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Capturing Nutrition Information for Food Items Reported in FoodAPS-2: 

Survey Methodological Enhancements for Evidence-Based Policymaking 

and Research

Linda Kantor1

Mark Denbaly1

Jeffrey Gonzalez2

Claire Milburn1

Lauren Miller1

Elina Page1

1

Economic Research Service

U.S. Department of Agriculture

2Bureau of Labor Statistics

Federal Committee on Statistical Methodology Research and Policy Conference

October 26, 2022

The findings and conclusions in this presentation are those of the authors 

and should not be construed to represent any official USDA or U.S. 

Government determination or policy.

Mention of trade names or commercial products in this 

presentation is solely for research purposes only and does 

not imply recommendation or endorsement by the U.S. 

Department of Agriculture.
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FoodAPS

National Household 

Food Acquisition and Purchase Survey

Comprehensive data on household food purchases 

and acquisitions.

• Food-at-home (FAH) retailers

• Food-away-from-home (FAFH) places

• Foods obtained for free

• All household members over seven days

• April 2012 to January 2013
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FoodAPS-1 Sample

Nationally representative of U.S. households (4,826) 

Four target populations:

• SNAP households (1,581)

• Non-SNAP households with income:

• < 100% of the federal poverty guideline (346)

• ≥ 100% and < 185% of the federal poverty guideline (851)

• ≥ 185% of the federal poverty guideline (2,048)
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Food Economics Questions 

for Evidence-Based Policymaking

• Food purchases and acquisitions

– What foods do households acquire, where, and at what price?

• Food environment, access and affordability

– Which consumers lack access to healthful, affordable food? 

– Does ease of access matter for nutritional quality of purchases?

• Food and nutrition safety net

– How do USDA food assistance programs like SNAP, WIC, and School 

Meals influence nutrition security, diet quality and well being?
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Households

• Income

• Food security

• Diet and nutrition 

knowledge

• Food assistance 

program 

participation

Individuals

• Demographics

• Eating occasions

• School attendance

• Dietary restrictions

Events

• Place

• Total paid

• Payment type

Items

• Descriptions

• Quantities

• Expenditures

• Prices

Data collection:

Paper food books, hand-held barcode 

scanners, receipts, in-person interviews, 

telephone interviews for FAFH items

FoodAPS-1 Survey
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Food Item 

Descriptions

Nutrition

Administrative

Store Location 

and 

Characteristics

Food 

Environment

Extant data were linked to reduce respondent 

burden and enhance data analysis. 

Integrating Nutrient Data

Barcode scanning 

data from IRI

USDA 

nutrition databases
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The USDA Healthy Eating Index (HEI) 

• Assesses how well a set of foods aligns with the Dietary Guidelines for Americans

• NHANES/WWEIA 24-hour recall food intake survey

• Updated with new each release of the Guidelines

FoodAPS: Evaluate how well the mix of foods acquired

by consumers (purchased or for free) aligns with dietary 

recommendations
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FoodAPS: HEI for Household Acquisitions, by Food Place and SNAP 

Participation

Mancino, L and J. Guthrie. 2018 “Supermarkets, Schools, and Social Gatherings: 

Where Supplemental Nutrition Assistance Program and Other U.S. Households 

Acquire Their Foods Correlates With Nutritional Quality” Amber Waves, U.S. 

Department of Agriculture, Economic Research Service
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FoodAPS-1 : Item Identification and Nutrient 

Coding Challenges 

FAH: 143,000 total; 55,000 unique items

• 34% items with no usable barcodes

• Most receipts were missing size/weight information

• Food book entries incomplete, illegible, inconsistent

FAFH: 115,000 total; 22,000 unique items

• 40% acquisitions free, no receipts 

• 14% match to MenuStat database

• 59% item weights/servings sizes were 

imputed
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The World Has Changed....

• First iPhone introduced 2010 (85% of Americans owned smartphones in 2021)

• GPS location technology

• Online grocery (expanded to SNAP) and restaurant ordering and payment

• Grocery and restaurant pick up and delivery

• “Ghost” kitchens

• “Groceraunts” (Supermarket hot food bars) 

• Meal delivery kits 

• FAFH$ > FAH$

• Dollar store expansion (esp. in rural areas)
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• Native smartphone application: Primary 

mode of data collection

• GPS-location services: Real-time links to 

food places

• Food item databases and APIs: Populate 

drop-down menus of food items

• Built-in camera: Receipt and food item 

picture upload, barcode scan

FoodAPS-2: FoodLogger

Capture higher quality data and reduce backend processing time
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Accurate and Detailed Food Item Information 

Across Wide Variety of 

Purchase and Acquisition Events

Comprehensive Data on Food Purchases 

and Acquisitions

Mention of trade names or commercial products in this 

presentation is solely for research purposes only and does 

not imply recommendation or endorsement by the U.S. 

Department of Agriculture
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Data Needs for Nutrition Assessment

HEI Scoring

• Item weight/portion size

• Ingredients

• Key nutrients

• Whole grain foods

• Food Pattern Equivalents (servings)

Healthful Alternatives

• Low, reduced or zero:

– Calories

– Fat

– Sodium

– Added sugars
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Where Was the Food Item Acquired?

GPS location service tracks 

user’s stops throughout each day

Google Places API

Displays map of potential food 

place location

Users can add stops with 

prompts from 

Google Places API

Mention of trade names or commercial products in this presentation is solely for research purposes 

only and does not imply recommendation or endorsement by the U.S. Department of Agriculture
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What type of food place is it?

Foodlogger assigns a 

reporting path 

using Google Places API

FAFH

Café

Restaurant

Bar

movie_theater

night_club

stadium

FAH

supermarket

grocery_or_supermarket

liquor_store

Drugstore

Pharmacy

User identifies place type 

for places not recognized by 

Google Places
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Linking to New External Databases for Real-Time

Food Item Identification (FAH)

USDA Global 

Mention of trade names or commercial Branded Food Products Database

products in this presentation is solely 

for research purposes only and does 

not imply recommendation or 

endorsement by the U.S. Department 

of Agriculture
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Detailed ingredient and nutrition data for grocery items

Source: U. S. Department of Agriculture, Agricultural Research 

Service, Food Data Central, Global Branded Food Products Database

https://fdc.nal.usda.gov/ 

Mention of trade names or commercial products in this presentation is solely for 

research purposes only and does not imply recommendation or endorsement by 

the U.S. Department of Agriculture
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Linking to New External Databases for Real-Time

Food Item Identification (FAFH)

Restaurant data

• Menu items 

• Nutrition data

Restaurant 

receipts

184,933 items from 853 restaurants

Credit card receipts Food item receipts

Mention of trade names or commercial products in this presentation is solely for 

research purposes only and does not imply recommendation or endorsement by 

the U.S. Department of Agriculture
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Database returns nutrition and portion size 

information on acquired food item

Nutritionix restaurant database

https://www.nutritionix.com/ Mention of trade names or commercial products in this presentation is solely for 

research purposes only and does not imply recommendation or endorsement by 

the U.S. Department of Agriculture
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Assign Nutrition Codes Using NHANES/WWEIA 

Food and Nutrition Databases

Food and Nutrition Database for Dietary Studies

Converts food and beverages consumed in What We Eat In America

survey into quantities and nutrients

• 7,641 individuals reported food intake: 24-hour recall (2017-2018)

• Food and beverage descriptions: 7,083 unique codes (6,286 foods, 767 beverages)

• Set of recipes for ingredients and nutrient calculations

• 65 nutrients and food components

• Updated every 2 years

• New foods reported, codes developed
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Food Pattern Equivalents Database

Food Patterns Equivalents Database (FPED) 

Converts the foods and beverages in the FNDDS to 37 USDA Food Patterns components

Bowman SA, Clemens JC, Friday JE, and Moshfegh AJ. 2020. Food Patterns Equivalents Database 2017-

2018: Methodology and User Guide [Online]. Food Surveys Research Group, Beltsville Human Nutrition 

Research Center, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, Maryland. 

October 2020. www.ars.usda.gov/nea/bhnrc/fsrg

• Fruits: citrus, melons and berries; other fruit; fruit juice

• Vegetables: dark green; red and orange (tomatoes; other red and orange); 

• starchy (potatoes, other starchy); other vegetables; beans, peas and lentils

• Grains: whole, refined

• Protein foods: meat (cured meat, organ meat); poultry; seafood (high omega-3, 

• low omega-3); eggs; soy products; nuts and seeds

• Dairy: milk; yogurt; cheese

• Oils: naturally present (e.g., fish); added (e.g., olive oil)

• Solid fats: naturally present (meat); added (butter)

• Added sugars

• Alcoholic drinks
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FoodAPS: Food Item Reporting and Nutrition Analysis

What: Lasagna (meat)

Place: Neighbor’s house

Ingredients:
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Opportunities for Growth

• New or complex foods may lack existing FNDDS codes 

• Meal kits and foods with multiple components 

• Foods as purchased vs. foods as eaten

Mention of trade names or commercial products in this presentation is solely for 

research purposes only and does not imply recommendation or endorsement by 

the U.S. Department of Agriculture
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ERS FoodAPS team

Mark Denbaly

David Dudgeon

Jeff Gonzalez

Claire Milburn (intern)

Lauren Miller

Elina Page

ERS Food Economics Division

Jay Variyam

Collaborators

Census Bureau

USDA Food and Nutrition Service

USDA National Agricultural Statistics Service

Contractor

Linda Kantor

linda.kantor@usda.gov

Contact us at:

foodaps@usda.gov

For more information, visit:

www.ers.usda.gov/foodaps

Thank you to the ERS FoodAPS Team and our Collaborators!