# Application-Specific Measurement Uncertainty Software for Measuring Enrofloxacin Residue in Aquatic Products Using the Quick Quantitative (QQ) Method

## TL;DR

A mobile app called AquaUncertainty Pal improves the reliability of rapid antibiotic residue testing in aquaculture by providing real-time uncertainty feedback.

## Contribution

A mobile application that integrates real-time measurement uncertainty computation into the QQ workflow for antibiotic residue screening.

## Key findings

- Pipetting precision improved from 4.1% to 1.79% with AquaUncertainty Pal.
- Inter-operator variability decreased by 52% using the app.
- Conformity assessment accuracy for samples near MRL increased from 25% to 70%.

## Abstract

Rapid tests are widely used to screen antibiotic residues in aquaculture products, but the results can vary because small operational differences (e.g., pipetting and interpretation) introduce uncertainty. We developed a mobile application (AquaUncertainty Pal) that calculates and visualizes measurement uncertainty during the Quick Quantitative (QQ) workflow and provides step-by-step guidance to users. Using a before–after study with frontline technicians and cross-validation against ISO/IEC 17025–accredited LC–MS/MS, we show that real-time uncertainty feedback improves pipetting consistency and reduces operator-to-operator variability. The proposed approach offers a practical way to make rapid on-site residue screening more reliable and more comparable across technicians and testing sites.

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.