ED-Stacking：A Construction Method of Few-shot Prediction Model for Beef Microbial Growth Based on Ensemble Learning

Under the current technological conditions, microbial detection was complicated and time-consuming, which leaded to the problem of lagging detecting results and limited sample size. In this paper proposed a construction method of few-shot predictive model for microbial growth in beef, called ED-Stacking, which was based on time series decomposition and ensemble learning, for early warning of microbial risks in food. First, empirical mode decomposition (EMD), discrete Fourier transform (DFT) and additive modeling were applied to construct a time series decomposition method EMD-DFT, which was used to extract the trend, period, and residual features in the microbial growth time series, and to provide training data for the subsequent prediction model. Second, these feature data were then utilized to train a single-layer linear neural network (SLN), extreme gradient boosting (XGBoost) and gradient boosting regression tree (GBRT). Finally, the stacking method in ensemble learning was used to fuse the three trained models to form ED-Stacking, a microbial growth prediction model with better performance in prediction. Results showed that ED-Stacking achieved 0.229 and 0.147 in MAE and MSE metrics, respectively, with lower prediction errors than the five baseline models of SLN, XGBoost, GBRT, GRU, and Transformer. Based on this model, the food quality classification was performed and the weighted precision of the classification, Weighted-Precision, reached 98.80%. Furthermore, the study also presented FMPvis, a visual analysis system for the prediction of microbial growth in food, which could display the prediction results and the food quality classification results, and helped users to analyze the trend of each environmental factor over time and its influence on the prediction results, so as to facilitate risk analysis and early warning. This approach contributes a new idea for early warning of microbial risk in food.