Exploration of the prediction and generation patterns of heterocyclic aromatic amines in roast beef based on Genetic Algorithm combined with Support Vector Regression.

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.

Study on failure mechanism of tight sandstone based on moment tensor inversion.

Understanding deep rocks' mechanical properties and failure evolution is crucial for efficient resource development. This study investigates the mechanical properties of tight sandstone and analyzes its acoustic emission (AE) characteristics using a combined discrete element model and moment tensor inversion. The AE activity during loading is categorized into three stages: crack initiation, stable crack propagation, and unstable crack propagation. Confining pressure loading suppresses AE activity during the crack initiation stage due to damage healing phenomenon. Moment tensor inversion reveals that tensile failure is the primary AE failure source, despite samples exhibiting splitting and shear failure modes. The proportion of AE failure types varies with stress levels and depends on the mechanical environment. Microcracks initiate at the ends of the sample and propagate inward along the loading direction, resulting in a blank area of AE events in the middle. This blank area can be utilized to predict specimen failure mode. The b value, representing the ratio of small to large magnitude events, decreases with increase of the confining pressure, indicating higher energy release during specimen failure under high confining pressure. The research results can provide a reference for predicting the failure of tight sandstone.