Could I please get help with this maple assignment? No idea where to even begin!!
# Diet Problems
# Text Reference: Section 1.10, p. 93
# The purpose of this set of exercises is to provide examples of vector
# equations which result from balancing nutrients in a diet.
# Section 1.10 shows how use a vector equation
#
> x[1]# a
> ``[1]# +
> x[2]# a
> ``[2]# + ... +
> x[n]# a
> ``[n]# = b
# to model a diet with a specified nutritional intake. Each vector a
> ``[i]# lists the nutrient composition of one unit (usually 100 grams)
# of foodstuff, and the corresponding weight
> x[i]# is the variable that represents the amount (number of units) of
# that foodstuff to be used in the diet. The vector b lists the amount
# of each nutrient that must be in the diet.
# Table 2 below is a listing of the nutritional value of many foods
# found in a typical kitchen. The nutrients are given per 100 grams of
# foodstuff. This data is taken from the USDA Nutrient Database for
# Standard Reference available at the United States Department of
# Agriculture website, whose web address is
# http://www.nal.usda.gov/fnic/foodcomp. The columns represent
# respectively the following foodstuffs: beef, brussels sprouts,
# carrots, chicken soup, egg, feta cheese, grapefruit, lentils, lettuce,
# milk, mushrooms, oil, onion, rice, salad dressing, salmon, soy sauce,
# spinach, tomato, and vanilla ice cream. Table 1 gives the standard
# serving size for each of these foodstuffs, and also gives a key to the
# columns in Table 2.
> T1 := matrix(
> [[`Key Number`, `Foodstuff`, `Serving Size`],
> [ 1, `Beef`, `6 oz. = 170 g`],
> [ 2, `Brussels Sprouts`, `1/2 cup = 78 g`],
> [ 3, `Carrots`, `1\ carrot = 61 g`],
> [ 4, `Chicken Soup`, `1 cup = 240 g`],
> [ 5, `Egg`, `1 egg = 61 g`],
> [ 6, `Feta Cheese`, `1/4 cup = 38 g`],
> [ 7, `Grapefruit`, `1/2 fruit = 123 g`],
> [ 8, `Lentils`, `1 cup = 198 g`],
> [ 9, `Lettuce`, `1/2 cup = 28 g`],
> [10, `Milk`, `1 cup = 244 g`],
> [11, `Mushrooms`, `1/2 cup = 35 g`],
> [12, `Oil`, `1 Tbsp. = 13.5 g`],
> [13, `Onion`, `1 onion = 110 g`],
> [14, `Rice`, `1 cup = 158 g`],
> [15, `Salad Dressing`, `1 cup = 250 g`],
> [16, `Salmon`, `1/2 fillet = 124 g`],
> [17, `Soy Sauce`, `1 Tbsp. = 18 g`],
> [18, `Spinach`, `1 cup = 180\ g`],
> [19, `Tomato`, `1 tomato = 123 g`],
> [20, `Vanilla Ice Cream`, `1/2 cup = 66 g`] ] ):
> evalm( T1 );
[Key Number Foodstuff Serving Size ]
[ ]
[ 1 Beef 6 oz. = 170 g ]
[ ]
[ 2 Brussels Sprouts 1/2 cup = 78 g ]
[ ]
[ 3 Carrots 1carrot = 61 g ]
[ ]
[ 4 Chicken Soup 1 cup = 240 g ]
[ ]
[ 5 Egg 1 egg = 61 g ]
[ ]
[ 6 Feta Cheese 1/4 cup = 38 g ]
[ ]
[ 7 Grapefruit 1/2 fruit = 123 g ]
[ ]
[ 8 Lentils 1 cup = 198 g ]
[ ]
[ 9 Lettuce 1/2 cup = 28 g ]
[ ]
[ 10 Milk 1 cup = 244 g ]
[ ]
[ 11 Mushrooms 1/2 cup = 35 g ]
[ ]
[ 12 Oil 1 Tbsp. = 13.5 g ]
[ ]
[ 13 Onion 1 onion = 110 g ]
[ ]
[ 14 Rice 1 cup = 158 g ]
[ ]
[ 15 Salad Dressing 1 cup = 250 g ]
[ ]
[ 16 Salmon 1/2 fillet = 124 g]
[ ]
[ 17 Soy Sauce 1 Tbsp. = 18 g ]
[ ]
[ 18 Spinach 1 cup = 180g ]
[ ]
[ 19 Tomato 1 tomato = 123 g ]
[ ]
[ 20 Vanilla Ice Cream 1/2 cup = 66 g ]
# Table 1: Serving Sizes of Various Foodstuffs
> C := vector( [`Nutrient`] ):
> N := vector(
> [`Calories (kcal)`,
> `Protein (g)`,
> `Fat (g)`,
> `Carbohydrates (g)`,
> `Calcium (mg)`,
> `Iron (mg)`,
> `Magnesium (mg)`,
> `Phosphorus (mg)`,
> `Potassium (mg)`,
> `Sodium ((mg)`,
> `Zinc (mg)`,
> `Copper (mcg)`,
> `Vitamin C (mg)`,
> `Thiamine (mg)`,
> `Riboflavin (mg)`,
> `Niacin (mg)`,
> `Pantothenic Acid (mg)`,
> `Vitamin B6 (mg)`,
> `Vitamin B12 (mcg)`,
> `Vitamin A (IU)` ] ):
> FSa := matrix( 1, 10, [[$1..10]] ):
> FSb := matrix( 1, 10, [[$11..20]] ):
> NFa := matrix(
> [ [215, 39, 43, 73, 152, 263, 30, 116, 14, 61.44],
> [26, 2.55, 1.03, 5.3, 10.33, 14.2, .55, 9.02, 1.62, 3.29],
> [11.5, .51, .19, 2.5, 11.44, 21.3, .1, .38, .2, 3.34],
> [0, 8.6, 10.1, 7.1, 1.04, 4.09, 7.68, 20.14, 2.37, 4.66],
> [7, 36, 27, 10, 42, 492.5, 11, 19, 36, 119.4],
> [3.1, 1.2, .5, .6, 1.19, .65, .12, 3.33, 1.1, .05],
> [27, 20, 15, 4, 9, 19.2, 8, 36, 6, 13.44],
> [211, 56, 44, 30, 148, 337, 9, 180, 45, 93.4],
> [367, 317, 323, 45, 101, 61.8, 129, 369, 290, 151.5],
> [69, 21, 35, 354, 270, 1116, 0, 2, 8, 49],
> [5290, .33, .2, .4, .92, 2.88, .07, 1.27, .25, .38],
> [.143, .083, .047, .1, .013, .032, .044, .251, .037, .01],
> [0, 62, 9.3, 0, 0, 0, 38.1, 1.5, 24, .94],
> [.11, .107, .097, .03, .044, .154, .034, .169, .1, .038],
> [.25, .08, .059, .07, .399, .844, .02, .073, .1, .162],
> [4.63, .607, .928, 1.8, .058, .991, .191, 1.06, .5, .084],
> [.34, .252, .197, 15, .934, .967, .283, .638, .17, .314],
> [.4, .178, 147, .02, .109, .424, .042, .178, .047, .042],
> [2.27, 0, 0, .13, .7, 1.69, 0, 0, 0, .357],
> [0, 719, 28129, 509, 654, 447, 259, 8, 2600, 126] ] ):
> NFb := matrix(
> [ [25, 884, 38, 130, 448.8, 149, 60, 23, 21, 201],
> [2.09, 0, 1.16, 2.69, 0, 25.56, 10.51, 2.9, .85, 3.5],
> [.42, 100, .16, .28, 50.1, 4.42, .1, .26, .33, 11],
> [4.65, 1, 8.63, 28.17, 2.5, 0, 5.57, 3.75, 4.64, 23.6],
> [5, .18, 20, 10, 0, 17, 20, 136, 5, 128],
> [1.24, .38, .22, 1.2, 0, .99, 2.38, 3.57, .45, .09],
> [10, .01, 10, 12, 0, 33, 40, 87, 11, 14],
> [104, 1.22, 33, 43, 0, 295, 130, 56, 24, 105],
> [370, 0, 157, 35, 7.5, 414, 212, 466, 222, 199],
> [.4, .04, 3, 1, .5, 86, 5586, 70, 9, 80],
> [.73, .06, .19, .49, 0, .71, .43, .76, .09, .69],
> [.492, 0, .06, .069, 0, .099, .135, .174, .074, .023],
> [3.5, 0, 6.4, 0, 0, 0, 0, 9.8, 19.1, .6],
> [.102, 0, .042, .163, 0, .196, .059, .095, .059, .041],
> [.449, 0, .02, .013, 0, .073, .152, .236, .048, .24],
> [40116, 0, .148, 1.476, 0, 8.526, 3.951, .49, .628, .116],
> [2.2, 0, .106, .39, 0, .865, .376, .145, .247, .581],
> [.097, 0, .116, .093, 0, .231, .2, .242, .08, .048],
> [0, 0, 0, 0, 0, 3.46, 0, 0, 0, .39],
> [0, 0, 0, 0, 0, 136, 0, 8190, 623, 409] ] ):
> T2a := blockmatrix( 2,2, [ C, FSa, N, NFa ] ):
> T2b := blockmatrix( 2,2, [ C, FSb, N, NFb ] ):
> evalm( T2a ); evalm( T2b );
[Nutrient , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10]
[Calories (kcal) , 215 , 39 , 43 , 73 , 152 , 263 , 30 , 116
, 14 , 61.44]
[Protein (g) , 26 , 2.55 , 1.03 , 5.3 , 10.33 , 14.2 , 0.55 ,
9.02 , 1.62 , 3.29]
[Fat (g) , 11.5 , 0.51 , 0.19 , 2.5 , 11.44 , 21.3 , 0.1 ,
0.38 , 0.2 , 3.34]
[Carbohydrates (g) , 0 , 8.6 , 10.1 , 7.1 , 1.04 , 4.09 ,
7.68 , 20.14 , 2.37 , 4.66]
[Calcium (mg) , 7 , 36 , 27 , 10 , 42 , 492.5 , 11 , 19 , 36
, 119.4]
[Iron (mg) , 3.1 , 1.2 , 0.5 , 0.6 , 1.19 , 0.65 , 0.12 ,
3.33 , 1.1 , 0.05]
[Magnesium (mg) , 27 , 20 , 15 , 4 , 9 , 19.2 , 8 , 36 , 6 ,
13.44]
[Phosphorus (mg) , 211 , 56 , 44 , 30 , 148 , 337 , 9 , 180 ,
45 , 93.4]
[Potassium (mg) , 367 , 317 , 323 , 45 , 101 , 61.8 , 129 ,
369 , 290 , 151.5]
[Sodium ((mg) , 69 , 21 , 35 , 354 , 270 , 1116 , 0 , 2 , 8 ,
49]
[Zinc (mg) , 5290 , 0.33 , 0.2 , 0.4 , 0.92 , 2.88 , 0.07 ,
1.27 , 0.25 , 0.38]
[Copper (mcg) , 0.143 , 0.083 , 0.047 , 0.1 , 0.013 , 0.032 ,
0.044 , 0.251 , 0.037 , 0.01]
[Vitamin C (mg) , 0 , 62 , 9.3 , 0 , 0 , 0 , 38.1 , 1.5 , 24
, 0.94]
[Thiamine (mg) , 0.11 , 0.107 , 0.097 , 0.03 , 0.044 , 0.154
, 0.034 , 0.169 , 0.1 , 0.038]
[Riboflavin (mg) , 0.25 , 0.08 , 0.059 , 0.07 , 0.399 , 0.844
, 0.02 , 0.073 , 0.1 , 0.162]
[Niacin (mg) , 4.63 , 0.607 , 0.928 , 1.8 , 0.058 , 0.991 ,
0.191 , 1.06 , 0.5 , 0.084]
[Pantothenic Acid (mg) , 0.34 , 0.252 , 0.197 , 15 , 0.934 ,
0.967 , 0.283 , 0.638 , 0.17 , 0.314]
[Vitamin B6 (mg) , 0.4 , 0.178 , 147 , 0.02 , 0.109 , 0.424 ,
0.042 , 0.178 , 0.047 , 0.042]
[Vitamin B12 (mcg) , 2.27 , 0 , 0 , 0.13 , 0.7 , 1.69 , 0 , 0
, 0 , 0.357]
[Vitamin A (IU) , 0 , 719 , 28129 , 509 , 654 , 447 , 259 , 8
, 2600 , 126]
[Nutrient , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20]
[Calories (kcal) , 25 , 884 , 38 , 130 , 448.8 , 149 , 60 ,
23 , 21 , 201]
[Protein (g) , 2.09 , 0 , 1.16 , 2.69 , 0 , 25.56 , 10.51 ,
2.9 , 0.85 , 3.5]
[Fat (g) , 0.42 , 100 , 0.16 , 0.28 , 50.1 , 4.42 , 0.1 ,
0.26 , 0.33 , 11]
[Carbohydrates (g) , 4.65 , 1 , 8.63 , 28.17 , 2.5 , 0 , 5.57
, 3.75 , 4.64 , 23.6]
[Calcium (mg) , 5 , 0.18 , 20 , 10 , 0 , 17 , 20 , 136 , 5 ,
128]
[Iron (mg) , 1.24 , 0.38 , 0.22 , 1.2 , 0 , 0.99 , 2.38 ,
3.57 , 0.45 , 0.09]
[Magnesium (mg) , 10 , 0.01 , 10 , 12 , 0 , 33 , 40 , 87 , 11
, 14]
[Phosphorus (mg) , 104 , 1.22 , 33 , 43 , 0 , 295 , 130 , 56
, 24 , 105]
[Potassium (mg) , 370 , 0 , 157 , 35 , 7.5 , 414 , 212 , 466
, 222 , 199]
[Sodium ((mg) , 0.4 , 0.04 , 3 , 1 , 0.5 , 86 , 5586 , 70 , 9
, 80]
[Zinc (mg) , 0.73 , 0.06 , 0.19 , 0.49 , 0 , 0.71 , 0.43 ,
0.76 , 0.09 , 0.69]
[Copper (mcg) , 0.492 , 0 , 0.06 , 0.069 , 0 , 0.099 , 0.135
, 0.174 , 0.074 , 0.023]
[Vitamin C (mg) , 3.5 , 0 , 6.4 , 0 , 0 , 0 , 0 , 9.8 , 19.1
, 0.6]
[Thiamine (mg) , 0.102 , 0 , 0.042 , 0.163 , 0 , 0.196 ,
0.059 , 0.095 , 0.059 , 0.041]
[Riboflavin (mg) , 0.449 , 0 , 0.02 , 0.013 , 0 , 0.073 ,
0.152 , 0.236 , 0.048 , 0.24]
[Niacin (mg) , 40116 , 0 , 0.148 , 1.476 , 0 , 8.526 , 3.951
, 0.49 , 0.628 , 0.116]
[Pantothenic Acid (mg) , 2.2 , 0 , 0.106 , 0.39 , 0 , 0.865 ,
0.376 , 0.145 , 0.247 , 0.581]
[Vitamin B6 (mg) , 0.097 , 0 , 0.116 , 0.093 , 0 , 0.231 ,
0.2 , 0.242 , 0.08 , 0.048]
[Vitamin B12 (mcg) , 0 , 0 , 0 , 0 , 0 , 3.46 , 0 , 0 , 0 ,
0.39]
[Vitamin A (IU) , 0 , 0 , 0 , 0 , 0 , 136 , 0 , 8190 , 623 ,
409]
# Table 2: Nutritional Values of Various Foods per 100 g of Foodstuff
# Questions:
# 1.
# Low carbohydrate diets are popular for weight loss. Compute (by hand)
# the amount of carbohydrates in each of the following dishes, and
# determine which would be better for such a dieter to choose. You will
# first need to use Table 1 to convert the kitchen measures into 100
# gram units, then use Table 2 to find the amount of carbohydrates in
# each ingredient.
>
# Spinach omelet: 1/4 cup spinach, 2 eggs, 1/8 cup milk, 1/2 Tbsp. oil
>
# Greek salad: 1 cup lettuce, 1/4 cup feta cheese, 1/2 of a tomato, 1/8
# cup salad dressing
>
# 2.
# To make a stir fry, fry beef and onions in a wok with oil, and top it
# with soy sauce.
# (a)
# You would like to make a stir fry with a total of 6 g carbohydrates,
# 50 g protein, and 3.5 mg vitamin C, and you'd like this dish to
# contain only 575 calories. Use Table 2 to set up a matrix equation
# which could be used to determine whether it is possible to make such a
# stir fry. Describe the steps you take to produce the vectors in the
# equation.
>
# (b)
# Find a precise recipe for the stir fry in part a). Convert your
# amounts to common kitchen measures using Table 1.
>
>
# 3.
# Table 2 has been incorporated into the matrix A.
# A := matrix( 20, 20,
# [[215, 39, 43, 73, 152, 263, 30, 116, 14, 61.44, 25, 884, 38, 130,
# 448.8, 149, 60, 23, 21, 201],
# [26, 2.55, 1.03, 5.3, 10.33, 14.2, 0.55, 9.02, 1.62, 3.29, 2.09, 0,
# 1.16, 2.69, 0, 25.56, 10.51, 2.9, 0.85, 3.5],
# [11.5, 0.51, 0.19, 2.5, 11.44, 21.3, 0.1, 0.38, 0.2, 3.34, 0.42, 100,
# 0.16, 0.28, 50.1, 4.42, 0.1, 0.26, 0.33, 11],
# [0, 8.6, 10.1, 7.1, 1.04, 4.09, 7.68, 20.14, 2.37, 4.66, 4.65, 0,
# 8.63, 28.17, 2.5, 0, 5.57, 3.75, 4.64, 23.6],
# [7, 36, 27, 10, 42, 492.5, 11, 19, 36, 119.4, 5, 0.18, 20, 10, 0, 17,
# 20, 136, 5, 128],
# [3.1, 1.2, 0.5, 0.6, 1.19, 0.65, 0.12, 3.33, 1.1, 0.05, 1.24, 0.38,
# 0.22, 1.2, 0, 0.99, 2.38, 3.57, 0.45, 0.09],
# [27, 20, 15, 4, 9, 19.2, 8, 36, 6, 13.44, 10, 0.01, 10, 12, 0, 33,
# 40, 87, 11, 14],
# [211, 56, 44, 30, 148, 337, 9, 180, 45, 93.4, 104, 1.22, 33, 43, 0,
# 295, 130, 56, 24, 105],
# [367, 317, 323, 45, 101, 61.8, 129, 369, 290, 151.5, 370, 0, 157, 35,
# 7.5, 414, 212, 466, 222, 199],
# [69, 21, 35, 354, 270, 1116, 0, 2, 8, 49, 0.4, 0.04, 3, 1, 0.5, 86,
# 5586, 70, 9, 80],
# [5290, 0.33, 0.2, 0.4, 0.92, 2.88, 0.07, 1.27, 0.25, 0.38, 0.73,
# 0.06, 0.19, 0.49, 0, 0.71, 0.43, 0.76, 0.09, 0.69],
# [0.143, 0.083, 0.047, 0.1, 0.013, 0.032, 0.044, 0.251, 0.037, 0.01,
# 0.492, 0, 0.06, 0.069, 0, 0.099, 0.135, 0.174, 0.074, 0.023],
# [0, 62, 9.3, 0, 0, 0, 38.1, 1.5, 24, 0.94, 3.5, 0, 6.4, 0, 0, 0, 0,
# 9.8, 19.1, 0.6],
# [0.11, 0.107, 0.097, 0.03, 0.044, 0.154, 0.034, 0.169, 0.1, 0.038,
# 0.102, 0, 0.042, 0.163, 0, 0.196, 0.059, 0.095, 0.059, 0.041],
# [0.25, 0.08, 0.059, 0.07, 0.399, 0.844, 0.02, 0.073, 0.1, 0.162,
# 0.449, 0, 0.02, 0.013, 0, 0.073, 0.152, 0.236, 0.048, 0.24],
# [4.63, 0.607, 0.928, 1.8, 0.058, 0.991, 0.191, 1.06, 0.5, 0.084,
# 40116, 0, 0.148, 1.476, 0, 8.526, 3.951, 0.49, 0.628, 0.116],
# [0.34, 0.252, 0.197, 15, 0.934, 0.967, 0.283, 0.638, 0.17, 0.314,
# 2.2, 0, 0.106, 0.39, 0, 0.865, 0.376, 0.145, 0.247, 0.581],
# [0.4, 0.178, 147, 0.02, 0.109, 0.424, 0.042, 0.178, 0.047, 0.042,
# 0.097, 0, 0.116, 0.093, 0, 0.231, 0.2, 0.242, 0.08, 0.048],
# [2.27, 0, 0, 0.13, 0.7, 1.69, 0, 0, 0, 0.357, 0, 0, 0, 0, 0, 3.46, 0,
# 0, 0, 0.39],
# [0, 719, 28129, 509, 654, 447, 259, 8, 2600, 126, 0, 0, 0, 0, 0, 136,
# 0, 8190, 623, 409]] );
>
# What does the
> j^(`th`)# column in this matrix represent? Which entry in this
# matrix tells you how much vitamin C is found in 100 g of vanilla ice
# cream?
>
# 4.
# A particularly math-savvy sumo wrestler wanted to adhere to a strict
# diet to maintain his weight and strength. Table 3 lists his desired
# nutritional intake for one day. The entries in Table 3 are stored in
# the vector \!\(TraditionalForm\`v\_1\) which follows this exercise.
# Using Table 2 he was able to decide on an optimal diet to give him
# this combination of nutrients. How much of each of the above foods
# were in his diet?
> H := vector( [`Amount`] ):
> S := vector( [`8279.12 kcal`,
> `608.81 g`,
> `387.6 g`,
> `604.48 g`,
> `4067.42 mg`,
> `93.34 mg`,
> `1714.73 mg`,
> `8488.03 mg`,
> `18023.48 mg`,
> `8846.38 mg`,
> `36009.75 mg`,
> `6.67 mcg`,
> `604.06 mg`,
> `6.77 mg`,
> `10.61 mg`,
> `28212.10 mg`,
> `103.11 mg`,
> `189.81 mg`,
> `51.78 mcg`,
> `95382.93 IU`] ):
> T3 := blockmatrix( 2, 2, [ C, H, N, S ] ):
> evalm( T3 );
[ Nutrient Amount ]
[ ]
[ Calories (kcal) 8279.12 kcal]
[ ]
[ Protein (g) 608.81 g ]
[ ]
[ Fat (g) 387.6 g ]
[ ]
[ Carbohydrates (g) 604.48 g ]
[ ]
[ Calcium (mg) 4067.42 mg ]
[ ]
[ Iron (mg) 93.34 mg ]
[ ]
[ Magnesium (mg) 1714.73 mg ]
[ ]
[ Phosphorus (mg) 8488.03 mg ]
[ ]
[ Potassium (mg) 18023.48 mg ]
[ ]
[ Sodium ((mg) 8846.38 mg ]
[ ]
[ Zinc (mg) 36009.75 mg ]
[ ]
[ Copper (mcg) 6.67 mcg ]
[ ]
[ Vitamin C (mg) 604.06 mg ]
[ ]
[ Thiamine (mg) 6.77 mg ]
[ ]
[ Riboflavin (mg) 10.61 mg ]
[ ]
[ Niacin (mg) 28212.10 mg ]
[ ]
[Pantothenic Acid (mg) 103.11 mg ]
[ ]
[ Vitamin B6 (mg) 189.81 mg ]
[ ]
[ Vitamin B12 (mcg) 51.78 mcg ]
[ ]
[ Vitamin A (IU) 95382.93 IU ]
# Table 3: Sumo Wrestler Diet
>
# The numerical entries from Table 3 can be entered into a Maple session
# with the command
# v1 := vector( [8279.12, 608.81, 387.6, 604.48, 4067.42, 93.34,
# 1714.73, 8488.03, 18023.48, 8846.38
# 36009.75, 6.67, 604.06, 6.77, 10.61, 28212.10,
# 103.11, 189.81, 51.78, 95382.93] );
>
# 5.
# The United States Food and Drug Administration (FDA) provides
# Recommended Daily Values for use on food labels.Table 4 gives the
# FDA's recommendations, which are also stored in the vector
# \!\(TraditionalForm\`v\_2\) which follows this exercise. Is it
# possible to combine the foods from the table to approximate these
# nutritional values?
> R := vector([`2000 kcal`,
> `50 g`,
> `65 g`,
> `300 g`,
> `1000 mg`,
> `18 mg`,
> `400 mg`,
> `1000 mg`,
> `3500 mg`,
> `2400 mg`,
> `15 mg`,
> `2 mcg`,
> `60 mg`,
> `1.5 mg`,
> `1.7 mg`,
> `20 mg`,
> `10 mg`,
> `2 mg`,
> `6 mcg`,
> `5000 IU`] ):
> T4 := blockmatrix( 2, 2, [ C, H, N, R ] ):
> evalm( T4 );
[ Nutrient Amount ]
[ ]
[ Calories (kcal) 2000 kcal]
[ ]
[ Protein (g) 50 g ]
[ ]
[ Fat (g) 65 g ]
[ ]
[ Carbohydrates (g) 300 g ]
[ ]
[ Calcium (mg) 1000 mg ]
[ ]
[ Iron (mg) 18 mg ]
[ ]
[ Magnesium (mg) 400 mg ]
[ ]
[ Phosphorus (mg) 1000 mg ]
[ ]
[ Potassium (mg) 3500 mg ]
[ ]
[ Sodium ((mg) 2400 mg ]
[ ]
[ Zinc (mg) 15 mg ]
[ ]
[ Copper (mcg) 2 mcg ]
[ ]
[ Vitamin C (mg) 60 mg ]
[ ]
[ Thiamine (mg) 1.5 mg ]
[ ]
[ Riboflavin (mg) 1.7 mg ]
[ ]
[ Niacin (mg) 20 mg ]
[ ]
[Pantothenic Acid (mg) 10 mg ]
[ ]
[ Vitamin B6 (mg) 2 mg ]
[ ]
[ Vitamin B12 (mcg) 6 mcg ]
[ ]
[ Vitamin A (IU) 5000 IU ]
# Table 4: FDA Daily Recommended Values
>
# The numerical entries from Table 4 can be entered into a Maple session
# with the command
# v2 := vector( [2000, 50, 65, 300, 1000, 18, 400, 1000, 3500,
# 2400, 15, 2, 60, 1.5, 1.7, 20, 10, 2, 6, 5000]
# );
>
>
# Reference
