A computer-based approach to assess the perception of composite odour intensity: a step towards automated olfactometry calibration.

The 2004 Nobel Prize in Physiology or Medicine laureates, Richard Axel and Linda Buck, have made smell a less enigmatic sense to study. In clinical routine, olfactory function is assessed using defined concentrations of a single defined substance, a setting which is uncommon in daily life. The present study was therefore conducted to evaluate the applicability of composite odours. Air was contaminated with different quantities of cyclohexanol, cyclohexanone and cyclohexane to generate 73 gas mixtures (one component: n = 21, two components: n = 40, three components: n = 12). The intensity of perception was estimated for each mixture by an average of 60.3 healthy individuals (4,403 assessments). An artificial neural network (ANN) was trained and validated using the contaminants' concentrations with the corresponding estimated intensities. The inter-rater variability was low, as 75.7% of the assessments did not exceed a difference beyond 0.5 from the corresponding median (considered correct predictions). The ANN correctly estimated 78.1% of the gas mixtures, and in terms of the regression task the ANN demonstrated a sufficient prediction performance (Pearson's correlation coefficient r = 0.883; R(2) = 0.757) and outperformed linear regression (r = 0.770; R(2) = 0.667). Evaluating extra ANNs for gas mixtures comprising one, two or three components, the predictive power did not decrease when complexity increased. The aforementioned results reflect nonlinearity in human perception. ANN technology helps simulate human perception of composite odour intensity which may be applicable to olfactometry calibration and systems biological mathematical modelling. The use of composite odours may represent real-life problems more adequately than single substances.