Statistical Data Mining Algorithms for Optimising Analysis of Spectroscopic Data from On-line NIR Mill Systems: Improving System Calibrations for Quality Measures (Old ID 22492)
Role
Principal Investigator
Description
NIR spectroscopy is a rapid, non-invasive method for determining cane quality attributes (e.g. CCS, brix, fibre and biomass). NIR methods for sugarcane are advanced and work well for 90% of cases. When NIR methods fail, industry must resort to expensive laboratory analysis. This project will involve using novel statistical data mining techniques applied to large NIR databases to investigate improved calibrations for cane quality measures. Specifically this project is interested cases that were previously difficult to calibrate. Improved calibrations will benefit industry through reduced costs associated with extensive laboratory analysis for samples unsuited for standard industry calibrations.
Date
01 Jan 2016 - 30 Jun 2019
Project Type
CONTRACT_RESEARCH
Keywords
NIR;Spectroscopy;Statistics;Sugarcane;Data Mining;Calibration
Funding Body
Sugar Research Australia
Amount
143000
Project Team
Ronald White;Yvette Everingham