GAN-enhanced E-nose analysis: VTAAE for temporal dynamics in beef quality assessment

Electronic nose (E-Nose) stands out as a promising solution for the rapid detection of meat quality owing to its non-destructive and low-cost nature. As the E-nose is an essential tool for aiding quality evaluation, it is crucial to analyze the complex time sequence information produced to ensure accurate quality recognition. This research presents a novel Variational Temporal Attention Autoencoder with Generative Adversarial Network (VTAAE-GAN) approach to analyze time series data collected from the e-nose to evaluate beef quality. The VTAAE-GAN approach encompasses two concepts including GAN for producing synthetic time series data similar to real E-nose data and VTAAE method for capturing temporal dependencies from the complex time series data to categorize the beef quality into respective classes including good, acceptable, spoiled, and excellent. The E-Nose dataset containing time series data from eleven metal oxide semiconductor gas sensors (MOX) of twelve beef cuts is taken for beef quality evaluation. Moreover, the comprehensive experimental assessment is performed by employing existing quality detection approaches in measures of accuracy F1-score, recall, and precision. The findings suggested that the VTAAE-GAN approach by achieving an accuracy of 98.71%, recall of 98.20%, F1-score of 98.04%, and precision of 97.90% outperformed other baseline models and also evidenced its stability in freshness evaluation. Further, Maximum Mean Discrepancy, Kulback–Leibler divergence, and Wasserstein Distance metrics are used to determine the authenticity of the generated time series data. It also illustrated impressive performance with lesser variations between the real and synthetic time series data and the superior experimental outcomes reveal that our VTAAE-GAN approach is capable of learning efficient temporal features from the time series data and producing significant outcomes. Overall, the GAN incorporated for producing synthetic time series boosts the quality evaluation ability of the proposed method under diverse characteristics of the E-nose data while the VTAAE with its attention mechanism and temporal dependencies enhanced the model to focus on specific time series and temporal dynamics which is crucial for evaluating the beef quality deterioration. As a result, the combination of GAN and VTAAE collectively contributed to the advancement of an automated beef quality monitoring system.