Development of a New Hyperspectral Imaging Technology with Autoencoder-Assisted Generative Adversarial Network for Predicting the Content of Polyunsaturated Fatty Acids in Red Meat

The establishment of a comprehensive predictive model for red meat polyunsaturated fatty acids holds profound significance for the food industry. However, challenges, such as intricate features and low chemical content bestow complexity upon this endeavor. In the study, an autoencoder-assisted generative adversarial network (AE-GAN) was used to address the intricacies of generative models in regression operations. Following numerous iterations, the AE-GAN generated samples akin to the original data. Upon the incorporation of these generated samples into training, the test set R2 values of Support Vector Regression, Random Forest and Fully Convolutional Network witnessed respective enhancements of 0.1589, 0.1482 and 0.2998. The outcomes underscore the efficacy of this novel approach in ameliorating the challenges faced by generative models in regression tasks, thereby augmenting the model's generalizability.