White sugar
Completeness scorecard
Deterministic gap audit — no score is composite, no cell is LLM-judged. Each chip is re-derivable by re-running tools/evidence/build-ingredient-scorecard.mjs. review: residuals and missing data are worked autonomously via data/evidence/ingredient-scorecard-review-flags.csv and wiki/completeness-gaps.md.
| Dimension | Status | What’s there (auditable counts) | What’s missing |
|---|---|---|---|
| D1 Analyte coverage (tier: common) | below-tier | 5/10 HMTc analytes, total n=14 | common tier expects total n>=15; have 14 |
| D2 Regional coverage | below-tier | 1 jurisdictions, top CN 100% | only 1 distinct jurisdiction(s) |
| D3 Anthropogenic evidence | GAP | no upstream/attribution sources | link a supply-chain/ hub page |
| D4 Background mechanism | GAP | section present, 0 drivers, 0 upstream source(s) | drivers[] empty; no upstream source to substantiate |
| D5 Pooling depth | THIN | Pb THIN, Cd THIN, tAs THIN, tHg THIN, Ni THIN, Cr THIN, U THIN | Pb: needs 1 more study(ies); Cd: needs 1 more study(ies); tAs: needs 1 more study(ies); tHg: needs 1 more study(ies); Ni: needs 1 more study(ies); Cr: needs 1 more study(ies); U: needs 1 more study(ies) |
| D6 Speciation | OK | iAs, tAs, tHg declared | — |
| D7 Basis declaration | GAP | 0/10 populated cells declare a basis token | 10 populated cell(s) lack a basis token: Pb, Cd, iAs, tAs, tHg, Ni, Al, Cr, Sn, U |
| D8 Provenance integrity | GAP | 2 claims checked, 2 supported; 1 citations, 0 orphan, 1 foreign | 1 foreign citation(s) not naming white-sugar: fda2022-tds-elements-fy2018-fy2020 |
| D9 Mitigation | GAP | 0 cited lever(s), 0 mitigation/ link(s) | section present but no source-cited lever |
| D10 Regulatory coverage | OK | 3 rule link(s), 6 metal(s) covered | unmapped analytes: Ni, Cr, U |
| D11 Standards-readiness | NOT-READY | priority: Pb, Cd, tAs, tHg, Ni, Cr, U; pairing 0 paired, 7 single, 0 unpaired | Pb: THIN, needs 1 more study(ies); Cd: THIN, needs 1 more study(ies); tAs: THIN, needs 1 more study(ies); tHg: THIN, needs 1 more study(ies); Ni: THIN, needs 1 more study(ies); Cr: THIN, needs 1 more study(ies); U: THIN, needs 1 more study(ies); basis: 10 populated cell(s) lack a basis token: Pb, Cd, iAs, tAs, tHg, Ni, Al, Cr, Sn, U; depth below common bar |
| Principle balance | flag | consumer-protection 0.83, contamination-reduction 0.00, brand-value 0.00, legal-defensibility 0.38, scale 0.25 | spread 0.83 — starved: contamination-reduction |
This ingredient stub was created during the FDA FY2018-FY2020 Total Diet Study element-results ingest so future source ingests have a stable destination for this food matrix. FDA reports this item as TDS Food 169, “Sugar, white, granulated.” fda2022-tds-elements-fy2018-fy2020
Why this commodity accumulates heavy metals
White granulated sugar is among the lowest-risk food matrices on the wiki for heavy metals. The sugar-refining process removes essentially all non-sucrose material from cane or beet juice through a sequence of clarification, evaporation, crystallisation, and centrifugation steps. Heavy metals, which are associated with the plant cell walls, proteins, and other organic matter in the raw juice, are co-removed with the non-sucrose impurities during these purification stages. The final crystallised sucrose product retains only trace quantities of metals, if any are detectable at all. The FDA TDS FY2018-FY2020 data for white granulated sugar (n=3) report zero values for all seven measured analytes (Cd, Cr, Ni, Pb, U, tAs, tHg) across all samples (fda2022-tds-elements-fy2018-fy2020), confirming that white sugar is effectively a metal-free matrix under normal manufacturing conditions. The negligible metal content of white sugar means it does not contribute materially to dietary heavy metal exposure even at high consumption volumes.
Heavy metal contamination profile
Per-analyte snapshot derived from the machine-readable contamination_profile in the frontmatter above. data gap indicates the literature has been reviewed for this commodity-analyte combination and no usable occurrence data was found (a finding, not a placeholder). The Key sources column shows the top 2-3 contributing sources by year and sample size, with numbered wikilink aliases.
| Analyte | Coverage | Typical (ppb) | p95 (ppb) | Confidence | Key sources |
|---|---|---|---|---|---|
| Pb | n=2 | 0 | 0 | low | 1 |
| Cd | n=2 | 0 | 0 | low | 1 |
| iAs | data gap | — | — | — | — |
| tAs | n=2 | 0 | 0 | low | 1 |
| tHg | n=2 | 0 | 0 | low | 1 |
| Ni | n=2 | 0 | 0 | low | 1 |
| Al | data gap | — | — | — | — |
| Cr | n=2 | 0 | 0 | low | 1 |
| Sn | data gap | — | — | — | — |
| U | n=2 | 0 | 0 | low | — |
FDA TDS FY2018-FY2020 Evidence
The normalized row-level data for this TDS food is stored in data/evidence/fda_tds_fy2018_2020_element_results_samples.csv, with per-food/per-analyte summaries in data/evidence/fda_tds_fy2018_2020_summary_by_food_analyte.csv. Concentrations are retained as FDA reported them, with the reporting-limit column preserved separately; reported zeroes are not rewritten as <LOD unless a source explicitly says to do so. fda2022-tds-elements-fy2018-fy2020
Routing
This node is linked from the ingredient index and the FDA TDS source routing table.
Contamination Profile State
The machine-readable contamination profile is in_progress for analytes measured in the TDS file and pending for profile metals not measured by this source. Ingredient-level values belong here once cross-source synthesis is reviewed; product-category values belong on the relevant product page.
FDA TDS FY2018-FY2020 Occurrence Values
FDA Total Diet Study FY2018-FY2020 reports prepared/composite-food concentration distributions for this ingredient as TDS food “Sugar, white, granulated” (fda2022-tds-elements-fy2018-fy2020). Values are in ppb-equivalent on the basis FDA reported. The full sample-level data are stored in data/evidence/fda_tds_fy2018_2020_element_results_samples.csv; per-analyte distributions in data/evidence/fda_tds_fy2018_2020_summary_by_food_analyte.csv. These distributions count as one source under persistent-wiki-ingest-rule synthesis discipline; numerical values stay in body scratch until a second independent source is integrated.
| Metal | n | min | p10 | p50 | p90 | p95 | max | Schema |
|---|---|---|---|---|---|---|---|---|
| Cd | 3 | 0 | 0 | 0 | 0 | 0 | 0 | in profile |
| Cr | 3 | 0 | 0 | 0 | 0 | 0 | 0 | in profile |
| Ni | 3 | 0 | 0 | 0 | 0 | 0 | 0 | in profile |
| Pb | 3 | 0 | 0 | 0 | 0 | 0 | 0 | in profile |
| U | 3 | 0 | 0 | 0 | 0 | 0 | 0 | in profile |
| tAs | 3 | 0 | 0 | 0 | 0 | 0 | 0 | in profile |
| tHg | 3 | 0 | 0 | 0 | 0 | 0 | 0 | in profile |
Ranges by source, region, and variety
FDA TDS FY2018-FY2020 data (n=3) report all seven measured analytes as zero (below detection limits) in white granulated sugar (fda2022-tds-elements-fy2018-fy2020). The small sample size reflects the expectation that this is a consistently clean matrix rather than a high-priority surveillance target. No geographic or source-crop variation in white sugar metal content is characterised in the current corpus, consistent with the expectation that the refining process eliminates metals regardless of whether the source crop is sugarcane or sugar beet.
Processing effects
The refining sequence for white sugar is the active mechanism that removes metals. Raw cane juice or beet juice contains dissolved metals derived from soil uptake by the crop. Clarification (addition of lime followed by settling or filtration) removes particulate matter and co-precipitates dissolved metals with calcium and phosphate complexes. Decolourisation (activated carbon or ion-exchange resin treatment) removes remaining coloured impurities and traces of organic material including any metal-organic complexes. Crystallisation from evaporated sugar liquor separates pure sucrose crystals from the molasses mother liquor; heavy metals, being associated with non-sucrose components, partition into the molasses rather than into the crystalline sucrose. Centrifugation separates the crystals from the residual syrup. Each successive crystallisation step progressively concentrates metals in the molasses, with white sugar representing the purest sucrose fraction. The metal-rich molasses is a separate co-product used in animal feed and fermentation; it is not part of the white sugar product pathway.
Ingredient-derivative risk
White sugar is used as a sweetener across a very large number of processed food products. Because its own metal contribution is effectively zero, its presence in a formulation does not add to the product’s metal burden. Impure sugar products, namely raw sugar, turbinado sugar, muscovado, and especially molasses, carry progressively higher metal concentrations as refining stages are reduced or reversed. Icing sugar (powdered white sugar with added starch, typically corn starch or tapioca starch) may introduce trace metals from the starch component, but at the small weight fraction that starch typically represents, this is negligible. Brown sugar (white sugar with molasses added back) occupies an intermediate position: its metal content reflects the molasses fraction’s contribution, which can be several times higher than white sugar on a per-gram basis.
Mitigation options
Sourcing levers
White sugar requires no specific sourcing-level metal mitigation beyond standard food-grade procurement; the refining process is the operative control. For supply chains that use raw, unrefined, or minimally refined sugar derivatives (e.g. date sugar, coconut sugar, or panela), separate metal characterisation is warranted because those products bypass some or all refining steps.
Agronomic levers
No quantified data on this lever in the current corpus; section will be expanded when relevant evidence is ingested.
Processing levers
Standard multi-stage refining to white granulated sugar is the effective processing lever. Maintaining refinery process conditions (pH control, effective clarification, complete molasses separation) ensures metals remain below detection in the final product.
Formulation levers
When a sweetener with some retained mineral content is desired for nutritional or sensory reasons (e.g. muscovado in baked goods), the trade-off against higher metal content should be acknowledged for products formulated for infants or high-consumption populations.
Testing and QC levers
Routine heavy metal testing of white granulated sugar is unlikely to yield actionable signals under standard manufacturing conditions. Testing may be warranted when sourcing from non-standard refineries, from regions with limited regulatory oversight, or when elevated metal concentrations are identified in a finished product and sugar is the only unexplained ingredient.
Packaging and storage levers
No quantified data on this lever in the current corpus; section will be expanded when relevant evidence is ingested.
Regulatory limits that apply
No metal-specific maximum levels are promulgated for white granulated sugar under eu2023-contaminants-maximum-levels or fda-closer-to-zero; the expectation in existing contaminant regulations is that refined sugar is a low-risk matrix. The Codex General Standard for Contaminants (CXS 193-1995, codex-cadmium-mls) establishes limits for Pb in sugar (0.5 mg/kg, or 500 ppb) and for certain other metals, but these limits are rarely triggered by commercially refined white sugar. EU food-grade specifications for sugar may set additional purity requirements that implicitly constrain metal content.
Sources
Auto-generated from source-page frontmatter. The “Used on this page for” column is populated by the orchestrator’s POPULATE-SOURCE-LEGEND action; pending entries appear as *[awaiting synthesis]*.
| # | Citation | Year | Type | Used on this page for |
|---|---|---|---|---|
| 1 | FDA 2022. FY2018-FY2020 TDS Elements Analytical Results, FDA Total Diet Study | 2022 | Government dataset | FDA TDS FY2018–FY2020 Cd, Cr, Ni, Pb, U, tAs, tHg occurrence distributions for Sugar, white, granulated (n=3); all analytes reported as zero (BDL) |
| 2 | Zhao et al. 2022. Exposure to Lead and Cadmium in the Sixth Total Diet Study — China, 2016–2019, China CDC Weekly | 2022 | Government report | CN Pb, Cd occurrence in 288 composite samples from the 24 provincial-level administrative divisions (PLADs) of the Sixth China Total Diet Study, covering… (n=288) |
| 3 | Wang et al. 2020. Contamination and health risk assessment of lead, arsenic, cadmium, and aluminum from a total diet study of Jilin Province, China, Food Science & Nutrition | 2020 | Peer-reviewed | CN Pb, tAs, Cd, Al occurrence in Jilin Province total-diet-study composites across 12 food groups and 48 product groups, with consumption inputs for 7700 residents… |
Page history
The five most recent substantive edits to this page. The full version history lives in git; when DOI minting comes online (see schema docs), each entry below will also link to a version-pinned DataCite DOI.
| Commit | Date | Description |
|---|---|---|
| b0f3d38 | 2026-06-12 | batch | corpus rescreen b04 old terminal skips |