Kent S. McKenzie

Correlation Between Cooked Rice Texture and Rapid Visco Analyser Measurements

ABSTRACT Although amylose content is considered the most important determinant of cooked rice texture, this constituent falls short as a predictor, because cultivars with similar amylose contents may differ in textural properties. Thus, amylography is used as one of a battery of tests, in addition to measuring amylose content, to improve differentiation of cultivars. The purpose of our study was to determine how well amylography conducted with a Rapid Visco Analyser (RVA) serves as a predictor of cooked rice texture, alone and in combination, with amylose content. Textural properties of 87 samples representing short-, medium-, and long-grain rice cultivars were assessed by descriptive sensory and instrumental texture profile (TPA) analyses and related to RVA measurements. None of the cooked rice textural attributes, whether measured by descriptive analysis or TPA, were modeled by RVA with high accuracy (i.e., high r2). Sensory texture attributes cohesiveness of mass, stickiness, and initial starchy coatin...

Prediction of cooked rice texture quality using near-infrared reflectance analysis of whole-grain milled samples

ABSTRACT Rice quality is based on chemical and physical properties affecting its appearance, flavor, and texture characteristics. Sensory quality can be assessed by a combination of descriptive sensory and physicochemical property evaluations. The purpose of the present study was to assess the potential of near-infrared reflectance spectroscopy (NIRS) and NIRS in combination with other physicochemical measurements for the determination of sensory texture attributes in whole-grain milled rice samples. Six rice samples representing combinations of variety and growing locations received treatments of two degrees of milling and five drying conditions to achieve final moisture levels of 12 or 15% (n = 120). Quality measurements of the cooked rice included sensory and instrumental texture analyses. Quality measurements of the uncooked rice included amylose and protein (chemical reference), whiteness, transparency, and degree of milling (appearance units of milled rice), and NIRS analyses. Partial least squares ...

Effects of Postharvest Processing on Texture Profile Analysis of Cooked Rice

ABSTRACT The effects of drying conditions, final moisture content, and degree of milling on the texture of cooked rice varieties, as measured by texture profile analysis, were investigated. Instrumentally measured textural properties were not significantly (α = 0.05) affected by drying conditions, with the exception of cohesiveness. Cohesiveness was lower in rice dried at lower temperatures (18°C or ambient) than in that dried at the higher commercial temperatures. Final moisture content and degree of milling significantly (α = 0.05) affected textural property values for adhesiveness, cohesiveness, hardness, and springiness; their effects were interdependent. The effects of deep milling were more pronounced in the rice dried to 15% moisture than that dried to 12%. In general, textural property values for hardness were higher and those for cohesiveness, adhesiveness, and springiness were lower in regular-milled rice dried to 15% moisture than in that dried to 12%. In contrast, hardness values were lower an...

Effects of Degree of Milling, Drying Condition, and Final Moisture Content on Sensory Texture of Cooked Rice

ABSTRACT Different cultures have different preferences for cooked rice flavor and texture characteristics. These differences provide opportunities for U.S. rice varieties to fit into global markets to meet consumer demands worldwide. It is important to assess the properties of U.S. rice varieties and determine the factors that influence their eating quality. Cooked rice texture attributes can be affected by postharvest handling practices, such as degree of milling, drying condition, and final moisture. This article reports the effects of postharvest handling parameters on the texture of cooked medium- and short-grain rice varieties grown in Arkansas (AR) and California (CA), as measured by descriptive sensory analysis. The rice samples were Bengal (AR), Koshihikari (AR), Koshihikari (CA), M-401 (AR), M-401 (CA), and M-202 (CA). The six rice varieties were regular- or deepmilled and dried under one of five drying conditions to achieve final moisture levels of 12 or 15% (n = 120). A trained sensory panel de...

Categorizing Rice Cultivars Based on Cluster Analysis of Amylose Content, Protein Content and Sensory Attributes

ABSTRACT Presently, rice cultivars are categorized according to grain dimensions, amylose content, and alkali spreading value (gelatinization temperature type). Categorization of rice cultivars based on total sensory impact is needed. This work endeavors to divide world rices into groups based on amylose, protein, flavor, and texture properties. Ninety-one rice samples representing 79 different cultivars and seven growing locations were separated into seven groups with Wards Cluster Analysis. Cluster 1 included a third of the rice samples and had cultivars with a large diversity of grain shapes and amylose contents. Mean attribute scores for this cluster were near the grand mean for the collective rice samples for nearly every sensory attribute. Cluster 2 included conventional U.S. short- and medium-grain cultivars. Cluster 3 included conventional U.S. medium cultivars that were produced in Louisiana. Mean sensory scores for this cluster characterized these cultivars as having relatively undesirable flav...

Near-Infrared Reflectance Analysis for Prediction of Cooked Rice Texture

ABSTRACT 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 (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.