Multi-Stage Corn-to-Syrup Process Monitoring and Yield Prediction Using Machine Learning and Statistical Methods
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View abstract on PubMed
Summary
This summary is machine-generated.Machine learning models accurately predict corn syrup quality (pH and dextrose equivalent) for semi-automated food production. Artificial neural networks offer the highest accuracy and noise tolerance for process control.
Area Of Science
- Food Science and Technology
- Chemical Engineering
- Data Science
Background
- Corn syrup is a vital food industry sweetener, but maintaining consistent quality (dextrose equivalent - DE) is challenging in semi-automated batch production.
- Existing process control methods often focus on continuous systems, leaving a gap for small to medium-sized factories.
- Developing robust, data-driven models is crucial for enhancing efficiency and product consistency.
Purpose Of The Study
- To develop and evaluate machine learning models for predicting key corn syrup quality parameters (feed pH and DE) in semi-automated settings.
- To identify critical process parameters influencing feed pH and DE using correlation analysis.
- To compare the performance of Artificial Neural Network (ANN), Support Vector Machine (SVM), and Linear Regression (LR) models.
Main Methods
- Correlation coefficients were calculated to identify key process parameters affecting feed pH and DE.
- ANN, SVM, and LR models were constructed to predict feed pH and DE using historical process data.
- Model performance was evaluated based on prediction accuracy, noise tolerance, and robustness with limited data.
Main Results
- Model accuracy ranged from 91% to 96%, with ANN models outperforming SVM and LR by 1-3%.
- ANN models demonstrated superior noise tolerance compared to SVM, while SVM performance degraded with high-dimensional data.
- Linear Regression models exhibited higher accuracy variation than ANN and SVM.
Conclusions
- Machine learning, particularly ANN, provides effective process control for semi-automated corn syrup production, enhancing quality consistency.
- The developed models are accurate and robust, even with limited datasets, offering practical solutions for small to medium-sized factories.
- Multi-stage modeling approaches show potential for improved accuracy, though trade-offs exist compared to single-stage methods.
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