Co-reporter:Alberto Pasamontes, Alexander A. Aksenov, Michael Schivo, Teri Rowles, Cynthia R. Smith, Lori H. Schwacke, Randall S. Wells, Laura Yeates, Stephanie Venn-Watson, and Cristina E. Davis
Environmental Science & Technology May 16, 2017 Volume 51(Issue 10) pp:5737-5737
Publication Date(Web):April 13, 2017
DOI:10.1021/acs.est.6b06482
Health assessments of wild cetaceans can be challenging due to the difficulty of gaining access to conventional diagnostic matrices of blood, serum and others. While the noninvasive detection of metabolites in exhaled breath could potentially help to address this problem, there exists a knowledge gap regarding associations between known disease states and breath metabolite profiles in cetaceans. This technology was applied to the largest marine oil spill in U.S. history (The 2010 Deepwater Horizon oil spill in the Gulf of Mexico). An accurate analysis was performed to test for associations between the exhaled breath metabolome and sonographic lung abnormalities as well as hematological, serum biochemical, and endocrine hormone parameters. Importantly, metabolites consistent with chronic inflammation, such as products of lung epithelial cellular breakdown and arachidonic acid cascade metabolites were associated with sonographic evidence of lung consolidation. Exhaled breath condensate (EBC) metabolite profiles also correlated with serum hormone concentrations (cortisol and aldosterone), hepatobiliary enzyme levels, white blood cell counts, and iron homeostasis. The correlations among breath metabolites and conventional health measures suggest potential application of breath sampling for remotely assessing health of wild cetaceans. This methodology may hold promise for large cetaceans in the wild for which routine collection of blood and respiratory anomalies are not currently feasible.
Co-reporter:Konstantin O. Zamuruyev, Hamzeh K. Bardaweel, Christopher J. Carron, Nicholas J. Kenyon, Oliver Brand, Jean-Pierre Delplanque, and Cristina E. Davis
Langmuir August 26, 2014 Volume 30(Issue 33) pp:10133-10142
Publication Date(Web):July 29, 2014
DOI:10.1021/la5004462
Combination of two physical phenomena, capillary pressure gradient and wettability gradient, allows a simple two-step fabrication process that yields a reliable hydrophobic self-cleaning condenser surface. The surface is fabricated with specific microscopic topography and further treatment with a chemically inert low-surface-energy material. This process does not require growth of nanofeatures (nanotubes) or hydrophilic–hydrophobic patterning of the surface. Trapezoidal geometry of the microfeatures facilitates droplet transfer from the Wenzel to the Cassie state and reduces droplet critical diameter. The geometry of the micropatterns enhances local coalescence and directional movement for droplets with diameter much smaller than the radial length of the micropatterns. The hydrophobic self-cleaning micropatterned condenser surface prevents liquid film formation and promotes continuous dropwise condensation cycle. Upon dropwise condensation, droplets follow a designed wettability gradient created with micropatterns from the most hydrophobic to the least hydrophobic end of the surface. The surface has higher condensation efficiency, due to its directional self-cleaning property, than a plain hydrophobic surface. We explain the self-actuated droplet collection mechanism on the condenser surface and demonstrate experimentally the creation of an effective wettability gradient over a 6 mm radial distance. In spite of its fabrication simplicity, the fabricated surface demonstrates self-cleaning property, enhanced condensation performance, and reliability over time. Our work enables creation of a hydrophobic condenser surface with the directional self-cleaning property that can be used for collection of biological (chemical, environmental) aerosol samples or for condensation enhancement.
Co-reporter:Mitchell M. McCartney, Yuriy Zrodnikov, Alexander G. Fung, Michael K. LeVasseur, Josephine M. Pedersen, Konstantin O. Zamuruyev, Alexander A. Aksenov, Nicholas J. Kenyon, and Cristina E. Davis
ACS Sensors August 25, 2017 Volume 2(Issue 8) pp:1167-1167
Publication Date(Web):July 28, 2017
DOI:10.1021/acssensors.7b00289
We have developed a simple-to-manufacture microfabricated gas preconcentrator for MEMS-based chemical sensing applications. Cavities and microfluidic channels were created using a wet etch process with hydrofluoric acid, portions of which can be performed outside of a cleanroom, instead of the more common deep reactive ion etch process. The integrated heater and resistance temperature detectors (RTDs) were created with a photolithography-free technique enabled by laser etching. With only 28 V DC (0.1 A), a maximum heating rate of 17.6 °C/s was observed. Adsorption and desorption flow parameters were optimized to be 90 SCCM and 25 SCCM, respectively, for a multicomponent gas mixture. Under testing conditions using Tenax TA sorbent, the device was capable of measuring analytes down to 22 ppb with only a 2 min sample loading time using a gas chromatograph with a flame ionization detector. Two separate devices were compared by measuring the same chemical mixture; both devices yielded similar peak areas and widths (fwhm: 0.032–0.033 min), suggesting reproducibility between devices.Keywords: chemical sensor; detectors; gas preconcentrator; microelectromechanical systems (MEMS); sorbent;
Co-reporter:Alexander A. Aksenov, Konstantin O. Zamuruyev, Alberto Pasamontes, Joshua F. Brown, Michael Schivo, Soraya Foutouhi, Bart C. Weimer, Nicholas J. Kenyon, Cristina E. Davis
Journal of Chromatography B 2017 Volumes 1061–1062(Volumes 1061–1062) pp:
Publication Date(Web):1 September 2017
DOI:10.1016/j.jchromb.2017.06.038
•Exhaled breath condensate metabolites study.•A practical methodology selection guide for optimal metabolite coverage of breath.•Comparison of mass spectrometry-based analysis methodologies for broad metabolite coverage.Breath analysis has been gaining popularity as a non-invasive technique that is amenable to a broad range of medical uses. One of the persistent problems hampering the wide application of the breath analysis method is measurement variability of metabolite abundances stemming from differences in both sampling and analysis methodologies used in various studies. Mass spectrometry has been a method of choice for comprehensive metabolomic analysis. For the first time in the present study, we juxtapose the most commonly employed mass spectrometry-based analysis methodologies and directly compare the resultant coverages of detected compounds in exhaled breath condensate in order to guide methodology choices for exhaled breath condensate analysis studies.Four methods were explored to broaden the range of measured compounds across both the volatile and non-volatile domain. Liquid phase sampling with polyacrylate Solid-Phase MicroExtraction fiber, liquid phase extraction with a polydimethylsiloxane patch, and headspace sampling using Carboxen/Polydimethylsiloxane Solid-Phase MicroExtraction (SPME) followed by gas chromatography mass spectrometry were tested for the analysis of volatile fraction. Hydrophilic interaction liquid chromatography and reversed-phase chromatography high performance liquid chromatography mass spectrometry were used for analysis of non-volatile fraction. We found that liquid phase breath condensate extraction was notably superior compared to headspace extraction and differences in employed sorbents manifested altered metabolite coverages. The most pronounced effect was substantially enhanced metabolite capture for larger, higher-boiling compounds using polyacrylate SPME liquid phase sampling. The analysis of the non-volatile fraction of breath condensate by hydrophilic and reverse phase high performance liquid chromatography mass spectrometry indicated orthogonal metabolite coverage by these chromatography modes.We found that the metabolite coverage could be enhanced significantly with the use of organic solvent as a device rinse after breath sampling to collect the non-aqueous fraction as opposed to neat breath condensate sample. Here, we show the detected ranges of compounds in each case and provide a practical guide for methodology selection for optimal detection of specific compounds.
Co-reporter:Mitchell M. McCartney, Sierra L. Spitulski, Alberto Pasamontes, Daniel J. Peirano, Michael J. Schirle, Raquel Cumeras, Jason D. Simmons, Jeffrey L. Ware, Joshua F. Brown, Alexandria J.Y. Poh, Seth C. Dike, Elizabeth K. Foster, Kristine E. Godfrey, Cristina E. Davis
Talanta 2016 Volume 146() pp:148-154
Publication Date(Web):1 January 2016
DOI:10.1016/j.talanta.2015.08.039
•A branch enclosure with a mobile detector isolates and measures volatiles from plants.•The system is adaptable for other volatile studies in contained research settings.•The system distinguished volatile differences between closely-related plant cultivars.•The system showed volatile-profile differences between healthy and infected citrus.Volatile organic compounds (VOCs) are off-gassed from all living organisms and represent end products of metabolic pathways within the system. In agricultural systems, these VOCs can provide important information on plant health and can ordinarily be measured non-invasively without harvesting tissue from the plants. Previously we reported a portable gas chromatography/differential mobility spectrometry (GC/DMS) system that could distinguish VOC profiles of pathogen-infected citrus from healthy trees before visual symptoms of disease were present. These measurements were taken directly from canopies in the field, but the sampling and analysis protocol did not readily transfer to a controlled greenhouse study where the ambient background air was saturated with volatiles contained in the facility. In this study, we describe for the first time a branch enclosure uniquely coupled with GC/DMS to isolate and measure plant volatiles. To test our system, we sought to replicate our field experiment within a contained greenhouse and distinguish the VOC profiles of healthy versus citrus infected with Candidatus Liberibacter asiaticus. We indeed confirm the ability to track infection-related trace biogenic VOCs using our sampling system and method and we now show this difference in Lisbon lemons (Citrus×limon L. Burm. f.), a varietal not previously reported. Furthermore, the system differentiates the volatile profiles of Lisbon lemons from Washington navels [Citrus sinensis (L.) Osbeck] and also from Tango mandarins (Citrus reticulata Blanco). Based on this evidence, we believe this enclosure-GC/DMS system is adaptable to other volatile-based investigations of plant diseases in greenhouses or other contained settings, and this system may be helpful for basic science research studies of infection mechanisms.
Co-reporter:Daniel J. Peirano;Alberto Pasamontes
International Journal for Ion Mobility Spectrometry 2016 Volume 19( Issue 2-3) pp:155-166
Publication Date(Web):2016 September
DOI:10.1007/s12127-016-0200-9
Modern differential mobility spectrometers (DMS) produce complex and multi-dimensional data streams that allow for near-real-time or post-hoc chemical detection for a variety of applications. An active area of interest for this technology is metabolite monitoring for biological applications, and these data sets regularly have unique technical and data analysis end user requirements. While there are initial publications on how investigators have individually processed and analyzed their DMS metabolomic data, there are no user-ready commercial or open source software packages that are easily used for this purpose. We have created custom software uniquely suited to analyze gas chromatograph / differential mobility spectrometry (GC/DMS) data from biological sources. Here we explain the implementation of the software, describe the user features that are available, and provide an example of how this software functions using a previously-published data set. The software is compatible with many commercial or home-made DMS systems. Because the software is versatile, it can also potentially be used for other similarly structured data sets, such as GC/GC and other IMS modalities.
Co-reporter:Alberto Pasamontes;William H. K. Cheung;Jason Simmons
Metabolomics 2016 Volume 12( Issue 3) pp:
Publication Date(Web):2016 March
DOI:10.1007/s11306-016-0959-z
Citrus tristeza virus (CTV) (genus Closterovirus) is a plant pathogen which infects economically important citrus crops, resulting in devastating crop losses worldwide. In this study, we analyzed leaf metabolite extracts from six sweet orange varieties and a mandarin × tangor cross infected with CTV collected at the Lindcove Research and Extension Center (LREC; Exeter, CA). In order to analyze low volatility small molecules, the extracts of leaf metabolites were derivatized by N-methyl-N-trimethylsilyl-trifluoracetamide (MSTFA). Chemical analysis was performed with gas chromatography/mass spectrometry (GC/MS) to assess metabolite changes induced by CTV infection. Principal Component Analysis (PCA) and Hotelling’s T2 were used to identify outliers within the set of samples. Partial Least Square Discriminant Analysis (PLS-DA) was applied as a regression method. A cross-validation strategy was repeated 300 times to minimize possible bias in the model selection. Afterwards, a representative model was built with a sensitivity of 0.66 and a specificity of 0.71. The metabolites which had the strongest contribution to differentiate between healthy and CTV-infected were found to be mostly saccharides and their derivatives such as inositol, d-fructose, glucaric and quinic acid. These metabolites are known to be endogenously produced by plants, possess important biological functions and often found to be differentially regulated in disease states, maturation processes, and metabolic responses. Based on the information found in this study, a method may be available that can identify CTV infected plants for removal and halt the spread of the virus.
Co-reporter:Raquel Cumeras;Alexander A. Aksenov
Analytical and Bioanalytical Chemistry 2016 Volume 408( Issue 24) pp:6649-6658
Publication Date(Web):2016 September
DOI:10.1007/s00216-016-9778-3
The natural porosity of eggshells allows hen eggs to become contaminated with microbes from the nesting material and environment. Those microorganisms can later proliferate due to the humid ambient conditions while stored in refrigerators, causing a potential health hazard to the consumer. The microbes’ volatile organic compounds (mVOCs) are released by both fungi and bacteria. We studied mVOCs produced by aging eggs likely contaminated by fungi and fresh eggs using the non-invasive detection method of gas-phase sampling of volatiles followed by gas chromatography/mass spectrometry (GC/MS) analysis. Two different fungal species (Cladosporium macrocarpum and Botrytis cinerea) and two different bacteria species (Stenotrophomas rhizophila and Pseudomonas argentinensis) were identified inside the studied eggs. Two compounds believed to originate from the fungi themselves were identified. One fungus-specific compound was found in both egg and the fungi: trichloromethane.
Co-reporter:William H. K. Cheung;Alberto Pasamontes;Daniel J. Peirano;Weixiang Zhao
Metabolomics 2015 Volume 11( Issue 6) pp:1514-1525
Publication Date(Web):2015 December
DOI:10.1007/s11306-015-0807-6
Citrus tristeza virus (CTV) (genus Closterovirus) is a plant pathogen which infects economically important citrus crops such as sweet oranges, mandarins, limes and grapefruit varietals. Within the last 70 years, an estimated 100 million citrus trees have been destroyed due to CTV infection worldwide. Present measures to contain CTV infection include scouts for visual assessment, and molecular analysis methods such as enzyme linked immunosorbent assay and reverse transcription polymerase chain reaction. Volatile organic compound (VOC) profiling may offer an alternative method of disease detection. In this study, we used a “Twister™” sorbent system for in-field VOC sampling. Chemical analysis was performed with thermal desorption gas chromatography time-of-flight mass spectrometry, and data were subjected to unsupervised and supervised analysis. Samples were collected from healthy trees, those with asymptomatic CTV, and those with CTV that were coinfected with a secondary unrelated bacterial infection of Spiroplasma citri, the causal agent of citrus stubborn disease (Stubborn). A total of 383 VOCs were detected across three classes: healthy control trees, CTV infected, and CTV coinfected with Stubborn. Mathematical models of this data were built to successfully differentiate: (a) healthy trees from CTV infected trees; (b) healthy trees from both CTV and CTV coinfected with Stubborn; and (c) to effectively differentiate between healthy trees and CTV infected trees, without consideration of Stubborn coinfection (the model would work on both singly or coinfected trees). The putative CTV biomarkers observed were terpenoid based (myrcene, carene, ocimene, bulnesene), two alcohols (n-undecanol, surfynol) and two acetones (geranyl acetone and neryl acetate).
Co-reporter:Alexander A. Aksenov, Alberto Pasamontes, Daniel J. Peirano, Weixiang Zhao, Abhaya M. Dandekar, Oliver Fiehn, Reza Ehsani, and Cristina E. Davis
Analytical Chemistry 2014 Volume 86(Issue 5) pp:2481
Publication Date(Web):February 2, 2014
DOI:10.1021/ac403469y
The viability of the multibillion dollar global citrus industry is threatened by the “green menace”, citrus greening disease (Huanglongbing, HLB), caused by the bacterial pathogen Candidatus Liberibacter. The long asymptomatic stage of HLB makes it challenging to detect emerging regional infections early to limit disease spread. We have established a novel method of disease detection based on chemical analysis of released volatile organic compounds (VOCs) that emanate from infected trees. We found that the biomarkers “fingerprint” is specific to the causal pathogen and could be interpreted using analytical methods such as gas chromatography/mass spectrometry (GC/MS) and gas chromatography/differential mobility spectrometry (GC/DMS). This VOC-based disease detection method has a high accuracy of ∼90% throughout the year, approaching 100% under optimal testing conditions, even at very early stages of infection where other methods are not adequate. Detecting early infection based on VOCs precedes visual symptoms and DNA-based detection techniques (real-time polymerase chain reaction, RT-PCR) and can be performed at a substantially lower cost and with rapid field deployment.
Co-reporter:Alexander A. Aksenov, Laura Yeates, Alberto Pasamontes, Craig Siebe, Yuriy Zrodnikov, Jason Simmons, Mitchell M. McCartney, Jean-Pierre Deplanque, Randall S. Wells, and Cristina E. Davis
Analytical Chemistry 2014 Volume 86(Issue 21) pp:10616
Publication Date(Web):September 25, 2014
DOI:10.1021/ac5024217
Changing ocean health and the potential impact on marine mammal health are gaining global attention. Direct health assessments of wild marine mammals, however, is inherently difficult. Breath analysis metabolomics is a very attractive assessment tool due to its noninvasive nature, but it is analytically challenging. It has never been attempted in cetaceans for comprehensive metabolite profiling. We have developed a method to reproducibly sample breath from small cetaceans, specifically Atlantic bottlenose dolphins (Tursiops truncatus). We describe the analysis workflow to profile exhaled breath metabolites and provide here a first library of volatile and nonvolatile compounds in cetacean exhaled breath. The described analytical methodology enabled us to document baseline compounds in exhaled breath of healthy animals and to study changes in metabolic content of dolphin breath with regard to a variety of factors. The method of breath analysis may provide a very valuable tool in future wildlife conservation efforts as well as deepen our understanding of marine mammals biology and physiology.
Co-reporter:Konstantin O. Zamuruyev, Hamzeh K. Bardaweel, Christopher J. Carron, Nicholas J. Kenyon, Oliver Brand, Jean-Pierre Delplanque, and Cristina E. Davis
Langmuir 2014 Volume 30(Issue 33) pp:10133-10142
Publication Date(Web):July 29, 2014
DOI:10.1021/la5004462
Combination of two physical phenomena, capillary pressure gradient and wettability gradient, allows a simple two-step fabrication process that yields a reliable hydrophobic self-cleaning condenser surface. The surface is fabricated with specific microscopic topography and further treatment with a chemically inert low-surface-energy material. This process does not require growth of nanofeatures (nanotubes) or hydrophilic–hydrophobic patterning of the surface. Trapezoidal geometry of the microfeatures facilitates droplet transfer from the Wenzel to the Cassie state and reduces droplet critical diameter. The geometry of the micropatterns enhances local coalescence and directional movement for droplets with diameter much smaller than the radial length of the micropatterns. The hydrophobic self-cleaning micropatterned condenser surface prevents liquid film formation and promotes continuous dropwise condensation cycle. Upon dropwise condensation, droplets follow a designed wettability gradient created with micropatterns from the most hydrophobic to the least hydrophobic end of the surface. The surface has higher condensation efficiency, due to its directional self-cleaning property, than a plain hydrophobic surface. We explain the self-actuated droplet collection mechanism on the condenser surface and demonstrate experimentally the creation of an effective wettability gradient over a 6 mm radial distance. In spite of its fabrication simplicity, the fabricated surface demonstrates self-cleaning property, enhanced condensation performance, and reliability over time. Our work enables creation of a hydrophobic condenser surface with the directional self-cleaning property that can be used for collection of biological (chemical, environmental) aerosol samples or for condensation enhancement.
Co-reporter:Dr. Alexer A. Aksenov;Christian E. Srock;Dr. Weixiang Zhao;Shankar Sankaran;Michael Schivo;Richart Harper;Dr. Carol J. Cardona;Dr. Zheng Xing;Dr. Cristina E. Davis
ChemBioChem 2014 Volume 15( Issue 7) pp:1040-1048
Publication Date(Web):
DOI:10.1002/cbic.201300695
Abstract
Volatile organic compounds (VOCs) emanating from humans have the potential to revolutionize non-invasive diagnostics. Yet, little is known about how these compounds are generated by complex biological systems, and even less is known about how these compounds are reflective of a particular physiological state. In this proof-of-concept study, we examined VOCs produced directly at the cellular level from B lymphoblastoid cells upon infection with three live influenza virus subtypes: H9N2 (avian), H6N2 (avian), and H1N1 (human). Using a single cell line helped to alleviate some of the complexity and variability when studying VOC production by an entire organism, and it allowed us to discern marked differences in VOC production upon infection of the cells. The patterns of VOCs produced in response to infection were unique for each virus subtype, while several other non-specific VOCs were produced after infections with all three strains. Also, there was a specific time course of VOC release post infection. Among emitted VOCs, production of esters and other oxygenated compounds was particularly notable, and these may be attributed to increased oxidative stress resulting from infection. Elucidating VOC signatures that result from the host cells response to infection may yield an avenue for non-invasive diagnostics and therapy of influenza and other viral infections.
Co-reporter:Dr. Alexer A. Aksenov;Dr. Andrea Gojova;Dr. Weixiang Zhao;Joshua T. Morgan;Shankar Sankaran;Dr. Christian E. Srock;Dr. Cristina E. Davis
ChemBioChem 2012 Volume 13( Issue 7) pp:1053-1059
Publication Date(Web):
DOI:10.1002/cbic.201200011
Abstract
The major histocompatibility complex (MHC), or human leukocyte antigen (HLA) gene-coding region in humans, plays a significant role in infectious disease response, autoimmunity, and cellular recognition. This super locus is essential in mate selection and kin recognition because of the organism-specific odor which can be perceived by other individuals. However, how the unique MHC genetic combination of an organism correlates with generation of the organism-specific odor is not well understood. In the present work, we have shown that human B-cells produce a set of volatile organic compounds (VOCs) that can be measured by GC-MS. More importantly, our results show that specific HLA alleles are related to production of selected VOCs, and that this leads to a cell-specific odor “fingerprint”. We used a C1R HLA class I A and B locus negative cell line, along with C1R cell lines that were stably transfected with specific A and B alleles. Our work demonstrates for the first time that HLA alleles can directly influence production of specific odor compounds at the cellular level. Given that the resulting odor fingerprint depends on expression of specific HLA sequences, it may yield information on unique human scent profiles, composition of exhaled breath, as well as immune response states in future studies.
Co-reporter:Abhinav Bhushan;Huilan Han;Alex Sutherl;Stefanie Boehme;Frank Yaghmaie
Applied Organometallic Chemistry 2010 Volume 24( Issue 7) pp:530-532
Publication Date(Web):
DOI:10.1002/aoc.1653
Abstract
There is a growing interest in developing advanced materials for thin film applications in biology, electronics, photonics and engineering. We report the development of hybrid inorganic/organic thin films containing nickel, iron and cobalt paramagnetic materials. By etching the resist in oxygen plasma after processing, most of the organic component of the resist was removed. The elemental chemical composition of the films was confirmed by energy dispersive X-ray spectroscopy. This process can potentially lead to patterning paramagnetic thin films containing paramagnetic materials by following standard photolithography protocols, obviating the need for a wet or vacuum metal deposition. Copyright © 2010 John Wiley & Sons, Ltd.
Co-reporter:Nicholas Strand, Abhinav Bhushan, Michael Schivo, Nicholas J. Kenyon, Cristina E. Davis
Sensors and Actuators B: Chemical 2010 Volume 143(Issue 2) pp:516-523
Publication Date(Web):7 January 2010
DOI:10.1016/j.snb.2009.09.052
A wide range of metabolites are measured in the gas phase of exhaled human breath, and some of these biomarkers are frequently observed to be up- or down-regulated in certain disease states. Portable breath analysis systems have the potential for a wide range of applications in health diagnostics. However, this is currently limited by the lack of concentration mechanisms to enhance trace metabolites found in the breath to levels that can be adequately recorded using miniaturized gas-phase sensors. In this study we have created chip-based polymeric pre-concentration devices capable of absorbing and desorbing breath volatiles for subsequent chemical analysis. These devices appear to concentrate chemicals from both environmental air samples as well as directly from exhaled human breath, and these devices may have applications in lab-on-a-chip-based environmental and health monitoring systems.
Co-reporter:Huilan Han, John Bissell, Frank Yaghmaie and Cristina E. Davis
Langmuir 2010 Volume 26(Issue 1) pp:515-520
Publication Date(Web):October 1, 2009
DOI:10.1021/la902195k
The development and processing of hybrid inorganic−organic thin film materials plays a critical role in advancing interdisciplinary sciences and device manufacturing. Here we present a novel approach to synthesize and deposit acrylate-containing organic/inorganic hybrid films. The material is based on a chemical solution and includes specifically desired metal dopants that are fully integrated into the backbone of the polymer structure. The film can be deposited by simple spin coating, and we confer photosensitive properties to the material making it directly patterned by traditional UV photolithography techniques. Film thickness, chemical characterization, and wet/dry etching capability of the film are also investigated. We believe this innovative material has the potential to be used in a broad range of applications for electronic, photonic, biology, and other interdisciplinary fields.
Co-reporter:Weixiang Zhao, Shankar Sankaran, Ana M. Ibáñez, Abhaya M. Dandekar, Cristina E. Davis
Analytica Chimica Acta 2009 Volume 647(Issue 1) pp:46-53
Publication Date(Web):4 August 2009
DOI:10.1016/j.aca.2009.05.029
This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.
Co-reporter:Weixiang Zhao, Cristina E. Davis
Analytica Chimica Acta 2009 Volume 651(Issue 1) pp:15-23
Publication Date(Web):28 September 2009
DOI:10.1016/j.aca.2009.08.008
This paper introduces the ant colony algorithm, a novel swarm intelligence based optimization method, to select appropriate wavelet coefficients from mass spectral data as a new feature selection method for ovarian cancer diagnostics. By determining the proper parameters for the ant colony algorithm (ACA) based searching algorithm, we perform the feature searching process for 100 times with the number of selected features fixed at 5. The results of this study show: (1) the classification accuracy based on the five selected wavelet coefficients can reach up to 100% for all the training, validating and independent testing sets; (2) the eight most popular selected wavelet coefficients of the 100 runs can provide 100% accuracy for the training set, 100% accuracy for the validating set, and 98.8% accuracy for the independent testing set, which suggests the robustness and accuracy of the proposed feature selection method; and (3) the mass spectral data corresponding to the eight popular wavelet coefficients can be located by reverse wavelet transformation and these located mass spectral data still maintain high classification accuracies (100% for the training set, 97.6% for the validating set, and 98.8% for the testing set) and also provide sufficient physical and medical meaning for future ovarian cancer mechanism studies. Furthermore, the corresponding mass spectral data (potential biomarkers) are in good agreement with other studies which have used the same sample set. Together these results suggest this feature extraction strategy will benefit the development of intelligent and real-time spectroscopy instrumentation based diagnosis and monitoring systems.
Co-reporter:Mary A. Molina, Weixiang Zhao, Shankar Sankaran, Michael Schivo, Nicholas J. Kenyon, Cristina E. Davis
Analytica Chimica Acta 2008 Volume 628(Issue 2) pp:155-161
Publication Date(Web):3 November 2008
DOI:10.1016/j.aca.2008.09.010
Analytical instruments that can measure small amounts of chemicals in complicated biological samples are often useful as diagnostic tools. However, it can be challenging to optimize these sensors using actual clinical samples, given the heterogeneous background and composition of the test materials. Here we use gas chromatography–differential mobility spectrometry (GC/DMS) to analyze the chemical content of human exhaled breath condensate (EBC). Ultimately, this system can be used for non-invasive disease diagnostics. Many parameters can be adjusted within this instrument system, and we implemented a factorial design-of-experiments to systematically test several combinations of parameter settings while concurrently analyzing effects and interactions.We examined four parameters that affect sensitivity and detection for our instrument, requiring a 24 factorial design. We optimized sensor function using EBC samples spiked with acetone, a known clinical biomarker in breath. Two outputs were recorded for each experiment combination: number of chemicals detected, and the amplitude of acetone signal. Our goal is to find the best parameter combination that yields the highest acetone peak while also preserving the largest number of other chemical peaks in the spectra. By optimizing the system, we can conduct further clinical experiments with our sensor more efficiently and accurately.
Co-reporter:Melissa D. Krebs, Robert D. Tingley, Julie E. Zeskind, Maria E. Holmboe, Joung-Mo Kang, Cristina E. Davis
Chemometrics and Intelligent Laboratory Systems 2006 Volume 81(Issue 1) pp:74-81
Publication Date(Web):March 2006
DOI:10.1016/j.chemolab.2005.10.001
Analyzing the response of analytical sensors to complex chemical mixtures is difficult, as signals may overlap and may vary between experiments. The analysis and comparison of data files depends on their repeatability, and misaligned signals will not be directly comparable. We present a method for aligning chromatographic data by the selection of landmarks, the matching of these landmarks across chromatograms, the calculation of a functional approximation describing the amount of shift of the landmarks between files using cubic spline interpolation, and the subsequent shifting of the data by the application of this functional approximation so that the two files are aligned. We demonstrate the effect of this alignment on subsequent principal component analysis.
Co-reporter:Melissa D. Krebs, Joung-Mo Kang, Sarah J. Cohen, Jeffrey B. Lozow, Robert D. Tingley, Cristina E. Davis
Sensors and Actuators B: Chemical 2006 Volume 119(Issue 2) pp:475-482
Publication Date(Web):7 December 2006
DOI:10.1016/j.snb.2005.12.058
Alignment of data is critical for analysis involving the comparison of multiple files. Analytical sensor data resulting from complex chemical mixtures can often be mis-aligned due to time-varying biases resulting from mechanical instrument variability. In addition to the necessity for time alignment, data from the micromachined differential mobility spectrometer (DMS) may be slightly shifted when comparing various data sets due to the effect of heat and flow variations on the compensation voltage (Vc). Thus, the data in this dimension can also benefit from alignment. We present here a method for the alignment of both dimensions (scans and Vc) of pyrolysis-DMS data, using a single file as reference. The Vc dimension is first aligned with respect to the reference file; this is a rigid shift and no interpolation is performed. This is an advantage as the Vc dimension has physical meaning and should not be altered by interpolation. The time (or scans) dimension is then aligned with respect to the reference by identifying common landmarks and interpolating according to a piecewise linear function calculated based on the amount of shift between the two files. The effect of a slight change in flow in the Vc dimension is examined using the nitrogen reactant ion peaks as a standard signal. This method is useful for further data processing in which multiple files are to be directly compared, and it could also be useful for two-dimensional alignment of data from other sensor modalities.
Co-reporter:Weixiang Zhao, Cristina E. Davis
Artificial Intelligence in Medicine (May 2011) Volume 52(Issue 1) pp:1-9
Publication Date(Web):May 2011
DOI:10.1016/j.artmed.2011.03.001
