The camera · Decision 2 of 12

Why we track added sugar, and what we mean by it

Snapkin says “added sugar” everywhere, because that is the phrase people use. The definition underneath is the World Health Organization's free sugars, which covers more.

Written and maintained by Mattias Geniar. Last updated .

What the app does
Counts WHO free sugars with a fruit-purée convention, and uses roughly 10% of your calorie target. No streak on it, no reward for staying under.
How sure we are Thin , One small study, or an engineering choice.
Less than anything else the app shows. It is a rough budget, not a measurement, which is why it has no confidence badge.

The evidence for it, and there is little of it

This is the weakest evidence on any of these pages, and the page says so throughout. Here is what there is.

10% of energy is the WHO’s limit on free sugars, and where the app’s daily limit comes from WHO guideline (2015) · below 10%: strong recommendation; below 5%: conditional
0.98 ICC for specialist estimates of added sugar in packaged foods; not validation of Snapkin or photo estimates Scapin et al. (2021) · 930 products, ICC
−0.80 kg weight change from eating less free sugar, in adults eating freely Te Morenga et al. (BMJ 2013) · and no effect once calories are matched

What “added sugar” means here

The definition underneath is the World Health Organization's free sugars: sugar added by a manufacturer, a cook or the person eating, plus honey, syrups, and fruit juice and juice concentrates. It does not include the sugar in whole fruit, in vegetables, or in plain milk and yoghurt. The app also counts fruit purées and smoothies, following the broader processing convention in the 2018 UK definition. WHO’s wording does not explicitly enumerate all these processed-fruit cases. Plain stewed or canned fruit with pieces retained is not automatically free sugar; added sweeteners still count.

The “Added Sugars” line on a US nutrition label (21 CFR 101.9) is not the same thing. Total sugars, the figure a European label declares, is a third thing again. The definitions differ for juice and for sugars naturally present in whole foods.

Three definitions of sugar, and where they differ What each one counts, for the same food.
Counted as sugar? Snapkin
(WHO free sugars)
US label
“Added Sugars”
EU label
“of which sugars”
Sugar stirred in, or added by a manufacturerYesYesYes
Honey, syrups, molassesYesYesYes
100% fruit juice, including correctly reconstituted juiceYesNoYes
Concentrated juice used as a sweetenerYesYes*Yes
Sugar in whole fruit and vegetablesNoNoYes
Lactose in plain milk and yoghurtNoNoYes

*US rules contain specific concentrate exceptions. See FDA guidance and the regulation linked above.

Unsweetened 100% orange juice can declare zero added sugars on a US label while its naturally present sugars count as WHO free sugars. The grams depend on the serving. Concentrated juice used to sweeten other foods can count as added sugar under FDA rules; reconstituting 100% juice is a different case.

The WHO guideline recommends keeping free sugars below 10% of energy, and suggests going below 5% if possible. Snapkin converts 10% of target calories to grams at 4 kcal/g and rounds to the nearest 5 g, so the displayed budget can be slightly above or below 10%. This is a rough tracking budget, not a precisely enforced WHO threshold.

How the number is worked out

Free sugars are not printed on any European label, so they have to be estimated. There is published specialist methods for estimating added and free sugars from food-composition and ingredient information. Our analyser prompt borrows their hierarchy of evidence; an AI following instructions is not the validated specialist procedure. Louie et al. (2015) set out ten steps, starting from hard evidence and ending with guesses by food category. Kibblewhite et al. (2017) restated them for WHO free sugars. Scapin et al. (2021) adapted them for a country whose labels declare neither total nor added sugars.

Scapin checked the method against US label-declared values on 930 products and reached an agreement score (ICC) of 0.98. An independent test of Louie's method (Davies et al., 2022) put it at R² 0.97 with a mean absolute error of 1.26 g per 100 g. Davies used a synthetic test set of 500 Australian products to compare the manual method with a machine-learning method. Both evaluations concern packaged-food information, not photographs.

0.98 ICC against US label-declared values, 930 products One specialist, ingredient lists in hand
1.26 g manual-method MAE per 100 g or 100 mL; 500 synthetic test products Davies et al. 2022, R² 0.97
4.6 g standard deviation between two trained researchers, per 100 g Louie, Lei & Rangan 2016, the same 5,740 foods

The reproducibility study asks a different question. Two people applying the same written steps to the same 5,740 foods differed with a standard deviation of 4.6 g per 100 g. That standard deviation cannot be divided by another study’s mean absolute error to claim a multiple of inaccuracy: the metrics, samples and comparisons differ.

The evidence against, and there is a lot of it

The weight effect is largely explained by energy intake

Te Morenga et al. (BMJ 2013): eating less sugar changes weight by −0.80 kg (95% CI −1.21 to −0.39) and eating more by +0.75 kg (0.30 to 1.19). But there was no significant weight difference when sugars replaced other carbohydrates at matched calories. This is a finding about body weight, not proof of no other health effects. WHO’s guideline also considers dental caries.

Estimating sugar from a photograph is the weakest thing this app does

One peer-reviewed photo study reporting total sugar is O'Hara et al. (2025), 114 photographs of weighed meals, assessed with ChatGPT-4. It measured total sugar, not free sugar. We have no validation showing that Snapkin can accurately separate the two from photographs. Hidden ingredients make that a particularly uncertain estimate.

Similar median calories do not mean accurate individual meals Differences between estimated and reference medians across 114 meals, not mean absolute percentage errors.
Sugar 32% low

Total sugar; this is not a free-sugar accuracy result

Protein 2.7% low

Same photographs, ChatGPT-4

Energy 0.1% high

Median-to-median difference; individual errors can cancel

0 35% difference between medians
O’Hara et al. compared a median energy estimate of 525 kcal with a reference median of 524.5 kcal: a 0.1% difference between medians. Individual-meal energy error was much larger: the paper reports average relative error of 27.3%. Total-sugar medians differed by −32%. Good ranking or similar medians does not establish accurate amounts for a particular meal.

“Carbs that turn into sugar” is a different thing, and we do not track it

Digestible carbohydrate, including starch, can affect blood glucose. Glycaemic load combines a food’s glycaemic index with the amount of available carbohydrate; it is distinct from free-sugar content. Zeevi et al. (Cell 2015) put 800 people on continuous glucose monitors across 46,898 meals and found that different people respond very differently to the same meal. Published glycaemic-index and load tables exist, but do not predict every individual’s response. Snapkin does not estimate your personal blood-glucose response.

So the sugar limit is a rough budget rather than a measurement, and it carries no confidence badge. The eating disorders page has the other half of this decision: there is no streak on the sugar limit, and no celebration for staying under it.