Microbiome data analysis via machine learning models: Exploring vital players to optimize kitchen waste composting system

Abstract

Composting, reliant on microorganisms, effectively treats kitchen waste. However, it is difficult to precisely understand the specific role of key microorganisms in the composting process by relying solely on experimental research. This study aims to employ machine learning models to explore key microbial genera and to optimize composting systems. After introducing a novel microbiome preprocessing approach, Stacking models were constructed (R2 is about 0.8). The SHAP method (SHapley Additive exPlanations) identified Bacillus, Acinetobacter, Thermobacillus, Pseudomonas, Psychrobacter, and Thermobifida as prominent microbial genera (Shapley values ranging from 3.84 to 1.24). Additionally, microbial agents were prepared to target the identified key genera, and experiments demonstrated that the composting quality score was 76.06 for the treatment and 70.96 for the control. The exogenous agents enhanced decomposition and improved compost quality in later stages. In summary, this study opens up a new avenue to identifying key microorganisms and optimizing the biological treatment process.