By a News Reporter-Staff News Editor at Robotics & Machine Learning -- A new study on Artificial Neural Networks is now available. According to news reporting out of Cesena, Italy, by VerticalNews editors, research stated, "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)."
Our news journalists obtained a quote from the research from the Department of Food Science, "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. 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."
According to the news editors, the research concluded: "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."
For more information on this research see: Evaluation of coffee roasting degree by using electronic nose and artificial neural network for off-line quality control. Journal of Food Science, 2012;77(9):C960-5. Journal of Food Science can be contacted at: Blackwell Publishing Inc, 350 Main St, Malden, MA 02148, USA. (Wiley-Blackwell - www.wiley.com/; Journal of Food Science - onlinelibrary.wiley.com/journal/10.1111/(ISSN)1750-3841)
Our news journalists report that additional information may be obtained by contacting S. Romani, Dept. of Food Science, Univ of Bologna, Piazza Goidanich 60, 47521 Cesena, FC, Italy. Additional authors for this research include C. Cevoli, A. Fabbri, L. Alessandrini and M. Dalla Rosa.
Publisher contact information for the Journal of Food Science is: Blackwell Publishing Inc, 350 Main St, Malden, MA 02148, USA.
Keywords for this news article include: Italy, Cesena, Europe, Machine Learning, Emerging Technologies, Artificial Neural Networks.
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