Recipe/Example/Cake
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Product
A plain cake (pound cake) with four ingredients, like this one. Or the category Pound cakes.
Data
Four scenarios:
Nutrient | Product | Ciqual Cake | Median Cake |
---|---|---|---|
Fat | 24 | 25.6 | 23 |
Sat. Fat | 15 | 15.8 | 7.4 |
Carbohydrates | 48 | 46 | 49 |
Sugars | 29 | 26.8 | 28 |
Fiber | 2 | 1.1 | 1.2 |
Proteins | 6 | 5.94 | 5.8 |
Salt | 1.4 | 0.52 | 0.8 |
For easy reuse:
Product (24,15,48,29,2,6,1.4) Ciqual (25.6,15.8,46,26.8,1.1,5.94,0.52) Median (23,7.4,49,28,1.2,5.8,0.8)
The nutritional values for the Ciqual ingredients.
Nutrient | Flour | Sugar | Egg | Butter |
---|---|---|---|---|
Fat | - | - | 9.83 | 82.9 |
Sat. Fat | - | - | 2.64 | 55.4 |
Carbohydrates | - | - | 0.27 | 0.9 |
Sugars | - | - | 0.27 | 0.83 |
Fiber | - | - | 0 | 0 |
Proteins | - | - | 12.7 | 0.7 |
Salt | - | - | 0.31 | 0.063 |
Eggs: (9.83,2.64,0.27,0.27,0,12.7,0.31) Butter: (82.9,55.4,0.90.83,0,0.7,0.063)
Least squares fit
R studio
The table below present four fit results using this. This compares the Ciqual ingredients against the OFF median ingredients. Four different least square methods have been used. the nnls and the glmnet force a solution with positive values.
model | Flour | Sugar | Eggs | Butter |
---|---|---|---|---|
lm | - | - | - | - |
lm forced intercept | - | - | - | - |
nnls | - | - | - | - |
glmnet | - | - | - | - |