Integrating Untargeted GC-MS Metabolomics, GNPS Molecular Networking, and Machine Learning for Sugarcane Byproduct...

Area of Science:

• Agricultural Chemistry
• Biotechnology
• Environmental Science

Background:

• Sugarcane industry generates significant waste, presenting environmental challenges.
• Circular economy and zero-waste principles necessitate effective residue valorization.
• Metabolomic profiling offers a powerful approach to understanding complex biomass composition.

Purpose of the Study:

• To explore the valorization of sugarcane residues using solvent-based extraction and metabolomic profiling.
• To identify optimal solvent-biomass pairings for recovering high-value compounds.
• To develop a data-driven framework for sustainable biorefinery processes.

Main Methods:

• Solvent-based extraction of metabolites from various sugarcane parts.
• Gas chromatography-mass spectrometry (GC-MS) with BSTFA derivatization for metabolite analysis.
• Automated spectral deconvolution, molecular networking (MSHub, GNPS, Cytoscape), and machine learning for data analysis.

Main Results:

• Specific solvent-biomass combinations significantly impacted yield and recovery of high-value compounds (policosanols, phytosterols, triterpenoids, phenolic acids).
• Sugarcane trash and filter cake showed promise for lipid-based compound recovery.
• Methanol and ethanol offered high extraction efficiency, while nonpolar solvents selectively enriched sterols and long-chain alcohols.

Conclusions:

• A data-driven framework integrating molecular data and statistical modeling enhances sugarcane residue valorization.
• Optimizing solvent systems for specific biomass types improves biorefinery efficiency and economic value.
• Findings contribute to the environmental sustainability of sugarcane waste management through effective valorization.