School of Food Science and Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui 230036, China.
Food Chem. 2025 Jan 15;463(Pt 1):141059. doi: 10.1016/j.foodchem.2024.141059. Epub 2024 Aug 31.
Heterocyclic aromatic amines (HAAs) are harmful byproducts in food heating. Therefore, exploring the prediction and generation patterns of HAAs is of great significance. In this study, genetic algorithm (GA) and support vector regression (SVR) are used to establish a prediction model of HAAs based on heating conditions, reveal the influence of heating temperature and time on the precursor and formation of HAAs in roast beef, and study the formation rules of HAAs under different processing conditions. Principal component analysis (PCA) showed that the effect on HAAs generation increases with the increase of heating temperature and time. The GA-SVR model exhibited near-zero absolute errors and regression correlation coefficients (R) close to 1 when predicting HAAs contents. The GA-SVR model can be applied for real-time monitoring of HAAs in grilled beef, providing technical support for controlling hazardous substances and intelligent processing of heat-processed meat products.
杂环胺（HAAs）是食物加热过程中产生的有害副产物。因此，探索 HAAs 的预测和生成模式具有重要意义。本研究采用遗传算法（GA）和支持向量回归（SVR）建立了基于加热条件的 HAAs 预测模型，揭示了加热温度和时间对烤牛肉中 HAAs 前体物和生成的影响，并研究了不同加工条件下 HAAs 的生成规律。主成分分析（PCA）表明，HAAs 生成的影响随加热温度和时间的增加而增加。GA-SVR 模型在预测 HAAs 含量时表现出接近零的绝对误差和接近 1 的回归相关系数（R）。GA-SVR 模型可用于实时监测烤牛肉中的 HAAs，为控制有害物质和热加工肉制品的智能加工提供技术支持。
