Posters Presented at the 12th Pangborn Sensory Science Symposium in Providence, Rhode Island. USA – 21st August 2017

Pangborn_2017_ThejaniPangborn_2017_Nadeesha_poster_1(corrected)Pangborn_2017_DamirPangborn_2017_Claudia_BiometricsPangborn_2017_Claudia_EyeTracking

Advertisements

New Paper: Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and artificial neural networks modelling techniques

By

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: sfuentes@unimelb.edu.au; Tel.: +61 3 9035 9670

Journal of The Science of Food and Agriculture

Full Article: CLICK HERE

ABSTRACT:

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.

NIRANN

Invited Speaker: 10th International Symposium on Viticulture and Oenology 2017

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.

Drone following a drone: Early disease detection project for Tomatoes

Footage of a drone following a drone carrying an hyper-spectral camera on a tomato crop. The project aims to generate machine learning algorithms to recognise early plant disease indications.

Melbourne Unmanned Aerial Vehicle System Platform (MUASIP) is a project with the Infrastructure Engineering Department, The Faculty of Veterinary and Agricultural Science and The Faculty of Science from The University of Melbourne.

For more information contact:

Rodger Young  |   MUASIP Platform Manager

Infrastructure Engineering / Environmental Monitoring

 

Building 174, Grattan St,  Parkville

The University of Melbourne, Victoria 3010 Australia

T: +61 3 8344 7018  M: 0417 504 593  E: riy@unimelb.edu.au   

unimelb.edu.au