Foodomics for Food Safety and Quality: A Critical Performance Analysis and Implementation Framework

Despite transformative analytical capabilities, foodomics technologies remain confined to research laboratories, with traditional methods dominating 87% of routine food testing. Critical evaluation of 127 studies (2020–2024) quantifies the persistent adoption gap. Published accuracies (mean 93.4%, 95% CI: 91.8–95.0%) systematically overestimate real-world performance, declining under independent validation (mean 81.2%, 95% CI: 78.5–83.9%) due to inadequate training sets, matrix effects, and statistical overfitting. Comprehensive economic analysis reveals true implementation costs ($400–800/sample for multi-omics) exceed reported expenses by 72% (95% CI: 68–76%), creating substantial economic barriers. Multi-omics integration provides marginal accuracy gains (mean 4.2 percentage points, 95% CI: 3.1–5.3) at 245% cost increases (95% CI: 205–285%), justifying deployment only when single-omics fails. Critical barriers include validation deficits (6% achieve multi-laboratory validation), insufficient reference materials (<30% of applications), and systematic underreporting of failures. Analysis of 75 documented failures reveals primary mechanisms: inadequate training sets (43%), matrix effects (28%), statistical overfitting (18%), insufficient sensitivity (15%), and uncharacterized confounding (12%). Strategic investments in processing-stable markers ($5–10M), reference material libraries ($20–40M), and cost-reduction technologies ($10–15M) could enable 60–75% expense reduction, expanding viable applications threefold by 2030.