Age (low) Age (high) Males AME Female AME
--------- ---------- --------- ----------
6 months < 1 0.22 0.22
1 2 0.31 0.28
2 3 0.37 0.34
3 4 0.41 0.38
4 5 0.44 0.41
5 6 0.48 0.43
6 7 0.52 0.47
7 8 0.56 0.51
8 9 0.60 0.56
9 10 0.65 0.61
10 11 0.70 0.66
11 12 0.77 0.70
12 13 0.84 0.75
13 14 0.91 0.78
14 15 0.98 0.80
15 16 1.04 0.82
16 17 1.09 0.82
17 18 1.11 0.82
18 30 1.00 0.79
30 60 0.97 0.77
60 > 60 0.80 0.69
--------- ---------- --------- ----------
Each person in the household is assigned an energy requirement based on a table of AMEs (as above). This individual AME is constructed by taking the ratio of their requirement to that of an adult male age 18-30 years. This is done for all members of the household. The individual AMEs are summed to give the total household AME.
Each person’s share is calculated as the ratio of their individual AME to the total household AME. For example, if you have 200g of wheat flour to divided over all HH members and your total HH AMEs sum (e.g.) to 6.5 then an adult male aged 18-30 years would get (1.0 / 6.5) * 200 or 30.8g while a boy aged four years would get (0.44 / 6.5) * 200 or 13.5g.
You can then use food tables to estimate individual energy intake from a given food. The idea can be extended to other nutrients / micronutrients.
I hope this is of some use.Answered:
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9 years agoMany thanks
Could I have with you the macros for estimating macronutrients and microtriments, or the optifood software?
Answered:
11 months agoMacros for estimating macronutrients and micronutrients
The following macros can be used to estimate macronutrients and micronutrients from food composition data:
Python
import numpy as np def estimate_macronutrients(food_composition_data): """Estimates macronutrients from food composition data. Args: food_composition_data: A NumPy array containing the food composition data, with the following columns: * food: Name of the food. * calories: Calories per 100 grams. * protein: Protein per 100 grams (grams). * fat: Fat per 100 grams (grams). * carbohydrates: Carbohydrates per 100 grams (grams). Returns: A NumPy array containing the estimated macronutrient intake, with the following columns: * food: Name of the food. * protein_intake: Protein intake (grams). * fat_intake: Fat intake (grams). * carbohydrate_intake: Carbohydrate intake (grams). """ protein_intake = food_composition_data['protein'] * food_composition_data['serving_size'] / 100 fat_intake = food_composition_data['fat'] * food_composition_data['serving_size'] / 100 carbohydrate_intake = food_composition_data['carbohydrates'] * food_composition_data['serving_size'] / 100 estimated_macronutrients = np.c_[food_composition_data['food'], protein_intake, fat_intake, carbohydrate_intake] return estimated_macronutrients def estimate_micronutrients(food_composition_data): """Estimates micronutrients from food composition data. Args: food_composition_data: A NumPy array containing the food composition data, with the following columns: * food: Name of the food. * micronutrients: A dictionary of micronutrient values, with the following keys: * vitamin_a: Vitamin A (IU). * vitamin_c: Vitamin C (mg). * vitamin_d: Vitamin D (IU). * vitamin_e: Vitamin E (mg). * calcium: Calcium (mg). * iron: Iron (mg). * potassium: Potassium (mg). * sodium: Sodium (mg). Returns: A NumPy array containing the estimated micronutrient intake, with the following columns: * food: Name of the food. * micronutrient_intake: A dictionary of micronutrient intake values, with the following keys: * vitamin_a: Vitamin A intake (IU). * vitamin_c: Vitamin C intake (mg). * vitamin_d: Vitamin D intake (IU). * vitamin_e: Vitamin E intake (mg). * calcium: Calcium intake (mg). * iron: Iron intake (mg). * potassium: Potassium intake (mg). * sodium: Sodium intake (mg). """ micronutrient_intake = {} for micronutrient in food_composition_data['micronutrients']: micronutrient_intake[micronutrient] = food_composition_data['micronutrients'][micronutrient] * food_composition_data['serving_size'] / 100 estimated_micronutrients = np.c_[food_composition_data['food'], micronutrient_intake] return estimated_micronutrients
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To use these macros, you would first need to create a NumPy array containing the food composition data. The food composition data can be obtained from a variety of sources, such as the USDA Food Composition Database. Once you have created the food composition data array, you can call the estimate_macronutrients()
or estimate_micronutrients()
macro to estimate the macronutrient or micronutrient intake, respectively.
For example, the following code shows how to use the estimate_macronutrients()
macro to estimate the macronutrient intake from a simple food composition data array:
Python
food_composition_data = np.array([ ['Apple', 52, 0.3, 0.25, 13.8], ['Banana', 89, 1.1, 0.3, 22.8], ['Orange', 47, 1, 0.2, 11.4] ]) estimated_macronutrients = estimate_macronutrients(food_composition_data) print(
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I think this some help.
Answered:
11 months agoI Really like your this post this post is very helpfull for me beause it gives me great help in my work
Answered:
11 months ago