The Label Can’t Tell You

One measurement can’t settle whether a food is healthy, and one expert can’t either. At both levels the fix turned out to be the same: don’t choose.

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Nutri-Score cannot be calculated from a Nutrition Facts panel, and neither can the other systems used to rate foods. Closing that gap means joining eight USDA and FDA sources that do not share a key — and once every system has what it needs, they turn out to disagree about half the shelf.

Authors

Josh Erndt-Marino, PhD

FRESH’s AI assistant

Published

July 30, 2026

The short version. Claims link to where they are worked out in full.

Nutri-Score cannot be calculated from a Nutrition Facts panel. Neither can the other systems used to rate foods. The label does not carry what they need.

  • The data exists, in pieces. Closing the gap means joining eight USDA and FDA sources that do not share a key — walk that path and the fragments collapse into one record per food.
  • Then the systems still disagree. Once every rating system finally has what it needs, they split on about half the shelf.
  • Same problem, two levels. No one measurement settles whether a food is healthy, and no one expert does either. At both levels the fix turned out to be the same: don’t choose. Integrate the measurements, run all the experts, and report the disagreement.
  • Which makes “Uncertain” a real verdict. Report the middle with its spread and a large share of products honestly resolve to uncertain — not because the analysis failed, but because the experts do not agree closely enough to place them.

The point. A single number on a package implies a settled question. For about half of what is on the shelf, it is not settled — and saying so is more useful than picking a side.

Ask whether a packaged food is healthy and you have asked a question no single measurement answers. The answer depends on nutrients, on food groups, on bioactives, on how heavily the food is processed, on what contaminants it carries, and on how much of it people actually eat.

It is not a question any single expert answers either. More than 200 food-rating systems have been published, and they disagree.

Same problem, two levels. It turned out to have the same answer at both.

No one measurement

Nutri-Score is the simplest of the five systems we work with, and the one printed on packaging across Europe. It needs energy, sugars, saturated fat, sodium, fibre and protein — all of which sit on a Nutrition Facts panel — plus the fruit, vegetable, nut and legume fraction of the product.

That last term is mandatory. It is not a nutrient, it is a food-group quantity, and it never appears on packaging. Across 80,000 label cells I checked, it appeared zero times.

So Nutri-Score cannot be computed from a label. Not computed poorly — not computed. And Nutri-Score is the least demanding of the five: it asks for seven inputs, NRF 4:3:3 for ten, NRF 9f.3 for twelve, Food Compass for fifty-four, FPro for fifty-eight. The panel does not grow to meet them.

Pool everything the five systems ask for and you get 236 distinct attributes of a food. A package is a small document. Here is how small.

A Nutrition Facts panel supplies 13 of the 236 attributes the five systems draw on
Matching the product to a USDA food supplies 192 of them — which is the entire reason the next eight databases matter.
What a package carries versus what a matched USDA record carries Two grids, each of 236 small cells, one cell per attribute the five rating systems draw on. In the first grid, 13 cells are filled — the mean number of attributes a US Nutrition Facts panel supplies. In the second grid, 192 cells are filled — the number available once the product is matched to a USDA food record. The remaining 44 cells stay empty in both. On the package a mean of 13 attributes After matching to a USDA record 192 attributes the remaining 44 are left empty and declared, not quietly filled
Each cell is one composition attribute the five systems draw on, 236 in all. “On the package” is the mean count a US Nutrition Facts panel supplies; “after matching” is what the joined USDA record carries.

The other attributes are not printed on any package, and no amount of squinting at one will produce them. They are in federal databases — which is where the second problem starts.

USDA has the answers, in eight places

The data exists. It just sits in eight separate USDA and FDA sources — survey foods, reference commodities, food-group equivalents, flavonoids, contaminants, iodine — on three keys that do not join. Food-group values exist twice, once per vocabulary. Flavonoids exist twice the same way. A question as ordinary as how much whole grain, and how much quercetin, is in this food crosses at least two vocabularies, and joining on food name is not a join.

The join is possible because USDA already documents how its survey foods are built out of reference commodities. That existing link works in both directions — the deeper commodity nutrient panel rolls up to the survey food, and the food-group and flavonoid axes push down onto the ingredients.

Eight sources, three identifier systems, one record per food
No single source carries more than 149 of the 236 attributes, so the join is not a convenience — it is the only way to get a whole food.
How eight federal sources sit on three identifier systems, and how two of them are joined Eight USDA and FDA sources sit on three identifier systems that do not join. FNDDS food codes identify survey foods and carry composition, food groups and flavonoids. SR or NDB numbers identify reference commodities and carry composition, food groups, flavonoids, contaminants and iodine. GTINs identify retail packages and carry only the label panel and ingredient statement. Food groups and flavonoids therefore exist twice, once in each vocabulary. A linkage USDA already publishes joins the first two, which lets the deeper nutrient panel roll up and the food-group and flavonoid axes push down. FNDDS food code survey foods, as people eat them composition food groups ×2 flavonoids ×2 one code per survey food — and each one is a recipe USDA’s own linkage already published, in both directions the deeper nutrient panel rolls up food groups and flavonoids push down SR / NDB number reference commodities, measured in a lab composition food groups ×2 flavonoids ×2 contaminants iodine GTIN the retail package label panel ingredient statement No shared key with either column. A package has to be matched to a food before any of this reaches it. Food groups and flavonoids exist twice — once in each vocabulary. Two columns using the same word are not a join.
Eight federal USDA and FDA sources, three identifier systems. The attribute counts are the same 236 as the figure above. Full architecture — which source wins each cell, and how the walk is validated — in the methodology companion.

Walk it and eight fragments collapse into one record per food.

That record is also honest about its own holes. Where USDA has genuinely never measured something — vitamin D isoforms, menaquinone-4, fluoride, individual tocotrienols — the gap gets declared rather than filled, because integration can propagate a measurement but it cannot create one.

Companion — how it is built

The methodology and benchmark summary draws the line this whole essay rests on. FRESH Composition — the integration and the matching, versioned, checksummed and deliberately judgment-free — sits underneath FRESH Food, the interpretive layer where the five expert systems, the Meta-Score and its stability, and the four verdicts live. Six figures on how the sources are joined, how the matches are benchmarked, and what the pipeline does not claim.

No one expert

With that record in hand, holes and all, the systems can finally run. One of them could not have run at all before it: NRF 9f.3 extends the classic nutrient-density score with a flavonoid term, and flavonoids were one of the two axes sitting in two vocabularies. Score whole-wheat bread — a food most people would call straightforwardly healthy — against roughly 6,450 US foods, and ask all five where it belongs.

Whole-wheat bread, placed by five published systems
Percentile of roughly 6,450 US foods. Each system is behaving correctly on its own terms — which is exactly what makes averaging them away the wrong move.
Whole-wheat bread scored by five published rating systems Whole-wheat bread placed against roughly 6,450 US foods. FPro, a processing score, puts it in the 12th percentile. NRF 9f.3 puts it in the 69th, Food Compass the 74th, Nutri-Score the 93rd, and NRF 4:3:3 the 99th. Taken together the five span the 12th to the 99th percentile, with a Meta-Score of 74.5 and a verdict of Uncertain. FPro processing 12th NRF 9f.3 nutrient density + flavonoids 69th Food Compass 54-input composite 74th Nutri-Score front-of-pack grade 93rd NRF 4:3:3 nutrient density + food groups 99th All five together the middle, with its spread Meta-Score 74.5 verdict: Uncertain 0 25th 50th 75th 100th percentile
Higher is healthier on each system's own terms. The Meta-Score reports the middle of the five together with how far apart they landed; here they land far enough apart that the verdict is Uncertain. Part one, Same Bread, Five Verdicts, works this example through.

Same loaf, near the bottom of the food supply and near the top of it, depending on who you ask.

That spread is not noise. FPro marks the loaf down for being an industrially produced bread; NRF 4:3:3 rewards its nutrient density. Both are behaving correctly, because they are measuring different things. Collapse them into one confident number and you have destroyed the only information that mattered. Report the middle with its spread and the honest answer is Uncertain.

That argument is the subject of Same Bread, Five Verdicts, part one of this series, and The Disagreement Has a Shape extends it across the whole carbohydrate supply.

Half the shelf

Extending it to the shelf is what the integration finally allows. Run the whole stack on 666 matched retail products and count how often the five land close enough together to say anything at all.

On half the shelf, the five experts do not agree closely enough to call it
666 matched retail products, each scored by all five systems and sorted by how far apart the five landed.
Verdicts across 666 matched retail products Of 666 matched retail products scored by all five systems, 13 percent come back Healthier, 20 percent Neutral and 16 percent Unhealthier — 49 percent in total with a verdict. The remaining 51 percent come back Uncertain, meaning the five systems landed too far apart to support any verdict. close enough to call — 49% too far apart to call — 51% 13% 20% 16% 51% Healthier Neutral Unhealthier Uncertain
666 matched retail products, scored on all five systems. Percentages are of products, not of sales. Every product behind this bar is browsable in the product surface below.
Companion — the products

The FRESH-Food product surface opens all 666 of them: label image, ingredient statement, the USDA food each was matched to, its intake tier, and where all five systems placed it. The 51% is easier to believe once you have watched two respected systems split over a cereal you have eaten.

That verdict gap counts as a finding only because of the integration underneath it. Fill a missing composition value quietly and the disagreement stops being a property of the systems and becomes an artifact of the filling.

Two levels, one shape. Don’t pick a measurement — integrate them. Don’t pick an expert — run them all and report how far apart they land. What falls out is uncomfortable: for half of a supermarket shelf, the most honest label we could print is nobody knows yet.

Resources

The published framework

Erndt-Marino J, O’Hearn M, Menichetti G. An integrative analytical framework to identify healthy, impactful, and equitable foods: a case study on 100% orange juice. International Journal of Food Sciences and Nutrition. 2023;74(6):668–684. doi:10.1080/09637486.2023.2241672

Erndt-Marino J, Ghirardelli A. Educational gaps and communication priorities for the healthfulness of carbohydrate foods. Journal of the American Nutrition Association. 2026. doi:10.1080/27697061.2026.2687436

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