The development of miniaturized spectrometers has revolutionized a wide range of industries. However, traditional off-the-shelf spectrometer designs suffer from essential deficiencies that are limiting their usage in important fields such as medical diagnostics or environmental monitoring. We propose adopting a set of new technologies and machine learning processes in designing and developing a new generation of miniature spectrometers. By utilizing state-of-the-art “off-the-shelf” detectors, advanced technologies such as 3D printing, advanced materials, and machine learning, we aim to design and develop a miniature spectrometer that is more accurate, efficient, and cost-effective. 3D scanning combined with machine learning will enhance the optimization process by reducing the need for manual adjustments and providing more accurate and reliable data. In addition, adopting new technologies will improve the overall quality and traceability of production.

Several research groups within the NASA's Earth sciences ( atmospheric measurements, air quality) will greatly benefits from this new design. This will be a major step in improving the Pandora NASA network retrievals.

We believe that this spectrometer will be of high interest to other US government agencies such as the, Environmental Protection Agency DOE Office of Environmental Management and the Bureau of Ocean Energy Management.