Pattern Recognition of Pyrolysis Bio-Oils by GC×GC-TOFMS with Tile-Based Feature Selection and Principal Component Analysis.

Chemometrics associated with advanced analytical separation methods are crucial for the chemical profiling of complex samples, such as bio-oil, enabling more accurate and efficient identification of differential features. The composition of bio-oils influences the selection of pretreatment methods for fuel production, which may include processes such as filtration, guard bed usage, or reactions such as hydrothermal liquefaction and esterification. This study focuses on the chemical profiling of pyrolytic bio-oils from sugar cane bagasse and straw using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). Chemometric approaches such as tile-based Fisher ratio analysis (FRA) and principal component analysis (PCA) are employed for the feature selection of class-differentiating analytes. Bio-oils from both feedstocks exhibited chromatographic profiles with subtle differences, which were observed in the composition and relative abundance of specific compound classes. Bagasse bio-oil was rich in phenolics and hexose derivatives, such as furans and aldehydes. In contrast, straw bio-oil presented a higher abundance of hydrocarbons and fatty acid methyl esters. Tile-based FRA enabled the identification of 16 differential features and the detection of low-intensity compounds, such as long-chain esters and hydrocarbons, not previously detected by the peak table-based approach. PCA based on these differential features explained 98.7% of the total variance (PC1 + PC2), clearly grouping bio-oils by feedstock origin. The findings highlight the potential of GC×GC-TOFMS and chemometrics for differentiating bio-oils, demonstrating the importance of advanced analytical techniques in studying biomass conversion processes and characterizing bioproducts.