmentally friendly and energy‐efficient techniques. These methods, which also apply other fresh waste coproducts, seek minimize environmental pollution associated with conventional systems. The investigation focused on application solar drying as a treatment waste. resulting data were employed model behavior using five machine learning algorithms. A thermokinetic was conducted under both natural forced convection establish optimal conditions for storing heads, are significant source high‐quality proteins human animal nutrition. Drying kinetics examined at three temperatures (60°C, 70°C, 90°C) two airflow rates (150 300 m 3 /h). identified air temperature most critical factor affecting wastes. Machine modeling conducted, evaluated models RNN, LSTM, GRU, LightGBM, CatBoost. CatBoost demonstrated superior performance in predicting moisture content. It achieved lowest Mean Squared Error 1.1491e − 06, Absolute 0.0006265, highest coefficient determination ( R 2 ) 99.99%. comparative analysis highlighted distinct differences predictive accuracy models, emerging effective.

Language: Английский

Citations

0
Image-Based Predictive Model to Optimize Drying Endpoints in the Chili Pepper Drying Process DOI
Dasong Yu,

Aekyeung Moon

Published: Sept. 11, 2024

Language: Английский

Citations

0