A TRACES establishment code check is a three-field match, not a string lookup. The validation passes only when the approval number, the country of the establishment and the activity category all line up against the official TRACES EU database. Treating it as a single-field comparison is the most common design error.
The record you need
Every validated row requires four inputs:
Field Purpose
sku Identifies the product line containing animal-origin ingredients
approval_number The establishment code issued to the facility
country Country of origin of the establishment
activity_type Meat, dairy, fishery or animal by-products
Missing any one of these makes the result unverifiable rather than merely incomplete.
Normalise before you compare
Most false failures come from input hygiene, not the reference data. Strip whitespace, remove non-printing characters introduced by copy and paste, and reject merged-cell exports before parsing rather than after.
approval_number = raw.strip() # stray spaces from pasted codes
approval_number = collapse_internal_ws(approval_number)
assert columns_aligned(row) # merged cells scramble mapping
A validation service can only tell you a code is unrecognised. It cannot tell you the code was fine and your parser mangled it. That distinction belongs on your side of the boundary. The full TRACES establishment code workflow covers the template structure this maps onto.
Model the failure states separately
Do not collapse outcomes into pass and fail. There are at least four states worth distinguishing: approved, unrecognised number, country mismatch, and activity category mismatch. Each has a different remediation path, and merging them forces a human to re-diagnose every flag by hand.
Keep the result, not just the verdict
Persist the validated row with its timestamp and outcome. The audit trail is a deliverable, not a log. When compliance reviews arrive, the question is what was checked and when.
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