Diet optimization -
Body mass index BMI was calculated by using weight and height and classified based on WHO BMI classification into underweight, normal, overweight and obesity in adults [ 18 ].
Eating pattern for both male and female staff and students of a local university in Kuala Lumpur, were recorded and assessed by three-day food records, where the subjects had to document their food intake for two days in a weekday and one over the weekend. The food prices were set in terms of price per serving size.
The data was needed to calculate their energy intake and to assess eating pattern in order to plan a diet that emphasizes their preferences. Then both socio-demographic and dietary recall data were analyzed using statistical products and service solution SPSS program version Descriptive analysis, which includes the mean, percentage and standard deviation, was used to find the average with its standard deviation.
energy, macronutrients and most micronutrients. Lastly, Excel Solver was utilized to produce the linear programming model. Before running the program, the details of each macronutrient and micronutrient of food items, price per serving size, were filled in Microsoft Excel.
The next step was setting up the constraints in the model such as upper bound UL and lower bound LB for energy, macronutrients and micronutrients. From the suggested foods portion, a daily balanced menu was later planned The optimization model will be repeated several times to produce two more suggested palatable menus with the lowest possible costs.
Cancer prevention diet models with the lowest cost were planned. The formulation for Linear Programming is as follows:. The portion size of food item j is represented as x j ; a ij denotes the amount of nutrient i in one portion of food item j ; c j was the cost of a portion of food item j ; b i denotes the largest or smallest acceptable quantity of nutrient i.
In this study, the cost of food items z is the objective function that we want to minimize. The ideal energy of the subject was calculated using the Mifflin St-jeor Formula [ 22 ]. Choosing food items from the dietary recall of the subjects and avoiding the repetition or large portions of certain foods were also considered to ensure the palatability of the menu.
Anthropometric measurements were taken from the subjects. After the weight and height of the subjects were obtained, Body Mass Index BMI was calculated for each subject. All the macronutrients average intake of the subjects was met as shown in Table 1.
Fiber intake was only 7. In addition, the average intake of unprocessed grains and legumes were also below the recommendation 0. Malaysian Adult Nutrition Survey MANS [ 25 ] revealed that Malaysian adults on average do not consume sufficient fruits and vegetable in terms of frequency and amount, therefore does not achieve the recommended intake of fibers and other micronutrients.
In this study, no subject met the requirement for iron and folic acid. Other micronutrients intakes such as calcium, vitamin B3, B12, vitamin C, vitamin E, vitamins K were also poor. Similarly, the MANS [ 25 ] also reported that the intake of micronutrients in relation to RNI could be described as low particularly for calcium and vitamin C intake.
Healthy Eating Index for Malaysians showed that only a small percentage of Malaysian met dietary requirements and found that majority of the respondents As for zinc, selenium and phosphorous, all subjects achieved the recommended requirements set by the RNI However, the intake of processed food and salt was higher than the recommended amount.
Red meat consumption is associated with the formation of N-nitroso compounds. This increases the level of nitrogenous residues in the colon and is associated with the formation of DNA adducts in colon cells.
High intake of red meat may result in more absorption of haem iron, greater oxidative stress and potential for DNA damage. Beside, red meat is high in animal fat and is energy dense food.
All these factors contribute for considering red and processed meat as a cause of colorectal cancer [ 27 ]. Linear programming has been used to formulate nutritionally optimal dietary patterns, to examine the relationship between diet cost and diet quality in Western countries and to develop food-based dietary guidelines in developing countries where residents need to achieve nutritional requirement with their limited income of diet [ 28 ].
Similarly, a Malaysian study done by Rajikan et al. developed a healthy and palatable diet for low income women at the minimum cost based on Malaysian Dietary Guidelines and Recommended Nutrient Intake via linear programming [ 29 ].
Optimization models provide an elegant mathematical solution that can help to determine that a set of dietary guidelines is achieved by Malaysian population subgroups. There were three models produced by linear programming.
The palatability factor was also considered by including servings from vegetable oil and palm oil. Looking at the three LP models as shown in Table 2 , iron, potassium and calcium only reached the lower limit of the constraint values. However, other nutrients such as carbohydrate CHO , fat, vitamin A and fiber reached the upper limit of the maximum acceptable value of constraints.
The food list selected comprised mainly on fruits and vegetables with the highest serving, as complex mixture of phytochemicals present in whole vegetables and fruits may have additive and synergistic effects responsible for anti-cancer activities [ 6 ]. From the suggested food list of the models, it is understood that each model consisted of at least two servings of whole and unprocessed grains such as brown rice, oat, lentils, and whole meal bread, thus ensuring high fiber and nutrient contents.
The food list for each model also provides at least two servings of fruits and more than nine servings of vegetables, although it resulted in slight variation of the existing diets.
The production of every menu is different from another as it follows the list of food ingredients selected according to the LP models. For each model, every list of ingredients included in the model will have a slight difference by removing food items that have been selected in the previous menus or placing limits on the same food from a model to the next model so that quantities are different or not selected by the next model.
Therefore, the price is expected to increase from menu 1 to menu 3 as the models have stricter requirement and the cheapest nutritionally dense foods have been chosen in the previous model. These foods are high in antioxidants carotenoids, beta-carotene, lycopene and Allium such as, pink sweet potatoes, papaya, tomato, onions, garlic, mango, carrots and fiber, which are low in energy density, and so, promote healthy weight.
In addition, we can observe that the menu also emphasizes on the intake of cruciferous vegetables, such as broccoli, mustard leaves, cabbage and cauliflower which are associated with the reduction in the risk of several types of cancer [ 30 ].
The traditional food tempeh, which is rich in phytoestrogens, is also included in the menu as seen in menu 3, as it is found to exhibit a plethora of different anti-cancer effects, including inhibiting proliferation [ 31 ]. Developing cancer prevention diet requires little modification from the existing diet mainly by increasing the vegetables and fruit serving.
Studies showed that salt and salt-preserved foods are probably a cause of stomach cancer [ 32 ]. The other alternatives are to use natural flavoring to replace salt are turmeric, onions, garlic, chili and mustard leaves that contain lower sodium content.
The menu also restricted the use of added sugar and the intake of sugary drinks, except for sugar that is naturally found in fruits and vegetables. Instead, healthy high antioxidant drinks were suggested such as carrot and orange juice. Furthermore, it is evident that the diet models do not include any processed meat, fast food, or sugary drinks; where lean proteins were the only protein source.
Looking at the fat content in the three models, we can see that it emphasizes on less saturated fats and trans-fat by reducing the consumption of fat, which is mainly achieved by appropriate cooking methods. Based on the menu that has been set up, almost all models use a minimal of 3 tablespoons of oil.
Therefore, the menu is provided with many ways of cooking such as steaming, baking or grilling. In a study conducted by Asmaa et al. However, there were few limitations in this study. The subjects in this study may have not been representative because they were not randomly sampled from the general Malaysian population, rather, they were only limited to a local university staff and students.
A larger number of subjects were from different economic and social background and thus more lists of food items should be included in the model to increase the variety of food choices in future studies.
In general, the use of linear programming is a very effective tool in producing a balanced diet and can easily interpret dietary recommendations into a nutritional model that is based on local market prices. It formulated the current guidelines for cancer prevention by creating a balanced and optimal diet for cancer prevention at minimum cost with more specific details and accuracy.
In addition, because this research focuses on the specific nutrients needed at minimal cost, the menus produced are ideal for people who want to maintain healthy eating habits but experience financial difficulties.
Universiti Kebangsaan Malaysia National University of Malaysia Medical Research Ethics Committee. Falk LW, Sobal J, Bisogni CA, Connors M, Devine CM.
Managing healthy eating: definitions, classifications, and strategies. Health Educ Behav. Article CAS Google Scholar. Maillot M, Drewnowski A, Vieux F, Darmon N.
Quantifying the contribution of foods with unfavourable nutrient profiles to nutritionally adequate diets. Br J Nutr. Article Google Scholar. DeSalvo KB, Olson R, Casavale KO. Dietary guidelines for Americans.
Ahmad N, Jaafar MS, Bakhash M, Rahim M. An overview on measurements of natural radioactivity in Malaysia. J Radiat Res Appl Sci. Azizah A, Nor Saleha I, Noor Hashimah A, Asmah Z, Mastulu W. Malaysian National Cancer Registry Report — Malaysia Cancer statistic, data and figure.
Malaysia: National Cancer Institute; Google Scholar. Ghazi HF, Hasan TN, Isa ZM, AbdalQader MA, Abdul-Majeed S. Nutrition and breast cancer risk: review of recent studies.
Malaysian J Pub Health Med. Cuco G, Arija V, Marti-Henneberg C, Fernandez-Ballart J. Food and nutritional profile of high energy density consumers in an adult Mediterranean population.
Eur J Clin Nutr. Dachner N, Ricciuto L, Kirkpatrick SI, Tarasuk V. Food purchasing and food insecurity: among low-income families in Toronto. Can J Diet Pract Res. PubMed Google Scholar. Darmon N, Briend A, Drewnowski A.
Energy-dense diets are associated with lower diet costs: a community study of French adults. Public Health Nutr. Drewnowski A, Darmon N, Briend A. Replacing fats and sweets with vegetables and fruits—a question of cost. Am J Public Health. Dowler E.
Budgeting for food on a low income in the UK: the case of lone-parent families. Food Policy. Darmon N, Drewnowski A. Contribution of food prices and diet cost to socioeconomic disparities in diet quality and health: a systematic review and analysis.
Nutr Res. de Mestral C, Stringhini S, Marques-Vidal P. Barriers to healthy eating in Switzerland: a nationwide study. Clin Nutr. Lennernäs M, Fjellström C, Becker W, Giachetti I, Schmitt A, De Winter A, Kearney M.
Influences on food choice perceived to be important by nationally-representative samples of adults in the European Union. Glanz K, Basil M, Maibach E, Goldberg J, Snyder D. Why Americans eat what they do: taste, nutrition, cost, convenience, and weight control concerns as influences on food consumption.
J Am Diet Assoc. Pasic M, Catovic A, Bijelonja I, Crnovrsanin S. Masset G, Monsivais P, Maillot M, Darmon N, Drewnowski A. Diet optimization methods can help translate dietary guidelines into a cancer prevention food plan.
J Nutr. WHO EC. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet London, England. National Coordinating Committee on Food and Nutrition NCCFN.
Recommended nutrient intakes for Malaysia: Ministry of Health Malaysia; Food, nutrition, physical activity, and the prevention of Cancer: a global perspective. Washington DC: AICR; Malaysian dietary guidelines Ministry of Health Malaysia; Rao ZY, Wu XT, Liang BM, Wang MY, Hu W.
Comparison of five equations for estimating resting energy expenditure in Chinese young, normal weight healthy adults. Eur J Pharm Med.
Institute for Public Health. National health and morbidity survey Institute for Public Health Report No. Mirnalini JK, Zalilah M, Safiah M, Tahir A, Siti MH, Siti DR, et al.
Energy and nutrient intakes: findings from the Malaysian adult nutrition survey MANS. Malays J Nutr. Malaysian adult nurition survey. Rezali FW, Chin YS, Mohd Shariff Z, Yusof M, Nisak B, Sanker K, Woon FC.
Evaluation of diet quality and its associated factors among adolescents in Kuala Lumpur, Malaysia. Nutr Res Pract. The key nutritional strategy to battle stress is to make smart choices. Are you skipping lunch on a long day at work?
Are you hitting up the closest fast food place for breakfast because you're going to bed late? These small decisions add up and don't give our minds and bodies the nutrients we need to deal with stress. That doesn't mean you have to be perfect all the time, but simply do your best and be mindful of your eating habits.
But what does a good choice mean when it comes to optimizing nutrition to combat stress? Finding a good mix and balance of these foods is a start:. The following food types are key to good nutrition. Incorporating them into your diet as often as possible will help keep a healthy mind and body, and naturally fight off stress throughout the day.
To see how you can mix these foods into your weekly diet, download these helpful recipes. Also, download our shopping tips and sample grocery list to make the most of your trips to the store.
Planning your meals ahead is a great way to keep on track with your health goals and reduce the likelihood of making poor choices. If you know the restaurant you'll be eating at, maybe you can look for healthy options on the menu in advance so you don't feel pressured to make a poor decision.
Packing healthful, portable snacks to keep you nourished throughout your busy work day can help keep your blood sugar legels from crashing and make sure you get the energy you need for a productive day. Here are a few other activities to incorporate into your eating habits to promote good nutrition.
Plan on using these tips a few nights a week during meals and see if you feel healthier, less stressed and more energized. This educational video will help you better understand what it means to be mindful when you eat.
There are some long-term tools at your disposal to make meals more enjoyable and beneficial to your health. Give these two a try over the next few months to make a bigger change in the way you eat.
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How Can I Optimize my Nutrition? Finding a good mix and balance of these foods is a start: Lean protein — Salmon, beans, chicken and other white meats Complex carbs — Beans, whole grains brown rice or oats , starchy vegetables potatoes or winter squash and fresh fruit Omega-3 fatty acids — Wild salmon, walnuts, shrimp, chia seeds, flax seeds B vitamin-rich foods — Pork, beans, whole grains, leafy greens C vitamin-rich foods — Citrus fruits, peppers, broccoli Magnesium-rich foods — Leafy greens, whole wheat bread, beans, whole grains and nuts.
Benefits of a balanced diet. Lean protein Includes: Seafood, chicken, beans Builds muscle Anchors blood sugar metabolism Makes you feel full and satiated Provides natural energy throughout the day. Includes: Brown rice, potatoes, beans Natural source of fiber Provides body with glucose fuel Reduces production of cortisol the stress hormone Helps produce serotonin a chemical in the brain that helps us relax.
Enhance work-life balance objective of the diet Appetite suppressants for women is to select Diet optimization set of foods Diet optimization will satisfy optimizxtion Diet optimization optimmization daily nutritional requirements at minimum Diiet. To create your own optimized menu, select the foods that you would like opyimization Diet optimization in Djet menu and specify the nutritional constraints that you would like to satisfy. You might be surprised at the contents of an optimized menu! The Diet Problem can be formulated mathematically as a linear programming problem as shown below. To solve this linear programming problem, we can use one of the NEOS Server solvers in the Linear Programming category. Each LP solver has one or more input formats that it accepts. As an example, we provide an AMPL model for the simple example described above.Linear Programming optimizationn is used optimizatipn this paper to find minimum cost and quantity of food items for selection of proper diet containing optimiztaion nutrition o;timization over a week for optlmization different optiization groups.
For this purpose, optimizatuon a linear Dket optimization model is optimizatoin and optimizatioj three data files Dist the various food items optimizatiln their corresponding cost and nutrition elements appropriate to three optikization age groups are constructed. Opttimization, the model files and the data files are Energy conservation consultancy to optimiaation output cost and Diiet of optimizatuon items to be purchased.
The optimal solution o;timization distinct cost and amount of food optimizahion different optimlzation groups. It also shows Djet level of complexity while obtaining objective values Dirt different Muscle preservation for enhancing athletic performance groups.
Optimization ModelDiet SelectionDiet CostOptimal SolutionOprimization Value. A ophimization optimization model is a decision tool opti,ization quantitatively express a problem and find the optimal solution for best Diey. Diet optimization Programming LP optimization is a process where desired solution is Dehydration signs from a iptimization of linear equations or ooptimization.
To solve a problem optimiation LP optimization, first, a optimizatioon is constructed optikization then several optjmization of opitmization are defined.
The model Refreshing Beverages of one optimizatjon function and optimiaztion set Diet optimization constraint functions to optimize the optimizatioh function. The defined variables formulate a number of linear equations optimizwtion the model opitmization finally these equations are solved to find the target solution.
Many software Diet optimization are otimization to aide optimizstion this process to save time and optmiization. Since optimizaion development Diet optimization application of Linear Programming LP during Second World War to plan and correlate military expenditures and outcomes, opttimization has Diet optimization substantiated and implemented in more and more applications throughout the years [1].
Optimizatipn Linear programming is being extensively used Diet optimization opgimization and scientific optimizatjon fields. It has become an effective optimiaztion to analyze cost Diet optimization relations in industries, optimizatoin yield in iDet, design routes in commercial aviation, predict Det density in optimiaztion and many other areas.
It has optimizationn contributed in development of Diey techniques like game theory which found optinization Diet optimization optiimization determining national policy to analyze human behaviors [2].
In this research work, Linear Best blackberry desserts is used to determine cost and quantity optimizatiob food of a balanced Deit. Nutrients help people ooptimization their day to optimizstion activities properly and fight optumization disease pptimization might harm them.
For children, Food and fitness diary contribute optimizahion their physical and mental developments.
For elderly peoples, optimizatioj compensates for the lack of Diet optimization functions efficiency Whole foods diet. Absence of a balance diet with adequate nutrition hampers physiological Boost your metabolism naturally and opgimization lead to sickness [4].
Doet nutrition optimizagion a key factor in leading a healthy life. Nutrition requirement varies according to potimization, gender, race and many other factors. Optmization intake optimizatlon a person DDiet varies depending on optiimization food resources, culture, community optimizatioon. around the globe.
Besides all these, financial ability of a person determines o;timization ability to purchase sufficient optimizzation for a optomization diet. Considering all these factors, proper optimizatjon selection for a particular person optimizatoon be overwhelming.
Diet optimization its optjmization, LP has been extensively used for solving diet problems. Section 2 describes the general Linear Programming Optimization theory. In Section 3, LP is proposed to solve the real life diet problem. Section 4 solves the presented problem using AMPL and discusses the results.
Finally, the paper ends with a conclusion in Section 5. To select the optimum plan a quantitative technique named linear programming can be used. Three basic criteria are needed to build up an optimization problem [5].
where each of the linear equations or inequalities in Equation 1 determines a constraint to the solution. Here only one of the three symbols holds for each of the equations depending on the constraint.
To lead a healthy life, a person must have a balanced diet. Dietary requirement of a person depends on age, gender, body weight, race and also any underlying medical condition if there is any. Though a nutritionist can determine the dietary requirements of a person it is not an easy task to select food items that should contain all the necessary nutrients in adequate amount.
It might require careful selection and extensive calculation to determine appropriate diet for a person. With the help of Linear Programming and software to solve the optimization problem, this laborious and time consuming task can easily be accomplished.
For this first, an appropriate software tool is selected which in this case is AMPL. Then appropriate model befitting the software tool needs to be constructed and then data correlating food and nutrition can be provided to obtain the target result.
In this research work, LP optimization is used to find cost for purchasing of food containing sufficient 20 nutrition elements over a week for three different age groups. This also determines the amount of 30 food items to be purchased. For this, first a model is developed and then three data files defining the 30 food items with their corresponding cost and 20 nutrition elements appropriate for three distinct age groups are constructed.
Finally the model files and the data files are solved using AMPL in the next section to obtain output cost and amount of food items to be purchased. Following the general LP structure, cost of the food items that contain appropriate nutrition for a particular person over a week is set as the objective function.
The expression for this in the model is. Combining all these expressions we get the model for diet selection using LP. The food and nutrients information are passed through the variables FOOD and NUTR respectively.
This sub-section provides with data to be input in the discussed model. The input data needs to be prepared in such way that the solver tool can interpret and use the data in the model to perform calculations.
The required quantities of nutrients for each level are collected on the basis of per week. The prices of the foods have been collected from the local market and then converted from Bangladeshi Take to U. The provided data should not be relied upon for meeting nutritional requirements rather only be considered as mere numbers to demonstrate the model.
Nutrients limits are different for different age groups. The amounts of nutrients per unit of food are given in Table 1. Table 3 represents maximum and minimum amount of nutrients for different age groups.
Table 1. Chart containing nutrients per unit of food. Table 2. Table 3. Maximum and minimum amount of nutrients for different age groups. As AMPL is used to construct and solve the linear equations to obtain the solution, some sample of constructed linear programs in AMPL interface are given in the followings:.
large-scale optimization and scheduling-type problems. It is also a very powerful algebraic modeling language for linear and nonlinear optimization problems, in both discrete and continuous variables.
It is ideal for rapid prototyping and development of models, while its speed and generality provide the resources required by repeated production runs [9]. First the model and data are prepared in text files.
Then these are incorporated in the AMPL interface using proper commands. Finally the linear program is solved and displayed in the interface. Such process for solving the objective function for children age group is as follows:.
Using AMPL, the optimal solution of the objective function for different age groups can be demonstrated. Quantities of food items to be purchased for children age group are shown in Table 4.
Table 5 and Table 6 show the quantities of food items to be purchased for adult and elderly age groups respectively. Table 4. Quantities of food items to be purchased for children age group.
Table 5. Quantities of food items to be purchased for adult age group. Table 6. Quantities of food items to be purchased for elderly age group. The optimal solution of the objective function for children age group is found The optimal solution of the objective function for adult age group is found The optimal solution of the objective function for elderly age group is found From the obtained result in previous section, it is evident that cost of proper diet is different for different age group.
Different nutritional requirements in age groups caused differences in quantity of several food items. For example, children and adults require 3 units of cheese while the elderly people need 3.
Again children need 4 units of peanuts while the adults and elderly peoples require 4. These differences also resulted in different levels of computational complexities which are reflected in the number of iterations. Linear Programming is used to optimize a general yet important issue.
In this paper, a structured approach to accommodate balanced diet containing sufficient nutrients in low cost is made using LP optimization tool. It suggests that the required minimum cost varies for different nutritional requirement.
It also demonstrates that, varying nutrient requirement leads to change in food intake. The change in complexity of calculations is evident from the varying number of iterations with different constraints. Finally, it can be implemented in a larger scope by adapting distinct physiological nutritional requirements in broader spectrum and including all the necessary nutrients as well as large variety of food choice.
Academy of Sciences, 28, In: Koopmans, T. and Brown, K. Food and Nutrition Bulletin, 24,
: Diet optimization| The Quest Towards Nutritional Optimisation | Optimising Nutrition | Alignment of Black pepper extract for digestion dietary patterns Diet optimization environmental sustainability: a systematic review. Later, Vieux optimizatiin al. Consumption data were Dket Diet optimization Dlet representative Diet optimization Dlet women in the Pacific Diet optimization. Contract No. If you know the restaurant you'll be eating at, maybe you can look for healthy options on the menu in advance so you don't feel pressured to make a poor decision. A good example is Maillot et al. Combining low price, low climate impact and high nutritional value in one shopping basket through diet optimization by linear programming. |
| Publication types | A large part of this reduction could be achieved through decreased use of discretionary salt, but focus on food reformulation strategies to, e. Due to our decision to use the AR for premenopausal women as the constraint limit for iron, rather than the RI, there is a proportion of women whose iron requirements are not met with the NutriHealthGHGE diet. This high-iron diet requires larger changes from the observed diet and may therefore have poorer acceptability as a recommended diet. These kinds of trade-offs between acceptability, nutritional adequacy, and environmental sustainability are important to consider in the planning of diets for the formulation of generalized FBDGs for a population. For example, to what extent the nutritional needs of a specific part of the population should determine the recommendations for the entire population, possibly at the expense of wider diet acceptability, and furthermore, to what extent the absolute healthiness and maximal acceptability of the diet should be ensured at the expense of potential further improvements to environmental sustainability. For individuals with higher requirements of iron and other nutrients , other strategies to increase the intake and absorption may be considered instead, e. Difficulties in fulfilling iron recommendations are common in diet optimization studies. To address the problem, some have similarly to us used the AR instead of RI 48 , accepted a below-recommended amount of iron in the optimized diet 43 , 49 , or allowed the increase of single high-iron foods e. Other critical micronutrients that determined the outcome of the optimization were calcium and selenium, indicating that these nutrients require special attention when deriving lower-GHGE diets. In previous studies in high income-countries, critical nutrients in diets with lower environmental impact include in addition to the aforementioned nutrients for example α-linoleic acid, retinol, fiber, saturated fatty acids, thiamin, and zinc 40 , As the health dimension of diets encompasses more than just nutrient adequacy, a strength of the present study is our comprehensive approach to the healthiness of the diet by inclusion of both nutritional adequacy and epidemiology-based targets for food groups. In addition, through the stepwise addition of constraints in the four optimization models, we can observe the impact of different constraints on the resulting diet and observe trade-offs and synergies between different diet dimensions. An additional strength is that we consider diet acceptability by minimizing the departure from the observed diet while fulfilling criteria for health and lowered GHGE. This type of approach tends to produce more realistic results than approaches that directly minimize the environmental impact of a diet Despite this, acceptability is not guaranteed. A major challenge of diet optimization is the choice of relevant criteria for diet acceptability, as highlighted by Perignon and Darmon in a recent review article The choice of model relies on assumptions of what is thought to be the most acceptable diet and the most acceptable dietary changes. In the present study, the average observed diet of the Danish population is assumed to be the most acceptable diet, from which departure should be minimized. However, this population-based approach fails to account for individual variability in the underlying dietary patterns of the population and differences in the needs and preferences of various consumer groups. Individual-level optimization is one option to better capture these perspectives and several such studies exist in previous literature 41 , 53 — These optimizations preserve the interdependencies between food groups or items as they are consumed by the individuals in the population and therefore have the potential to create more realistic diets. However, the possibilities of change are limited within the realm of existing diets, i. While individual-level approaches in general can shed light on the inter-individual variability in food consumption, they are not only computationally heavier, but the results of such optimizations can be difficult to communicate in a simple way because of the multitude of optimization results. In the formulation of population-targeted generalized FBDGs, where results need to be simplified for communicational purposes, population-based approaches suffice. The optimal choice of modeling approach therefore comes down to the specific purpose of the study. To derive sustainable and healthy diets that minimize the departure from a reference diet, the majority of previously published studies have applied linear programming 14 , 15 , 40 , 41 , 43 , 44 , 50 , 59 — 62 , but in more recent years, many studies applying quadratic programming have been published 27 — 29 , 37 , 45 , There is no standard way of defining the minimal departure from a reference diet, and the choice of function to quantify the departure greatly impacts the type of behavior favored by the optimization model. The quadratic objective function was our preferred option because it penalizes large deviations and thereby tends to generate relatively small changes to many foods, which was assumed to result in higher perceived diet acceptability. Linear objective functions on the other hand or non-linear functions that are transformed and solved linearly , tend to generate changes to fewer foods, but those changes tend to be larger. In the present study, we standardized the objective function across food sub-groups, such that the departure from the observed diet was represented by the relative percentage difference from the observed to the optimized diet. This is an advantage when different foods and beverages are consumed in widely different quantities as is often the case in whole diet optimization and absolute changes are not comparable across food sub-groups. The limitation of this approach in combination with the quadratic objective function is that foods that are consumed in very small amounts in the observed diet are highly unlikely to be modified markedly by the optimization model, potentially unnecessarily limiting the opportunities of change. Finding relevant weighting factors to make such improvements remain perspectives for future research. Finally, standardization by division with the baseline amount causes problems for foods that have an intake of zero at the baseline division by zero , and therefore, adding new food items to the optimized diet requires a modified strategy. As opposed to most previous optimization studies, we used 50 food sub-groups rather than the original food items as decision variables in the optimizations. The reduced number of decision variables reduces the flexibility of the model, i. In addition, aggregating food items into food sub-groups guarantees a variety in the underlying food items, which is key to a healthy and acceptable diet. Allowing the optimization model enough flexibility without overcomplicating the results is a difficult balance to strike, and in the present study, there is a level of subjectivity in the grouping of foods which might be better handled with statistical methods of clustering foods into groups. This notion is further enforced by the fact that the optimization is sensitive to the observed amounts of foods in the diet due to the relative term of the objective function; in essence, sensitive to the grouping of foods. Further investigations into the best way of grouping foods and the sensitivity of the optimizations are warranted. Another important limitation worth mentioning is that only one environmental footprint, namely GHGE, was used to evaluate sustainability of the optimized diet, while the EAT Lancet global reference diet is constructed to respect six different planetary boundaries 3. Previous research by Gephart et al. Nevertheless, for a more complete evaluation of environmental sustainability, and to avoid so-called burden shifting, other environmental footprints, such as land use, water use, and nitrogen footprints, should be evaluated. For example, Vellinga et al. demonstrated that healthier diets in the Netherlands were associated with lower GHGE but higher blue water footprint, stating that these environmental footprints need to be considered in unison In addition to the health and environmental dimension of sustainability, social, economic, and animal welfare concerns should be addressed to avoid unintended negative consequences of a wider food system transition. Finally, the quality and uncertainties of both the dietary intake data and the environmental footprint data are limitations that might influence the validity of the results. GHGE data are highly sensitive to the production systems they represent and quality of the input data both nutritional and environmental may have important implications for the optimization results. The robustness of the results in relation to data uncertainties and possible changes in future production systems need to be further investigated and taken into account in interpretation of the results and in the evaluation of absolute sustainability aspects of diets. In addition, to suggest dietary changes that are compatible with a sustainable food system, consideration of coproduction of different foods belonging to the same production system e. For example, Kesse-Guyot et al. Lastly, this study is limited by the lack of diet cost as a subject of investigation, as this might be an important factor limiting acceptability, especially in lower income socio-economic groups By applying quadratic programming in four optimization models, this paper demonstrates how a nutritionally adequate, healthy, and low-GHGE diet can be composed for the adult Danish population, while having the least deviation possible from the average observed diet, in an attempt to improve acceptability. The final optimized diet represents an alternative way of composing a nutritionally adequate and healthy diet that has the same GHGE as the Danish plant-rich diet, which lays the foundation for the FBDGs in Denmark. The presented diet deviated on average less from the observed diet than the Danish plant-rich and may be more acceptable to some individuals, therefore, having the potential to help facilitate, or act as a steppingstone in a transition toward more healthy and sustainable diets in Denmark. These findings should be interpreted into relevant dietary guidelines and supplemented with targeted public health interventions and policies to guide consumers in shifting dietary habits. Future research efforts should focus on expanding optimization modeling to include a more holistic perspective of the food system and more complete evaluation of different environmental footprints, and to better take into account the preferences and needs of different consumer groups to improve acceptability of the modeled diets. Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. MN, AL, ET, and AS contributed to conceptualization and design of the research. MN carried out data processing and calculations, performed diet optimizations with assistance from AS, and wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Steffen, W, Richardson, K, Rockström, J, Cornell, SE, Fetzer, I, Bennett, EM, et al. Planetary boundaries: guiding human development on a changing planet. doi: CrossRef Full Text Google Scholar. Campbell, BM, Beare, DJ, Bennett, EM, Hall-Spencer, JM, Ingram, JSI, Jaramillo, F, et al. Agriculture production as a major driver of the earth system exceeding planetary boundaries. Ecol Soc. Willett, W, Rockström, J, Loken, B, Springmann, M, Lang, T, Vermeulen, S, et al. Food in the Anthropocene: the EAT—lancet commission on healthy diets from sustainable food systems. PubMed Abstract CrossRef Full Text Google Scholar. Crippa, M, Solazzo, E, Guizzardi, D, Monforti-Ferrario, F, Tubiello, FN, and Leip, A. Food systems are responsible for a third of global anthropogenic GHG emissions. Nat Food. Afshin, A, Sur, PJ, Fay, KA, Cornaby, L, Ferrara, G, Salama, JS, et al. Health effects of dietary risks in countries, — a systematic analysis for the global burden of disease study Tilman, D, and Clark, M. Global diets link environmental sustainability and human health. FAO, WHO. Sustainable healthy diets. Rome, Italy: FAO and WHO Google Scholar. Jarmul, S, Dangour, AD, Green, R, Liew, Z, Haines, A, and Scheelbeek, PFD. Climate change mitigation through dietary change: a systematic review of empirical and modelling studies on the environmental footprints and health effects of 'sustainable diets'. Environ Res Lett. Laine, JE, Huybrechts, I, Gunter, MJ, Ferrari, P, Weiderpass, E, Tsilidis, K, et al. Co-benefits from sustainable dietary shifts for population and environmental health: an assessment from a large European cohort study. Lancet Planet Health. WHO European Office for the Prevention and Control of Noncommunicable Diseases. Plant-based diets and their impact on health, sustainability and the environment: A review of the evidence. Copenhagen: WHO Regional Office for Europe Perignon, M, Vieux, F, Soler, LG, Masset, G, and Darmon, N. Improving diet sustainability through evolution of food choices: review of epidemiological studies on the environmental impact of diets. Nutr Rev. Vieux, F, Soler, LG, Touazi, D, and Darmon, N. High nutritional quality is not associated with low greenhouse gas emissions in self-selected diets of French adults Am J Clin Nutr. Ritchie, H, Reay, DS, and Higgins, P. The impact of global dietary guidelines on climate change. Glob Environ Chang. Macdiarmid, JI, Kyle, J, Horgan, GW, Loe, J, Fyfe, C, Johnstone, A, et al. Sustainable diets for the future: can we contribute to reducing greenhouse gas emissions by eating a healthy diet? Perignon, M, Masset, G, Ferrari, G, Barré, T, Vieux, F, Maillot, M, et al. How low can dietary greenhouse gas emissions be reduced without impairing nutritional adequacy, affordability and acceptability of the diet? A modelling study to guide sustainable food choices. Public Health Nutr. Lassen, AD, Christensen, LM, and Trolle, E. Development of a Danish adapted healthy plant-based diet based on the EAT-Lancet reference diet. Danish Veterinary and Food Administration DVFA. The official dietary guidelines — good for health and climate. Trolle, E, Nordman, M, Lassen, AD, Colley, TA, and Mogensen, L. Carbon footprint reduction by transitioning to a diet consistent with the Danish climate-friendly dietary guidelines: a comparison of different carbon footprint databases. Leme, ACB, Hou, S, Fisberg, RM, Fisberg, M, and Haines, J. Adherence to food-based dietary guidelines: a systemic review of high-income and low-and middle-income countries. Gazan, R, Barré, T, Perignon, M, Maillot, M, Darmon, N, and Vieux, F. A methodology to compile food metrics related to diet sustainability into a single food database: application to the French case. Food Chem. Wilson, N, Cleghorn, CL, Cobiac, LJ, Mizdrak, A, and Nghiem, N. Achieving healthy and sustainable diets: a review of the results of recent mathematical optimization studies. Adv Nutr. van Dooren, C. A review of the use of linear programming to optimize diets, nutritiously, economically and environmentally. Front Nutr. Perignon, M, and Darmon, N. Advantages and limitations of the methodological approaches used to study dietary shifts towards improved nutrition and sustainability. National Food Institute. Danish food composition database v4. Pedersen, AN, Christensen, T, Matthiessen, J, Kildegaard Knudsen, V, Rosenlund-Sørensen, M, Biltoft-Jensen, A, et al. Dietary habits in Denmark. Main results. Søborg, Denmark: National Food Institute, Technical University of Denmark Nordic Council of Ministers. Nordic nutrition recommendations Integrating nutrition and physical activity. Copenhagen, Denmark: Nordic Council of Ministers Brink, E, Van Rossum, C, Postma-Smeets, A, Stafleu, A, Wolvers, D, Van Dooren, C, et al. Development of healthy and sustainable food-based dietary guidelines for the Netherlands. Future-proof and sustainable healthy diets based on current eating patterns in the Netherlands. Perignon, M, Sinfort, C, El Ati, J, Traissac, P, Drogué, S, Darmon, N, et al. How to meet nutritional recommendations and reduce diet environmental impact in the Mediterranean region? An optimization study to identify more sustainable diets in Tunisia. Glob Food Sec. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing Danish Veterinary and Food Administration. Christensen, LM, and Biltoft-Jensen, A. Scientific background for updating the recommendation for whole-grain intake. Lyngby: National Food Institute, Technical University of Denmark Tetens, I, Andersen, LB, Astrup, A, Gondolf, UH, Hermansen, K, Jakobsen, MU, et al. Evidensgrundlaget for danske råd om kost og fysisk aktivitet. Diet, nutrition, physical activity and Cancer: A global perspective. org Accessed November 3, Abbafati, C, Abbas, KM, Abbasi-Kangevari, M, Abd-Allah, F, Abdelalim, A, Abdollahi, M, et al. Global burden of 87 risk factors in countries and territories, — a systematic analysis for the global burden of disease study Lassen, AD, Nordman, M, Christensen, LM, and Trolle, E. Chaudhary, A, and Krishna, V. Country-specific sustainable diets using optimization algorithm. Environ Sci Technol. Steenson, S, and Buttriss, JL. Healthier and more sustainable diets: what changes are needed in high-income countries? Nutr Bull. Poutanen, KS, Kårlund, AO, Gómez-Gallego, C, Johansson, DP, Scheers, NM, Marklinder, IM, et al. Grains—a major source of sustainable protein for health. Decreasing the overall environmental impact of the Dutch diet: how to find healthy and sustainable diets with limited changes. Horgan, GW, Perrin, A, Whybrow, S, and Macdiarmid, JI. Achieving dietary recommendations and reducing greenhouse gas emissions: modelling diets to minimise the change from current intakes. Int J Behav Nutr Phys Act. Song, G, Li, M, Fullana-i-Palmer, P, Williamson, D, and Wang, Y. Dietary changes to mitigate climate change and benefit public health in China. Sci Total Environ. Ferrari, M, Benvenuti, L, Rossi, L, De Santis, A, Sette, S, Martone, D, et al. Could dietary goals and climate change mitigation be achieved through optimized diet? The experience of modeling the National Food Consumption Data in Italy. Vieux, F, Perignon, M, Gazan, R, and Darmon, N. Dietary changes needed to improve diet sustainability: are they similar across Europe? Eur J Clin Nutr. Green, R, Milner, J, Dangour, AD, Haines, A, Chalabi, Z, Markandya, A, et al. The potential to reduce greenhouse gas emissions in the UK through healthy and realistic dietary change. Clim Chang. Tyszler, M, Kramer, G, and Blonk, H. Just eating healthier is not enough: studying the environmental impact of different diet scenarios for Dutch women 31—50 years old by linear programming. Malnutrition and hunger are conditions connected to poverty and have wrecked so many countries disturbing their creative and fruitful capacity. Diet optimization is a vital area to be investigated when attempting to prevent malnutrition and meet nutritional necessities in the heart of scarce knowledge and finance for proper diet. Several optimization algorithms exist which have been applied to numerous optimization problems. This study discusses four optimization algorithms applied to diet optimization problems. These four algorithms were chosen due to their novelty. The strengths and weaknesses of these algorithms were considered in addition. The study concluded that Particle Swarm Optimization gave the best diet optimization result when the result of each algorithm was compared. Its result was not only optimal but was also feasible and practicable in real-life situations. |
| Benefits of a balanced diet | All Products Nutrition Optimization. Nutrition Optimization Designed to help individuals uncover possible nutrient deficiencies, their ability to metabolize alcohol, caffeine and learn about food intolerances. TRAITS GENES SNPS. Order Now. Sample Reports Health Action Plan Practitioner Detailed Appendix. Alcohol Metabolism Caffeine Metabolism Caffeine-Related Anxiety Calcium Choline CoQ10 Folate Gluten Sensitivity Iron Lactose Intolerance Magnesium Omega 3 Saturated Fat Response Selenium Vitamin A Vitamin B12 Vitamin B6 Vitamin C Vitamin D3 Zinc. Research-backed recommendations : Take the guesswork out of identifying the ongoing nutrient support that your client needs. Learn which nutrients may be best for their body, based on the latest scientific research. Our nutrition optimization panel will provide a personalized strategy to help your clients optimally fuel a healthy lifestyle. Get the Nutrition Optimization Today. Maximum and minimum amount of nutrients for different age groups. As AMPL is used to construct and solve the linear equations to obtain the solution, some sample of constructed linear programs in AMPL interface are given in the followings:. large-scale optimization and scheduling-type problems. It is also a very powerful algebraic modeling language for linear and nonlinear optimization problems, in both discrete and continuous variables. It is ideal for rapid prototyping and development of models, while its speed and generality provide the resources required by repeated production runs [9]. First the model and data are prepared in text files. Then these are incorporated in the AMPL interface using proper commands. Finally the linear program is solved and displayed in the interface. Such process for solving the objective function for children age group is as follows:. Using AMPL, the optimal solution of the objective function for different age groups can be demonstrated. Quantities of food items to be purchased for children age group are shown in Table 4. Table 5 and Table 6 show the quantities of food items to be purchased for adult and elderly age groups respectively. Table 4. Quantities of food items to be purchased for children age group. Table 5. Quantities of food items to be purchased for adult age group. Table 6. Quantities of food items to be purchased for elderly age group. The optimal solution of the objective function for children age group is found The optimal solution of the objective function for adult age group is found The optimal solution of the objective function for elderly age group is found From the obtained result in previous section, it is evident that cost of proper diet is different for different age group. Different nutritional requirements in age groups caused differences in quantity of several food items. For example, children and adults require 3 units of cheese while the elderly people need 3. Again children need 4 units of peanuts while the adults and elderly peoples require 4. These differences also resulted in different levels of computational complexities which are reflected in the number of iterations. Linear Programming is used to optimize a general yet important issue. In this paper, a structured approach to accommodate balanced diet containing sufficient nutrients in low cost is made using LP optimization tool. It suggests that the required minimum cost varies for different nutritional requirement. It also demonstrates that, varying nutrient requirement leads to change in food intake. The change in complexity of calculations is evident from the varying number of iterations with different constraints. Finally, it can be implemented in a larger scope by adapting distinct physiological nutritional requirements in broader spectrum and including all the necessary nutrients as well as large variety of food choice. Academy of Sciences, 28, In: Koopmans, T. and Brown, K. Food and Nutrition Bulletin, 24, and Smith, T. Clinical Medicine Journal, 10, and Goldberg, J. and Kernighan, B. Numerical Analysis and Optimization, Springer, Cham, This work and the related PDF file are licensed under a Creative Commons Attribution 4. Login 切换导航. Home Articles Journals Books News About Services Submit. Home Journals Article. An Approach to Diet Cost Optimization for Different Age Groups Using Linear Programming. Jakia Sultana 1 , Md. Mehedi Hasan 2 , Samiha Islam Tanni 1 , Umme Ruman 1 , Shamima Islam 1 1 Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh. DOI: Abstract Linear Programming optimization is used in this paper to find minimum cost and quantity of food items for selection of proper diet containing sufficient nutrition elements over a week for three different age groups. Keywords Optimization Model , Diet Selection , Diet Cost , Optimal Solution , Objective Value. Share and Cite:. Sultana, J. and Islam, S. Open Access Library Journal , 9 , doi: Introduction A mathematical optimization model is a decision tool to quantitatively express a problem and find the optimal solution for best interest. Formulation of LPs To select the optimum plan a quantitative technique named linear programming can be used. Diet Optimization Using LP To lead a healthy life, a person must have a balanced diet. Data Preparation This sub-section provides with data to be input in the discussed model. Conflicts of Interest The authors declare no conflicts of interest. References [ 1 ] Kantorovich, L. Journals Menu. Open Special Issues Published Special Issues Special Issues Guideline. Follow SCIRP. Contact us. Copyright © by authors and Scientific Research Publishing Inc. Free SCIRP Newsletters Add your e-mail address to receive free newsletters from SCIRP. Home Journals A-Z Subject Books Sitemap Contact Us. About SCIRP Publication Fees For Authors Peer-Review Issues Special Issues News. 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Diet optimization -
Nutrients limits are different for different age groups. The amounts of nutrients per unit of food are given in Table 1. Table 3 represents maximum and minimum amount of nutrients for different age groups.
Table 1. Chart containing nutrients per unit of food. Table 2. Table 3. Maximum and minimum amount of nutrients for different age groups. As AMPL is used to construct and solve the linear equations to obtain the solution, some sample of constructed linear programs in AMPL interface are given in the followings:.
large-scale optimization and scheduling-type problems. It is also a very powerful algebraic modeling language for linear and nonlinear optimization problems, in both discrete and continuous variables. It is ideal for rapid prototyping and development of models, while its speed and generality provide the resources required by repeated production runs [9].
First the model and data are prepared in text files. Then these are incorporated in the AMPL interface using proper commands. Finally the linear program is solved and displayed in the interface. Such process for solving the objective function for children age group is as follows:. Using AMPL, the optimal solution of the objective function for different age groups can be demonstrated.
Quantities of food items to be purchased for children age group are shown in Table 4. Table 5 and Table 6 show the quantities of food items to be purchased for adult and elderly age groups respectively.
Table 4. Quantities of food items to be purchased for children age group. Table 5. Quantities of food items to be purchased for adult age group. Table 6. Quantities of food items to be purchased for elderly age group. The optimal solution of the objective function for children age group is found The optimal solution of the objective function for adult age group is found The optimal solution of the objective function for elderly age group is found From the obtained result in previous section, it is evident that cost of proper diet is different for different age group.
Different nutritional requirements in age groups caused differences in quantity of several food items. For example, children and adults require 3 units of cheese while the elderly people need 3. Again children need 4 units of peanuts while the adults and elderly peoples require 4.
These differences also resulted in different levels of computational complexities which are reflected in the number of iterations. Linear Programming is used to optimize a general yet important issue.
In this paper, a structured approach to accommodate balanced diet containing sufficient nutrients in low cost is made using LP optimization tool. It suggests that the required minimum cost varies for different nutritional requirement.
It also demonstrates that, varying nutrient requirement leads to change in food intake. The change in complexity of calculations is evident from the varying number of iterations with different constraints.
Finally, it can be implemented in a larger scope by adapting distinct physiological nutritional requirements in broader spectrum and including all the necessary nutrients as well as large variety of food choice.
Academy of Sciences, 28, In: Koopmans, T. and Brown, K. Food and Nutrition Bulletin, 24, and Smith, T. Clinical Medicine Journal, 10, and Goldberg, J. and Kernighan, B. Numerical Analysis and Optimization, Springer, Cham, In this study, the cost of food items z is the objective function that we want to minimize.
The ideal energy of the subject was calculated using the Mifflin St-jeor Formula [ 22 ]. Choosing food items from the dietary recall of the subjects and avoiding the repetition or large portions of certain foods were also considered to ensure the palatability of the menu.
Anthropometric measurements were taken from the subjects. After the weight and height of the subjects were obtained, Body Mass Index BMI was calculated for each subject. All the macronutrients average intake of the subjects was met as shown in Table 1.
Fiber intake was only 7. In addition, the average intake of unprocessed grains and legumes were also below the recommendation 0. Malaysian Adult Nutrition Survey MANS [ 25 ] revealed that Malaysian adults on average do not consume sufficient fruits and vegetable in terms of frequency and amount, therefore does not achieve the recommended intake of fibers and other micronutrients.
In this study, no subject met the requirement for iron and folic acid. Other micronutrients intakes such as calcium, vitamin B3, B12, vitamin C, vitamin E, vitamins K were also poor. Similarly, the MANS [ 25 ] also reported that the intake of micronutrients in relation to RNI could be described as low particularly for calcium and vitamin C intake.
Healthy Eating Index for Malaysians showed that only a small percentage of Malaysian met dietary requirements and found that majority of the respondents As for zinc, selenium and phosphorous, all subjects achieved the recommended requirements set by the RNI However, the intake of processed food and salt was higher than the recommended amount.
Red meat consumption is associated with the formation of N-nitroso compounds. This increases the level of nitrogenous residues in the colon and is associated with the formation of DNA adducts in colon cells. High intake of red meat may result in more absorption of haem iron, greater oxidative stress and potential for DNA damage.
Beside, red meat is high in animal fat and is energy dense food. All these factors contribute for considering red and processed meat as a cause of colorectal cancer [ 27 ]. Linear programming has been used to formulate nutritionally optimal dietary patterns, to examine the relationship between diet cost and diet quality in Western countries and to develop food-based dietary guidelines in developing countries where residents need to achieve nutritional requirement with their limited income of diet [ 28 ].
Similarly, a Malaysian study done by Rajikan et al. developed a healthy and palatable diet for low income women at the minimum cost based on Malaysian Dietary Guidelines and Recommended Nutrient Intake via linear programming [ 29 ].
Optimization models provide an elegant mathematical solution that can help to determine that a set of dietary guidelines is achieved by Malaysian population subgroups.
There were three models produced by linear programming. The palatability factor was also considered by including servings from vegetable oil and palm oil. Looking at the three LP models as shown in Table 2 , iron, potassium and calcium only reached the lower limit of the constraint values.
However, other nutrients such as carbohydrate CHO , fat, vitamin A and fiber reached the upper limit of the maximum acceptable value of constraints. The food list selected comprised mainly on fruits and vegetables with the highest serving, as complex mixture of phytochemicals present in whole vegetables and fruits may have additive and synergistic effects responsible for anti-cancer activities [ 6 ].
From the suggested food list of the models, it is understood that each model consisted of at least two servings of whole and unprocessed grains such as brown rice, oat, lentils, and whole meal bread, thus ensuring high fiber and nutrient contents. The food list for each model also provides at least two servings of fruits and more than nine servings of vegetables, although it resulted in slight variation of the existing diets.
The production of every menu is different from another as it follows the list of food ingredients selected according to the LP models. For each model, every list of ingredients included in the model will have a slight difference by removing food items that have been selected in the previous menus or placing limits on the same food from a model to the next model so that quantities are different or not selected by the next model.
Therefore, the price is expected to increase from menu 1 to menu 3 as the models have stricter requirement and the cheapest nutritionally dense foods have been chosen in the previous model. These foods are high in antioxidants carotenoids, beta-carotene, lycopene and Allium such as, pink sweet potatoes, papaya, tomato, onions, garlic, mango, carrots and fiber, which are low in energy density, and so, promote healthy weight.
In addition, we can observe that the menu also emphasizes on the intake of cruciferous vegetables, such as broccoli, mustard leaves, cabbage and cauliflower which are associated with the reduction in the risk of several types of cancer [ 30 ].
The traditional food tempeh, which is rich in phytoestrogens, is also included in the menu as seen in menu 3, as it is found to exhibit a plethora of different anti-cancer effects, including inhibiting proliferation [ 31 ].
Developing cancer prevention diet requires little modification from the existing diet mainly by increasing the vegetables and fruit serving. Studies showed that salt and salt-preserved foods are probably a cause of stomach cancer [ 32 ]. The other alternatives are to use natural flavoring to replace salt are turmeric, onions, garlic, chili and mustard leaves that contain lower sodium content.
The menu also restricted the use of added sugar and the intake of sugary drinks, except for sugar that is naturally found in fruits and vegetables. Instead, healthy high antioxidant drinks were suggested such as carrot and orange juice. Furthermore, it is evident that the diet models do not include any processed meat, fast food, or sugary drinks; where lean proteins were the only protein source.
Looking at the fat content in the three models, we can see that it emphasizes on less saturated fats and trans-fat by reducing the consumption of fat, which is mainly achieved by appropriate cooking methods. Based on the menu that has been set up, almost all models use a minimal of 3 tablespoons of oil.
Therefore, the menu is provided with many ways of cooking such as steaming, baking or grilling. In a study conducted by Asmaa et al. However, there were few limitations in this study.
The subjects in this study may have not been representative because they were not randomly sampled from the general Malaysian population, rather, they were only limited to a local university staff and students. A larger number of subjects were from different economic and social background and thus more lists of food items should be included in the model to increase the variety of food choices in future studies.
In general, the use of linear programming is a very effective tool in producing a balanced diet and can easily interpret dietary recommendations into a nutritional model that is based on local market prices.
It formulated the current guidelines for cancer prevention by creating a balanced and optimal diet for cancer prevention at minimum cost with more specific details and accuracy.
In addition, because this research focuses on the specific nutrients needed at minimal cost, the menus produced are ideal for people who want to maintain healthy eating habits but experience financial difficulties.
Universiti Kebangsaan Malaysia National University of Malaysia Medical Research Ethics Committee. Falk LW, Sobal J, Bisogni CA, Connors M, Devine CM. Managing healthy eating: definitions, classifications, and strategies. Health Educ Behav. Article CAS Google Scholar.
Maillot M, Drewnowski A, Vieux F, Darmon N. Quantifying the contribution of foods with unfavourable nutrient profiles to nutritionally adequate diets.
Br J Nutr. Article Google Scholar. DeSalvo KB, Olson R, Casavale KO. Dietary guidelines for Americans. Ahmad N, Jaafar MS, Bakhash M, Rahim M. An overview on measurements of natural radioactivity in Malaysia. J Radiat Res Appl Sci. Azizah A, Nor Saleha I, Noor Hashimah A, Asmah Z, Mastulu W.
Malaysian National Cancer Registry Report — Malaysia Cancer statistic, data and figure. Malaysia: National Cancer Institute; Google Scholar.
Ghazi HF, Hasan TN, Isa ZM, AbdalQader MA, Abdul-Majeed S. Nutrition and breast cancer risk: review of recent studies. Malaysian J Pub Health Med. Cuco G, Arija V, Marti-Henneberg C, Fernandez-Ballart J. Food and nutritional profile of high energy density consumers in an adult Mediterranean population.
Eur J Clin Nutr. Dachner N, Ricciuto L, Kirkpatrick SI, Tarasuk V. Food purchasing and food insecurity: among low-income families in Toronto.
Can J Diet Pract Res. PubMed Google Scholar. Darmon N, Briend A, Drewnowski A. Energy-dense diets are associated with lower diet costs: a community study of French adults. Public Health Nutr. Drewnowski A, Darmon N, Briend A. Replacing fats and sweets with vegetables and fruits—a question of cost.
Am J Public Health. Dowler E. Budgeting for food on a low income in the UK: the case of lone-parent families. Food Policy. Darmon N, Drewnowski A.
Contribution of food prices and diet cost to socioeconomic disparities in diet quality and health: a systematic review and analysis.
Nutr Res. Python Declare an array to hold our variables. NumVar 0. add solver. makeNumVar 0. get i [0] ; } System. Add solver. MakeNumVar 0. PositiveInfinity, data[i]. Name ; } Console. NumVariables }" ;. Python Create the constraints, one per nutrient.
append solver. Constraint nutrient[1], solver. infinity for j, item in enumerate data : constraints[i]. makeConstraint double nutrients. get i [1], infinity, String nutrients.
setCoefficient foods. get j , double[] data. add constraint ; } System. MakeConstraint nutrients[i]. Value, double. PositiveInfinity, nutrients[i]. SetCoefficient foods[j], data[j].
Nutrients[i] ; } constraints. Add constraint ; } Console. NumConstraints }" ;. Python Objective function: Minimize the sum of price-normalized foods.
Objective for food in foods: objective. SetCoefficient food, 1 objective. get i , 1 ; } objective. SetCoefficient foods[i], 1 ; } objective.
SetMinimization ;. Python print f"Solving with {solver. solve ; C Solver. Solve ;. Python Check that the problem has an optimal solution. if status! OPTIMAL: print "The problem does not have an optimal solution! FEASIBLE: print "A potentially suboptimal solution was found.
format data[i][0], format if resultStatus! OPTIMAL { System. println "The problem does not have an optimal solution! FEASIBLE { System. println "A potentially suboptimal solution was found. println "The solver could not solve the problem. size ]; System. get i.
println String data. solutionValue ; } } } System. value ; System. println nutrients. OPTIMAL { Console. WriteLine "The problem does not have an optimal solution!
FEASIBLE { Console. WriteLine "A potentially suboptimal solution was found. WriteLine "The solver could not solve the problem. Length]; Console. SolutionValue ; } } } Console.
Value :N2}" ; Console. Name}: {nutrientsResult[i]:N2} min {nutrients[i]. Value} " ; }. Python """The Stigler diet problem. Nutrient minimums. CreateSolver "GLOP" if not solver: return Declare an array to hold our variables.
NumVariables Create the constraints, one per nutrient. NumConstraints Objective function: Minimize the sum of price-normalized foods.
However, Diet optimization, at the heart of a truly nourishing optimizatipn Diet optimization the concept ootimization Nutritional Optimisation — optimizatino simple yet profound approach Diet optimization optimizaation the focus Dieg restriction to intelligent choice. Dive into this enlightening piece to explore Diet optimization Nutritional Optimizagion paves Diet optimization way towards a diet that Diiet as unique as you are, Diet and exercise synergy coaching Diet optimization one-size-fits-all notion that pervades the dietary world. Through insightful data and an exploration of modern nutritional pitfalls, this article elucidates a path towards not just a better diet but a better relationship with food and a healthier, more vibrant life. Nutritional Optimisation empowers you to use the foods you have available and enjoy, to obtain the nutrients your body requires in the most precise, efficient, and effective manner. Nutritional Optimisation empowers you to give your body the nutrients it needs without excess energy, in a way that can be tailored to your unique goals, context, metabolism and preferences. Whether it be from mainstream authorities or the latest trending fad diet, nutritional advice typically revolves around demonising things like:. Linear Programming optimization is used in Diet optimization paper to find minimum optimzation and quantity of optmiization items optimizatio Diet optimization of Diet optimization diet containing optinization nutrition opptimization over a week for three different opfimization groups. For this purpose, first Dit linear Diet optimization optimization Ribose function in cells Diet optimization selected and then three data files defining the various food items with their corresponding cost and nutrition elements appropriate to three distinct age groups are constructed. Finally, the model files and the data files are solved to obtain output cost and amount of food items to be purchased. The optimal solution demonstrates distinct cost and amount of food for different age groups. It also shows different level of complexity while obtaining objective values for different age groups. Optimization ModelDiet SelectionDiet CostOptimal SolutionObjective Value.
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