Product Development & Innovation PDF

Product Development & Innovation Lesson 6: Product Formulation ❑ Optimization of Product Using Response Surface Methodology ❑ Formulation Design Specific Learning Outcome ❑ Explain product optimization using response surface methodology ❑ Design a food product through the application...

Product Development & Innovation Lesson 6: Product Formulation ❑ Optimization of Product Using Response Surface Methodology ❑ Formulation Design Specific Learning Outcome ❑ Explain product optimization using response surface methodology ❑ Design a food product through the application of knowledge of optimization and formulation design August 25-26, 2020 RESPONSE SURFACE METHODOLOGY Improving system performance without increasing cost of production and process time while maintaining the required quality attributes is the main objective of food processing and manufacturing. Finding the optimum processing condition and formulation for food products of high quality and high marketability is paramount importance for successful product. The method used for coming up with optimal processing condition and combination of ingredients with the best output is called optimization Optimization means finding the best quality criteria (product and process efficiency) while saving time and cost RESPONSE SURFACE METHODOLOGY Modeling precedes optimization and helps establish a quantitative relationship between independent and dependent/response variables. In the food industry, models help practitioners and scientists to think about processes that are too complicated to understand in every detail Modeling and optimization of processes including food processes has been done through focusing on the effect of changes in one parameter on a response keeping all other factors constant. This is called one-variable-at-a-time technique. RESPONSE SURFACE METHODOLOGY One-variable-at-a-time method Major limitation is that the interactive effects among the variables are not accounted for and there is a lack of explanation of the complete effect of the factors on the response or an overview of the variables’ behavior within the entire experimental space. One-variable-at-a-time method increases the number of experimental runs required to conduct the research, which eventually leads to increased cost and time to do the research. RESPONSE SURFACE METHODOLOGY RSM is a collection of statistical and mathematical techniques useful for developing, improving, and optimizing processes. applications in the design, development, and formulation of new products improvement of existing product designs. The most extensive applications of RSM are in the food industry, particularly in situations where multiple input variables potentially influence the quality characteristics of the product or the process. Extensively used in modeling and optimizing food processing operations and formulation of products. RESPONSE SURFACE METHODOLOGY Major food process operations like drying, extrusion, fermentation, baking and cooking operations have been modeled and optimized using RSM. Food product formulations and product design and development has been carried out using RSM. Several experimental designs including factorial designs, central composite design with its variants, D-optimal design, and mixture designs have been used with RSM The RSM’s major advantage is generating large amount of information from a reduced number of experimental runs that are required to evaluate multiple parameters and their interactions RESPONSE SURFACE METHODOLOGY Terms Used in RSM Experimental domain Series of tests that form an experiment Dependent variables or responses Output variables which are influenced by several independent variables. Residual The difference between the experimental and calculated (predicted) result for a determinate set of conditions. A low value of this index is necessary for a good mathematical model fitted on experimental data. RESPONSE SURFACE METHODOLOGY Terms Used in RSM Runs The experimental field that will be investigated, which is defined by the maximum and minimum limits of the independent variables. Experimental design A specific system of experiments defined by a matrix created with the different level combinations of the independent variables. Central composite, Box-Behnken, and Doehlert designs are examples of experimental designs. Independent variables or factors input variables which can be changed independently of each other RESPONSE SURFACE METHODOLOGY The relationship between the independent variables and the response can be represented by where y is the response, f is the unknown function of response, x1, x2, … xn denote the independent variables and n is the number of independent variables, ε is error that represents other sources of variability which is not explained by the mathematical relationship (by the function, f). RESPONSE SURFACE METHODOLOGY 1. Selection/identification of key process or independent variables and their levels Many factors often affect food manufacturing process, both formulation/ingredient related and process parameter related. The independent variables to be studied are selected based on experience, research results obtained from literature or preliminary experiments. If there are too many variables involved, as is the case in most new food product development, screening procedure should be used to identify those that critically influence the responses of interest. RESPONSE SURFACE METHODOLOGY 1. Selection/identification of key process or independent variables and their levels Numerous input variables usually affect the output response of a system or process in food and biological materials, but taking the individual effects of each of these parameters on process response is impossible due to the high costs involved and the number of runs required. Therefore, the first and most crucial step in RSM analysis is the determination of the factors which have the largest influence on the response and their ranges. This process is called “screening”. RESPONSE SURFACE METHODOLOGY 1. Selection/identification of key process or independent variables and their levels Screening designs allow the researcher to look at the effects of several variables each of which takes on two levels with less number of runs. Those significant variables are then selected for further optimization. Screening designs like Plackett-Burman and Saturated fractional factorial designs are commonly used in food processing and formulation. Some specifically designed preliminary experiments are conducted using screening designs and they enable the food researcher to estimate the effect of each factor and to select the most significant and critical variables from the potential variables with minimum experimental efforts RESPONSE SURFACE METHODOLOGY 1. Selection/identification of key process or independent variables and their levels Screening can be done using full or fractional factorial designs (for 2–4 factors) and Plackett-Burman design (for 5 or more factors). In such designs, only main effects are estimated; interactions between independent variables are usually considered insignificant and are neglected. The Plackett-Burman design type is a two-level fractional factorial screening design for studying N-1 variables using N runs, where N is a multiple of 4. The levels of the selected variables should be identified carefully. Inappropriate selection of independent variable levels would directly lead to an incorrect optimization of the process RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design After selection of the food quality attributes of interest (response) and identifying the significant independent variables, the next step of statistical food product design and development is to design an appropriate experiment. The basic goal of RSM is to guide the experimenter in finding the optimum points DOE determines the points where the response should be evaluated. The 3n factorial, the central composite design (CCD), the Box–Behnken Design (BBD), the D-optimal designs (constrained designs) and mixture designs are commonly used in RSM RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design Some computer packages offer optimal designs based on the special criteria and input from the user. These designs differ from one another with respect to their selection of experimental points, number of runs and blocks. Design-Expert ✔ Specifically designed for DOE ✔ Offers comparative tests, screening, characterization, optimization, robust parameter design, mixture designs and combined designs. ✔ Provides test matrices for screening up to 50 factors. ✔ Graphical tools to identify the impact of each factor on the desired outcomes and reveal abnormalities in the data. RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design R software ✔ Provides functions to generate response-surface designs, fit first- and second-order response-surface models. ✔ It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux, Windows and Mac). ✔ Free for use. RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design MATLAB ✔ RSMdemo (interactive response surface demonstration) statistical toolbox in MATLAB opens a group of three graphical user interfaces for interactively investigating response surface methodology (RSM), nonlinear fitting, and the design of experiments. RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design Minitab ✔ Surface design to model curvature in data and identify factor settings that optimize the response. ✔ Predict responses for different factor settings. ✔ Plot the relationships between the factors and the response. ✔ Find settings that optimize one or more responses. RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design ReliaSoft’s DOE++ ✔ Design of experiments (DOE). ✔ Analysis of response data. ✔ Extensive plotting capabilities to present analysis results graphically. ✔ Powerful optimization utility. RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design JMP ✔ Powerful in selection of experimental design. ✔ RSM analysis and optimization. STATGRAPHICS ✔ DOE, response surface design and optimization. STATISTA ✔ DOE, response surface design and optimization. RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design a) Full factorial (3n factorial design) Suitable for supporting the building of a quadratic model, if there are less than four significant variables (n ≤ 4) selected for modeling in the food systems and chemical processes. A 3n experimental design supplies 3n degrees of freedom, in which one is fixed for determining the total average value β0 (constant term) in the model. The remaining (3n - 1) degrees of freedom then allow estimation and calculation of the effects of each factor, the interactions between and among factors, and the curvature in the system. RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design a) Full factorial (3n factorial design) constructed by the combination of all the possible test levels of each variable can be divided into four subgroups: a 2n factorial plan with 2n trials, 2n central points of all the surfaces, border middle points and one central point (in practice, this should be repeatedly performed) RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design a) Full factorial (3n factorial design) appropriate to obtain a lot of information about the main effects in a proportionately small numbers of runs. is not appropriate to evaluate the interaction between factors due to deficiency of this design to provide information about interactions. most labor-intensive experiment design If the number of design variables becomes large, a fraction of a full factorial design can be used at the cost of estimating only a few combinations among variables  RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design b) Central Composite Designs (CCD) consists of factorial points, a central point, and axial points which are at a distance a from the central point. can predict linear and quadratic models with high quality. provides a reasonable amount of information for testing lack-of-fit, while not involving an unusually large number of experimental runs  RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design b) Central Composite Designs (CCD) Used to estimate parameters of a full second-degree model in all scientific research areas. One advantage is its efficiency with respect to the smaller number of runs required with each factor having 3 or 5 levels. Another advantage is that it can be constructed in a sequential program of experimentation by building onto information gathered previously from a 2n factorial design.  RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design b) Central Composite Designs (CCD) If a linear model based on a 2n factorial design turns out to be insignificant, then some extra trials can be designed, according to the principles of a CCD, to repair the model. Can predict linear and quadratic models with high quality. provides a reasonable amount of information for testing lack-of-fit, while not involving an unusually large number of experimental runs. appropriate to study factors with three and/or five levels. However, a CCD considers extreme points, which is not advisable for special processes like the extraction of a compound sensitive to high temperatures and pressures.  RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design c) BOX-Behnken design comprises a specific subset of the factorial combinations from the 3n factorial design that are formed by combining 2n factorials with incomplete block designs. designs are usually very efficient in terms of the number of required runs, and they are either rotatable or nearly rotatable. experimental points are situated on a hypersphere equally distant from the central point. approach avoids performing optimization under extreme conditions. is not appropriate for studying factors with more than three levels. only triplex levels (−1, 0, +1) are applied.  RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design c) BOX-Behnken design Applying this design is popular in food processes due to its economical design. appropriate to evaluate interaction between factors and especially to study processes without extreme points (where high levels of factors involved in the process is difficult to implement) such as high temperature and pressure next to each other  RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design d) D-Optimal design (constrained designs) The factorial designs are not always applicable for some food processes because of functional or technical restriction. There are combinations of some factor levels that are not practically possible to conduct the experiment. For example, in a roasting operation combining the highest temperature and the longest time may result in a product that is over roasted which is not fit for sensory evaluation whereas the high temperature can be combined with other shorter roasting times.  RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design d) D-Optimal design (constrained designs) Every trial under factorial design must be performed and the trial number increases rapidly beyond affordable limit when the number of factors increase. On the other hand, though CCD offers a smaller number of trials, it requires the exact setting of the test levels at the defined values and cannot be changed or is not flexible to handle constraints. D-optimal design was developed to overcome these shortcomings or exclude practically unsound scenarios  RESPONSE SURFACE METHODOLOGY 2. Selection of the experimental design d) D-Optimal design (constrained designs) test level of each variable can be selected flexibly and a variable can be tested at as many levels as the researcher wants. The number of levels of the different factors can be different or same. are computer-generated. “D-optimal” means that these designs are selected from the list of valid candidate runs that provide as much orthogonality between the columns of the design matrix as possible. Designs have been used in optimizing food ingredients (D-optimal mixture designs) and process conditions  RESPONSE SURFACE METHODOLOGY 3. Selection of the best regression model Building a model is one of the most important steps in food process and product design. After the experiments have been conducted and the data collected, the intended model is fitted to the data by using regression analysis least square minimization technique. The two important criteria for selecting a usable and precise model from the alternative equations are: the model with the highest precision for accurate application and the model with the simplest form for easy application. Polynomials have been used extensively in empirical modeling of chemical, biological, and food research systems  RESPONSE SURFACE METHODOLOGY 3. Selection of the best regression model The second order model can be written as follows where β0, βj, βjj and βjk are regression coefficients for intercept, linear, quadratic and interaction terms respectively and Xj, and Xk, are coded independent variables. RESPONSE SURFACE METHODOLOGY 4. Verification of the accuracy of the model After designing the experiments and fitting the measured responses into the selected model, the accuracy of the response model should be checked. Determination of statistical significance of the model is done using analysis of variance (ANOVA). Total sum of squares of y (SST) where n is the number of observations, y is the ith observation, y¯ is the mean value of all observations RESPONSE SURFACE METHODOLOGY 4. Verification of the accuracy of the model Regression sum of squares (SSR) where n is the number of observations, y is the ith observation, y¯ is the mean value of all observations, and yˆ i is the predicted response RESPONSE SURFACE METHODOLOGY 4. Verification of the accuracy of the model Error sum of squares (SSE) where n is the number of observations, y is the ith observation, yˆ i is the predicted response Parameter Sum of Degree MS F value the of square freedom Regression SSR p-1 SSR MSregression where p is the p–1 MSresiduals number of Residuals SSE n-p SSE coefficients of n–p the model and m Lack of fit SSEL m-p SSEL MSLack of fit is the numbers m–p MSPure error of levels used in the investigation Pure error SSEP n-m SSEP n–m Total SST n-1 F value RESPONSE SURFACE METHODOLOGY 5. Graphical presentation of the model equation Model building is not the only and ultimate objective of food process and product design. The interest of food product and process designers focus on the effect of different factors on the quality attributes. The question usually is which variables and in which ranges have significant effects on specific quality attributes or response variables. The other question could be under what conditions should food be processed to get a pre-defined quality attribute. Only a significant and precise model can supply reliable and essential information for the food researcher. RESPONSE SURFACE METHODOLOGY 5. Graphical presentation of the model equation Generally, two approaches are used to extract this information from the model: the graphical and numerical method. The predictive models are used to generate contours and response surfaces within the experimental range. The response surface plot is the theoretical three-dimensional plot (3D surface) showing the relationship between the response and the independent variables RESPONSE SURFACE METHODOLOGY 5. Graphical presentation of the model equation 3D surface (Maximum response) 3D surface (Minimum response) RESPONSE SURFACE METHODOLOGY 5. Graphical presentation of the model equation Proper interpretation of contour plots is an important part of the optimization exercise. When the contour plot displays ellipses or circles, the center of the system refers to a point of maximum or minimum response. RESPONSE SURFACE METHODOLOGY 5. Graphical presentation of the model equation Contour plot (Maximum response) Contour plot (Minimum response) RESPONSE SURFACE METHODOLOGY 5. Graphical presentation of the model equation Sometimes, contour plot may display hyperbolic or parabolic system of the contours. In this case, the stationary point is called a saddle point and it is neither a maximum nor a minimum point. These plots give useful information about the model fitted but they may not represent the true behavior of the system. It is important to keep in mind that the contours or the 3D surfaces represent contours or surfaces of estimated response and the general nature of the system that arises as a result of a fitted model, not the true structure. RESPONSE SURFACE METHODOLOGY 5. Graphical presentation of the model equation the optimum point cannot be considered as a maximum or minimum because it depends on the direction of travel from the central point, meaning that the center is maximum in one direction and minimum in another one RESPONSE SURFACE METHODOLOGY 6. Prediction and determination of optimal operating conditions Prediction of food quality attributes enables the researcher to estimate the response variable given the independent variables in the experimental region where no trials have been conducted. Helps in calculating the possible independent variables for a given response value. Apart from prediction, researchers are also interested in optimization which is an important step in statistical food process and product design. RESPONSE SURFACE METHODOLOGY 6. Prediction and determination of optimal operating conditions Optimization gives more detailed information about the level combinations of the independent variables that will result in optimum food quality attributes. This information from the optimization is reliable only if the model built is significant and adequately describes the relationship between the independent and the response variables. In food and beverages, the researcher must often deal with multiple quality attributes (physicochemical properties and sensory attributes) as desirable responses. There are several aspects that complicate the process of choosing a best alternative when considering multiple attributes to the decision-making. RESPONSE SURFACE METHODOLOGY 6. Prediction and determination of optimal operating conditions There are almost no perfect practical decisions where it is possible to get the optimal result for each response or criterion in a single choice. Therefore, for most situations, it is necessary to make trade-offs between the different objectives among the quality attributes. As a result, optimizing based on multiple objectives should provide mechanisms for incorporating the experimenter’s priorities and preferences. An optimum product may be achieved with different combinations of levels of the variables. The optimal levels of the independent variables that give the ‘best’ product can be determined using numerical and graphical techniques RESPONSE SURFACE METHODOLOGY PROCEDURE RESPONSE SURFACE METHODOLOGY PROCEDURE RESPONSE SURFACE METHODOLOGY PROCEDURE RESPONSE SURFACE METHODOLOGY PROCEDURE RESPONSE SURFACE METHODOLOGY PROCEDURE RESPONSE SURFACE METHODOLOGY PROCEDURE FORMULATION DESIGN Making a Food Concept into a New Food Product – Start with a Plan Once a new product concept is decided upon, your team will need a formulation starting point. Finding existing formulations and recipes and then modifying and combining them as needed is often the easiest way to start. Look up usage levels for ingredients, especially if specialty or industrial ingredients are needed (gums, modified starches, etc.). Study the existing recipes, formulations, and processing instructions and look for similarities and differences. Think through ingredient functionality and purpose of processing steps to choose the best starting place(s). FORMULATION DESIGN Making 2-3 Formulations Convert volume measurements to weights and calculate percent by weight right away. Converting volume measurements to weights can be done by carefully weighing out volumetric measurements of ingredients or by looking at reputable references such as the USDA FoodData Central. Weights -> Percents: Ingredient Weight/Total Weight = % by Weight For Baker’s Weight: % by weight of an ingredient/% of flour weight(s) in formula Converting to weights makes it easier to track variable changes. FORMULATION DESIGN Making 2-3 Formulations Take notes and record observations while making the formulation and evaluate the finished product in detail. Determine which formulation and process worked the best and why. FORMULATION DESIGN Decide what changes need to be made to reach the optimum gold standard food product. Based on the characteristics of the best iteration and the changes that need to be made, make a list of the most effective variables to test. There is not enough time or resources to test each variable, so it is important to think through the system and use educated reasoning to narrow down variables to test. FORMULATION DESIGN Decide what changes need to be made to reach the optimum standard food product. Keep cost in mind. If you are using an expensive ingredient, could you use a less expensive ingredient and get almost the same functionality and product quality? It is recommended to make the smallest batch size that works on equipment and is needed for sensory evaluation. Once variables are chosen, start testing one variable at a time. It is important to only change one ingredient type, ingredient amount, or processing step at a time to track and understand the results of each experiment. FORMULATION DESIGN Adjusting and Tracking Variables When adding a new ingredient, research usage levels. Depending on the application, it is often most efficient to test the high usage level first. This is the best way to see and evaluate the functionality of the ingredient. Addition vs. Substitution: When adjusting a formulation, it can either be done by addition or by substitution. Addition is taking the existing amounts of ingredients and simply adding another ingredient. This method dilutes or reduces the percentages of other ingredients. It makes the most sense if the ingredient function is different than other ingredients already in the formula. FORMULATION DESIGN Adjusting and Tracking Variables Addition vs. Substitution: Substitution takes out all or part of one or more ingredients and replaces that amount with another ingredient. This keeps the amounts and percentages of the other ingredients the same, but it replaces one ingredient functionality with another ingredient functionality. Substitution makes the most sense if the new ingredient has a similar function to another ingredient already in the formula. It can be difficult to decide on addition versus substitution. Think through the formulation and the variable you want to test and then evaluate both options to determine which makes the most sense. FORMULATION DESIGN Pancake Formulation with Ingredient Functionality Pancake Ingredients Percent by Weight Ingredient Function All-purpose Flour 30.8 Gluten Structure (Minimal), Starch Gelatinization Baking Powder 2.5 Leavening Sugar 2.1 Sweetness, Tenderizing (Minimal), Maillard Browning Salt 1.0 Taste Milk, 2% 52.7 Hydrate Dry Ingredients, Flavor Egg 8.5 Hydrate Dry Ingredients, Structure, Maillard Browning Oil 2.4 Tenderizing, Flavor (Minimal) Total 100.0  Pancake Formulation with Fiber Ingredient Added Option 1 - Addition Option 2 – Substitution Pancake Ingredients Weight ( grams) Percent by Weight Weight (grams) Percent by Weight All-purpose flour 30.8 180.0 29.2 149.2 25.5 Fiber Ingredient 31.4 5.1 29.8 5.1 Baking Powder 2.5 14.4 2.3 14.4 2.5 Sugar 2.1 12.0 1.9 12.0 2.1 Salt 1.0 6.0 1.0 6.0 1.0 Milk, 2% 52.7 309.0 50.1 309.0 52.8 Egg 8.5 50.0 8.1 50.0 8.6 Oil 2.4 14.0 2.3 14.0 2.4 Total 100.0 616.8 100.0 584.4 100.0 FORMULATION DESIGN How recipes and formulas different? A recipe is made at home, using cups, tablespoons and pinches. However, a formula uses precise weights of the ingredients for developing a product, like grams, kilograms and pounds. These weights are subsequently converted into percentages, so that the food and beverage manufacturers know the exact amount of every ingredient that goes into developing the product. FORMULATION DESIGN How to convert a recipe into a formula? 1. Understand the recipe Understand what goes into making a product: the ingredients, their quantities and the steps involved in formulating it. FORMULATION DESIGN How to convert a recipe into a formula? 2. Enlist the ingredients List all the ingredients that go into developing the product. Categorize the ingredients based on the form, such as fresh, canned or frozen. If needed, also include the brand name of the ingredient. Ensure that all ingredients are included for formulating the product. FORMULATION DESIGN How to convert a recipe into a formula? 3. List each ingredient’s unit of measurement in weight The ingredients in a recipe are measured by volume (i.e. cups or tablespoons) used for preparing the dish at home. These should be measured in weight before proceeding any further. FORMULATION DESIGN FORMULATION DESIGN How to convert a recipe into a formula? 4.Convert the weight into percentage Add the measure of each ingredient and divide the measure by the total weight. FORMULATION DESIGN FORMULATION DESIGN How to convert a recipe into a formula? 5.Test and Adjust Use the formula to make a small test batch. They are then tasted and adjusted for flavor, texture, and consistency as desired. Make any necessary changes and adjust the formula accordingly. At this stage, the cost of preparing the biscuits is also calculated, which may depend on the ingredients FORMULATION DESIGN How to convert a recipe into a formula? 6.Record and Standardize the formula When you are satisfied with the formula, write it down properly for future reference. Include any particular directions, measurements, and steps.  FORMULATION DESIGN How to convert a recipe into a formula? 7.Refine the formula Test the formula several times to ensure consistency in results of food product development. Testing out a formula takes several trials and errors before perfecting it and proceeding with new product development. FORMULATION DESIGN Why convert a recipe into a formula? A. Consistent quality Using the weight of the ingredients instead of the measures brings consistency in the finished product. Discrepancy may arise in measurements from one person to another. FORMULATION DESIGN Why convert a recipe into a formula? B. Formulas facilitate scalability Formulas allow adjustment in quantities, allowing for greater flexibility in scaling up or down per the requirements. Formulas ensure that there is no compromise in the integrity of the product. FORMULATION DESIGN Why convert a recipe into a formula? C. More cost-effective Weighing the ingredients gives a better control over the cost of developing a product. Manufacturers can understand the unit costs, the Cost of Goods Sold (COGS) and the product margins. FORMULATION DESIGN Why convert a recipe into a formula? D. Facilitates automation Food processing is automated, formulas are preferred over recipes. Processors can be programmed to follow formulas, and this can lead to increased efficiency and reduced errors. FORMULATION DESIGN Why convert a recipe into a formula? E. Standardization Formulas assist in industry standardization by giving a standardized set of instructions that can be followed consistently. This is critical for guaranteeing product quality and compliance with international regulatory standards. FORMULATION DESIGN Why convert a recipe into a formula? F. Helps with product labelling Consistency in the quantity of ingredients used will ensure a more uniform product labelling in relation to its nutritional value and label information. FORMULATION DESIGN Adjusting and Tracking Variables Addition (if the ingredient function is different than other ingredients already in the formula) Substitution from similar ingredient(s) (if the ingredient is similar to another ingredient already in the formula) In this scenario, deciding between addition and substitution will likely center around the fiber ingredient characteristics. If it is a fiber ingredient like wheat bran or oat bran, the fiber ingredient will function similarly enough to flour for substitution to make the most sense. If the fiber is soluble with low viscosity like inulin or resistant maltodextrin, the fiber ingredient does not have similar functionality to the ingredients listed and addition may make more sense. FORMULATION DESIGN Material Balance Track the material in the system. This tracking will provide context for ingredient functionality and is necessary for generating the nutrition facts panel. Measure and record data throughout the formulation testing, NOT just once the standard/best formulation is reached. Measurements include processing loss, moisture loss or gain, and fat loss or gain and will depend on the formulation. The most common material balance measurement is moisture loss through cooking, baking, or dehydrating. It can be as simple as measuring the weight of the product before and after the processing step to calculate water loss. FORMULATION DESIGN Material Balance FORMULATION DESIGN Tracking Moisture Content It helps us understand what is happening through the process, especially when comparing one experiment to the next. For instance, if the first experiment included raw fruit and the second experiment included frozen fruit puree, how did the initial water content of the fruit (and possibly the particle size) affect the final water content and consistency of the product? If the first experiment included a step to cook a filling on top of the stove for 5 minutes and the second experiment modified that step to cook a filling for 15 minutes, how much water was cooked off in both and how did that affect the finished product texture, color, and flavor? FORMULATION DESIGN Tracking Moisture Content Tracking moisture content helps convert liquid ingredients to dry ingredients – like fluid milk to nonfat dry milk or liquid egg to egg powder. It allows an accurate Nutrition Facts Panel to be generated. The Nutrition Facts Panel serving size is based on the finished product weight. If moisture is lost through cooking or baking, the calories and nutrients are concentrated in the final product. If water is added through processing, it dilutes the calories and nutrients. Tracking moisture through an experiment makes sure you are paying attention to details and observing what is happening during the formulation experimentation process. FORMULATION STEPS Formulation Steps – Where to Start Think through the processing steps to make a food. What is the function of each step? Does the order of the steps matter? To help answer these questions, think about what would happen if all of the ingredients were mixed together in one step Common Processing Steps Processing Method Definition Example(s) Baked Products: Creaming Mixing fat and sugar together vigorously to create an Shortened Cakes and air-in-fat foam Cookies Beating Very vigorous agitation of food mixtures using an electric Shortened Cakes, egg mixer at high speed or a wooden spoon to trap air and/or white foams like in Angel develop gluten or an emulsion Food Cake FORMULATION STEPS Formulation Steps – Where to Start Common Processing Steps Processing Method Definition Example(s) Baked Products: Stirring/ moderate The gentle blending of ingredients when trapping of air and Muffins, various quick mixing development of gluten are not necessary breads Folding Very gentle manipulation used to bring batter up from the Angel food cake, soufflé, bottom of the mixing bowl while incorporating dry chiffon cake ingredients or another batter, all without releasing air from the foam FORMULATION STEPS Formulation Steps – Where to Start Common Processing Steps Processing Method Definition Example(s) Baked Products: Cutting In Process of cutting solid fats (generally mixed with flour) into Biscuits and Pastry small pieces using a pastry blender Kneading Folding over a ball of dough and pressing it with either the Biscuits, Yeast Bread, fingertips or the heels of both hands, depending upon the Pizza Crust amount of gluten needing to be developed and the ratio of ingredients FORMULATION STEPS Formulation Steps – Where to Start Common Processing Steps Processing Method Definition Example(s) Size Reduction: Cutting/ Chopping Reducing the size of an ingredient to medium to small Fruits, Vegetables, Nuts pieces Grinding/ Milling Reducing the size of a typically dry ingredient to a very Grains, Nuts small piece or powder Blending Reducing the size and mixing ingredients together, typically Fruits, Vegetables, with a food processor or blender, to create a liquid or paste Juices, Nuts, Peanuts FORMULATION STEPS Formulation Steps – Where to Start Common Processing Steps Processing Method Definition Example(s) Shaping: Rolling/ Laminating Flattening a dough to a given thickness, potentially layering Pizza crust, Biscuits, dough and fat layers together to laminate for a flaky baked Fondant product Cutting or Pressing Using a set shape to form a dough, could use a cutter, Sugar cookies, Oreos, Shapes press, or pan Tortilla Extruding Pressing a dough or batter through a tube with a Pasta, Spritz cookies, specifically shaped opening Sausage & Hot Dogs FORMULATION STEPS Formulation Steps – Where to Start Common Processing Steps Processing Method Definition Example(s) Shaping: Molding Using a specific 3-D shape to form a coating and/or dough Candies with fillings such as peanut butter cups and peppermint patties Coating Adding a layer to the outside of a food; the layer could be Cheetos, Peanut Butter made up of dry ingredients, wet ingredients, or a melting Balls, M&Ms coating that will set upon cooling FORMULATION STEPS Formulation Steps – Where to Start Common Processing Steps Processing Method Definition Example(s) Mixing: Hydrating Mixing of ingredients with the main purpose of water Hydrocolloids, Leavening, hydrating dry ingredients to get functionality from the dry Gluten Development ingredients Shear / High-Speed Mixing of ingredients with the purpose of particle size Salad Dressings Mixing / Emulsifying reduction and/or emulsion formation Homogenizing Processing a liquid under pressure with the goal of particle Milk, Beverages size reduction to inhibit separation FORMULATION STEPS Formulation Steps – Where to Start Common Processing Steps Processing Method Definition Example(s) Water Separation: Dehydrating Removal of water from a food, typically slowly using heat Fruits & Vegetables and forced air Centrifuging Separation of particles based on density, often a liquid Fruit Purees separated from a semi-solid Physical Pressure Using physical pressure to squeeze out free water, often Cheese, Vegetables using cheesecloth Straining Using a filter to remove solids from a liquid. The filter size Apple Cider, Tea, Coffee affects the separation and can include cheesecloth and finer filter paper FORMULATION STEPS Formulation Steps – Where to Start Common Processing Steps Processing Method Definition Example(s) Physical/Chemical Reaction: Fermentation / Allowing beneficial bacteria, yeast, or enzymes to convert Sauer Kraut, Yogurt, Enzyme Reaction food through controlled breakdown, production of acid, Yeast Bread, Soy Sauce alcohol, and/or carbon dioxide Protein Coagulation Adding an enzyme, salt, acid, physical agitation, or heat to Cheese, Tofu, Egg White cause proteins to change shape and become less soluble Foams, Cooked Eggs, Cooked Meat FORMULATION STEPS Formulation Steps – Where to Start Common Processing Steps Processing Method Definition Example(s) Heating: Cooking Heating with a direct heat source, often with a liquid Soups, Gravies, Pudding present, typically on the stovetop in a conventional kitchen Baking Heating in an oven, typically referring to baked products Cookies, Brownies, Cake Roasting Heating in an oven, dry heat method Chicken, Nuts Frying Heating in liquid oil for efficient heat transfer French Fries, Chicken Nuggets, Funnel Cakes FORMULATION STEPS Formulation Steps – Where to Start Common Processing Steps Processing Method Definition Example(s) Cooling/Freezing Refrigerating Cooling a food product to under 40 degrees Fahrenheit Various Freezing Cooling a food product to ~ 0 degrees Fahrenheit, Various converting water to ice in the food, speed of freezing affects product quality FORMULATION STEPS Formulation Steps – Where to Start Next think about where the water is added or removed in the system and what ingredients need to be hydrated to function fully in the food (gums, gluten proteins, leavening, protein powders, etc.). Is there enough water in the system to hydrate all of the ingredients like in a beverage? Is there limited water in the system leading to minimal hydration of ingredients like in cookies (where often the only water is from the water in the eggs)? Is it somewhere in between? FORMULATION STEPS Formulation Steps – Where to Start Next think about where the water is added or removed in the system and what ingredients need to be hydrated to function fully in the food (gums, gluten proteins, leavening, protein powders, etc.). How much sugar and/or salt are in the system? Both pull water more than other ingredients. Consider if the water-containing ingredients need to be mixed with the dry ingredients that need the most hydration first. For instance, gums often need to be mixed with water first before being mixed with other ingredients. FORMULATION STEPS Formulation Steps – Where to Start Next think about where the water is added or removed in the system and what ingredients need to be hydrated to function fully in the food (gums, gluten proteins, leavening, protein powders, etc.). How much sugar and/or salt are in the system? Both pull water more than other ingredients. Consider if the water-containing ingredients need to be mixed with the dry ingredients that need the most hydration first. For instance, gums often need to be mixed with water first before being mixed with other ingredients. FORMULATION STEPS Flow Diagram Starting Point Once the processing steps have been determined and tested with parameters set (time, temperature, speed, etc.), your team will construct a flow diagram based on the processing steps (not necessarily the equipment). This is helpful as a transition step to processing to determine larger pieces of processing equipment. As an example, consider the processing steps needed to make a chocolate chip cookie. Here is a typical set of instructions for a chocolate chip cookie recipe modified from Nestle Toll House: Step 1. Preheat oven to 375° F. FORMULATION STEPS Flow Diagram Starting Point Step 2. Combine flour, baking powder, and salt in a small bowl. Beat butter, granulated sugar, and brown sugar in a large mixer bowl until creamy. Beat in eggs and vanilla extract. Gradually add in flour mixture. Stir in chocolate chips. Drop by rounded tablespoon onto ungreased baking sheets. Step 3. Bake for 9 to 11 minutes or until golden brown. Cool on baking sheets for 2 minutes; remove to wire racks to cool completely.