Reagent-Free Prediction of Free-Ammonia Toxicity in Algal Systems Using Chlorophyll Fluorescence Transients and Interpretable Sparse Regression

High Resolution Image Download MS PowerPoint Slide Accurate assessment of free ammonia (NH 3 ) is critical for managing nutrient-rich effluents and algal-based treatment systems, but conventional assays are reagent-intensive and poorly suited to real-time monitoring. Chlorophyll a fluorescence offers a rapid, reagent-free proxy for photosynthetic health, yet widely used indices such as F v / F m respond nonspecifically to diverse stresses and rarely yield quantitative NH 3 concentrations. Here, we develop a reagent-free framework that predicts NH 3 toxicity in microalgae from fast chlorophyll fluorescence transients (O–J–I–P; OJIP) analyzed with interpretable sparse regression. Three species ( Chlorella vulgaris, Acutodesmus obliquus, and Arthrospira platensis ) were exposed to environmentally relevant NH 3 levels, and OJIP responses were monitored for 35 h. Among several multivariate approaches, Lasso regression optimized by nested cross-validation provided the best compromise between accuracy ( R 2 = 0.93–0.98 within species) and transferability across taxa ( R 2 = 0.81–0.87 for green algae). The selected OJIP parameters captured conserved fluorescence signatures of NH 3 -induced stress and retained predictive power under light-induced photoinhibition that markedly depressed F v / F m . Incorporating model evaluation diagnostics enabled transparent uncertainty quantification and discrimination of extrapolative predictions. This workflow converts routine OJIP measurements into real-time, reagent-free, and quantitative information on ammonia toxicity in algal cultures and high-ammonia effluents, providing a transparent biosensing platform for environmental risk assessment.