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. 2023 Jun 1;12(11):2241.
doi: 10.3390/foods12112241.

Prediction of Safety Risk Levels of Benzopyrene Residues in Edible Oils in China Based on the Variable-Weight Combined LSTM-XGBoost Prediction Model

Cheng Hao  1   2 , Qingchuan Zhang  1   2 , Shimin Wang  1   2 , Tongqiang Jiang  1   2 , Wei Dong  1   2
Affiliations

Prediction of Safety Risk Levels of Benzopyrene Residues in Edible Oils in China Based on the Variable-Weight Combined LSTM-XGBoost Prediction Model

Cheng Hao et al. Foods. .

Abstract

To assess and predict the food safety risk of benzopyrene (BaP) in edible oils in China, this study collected national sampling data of edible oils from 20 Chinese provinces and their prefectures in 2019, and constructed a risk assessment model of BaP in edible oils with consumption data. Initially, the k-means algorithm was used for risk classification; then the data were pre-processed and trained to predict the data using the Long Short-Term Memory (LSTM) and the eXtreme Gradient Boosting (XGBoost) models, respectively, and finally, the two models were combined using the inverse error method. To test the effectiveness of the prediction model, this study experimentally validated the model according to five evaluation metrics: root mean square error (RMSE), mean absolute error (MAE), precision, recall, and F1 score. The variable-weight combined LSTM-XGBoost prediction model proposed in this paper achieved a precision of 94.62%, and the F1 score value reached 95.16%, which is significantly better than other neural network models; the results demonstrate that the prediction model has certain stability and feasibility. Overall, the combined model used in this study not only improves the accuracy but also enhances the practicality, real-time capabilities, and expandability of the model.

Keywords: BaP; LSTM; XGBoost; edible oil; risk assessment; risk prediction.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of k-means clustering algorithm.
Figure 6
Figure 6
LSTM-XGBoost-based variable-weight combined prediction model.
Figure 2
Figure 2
Flow chart of the variable-weight combined LSTM-XGBoost prediction model.
Figure 3
Figure 3
Hidden layer cell of LSTM model.
Figure 4
Figure 4
LSTM model construction.
Figure 5
Figure 5
XGBoost model tuning results.
Figure 7
Figure 7
Elbow method to determine k value.
Figure 8
Figure 8
Probability density distribution of subgroup 1.
Figure 9
Figure 9
Probability density distribution of subgroup 2.
Figure 10
Figure 10
Probability density distribution of subgroup 3.
Figure 11
Figure 11
RMSE for ILCR, MOE, and NIPI indicators.
Figure 12
Figure 12
MAE for ILCR, MOE, and NIPI indicators.
See this image and copyright information in PMC

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