Adaptive spectroscopy for rapid chemical identification

Abstract

Spectroscopic chemical identification is fundamentally a classification task where sensor measurements are compared to a library of known compounds with the hope of determining an unambiguous match. When the measurement signal-to-noise ratio (SNR) is very low (e.g. from short exposure times, weak analyte signatures, etc.), classification can become very challenging, requiring a multiple-measurement framework such as sequential hypothesis testing, and dramatically extending the time required to classify the sample. There are a wide variety of defense, security, and medical applications where rapid identification is essential, and hence such delays are disastrous. In this paper, we discuss an approach for adaptive spectroscopic detection where the introduction of a tunable spectral filter enables the system to measure the projection of the sample spectrum along arbitrary bases in the spectral domain. The net effect is a significant reduction in time-to-decision in low SNR cases. We describe the general operation of such an instrument, present results from initial simulations, and report on our experimental progress.