Abstract
The current work describes and compares the performance of hyperbolic mode resonance (HMR)-based sensors for the detection of acetone at parts per billion (ppb) concentrations using ensemble machine learning (EML) techniques. A pair of HMR based-sensors with resonances located in the visible (VIS) and mid infrared (MIR) regions were obtained in order to train a set of ensemble machine learning models. The response of the detection system formed by both devices in the VIS and MIR regions, with the help of the EML system, allowed the limit of detection (LoD) of the sensors to be reduced by an order of magnitude. It is the first time that HMR-based sensors are shown in practical applications, at the same time that their performance is improved using EML techniques. This opens new avenues for the use of this type of HMR-based sensors for the detection of other substances, in addition to improving the performance of any optoelectronic sensor using EML techniques.
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