摘要：This study presents a method laying the groundwork for systematically monitoring food quality and the healthfulness of consumers’ point-of-sale grocery purchases. The method automates the process of identifying United States Department of Agriculture (USDA) Food Patterns Equivalent Database (FPED) components of grocery food items. The input to the process is the compact abbreviated descriptions of food items that are similar to those appearing on the point-of-sale sales receipts of most food retailers. The FPED components of grocery food items are identified using Natural Language Processing techniques combined with a collection of food concept maps and relationships that are manually built using the USDA Food and Nutrient Database for Dietary Studies, the USDA National Nutrient Database for Standard Reference, the What We Eat In America food categories, and the hierarchical organization of food items used by many grocery stores. We have established the construct validity of the method using data from the National Health and Nutrition Examination Survey, but further evaluation of validity and reliability will require a large-scale reference standard with known grocery food quality measures. Here we evaluate the method’s utility in identifying the FPED components of grocery food items available in a large sample of retail grocery sales data (~190 million transaction records).
