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Prediction of Texture of Cooked White Rice by Near-Infrared Reflectance Analysis of Whole-Grain Milled Samples

¹Dept. Food Science, University of Arkansas, Fayetteville, AR 72704. ²Corresponding author., Phone: 501-575-6822., Fax: 501-575-6936. E-mail: jfmeull@uark.edu ³Dept. Agricultural Statistics, University of Arkansas, Fayetteville, AR 72701. ⁴Dept. Agricultural Engineering, Michigan State University, East Lansing, MI

Although much work has been done using near-infrared (NIR) spectroscopy with rice, little is currently known about the effectiveness of NIR to predict functional attributes of rice such as cooked rice texture, especially as they are influenced by postharvest parameters. In this study, NIR spectroscopy was used for predicting cooked rice texture as affected by postharvest history. Cooked rice texture attributes were evaluated by a nine-member trained descriptive panel, and milled white rice was scanned using a near-infrared (NIR) spectrophotometer. Sensory attribute models were developed using partial least squares regression in combination with jack-knife, a model optimization method, using NIR reflectance spectra (400–2,500 nm) and 1st and 2nd derivatives. Cooked rice adhesion to lips (R² = 0.88), hardness (R² = 0.79), cohesiveness of mass (three chews) (R² = 0.79), and toothpack (R² = 0.85) were satisfactorily fitted (n = 201–202) using the 2nd derivative spectra. Other attributes evaluated, such as cohesiveness of mass (eight chews) (R² = 0.69), roughness of mass (R² = 0.49), and toothpull (R² = 0.76) were less successfully modeled. In addition, jack-knife significantly improved model statistics. Overall, NIR spectroscopy had potential application for predicting cooked rice texture. This finding is especially significant for applications such as breeding programs, where the amount of material available is limited.