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Your search keyword '"Harynuk, James J."' showing total 244 results
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244 results on '"Harynuk, James J."'

2. Preclinical modeling of metabolic syndrome to study the pleiotropic effects of novel antidiabetic therapy independent of obesity

Author

Mochel, Jonathan P., Ward, Jessica L., Blondel, Thomas, Kundu, Debosmita, Merodio, Maria M., Zemirline, Claudine, Guillot, Emilie, Giebelhaus, Ryland T., de la Mata, Paulina, Iennarella-Servantez, Chelsea A., Blong, April, Nam, Seo Lin, Harynuk, James J., Suchodolski, Jan, Tvarijonaviciute, Asta, Cerón, José Joaquín, Bourgois-Mochel, Agnes, Zannad, Faiez, Sattar, Naveed, and Allenspach, Karin

Published
2024
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3. Untargeted Region of Interest Selection for GC-MS Data using a Pseudo F-Ratio Moving Window ($\psi$FRMV)

Author

Giebelhaus, Ryland T., Armstrong, Michael D. Sorochan, de la Mata, A. Paulina, and Harynuk, James J.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

There are many challenges associated with analysing gas chromatography - mass spectrometry (GC-MS) data. Many of these challenges stem from the fact that electron ionisation can make it difficult to recover molecular information due to the high degree of fragmentation with concomitant loss of molecular ion signal. With GC-MS data there are often many common fragment ions shared among closely-eluting peaks, necessitating sophisticated methods for analysis. Some of these methods are fully automated, but make some assumptions about the data which can introduce artifacts during the analysis. Chemometric methods such as Multivariate Curve Resolution, or Parallel Factor Analysis are particularly attractive, since they are flexible and make relatively few assumptions about the data - ideally resulting in fewer artifacts. These methods do require expert user intervention to determine the most relevant regions of interest and an appropriate number of components, $k$, for each region. Automated region of interest selection is needed to permit automated batch processing of chromatographic data with advanced signal deconvolution. Here, we propose a new method for automated, untargeted region of interest selection that accounts for the multivariate information present in GC-MS data to select regions of interest based on the ratio of the squared first, and second singular values from the Singular Value Decomposition of a window that moves across the chromatogram. Assuming that the first singular value accounts largely for signal, and that the second singular value accounts largely for noise, it is possible to interpret the relationship between these two values as a probabilistic distribution of Fisher Ratios. The sensitivity of the algorithm was tested by investigating the concentration at which the algorithm can no longer pick out chromatographic regions known to contain signal.

Published
2022
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4. PARAFAC2$\times$N: Coupled Decomposition of Multi-modal Data with Drift in N Modes

Author

Armstrong, Michael D. Sorochan, Hinrich, Jesper Løve, de la Mata, A. Paulina, and Harynuk, James J.

Subjects
Statistics - Applications, Computer Science - Machine Learning
Abstract

Reliable analysis of comprehensive two-dimensional gas chromatography - time-of-flight mass spectrometry (GC$\times$GC-TOFMS) data is considered to be a major bottleneck for its widespread application. For multiple samples, GC$\times$GC-TOFMS data for specific chromatographic regions manifests as a 4th order tensor of I mass spectral acquisitions, J mass channels, K modulations, and L samples. Chromatographic drift is common along both the first-dimension (modulations), and along the second-dimension (mass spectral acquisitions), while drift along the mass channel and sample dimensions is for all practical purposes nonexistent. A number of solutions to handling GC$\times$GC-TOFMS data have been proposed: these involve reshaping the data to make it amenable to either 2nd order decomposition techniques based on Multivariate Curve Resolution (MCR), or 3rd order decomposition techniques such as Parallel Factor Analysis 2 (PARAFAC2). PARAFAC2 has been utilised to model chromatographic drift along one mode, which has enabled its use for robust decomposition of multiple GC-MS experiments. Although extensible, it is not straightforward to implement a PARAFAC2 model that accounts for drift along multiple modes. In this submission, we demonstrate a new approach and a general theory for modelling data with drift along multiple modes, for applications in multidimensional chromatography with multivariate detection.

Published
2022

15. Multivariate Optimization Procedure for Dynamic Headspace Extractions Coupled to GC(xGC)

Author

Dias, Ryan P., Nam, Seo Lin, Schmidt, Sheri A., Mata, A. Paulina de la, and Harynuk, James J.

Subjects
Volatile organic compounds -- Analysis, Chemistry, Science and technology, University of Alberta
Abstract

Volatile organic compounds (VOCs) are ubiquitous chemicals of great interest in the study of aromas and flavours of foods. Many recent studies present optimized headspace (HS) and dynamic headspace (DHS) methods for specific sample types; however, the literature does not present (to the best of our knowledge) a generalized procedure for the thorough optimization of a DHS extraction.This article presents an approach using design of experiments (DoE) for the optimization of DHS extraction parameters.The approach is demonstrated for two different food sample types with diverse populations of VOCs: active sourdough colony as an example with a high moisture content, and sourdough bread as an example with a lower moisture content. Optimized methods are assessed for VOC extraction reproducibility and exhaustiveness; guidelines for DHS optimization are presented. KEY POINTS * Design of experiments (DoE) modelling for dynamic headspace (DHS) extractions. * Sample type impacts on design of DHS extractions. * DoE yields reproducible, predictable extraction methods., Mixtures of volatile organic compounds (VOCs) emanate from foods and beverages during human consumption, and these compounds blend together to create flavours and aromas. While many VOCs are present endogenously [...]

Published
2022

16. Discriminating extra virgin olive oils from common edible oils: Comparable performance of PLS‐DA models trained on low‐field and high‐field 1H NMR data.

Author

Head, Thomas, Giebelhaus, Ryland T., Nam, Seo Lin, de la Mata, A. Paulina, Harynuk, James J., and Shipley, Paul R.

Abstract

Introduction: Olive oil, derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or fraudulently mislabel oils as olive to increase profitability. Adulterated products can cause allergic reactions in sensitive individuals and can lack compounds which contribute to the perceived health benefits of olive oil, and its corresponding premium price. Objective: There is a need for robust methods to rapidly authenticate olive oils. By utilising machine learning models trained on the nuclear magnetic resonance (NMR) spectra of known olive oil and edible oils, samples can be classified as olive and authenticated. While high‐field NMRs are commonly used for their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate for routine screening purposes. Low‐field benchtop NMR presents an affordable alternative. Methods: We compared the predictive performance of partial least squares discrimination analysis (PLS‐DA) models trained on low‐field 60 MHz benchtop proton (1H) NMR and high‐field 400 MHz 1H NMR spectra. The data were acquired from a sample set consisting of 49 extra virgin olive oils (EVOOs) and 45 other edible oils. Results: We demonstrate that PLS‐DA models trained on low‐field NMR spectra are highly predictive when classifying EVOOs from other oils and perform comparably to those trained on high‐field spectra. We demonstrated that variance was primarily driven by regions of the spectra arising from olefinic protons and ester protons from unsaturated fatty acids in models derived from data at both field strengths. This study compares the performance of partial least squares discrimination analysis (PLS‐DA) models trained on low‐field (LF) and high‐field (HF) nuclear magnetic resonance (NMR) spectra when distinguishing extra virgin olive oil (EVOO) from other edible oils. When predicting on an external dataset, LF NMR models were highly effective in classifying EVOO and had predictive performance comparable to HF models. These findings support LF NMR trained machine learning algorithms as a rapid, cost‐effective tool for EVOO authentication. [ABSTRACT FROM AUTHOR]

Published
2024
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18. A Preclinical Model of Obesity-Independent Metabolic Syndrome for Studying the Effects of Novel Antidiabetic Therapy Beyond Glycemic Control

Author

Mochel, Jonathan P, primary, Ward, Jessica L., additional, Blondel, Thomas, additional, Kundu, Debosmita, additional, Merodio, Maria M., additional, Zemirline, Claudine, additional, Guillot, Emilie, additional, Giebelhaus, Ryland T., additional, Mata, Paulina de la, additional, Iennarella-Servantez, Chelsea A., additional, Blong, April, additional, Nam, Seo Lin, additional, Harynuk, James J., additional, Suchodolski, Jan, additional, Tvarijonaviciute, Asta, additional, Cerón, José Joaquín, additional, Bourgois-Mochel, Agnes, additional, Zannad, Faiez, additional, Sattar, Naveed, additional, and Allenspach, Karin, additional

Published
2023
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23. Antibacterial Activity and Untargeted Metabolomics Profiling of Acalypha arvensis Poepp.

Author

Thesnor, Valendy, Molinié, Roland, Giebelhaus, Ryland T., de la Mata Espinosa, A. Paulina, Harynuk, James J., Bénimélis, David, Vanhoye, Bérénice, Dunyach-Rémy, Catherine, Sylvestre, Muriel, Cheremond, Yvens, Meffre, Patrick, Cebrián-Torrejón, Gerardo, and Benfodda, Zohra

Subjects
ETHYL esters, ANTIBACTERIAL agents, DRUG discovery, FATTY acid methyl esters, STAPHYLOCOCCUS aureus, PLANT classification
Abstract

The search for potent antimicrobial compounds is critical in the face of growing antibiotic resistance. This study explores Acalypha arvensis Poepp. (A. arvensis), a Caribbean plant traditionally used for disease treatment. The dried plant powder was subjected to successive extractions using different solvents: hexane (F1), dichloromethane (F2), methanol (F3), a 50:50 mixture of methanol and water (F4), and water (F5). Additionally, a parallel extraction was conducted using a 50:50 mixture of methanol and chloroform (F6). All the fractions were evaluated for their antimicrobial activity, and the F6 fraction was characterized using untargeted metabolomics using SPME-GC×GC-TOFMS. The extracts of A. arvensis F3, F4, and F5 showed antibacterial activity against Staphylococcus aureus ATCC 25923 (5 mg/mL), MRSA BA22038 (5 mg/mL), and Pseudomonas aeruginosa ATCC 27853 (10 mg/mL), and fraction F6 showed antibacterial activity against Staphylococcus aureus ATCC 29213 (2 mg/mL), Escherichia coli ATCC 25922 (20 mg/mL), Pseudomonas aeruginosa ATCC 27853 (10 mg/mL), Enterococcus faecalis ATCC 29212 (10 mg/mL), Staphylococcus aureus 024 (2 mg/mL), and Staphylococcus aureus 003 (2 mg/mL). Metabolomic analysis of F6 revealed 2861 peaks with 58 identified compounds through SPME and 3654 peaks with 29 identified compounds through derivatization. The compounds included methyl ester fatty acids, ethyl ester fatty acids, terpenes, ketones, sugars, amino acids, and fatty acids. This study represents the first exploration of A. arvensis metabolomics and its antimicrobial potential, providing valuable insights for plant classification, phytochemical research, and drug discovery. [ABSTRACT FROM AUTHOR]

Published
2023
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24. PARAFAC2×N:Coupled decomposition of multi-modal data with drift in N modes

Author

Sorochan Armstrong, Michael D., Hinrich, Jesper Løve, de la Mata, A. Paulina, Harynuk, James J., Sorochan Armstrong, Michael D., Hinrich, Jesper Løve, de la Mata, A. Paulina, and Harynuk, James J.

Abstract

Analysis of GC×GC-TOFMS data for large numbers of poorly-resolved peaks, and for large numbers of samples remains an enduring problem that hinders the widespread application of the technique. For multiple samples, GC×GC-TOFMS data for specific chromatographic regions manifests as a 4th order tensor of I mass spectral acquisitions, J mass channels, K modulations, and L samples. Chromatographic drift is common along both the first-dimension (modulations), and along the second-dimension (mass spectral acquisitions), while drift along the mass channel is for all practical purposes nonexistent. A number of solutions to handling GC×GC-TOFMS data have been proposed: these involve reshaping the data to make it amenable to either 2nd order decomposition techniques based on Multivariate Curve Resolution (MCR), or 3rd order decomposition techniques such as Parallel Factor Analysis 2 (PARAFAC2). PARAFAC2 has been utilised to model chromatographic drift along one mode, which has enabled its use for robust decomposition of multiple GC-MS experiments. Although extensible, it is not straightforward to implement a PARAFAC2 model that accounts for drift along multiple modes. In this submission, we demonstrate a new approach and a general theory for modelling data with drift along multiple modes, for applications in multidimensional chromatography with multivariate detection. The proposed model captures over 99.9% of variance for a synthetic data set, presenting an extreme example of peak drift and co-elution across two modes of separation.

Published
2023

43. Retention and release of odorants in cotton and polyester fabrics following multiple soil/wash procedures

Author

Abdul-Bari, M Mukhtar, McQueen, Rachel H, de la Mata, A Paulina, Batcheller, Jane C, and Harynuk, James J

Subjects
parasitic diseases, technology, industry, and agriculture, psychological phenomena and processes
Abstract

Odorous clothing can be an annoying and unpleasant problem particularly when odorants are not effectively removed via laundering. Cotton and polyester knit fabrics were soiled with three selected odorants, representing different polarities and lipophilicities (i.e. octanoic acid, 2-nonenal, dodecane). Fabrics were subjected to 1, 5 and 10 soil/wash cycles using a regular liquid detergent (Tide�� Free and Gentle) or a sport liquid detergent (Tide�� Plus Febreze Sport). Odorants released into the headspace were collected using solid phase micro-extraction (SPME), and odorants retained within the fabric were collected using solvent extraction. Analysis of odorant peaks was carried out using gas chromatography-flame ionization detection (GC-FID). Prior to laundering, higher amounts of all odorants were released into the headspace above polyester fabrics than above cotton fabrics. Cotton fabrics retained more octanoic acid within the fabric and lower amounts of 2-nonenal than polyester. Laundering was more effective at removing odorants from cotton than from polyester, and the polar octanoic acid was more readily removed than the two nonpolar odorants from both fabrics. Accumulation of odorants occurred as soil/wash cycles increased from 1 to 5 cycles. However, between 5 and 10 soil/wash cycles the amounts of compounds did not significantly increase, with significantly lower amounts of octanoic acid extracted from cotton at 10 cycles compared to 5 cycles. The results from this study indicate that incomplete removal of odorants during washing, especially from oleophilic polyester fabrics, is a cause for odor build-up in clothing.

Published
2022
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