Reliability Improvement of Odour Detection Thresholds Bibliographic Data

Odour control is an important industrial issue as it is a criterion in purchase of a material. The minimal concentration of a pure compound allowing to perceive its odour, called Odour Detection Threshold (ODT), is a key of the odour control. Each compound has its own ODT. Literature is the main source to obtain ODT, but a lot of compounds are not reported and, when reported, marred by a high variability. This paper proposes a supervised cleaning methodology to reduce uncertainty of available ODTs and a prediction of missing ODTs on the base of physico-chemical variables. This cleaning leads to eliminate 39% of reported compounds while conducting 84% of positive scenarios on 37 comparisons. Missing ODTs are predicted with an error of 0.83 for the train and 1.14 for the test (log10 scale). Given the uncertainty of data, the model is sufficient. This approach allows working with a lower uncertainty and satisfactory prediction of missing ODTs.