Quick Quantitative (QQ) immunoassays have been increasingly applied for the measurement of enrofloxacin (ENR) and ciprofloxacin (CIP) residues in aquaculture due to their speed and convenience. However, their quantitative reliability remains limited because measurement uncertainty (MU) is rarely considered during field testing. To enhance the metrological reliability of QQ-based residue analysis, we developed AquaUncertainty Pal, a mobile application that embeds real-time MU computation into the QQ workflow. The software automatically evaluates uncertainty sources during sampling and pipetting, visualizes the uncertainty budget, and guides users through optimized operations. The framework was validated against ISO/IEC 17025–accredited LC–MS/MS and assessed through a user study involving 20 frontline technicians. With the integrated software, pipetting precision (RSD) at 100 μL improved from 4.1% to 1.79%, the inter-operator variability (CV) decreased by 52%, and conformity assessment accuracy for samples near the maximum residue limit (MRL) increased from 25% to 70%. This suggests that real-time MU visualization effectively guided technicians toward consistent pipetting and interpretation behavior. These results demonstrate that integrating MU into the QQ workflow is both feasible and effective, substantially improving reliability and providing a replicable digital framework for uncertainty-informed residue monitoring in aquaculture.

Enrofloxacin use in aquaculture can lead to residue accumulation in fish tissues, creating challenges for food safety control and residue risk assessment. Residue prediction across published studies remains difficult because concentrations are influenced by species, tissue matrix, administration conditions, environmental factors, and between-study heterogeneity. Here, we constructed a literature-derived database to model matrix-specific enrofloxacin concentrations in fish. After duplicate removal, fish-focused filtering, source cleaning, and exclusion of records with missing critical variables, 1254 records from 39 source groups with traceable literature identifiers were retained from 2275 extracted records. Eleven predictors were used, and eight machine learning models were trained using log1p-transformed concentrations. Under a random row-level 80:20 split, the histogram gradient-boosting decision tree achieved the best row-level performance, with R²_log = 0.8889 and root-mean-squared error in log1p space (RMSE_log) = 0.2728. In contrast, stratified source-grouped validation showed markedly reduced performance, indicating limited cross-study generalization to unseen source groups. ExtraTrees showed the most stable grouped validation performance and was used for model-based interpretation. Species, tissue or biological matrix, dosing frequency, and sampling time point were highly ranked model-associated predictors. This study provides a transparent literature integration framework for residue pattern modeling and predictor prioritization, while highlighting the need for standardized residue depletion data and external validation.