Wenes Ramos da Silva

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.

Cow's Milk Protein Allergy (CMPA) is one of the most recurrent pediatric conditions of food allergies, which occur in early childhood. The diagnosis is not easy and demands oral food challenge tests (OFC). 24 metabolomic profiles of fecal samples from infants suspected of CMPA were assessed while searching for a possible intestinal metabolite that can be used as a biomarker of CMPA. The children were previously diagnosed with an open OFC. Fece samples were extracted and directly analyzed by ultra-high resolution mass spectrometry (HRMS) using an FT-Orbitrap mass spectrometer. Metabolomic profiles were initially treated by principal component analysis (PCA) which was not efficient in distinguishing the samples to propose a diagnosis. Then, the metabolomic profile of a specific m/z range obtained in the negative mode of analysis was successfully subjected to orthogonal partial least squares discriminant analysis (OPLS-DA) which separated the two groups with and without CMPA. The model fit was R² = 0.88, with a predictive capability of Q² = 0.52 in the test with 2000 permutations and significance p-value < 0.05. In this preliminary study, the model obtained using the metabolomic profile showed significant validation values, indicating the potential to distinguish between the two groups of interest, suggesting its use as a possible diagnostic tool for CMPA patients.