Noise-Aware Bayesian Optimization for Precision Improvement of Micro-Volume Liquid Handling in In Vitro Diagnostics

Micro-volume dispensing precision is critical for in vitro diagnostic (IVD) analyzers. We present a noise-aware Bayesian optimization framework that minimizes the within-run coefficient of variation (CV) of a 50 μL dispensing process by optimizing five pump-control variables. Each setting was tested in five independent batches; the group mean CV was used as the response, and the squared group standard error was supplied to an automatic relevance determination Gaussian process as observation-noise variance. From 33 tested combinations, the lowest measured mean CV was 0.266% (SD, 0.044%). An engineering-rounded setting then achieved 0.270% (SD, 0.050%), an 81.8% relative reduction versus engineer-selected settings (1.480%, SD, 0.083%). In a retrospective surrogate-based replay, the noise-aware strategy reached CV < 1.5% in 2.8 ± 1.2 iterations, compared with 4.1 ± 2.0, 6.5 ± 3.4, and 11.3 ± 5.1 for homoscedastic GP, standard GP, and random search. Deionized water, diluted human serum, and 5% bovine serum albumin all yielded mean CVs below 0.45%. The method thus identified a repeatable low-CV operating region with a limited physical-experiment budget; prospective algorithmic comparisons and multi-instrument validation remain necessary.