Search a nutrition tracker or reference table for split red lentils, basmati rice or spinach, and you will often find several entries with slightly different calorie, fiber or iron values.
Those differences rarely mean one table is fabricated. As USDA FoodData Central documentation explains, food composition figures vary because datasets serve different purposes and because real agricultural foods vary in genetics, growing conditions and moisture content.
The five data types inside USDA FoodData Central
Most English-language nutrition apps draw their core records from USDA FoodData Central, maintained by the U.S. Department of Agriculture's Agricultural Research Service (ARS). Rather than a single uniform table, FoodData Central contains five distinct data types, each compiled with different methods and metadata:
- Foundation Foods: Analytical measurements of minimally processed single foods, published with sample counts, growing locations, cultivars and laboratory methods so users can see natural variability.
- SR Legacy (Standard Reference): The historic USDA reference compilation (final release April 2018) combining laboratory analyses, scientific literature and calculated values across thousands of raw and cooked foods.
- Survey Foods (FNDDS): The Food and Nutrient Database for Dietary Studies, which provides 65 nutrient values for everyday foods and mixed dishes reported in the What We Eat in America (NHANES) survey.
- Branded Foods: Label figures supplied by food manufacturers for hundreds of thousands of barcoded retail products.
- Experimental Foods: Specialized research datasets examining novel cultivars, growing conditions or analytical techniques.
Why two laboratory samples of the same food differ
Even when two laboratories analyze the exact same botanical species, their results will not match to the last decimal place. USDA Foundation Foods documentation highlights that nutrient levels in whole foods shift with plant genetics (variety or cultivar), soil mineral composition, weather and geographic growing region, harvest maturity, storage duration and processing.
In home cooking, moisture content is the single biggest driver of apparent database disagreement. Because water adds weight (1 gram per milliliter) without adding calories, a cooked grain or lentil sample that absorbed slightly more water during boiling has fewer calories, grams of protein and milligrams of iron per 100 grams of cooked weight than a firmer, drier sample of the exact same grain.
How to choose consistent entries for kitchen tracking
Small 5% to 10% differences between two reference records reflect normal biological variation rather than a logging error. Large 50% to 200% discrepancies almost always happen when a user accidentally compares a raw dry entry against a cooked boiled entry, or picks a Branded package record whose household serving was rounded.
Follow three habits to keep your food log consistent:
- Match the preparation state first: Verify whether the database entry specifies raw/dry versus cooked/boiled/drained before comparing per-100-gram numbers.
- Choose Foundation Foods or SR Legacy for whole staples: Use analytical USDA reference entries for single ingredients like lentils, rice, vegetables, nuts, seeds, spices and dairy.
- Use Branded entries only when matching a specific retail package, and remember that branded panels omit most non-mandatory vitamins and minerals.
Sources & Classical References
- USDA FoodData Central: Data Type Documentation — Defines Foundation Foods, SR Legacy, Survey Foods (FNDDS), Branded Foods and Experimental Foods, and explains genetic, environmental and processing variability in nutrient values.
- FDA: How to Understand and Use the Nutrition Facts Label — Explains how retail Nutrition Facts labels report rounded per-serving values for packaged foods.
