Thermal Food Processing

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Thermal Processing of Canned Foods 351 FIGURE 11.11 Chocolate drink in 15.5-fluid oz glass bottle processed at 119.4°C (246.9°F) and 125.6°C (258.0°F) retort temperatures (RT), respectively. Left: Modeling the heat penetration data with RT of 119.4°C (246.9°F). Right: Predicting the product temperatures with RT of 125.6°C (258.0°F) (jh = 1.56, fh = 10.50, jc = 0.73, fc = 10.86). FIGURE 11.12 Effect of the come-up and cooling retort temperature profiles on the NumeriCAL modeling results (°F). Product: 300 × 405 canned condensed mushroom soup (2:1, v/v) processed in a hydrostatic sterilizer simulator. Left: Modeling the heat penetration data. Right: Predicting product temperatures with longer come-up and cooling profiles (jh = 1.79, fh = 44.79, jc = 1.56, fc = 50.70).
352 Thermal Food Processing: New Technologies and Quality Issues FIGURE 11.13 NumeriCAL simulation results (°F) under process deviation conditions. Product: 10% bentonite in 300 × 407 cans. Left: Normal heat penetration data modeling results. Right: Model-predicted time–temperature under multiple process temperature deviation conditions (jh = 2.83, fh = 41.42, jc = 1.42, fc = 50.85). heating and cooling factors was then used to predict the product temperatures for the same product with longer come-up and cooling-down times (21.5 min for simulating hydrostatic sterilizer feed and discharge leg residence time). The results were plotted in the right curve and show that the model-predicted product temperatures agree well with the measurement data. The result suggests that the NumeriCAL heating and cooling factors used for the process design in this case are not affected by either the come-up profile or cooling-down profile. Berry recognized that the come-up time and its profile have a profound effect on Ball’s heating factors, particularly the jh value when using 42% come-up time correction. 35 If the heating factors were developed from the fastest come-up profile, it could cause food safety problems when these heating factors were used for the process development for a sterilizer actually having a longer come-up time. He recommended using a true jh value for the process development. Table 11.2 shows various come-up time effects in a hydrostatic sterilizer simulator on both Ball’s and NumeriCAL jh values. From feed leg time 4.5 to 21.5 min, we found that the Ball jh values ranged from 1.49 to 1.90, whereas the NumeriCAL obtained jh value was consistent, which was around 1.71. Since NumeriCAL uses actual come-up and cooling-down retort temperatures, the developed jh values reflect the true jh value, which has less effect on the come-up time profile. Figure 11.13 shows the NumeriCAL simulation results under process deviation conditions (10% Bentonite in 300 × 407 cans), with the jh value larger than 2.0. The left curve shows the normal heat penetration data modeling results
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Thermal Food Processing
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87. Polysaccharide Association Stru
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133. Handbook of Frozen Foods, edit
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Published in 2006 by CRC Press Tayl
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Various alternative thermal process
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Professor Sun is a fellow of the In
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Antonio Martínez Instituto de Agro
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Contents Part I: Modeling of Therma
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Chapter 17 Pressure-Assisted Therma
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1 CONTENTS Thermal Physical Propert
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Thermal Physical Properties of Food
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Heat and Mass Transfer in Thermal F
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5 CONTENTS Modeling Thermal Process
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Modeling Thermal Processing Using C
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6 CONTENTS Thermal Processing of Me
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Thermal Processing of Meat Products
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Thermal Processing of Poultry Produ
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9 CONTENTS Thermal Processing of Da
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Thermal Processing of Dairy Product
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10 CONTENTS UHT Thermal Processing
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14 CONTENTS Ohmic Heating for Food
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Ohmic Heating for Food Processing 4
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15 CONTENTS Radio Frequency Dielect
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Radio Frequency Dielectric Heating
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16 CONTENTS Infrared Heating Noboru
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Infrared Heating 495 0.78 1.4 Far-i
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Infrared Heating 497 A black body a
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Infrared Heating 523 15. T Takeo. F
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Infrared Heating 525 55. CM Liu, N
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Thermal Food Processing

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