Predicting fermentability of wood hydrolyzates with responses from electronic noses

The fermentability of lignocellulose hydrolyzates have been predicted from the responses of a combination of chemical gas sensors. The hydrolyzates we re prepared by dilute-acid hydrolysis of wood from pine, aspen, birch, and spruce. The volatile emission from the hydrolyzates before fermentation was measured, and the sensor array response pattern was compared with the obse rved fermentability of the hydrolyzates, i.e. with the final ethanol concen tration after fermentation and the maximum specific ethanol production rate . Two concentration parameters in the hydrolyzates, furfural and the sum of furfural and 5-(hydroxymethyl)furfural (HMF), were also predicted from the responses. The sensors used were metal oxide semiconductor field effect tr ansistors (MOSFET), tin oxide semiconductor devices, and conductive polymer sensors configured in two sensor arrays. The sensor array response pattern was analyzed by principal component analysis and artificial neural network s. Predictions from artificial neural networks deviated from measured value s with less than 15%.