Real-time concentration prediction in column chromatography purification using NIR optical sensing and evolutionary...

Ginsenosides, the principal bioactive constituents of American ginseng (Panax quinquefolius), require precise monitoring during column chromatography to ensure product quality. Conventional assays are time-consuming and unsuited for real-time process control, particularly for low-abundance compounds. Here, we present a rapid, non-destructive near-infrared (NIR) optical sensing system coupled with an Evolutionary Attention-based Long Short-Term Memory (EA-LSTM) model to predict ginsenoside concentrations dynamically. The approach exploits the high-speed acquisition and reproducibility of NIR spectra while leveraging EA-LSTM's temporal feature extraction and adaptive attention to focus on chemically informative elution segments. Offline spectra from eight elution batches, referenced by UPLC, enabled model training and validation, with high-frequency sampling simulated to mimic industrial deployment. The system demonstrated high accuracy (R² = 0.9841, RPD = 7.94) and robustness under dynamic conditions (R² = 0.9694, RPD = 5.72, response time = 0.34 ms), highlighting its potential for real-time, non-destructive monitoring and intelligent control in natural product purification.