High-Resolution Analysis of Growth and Transpiration of Quinoa Under Saline Conditions.

The Plantarray 3.0 phenotyping platform® was used to monitor the growth and water use of the quinoa varieties Pasto and selRiobamba under salinity (0-300 mM NaCl). Salinity reduced the cumulative transpiration of both varieties by 60% at 200 mM NaCl and by 75 and 82% at 300 mM NaCl for selRiobamba and Pasto, respectively. Stomatal conductance was reduced by salinity, but at 200 mM NaCl Pasto showed a lower reduction (15%) than selRiobamba (35%), along with decreased specific leaf area. Diurnal changes in water use parameters indicate that under salt stress, daily transpiration in quinoa is less responsive to changes in light irradiance, and stomatal conductance is modulated to maximize CO2 uptake and minimize water loss following the changes in VPD (vapor pressure deficit). These changes might contribute to the enhanced water use efficiency of both varieties under salt stress. The mechanistic crop model LINTUL was used to integrate physiological responses into the radiation use efficiency of the plants (RUE), which was more reduced in Pasto than selRiobamba under salinity. By the end of the experiment (eleven weeks after sowing, six weeks after stress), the growth of Pasto was significantly lower than selRiobamba, fresh biomass was 50 and 35% reduced at 200 mM and 70 and 50% reduced at 300 mM NaCl for Pasto and selRiobamba, respectively. We argue that contrasting water management strategies can at least partly explain the differences in salt tolerance between Pasto and selRiobamba. Pasto adopted a "conservative-growth" strategy, saving water at the expense of growth, while selRiobamba used an "acquisitive-growth" strategy, maximizing growth in spite of the stress. The implementation of high-resolution phenotyping could help to dissect these complex growth traits that might be novel breeding targets for abiotic stress tolerance.

Comprehensive two-dimensional gas chromatography thermodynamic modeling and selectivity evaluation for the separation of polychlorinated dibenzo-p-dioxins and dibenzofurans in fish tissue matrix.

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful tool for complex separations. The selectivity and sensitivity benefits from thermally modulated GC×GC were applied to the analysis of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). Thermodynamic indices of 50 PCDD/Fs, including the 17 toxic 2378-substituted congeners, were collected and used to model one-dimensional and two-dimensional separations with the Rtx-Dioxin2 and Rxi-17SilMS capillary GC columns. Thermodynamic modeling was used to determine the optimal conditions to take advantage of the selectivity differences between the Rxi-17SilMS and Rtx-Dioxin2 to separate all PCDD/Fs congeners from the 2378-substituted compounds by GC×GC. The modeled elution order patterns closely matched the experimental elution order in 40 of the 45 tetrachlorinated through hexchlorinated compounds analyzed. The heptachlorinated and octachlorinated congeners were not included in the elution order modeling as they are readily resolved from other dioxin congeners. The Rxi-17SilMS crossed with the Rtx-Dioxin2 was able to separate all 2378-substituted compounds in a single separation in a fish matrix. Thirty-three additional PCDD/F congeners were added to the fish matrix that coelute with the 2378-substituted congeners. The Rxi-17SilMS crossed with the Rtx-Dioxin2 was able to fully resolve 11 of the 2378-substituted congeners with the other six congeners exhibiting coelutions with only one other congener.

Thermodynamic modeling of comprehensive two dimensional gas chromatography isovolatility curves for second dimension retention indices based analyte identification.

A method to thermodynamically model the alkane isovolatility curves of a comprehensive two dimensional gas chromatography (GC × GC) separation is presented. This method omits all instrument modifications, additional chromatogram collection, or method alterations which typical isovolatility curve generation requires. Provided that the thermodynamic indices of reference alkanes are available, chromatographers only need to specify the GC × GC method parameters of their separation to output the isovolatility curves. The curves can then be used alongside reference retention indices to generate two dimensional retention times for each analyte. Agreement between the modeled and experimental retention times provides a secondary mechanism for compound identification, supporting the results of a mass spectral search. The technique was used to model the retention times of a GC × GC separation of aromatic hydrocarbons, achieving an average first dimension retention time modeling error of 11 s and an average second dimension retention time modeling error of 0.09 s. Retention indices modeled retention times provide a simpler analyte identification procedure compared to conventional two dimensional retention indices matching.

Retention time prediction of hydrocarbons in cryogenically modulated comprehensive two-dimensional gas chromatography: A method development and translation application.

Thermodynamic modeling of GC × GC separations provides a tool for rapid method evaluation and optimization. Separations of 95 hydrocarbons on two cryogenically modulated GC × GC systems (atmospheric outlet and vacuum outlet) are modeled, displaying average second dimension retention time modeling absolute errors of 0.17 s and 0.12 s respectively, and generating modeled chromatograms which sufficiently represent experimental data. A web-based GC × GC modeling routine is presented which allows users to model separations, currently focused on hydrocarbons, with full control over all system parameters. The method translation capabilities of the application are further demonstrated by replicating Piotrowski et al.'s GC × GC-HRT temporal distribution plots of hydraulic fracturing flowback fluid hydrocarbons [28].

Retention time prediction in thermally modulated comprehensive two-dimensional gas chromatography: Correcting second dimension retention time modeling error.

Thermodynamic retention modeling to a thermally modulated comprehensive two-dimensional gas chromatography (GC × GC) system run under constant flow is performed. Significant errors in modeled second dimension retention time (tr,2) were observed, in line with past work on thermally modulated GC × GC modeling. A comprehensive study of tr,2 modeling error for alkane separations across a wide range of heating ramp rates and carrier gas flow rates was performed. Modeling errors were found to be systematic and a function of analyte elution temperature and mobile phase velocity. A model to account for these systematic errors was generated, and associated coefficients were determined which reduced average tr,2 retention time error in 144 hydrocarbon separations by an order of magnitude resulting in significant improvement in prediction accuracy. The model was used to correct the separation of 139 Grob mix analyte separations, providing an average tr,2 modeling error of 0.030 ± 0.022 s. The model successfully predicted the separation of n-alkanes on a longer second dimension column configuration.