Faculty of Engineering

Page 1. PRESSURE DROP ESTIMATION IN TUBE FLOW OF NETWORKS NON-NEWTONIAN FLUID FOODS BY NEURAL PAWAN P. SINGH' Agricultural Biotechnology Room 203 Deparment of Food Science and Toxicology University of Idaho MOSCOW. ID 83844-2312 AND ...

Fourteen varieties of Thai indica rice, cooked with five water-to-rice ratios ranging from 1.3 to 2.5 on a weight basis, were characterised by sensory and instrumental texture profile analysis. The potential of near infrared (NIR) reflectance spectroscopy was investigated as an alternative tool for evaluating eating quality attributes of cooked rice by developing predictive models for sensory hardness, stickiness and glossiness. Partial least squares regression models were developed which predicted sensory hardness and stickiness slightly better than the glossiness with r2v values ranging from 0.88 to 0.91 and standard errors of prediction ( SEP) lower than 0.4 unit score on nine-point sensory intensity scales. Results indicated that NIR spectroscopy-based models could be used for estimating the sensory hardness, stickiness and glossiness scores of cooked rice with higher accuracy (lower SEP) compared to the instrumental texture profile analysis based-models.

The quality attributes of pasteurized milk such as total viable count (TVC), pH, conductivity and titratable acidity (TA) were determined during storage at 6, 15 and 25°C. The quality deterioration was indicated by an increase in initial TVC, conductivity and TA, and decrease in pH. The TVC increased from initial 103.6 CFU/ml to about 106-107 CFU/ml indicating spoilage of milk. The milk could be kept without spoilage for about 4, 1 and 0.35 day during storage at 6, 15 and 25°C, respectively. Changes in pH and TA could only be detected after TVC reached a level of about 106 CFU/ml.). Results showed that both multiple regression and Gompertz models could be used for shelf-life prediction of pasteurized milk. However, the Gompertz model resulted in superior fitting to experimental data (r2 = 0.98) compared to the multiple regression model (r2 = 0.76).