Microwave detection technique combined with deep learning algorithm facilitates quantitative analysis of heavy metal Pb residues in edible oils

Abstract

The heavy metal content in edible oils is intricately associated with their suitability for human consumption. In this study, standard soybean oil was used as a sample to quantify the specified concentration of heavy metals using microwave sensing technique. In addition, an attention-based deep residual neural network model was developed as an alternative to traditional modeling methods for predicting heavy metals in edible oils. In the process of microwave data processing, this work continued to discuss the impact of depth on convolutional neural networks. The results demonstrated that the proposed attention-based residual network model outperforms all other deep learning models in all metrics. The performance of this model was characterized by a coefficient of determination (R²) of 0.9605, a relative prediction deviation (RPD) of 5.0479, and a root mean square error (RMSE) of 3.1654 mg/kg. The research findings indicate that the combination of microwave detection technology and chemometrics holds significant potential for assessing heavy metal levels in edible oils.