Near‐Infrared Reflectance Analysis for Prediction of Cooked Rice Texture

Abstract

ABSTRACTThe 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 (R2calibration 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 R2calibration = 0.96 and R2validation = 0.90. Key wavelengths contributing to the models describing the texture attributes were wavelengths also contributing to models for amylose, protein, and lipid contents.