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Near‐Infrared Reflectance Analysis for Prediction of Cooked Rice Texture
Author(s) -
Champagne Elaine T.,
BettGarber Karen L.,
Grimm Casey C.,
McClung Anna M.,
Moldenhauer Karen A.,
Linscombe Steve,
McKenzie Kent S.,
Barton Franklin E.
Publication year - 2001
Publication title -
cereal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 100
eISSN - 1943-3638
pISSN - 0009-0352
DOI - 10.1094/cchem.2001.78.3.358
Subject(s) - texture (cosmology) , partial least squares regression , chemistry , amylose , near infrared spectroscopy , food science , wavelength , cultivar , multivariate statistics , sensory system , analytical chemistry (journal) , statistics , mathematics , artificial intelligence , botany , optics , chromatography , psychology , computer science , starch , physics , image (mathematics) , cognitive psychology , biology
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.