关键词: 数据挖掘, 食品加工, 系统工程, 可解释性, 神经网络

Abstract:

The production process of corn deep processing to produce fructose has problems of outdated control and lack of refinement in production and processing. However, due to the complexity of the process, it is difficult to establish and optimize mechanism based models. Big data technology offers an effective solution by utilizing a substantial volume of production data to uncover process insights and identify key points. Initially, crucial target variables of this process were selected. Using big data technology, the original production data underwent preprocessing steps such as handling missing values, addressing outliers, noise reduction, and dimensionality reduction. Subsequently, three machine learning models—random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN) were constructed, all achieving R² values exceeding 0.90. Lastly, the SHAP method is used to explain different machine learning models, validate the credibility of the models, obtain the contribution levels of different features to the prediction results, and integrate the results of explanations from different models. This process generates a ranking of the importance of different points in the production process. Combining this with production experience, a mechanistic analysis is conducted to obtain the final key point table.

Key words: data mining, food processing, systems engineering, explainability, neural network