Development of deep learning and equation-based models for predicting oil freshness in snack frying processes and implementation of real-time prediction system

Key Points

• The study aims to predict the quality indicator of frying oil, total polar compounds (TPM), during snack manufacturing.
• Developed parallel deep learning model (LSTM) and equation-based model using industrial frying process data.
• Collected data from an actual industrial continuous frying machine.
• Implemented a user-friendly graphical interface for real-time prediction and visualization of TPM.
• Successfully demonstrated the possibility of predicting TPM at the end of the frying process.
• The user interface facilitated real-time monitoring of oil freshness.
• Highlighted the potential for improving quality control by integrating predictive models.