Abstract

The performance of different enzymes towards the bioprocessing of aroma-related compounds was investigated and a strategy based on GC-FID analysis was developed to facilitate assessment of the stages of characterisation, screening and optimisation, including chiral ratio determination. Characterisation included activity assays (UV–Vis and GC-FID), protein quantification (NanoDrop spectrophotometry) and molar mass estimation (SDS-PAGE electrophoresis). Screening experiments assessed different enzymes, substrates, solvents, acyl donors or mediators. Aroma-related substrates comprised terpene and phenolic compounds. The enzymes tested included the lipases CALA (Sigma-Aldrich), NZ-435, LZ-TLIM, NC-ADL, LZ-CALBL and the laccases NZ-51003 and DL-IIS (all from Novozymes). Among those, NZ-435 and NZ-51003 had the highest activities in the characterisation stage and, along with CALA, achieved conversions above 70% for citronellol (lipases) or 50% for eugenol (laccases) at the screening stage. The lipases had preference for the primary alcohol and laccases for phenolic compounds, among the tested substrates. The transesterification reaction between the lipase CALA and the standards mixture (citronellol, menthol, linalool) was used to demonstrate the optimisation stage, where the best levels of temperature, enzyme and acyl donor concentrations were investigated. Optimum conditions were found to be 37–40 °C, 3–4 mg/mL of enzyme and 58–60% (v/v) vinyl acetate. Additional confirmation experiments using the same terpene standards mixture and citronella oil sample, gave a conversion of > 95% for citronellol after 1 h (for both, standards mixture and sample), and 20% or 74% for menthol after 1 h or 24 h, respectively. None of the tested enzymes demonstrated significant enantioselectivity under the tested conditions. The GC-FID approach demonstrated here was suitable to determine the reaction profiles and chiral ratio variations for biocatalysed reactions with aroma compounds in low complexity samples. Advanced separations will be applied to more complex samples in the future.

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