Authors : Bright Amankwah; Lawrence Anebi Enyejo

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/5xr5dkfa

Scribd : https://tinyurl.com/y6wrr4w8

DOI : https://doi.org/10.38124/ijisrt/26May1819

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : High-throughput metabolomics remains constrained by analytical bottlenecks including prolonged chromatographic cycle times, inefficient instrument utilization, high solvent consumption, limited predictive maintenance capability, and escalating per-sample operational costs that restrict scalable analytical service commercialization. This study proposes a novel intelligent metabolomics framework termed AIDCL-MS (Artificial Intelligence-Driven Dual-Column Liquid Chromatography–Mass Spectrometry System) for accelerated metabolite profiling and economically optimized analytical laboratory entrepreneurship. The framework integrates a synchronized dual-column LC architecture with machine learning-guided sample routing, adaptive gradient scheduling, predictive instrument health diagnostics, and automated spectral deconvolution to maximize throughput while maintaining analytical precision. The proposed intelligent control engine incorporates a hybrid optimization algorithm named Reinforced Bayesian Chromatographic Orchestration Network (RBCON), combining Deep Reinforcement Learning (DRL), Bayesian Optimization, and temporal anomaly prediction for dynamic workflow control. RBCON continuously optimizes column switching intervals, solvent gradient parameters, injection timing, ion source stability, and queue prioritization using real-time telemetry from pump pressure, retention drift, ion suppression indicators, and detector response profiles. Comparative benchmarking is conducted against conventional single-column LC-MS workflows, static dual-column switching systems, Random Forest-based scheduling, XGBoost optimization pipelines, and Long Short-Term Memory (LSTM)-driven chromatographic prediction models.

Keywords : LC-MS Metabolomics; Dual-Column Chromatography; Artificial Intelligence Optimization; High-Throughput Metabolite Profiling; Analytical Service Entrepreneurship.

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