Machine learning to correct for nonphotochemical quenching in high-frequency, in vivo fluorometer data

Nonphotochemical quenching (NPQ) is a response mechanism in plants and algae that allows them to process and dissipate excess excitation energy as heat safely. Collecting fluorescence data from these plants and algae in surface water environments can incur errors from NPQ, ultimately leading to inaccurate calculations of chlorophyll concentration for environmental and industrial water quality monitoring. Rensselaer inventors developed a novel approach to correcting NPQ-skewed fluorescence data by employing trained machine-learning modules that can be applied to fluorescence detection system