Machine Learning and Artificial Intelligence can help a great deal in vineyard operations and management to produce the best wine possible. An article discussing 5 ways that this can be done has been published in the Pursuit magazine from The university of Melbourne that showcase CUTTING-EDGE RESEARCH AND INSIGHTFUL COMMENTARY BY WORLD-LEADING EXPERTS.
2017 June 25th – 28th June. Fourth International Conference on Cocoa, Coffee and Tea (Poster). Turin, Italy.
Chacon G., Fuentes S., Gonzalez Viejo C., Zhang P.
Claudia Gonzalez Viejo 1, Sigfredo Fuentes 1*, Damir Torrico1, Kate Howell1, and Frank R. Dunshea1.
1 University of Melbourne, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, VIC 3010, Australia
* Correspondence: email@example.com; Tel.: +61 3 9035 9670
Journal of The Science of Food and Agriculture
Full Article: CLICK HERE
Beer quality is mainly defined by its color, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical compounds are usually time-consuming and costly. This study used rapid methods to evaluate 15 foam and color-related parameters using a robotic pourer (RoboBEER) and chemical fingerprinting using near infrared spectrometry (NIR) from six replicates of 21 beers from three types of fermentation. Results from NIR were used to create partial least squares regression (PLS) and artificial neural networks (ANN) models to predict four chemometrics such as pH, alcohol, Brix and maximum volume of foam.
By Stuart Winthrope, University of Melbourne
Link to the full article CLICK HERE
Dr Sigfredo Fuentes has been invited to the 10th International Symposium on Viticulture and Oenology to be held in Wuzhong, Ningxia – China from the 20th to the 22nd of April 2017.
Presentation: Detection of berry cell death and smoke contamination in berries using near infrared spectroscopy and machine learning algorithms.