Texture relationships were studied using both sensory and instrumental texture proJle analysis (TPA) techniques to evaluate twenty-one food samples from a wide variety of foods. High linear correlations were found between sensory and instrumental TPA parameters for hardness (r = 0.76) and springiness (r = 0.83). No sign@cant correlations were found between sensory and instrumental TPAparameters for cohesiveness and chewiness. Logarithmic transfonnations of data 'Corresponding author Journal of Sensory Studies 13 (1998). 77-93. All Rights Reserved. 'Copyright I998 by Food & Nutrition Press, Inc.. Trumbull. Connecticut. 77 78 J.-F. MEULLENET ETAL.. improved correlations between sensory attributes and their instrumental corollaries. The correlation between sensory hardness and the logarithm ofinstrumental hardness was improved to r=O.%. The correlation between the logarithm of both sensory and instrumental springiness was improved to r = 0.86. The correlation between the logarithms of both sensory and instrumental chewiness was improved to r = 0.54, which was signiJicant at P < 0.05.

J. Gross, B.P. Marks and M. Daniels. Cereal Chem. 75, 714-720 (1998).A trained texture profiling panel and an extrusion cell fitted into the TA.XT2 Texture Analyzer were used to quantify textural characteristics of three cooked rice cultivars exhibiting small differences in tlexture. Nine sensory and five instrumental parameters were used to establish predictive models for the sensory characteristics evaluated. Most effectively pred3icted were hardness (R2=@.62) and toothpack (R2=0.70). Partial least square regression techniques gave the following RAP (relative ability of prediction) values: hardness 0.52, cohesiveness of mass 0.60, toothpull 0.73, and toothpack 0.54. These values take into account the accuracy of the sensory methodology and provide a more accurate evaluation of the quality of the predictive models. Poor predictability was related to small textural differences between test samples, and need for better statistical methods applicable to such situations.A. S. Sz.

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Dental microwear has been used for decades to reconstruct the diets of fossil hominins and bioarchaeological populations. The basic theory has been that hard-brittle foods (e.g., nuts, bone) require crushing and leave pits as they are pressed between opposing cheek-tooth surfaces, whereas soft-tough foods (e.g., grass blades, meat) require shearing and leave scratches as they are dragged along opposing surfaces that slide past one another. However, recent studies have called into question the efficacy of microwear as an indicator of diet. One issue has been the limited number of in vitro studies providing empirical evidence for associations between microwear pattern and chewing behavior. We here describe a new study using a chewing simulator, the BITE Master II, to examine the effects of angle of approach between opposing teeth and food consistency on microwear surface texture. Results indicate that opposing teeth that approach one another: 1) perpendicular to the occlusal plane (crushing) result in pits; 2) parallel to the occlusal plane (shearing) result in striations in the direction of movement; and 3) oblique to the occlusal plane (45°) result in both striations and pits. Results further suggest that different food types and abrasive loads affect the propensity to accumulate microwear features independent of feature shapes.

In both domestic and international markets, the end‐use quality of rice affects its market value and acceptability to consumers. The effect of various postharvest processing treatments on sensory characteristics of cooked rice was investigated using sensory descriptive methods. Cooked rice quality was affected (P < 0.05) by rough rice wet holding, drying temperature, storage temperature, and storage duration. Cohesiveness of mass and hardness of sample were significantly affected by the temperature of drying. A higher storage temperature reduced the cohesiveness of mass and gluiness, while sample hardness, clumpiness, and geometry of slurry increased. Storage duration had more profound effects on the sensory attributes studied. Perceived starchy note, clumpiness, gluiness, and overall sensory impression decreased after four weeks of storage. Storage duration also influenced hardness, moisture absorption, sulfury notes, and cardboardy notes.

Cereal Chem. 77(4):501-506Sensory texture attributes of cooked rice from two cultivars (Bengal and Cypress) harvested in 1997 (56 samples) were predicted using extrusion and compression tests along with spectral stress strain analysis. Predictive models for each of nine sensory texture attributes studied were evaluated using force values from the instrumental tests in conjunction with partial least squares regression. All sensory attributes were well predicted using both the extrusion and compression tests (relative ability of prediction > 0.70). However, the extrusion test consistently provided more accurate and discriminative predicted models (root mean square error of prediction < 0.55, S tot / RMSEP > 2.0). Spectral stress strain analysis predictive models for adhesiveness to lips and hardness were explained.

Sensory texture characteristics of cooked rice for three cultivars (74 samples) were predicted using an extrusion cell and a novel data analysis method (i.e. Spectral Stress Strain Analysis). Eight sensory texture characteristics were evaluated and force values from the instrumental tests were used in combination with Partial Least Squares regression to evaluate predictive models for each of the sensory attributes studied. Relative Ability of Prediction (RAP) values were evaluated for each model; they ranged from 0.06 to 0.85. Satisfactory models are proposed for the two major texture characteristics of cooked rice, namely hardness (RAP=0.85) and stickiness as evaluated by adhesion to lips (RAP=0.76). Other sensory attributes such as roughness of mass (RAP=0.73) and toothpack (RAP=0.81) were also satisfactorily predicted. Sensory attributes such as toothpull (RAP=0.12) and loose particles (RAP=0.06) could not be predicted using the Spectral Stress Strain Analysis.

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