Scholarlink

<p>To address the challenges of aflatoxin M<sub>1</sub> (AFM<sub>1</sub>) detection in the dairy industry, this study proposes a neural network model named STANet, based on a spatiotemporal attention mechanism, for probabilistic prediction of exceeding AFM<sub>1</sub> in raw milk. This study utilizes nearly 1 million raw milk spatiotemporal monitoring data from December 2022 to October 2024 of a Chinese dairy company, generates high-quality synthetic data to alleviate the sample imbalance problem by the TABSYN method, and effectively integrates the spatiotemporal information with the basic features by using the dynamic weight fusion mechanism and attention pooling process. The experimental results show that STANet significantly outperforms other models in terms of accuracy (0.835), especially in the identification of sparse positive samples, with F1 scores of 0.842 and 0.828 for category 0 and category 1, respectively. The ablation experiments further validate the spatiotemporal features and dynamic weighting mechanism to enhance the performance of the model. Feature importance analysis reveals the significant contribution of features to the prediction. This study provides a data-driven framework to support the early probabilistic detection and monitoring of AFM<sub>1</sub> exceedance, which has the potential to reduce screening costs in large-scale raw milk quality control. Future improvements may be achieved by incorporating biochemical validation experiments and refining data pre-processing strategies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

STANet: Spatiotemporal Attention Network for Aflatoxin M1 Prediction in Raw Milk

  • 期刊论文

  • 发表日期：

    2026年3月16日

查看全文
  • Long Wang,
  • Xiaodong Song,
  • Haohan Ding,
  • Xiaohui Cui,
  • David I. Wilson,
  • Wei Yu,
  • Cheng Zhang,
  • Guanjun Dong

摘要

To address the challenges of aflatoxin M1 (AFM1) detection in the dairy industry, this study proposes a neural network model named STANet, based on a spatiotemporal attention mechanism, for probabilistic prediction of exceeding AFM1 in raw milk. This study utilizes nearly 1 million raw milk spatiotemporal monitoring data from December 2022 to October 2024 of a Chinese dairy company, generates high-quality synthetic data to alleviate the sample imbalance problem by the TABSYN method, and effectively integrates the spatiotemporal information with the basic features by using the dynamic weight fusion mechanism and attention pooling process. The experimental results show that STANet significantly outperforms other models in terms of accuracy (0.835), especially in the identification of sparse positive samples, with F1 scores of 0.842 and 0.828 for category 0 and category 1, respectively. The ablation experiments further validate the spatiotemporal features and dynamic weighting mechanism to enhance the performance of the model. Feature importance analysis reveals the significant contribution of features to the prediction. This study provides a data-driven framework to support the early probabilistic detection and monitoring of AFM1 exceedance, which has the potential to reduce screening costs in large-scale raw milk quality control. Future improvements may be achieved by incorporating biochemical validation experiments and refining data pre-processing strategies.