Publication no. C-2001-0416-04R |  VIEW ARTICLE

Near-Infrared Reflectance Analysis for Prediction of Cooked Rice Texture.

Elaine T. Champagne (1,2), Karen L. Bett-Garber (1), Casey C. Grimm (1), Anna M. McClung (3), Karen A. Moldenhauer (4), Steve Linscombe (5), Kent S. McKenzie (6), and Franklin E. Barton II (7). (1) USDA ARS Southern Regional Research Center, New Orleans, LA. (2) Corresponding author. Fax: 504-286-4430. E-mail: <etchamp@nola.srrc.usda.gov> (3) USDA ARS Rice Research Unit, Beaumont, TX. (4) University of Arkansas Rice Research and Extension Center, Stuttgart, AR. (5) Louisiana State University Rice Research Station, Crowley, LA. (6) California Cooperative Rice Research Foundation, Biggs, CA. (7) USDA ARS Richard B. Russell Research Center, Athens, GA. Cereal Chem. 78(3):358-362. Accepted February 13, 2001. This article is in the public domain and not copyrightable. It may be freely reprinted with customary crediting of the source. American Association of Cereal Chemists, Inc., 2001.

The ability of near-infrared (NIR) spectroscopy to predict sensory texture attributes of diverse rice cultivars was examined. The sensory texture of 87 samples representing 77 different short-, medium-, and long-grain cultivars was evaluated by trained panelists using descriptive analysis. Correlations between sensory texture attributes and NIR reflectance data were examined using the multivariate method of partial least squares (PLS) regression. Texture attributes (hardness, initial starchy coating, cohesiveness of mass, slickness, and stickiness) measured by panelists in the early evaluation phases were successfully predicted (R(^2)(calibration) 0.71-0.96). Cohesiveness of mass, the maximum degree to which the sample holds together in a mass while chewing, was best modeled with R(^2)(calibration) = 0.96 and R(^2)(validation) = 0.90. Key wavelengths contributing to the models describing the texture attributes were wavelengths also contributing to models for amylose, protein, and lipid contents.