Data-driven Mid-IR spectroscopy for contaminant analysis in wastewater (Conference Presentation)

We present innovative methods for analyzing mid-infrared (mid-IR) spectroscopy data to identify and quantify analytes in complex water matrices. This approach leverages the high specificity of mid-IR spectroscopy for chemical fingerprinting while overcoming traditional challenges associated with the rapid attenuation of IR signals in aqueous environments. By combining state-of-the-art pre-concentration techniques with robust data processing algorithms and machine learning, we significantly enhance measurement sensitivity and selectivity. The analytics-driven sensing platform incorporating these methods enables detailed environmental analysis, particularly for monitoring contaminants in wastewater and natural water bodies. The identification of diverse chemical species facilitates real-time monitoring and predictive analysis in environmental remediation processes. Additional applications include tracking pollutants, optimizing wastewater treatment operations, and ensuring compliance with environmental regulations. This work underscores the importance of integrating advanced data analytics with mid-IR spectroscopy for comprehensive environmental monitoring and management.