Augmentations for selective multi-species quantification from infrared spectroscopic data

Sensitivity and selectivity are arguably the two most important qualities in a new sensor design. While many spectroscopic sensors developed in laboratory conditions achieve high sensitivity and selectivity, they are not always applicable to real-world conditions. Challenges in real-world applications come from corruptions like noise and interference. This study leverages machine learning methods for accurate and robust quantification under such corruptions. We propose simple yet effective augmentation strategies that promote robustness against unknown interference. The performance of the proposed augmentations is compared under varying levels of interference and noise. We demonstrate our methodology for a gas sensing application using infrared spectroscopy data. We focus on quantifying common volatile organic compounds (VOCs) in a realistic scenario with several unknown interfering species. The findings of this work put us a step closer to creating a robust and widely-applicable sensing platform.Synopsis: Interference is a major obstacle in the development of reliable spectroscopic gas sensors. This study explores data augmentation strategies to tackle unknown interference and noise to propose a sensing strategy for volatile organic compounds.

Automated summary

Sensitivity and selectivity are crucial qualities in sensor design, but many spectroscopic sensors developed in laboratories lack applicability to real-world conditions. This study addresses challenges in real-world applications, such as noise and interference, by leveraging machine learning and proposing innovative augmentation strategies. The proposed strategies are tested under varying levels of interference and noise using infrared spectroscopy data for gas sensing, specifically focusing on quantifying volatile organic compounds in the presence of unknown interfering species. The findings bring us closer to creating a robust and widely-applicable sensing platform.