A total volatile basic nitrogen detection method for yellow croaker based on an electronic nose combined with an AIE ‐ LSTM regression model

Abstract BACKGROUND As a rapid detection method, the electronic nose exhibits enormous potential in food quality monitoring. However, the response data generated by electronic nose detection are highly complex time‐series data. Traditional data analysis models struggle to fully resolve such long‐sequence non‐linear signals, leading to insufficient feature extraction and poor prediction accuracy. RESULTS We propose a novel total volatile basic nitrogen (TVB‐N) prediction method based on the attention improved encoder long short‐term memory (AIE‐LSTM) hybrid network with a dual‐stream feature fusion architecture. A modified informer encoder captures temporal dependencies via multi‐head attention, and a temporal down‐sampling layer compresses sequence dimensions at the same time as preserving key trend features. In addition, a bidirectional LSTM network processes raw voltage sequences and manually designed physical features separately. Finally, a multi‐level feature fusion mechanism integrates these two types of features through fully connected layers to output predictions. Experimental results demonstrated that the AIE‐LSTM model achieved the optimal TVB‐N prediction performance across nine batches of electronic nose datasets, with an average coefficient of determination ( R 2 ) of 0.960, as well as the lowest relative standard deviation of R 2 , root mean square error (RMSE) and mean absolute error (MAE). Notably, the model exhibited the best performance in the fourth batch, where the R 2 , RMSE and MAE between the predicted and actual TVB‐N values reached 0.979, 0.988 and 0.589, respectively. CONCLUSION This model significantly improves the prediction accuracy of fish TVB‐N content, providing a reliable technical solution for the rapid, non‐destructive and accurate detection of fish freshness. © 2026 Society of Chemical Industry.