Reliability Improvement of Odour Detection Thresholds Bibliographic Data
Résumé
Odour control is an important industrial and economical issue as it is a criterion in purchase and use of a material. The minimal concentration of a pure compound allowing to perceive its odour, called Odour Detection Threshold (ODT), is a key parameter of the odour control. Each compound has its own ODT. Bibliographic data are the main source of information to obtain ODTs. Nevertheless, there are a lot of not documented compounds and available ODTs are marred by a high variability. Another expensive and time-consuming way to obtain ODT is the measurement. This paper proposes a cleaning methodology to reduce available ODTs data uncertainty. This methodology will be consolidated by our own measurements. Next, we predict missing ODTs as a function of chemical and physical certain variables. The cleaning proposed leads to eliminate 39% of compounds with at least one ODT and 84% of positive cases (with a comparison on 37 compounds). The missing ODTs are predicted with an error of 0.83 for the train and 1.14 for the test (on a log10 scale). Given the uncertainty of data, the model is sufficient. This approach leads to work with a lower uncertainty on available ODTs and predict missing ODTs with a satisfactory model.
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