(587g) A Device Using Neural Networks and Artificial Noses for the Monitoring of Wine Fermentation
This paper is concerned with the development of an artificial nose to monitor the fermentation process in wine. The design objective of the device is to provide the wine industry with a light weight, compact, and accurate sensing device. Other design factors that were considered included ease of installation, level of maintenance, and lifetime of the device. Data was generated from sensor output versus concentration plots and this data was then classified with the use of a neural network model. Data was classified into three outputs, stage 1, stage 2, and stage 3 of fermentation. The neural network data was trained, cross validated, and ultimately tested. This model was used classify the data, giving accuracy results of 100% for all three fermentation stages.
A customer satisfaction model was then developed by varying design characteristics. This model ultimately resulted in consumer preferences that were used to calculate product demands for varying product prices. These demands were then used to obtain the optimal design in terms of consumer satisfaction and profitability showing that the production and marketing of tis device is a profitable venture under the conditions analyzed.