Synergizing Meat Science and AI: Enhancing Long-Chain Saturated Fatty Acids Prediction

In the field of the food industry, establishing a global predictive model for the content of long-chain saturated fatty acids (LC-SFAs) in red meat is of profound significance. However, this work requires the accumulation of a large number of diverse samples for model calibration. To address these formidable challenges, the Generative Inference Adversarial Autoencoder (GI-AAE) was adopted to enhance the model training process. Through multiple iterations, GI-AAE successfully generated simulated samples of higher quality than traditional generative adversarial networks and autoencoders. After incorporating these generated samples into the training dataset, multiple sets of models showed significant improvements. These results clearly demonstrated that our approach can effectively overcome the challenges posed by generative models in the regression domain, greatly enhancing the model's generalization ability. This advancement holds significant potential for applications in the food industry.