Plasmonic Mid-Infrared Filter Array-Detector Array Chemical Classifier Based on Machine Learning

Numerous applications exist for chemical detection, ranging from the industrial production of chemicals to pharmaceutical manufacturing, environmental monitoring, and hazardous risk control. For many applications, infrared absorption spectroscopy is the favored technique, due to attributes that include short response time, high specificity, minimal drift, in situ operation, negligible sample disruption, and reliability. The workhorse instrument for infrared absorption is the Fourier transform infrared (FTIR) spectrometer. While such systems are suitable for many purposes, new applications would be enabled by small, lightweight, low power and low cost infrared microspectrometers. Here we perform a detailed study on a microspectrometer chemical classifier comprising an array of plasmonic mid-infrared spectral filters used with a photodetector array, whose outputs are analyzed by a machine learning algorithm. We conduct simulations (including noise), demonstrating the identification of six gas-phase and six liquid-phase chemicals. We study the performance of our method at detecting the concentration of acetylene.