A novel clustering-regression machine learning framework for biomass classification and biochemical composition...

Biomass elemental and biochemical compositions determine its conversion behavior and utilization potential. However, a standardized classification system based on these intrinsic characteristics is lacking, and different biomass types follow distinct processing pathways. In comparison, biomass biochemical analysis is time-consuming and costly, limiting its large-scale evaluation and application. To address these challenges, this study developed a novel clustering-regression machine learning (ML) framework that predicts the main biochemical components of different biomass types based on their elemental composition. First, Principal Component Analysis (PCA) achieved effective dimensionality reduction, with a total explained variance of 93.2 %. Using a PCA-assisted clustering model, biomass was effectively clustered into lipid-/protein-rich and lignocellulosic groups with a high silhouette score of 0.605. Subsequently, regression model 1 predicted protein and lipid contents in lipid-/protein-rich biomass, achieving average test R² of 0.88 and average validation R² of 0.81. For lignocellulosic biomass, regression model 2 predicted fibres and lignin contents, achieving average test R² of 0.77 and average validation R² of 0.66. Clustering and regression models all underwent thorough validation and testing, showing strong generalization. To make the integrated clustering-regression framework more user-friendly, a software application is established. Users can input elemental composition to identify biomass clusters and predict the corresponding main component contents. This study provides a reliable predictive tool for screening suitable feedstocks for targeted product manufacturing.