@article{,

title = {A review of digital innovations for diet monitoring and precision nutrition},

author = {B. J. Mortazavi and R. Gutierrez-Osuna},

url = {https://journals.sagepub.com/doi/full/10.1177/19322968211041356

https://psi.engr.tamu.edu/wp-content/uploads/2023/11/digitalInnovations2023bobak.pdf},

year  = {2023},

date = {2023-01-01},

urldate = {2021-07-14},

journal = {Journal of Diabetes Science and Technology},

keywords = {Chemical sensors, Diabetes, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{diana2022mesoporous,

title = {Silane-modified mesoporous silica nanocoatings for selective mid-infrared waveguide-based gas sensing},

author = {D. Al Husseini and P. T. Lin and S. A. Sukhishvili and J. Zhou and J. Li and P. T. Lin and R. Gutierrez-Osuna and G. L. Coté},

year  = {2022},

date = {2022-09-29},

journal = {Advanced Materials Interfaces},

keywords = {Chemical sensors},

pubstate = {forthcoming},

tppubtype = {article}

}

@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}

}

@article{anurag2021aminoacids,

title = {Predicting the macronutrient composition of mixed meals from dietary biomarkers in blood},

author = {A. Das and B. Mortazavi and S. Sajjadi and T. Chaspari and L. E. Ruebush and N. E. Deutz and G. L. Cote and R. Gutierrez-Osuna},

url = {https://ieeexplore.ieee.org/document/9645322

https://psi.engr.tamu.edu/wp-content/uploads/2021/12/jbhi-aminoacids-2021-preprint.pdf},

year  = {2022},

date = {2022-06-04},

urldate = {2021-12-05},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {26},

number = {6},

pages = {2726-2736},

keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{sudip-2022sab,

title = {On-line drift-compensation for continuous monitoring  with arrays of cross-sensitive chemical sensors},

author = {Sudip Paul and Rohit Sharma and Prashant Tathireddy and Ricardo Gutierrez-Osuna},

year  = {2022},

date = {2022-05-17},

journal = {Sensors and Actuators B: Chemical},

keywords = {Chemical sensors},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{junchao-2022-scirep,

title = {Detection of Volatile Organic Compounds Using Mid-Infrared Silicon Nitride Waveguide Sensors},

author = {Junchao Zhou and Diana Al Husseini and Junyan Li and Zhihai Lin and Svetlana Sukhishvili and Gerard L. Coté and Ricardo Gutierrez-Osuna and Pao Tai Lin},

url = {https://www.nature.com/articles/s41598-022-09597-9 

https://psi.engr.tamu.edu/wp-content/uploads/2022/04/s41598-022-09597-9.pdf},

year  = {2022},

date = {2022-03-15},

urldate = {2022-03-15},

journal = {Scientific Reports},

volume = { 12},

number = {5572 },

keywords = {Chemical sensors},

pubstate = {published},

tppubtype = {article}

}

@article{martin2021clinicalnutrition,

title = {Postprandial concentration of circulating branched chain amino acids are able to predict the carbohydrate content of the ingested mixed meal},

author = {M. Hagve and S. Y. Simbo and L. E. Ruebush and M. P. K. J. Engelen and R. Gutierrez-Osuna and B. J. Mortazavi and G. L. Cote and N. E. P. Deutz},

url = {https://www.sciencedirect.com/science/article/pii/S0261561421003502

https://psi.engr.tamu.edu/wp-content/uploads/2021/07/1-s2.0-S0261561421003502-main.pdf},

year  = {2021},

date = {2021-07-13},

journal = {Clinical Nutrition},

keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{alhusseini2020chemComm,

title = {All-Nanoparticle Layer-by-layer Coatings for Mid-IR On-Chip Gas Sensing},

author = {D. Al Husseini and J. Zhou and D. Willhelm and T. Hastings and G. S. Day and H.-C. Zhou and G. L. Coté and X. Qian and R. Gutierrez-Osuna, P. T. Lin and S. Sukhishvili},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2020/10/d0cc05513a.pdf

https://pubs.rsc.org/en/content/articlelanding/2020/cc/d0cc05513a#!divAbstract},

year  = {2020},

date = {2020-10-22},

journal = {Chemical Communications},

keywords = {Chemical sensors},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@article{li2016sj,

title = {Using Field Asymmetric Ion Mobility Spectrometry for Odor Assessment of Automobile Interior Components},

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/li2016sj-1.pdf},

year  = {2016},

date = {2016-05-01},

journal = {IEEE Sensors Journal},

volume = {in press},

keywords = {Chemical sensors, Electronic nose, Machine olfaction},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{rakeshmixturessab13,

title = {Active temperature modulation of metal-oxide sensors for quantitative analysis of gas mixtures},

author = {R Gosangi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/rakeshmixturessab13.pdf},

year  = {2013},

date = {2013-04-15},

journal = {Sensors and Actuators B: Chemical},

volume = {185},

pages = {201-210},

keywords = {Active sensing, Chemical sensors, Metal-oxide sensors},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{gosangi2010sj,

title = {Active temperature programming for metal-oxide chemoresistors},

author = {R Gosangi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gosangi2010sj-1.pdf},

year  = {2010},

date = {2010-01-01},

journal = {Sensors Journal, IEEE},

volume = {10},

number = {6},

pages = {1075--1082},

publisher = {IEEE},

abstract = {Modulating the operating temperature of metal-oxide (MOX) chemical sensors gives rise to gas-specific signatures that 

provide a wealth of analytical information. In most cases, the operating temperature is modulated according to a standard waveform (e.g., ramp, sine wave). A few studies have approached the optimization of temperature profiles systematically, but these optimizations are performed offline and cannot adapt to changes in the environment. Here, we present an “active perception” strategy based on Partially Observable Markov Decision Processes (POMDP) that allows the temperature program to be optimized in real time, as the sensor reacts to its environment. We characterize the method on a ternary classification problem using a simulated sensor model subjected to additive Gaussian noise, and compare it against two “passive” approaches, a naïve Bayes classifier and a nearest neighbor classifier. Finally, we validate the method in real time using a Taguchi sensor exposed to three volatile compounds. Our results show that the POMDP outperforms both passive approaches and provides a strategy to balance classification performance and sensing costs.},

keywords = {Active sensing, Chemical sensors, Metal-oxide sensors, Temperature modulation},

pubstate = {published},

tppubtype = {article}

}

@article{gutierrez2010arac,

title = {Adaptive Microsensor Systems},

author = {R Gutierrez-Osuna and A Hierlemann},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2010arac.pdf},

year  = {2010},

date = {2010-01-01},

journal = {Annual Review of Analytical Chemistry},

volume = {3},

pages = {255--276},

publisher = {Annual Reviews},

abstract = {We provide a broad review of approaches for developing chemosensor systems whose operating parameters can adapt in response to environmental changes or application needs. Adaptation may take place at the instrumentation level (e.g., tunable sensors) and at the data-analysis level (e.g., adaptive classifiers). We discuss several strategies that provide tunability at the device level: modulation of internal sensing parameters, such as frequencies and operation voltages; variation of external parameters, such as exposure times and catalysts; and development of compact microanalysis systems with multiple tuning options. At the data-analysis level, we consider adaptive filters for change, interference, and drift rejection; pattern classifiers that can adapt to changes in the statistical properties of training data; and active-sensing techniques that can tune sensing parameters in real time. We conclude with a discussion of future opportunities for adaptive sensing in wireless distributed sensor systems.},

keywords = {Active sensing, Chemical sensors},

pubstate = {published},

tppubtype = {article}

}

@article{hierlemann2008higher,

title = {Higher-order chemical sensing},

author = {A Hierlemann and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/hierlemann2008higher.pdf},

year  = {2008},

date = {2008-01-01},

journal = {Chemical reviews},

volume = {108},

number = {2},

pages = {563},

keywords = {Active sensing, Chemical sensors},

pubstate = {published},

tppubtype = {article}

}

@article{raman2007neuromorphic,

title = {Neuromorphic processing for optical microbead arrays: dimensionality reduction and contrast enhancement},

author = {B Raman and T Kotseroglou and L Clark and M Lebl and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2007neuromorphic.pdf},

year  = {2007},

date = {2007-01-01},

journal = {Sensors Journal, IEEE},

volume = {7},

number = {4},

pages = {506--514},

publisher = {IEEE},

abstract = {This paper presents a neuromorphic approach for sensor-based machine olfaction that combines a portable chemical detection system based on microbead array technology with a biologically inspired model of signal processing in the olfactory bulb. The sensor array contains hundreds of microbeads coated with solvatochromic dyes adsorbed in, or covalently attached on, the matrix of various microspheres. When exposed to odors, each bead sensor responds with corresponding intensity changes, spectral shifts, and time-dependent variations associated with the fluorescent sensors. The bead array responses are subsequently processed using a model of olfactory circuits that capture the following two functions: chemotopic convergence of receptor neurons and center on-off surround lateral interactions. The first circuit performs dimensionality reduction, transforming the high-dimensional microbead array response into an organized spatial pattern (i.e., an odor image). The second circuit enhances the contrast of these spatial patterns, improving the separability of odors. The model is validated on an experimental dataset containing the responses of a large array of microbead sensors to five different analytes. Our results indicate that the model is able to significantly improve the separability between odor patterns, compared to that available from the raw sensor response.},

keywords = {Chemical sensors, Neuromorphic models},

pubstate = {published},

tppubtype = {article}

}

@article{raman2006processing,

title = {Processing of chemical sensor arrays with a biologically inspired model of olfactory coding},

author = {B Raman and P A Sun and A Gutierrez-Galvez and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2006processing.pdf},

year  = {2006},

date = {2006-01-01},

journal = {Neural Networks, IEEE Transactions on},

volume = {17},

number = {4},

pages = {1015--1024},

publisher = {IEEE},

abstract = {This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology.},

keywords = {Chemical sensors, Neuromorphic models},

pubstate = {published},

tppubtype = {article}

}

@article{raman2006concentration,

title = {Concentration normalization with a model of gain control in the olfactory bulb},

author = {B Raman and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2006concentration.pdf},

year  = {2006},

date = {2006-01-01},

journal = {Sensors and Actuators B: Chemical},

volume = {116},

number = {1},

pages = {36--42},

publisher = {Elsevier},

abstract = {This article presents a biologically inspired model capable of removing concentration effects from the multivariate response of a gas sensor array. The model is based on the first stage of lateral inhibition in theolfactorybulb, which is mediated by periglomerular interneurons. To simulate inputs to the olfactorybulb, signals from a chemosensor array are first processed with a self-organizing model of chemotopic convergence proposed earlier, which leads to odor-specific spatial patterning. Subsequently, a shunting lateral inhibitory network, modeled after the role of periglomerular cells in the olfactorybulb, is used to compress concentration information. The model is validated using experimental data from an array of temperature-modulated metal-oxide chemoresistors.},

keywords = {Chemical sensors, Neuromorphic models},

pubstate = {published},

tppubtype = {article}

}

@article{raman2006contrast,

title = {Contrast enhancement of gas sensor array patterns with a neurodynamics model of the olfactory bulb},

author = {B Raman and T Yamanaka and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2006contrast.pdf},

year  = {2006},

date = {2006-01-01},

journal = {Sensors and Actuators B: Chemical},

volume = {119},

number = {2},

pages = {547--555},

publisher = {Elsevier},

abstract = {We propose a biologically inspired signal processing model capable of enhancing the discrimination of multivariate patterns from gassensorarrays. The model captures two functions in the early olfactorypathway: chemotopic convergence of sensory neurons onto the olfactorybulb, and center on–off surround lateral interactions. Sensor features are first topologically projected onto a two-dimensional lattice according to their selectivity profile, leading to odor-specific spatial patterning. The resulting patterns serve as inputs to a network of mitral cells with center on–off surround lateral inhibition, which enhances the initialcontrast among odors and decouples odor identity from intensity. The model is validated using experimental data from an array of temperature-modulated metal-oxide sensors. Our results indicate that the model is able to improve the separability between odor patterns that is available at the inputs.},

keywords = {Chemical sensors, Neuromorphic models},

pubstate = {published},

tppubtype = {article}

}

@article{gutierrez2006contrast,

title = {Contrast enhancement and background suppression of chemosensor array patterns with the KIII model},

author = {A Gutierrez-Galvez and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2006contrast.pdf},

year  = {2006},

date = {2006-01-01},

journal = {International journal of intelligent systems},

volume = {21},

number = {9},

pages = {937--953},

publisher = {Wiley Online Library},

abstract = {Inspired by the ability of the olfactory bulb to enhance the contrast between odor representations, we propose a new hebbian learning rule that is able to increase the separability of odor patterns from gas sensor arrays. The proposed learning rule employs a hebbian term to build associations within odors and an anti-hebbian term to reduce correlated activity across odors. In addition to increasing the separability of patterns, the new learning rule can also achieve odor background suppression when combined with a habituation term. These two functions are demonstrated on Freeman's KIII, a neurodynamics model of the olfactory system. The system is first characterized on synthetic data, and also validated on experimental data from an array of chemical sensors exposed to organic solvents.},

keywords = {Chemical sensors, Neuromorphic models},

pubstate = {published},

tppubtype = {article}

}

@article{gutierrez2006increasing,

title = {Increasing the separability of chemosensor array patterns with Hebbian/anti-Hebbian learning},

author = {A Gutierrez-Galvez and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2006increasing.pdf},

year  = {2006},

date = {2006-01-01},

journal = {Sensors and Actuators B: Chemical},

volume = {116},

number = {1},

pages = {29--35},

publisher = {Elsevier},

abstract = {The olfactory bulb is able to enhance the contrast between odor representations through a combination of excitatory and inhibitory circuits. Inspired by this mechanism, we propose a newHebbian/anti-Hebbianlearning rule to increase the separability of sensor-arraypatterns in a neurodynamics model of the olfactory system: the KIII. In the proposed learning rule, a Hebbian term is used to build associations within odors and an anti-Hebbian term is used to reduce correlated activity across odors. The KIII model with the new learning rule is characterized on synthetic data and validated on experimental data from an array of temperature-modulated metal-oxide sensors. Our results show that the performance of the model is comparable to that obtained with Linear Discriminant Analysis (LDA). Furthermore, the model is able to increase patternseparability for different concentrations of three odorants: allyl-alcohol, tert-butanol, and benzene, even though it is only trained with the gas sensor response to the highest concentration.},

keywords = {Chemical sensors, Neuromorphic models},

pubstate = {published},

tppubtype = {article}

}

@article{raman2005sensors,

title = {Concentration normalization with a model of gain control in the olfactory bulb},

author = {B Raman and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2005sensors.pdf},

year  = {2005},

date = {2005-04-13},

booktitle = {Proceedings of the 11th International Symposium on Olfaction and Electronic Nose},

journal = {Sensors and Actuators B: Chemical},

volume = {116},

number = {1},

pages = {36-42},

abstract = {This article presents a biologically-inspired model to remove concentration effects from the multivariate response of a gas sensor array. The model is based on the first stage of lateral inhibition in the olfactory bulb, mediated by periglomerular interneurons. To simulate inputs to the olfactory bulb, sensor-array data are processed with a self-organizing model of chemotopic convergence proposed earlier, which leads to odorspecific spatial patterning. Subsequently, a shunting lateral inhibitory network, modeled after the role of periglomerular cells, compresses the concentration information. The model is validated using experimental data from an array of temperature-modulated metaloxide sensors.},

keywords = {Chemical sensors, Neuromorphic models, Temperature modulation},

pubstate = {published},

tppubtype = {article}

}

@article{rodríguez2004fusion,

title = {Fusion of three sensory modalities for the multimodal characterization of red wines},

author = {M L Rodriguez-Mendez and A Arrieta and V Parra and A Bernal and A Vegas and S Villanueva and R Gutierrez-Osuna and J A de Saja},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/rodríguez2004fusion.pdf},

year  = {2004},

date = {2004-01-01},

journal = {Sensors Journal, IEEE},

volume = {4},

number = {3},

pages = {348--354},

publisher = {IEEE},

abstract = {This work represents the first attempt to develop a sensory system, specifically designed for the characterization of wines, which combines three sensory modalities: an array of gas sensors, an array of electrochemical liquid sensors, and an optical system to measure color by means of CIElab coordinates. This new analytical tool, that has been called "electronic panel," includes not only sensors, but also hardware (injection system and electronics) and the software necessary for fusing information from the three modules. Each of the three sensory modalities (volatiles, liquids, and color) has been designed, tested, and optimized separately. The discrimination capabilities of the system have been evaluated on a database consisting of six red Spanish wines prepared using the same variety of grape (tempranillo) but differing in their geographic origins and aging stages. Sensor signals from each module have been combined and analyzed using pattern recognition techniques. The results of this work show that the discrimination capabilities of the system are significantly improved when signals from each module are combined to form a multimodal feature vector.},

keywords = {Chemical sensors, Electronic nose},

pubstate = {published},

tppubtype = {article}

}

@article{pasini2004use,

title = {Use of a gas-sensor array for detecting volatile organic compounds (VOC) in chemically induced cells},

author = {P Pasini and N Powar and R Gutierrez-Osuna and S Daunert and A Roda},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/pasini2004use.pdf},

year  = {2004},

date = {2004-01-01},

journal = {Analytical and bioanalytical chemistry},

volume = {378},

number = {1},

pages = {76--83},

publisher = {Springer},

abstract = {An application of gas sensors for rapid bioanalysis is presented. An array of temperature-modulated semiconductor sensors was used to characterize the headspace above a cell culture. Recombinant Saccharomyces cerevisiae yeast cells, able to respond to 17β-estradiol by producing a reporter protein, were used as a model system. Yeast cells had the DNA sequence of the human estrogen receptor stably integrated into the genome, and contained expression plasmids carrying estrogen-responsive sequences and the reporter gene lac-Z, encoding the enzyme β-galactosidase. The sensor-response profiles showed small but noticeable discrimination between cell samples induced with 17β-estradiol and non-induced cell samples. The sensor array was capable of detecting changes in the volatile organic compound composition of the headspace above the cultured cells, which can be associated with metabolic changes induced by a chemical compound. This finding suggests the possibility of using cross-selective gas-sensor arrays for analysis of drugs or bioactive molecules through their interaction with cell systems, with the advantage of providing information on their bioavailability.},

keywords = {Chemical sensors, Metal-oxide sensors, Temperature modulation},

pubstate = {published},

tppubtype = {article}

}

@article{gutierrez2003habituation,

title = {Habituation in the KIII olfactory model with chemical sensor arrays},

author = {R Gutierrez-Osuna and A Gutierrez-Galvez},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2003habituation.pdf},

year  = {2003},

date = {2003-01-01},

journal = {Neural Networks, IEEE Transactions on},

volume = {14},

number = {6},

pages = {1565--1568},

publisher = {IEEE},

abstract = {This paper presents a novel combination of chemical sensors and the KIII model for simulating mixture perception with a habituation process triggered by local activity. Stimuli are generated by partitioning feature space with labeled lines. Pattern completion is demonstrated through coherent oscillations across granule populations using experimental odor mixtures.},

keywords = {Chemical sensors, Neuromorphic models},

pubstate = {published},

tppubtype = {article}

}

@article{gutierrez2003transient,

title = {Transient response analysis for temperature-modulated chemoresistors},

author = {R Gutierrez-Osuna and A Gutierrez-Galvez and N Powar},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2003transient.pdf},

year  = {2003},

date = {2003-01-01},

journal = {Sensors and Actuators B: Chemical},

volume = {93},

number = {1-3},

pages = {57--66},

publisher = {Elsevier},

abstract = {This article presents a sensor excitation and signal processing approach that combines temperature modulation and transientanalysis to enhance the selectivity and sensitivity of metal-oxide gas sensors. A staircase waveform is applied to the sensor heater to extract transient information from multiple operating temperatures. Four different transientanalysis techniques, Pade–Z-transform, multi-exponential transient spectroscopy (METS), window time slicing (WTS) and a novel ridge regression solution, are evaluated on the basis of their ability to improve the sensitivity and selectivity of the sensor array. The techniques are validated on two experimental databases containing serial dilutions and mixtures of organic solvents. Our results indicate that processing of the thermal transients significantly improves the sensitivity of metal-oxide chemoresistors when compared to the quasi-stationary temperature-modulatedresponses.},

keywords = {Chemical sensors, Metal-oxide sensors, Temperature modulation},

pubstate = {published},

tppubtype = {article}

}

@article{gutierrez1999transient,

title = {Transient response analysis of an electronic nose using multi-exponential models},

author = {R Gutierrez-Osuna and H T Nagle and S S Schiffman},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez1999transient.pdf},

year  = {1999},

date = {1999-01-01},

journal = {Sensors and Actuators B: Chemical},

volume = {61},

number = {1-3},

pages = {170--182},

publisher = {Elsevier},

abstract = {The purpose of this study is to model the transient response of conductivity-based gas sensors in the context of odor recognition with an electronic nose. Commonly, only the steady-state response of the sensor is used for pattern recognition, ignoring the transient response, which conveys useful discriminatory information. The transient response is modeled as a sum of real exponential functions that represent the different decay processes that occur during sampling of the gas into the sensor chamber and adsorption of the odor compounds onto the sensing element. Four multi-exponential models are reviewed: Gardner transform, multi-exponential transient spectroscopy, Pade-Laplace and Pade-Z transforms. Validation on experimental data from an array of conducting-polymer gas sensors shows that the Pade-Laplace and Pade-Z models have better resolution capabilities than the two spectral transforms.},

keywords = {Chemical sensors, Electronic nose, Temperature modulation},

pubstate = {published},

tppubtype = {article}

}

@article{gutierrez1999method,

title = {A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors},

author = {R Gutierrez-Osuna and H T Nagle},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez1999method.pdf},

year  = {1999},

date = {1999-01-01},

journal = {Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on},

volume = {29},

number = {5},

pages = {626--632},

publisher = {IEEE},

abstract = {The performance of a pattern recognition system is dependent on, among other things, an appropriate data-preprocessing technique, In this paper, we describe a method to evaluate the performance of a variety of these techniques for the problem of odour classification using an array of gas sensors, also referred to as an electronic nose. Four experimental odour databases with different complexities are used to score the data-preprocessing techniques. The performance measure used is the cross-validation estimate of the classification rate of a K nearest neighbor voting rule operating on Fisher's linear discriminant projection subspace.},

keywords = {Chemical sensors},

pubstate = {published},

tppubtype = {article}

}