What question did this study set out to answer?

The research aims to valorize sugarcane byproducts using metabolomic profiling and machine learning techniques.

April 10, 2026Open Access

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

TPThapanee PruksatrakulNational Science and Technology Development Agency CSChanakarn SangsumNational Science and Technology Development Agency SMSasina MakmaiNational Science and Technology Development Agency

Key Points

The research aims to valorize sugarcane byproducts using metabolomic profiling and machine learning techniques.
Conducted solvent-based extraction from sugarcane residues.
Analyzed metabolites using gas chromatography-mass spectrometry (GC-MS) after BSTFA derivatization.
Performed molecular networking for structural dereplication of metabolites.
Applied machine learning techniques to identify factors influencing compound yield.”],
results":["Specific solvent-biomas pairings significantly affected yield and compound specificity.","Methanol and ethanol showed the highest extraction efficiency across samples.","Nonpolar solvents enriched sterols and long-chain alcohols effectively.","Identified key chemotypic drivers for biomass differentiation."]},
simple_explanation":"Sugarcane creates a lot of leftover materials that can be wasteful. This study looks at how to make those leftovers more useful. By using certain solvents, researchers can extract valuable compounds from sugarcane. They found that using methanol and ethanol is best for getting the most out of the biomass while keeping the process eco-friendly. This helps reduce waste and makes better use of sugarcane resources! 🍃"} }
Specific solvent-biomas pairings significantly affected yield and compound specificity.
Methanol and ethanol showed the highest extraction efficiency across samples.
Nonpolar solvents enriched sterols and long-chain alcohols effectively.
Identified key chemotypic drivers for biomass differentiation.

Abstract

The global sugar industry generates substantial byproducts and waste, posing significant environmental challenges. In alignment with circular economy and zero-waste principles, this study explores the valorization of sugarcane residues through solvent-based extraction and advanced metabolomic profiling. Various organic solvents were employed to extract metabolites from different sugarcane parts, followed by N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) derivatization and analysis using gas chromatography-mass spectrometry (GC-MS). Automated spectral deconvolution and molecular networking, conducted via open-source platforms (MSHub, GNPS, and Cytoscape), enabled structural dereplication and clustering of metabolite spectra. Integration of metabolomic data with sample metadata facilitated system-level comparisons of chemical diversity and metabolite abundance across extraction conditions. Machine learning techniques, particularly random forest and multivariate statistical analyses, were applied to the metabolomic data set. These approaches enabled the identification of chemotypic drivers responsible for differentiating biomass types and extraction solvent systems. Results revealed that specific solvent–biomass pairings significantly influenced both the yield and specificity of high-value compounds, including policosanols, phytosterols, triterpenoids, and phenolic acids. Notably, trash and filter cake emerged as promising matrices for lipid-based compound recovery. Methanol and ethanol provided the highest overall extraction efficiency, whereas nonpolar solvents such as tert-butyl methyl ether (TBME) and hexane enabled selective enrichment of sterols and long-chain alcohols. By integrating molecular data with statistical modeling and yield analysis, this study presents a data-driven framework for optimizing biorefinery processes. These findings offer critical insights into aligning solvent systems with specific biomass types to enhance the efficiency, economic value, and environmental sustainability of sugarcane residue valorization.

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