Artificial intelligence-enabled real-time monitoring of intracellular lipid dynamics in microalgae via optical sensors and kinetic early-warning differentiation.

Intracellular lipid droplets synthesized by microalgae are high-value molecules with broad industrial demand, but real-time monitoring of the dynamics of intracellular lipid in bioreactors remains an unresolved challenge. This study presented the first artificial intelligence-enabled sensing system for non-invasive, real-time monitoring and anticipation of intracellular lipid droplet dynamics using chrono-spectral color evolution in microalgal photobioreactors coupled with a short-term optical memory intelligent module. An Internet of Things(IoT)-enabled data acquisition pipeline was implemented to automatically acquire color data from a Chlorella sp. photobioreactor and intracellular lipids and chlorophyll were quantified by epifluorescence microscopy across > 3,000 individual cells. Time-aware supervised machine learning models incorporating short-term optical memory enabled accurate inference of intracellular lipid droplets and chlorophyll, achieving correlation coefficient values above 0.86 with normalized root mean square errors below 13.5 %. Kinetic Savitsky-Golay linear differentiation revealed early-warning indicators of intracellular metabolic transitions, providing 8-10 h of lead time for lipid accumulation and chlorophyll decline at the single-cell level, an intervention window beyond the reach of conventional offline methods. At a fraction of the cost of microscopy, this artificial intelligence-enabled technology enables intracellular monitoring and anticipatory control compatible with self-driving bioreactors, internet of biological things ecosystems, and intelligent process analytical technology frameworks.