Computerized delimitation of odorant areas in gas-chromatography-olfactometry by kernel density estimation: Data processing on French white wines

•A new method to build a total olfactogram from GC-O data based on kernel density estimation has been proposed.•The program is able to delimit automatically the odorant areas only from the odor events' linear retention indices.•The proposed method has been validated by comparing computed odorant areas with human delimiting used as benchmark.•The range of application of the method in terms of number of samples and number of assessors has been studied.

GC-O using the detection frequency method gives a list of odor events (OEs) where each OE is described by a linear retention index (LRI) and by the aromatic descriptor given by a human assessor. The aim of the experimenter is to gather OEs in a total olfactogram on which he tries to delimit odorant areas (OAs), then to compute each detection frequency. This paper proposes a computerized mathematical method based on kernel density estimation that makes up the total olfactogram as continuous and differentiable function from the OEs LRI only. The corresponding curve looks like a chromatogram, the peaks of which are potential OAs. The limits of an OA are the LRI of the two minima surrounding the peak. The method was applied on a big data set: 18 white wines, 17 assessors, 13,037 OEs. A previous manual delimitation made by the experimenter was used as benchmark to test the quality of the rendition by the computed delimitation. A contingency table containing the numbers of OEs that belonged to both benchmark OAs and computed OAs was built. This table enabled to assess the quality of the global rendition (Tschuprow's T coefficients) and the quality of individual rendition of each benchmark OA. In order to define a suitable range of application, the kernel-based method was tested on sub-sets from the global dataset, by randomly drawing n wines out of 18 and p assessors out of 17. The method gave very satisfying results for at least n = 9 wines, p = 7 assessors for the peaks gathering at least (n + p)/2 OEs.