Start with the food description
Match the food itself before comparing numbers. Check raw versus cooked, preparation method, cut, skin, drained status, added salt or fat, brand, fortification, and whether the portion includes bone or another inedible part. “Chicken breast” is not specific enough if one entry is raw and skinless while another is roasted with skin.
Know where the data came from
USDA FoodData Central contains several data types. Foundation Foods include analytical data and metadata for individual samples. FNDDS is designed around foods and portions reported in national dietary surveys. Branded Foods largely reflect manufacturer label data and are updated more frequently. SR Legacy is a historical dataset.
These sources serve different purposes. A branded yogurt should usually match its current label; a basic raw ingredient may be better represented by a current analytical or survey-linked entry.
Check the date and package
Recipes and labels change. For branded foods, compare serving size and a few nutrients with the package in your hand. If they disagree, the package may be newer, the database record may represent another market, or you may have selected a similar product. Save the exact entry you used so the same food does not silently change from day to day.
Use gram-based values carefully
Per-100-gram data are useful for scaling, but only after the entry matches the state of the food. Converting a mismatched raw entry to the exact cooked gram does not fix the mismatch. Portion descriptions can also be averages; “one medium” is less reproducible than an actual edible gram weight.
Treat natural variability honestly
Agricultural foods vary by cultivar, season, feed, growing conditions, trimming, and cooking. Analytical values are estimates of samples, not a chemical promise about every apple or fillet. More decimal places do not eliminate biological variation.
For repeated tracking, consistency of entry and measurement often matters more than hunting for a single supposedly perfect value.
A quick selection checklist
Ask: Is the food state correct? Is the brand or generic type appropriate? Does the serving weight make sense? Is the data source suitable? Does the entry include additions such as salt or oil? Can I reuse this same entry later? If those answers are clear, you have a defensible estimate.