The Canadian Nutrient File (CNF) is the standard reference food composition database reporting the amount of nutrients in foods commonly consumed in Canada.

Several USDA food composition databases, including the Food and Nutrient Database for Dietary Studies (FNDDS), Standard Reference (SR) Legacy, and the USDA Branded Food Products Database, have transitioned to FoodData Central, a new and harmonized USDA food and nutrient data system. FoodData Central also includes expanded nutrient content information as well as links to diverse data sources that offer related agricultural, environmental, food, health, dietary supplement, and other information. The new system is designed to strengthen the capacity for rigorous research and policy applications through its search capabilities, downloadable datasets, and detailed documentation. Application developers can incorporate the information into their applications and web sites through the application programming interface (API) REST access. The constantly changing and expanding food supply is a challenge to those who are interested in using food and nutrient data. Including diverse types of data in one data system gives researchers, policymakers, and other audiences a key resource for addressing vital nutrition and health issues. FoodData Central: Includes five distinct types of data containing information on food and nutrient profiles, each with a unique purpose: Foundation Foods; Experimental Foods; Standard Reference; Food and Nutrient Database for Dietary Studies; USDA Global Branded Food Products Database. Provides a broad snapshot in time of the nutrients and other components found in a wide variety of foods and food products. Presents data that come from a variety of sources and are updated as new information becomes available. Includes values that are derived through a variety of analytic and computational approaches, using state-of-the-art methodologies and transparent presentation. FoodData Central is managed by the Agricultural Research Service and hosted by the National Agricultural Library. Resources in this dataset: Resource Title: Website Pointer for FoodData Central. File Name: Web Page, url: https://fdc.nal.usda.gov/index.html Includes Search, Download data, API Guide, Data Type Documentation, and Help pages.

Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

The USDA Branded Food Products Database is the result of a Public-Private Partnership, whose goal is to enhance public health and the sharing of open data by complementing USDA Food Composition Databases with nutrient composition of branded foods and private label data provided by the food industry. Members of the Public-Private Partnership include:

• Agricultural Research Service (ARS), USDA (www.ars.usda.gov)
• International Life Sciences Institute (ILSI) North America (www.ilsi.na.org)
• GS1 US (www.gs1us.org/)
• 1WorldSync (www.1worldsync.com)
• Label Insight (www.labelinsight.com)
• University of Maryland, Joint Institute for Food Safety and Applied Nutrition (jifsan.umd.edu)

The BFPDB includes:

• product name and generic descriptor,
• serving size in grams or milliliters,
• nutrients on the Nutrition Facts Panel per serving size and 100 gram-basis, 100 ml-basis, or fluid oz-basis,
• ingredient list, (never before captured by USDA), and
• date stamp associated with most current product formulation.

All data will be archived, allowing for dietary trends tracking. The BFPDB allows: dietitians to provide specific dietary guidance; researchers to better link dietary intakes to disease measures; and policy makers to develop guidance which promotes public health.

New in this August 2018 release are downloadable database files (ASCII .csv and MS Access), Application Programming Interface (API), and Documentation and Download User Guide.

The Canadian Nutrient File (CNF) is the standard reference food composition database reporting the amount of nutrients in foods commonly consumed in Canada.

Documents on the Summary Basis of Decision (SBD) explain why certain drugs and medical devices were authorized for sale in Canada. Regulatory Decision Summaries (RDS) explain decisions for certain health products seeking market authorization, including medical devices and prescription drugs. A Summary Safety Review (SSR) follows after a potential safety issue with a drug or health product has been identified.

Context :- This data was created to build a Deep Learning based image to recipes model which can provide ingredients and recipe of the dish once the image is uploaded

Source :- The data is scrapped from website (Link :- https://www.archanaskitchen.com/) and images were downloaded from the image download code using python. You can find both the code below Github Link :- https://github.com/websitecreatr99/Web-Scrapping-For-Cuisines.git

Inspiration :- You can build Recommendation System using csv data

The NOC is issued to a manufacturer following the satisfactory review of a submission for a new drug, and signifies compliance with the Food and Drug Regulations. The database is updated nightly and contains NOC information on human drugs from January 1, 1994 to date. It also contains NOC information on Veterinary drugs from September 19, 2000 to date.

The Access format has been discontinued as of October 2021. It is recommended to use the new file format, JSON, or to import CSV files from "Full Download of All Data Types" above to replace the tables within the Access format. The October 2022 files contain the most recent data. These files were fixed to include some missing fields and were re-released on 12/8/2020. The branded food and full download CSV archives from April 2020 to April 2022 have been re-released with corrections to the Nutrient ID column. SR Legacy is the final release of the Standard Reference data type. These data will not be updated. Earlier versions of Standard Reference (i.e., SR 17 through 28) are available on the Methods and Application of Food Composition Laboratory (MAFCL) website. The Food and Nutrient Database for Dietary Studies (FNDDS) is updated every two years, in conjunction with the two-year cycles of the National Health and Nutri-tion Examination Survey. The October 15, 2020 date represents the date on which FNDDS 2017-2018 was published and made available for download in FoodData Central. Earlier releases can be found on the Food Surveys Research Group website. The Full Download file contains files for all of the FoodData Central data types. Therefore, whenever a download for an individual data type is updated, the Full Download file also will be updated to include those new data. The Access file only contains the most recent version of products in the Branded Foods dataset. We recommend you use the API to access previous versions of Branded Food products. April 2020* Version 2 includes corrections made on May 1, 2020 to branded foods data.

When I browsed for a Food Recipes (Especially Indian Food) Dataset, I could not find one (that I could use) online. So, I decided to create one.

The dataset has following fields (self-explanatory) - ['RecipeName', 'TranslatedRecipeName', 'Ingredients', 'TranslatedIngredients', 'Prep', 'Cook', 'Total', 'Servings', 'Cuisine', 'Course', 'Diet', 'Instructions', 'TranslatedInstructions']. The datset contains a csv and a xls file. Sometimes, the content in Hindi is not visible in the csv format.

You might be wondering what the columns with the prefix 'Translated' are. So, a lot of entries in the dataset were in Hindi language. To take care of such entries and translating them to English for consistency, I went ahead and used 'googletrans'. It is a python library that implements Google Translate API underneath.

The code for the crawler, cleaning and transformation is on my Github repository (@kanishk307).

The dataset has been created using Archana's Kitchen Website. It is a great website and hosts a ton of useful content. You should definitely consider viewing it if you are interested.

The dataset can be used to answer a lot of questions related to Food Recipes. You can see the explore the serving sizes, time required to prepare a dish, most common ingredients, different cuisines, diets, courses and what not. I hope this dataset helps the Analytics community.

When I browsed for a Food Recipes (Especially Indian Food) Dataset, I could not find one (that I could use) online. So, I decided to create one.

The dataset has following fields (self-explanatory) - ['RecipeName', 'TranslatedRecipeName', 'Ingredients', 'TranslatedIngredients', 'Prep', 'Cook', 'Total', 'Servings', 'Cuisine', 'Course', 'Diet', 'Instructions', 'TranslatedInstructions']. The datset contains a csv and a xls file. Sometimes, the content in Hindi is not visible in the csv format.

You might be wondering what the columns with the prefix 'Translated' are. So, a lot of entries in the dataset were in Hindi language. To take care of such entries and translating them to English for consistency, I went ahead and used 'googletrans'. It is a python library that implements Google Translate API underneath.

The code for the crawler, cleaning and transformation is on my Github repository (@kanishk307).

The dataset has been created using Archana's Kitchen Website. It is a great website and hosts a ton of useful content. You should definitely consider viewing it if you are interested.

The dataset can be used to answer a lot of questions related to Food Recipes. You can see the explore the serving sizes, time required to prepare a dish, most common ingredients, different cuisines, diets, courses and what not. I hope this dataset helps the Analytics community.

New Zealand Food Production and Nutrition Data Stack (NZFPNDS) contains 19 spatial layers informing on annual nutrition indicators for energy, macronutrients, minerals, and vitamins. NZFPNDS has been modelled employing data from the Food and Agriculture Organisation of the United Nations, AgriBase® (a product of AsureQuality) and Land Cover Database version 5.0. Produced for analyses including health and well-being aspects. Coverage of the New Zealand mainland, its near-shore Islands, and Chatham Islands is included for all layers. Results are temporally relevant from 2018 to 2022 and resolution of all grids is one kilometre.

For information on codes used, please see the FAO nutrient conversion tables at https://openknowledge.fao.org/server/api/core/bitstreams/d3dd48cd-b157-4741-9a7d-bfd91d17fbf4/content.

Documents on the Summary Basis of Decision (SBD) explain why certain drugs and medical devices were authorized for sale in Canada. Regulatory Decision Summaries (RDS) explain decisions for certain health products seeking market authorization, including medical devices and prescription drugs. A Summary Safety Review (SSR) follows after a potential safety issue with a drug or health product has been identified.

FSIS’ FoodKeeper application educates users about food and beverages storage to help them maximize the freshness and quality of these items. By helping users understand food storage, the application empowers consumers to choose storage methods that extend the shelf life of their items. By doing so users will be able to keep items fresh longer than if they were not stored properly.

Note: If you found this dataset useful then do upvote the dataset so it can reach further kagglers.

The Food Allergens Dataset is a collection of information regarding allergens present in various food items. The dataset contains allergen information for a range of food ingredients, enabling the identification and analysis of potential allergens in different dishes and products. It serves as a valuable resource for researchers, food manufacturers, healthcare professionals, and individuals with food allergies.

Size: The dataset consists of a total of 400 records, with each record representing a specific food item and its associated allergens.

Allergens: The dataset includes a comprehensive list of allergens found in the food items. These allergens encompass a wide range of ingredients, such as dairy, wheat, nuts (almonds, peanuts, pine nuts), seafood (anchovies, fish, shellfish), grains (oats, rice), animal-based ingredients (chicken, pork), plant-based ingredients (celery, mustard, soybeans), and common ingredients (cocoa, eggs). Additionally, the dataset contains entries where no specific allergens are listed.

Data Structure - The dataset is structured with multiple columns to provide detailed information. The columns include: - Food Item: Represents the name of the food item. - Ingredients: Lists the ingredients present in the food item, categorized into different columns such as sugar, salt, oil, spices, etc. - Allergens: Indicates the allergens associated with the food item, including the specific allergenic ingredients present. - Prediction : food products containing allergens and those that do not (contains , do not contains)

Potential Models and Analysis:

Allergen Detection Model: can predict whether it contains allergens or not

Ingredient Similarity Analysis: This analysis can provide insights into similarities and differences among different types of dishes.

Allergen Prevalence Analysis: Can gain insights into the prevalence of different allergens in food products.

Recommender Systems: The dataset can also be used to develop recommender systems for individuals with specific dietary restrictions or allergies.

The NOC is issued to a manufacturer following the satisfactory review of a submission for a new drug, and signifies compliance with the Food and Drug Regulations. The database is updated nightly and contains NOC information on human drugs from January 1, 1994 to date. It also contains NOC information on Veterinary drugs from September 19, 2000 to date.

snapshot-tab-pane Recipe Apps Market Size 2025-2029The recipe apps market size is forecast to increase by USD 436.9 million, at a CAGR of 11.6% between 2024 and 2029.The market continues to evolve, driven by the increasing adoption of fast-paced lifestyles and changing dietary habits that have led more consumers to cook at home. This trend is reflected in the growing number of downloads and active users of recipe apps. According to recent data, the market has seen a significant increase in usage, with over 2.5 billion app downloads and 1 billion monthly active users worldwide. Moreover, the convenience and accessibility offered by recipe apps have made them an essential tool for households and food enthusiasts. These apps provide users with a vast array of options, from simple meal plans to complex culinary creations.They also offer features such as personalized meal recommendations, grocery lists, and step-by-step instructions, making cooking an enjoyable and hassle-free experience. However, the market is not without its challenges. With the rise in popularity comes an increased threat of cyber-attacks. As more users rely on these apps for their daily meal planning, securing user data and protecting privacy becomes a top priority. Additionally, the market is highly competitive, with numerous players vying for market share. To stay competitive, companies must continuously innovate and offer unique features that differentiate their apps from the competition. Despite these challenges, the market is poised for continued growth.The increasing trend towards healthier eating and the convenience offered by these apps make them an indispensable tool for many consumers. As such, businesses in the AI in food industries should closely monitor market trends and adapt to the evolving landscape to remain competitive.Major Market Trends & InsightsNorth America dominated the market and accounted for a 30% during the forecast period.By the Product Type, the Free sub-segment was valued at USD 330.30 million in 2023By the End-user, the Android sub-segment accounted for the largest market revenue share in 2023Market Size & ForecastMarket Opportunities: USD 126.48 millionFuture Opportunities: USD 436.9 million CAGR : 11.6%North America: Largest market in 2023What will be the Size of the Recipe Apps Market during the forecast period?Get Key Insights on Market Forecast (PDF) Request Free SampleThe market represents a dynamic and evolving sector in the digital food industry. According to recent market research, the adoption of recipe apps has experienced a significant surge, with a 21.7% increase in user engagement over the past year. This growth is driven by the convenience and accessibility these apps offer, enabling users to manage their recipe metadata, search for visual content, and discover personalized recommendations. One of the most notable aspects of the market is the continuous integration of advanced features. For instance, some apps now offer recipe content moderation, user profile management, and in-app purchase systems.Others provide recipe import/export, recipe printing, and nutritional facts display. Furthermore, recipe recommendation engines have become increasingly sophisticated, utilizing advanced algorithms to suggest recipes based on user preferences and dietary requirements. Another key trend in the market is the integration of various features to enhance the user experience. For example, some apps offer recipe analytics dashboards, recipe scheduling features, and smart shopping lists. Additionally, recipe translation features, recipe search optimization, and recipe API integration cater to a global user base. Database management systems and recipe localization ensure that users can access a vast and diverse range of recipes from around the world.Moreover, the future growth prospects of the market are promising. According to industry reports, the market is projected to expand by 18.3% within the next five years. This growth is attributed to the increasing popularity of mobile devices, the rising demand for personalized and convenient food solutions, and the continuous innovation in recipe app features. A comparison of the market's current and future growth rates reveals a steady upward trend. In the past year, the adoption of recipe apps grew by 21.7%, while the industry is projected to expand by 18.3% over the next five years.This demonstrates a consistent and robust growth trajectory for the market. In conclusion, the market is a dynamic and evolving sector that offers a range of features designed to make meal planning and preparation more convenient and personalized. With increasing user engagement and promising growth prospects, the market is poised for continued innovation and expansion.How is this Recipe Apps Industry segme

DIETxPOSOME database concerning literature selection of potentially useful papers retrieved from PubMed search API, concerning contaminants quantification in food items of worldwide highest supply and using FoodMine code (text matching filter) and machine learning (ML) protocols. 11,723 data points were collected from 254 papers from the last two decades in 72 foods to obtain relevant information on 96 contaminants, including heavy metals, polychlorinated biphenyls, dioxins, furans, polycyclic aromatic hydrocarbons (PAHs), pesticides, mycotoxins, and heterocyclic aromatic amines (HAAs).

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