Visual Data Mining Used to Assess the Role of Individual Kernel Moisture Content on Milling Quality

• Activity Number: 425
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Consulting
• Author(s): Andy Mauromoustakos*+ and Rusty Bautista and Terry Siebenmorgen
• Companies: University of Arkansas and University of Arkansas and University of Arkansas
• Keywords: JMP ; visual data mining ; Harvest moisture content ; Head rice yield ; Milling quality

Abstract

The objective of this study was to quantify the effects of rice kernel properties, associated with kernel moisture content (MC) distributions, on milling quality as harvest MC varied. Multiple samples of rice cultivars Bengal, Cypress, and Drew were harvested at various MCs from northeast and southeast Arkansas in 1999 and 2000. Additional sample sets of various rice cultivars were collected in 2004, 2005, and 2006 at various locations in Arkansas, Mississippi, and Missouri. Head rice yield reduction (HRYR) increased with increased percentages of both high and low MC kernels. Optimal HMCs ranged from 18% to 22% for long-grain cultivars, from 19.0% to 20.4% for medium-grain Bengal, and from 17.7% to 19.0% for long-grain hybrid XP723. JMP was used as a tool to facilitate this data mining exercise via filtering and interactive displays and tables to aid the discovery process.