The Cannabis market is experiencing steady global growth. Cannabis herbal extracts (CHE) are interesting and sought-after products for many clinical conditions. The medical potential of these formulations is mainly attributed to neutral cannabinoids, such as cannabidiol (CBD), tetrahydrocannabinol (THC), and cannabinol (CBN), and their non-standardized content poses a significant fragility in these pharmaceutical inputs. High-resolution mass spectrometry portrays a powerful alternative to their accurate monitoring; however, further analytical steps need to be critically optimized to keep up with instrumental performance. In this study, Full Factorial and Box–Behnken designs were employed to achieve a multivariate optimization of CBD, THC, and CBN extraction from human and veterinary commercial CHE using a minimum methanol/hexane 9:1 volume and short operational times. A predictive model was also constructed using the Response Surface Methodology and its accuracy was validated. Agitation and sonication times were identified as the most significant operational extraction parameters, followed by the extraction mixture volume. The final setup of the optimized method represented a significantly faster and cheaper protocol than those in the literature. The selected neutral cannabinoids quantification was conducted using ultra high-performance liquid chromatography coupled to high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) with a precision of <15%, accuracy of 69–98%, sensitivity of 23–39 ng kg⁻¹, and linearity regarding pharmaceutical requirements. State-of-the-art levels of analytical sensitivity and specificity were achieved in the target quantification due to high-resolution mass spectrometry. The developed method demonstrated reliability and feasibility for routine analytical applications. As a proof-of-concept, it enabled the efficient processing of 16 samples of commercial CHE within a three-hour timeframe, showcasing its practicality and reproducibility, and highlighting its potential for broader adoption in similar scenarios for both human and veterinary medicines.

Incorporating renewable carbon into conventional petroleum refining processes, without requiring significant investments in new infrastructure, is a promising strategy for advancing the energy transition. Among such processes, coking is a thermal cracking process that converts heavy petroleum residues into higher-value products such as diesel-range hydrocarbons. However, the introduction of renewable carbon sources, such as bio-oils, may lead to the formation of oxygenated byproducts which can promote gum formation and reduce combustion efficiency, particularly acid species such as carboxylic acids and phenols. To address these challenges and assess the feasibility of integrating renewable carbon into the coking process, slow pyrolysis bio-oil (BO) was coprocessed with vacuum residue (VR) in a coking process unit using three feed compositions (100% VR, 95% VR + 5% BO, and 90% VR + 10% BO) to evaluate the contribution of BO in coprocessing liquid products. Acidic fractions were analyzed using electrospray ionization in negative mode [ESI(−)] with Orbitrap high-resolution mass spectrometry (HRMS) to obtain a detailed chemical characterization of polar compounds. This study also employs an integrated data-mining approach using Venn diagrams and heatmaps to evaluate the detected peaks, moving beyond conventional major-class analysis. A Venn diagram approach was employed to identify ions shared between the BO and the coprocessed liquid products, indicating compounds of biogenic origin. A total of 23 ions were selected for tandem mass spectrometry (MS/MS) experiments to propose their molecular structures. The compounds were proposed as consistent with lignocellulosic-derived molecules and were grouped as carboxylic acids, methoxyphenols, hydroxybenzofurans, benzenediols, diphenols, and carbohydrates. The peak/class ratio revealed pronounced biogenic contribution, observed in the 10% BO coprocessed liquid product. The heatmaps highlight biogenic compounds with distinct distribution patterns in the HRMS data. Specifically, biogenic compounds were considered when they were not detected in the fossil matrix or showed a 2-fold increase in relative intensity upon bio-oil incorporation. Consequently, these oxygenated acidic compounds serve as specific tracers for monitoring renewable carbon content in the blends.