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Free Shipping Free global shipping No minimum order. Physical Properties of Food Materials 1. Fluid Flow 2. Heat and Mass Transfer: Basic Principles 3. Reaction Kinetics 4. Elements of Process Control 5. Size Reduction 6. Mixing 7. Filtration and Expression 8. Centrifugation 9. Membrane Processes Extraction Adsorption and Ion Exchange Distillation Crystallization and Dissolution Extrusion Spoilage and Preservation of Foods Thermal Processing Thermal Processes, Methods and Equipment Refrigeration: Chilling and Freezing Refrigeration: Equipment and Methods Evaporation Dehydration No Downloads.

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- Fundamentals of Food Process Engineering : Romeo T. Toledo : .
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Embeds 0 No embeds. No notes for slide. Fundamentals of food process engineering by toledo 1. The Editorial Board has outlined the most appropriate and complete content for each food science course in a typical food science program, and has identified textbooks of the highest quality, written by leading food science educators. Series Editor Dennis R.

Heldman, Ph. Golden, Ph. Johnson, Ph. Shafiur Rahman, Ph. Vaclavik and Elizabeth W. Potter and Joseph H. Toledo Introduction to Food Processing, P.

Jay, Martin J. Loessner, and David A. Heldman and Richard W. Marriott and Robert B.

## Food Process Engineering and Technology

Lawless and Hildegarde Heymann 3. Toledo University of Georgia Athens, Georgia 4. All rights reserved. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

The author expresses his gratitude to colleagues who have adopted this textbook and to those who have made constructive criticisms on the material. This new edition not only incorporates changes suggested by colleagues, but additional material has been added to include facilitated problem solving using a computer, and new food processing and food product technologies. The expanded coverage may result in not enough time available in a school term to cover all areas; therefore, instructors are advised to carefully peruse the book and select the most appropriate sections to cover in a school term.

The advantage of the expanded coverage is the elimination of the need for a supplementary textbook. This theme continues in the third edition. In addition to the emphasis on problem solving, technological principles that form the basis for a process are presented so that the process can be better understood and selection of processing parameters to maximize product quality and safety can be made more effective. The third edition incorporates most of what was in the second edition with most of the material updated to include the use of computers in problem solving.

Use of the spreadsheet and macros such as the determinant for solving simultaneous linear equations, the solver function, and programming in Visual BASIC are used throughout the book. The manual problem-solving approach has not been abandoned in favor of the computer approach. Thus, users can still apply the concepts to better understand a process rather than just mechanically entering inputs into a pre-programmed algorithm. Athens, Georgia Romeo T. Toledo 7. Contents Preface. Contents ix 3.

## Fundamentals and Operations in Food Process Engineering - CRC Press Book

Contents xi 6. Contents xiii 9.

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Contents xv Contents xvii A. In algebraic expressions, variables are represented by letters from the end of the alphabet. In physics and engineering, any letter of the alphabet and Greek letters are used as symbols for physical quantities. A function represents the mathematical relationship between variables. Variables may be dependent or independent. In physical or chemical systems, the interdependence of the variables is determined by the design of the experiment. For example, when determining the loss of ascorbic acid in stored canned foods, ascorbic acid concentration is the dependent variable and time is the independent variable.

On the other hand, if an experiment involves taking a sample of a food and measuring both moisture content and water activity, either of these two variables may be designated as the dependent or independent variable. A data point for a response variable that depends on only one independent variable univariate will be a number pair, whereas with response variables that depend on several independent variables multivariate , a data point will consist of a value for the response variable and one value each for the treatment variables.

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Experimental data are often presented as a table of numerical values of the variables or as a graph. The graph traces the path of the depen- dent variable as the values of the independent variables are changed. For univariate responses, the graph will be two-dimensional, and multivariate responses will be represented by multidimensional graphs.

When any of the variables has an exponent other than one, the graph will be a curve in rectangular coordinates. Both abscissa and ordinate are in the arithmetic scale and the distance from the origin measured along or parallel to the abscissa or ordinate to the point under consideration is directly proportional to the value of the coordinate of that point.

Scaling of the abscissa and ordinate is done such that the data points, when plotted, will be symmetrical and centered within the graph. The Cartesian coordinate system is divided into four quadrants with the origin in the center. The upper right quadrant represents points with positive coordinates, the left right quadrant represents negative values of the variable on the abscissa and positive values for the variable on the ordinate, the lower left quadrant represents negative values for both variables, and the lower right quadrant represents positive values for the variable on the abscissa and negative values for the variable on the ordinate.

Equations are useful for presenting experimental data because they can be mathematically manipulated. Furthermore, if the function is continuous, interpolation between experimentally derived values for a variable may be possible.

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Statistical procedures are based on minimizing the sum of squares for the difference between the experimental values and values predicted by the equation. When linear regression is used on experimental data, the slope and the intercept of the line are calculated. The line must pass through the point that represents the mean of x and the mean of y. A line can then be drawn easily using either the point-slope or the slope-intercept forms of the equation for the line.

Values for r that is much different from 1. Example 1. Data is collected by providing feed and water to the animal so the animal can feed at will, determining the amount of feed consumed, and weighing each animal at designated time intervals. The PER may be calculated from the slope of the regression line for weight of the animal y against cumulative weight of protein consumed x.

The data expressed as x, y where x is the amount of feed consumed and y is the weight are as follows: 0, Perform a regression analysis and determine the PER. The regression and graphing can also be performed using a spreadsheet as discussed later in this chapter. The PER is the slope of the line, 2. The term with the exponent 1 is the linear term.

Stepwise regression analysis may be performed, that is, additional terms are added to the polynomial, and the contribution of each additional term in reducing the error sum of squares is evaluated. Determinants can be used to determine the constants for an nth order polynomial. Techniques for solving determinants manually and using a spreadsheet program are discussed later in this chapter. For the second-order polynomial quadratic equation, the constants a, b, and c are solved by substituting the values of N, x, x2 , x3 , x4 , xy, and x2 y, into the three equations above and solving them simultaneously.

Linearizationalsointroducescomplexerrorsparticularly when two measured variables both appear in a linearized term. One commonly used software is Systat. Select Window and on the pop-up menu, select Worksheet. Data may then be entered in the worksheet. Data may then be saved by selecting File and Save. Enter the Filename with the. Systat variables available to you are.

Then select Extended Long and OK to get back to the main menu. The Systat toolbar then becomes active. Select Stats in the Systat Main menu and select Nonlin in the pop-up menu. Follow the prompts. First select Loss Function and enter Loss function that is to be minimized. Usually this will be the sum of squares of the value of the dependent variable and the estimate. Although the sum of squares is the default, sometimes the program does not do the required iterations if nothing is entered for the loss function.

Then select OK and when the display returns to the Systat Main menu, select Stats again, select Nonlin in the pop-up menu, and select Model. Enter number of iterations. Select OK and Systat will return values of the parameter estimates and the loss function.