Evaluation of Coffee Roasting Degree by Using Electronic Nose and Artificial Neural Network for Off-line Quality Control
Version of Record online: 21 AUG 2012
© 2012 Institute of Food Technologists®
Journal of Food Science
Volume 77, Issue 9, pages C960–C965, September 2012
How to Cite
Romani, S., Cevoli, C., Fabbri, A., Alessandrini, L. and Dalla Rosa, M. (2012), Evaluation of Coffee Roasting Degree by Using Electronic Nose and Artificial Neural Network for Off-line Quality Control. Journal of Food Science, 77: C960–C965. doi: 10.1111/j.1750-3841.2012.02851.x
- Issue online: 7 SEP 2012
- Version of Record online: 21 AUG 2012
- MS 20120272 Submitted 2/22/2012, Accepted 6/6/2012.
- artificial neural network;
- electronic nose;
- roasting degree
Abstract: An electronic nose (EN) based on an array of 10 metal oxide semiconductor sensors was used, jointly with an artificial neural network (ANN), to predict coffee roasting degree. The flavor release evolution and the main physicochemical modifications (weight loss, density, moisture content, and surface color: L*, a*), during the roasting process of coffee, were monitored at different cooking times (0, 6, 8, 10, 14, 19 min). Principal component analysis (PCA) was used to reduce the dimensionality of sensors data set (600 values per sensor). The selected PCs were used as ANN input variables. Two types of ANN methods (multilayer perceptron [MLP] and general regression neural network [GRNN]) were used in order to estimate the EN signals. For both neural networks the input values were represented by scores of sensors data set PCs, while the output values were the quality parameter at different roasting times. Both the ANNs were able to well predict coffee roasting degree, giving good prediction results for both roasting time and coffee quality parameters. In particular, GRNN showed the highest prediction reliability.
Practical Application: Actually the evaluation of coffee roasting degree is mainly a manned operation, substantially based on the empirical final color observation. For this reason it requires well-trained operators with a long professional skill. The coupling of e-nose and artificial neural networks (ANNs) may represent an effective possibility to roasting process automation and to set up a more reproducible procedure for final coffee bean quality characterization.