=Paper=
{{Paper
|id=Vol-2028/paper28
|storemode=property
|title=A Proposed General Formula to Create and Analyze Baking Recipes
|pdfUrl=https://ceur-ws.org/Vol-2028/paper28.pdf
|volume=Vol-2028
|authors=Michael Ohene
|dblpUrl=https://dblp.org/rec/conf/iccbr/Ohene17
}}
==A Proposed General Formula to Create and Analyze Baking Recipes==
https://ceur-ws.org/Vol-2028/paper28.pdf
245
A Proposed General Formula to Create and
Analyze Baking Recipes
Michael Ohene
EasierBaking,
1301 St. John Street, Apt. 206, Lafayette, LA 70506
michael@easierbaking.com
http://easierbaking.com
Abstract. A mathematical formula for characterizing baking recipes
is presented as part of the 2017 Computer Cooking Contest Open Chal-
lenge. The formula produces three characteristic values, which along with
common knowledge rules and classification, form the basis of two com-
puter applications: Random Recipe Generator, which creates recipes, and
Recipe Report Card, which analyzes recipes.
Keywords: recipe analysis, recipe creation, recipe classification
1 Introduction
The mystery of baking recipes has existed for many years despite many attempts
to discover a formula or set of rules to describe them [1], [2]. The discovery of
a universal formula or set of rules would, at least, form a basis for answering
key questions governing baking. Of particular interests are the abilities to create
custom recipes and to discover new uses for ingredients in baking. In lieu of a
universal formula, creating new recipes by adaptation remains popular, however,
this approach results in recipes limited by their reference recipe.
Adaptation has been formalized in research communities, where it involves
creating new recipes by the introduction of substitute ingredients [6], primarily in
a like-for-like relationship, and adaptation rules. The methods for substituting
ingredients have involved evaluating the validity of substitutions by a scoring
procedure [3] and by ingredient generalization through a cooking ontology [7].
This paper outlines an extended, generalized substitution process where any
ingredient is a candidate for substitution. The only restrictions are common
knowledge rules placed on baked good recipes (e.g., ”Cobbler must not contain
water”, ”Pie crust must contain water”). To avoid the tedious work alluded to
in [7], the scope of this procedure shall be limited to baked goods.
1.1 What is a Baking Recipe?
A baking recipe provides a list of ingredients and measurements, which includes
instructions for combining the ingredients. Each ingredient may be considered
Copyright © 2017 for this paper by its authors. Copying permitted for private and
academic purpose. In Proceedings of the ICCBR 2017 Workshops. Trondheim, Norway
246
2 Michael Ohene
either a wet ingredient, a dry ingredient, or semi-wet ingredient. In the following
procedure, first detailed in [5], wet and semi-wet ingredients are given constant
values (see Table 1), while flavorings, leavenings (e.g., baking powder, baking
soda, yeast, etc.), seasonings (e.g., salt), and food pieces (e.g., shredded coconut,
walnut pieces, sesame seeds, etc.) are ignored. The constant values are multiplied
by their respective measurements (usually in cups) to yield a numerical product.
The products are summed and finally divided by the dry ingredient product(s),
obtained from values in Table 1, to yield solutions called the moistness, fat, and
egg value [5]. These characteristic values (i.e., the moistness value, the fat value,
and the egg value) complete the characterization of baked good recipes.
Ingredients Value per Cup
Wet Ingredients
Water/Juice/Water/Milk 1
Butter/Oil 0.50
Banana 0.375
*Large egg (50 grams) 0.167, 1
Honey/Molasses 0.70
Dry Ingredients
Flour (all-purpose, cocoa powder, whole-wheat) 1
Old-fashioned rolled oats 0.50
Semi-wet Ingredients
Ground nuts (almond, pecans, walnuts) 0.33
Table 1. Constants for common wet, dry, and semi-wet ingredients. The large egg
constant does not use a per cup value. *Large eggs each have a value of 0.167 in
the moistness calculation and 1 in the egg calculation. Constants for common dry
ingredients.
Ingredients Measure Wet Value Dry Value
All-purpose flour, Cups(g) 21/2, (352g) — 2.50
Butter, Tbsp(g) 16, (224g) 0.50 —
Egg, #(g) 1, (50g) 0.167 —
Confectioner’s sugar, Cups(g) 11/2, (120g) — —
Vanilla extract, tsp(g) 1, (4g) — —
Almond extract, tsp(g) 1/2, (2g) — —
Salt, tsp(g) 1/2, (3g) — —
Baking soda, tsp(g) 1, (5g) — —
Cream of Tartar, tsp(g) 1, (5g) — —
Table 2. Mary’s Sugar Cookie recipe with moistness values. [12]
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Creation and Analysis of Baking Recipes 3
1Cup 1 1
16T bsp ∗ 16T bsp ∗ 2 + 1 ∗ 6
= 0.27 (1)
2.50 ∗ 1
Equation (1) shows the wet-over-dry ingredient equation used to calculate
the moistness value from Mary’s Sugar Cookie recipe in Table 2. A similar equa-
tion is used to calculate the fat value in equation (2), only using ingredients
that are considered fats. The egg value requires a number-of-eggs-per-cup-of-
dry-ingredients calculation shown in equation (3).
1Cup 1
16T bsp ∗ 16T bsp ∗ 2
= 0.20 (2)
2.50 ∗ 1
1
= 0.4 (3)
2.50 ∗ 1
The general linear equation
1
( )(q1 i1 + q2 i2 + ...qn in ) = y, y (4)
qn+1 in+1
defines baked goods through the use of characteristic values, where i is the
ingredient constant, q is the quantity, and n is the nth ingredient. The term y, y
refers to the numerical range in moistness, fat, or egg value of a baked good.
y represents the lower limit and y represents the upper limit of the numerical
range.
1.2 Knowledge Acquisition
To accurately define the numerical ranges corresponding to baked goods, the
acceptability of recipes and recipe reviews were considered. Instead of analyzing
the reliability of users as in [3], the sheer number of reviews and the selec-
tion of recipe-focused review sites - as opposed to blogger-focused review sites -
served to minimize unreliable reviews. The recipe review ratings and the ”make
it again” ratings served to define ”acceptability”. From this point the acceptable
linear equations were constructed from equation (4) to determine the unknown
constants.
From the collection of recipes, acceptable recipes tended fall within the pre-
defined numerical ranges, thereby satisfying equation (4). Unacceptable recipes
tended to fall outside the predefined numerical ranges of the baked goods. Ex-
ample deviations from these generalized numerical ranges for cakes are presented
in bold text in Table 3. By generalized, it is meant that the numerical range used
for cakes in Table 3 are aggregations of several independent numerical ranges
representing a variety of cakes (e.g., the egg value for pound cake only occupies
a portion of the 1.00-3.50 egg range).
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4 Michael Ohene
Characteristic Values Comments,
Recipes
Moistness Fat Egg (Exceptions)
Cakes 0.68-1.15 0.13-0.34 1.00-3.50
Basic 1-2-3-4 Layer 0.72 0.17 1.3
Devil’s Food 0.83 0.18 1.1
Glazed Lemon-Thyme 1.86 0.67 2.7
Glazed Lemon-Thyme (corrected) 0.8 0.27 1
Confetti 1.29 0.38 2.5 Possible bad recipe
Pineapple Curry 0.7 0.13 1
Classic Pound 0.87 0.29 2.3
Blueberry Cornmeal 1.05 0.16 1.3
German Chocolate 0.64 0.07 0.2 Possible bad recipe
Nejla’s Yogurt 0.6 0 1.5 (Sponge cake)
Italian Cream 0.75 0.21 1.6
Champagne 0.62 0.18 0.5 (Wedding cake)
Tasted Just Like Wedding 0.59 0.17 0.3 (Wedding cake)
Angel Bean Food 0.27 0 1.6 (Angel food cake)
Old-Fashioned Coconut 0.81 0.17 1.3
Peanut Butter and Chocolate Swirl 0.88 0.25 1.5
Pecan Crumble 0.89 0.13 1
Guinness Stout 1.00 0.1 0.9 Possible bad recipe
Vanilla Bean Angel Food 0.97 0 2 (Angel food cake)
Strawberry and Cream 0.81 0.25 1.3
Caramel 0.72 0.17 1.3
Meyer Lemon 0.98 0.21 1.1
Old-Fashioned Red Velvet 1.01 0.2 1.6
Spiced Crumb 0.96 0.17 1.3
Blood Orange 1.3 0.17 2
Blood Orange (corrected) 1.13 0.17 2
Hummingbird 0.77 0.17 1
Strawberry Buttermilk 1.01 0.13 1.3
Tres Leches 1.06 0.2 1.3
Rum-Soaked 0.86 0.22 3
Upside Down Chocolate 0.97 0.2 1.5
Cardamom Flourless 1.60 0.6 6 Possible bad recipe
Carrot 0.91 0.17 1.3
Pear Almond 0.52 0.25 1.5
Pear Almond (corrected) 0.67 0.25 1.5 Possible bad recipe
Table 3. Characteristic values from the 2016 bakeFromScratch Special Edition. The
bold values are the values that fall outside the numerical range for (cakes). Some
recipes, labeled (corrected ), were corrected in the online edition of the magazine after
receiving reader feedback. (Moistness) corresponds to the thinness of the batter. [10]
249
Creation and Analysis of Baking Recipes 5
2 Random Recipe Generator
Random Recipe Generator uses characteristic values to provide users with unique,
randomly generated recipes. The program simply converts characteristics values
to recipes.
The Random Recipe Generator functions using a clickable photo grid of baked
goods and two pull-down menus. The two pull-down menus allow users to choose
their ”Fat Level” and ”Sweetness”, by choosing between ”Low Fat”, ”Regular
Fat”, or ”High Fat” and ”Not too Sweet”, ”Sweet”, or ”Really Sweet”, respec-
tively [4].
2.1 Choosing Characteristic Values
The steps for choosing a random recipe are as follows:
1) A click by the user selects the numerical ranges that define a baked good.
2) From the user’s choice for fat level, the numerical range for fat, x2 , is chosen.
3) Once the numerical range for fat, x2 = [x2 , x2 ], is chosen, a random fat value,
x2 , is chosen and the other two values, the moistness value, x1 , and the egg
value, x3 , are chosen according to the value x2 . Specifically,
a second value, x3 , in the numerical range for eggs, x3 = [x3 , x3 ], is randomly
chosen, which in turn automatically sets the third value, x1
or
a second value, x3 , in the numerical range for eggs, x3 = [x3 , x3 ], is randomly
chosen, then a constant value is chosen such that a third value, x1 , lies within
[x1 , x1 ].
2.2 Converting the Characteristic Values into a Recipe
After the process of choosing characteristic values based on the user input occurs,
a base ingredient, i.e., an initial guess, is chosen, and the remaining ingredients
are then substituted into equation (4). The possible measurements for the in-
gredients are defined by values in the Random Recipe Generator’s database. In
addition to measurement limits, the database also contains predefined, ingre-
dient combinations. When equation (4)’s variables are replaced by quantities
and ingredient constants, there exists some distance/error between the original
random recipe’s characteristic value vector, x, and the substitution attempt’s
(adaptation’s) characteristic value vector, si , which can be calculated as the
Euclidean distance, equation (5).
q
d(x, si ) = (x1 − si,1 )2 + (x2 − si,2 )2 + (x3 − si,3 )2 . (5)
There are 1410 iterations, i, of the ingredient substitution process, producing
the distance values d(x, s1 ), ..., d(x, s1410 ). The ingredient substitution attempt
(adaptation) with the shortest distance, arg min d(x, si ), is selected and pre-
sented to the user.
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6 Michael Ohene
2.3 Discovering New Ingredient Uses
Case-based reasoning differs from Random Recipe Generator’s procedure, but it
would be erroneous to say the current procedure did not utilize a case base. In
fact, Random Recipe Generator relies upon a numerical abstraction of the recipe
case base mentioned in the Knowledge Acquisition section. This abstraction
helps to eliminate the detailed knowledge usually required to create recipes and
completely eliminates the need for recipe retrieval.
In addition, instead of a more detailed formal concept analysis (FCA) ap-
proach described in [9], the only additional information needed to create a recipe
is a generalized classification structure (e.g., whether an ingredient is a nut, egg,
dairy, dry ingredient, chocolate, etc.). In other words, any ingredient can be
added to the Random Recipe Generator database and incorporated into recipes
as long as its classification and ingredient constant are known. As an exam-
ple, Table 4 shows three recipes for chocolate chip cookies using peanut butter,
ground almonds, and all-purpose wheat flour.
Low Fat
Ingredients
Not Too Sweet Sweet Really Sweet
All-purpose flour, Cups(g) 13/4, (247g) 11/2, (211g) 21/4, (317g)
Ground almonds, Cups(g) 13/4, (210g) — —
Peanut butter, Cups(g) — — 1/2, (129g)
Butter, Tbsp(g) 14, (196g) 8, (112g) 10, (140g)
Egg, #(g) 2, (100g) 1, (50g) 2, (100g)
Egg yolk, #(g) — — —
Brown sugar, Cups(g) 2/3 Tbsp, (147g) 1/2, (110g) 1, (220g)
White sugar, Cups(g) 2/3, (133g) 1/2, (100g) 1, (200g)
3
Chocolate chips, Cups(g) 1 /4, (319g) 1, (182g) 2, (365g)
Vanilla extract, tsp(g) 13/4, (8g) 11/4, (5g) 13/4, (8g)
Salt, tsp(g) 1/2, (3g) 1/4,(2g) 1/2, (3g)
Baking soda, tsp(g) 3/4, (4g) 1/2, (2g) 3/4, (4g)
Table 4. Three chocolate chip cookie recipes. (Not Too Sweet) , (Sweet), and (Really
Sweet) correspond to low, normal, and high sweetness.
3 Recipe Report Card
A logical extension of the work in Table 4 is the development of a recipe analysis
tool. In this role, Recipe Report Card serves to create an alternative to the
traditional recipe review, i.e., to provide accurate, objective feedback for baking
recipes. The use of the Recipe Report Card creates baking recipes which can be
customized and prescreened. In addition, if the recipe’s characteristic values fall
within a predefined numerical range and satisfy common knowledge rules (e.g.,
”Brownie must contain chocolate”), the recipe is labeled and feedback about the
251
Creation and Analysis of Baking Recipes 7
recipe’s sweetness and flavor is provided to the user. The predefined numerical
ranges are approximated in Table 5.
4 Conclusion and Future Work
A proposed mathematical formula for baking recipes was shown capable of iden-
tifying unacceptable recipes. The results also produced logical mathematical
groupings of baked good recipes. Through the Random Recipe Generator, it
was shown that it is possible to generate different recipes from characteristic
values via ingredient constants.
The next task for both the Recipe Report Card and the Random Recipe Gen-
erator is to produce structured lists of baking recipes. Other areas of investigation
include the discovery of additional ingredient constants and the continued de-
velopment of the current mathematical formula to address dairy-based desserts
(e.g., ice cream, cheesecake, and custards).
References
1. Corriher, S.: Bakewise: The Hows and Whys of Successful Baking. Scribner, New
York (2008)
2. Ruhlman, M.: Ratio: The Simple Codes Behind the Craft of Everyday Cooking.
Scribner, New York (2010)
3. Wolf, K.I., Goetze, S., Wallhoff, F.: CooCo, What Can I Cook Today? Surprise Me.
Workshop Proceedings from the Twenty-Third International Conference on Case-
Based Reasoning. ICCBR 2015. Columbus (2015)
4. Ohene, M.: Michael Ohene’s Cookie Recipe Generator. Cooking
for Engineers, http://www.cookingforengineers.com/article/309/
Michael-Ohenes-Cookie-Recipe-Generator
5. Ohene, M.: Analyzing a Baking Recipe. Cooking for Engineers, http://www.
cookingforengineers.com/article/280/Analyzing-a-Baking-Recipe
6. Chef Watson, http://www.ibmchefwatson.com
7. Badra F., Cordier A., Lieber J.: Opportunistic Adaptation Knowledge Discovery. In:
McGinty L., Wilson D.C. (eds) Case-Based Reasoning Research and Development.
ICCBR 2009. LNCS, vol. 5650. Springer, Berlin, Heidelberg (2009)
8. Gaillard E., Lieber J., Nauer E.: Improving Ingredient Substitution Using Formal
Concept Analysis and Adaptation of Ingredient Quantities with Mixed Linear Op-
timization. Proceedings from the Twenty-Third International Conference on Case-
Based Reasoning. ICCBR 2015. Columbus (2015)
9. Gaillard E., Lieber J., Nauer E., Cordier A.: How Case-Based Reasoning on e-
Community Knowledge Can Be Improved Thanks to Knowledge Reliability. Case-
Based Reasoning Research and Development, Sep 2014, Cork, Ireland, Ireland. 8765,
155 - 169 (2014)
10. bakeFromScratch, http://www.bakefromscratch.com/
11. Megan’s Granola, http://allrecipes.com/recipe/98390/megans-granola/
12. Mary’s Sugar Cookies, http://allrecipes.com/recipe/23750/
marys-sugar-cookies/
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8 Michael Ohene
Fat Values
0.00-0.05 0.05-0.10 0.10-0.20 0.20-0.34
Egg Values
0-0.5 0.5-1.0 0-0.5 0.5-1.0 1.0-2.0 0-0.5 0.5-1.0 1.0-2.0 0-0.5 0.5-1.0 1.0-2.0 2.0-3.0
Moistness
0.00-0.05
0.05-0.10
0.10-0.15
0.15-0.20 streu
0.20-0.25 gran cook bcook cook
0.25-0.30 angel pnuss bisco cook cook bcook cook
0.30-0.35 bd chal bd bisco bisco cook cook bcook cook
0.35-0.40 bd chal bd bri bisco pie ging cook
0.40-0.45 cia chal sco king king cobb pie
0.45-0.50 nokn sco dane bri brown
0.50-0.55 biscu cobb bri croiss brown
0.55-0.60 cobb muff croiss
0.60-0.65 baba muff kuge
0.65-0.70 muff b.bd coff lbs
0.70-0.75 sav eng muff coffee lbs lbs
0.75-0.80 muff coffee lbs
0.80-0.85 cake tea lbs
0.85-0.90 cake apl lbs
0.90-0.95 cake ct
0.95-1.00 cake ct
1.00-1.05 cake ct
1.05-1.10
1.10-1.15
Table 5. Distribution of baked good characteristic values. The following abbreviations
were used. (angel ) - angel food cake; (apl ) - apple cake; (baba) - baba al rhum; (b.bd ) -
banana bread; (bisco) - biscotti; (biscu) - biscuit; (bd ) - bread; (bri) - brioche; (chal ) -
challah; (cia) - ciabatta; (ct) - carrot cake; (coff ) - coffee cake; (dane) - danish; (cobb)
- cobbler; (ging) - gingerbread; (gran) - granola; (king) - king cake; (kuge) - kugelhof ;
(muff ) - muffin; (nokn) - no knead bread; (eng) - Old English cake; (pie) - pie crust;
(pnuss) - pfeffernuesse; (lbs) - pound cake; (sav ) -savarin; (sco) - scone; (streu) -
streusel; (tea) - tea cake; (bcook ) signifies pate brisee, butter cookies, Mexican wedding
cookies, k’ak, nuhood al-adhraa, and other eggless cookies. (cook ) signifies chocolate
chip cookies, oatmeal cookies, snickerdoodles, and other cookies that contain eggs.