Volatile phenols (VPs) derived from smoke-exposed grapes are known to confer a smoky flavor to wine. Current methods for determination of VPs in grape berries either involve complex sample purification/derivatization steps or employ two analytical platforms for free and bound VP fractions. We report here a simple gas chromatography-tandem mass spectrometry (GC-MS/MS) method for quantification of both free and bound VPs in grapes, based on optimized (1) GC-MS/MS parameters, (2) an analyte extraction procedure, and (3) phenol glycoside hydrolysis conditions. Requiring neither sample cleanup nor a derivatization step, this method is sensitive (LOD ≤ 1 ng/g berries) and reproducible (RSD < 12% for repeated analyses) and is expected to significantly reduce the sample turnover time for smoke taint detection in vineyards.

Cannabis and its secondary metabolite content have recently seen a surge in research interest. Cannabis terpenes and terpenoids in particular are increasingly the focus of research efforts due to the possibility of their contribution to the overall therapeutic effect of medicinal cannabis. Current methodology to quantify terpenes in cannabis biomass mostly relies on large quantities of biomass, long extraction protocols, and long GC gradient times, often exceeding 60 min. They are therefore not easily applicable in the high-throughput environment of a cannabis breeding program. The method presented here, however, is based on a simple hexane extract from 40 mg of biomass, with 50 μg/mL dodecane as internal standard, and a gradient of less than 30 min. The method can detect 48 individual terpenes and terpenoids and was validated for selectivity, linearity, LOD/LOQ, precision, intermediate precision, and accuracy (recovery) for 22 terpenes and terpenoids. The validation parameters are comparable to previously published studies that employ significantly longer runtimes and/or more complex extraction protocols. It is currently being applied to medicinal cannabis precision breeding programs.

An accurate dry matter intake (DMI) measurement method is of great importance for determining the performance of dairy cows. While n-alkane technique is commonly used to estimate the DMI of ruminants, the protocols used to measure alkanes are complex, time consuming and low throughput. Herein we present a simplified and miniaturised protocol that combines a smaller sample matrix (60 mg of faeces or herbage), a shorter (3 h) saponification step and a direct purification of the crude extract by filtration through silica powder or mixing with silica powder prior to GC-FID analysis. In addition to a much higher throughput compared with the existing methods, by skipping the commercial SPE columns and reducing the organic solvent volume, our miniaturised protocol should significantly reduce the sample processing cost and is thus suitable for alkane determination for large-scale experiments.

Paratuberculosis is a debilitating disease of ruminants that causes significant economic loss in both cattle and sheep. Early detection of the disease is crucial to controlling the disease; however, current diagnostic tests lack sensitivity. This study evaluated the potential for volatile organic compounds (VOCs) detected by gas chromatography and an electronic nose (eNose) for use as diagnostic tools to differentiate between Map-infected and non-infected cattle and sheep. Solid-phase micro-extraction gas chromatography–mass spectrometry (SPME GC-MS) was used to quantify VOCs from the headspace of faecal samples (cattle and sheep), and partial least squares–discriminant analysis (PLS-DA) was used to determine the suitability as a diagnostic tool. Both the cattle and sheep models had high specificity and sensitivity, 98.1% and 92.3%, respectively, in cattle, and both were 100% in sheep. The eNose was also able to discriminate between Map-infected and non-infected sheep and cattle with 88.9% specificity and 100% sensitivity in sheep and 100% specificity and sensitivity in cattle. This is the first time that VOC analysis by eNose and GCMS has been used for identification of Map in cattle and sheep faeces. GCMS also allowed the identification of putative disease biomarkers, and the eNose diagnostic capability suggests it is a promising tool for point-of-care diagnosis for Map detection on farms.