The Experts below are selected from a list of 180 Experts worldwide ranked by ideXlab platform
I. Church - One of the best experts on this subject based on the ideXlab platform.
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modelling of texture changes of Dry Peas in long time cooking
Food Control, 1998Co-Authors: G. Xie, R Xiong, I. ChurchAbstract:Texture changes of soaked dehydrated Peas cooked at four temperatures (70, 80, 90 and 100°C) for up to 240 min, were studied using both sensory and instrumental means. It was observed that, for long time cooking, texture change v time in a semi-log scale forms a tail. To describe this phenomenon two kinetic models were developed and compared to the first order reaction kinetics and Huang and Bourne model. Both models have two parameters k and Fmin which can be estimated by both linear and non-linear regression. The model 2 (non-linear) produced the best full model prediction, followed by model 1 (non-linear) and Rizvi-Tong model, and the worst results come from the first order reaction kinetics.
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Comparison of kinetics, neural network and fuzzy logic in modelling texture changes of Dry Peas in long time cooking
LWT - Food Science and Technology, 1998Co-Authors: Guozhong Xie, R Xiong, I. ChurchAbstract:Abstract Kinetic, neural network and fuzzy logic models were proposed to model the textural changes of Dry Peas cooked at 70, 80, 90 and 100 °C for up to 240 min. The results were compared to the first order kinetic and Rizvi and Tong (R-T) models. It was observed that the textural changes in cooked Peas vs. time did not follow the first order reaction kinetic model. The neural network model consistently produced the best fit to the experimental data, and the first-order-reaction kinetic model the worst. The performance of the other three models, that is, the proposed kinetic, fuzzy and R-T, varied. The models were also validated and a similar pattern was observed. Compared to the traditional kinetic models, the neural network and fuzzy logic models are more flexible. They use the weight matrix (NN model) or membership function and fuzzy rules, instead of activation energy and constant.
G. Xie - One of the best experts on this subject based on the ideXlab platform.
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Comparison of textural changes of Dry Peas in sous vide cook-chill and traditional cook-chill systems
Journal of Food Engineering, 2000Co-Authors: G. XieAbstract:The effect of sous vide cook-chill (SVCC) and traditional cook-chill (CC) processes on the textural changes of Dry Peas was experimentally studied. For the same heat treatment larger peak puncture and compression forces were found for SVCC Peas, which was mainly caused by the lack of extra water in the vacuum sealed pouch during cooking. A kinetic model was used to model the textural changes of cooked Peas and values of the apparent activation energy were obtained. They were 146.7 kJ/mole (CC) and 125.8 kJ/mole (SVCC) for the peak compression force and 112.4 kJ/mole (CC) and 168.3 kJ/mole (SVCC) for the peak puncture force. This work demonstrated that higher cooking temperature not only helps to increase the rate of water take up of the Peas during CC cooking but also has a significant impact on pea texture changes.
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modelling of texture changes of Dry Peas in long time cooking
Food Control, 1998Co-Authors: G. Xie, R Xiong, I. ChurchAbstract:Texture changes of soaked dehydrated Peas cooked at four temperatures (70, 80, 90 and 100°C) for up to 240 min, were studied using both sensory and instrumental means. It was observed that, for long time cooking, texture change v time in a semi-log scale forms a tail. To describe this phenomenon two kinetic models were developed and compared to the first order reaction kinetics and Huang and Bourne model. Both models have two parameters k and Fmin which can be estimated by both linear and non-linear regression. The model 2 (non-linear) produced the best full model prediction, followed by model 1 (non-linear) and Rizvi-Tong model, and the worst results come from the first order reaction kinetics.
R Xiong - One of the best experts on this subject based on the ideXlab platform.
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Use of hyperbolic and neural network models in modelling quality changes of Dry Peas in long time cooking
Journal of Food Engineering, 1999Co-Authors: R XiongAbstract:Abstract Quality of Dry Peas cooked at 70°C, 80°C, 90°C and 100°C for up to 240 min was assessed using both sensory evaluation and instrumental measurement. The quality changes of the Peas cooked at each temperature were modelled using the primary models, i.e. hyperbolic model and its two linear versions. Both Davey’s modified Arrhenius model and neural network (1-9-16) model were used as secondary models to predict the parameters of the primary models from temperature. Compared to the first order reaction kinetic model the hyperbolic model and its two linear versions significantly improved the fitness to the experimental data. Among the three proposed primary models the performance of the hyperbolic and second linear version models was similar and better than that of the first linear version model. For the full model prediction the performance of the neural network model was better than that of the Davey model.
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modelling of texture changes of Dry Peas in long time cooking
Food Control, 1998Co-Authors: G. Xie, R Xiong, I. ChurchAbstract:Texture changes of soaked dehydrated Peas cooked at four temperatures (70, 80, 90 and 100°C) for up to 240 min, were studied using both sensory and instrumental means. It was observed that, for long time cooking, texture change v time in a semi-log scale forms a tail. To describe this phenomenon two kinetic models were developed and compared to the first order reaction kinetics and Huang and Bourne model. Both models have two parameters k and Fmin which can be estimated by both linear and non-linear regression. The model 2 (non-linear) produced the best full model prediction, followed by model 1 (non-linear) and Rizvi-Tong model, and the worst results come from the first order reaction kinetics.
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Comparison of kinetics, neural network and fuzzy logic in modelling texture changes of Dry Peas in long time cooking
LWT - Food Science and Technology, 1998Co-Authors: Guozhong Xie, R Xiong, I. ChurchAbstract:Abstract Kinetic, neural network and fuzzy logic models were proposed to model the textural changes of Dry Peas cooked at 70, 80, 90 and 100 °C for up to 240 min. The results were compared to the first order kinetic and Rizvi and Tong (R-T) models. It was observed that the textural changes in cooked Peas vs. time did not follow the first order reaction kinetic model. The neural network model consistently produced the best fit to the experimental data, and the first-order-reaction kinetic model the worst. The performance of the other three models, that is, the proposed kinetic, fuzzy and R-T, varied. The models were also validated and a similar pattern was observed. Compared to the traditional kinetic models, the neural network and fuzzy logic models are more flexible. They use the weight matrix (NN model) or membership function and fuzzy rules, instead of activation energy and constant.
E. G. Kokko - One of the best experts on this subject based on the ideXlab platform.
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bacterial suppression of basal pod rot and end rot of Dry Peas caused by sclerotinia sclerotiorum
Canadian Journal of Microbiology, 1993Co-Authors: H. C. Huang, E. G. Kokko, L J Yanke, R C PhillippeAbstract:Morphological and biochemical characteristics indicate that the two bacterial strains used in this study belong to Bacillus cereus Frankland and Frankland. Tests in vitro revealed that strains of B. cereus differ in their antagonistic activities on Sclerotinia sclerotiorum (Lib.) de Bary. Vegetative growth and ascospore germination of S. sclerotiorum were inhibited by diffusible metabolites induced by B. cereus strain alf-87A, but were unaffected by strain B43. In vivo studies showed that the antagonistic strain alf-87A, when sprayed onto pea plants (Pisum sativum L.) at the pod development stage, reduced the incidence of basal pod rot from infection by airborne ascospores of S. sclerotiorum by 39–55%. This treatment also significantly (P < 0.05) reduced the severity of basal pod rot by decreasing lesion size. Strain alf-87A significantly reduced the incidence of end pod rot. Spraying pea plants with strain B43 of B. cereus was not consistently effective in reducing basal and end pod rots. Scanning electr...
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Pod rot of Dry Peas due to infection by ascospores of Sclerotinia sclerotiorum.
Plant Disease, 1992Co-Authors: H. C. Huang, E. G. KokkoAbstract:Dry Peas (Pisum sativum) were artificially inoculated with airbone ascospores of Sclerotinia sclerotiorum over an 8-day period at the pod development stage. Pod rot lesions at the flower end of pods (basal rot) developed in 71.8% of pods. Lesion incidence at the distal end of pods (end rot) averaged 17.8%, whereas that on other parts of the pod tissues averaged 11.8%. The scanning electron microscopy studies illustrated that the high incidence of basal rot was attributable to close contact between pod tissues and the stamens (.)
F. J. Muehlbauer - One of the best experts on this subject based on the ideXlab platform.
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Dry Pea (Pisum Sativum L.) Canning Quality as Influenced by Soak Time, Soak Solution, and Cultivar
Journal of Food Science, 2006Co-Authors: Stephen R. Drake, F. J. MuehlbauerAbstract:Hydration ratio of Dry Peas increased as soak time increased. Drained weights for the cv. Alaska decreased as soak time was increased, but drained weight for the cv. Garfield remained constant. Shear values increased for‘Alaska’, but decreased for‘Garfield’as soak time was extended. Moisture content decreased for‘Alaska’, but increased for‘Garfield’as soak time was extended. Objective color remained constant regardless of soak time. Sensory integrity (wholeness) was improved as soak time was extended for‘Alaska’Peas. The addition of 0.5% CaCl2 solution to canned Peas reduced hydration ratios, drained weights, and color, and increased shear values and sensory integrity and appearance.
