Global Food Nutrient Database

Columns

• Food: The name or type of the food item.
• Caloric Value: Total energy provided by the food, measured in kilocalories (kcal) per 100 grams.
• Fat (in g): Total amount of fats in grams per 100 grams, including saturated, monounsaturated, and polyunsaturated breakdowns.
• Saturated Fats (in g): Amount of saturated fats (fats that typically raise cholesterol levels) in grams per 100 grams.
• Monounsaturated Fats (in g): Amount of monounsaturated fats (considered heart-healthy fats) in grams per 100 grams.
• Polyunsaturated Fats (in g): Amount of polyunsaturated fats (including essential fats the body needs but cannot produce) in grams per 100 grams.
• Carbohydrates (in g): Total carbohydrates in grams per 100 grams, including sugars.
• Sugars (in g): Total sugars in grams per 100 grams, a subset of carbohydrates.
• Protein (in g): Total proteins in grams per 100 grams, essential for body repair and growth.
• Dietary Fiber (in g): Fiber content in grams per 100 grams, important for digestive health.
• Cholesterol (in mg): Cholesterol content in milligrams per 100 grams, pertinent for cardiovascular health.
• Sodium (in mg): Sodium content in milligrams per 100 grams, crucial for fluid balance and nerve function.
• Water (in g): Water content in grams per 100 grams, which affects the food’s energy density.
• Vitamin A (in mg): Amount of Vitamin A in micrograms per 100 grams, important for vision and immune functioning.
• Vitamin B1 (Thiamine) (in mg): Essential for glucose metabolism.
• Vitamin B11 (Folic Acid) (in mg): Crucial for cell function and tissue growth, particularly important in pregnancy.
• Vitamin B12 (in mg): Important for brain function and blood formation.
• Vitamin B2 (Riboflavin) (in mg): Necessary for energy production, cell function, and fat metabolism.
• Vitamin B3 (Niacin) (in mg): Supports digestive system, skin, and nerve health.
• Vitamin B5 (Pantothenic Acid) (in mg): Necessary for making blood cells and helps convert food into energy.
• Vitamin B6 (in mg): Important for normal brain development and keeping the nervous and immune systems healthy.
• Vitamin C (in mg): Important for the repair of all body tissues.
• Vitamin D (in mg): Crucial for the absorption of calcium, promoting bone growth and health.
• Vitamin E (in mg): Acts as an antioxidant, helping to protect cells from damage caused by free radicals.
• Vitamin K (in mg): Necessary for blood clotting and bone health.
• Calcium (in mg): Vital for building and maintaining strong bones and teeth.
• Copper (in mg): Helps with collagen formation, increases iron absorption, and plays a role in energy production.
• Iron (in mg): Essential for the creation of red blood cells.
• Magnesium (in mg): Important for many bodily processes including regulation of muscle and nerve function, blood sugar levels, blood pressure, and making protein, bone, and DNA.
• Manganese (in mg): Involved in the formation of bones, blood clotting factors, and enzymes that play a role in fat and carbohydrate metabolism, calcium absorption, and blood sugar regulation.
• Phosphorus (in mg): Helps with the formation of bones and teeth and is necessary for the body to make protein for the growth, maintenance, and repair of cells and tissues.
• Potassium (in mg): Helps regulate fluid balance, muscle contractions, and nerve signals.
• Selenium (in mg): Important for reproduction, thyroid gland function, DNA production, and protecting the body from damage caused by free radicals and infection.
• Zinc (in mg): Necessary for the immune system to properly function and plays a role in cell division, cell growth, wound healing, and the breakdown of carbohydrates.
• Nutrition Density: A metric indicating the nutrient richness of the food per calorie.

Distribution

• FOOD-DATA-GROUP1.csv (97.24 kB)
• FOOD-DATA-GROUP2.csv (51.71 kB)
• FOOD-DATA-GROUP3.csv (97.8 kB)
• FOOD-DATA-GROUP4.csv (38.71 kB) Specific numbers for rows/records are not available in the provided details.

Usage

1. Nutritional Pattern Analysis: Machine learning algorithms can analyse trends and patterns in food consumption, linking them to nutritional impacts.
2. Diet Recommendation Systems: Integrating this dataset with broader dietary data allows machine learning models to recommend dietary adjustments.
3. Predictive Modelling for Health Impacts: With sufficient data linking food consumption to health outcomes, predictive models could forecast health impacts.
4. Ingredient Optimisation: Machine learning can aid in formulating new recipes by predicting nutritional content based on ingredients.
5. Consumer Behaviour Analysis: Classification or regression models can predict consumer preferences for certain food types based on nutritional information and demographic data.
6. Quality Control: Machine learning models can be trained to predict the quality and consistency of food based on variations in manufacturing parameters and ingredient quality.
7. Text Analysis for Marketing Insights: Natural language processing (NLP) techniques applied to product reviews and feedback can extract consumer sentiments related to specific nutritional aspects.
8. Supply Chain Optimisation: Analysing sales and nutritional preferences with machine learning can optimise supply chains by predicting demand fluctuations based on health trends and nutritional awareness.
9. Educational Tools: Developing interactive machine learning applications that teach users about nutrition using food as a case study.
10. Integration with Fitness and Health Tracking Apps: Machine learning models can integrate food consumption data into broader dietary tracking tools, providing users with insights into their overall dietary goals.

Coverage

License

Who Can Use It

• Researchers in nutritional science for in-depth analysis.
• Dietitians planning meals and dietary regimes.
• Healthcare providers advising on dietary options for patients.
• Individuals tracking their food intake and personal nutrition.
• Manufacturers developing healthier food products.
• Businesses analysing consumer preferences and optimising supply chains in the food industry.
• Educators creating tools to teach about nutrition.

Dataset Name Suggestions

• Global Food Nutrient Database
• Dietary Health Data
• Nutritional Content Database
• Food Composition for AI
• Predictive Nutrition Dataset

Attributes