2022
|
Zhou, J.; Husseini, D. Al; Li, J.; Lin, Z.; Sukhishvili, S.; Cote, G. L.; Gutierrez-Osuna, R.; Lin, P. T. Mid-Infrared Serial Microring Resonator Array for Real-time Detection of Vapor Phase Volatile Organic Compounds Journal Article Forthcoming In: Analytical Chemistry, Forthcoming. @article{junchao2022ac,
title = {Mid-Infrared Serial Microring Resonator Array for Real-time Detection of Vapor Phase Volatile Organic Compounds},
author = {J. Zhou and D. Al Husseini and J. Li and Z. Lin and S. Sukhishvili and G. L. Cote and R. Gutierrez-Osuna and P. T. Lin },
year = {2022},
date = {2022-07-19},
journal = {Analytical Chemistry},
keywords = {Chemical sensors, Infrared spectroscopy},
pubstate = {forthcoming},
tppubtype = {article}
}
|
2021
|
Husseini, D. Al; Karanth, Y.; Zhou, J.; Willhelm, D.; Qian, X.; Gutierrez-Osuna, R.; Coté, G. L.; and P. Tai Lin,; Sukhishvili, S. A. Surface Functionalization Utilizing Mesoporous Silica Nanoparticles for Enhanced Evanescent-Field Mid-Infrared Waveguide Gas Sensing Journal Article In: Coatings, vol. 11, no. 118, pp. 1-12, 2021. @article{alhusseini2021coatings,
title = {Surface Functionalization Utilizing Mesoporous Silica Nanoparticles for Enhanced Evanescent-Field Mid-Infrared Waveguide Gas Sensing},
author = {D. Al Husseini and Y. Karanth and J. Zhou and D. Willhelm and X. Qian and R. Gutierrez-Osuna and G. L. Coté and and P. Tai Lin and S. A. Sukhishvili},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2021/01/ahhusseini2021coatings.pdf
https://www.mdpi.com/2079-6412/11/2/118},
year = {2021},
date = {2021-01-21},
journal = {Coatings},
volume = {11},
number = {118},
pages = {1-12},
keywords = {Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {article}
}
|
2018
|
Jin, Tiening; Zhou, Juntao; Wang, Zelun; Gutierrez-Osuna, Ricardo; Ahn, Charles; Hwang, Wonjun; Park, Ken; Lin, Pao-Tai Real-time Gas Mixture Analysis Using Mid-infrared Membrane Microcavities Journal Article In: Analytical Chemistry, vol. 90, no. 7, pp. 4348-4353, 2018. @article{jin2018-ac,
title = {Real-time Gas Mixture Analysis Using Mid-infrared Membrane Microcavities},
author = {Tiening Jin and Juntao Zhou and Zelun Wang and Ricardo Gutierrez-Osuna and Charles Ahn and Wonjun Hwang and Ken Park and Pao-Tai Lin },
url = {https://www.ncbi.nlm.nih.gov/pubmed/29509404},
year = {2018},
date = {2018-03-08},
journal = {Analytical Chemistry},
volume = {90},
number = {7},
pages = {4348-4353},
keywords = {Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {article}
}
|
2017
|
Karkamkar, P; Gutierrez-Osuna, R Optical Computation of Chemometrics Projections using a Digital Micromirror Device Proceedings Article In: Proc. International Symposium on Olfaction and Electronic Nose (ISOEN), 2017. @inproceedings{karkamkar2017isoen,
title = {Optical Computation of Chemometrics Projections using a Digital Micromirror Device},
author = {P Karkamkar and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/purvesh2017isoen.pdf},
year = {2017},
date = {2017-03-15},
booktitle = {Proc. International Symposium on Olfaction and Electronic Nose (ISOEN)},
keywords = {Active sensing, Infrared spectroscopy},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Wang, Z; Gutierrez-Osuna, R Mixture quantification in the presence of unknown interferences Proceedings Article In: Proc. International Symposium on Olfaction and Electronic Nose (ISOEN), 2017. @inproceedings{wang2017isoen,
title = {Mixture quantification in the presence of unknown interferences},
author = {Z Wang and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/wang2017isoen.pdf},
year = {2017},
date = {2017-03-15},
booktitle = {Proc. International Symposium on Olfaction and Electronic Nose (ISOEN)},
journal = {Proc. International Symposium on Olfaction and Electronic Nose (ISOEN)},
keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2016
|
Huang, J; Gutierrez-Osuna, R Active wavelength selection for mixture identification with tunable mid-infrared detectors Journal Article In: Analytica Chimica Acta, vol. in press, 2016. @article{huang2016aca,
title = {Active wavelength selection for mixture identification with tunable mid-infrared detectors},
author = {J Huang and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/huang2016aca.pdf},
year = {2016},
date = {2016-08-08},
journal = {Analytica Chimica Acta},
volume = {in press},
keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {article}
}
|
2015
|
Li, J; Gutierrez-Osuna, R; Hodges, R D; Luckey, G; Crowell, J; Schiffman, S S; Nagle, H T Odor Assessment of Automobile Interior Components Using Ion Mobility Spectrometry Proceedings Article In: IEEE Sensors Conference, 2015. @inproceedings{li2015sensors,
title = {Odor Assessment of Automobile Interior Components Using Ion Mobility Spectrometry},
author = {J Li and R Gutierrez-Osuna and R D Hodges and G Luckey and J Crowell and S S Schiffman and H T Nagle},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/li2015sensors.pdf},
year = {2015},
date = {2015-11-01},
booktitle = {IEEE Sensors Conference},
keywords = {Chemical sensors, Infrared spectroscopy, Olfaction},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Huang, J; Gutierrez-Osuna, R Active wavelength selection for mixture analysis with tunable infrared detectors Journal Article In: Sensors and Actuators B: Chemical, vol. 208, pp. 245–257, 2015. @article{huang2014sab,
title = {Active wavelength selection for mixture analysis with tunable infrared detectors},
author = {J Huang and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/huang2014sab.pdf},
year = {2015},
date = {2015-01-01},
journal = {Sensors and Actuators B: Chemical},
volume = {208},
pages = {245–257},
keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {article}
}
|
2013
|
Huang, J; Gutierrez-Osuna, R Active analysis of chemical mixtures with multi-modal sparse non-negative least squares Conference 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013. @conference{jinicassp2013,
title = {Active analysis of chemical mixtures with multi-modal sparse non-negative least squares},
author = {J Huang and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/jinicassp2013.pdf},
year = {2013},
date = {2013-02-28},
booktitle = {38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {conference}
}
|
2012
|
Huang, J; Gutierrez-Osuna, R Active Analysis of Chemical Mixtures with Multi-modal Sparse Non-negative Least Sqares Technical Report 2012. @techreport{huang2012techreport,
title = {Active Analysis of Chemical Mixtures with Multi-modal Sparse Non-negative Least Sqares},
author = {J Huang and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/huang2012techreport.pdf},
year = {2012},
date = {2012-12-05},
abstract = {New sensor technologies such as Fabry-Pérot interferometers (FPI) offer low-cost and portable alternatives to traditional infrared absorption spectroscopy for chemical analysis. However, with FPIs the absorption spectrum has to be measured one wavelength at a time. In this work, we propose an active-sensing framework to select a subset of wavelengths that best separates the specific components of a chemical mixture. Compared to passive feature-selection approaches, in which the subset is elected offline, active sensing selects the next feature on-the-fly based on previous measurements so as to reduce uncertainty. We propose a novel multi-modal non-negative least squares method (MM-NNLS) to solve the underlying linear system, which has multiple near-optimal solutions. We tested the framework on mixture problems of up to 10 components from a library of 100 chemicals. MM-NNLS can solve complex mixtures using only a small number of measurements, and outperforms passive approaches in terms of sensing efficiency and stability},
keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {techreport}
}
New sensor technologies such as Fabry-Pérot interferometers (FPI) offer low-cost and portable alternatives to traditional infrared absorption spectroscopy for chemical analysis. However, with FPIs the absorption spectrum has to be measured one wavelength at a time. In this work, we propose an active-sensing framework to select a subset of wavelengths that best separates the specific components of a chemical mixture. Compared to passive feature-selection approaches, in which the subset is elected offline, active sensing selects the next feature on-the-fly based on previous measurements so as to reduce uncertainty. We propose a novel multi-modal non-negative least squares method (MM-NNLS) to solve the underlying linear system, which has multiple near-optimal solutions. We tested the framework on mixture problems of up to 10 components from a library of 100 chemicals. MM-NNLS can solve complex mixtures using only a small number of measurements, and outperforms passive approaches in terms of sensing efficiency and stability |
Huang, J; Gosangi, R; Gutierrez-Osuna, R Active Concentration-Independent Chemical Identification with a Tunable Infrared Sensor Journal Article In: Sensors Journal, IEEE, 2012. @article{huang2012sj,
title = {Active Concentration-Independent Chemical Identification with a Tunable Infrared Sensor},
author = {J Huang and R Gosangi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/huang2012sj.pdf},
year = {2012},
date = {2012-09-03},
journal = {Sensors Journal, IEEE},
abstract = {This paper presents an active sensing framework for concentration-independent identification of volatile chemicals using a tunable infrared interferometer. The framework operates in real time to generate a sequence of absorption lines that can best discriminate among a given set of chemicals. The active-sensing algorithm was previously developed to optimize temperature programs for metal-oxide chemosensors. Here, we adapt it to tune a non-dispersive infrared spectroscope based on a Fabry-Perot interferometer (FPI). We also extend this framework to allow the identification of chemical samples irrespective of their concentrations. Namely, we use non-negative matrix factorization (NMF) to create concentration-independent absorption profiles of different chemicals, and then employ linear least squares to fit sensor observations to the response profiles. We tested the framework on a simulated classification problem with 27 chemicals and compared against a passive sensing approach; active sensing consistently outperformed passive sensing in terms of classification performance for various sensing budgets and at various levels of sensor noise. We also validated the approach experimentally using a commercial FPI sensor and a database of eight household chemicals. Our results show the method is able to predict the sample identity irrespective of concentration.},
keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {article}
}
This paper presents an active sensing framework for concentration-independent identification of volatile chemicals using a tunable infrared interferometer. The framework operates in real time to generate a sequence of absorption lines that can best discriminate among a given set of chemicals. The active-sensing algorithm was previously developed to optimize temperature programs for metal-oxide chemosensors. Here, we adapt it to tune a non-dispersive infrared spectroscope based on a Fabry-Perot interferometer (FPI). We also extend this framework to allow the identification of chemical samples irrespective of their concentrations. Namely, we use non-negative matrix factorization (NMF) to create concentration-independent absorption profiles of different chemicals, and then employ linear least squares to fit sensor observations to the response profiles. We tested the framework on a simulated classification problem with 27 chemicals and compared against a passive sensing approach; active sensing consistently outperformed passive sensing in terms of classification performance for various sensing budgets and at various levels of sensor noise. We also validated the approach experimentally using a commercial FPI sensor and a database of eight household chemicals. Our results show the method is able to predict the sample identity irrespective of concentration. |
2011
|
Huang, J; Gosangi, R; Gutierrez-Osuna, R Active Sensing with Fabry-Perot Infrared Interferometers Conference Proceedings of the 14th International Symposium on Olfaction and Electronic Nose, 2011. @conference{huang2011active,
title = {Active Sensing with Fabry-Perot Infrared Interferometers},
author = {J Huang and R Gosangi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/huang2011active.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings of the 14th International Symposium on Olfaction and Electronic Nose},
journal = {AIP Conference Proceedings},
pages = {31-32},
abstract = {In this article, we describe an active‐sensing framework for infrared (IR) spectroscopy. The goal is to generate a sequence of wavelengths that best discriminates among chemicals. Unlike feature‐selection strategies, the sequence is selected on‐the‐fly as the device acquires data. The framework models the problem as a Partially Observable Markov Decision Process (POMDP), which is solved by a greedy myopic algorithm. In previous work [1], we had applied this framework to temperature‐modulated metal oxide sensor. Here, we adapt the framework to a tunable IR sensor based on Fabry‐Perot interferometers (FPI). FPIs provide a low‐cost alternative to traditional Fourier Transform Infrared Spectroscopy (FTIR), though at the expense of a narrower effective range and lower spectral resolution. Here, we first test whether the framework can scale up to large problems consisting 27 chemicals with 60 dimensions; our previous work on metal oxide sensors employed three chemicals and 7 dimensions. For this purpose, FPI spectra are simulated from FTIR. Then we validate the framework experimentally on 3 chemicals using a prototype instrument based on FPIs. These preliminary results are encouraging and indicate that the framework is able to solve classification problems of reasonable size.},
keywords = {Active sensing, Infrared spectroscopy},
pubstate = {published},
tppubtype = {conference}
}
In this article, we describe an active‐sensing framework for infrared (IR) spectroscopy. The goal is to generate a sequence of wavelengths that best discriminates among chemicals. Unlike feature‐selection strategies, the sequence is selected on‐the‐fly as the device acquires data. The framework models the problem as a Partially Observable Markov Decision Process (POMDP), which is solved by a greedy myopic algorithm. In previous work [1], we had applied this framework to temperature‐modulated metal oxide sensor. Here, we adapt the framework to a tunable IR sensor based on Fabry‐Perot interferometers (FPI). FPIs provide a low‐cost alternative to traditional Fourier Transform Infrared Spectroscopy (FTIR), though at the expense of a narrower effective range and lower spectral resolution. Here, we first test whether the framework can scale up to large problems consisting 27 chemicals with 60 dimensions; our previous work on metal oxide sensors employed three chemicals and 7 dimensions. For this purpose, FPI spectra are simulated from FTIR. Then we validate the framework experimentally on 3 chemicals using a prototype instrument based on FPIs. These preliminary results are encouraging and indicate that the framework is able to solve classification problems of reasonable size. |
2007
|
Nogueira, F G; Felps, D; Gutierrez-Osuna, R Development of an infrared absorption spectroscope based on linear variable filters Journal Article In: Sensors Journal, IEEE, vol. 7, no. 8, pp. 1183–1190, 2007. @article{nogueira2007development,
title = {Development of an infrared absorption spectroscope based on linear variable filters},
author = {F G Nogueira and D Felps and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/nogueira2007development.pdf},
year = {2007},
date = {2007-01-01},
journal = {Sensors Journal, IEEE},
volume = {7},
number = {8},
pages = {1183--1190},
publisher = {IEEE},
abstract = {The objective of this research is to develop a low-cost infrared absorption spectroscope based on linear variable filter technology for the automated detection of concentrated gases and vapors, and the semiautomated detection of liquids. This instrument represents an alternative to electronic-nose devices based on cross-selective gas sensor arrays. Instead, the proposed instrument uses the concept of computational ldquopseudosensors,rdquo whereby spectral lines in an analytical instrument are clustered into groups and used as independent variables. We characterize the system on a database of chemical mixtures, and evaluate it on two real-world applications in the foodstuffs domain: oil adulteration and trans-fatty acid detection. Our results show that the proposed system is a viable low-resolution, low-cost analytical technique for niche applications.},
keywords = {Infrared spectroscopy},
pubstate = {published},
tppubtype = {article}
}
The objective of this research is to develop a low-cost infrared absorption spectroscope based on linear variable filter technology for the automated detection of concentrated gases and vapors, and the semiautomated detection of liquids. This instrument represents an alternative to electronic-nose devices based on cross-selective gas sensor arrays. Instead, the proposed instrument uses the concept of computational ldquopseudosensors,rdquo whereby spectral lines in an analytical instrument are clustered into groups and used as independent variables. We characterize the system on a database of chemical mixtures, and evaluate it on two real-world applications in the foodstuffs domain: oil adulteration and trans-fatty acid detection. Our results show that the proposed system is a viable low-resolution, low-cost analytical technique for niche applications. |