Explainable Machine Learning for Predicting Lactococcus Abundance in Rump Steak Enhanced by Generative AI-Based Metagenomic Data

The microbial spoilage of meat products poses a significant challenge to food safety and shelf-life, primarily due to the proliferation of spoilage-related bacteria under varying environmental conditions. Among beneficial microorganisms, Lactococcus lactis species are notable for their ability to produce nisin, a natural antimicrobial that enhances the microbial stability of meat. This study presents an interpretable machine learning framework to predict the relative abundance of Lactococcus in meat using 16S rRNA sequencing data, enhanced by synthetic data generation and explainable AI. A dataset comprising microbial profiles from rump steak samples across seasonal and temporal variables was expanded using a Generative Adversarial Network (GAN), addressing data scarcity. Multiple regression models, including Artificial Neural Networks, Random Forest, Gradient Boosting, and k-Nearest Neighbors, were trained and evaluated. The ANN model achieved the highest performance (\((R^2 = 0.89)\)) when trained on synthetic data and tested on real samples. Explainable AI analysis revealed Lactobacillus , Carnobacterium , and seasonal variables as the most influential predictors. Notably, seasonal shifts significantly impacted Lactococcus abundance, with higher levels observed in autumn. This study demonstrates the effectiveness of GAN-augmented data and interpretable machine learning in modeling microbial behavior, providing a scalable, cost-efficient tool for predictive microbiology. The approach not only enhances our understanding of microbial co-occurrence patterns but also supports the development of intelligent, data-driven strategies for natural food preservation.