Earwax is a readily accessible biological matrix that has the potential to be used in disease diagnostics. However, its semisolid nature and high chemical complexity have hampered efforts to investigate its potential to reveal disease markers. This is because more conventional methods of analysis such as gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry yield unsatisfactory results due to the presence of many nonvolatile and/or coeluting compounds, which in some cases have very similar mass spectrometric profiles. In addition, these routine methods often require the sample to be saponified, which dramatically increases the complexity of the analysis and makes it difficult to determine which compounds are actually present versus those that are produced by saponification. In this study, two-dimensional GC mass spectrometry (GC × GC–MS) was successfully applied for the characterization of the chemical components of earwax from healthy donors using nonpolar (primary) and midpolar (secondary) columns without saponification. Over 35 of the compounds that were identified are reported for the first time to be detected in unsaponified earwax. The resulting GC × GC–MS contour plots revealed visually recognizable compound class clusters of previously reported groups including alkanes, alkenes, fatty acids, esters, triglycerides, and cholesterol esters, as well as cholesterol and squalene. The application of GC × GC–MS revealed results that provide a foundation upon which future studies aimed at comparing healthy donor earwax to that from individuals exhibiting various disease states can be accomplished.

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Summary Analysis of wood transects in a manner that preserves the spatial distribution of the metabolites present is highly desirable to among other things: (1) facilitate ecophysiology studies that reveal the association between chemical make‐up and environmental factors or climatic events over time; and (2) investigate the mechanisms of the synthesis and trafficking of small molecules within specialised tissues. While a variety of techniques could be applied to achieve these goals, most remain challenging and impractical. Laser ablation direct analysis in real time imaging–mass spectrometry (LADI‐MS) was successfully used to survey the chemical profile of wood, while also preserving the small‐molecule spatial distributions. The tree species Entandrophragma candollei Harms, Millettia laurentii DeWild., Pericopsis elata (Harms) Meeuwen, Dalbergia nigra (Vell.) Benth. and Dalbergia normandii Bosser & R.Rabev were analysed. Several compounds were associated with anatomical features. A greater diversity was detected in the vessels and parenchyma compared with the fibres. Analysis of single vessels revealed that the chemical fingerprint used for timber identification is mainly determined by vessel content. Laser ablation direct analysis in real time imaging–mass spectrometry offers unprecedented opportunities to investigate the distribution of metabolites within wood samples, while circumventing the issues associated with previous methods. This technique opens up new vistas for the discovery of small‐molecule biomarkers that are linked to environmental events.

Many diseases remain difficult to identify because the occurrence of characteristic biomarkers within traditional matrices such as blood and urine remain unknown. Disease diagnosis could, therefore, benefit from the analysis of readily accessible, non-traditional matrices that have a high chemical content and contain distinguishing biomarkers. One such matrix is cerumen (i.e., earwax), whose chemical complexity can pose challenges when analyzed by conventional methods. A combination of cerumen chemical profiles analyzed by gas chromatographymass spectrometry (GC–MS) and direct analysis in real timehigh-resolution mass spectrometry (DART-HRMS) were investigated to ascertain the possible presence of the rare otolaryngological disorder Ménière’s disease. This illness is currently identified via “diagnosis by exclusion” in which the disease is distinguished from others with overlapping symptoms by the process of elimination. GC–MS revealed a chemical profile difference between those with and without a Ménière’s disease diagnosis by a visually apparent diminution of the compounds present in the Ménière’s disease samples. DART-HRMS revealed that the two classes could be differentiated using three fatty acids: cis-9-hexadecenoic acid, cis-10-heptadecenoic acid, and cis-9-octadecenoic acid. These compounds were subsequently quantified by GC–MS and overall, the amounts of these fatty acids were decreased in Ménière’s disease patients. The average levels for non-Ménière’s disease samples were 7.89 μg/mg for cis-9-hexadecenoic acid, 0.87 μg/mg for cis-10-heptadecenoic acid, and 4.94 μg/mg for cis-9-octadecenoic acid. The average levels for Ménière’s disease samples were 1.70 μg/mg for cis-9-hexadecenoic acid, 0.13 μg/mg for cis-10-heptadecenoic acid, and 2.07 μg/mg for cis-9-octadecenoic acid. The confidence levels for cis-9-hexadecenoic acid, cis-10-heptadecenoic acid, and cis-9-octadecenoic acid were 98.7%, 99.9%, and 95.4%, respectively. The results suggest that assessment of the concentrations of these fatty acids could be a useful clinical tool for the more rapid and accurate detection of Ménière’s disease.