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The sensory-chemical profiles of Finnish honeys (labeled as buckwheat, cloudberry-bog, lingonberry, sweet clover, willowherb and multifloral honeys) were investigated using a multi-analytical approach. The sensory test (untrained panel, n = 62) was based on scaling and check-all-that-apply (CATA) methods accompanied with questions on preference and usage of honey. The results were correlated with corresponding profiles of odor-active compounds, determined using gas chromatography coupled with mass spectrometry/olfactometry (GC–MS/O). Botanical origins and chemical compositions including sugars were evaluated using NMR spectroscopy. A total of 73 odor-active compounds were listed based on GC–O. Sweet and mild honeys with familiar sensory properties were preferred by the panelists (PCA, R2X(1) = 0.7) while buckwheat and cloudberry-bog honeys with strong odor, flavor and color were regarded as unfamiliar and unpleasant. The data will give the honey industry novel information on honey properties in relation to the botanical origin, and consumer preference.

Background: Identification of cancer biomarkers to allow early diagnosis is an urgent need for many types of tumors, whose prognosis strongly depends on the stage of the disease. Canine olfactory testing for detecting cancer is an emerging field of investigation. As an alternative, here we propose to use GC-Olfactometry (GC/O), which enables the speeding up of targeted biomarker identification and analysis. A pilot study was conducted in order to determine odor-active compounds in urine that discriminate patients with gastrointestinal cancers from control samples (healthy people). Methods: Headspace solid phase microextraction (HS-SPME)-GC/MS and GC-olfactometry (GC/O) analysis were performed on urine samples obtained from gastrointestinal cancer patients and healthy controls. Results: In total, 91 key odor-active compounds were found in the urine samples. Although no odor-active biomarkers present were found in cancer carrier’s urine, significant differences were discovered in the odor activities of 11 compounds in the urine of healthy and diseased people. Seven of above mentioned compounds were identified: thiophene, 2-methoxythiophene, dimethyl disulphide, 3-methyl-2-pentanone, 4-(or 5-)methyl-3-hexanone, 4-ethyl guaiacol and phenylacetic acid. The other four compounds remained unknown. Conclusions: GC/O has a big potential to identify compounds not detectable using untargeted GC/MS approach. This paves the way for further research aimed at improving and validating the performance of this technique so that the identified cancer-associated compounds may be introduced as biomarkers in clinical practice to support early cancer diagnosis.

A lot of research has been conducted in correlating the sensory properties of food with different analytical measurements in recent years. Various statistical methods have been used in order to get the most reliable results and to create prediction models with high statistical performance. The current review summarises the latest practices in the field of correlating attributes from sensory analysis with volatile data obtained by gas chromatographic analysis. The review includes the origin of the data, different pre-processing and variable selection methods and finally statistical methods of analysis and validation. Partial least squares regression analysis appears as the most commonly used statistical method in the area. The main shortcomings were identified in the steps of pre-processing, variable selection and also validation of models that have not gained enough attention. As the association between volatiles and sensory perception is often nonlinear, future studies should test the application of different nonlinear techniques.