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Investigation into the Efficacy of a Neural Network Approach to Concentration Measurements of Ethanol in Water Based on Multianalyzer Inputs

This study explores the quantitative capabilities of the multianalyzer tensiograph for applications such as distilling. Two standard tensiograph trace features (from the many on offer) were selected in this study of pure ethanol-water solutions for a comparative study with a neural net approach based on the entire data set. The advantage of the latter approach is that it can learn to extract good estimates of an output variable based on (possibly interfering) multivariate inputs such as the measurands found in complex products. The conclusion of this study is that, quantitatively, the multianalyzer has the potential of approaching the sensitivities required for the fast qualitative and quantitative analysis of whiskies. This could make it useful as a supplement to the slow gas chromatograph-mass spectroscopic techniques currently employed in the industry.