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Odor-active compounds are commonly analyzed using gas chromatography–olfactometry (GC–O). However, there are only limited guidelines available for panelist training with this technique. In the current study, 29 volunteers were trained to detect, describe, and rate the intensity of odors. In addition, three GC–O methods, i.e., aroma extraction dilution method, detection frequency, and posterior intensity (PI), were used to evaluate the newly trained panelists’ ability to analyze key compounds of kvass (fermented nonalcoholic drink) aroma. A five-step approach is proposed for training as follows: (1) introduction of the method; (2) vocabulary training using standard compounds and learning the use of the scale; (3) training with the reference mixture A; (4) training with the real product of interest—the beverage kvass; and (5) monitoring and further training of the panel. Following these steps, all panelists learned how to perform GC–O analysis. Some variances among subjects were observed; however, the background of the trainees was found to be insignificant. Assessors for the “professional” GC–O panel were chosen for further training and included people with a sensory and food science background, but also ordinary consumers. The PI method, where subjects rate odor intensity after a peak eluted, was found to provide a sufficient amount of data for key compound analysis. The method enabled easy data handling, provided valuable feedback for panel monitoring, and aided in the selection process to decide which assessors would be suitable for further training and placement on a professional panel.

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.

The aroma profiles of thirteen different honey samples from four botanical origins: heather (Calluna vulgaris), raspberry (Rubus idaeus), rape (Brassica napus), alder buckthorn (Frangula alnus) and the blossoms of the four corresponding flowers were investigated to find odour-active compounds exclusively representing specific honeys based on odour-active compounds from the blossoms. Gas-chromatography-mass spectrometry (GC-MS) and gas-chromatography-olfactometry were used to determine and identify the odour-active compounds. Data was analysed using agglomerative hierarchical clustering and correspondence analysis. Honeys from the same botanical origin clustered together; however, none of the identified compounds were exclusive to a particular honey/blossom combination. Heather honey had the flavour profile most different to the others. Isophorone and 2-methylbutyric acid were found only in heather honeys. Heather honey was characterised by having more "sweet" and "candy-like" notes, raspberry honeys had more "green" notes, while alder buckthorn had more "honey" and "floral" notes.