Error detection/Roadmap

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Contents

Objectives

  • Detect potential errors in order to correct them and prevent them
  • Do not take into account the products whose information is questionable in the calculations of averages

Ideas to automatically detect products that contain errors entering information

Rules

  • Sum of components of nutritional table well above 100g
  • Sum of ingredients well over 100g (detection and complicated products )
  • Confusion between "l" ( lowercase letter L ) and 1 (one) in the list of ingredients because of the OCR, which gives: nom_de_l'ingrédient l5 %
  • Sum of " that sugar / starch / etc . " than carbohydrates (and the same for fat etc.).
  • Energy over 4000 kJ
  • Height from the upper portion to the size of package
  • "CO2 Carbon Footprint" greater than 3 000g
  • Over 12 additives
  • Quantity of "sugars" greater than the amount of "carbohydrate"
  • Sum of fatty acids greater than the amount of "lipids"
  • Use the EU Organic codes to check whether the certification country is the same as the production country
  • Year in a label is 2 or more greater than best before.
  • etc.

Methods

  • Consistency checks within a category
  • Consistency checks with logical or biological rules
  • Consistency checks with Laws
  • Consistency checks with outside corpora
  • Consistency checks of contributed data with OCR results
  • Bots : several bots already exist.

Note

Each track (each rule) must have a unique number associated clearly: Once an accurate method of error detection is translated computer (is implemented), then this method becomes a rule. The automatically assigned number to this rule allows contributors to designate it unambiguously.