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Multi-Stage Corn-to-Syrup Process Monitoring and Yield Prediction Using Machine Learning and Statistical Methods.

Sheng-Jen Hsieh¹, Jeff Hykin²

¹Department of Engineering Technology & Industrial Distribution and Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

Sensors (Basel, Switzerland)

|October 16, 2024

Summary

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.

Keywords:

ANN SVM corn syrup dextrose equivalent value machine learning noise tolerant prediction process control

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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.