Systematic review identifies titanium dioxide levels in food, proposing a threshold for regulatory use.
The regulatory status of titanium dioxide (TiO 2 ) as a food additive varies per jurisdiction (i.e., banned in some jurisdictions, notably the European Union, while others continue to authorize its use). The lack of a harmonized method for measuring TiO 2 as a food additive, along with the absence of an established threshold to distinguish intentional addition from background presence (e.g., from natural sources, agricultural practices, cross-contamination) have led to challenges in compliance verification and enforcement, particularly in jurisdictions applying a ban. This study aimed (i) to systematically review reported concentrations of titanium or titanium dioxide in foods with and without declared use of this additive, and (ii) to derive a concentration-based screening threshold suitable for regulatory applications. A systematic literature review identified 50 peer-reviewed studies, yielding 797 quantitative observations of TiO 2 in foods. Concentrations were harmonized and expressed as TiO 2 (mg kg −1 , wet weight). Data were separated into “Declared” and “Not Declared” subsets and analysed using descriptive statistics, outlier detection, and distribution fitting. Foods declaring TiO 2 exhibited high and variable concentrations (mean: 1762.3 mg kg −1 ; median: 220 mg kg −1 ), whereas foods without declaration showed consistently low levels after outlier exclusion (mean: 2.87 mg kg −1 ; maximum: 30 mg kg −1 ). Based on the upper bound of background concentrations, a conservative screening threshold of 30 mg kg −1 is proposed: concentrations above this level are likely to reflect intentional additive use and would warrant further investigation. However, foods containing pearlescent pigments may carry TiO 2 levels below this threshold and would require particular attention. Thus, the proposed threshold provides a pragmatic, analytically feasible tool to support enforcement of TiO 2 bans and complements existing analytical detection methods by accounting for unavoidable background presence.
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Domínguez et al. (2026) studied this question.
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