Reagent-Free Prediction of Free-Ammonia Toxicity in Algal Systems Using Chlorophyll Fluorescence Transients and...

Accurate assessment of free ammonia (NH₃) 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 NH₃ concentrations. Here, we develop a reagent-free framework that predicts NH₃ 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 NH₃ 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 (R² = 0.93-0.98 within species) and transferability across taxa (R² = 0.81-0.87 for green algae). The selected OJIP parameters captured conserved fluorescence signatures of NH₃-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.