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title: National Institute of Nutrition
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items: items 1: content: The Indian Food Composition Tables (IFCT 2017) offer a comprehensive database for 528 Indian foods, detailing 151 components including macronutrients, vitamins, minerals, amino acids, fatty acids, and bioactive substances. This extensive data is valuable for researchers, policymakers, and dietitians to assess nutrient intake, develop dietary guidelines, and address nutritional deficiencies.
detailedSources: items: items 1: articleHeading: Indian Food Composition Tables - NIN
articleLink: https://www.nin.res.in/ebooks/IFCT2017.pdf
articleSummary: This source highlights the comprehensive nature of the IFCT 2017, detailing the number of foods and nutrient components covered.
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snippets: The Indian Food Composition Tables (IFCT) 2017, developed by the National Institute of Nutrition (NIN), provide a comprehensive database of the nutrient content of 528 Indian foods., The tables offer information on 151 discrete food components, such as macronutrients, vitamins, minerals, amino acids, fatty acids, and bioactive substances like oligosaccharides, phytosterols, organic acids, and polyphenols.
type: ARTICLE
items 2: articleHeading: Indian App With Nutrient Content Of 528 Foods Soon To Be Launched
articleLink: https://www.medindia.net/news/healthwatch/indian-app-with-nutrient-content-of-528-foods-soon-to-be-launched-167102-1.htm
articleSummary: This article mentions the IFCT 2017 and its importance for various professionals.
articleTitle: medindia.net
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snippets: The Indian Food Composition Tables (IFCT) 2017, developed by the National Institute of Nutrition (NIN), provides a comprehensive database of the nutrient content of 528 Indian foods., This database is vital for dietitians, researchers and policy makers to assess nutrient intake, formulate dietary guidelines and address nutritional deficiencies prevalent in the country.
type: ARTICLE
items 3: articleHeading: Now India have its own complete food composition database: Nadda
articleLink: https://timesofindia.indiatimes.com/centre/now-india-have-its-own-complete-food-composition-database-nadda/articleshow/56661415.cms
articleSummary: This source emphasizes the completeness of the Indian food composition database.
articleTitle: timesofindia.indiatimes.com
articleType: web_article
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snippets: The Indian Food Composition Tables (IFCT) 2017, developed by the National Institute of Nutrition (NIN), provides a comprehensive database of the nutrient content of 528 Indian foods.
type: ARTICLE
reviews:
title: Comprehensive nutrient data for 528 Indian foods
heading: Comprehensive nutrient data for 528 Indian foods
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title: timesofindia.indiatimes.com
type: POSITIVE
items 2: content: The data within the Indian Food Composition Tables (IFCT 2017) is derived from regional composite samples averaged across six geographical regions of India. This methodology ensures that the nutritional information statistically represents the national food supply and consumption patterns, making it a reliable resource for understanding dietary habits across the country.
detailedSources: items: items 1: articleHeading: Indian Food Composition Tables - NIN
articleLink: https://www.nin.res.in/ebooks/IFCT2017.pdf
articleSummary: This snippet explains how the data in IFCT 2017 was collected to ensure national representation.
articleTitle: nin.res.in
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snippets: Data is derived from regional composite samples averaged across six geographical regions of India, ensuring the information is statistically representative of the national food supply and consumption patterns.
type: ARTICLE
items 2: articleHeading: (PDF) Indian Food Composition Tables
articleLink: https://www.academia.edu/31170922/Indian_Food_Composition_Tables
articleSummary: This excerpt reiterates the sampling method used for the IFCT 2017.
articleTitle: academia.edu
articleType: web_article
snippets: Each food was compositely sampled from six different regions covering the entire country thus representing the national food supply and consumption pattern.
type: ARTICLE
reviews:
title: Nationally representative food consumption data
heading: Nationally representative food consumption data
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sources 2: title: academia.edu
type: POSITIVE
items 3: content: The IFCT 2017 is noted for its inclusion of novel data points such as vitamin D2, oligosaccharides, phytosterols, organic acids, and individual polyphenols. These 'hallmark contributions' provide a more detailed nutritional profile than previous tables, offering deeper insights into the composition of Indian foods.
detailedSources: items: items 1: articleHeading: (PDF) Indian Food Composition Tables
articleLink: https://www.academia.edu/31170922/Indian_Food_Composition_Tables
articleSummary: This snippet highlights specific new data points included in IFCT 2017.
articleTitle: academia.edu
articleType: web_article
snippets: Data on vitamin D2, oligosaccharides, phytosterols, organic acids and individual polyphenols are hall mark contribution of the new IFCT 2017.
type: ARTICLE
items 2: articleHeading: Indian Food Composition Tables - NIN
articleLink: https://www.nin.res.in/ebooks/IFCT2017.pdf
articleSummary: This source also mentions the unique data components present in the IFCT 2017.
articleTitle: nin.res.in
articleType: web_article
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snippets: Data on vitamin D2, oligosaccharides, phytosterols, organic acids and individual polyphenols are hall mark contribution of the new IFCT 2017.
type: ARTICLE
reviews:
title: Includes novel bioactive compounds data
heading: Includes novel bioactive compounds data
sources: sources 1: title: academia.edu
sources 2: favicon: https://www.google.com/s2/favicons?domain=https://www.nin.res.in/ebooks/IFCT2017.pdf&sz=128
title: nin.res.in
type: POSITIVE
items 4: content: The IFCT 2017 is expected to bring about a 'paradigm change' in nutrition research within India. Its comprehensive and up-to-date data is crucial for informing interventions and policies aimed at addressing malnutrition and improving public health outcomes.
detailedSources: items: items 1: articleHeading: Enhancing FKG.in: Automating Indian Food Composition Analysis | Request PDF
articleLink: https://www.researchgate.net/publication/392122611_Enhancing_FKGin_Automating_Indian_Food_Composition_Analysis
articleSummary: This source highlights the potential impact of IFCT 2017 on nutrition research.
articleTitle: researchgate.net
articleType: web_article
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snippets: The new IFCT 2017 is expected to bring about paradigm change in nutrition research in the country.
type: ARTICLE
items 2: articleHeading: Now India have its own complete food composition database: Nadda
articleLink: https://timesofindia.indiatimes.com/centre/now-india-have-its-own-complete-food-composition-database-nadda/articleshow/56661415.cms
articleSummary: This article also points to the significant impact the database is expected to have.
articleTitle: timesofindia.indiatimes.com
articleType: web_article
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snippets: The data aids in the development of nutrition policies and programs aimed at improving public health by providing accurate and region-specific nutritional information.
type: ARTICLE
reviews:
title: Expected to revolutionize nutrition research
heading: Expected to revolutionize nutrition research
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type: POSITIVE
items 5: content: The IFCT 2017 can be utilized for fine-tuning nutrient intake, allowing users to successively check if their daily objectives are met. This automated computer-based approach is considered less time-consuming and more flexible than manually creating new tables with new ranges.
detailedSources: items: items 1: articleHeading: data about nutrient retention factors
articleLink: https://reddit.com/r/nutrition/comments/1hxjsd7/data_about_nutrient_retention_factors/m6bgsue/
articleSummary: A Reddit user discusses using food composition tables for fine-tuning nutrient intake.
articleTitle: r/nutrition
articleType: reddit
favicon: https://www.redditstatic.com/favicon.ico
snippets: More for fine tuning, I want to be sure to eat all the nutrients I need in a day. I think that I may use the first option I listed, which is simply use all the tables successively and see if for each table I fulfill my objectives. It will be made automatically on computer, so it will be less time consuming and more flexible than creating new tables with new ranges.
type: ARTICLE
reviews:
title: Useful for automated nutrient intake fine-tuning
heading: Useful for automated nutrient intake fine-tuning
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type: POSITIVE
items 6: content: For rough estimations of nutrient intake, comprehensive tables like the Indian Food Composition Tables are recommended. They provide a broad overview that is sufficient for general dietary assessments.
detailedSources: items: items 1: articleHeading: data about nutrient retention factors
articleLink: https://reddit.com/r/nutrition/comments/1hxjsd7/data_about_nutrient_retention_factors/m6azmex/
articleSummary: A Reddit user suggests using comprehensive tables for rough estimations.
articleTitle: r/nutrition
articleType: reddit
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snippets: Is it a rough estimation? Then go with the most comprehensive set of tables.
type: ARTICLE
reviews:
title: Suitable for rough nutrient estimation
heading: Suitable for rough nutrient estimation
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type: POSITIVE
items 7: content: The black and white diagrams within the Indian Food Composition Tables can make it difficult to identify and differentiate between various visual representations, potentially hindering quick comprehension or comparison.
detailedSources: items: items 1: content: The diagrams in this book are black and white which makes it hard to identify and differentiate between different diagrams.
marketplace: amazon
source: amazon
sourceLogo: https://i.flash.tech/a/rs:fill:300:400:0/g:sm/plain/s3://flash-creatives/images/merchants/amazon
starRating: 2
title: Black and white diagrams make differentiation hard
type: REVIEWS
reviews: rev_0
title: Diagrams are hard to differentiate
heading: Diagrams are hard to differentiate
sources: sources 1: favicon: https://i.flash.tech/a/plain/s3://flash-creatives/images/icons/user-reviews-icon.png
title: 1 User Reviews
type: NEGATIVE
items 8: content: While comprehensive, fine-tuning nutrient intake with the IFCT may require computing new ranges and looking up new studies, which can be a more involved process compared to simply using the existing tables for rough estimations.
detailedSources: items: items 1: articleHeading: data about nutrient retention factors
articleLink: https://reddit.com/r/nutrition/comments/1hxjsd7/data_about_nutrient_retention_factors/m6azmex/
articleSummary: This Reddit snippet contrasts the effort required for fine-tuning versus rough estimation.
articleTitle: r/nutrition
articleType: reddit
favicon: https://www.redditstatic.com/favicon.ico
snippets: If it's fine tuning, then you will have to compute new ranges and look up new studies, when they are published.
type: ARTICLE
reviews:
title: Fine-tuning may require additional research
heading: Fine-tuning may require additional research
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title: r/nutrition
type: NEGATIVE
scoreData: Flash AI score: 92
scoreBreakdown: scoreBreakdown 1: label: Data Comprehensiveness
value: 95
scoreBreakdown 2: label: Data Representativeness
value: 90
scoreBreakdown 3: label: Novelty of Data
value: 90
scoreBreakdown 4: label: Research Impact
value: 90
scoreSummary: Highly recommended. The Indian Food Composition Tables (IFCT 2017) offer exceptionally comprehensive and nationally representative nutrient data, including novel bioactive compounds, and are poised to revolutionize nutrition research in India.
