Characterization of 9 gas chromatography columns by Kovats and Lee retention indices for dioxin analysis

Stultz, Conner; Dorman, Frank L.

doi:10.1016/j.chroma.2019.460701

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…This approach is also applicable with change in 2 D column diameter. Another recent approach reported Kovats’ and Lee retention indexes for polychlorinated dibenzo- p -dioxins and furans, polychlorinated biphenyls, and polychlorinated naphthalenes (PCNs) . Another used internal standards proposed by U.S. EPA Method 8270 for analysis of semivolatile compounds .…”

Section: Discussionmentioning

confidence: 99%

“…Another recent approach reported Kovats' and Lee retention indexes for polychlorinated dibenzo-p-dioxins and furans, polychlorinated biphenyls, and polychlorinated naphthalenes (PCNs). 80 Another used internal standards proposed by U.S. EPA Method 8270 for analysis of semivolatile compounds. 81 This simple regression method includes analyte retention time and elution temperature into the 2 D column.…”

Section: ■ Data Analysismentioning

confidence: 99%

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Comprehensive Two-Dimensional Gas Chromatography Advances in Technology and Applications: Biennial Update

2019

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Section: Discussionmentioning

confidence: 99%

Section: ■ Data Analysismentioning

confidence: 99%

Comprehensive Two-Dimensional Gas Chromatography Advances in Technology and Applications: Biennial Update

2019

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“…Constituents in macadamia nuts were identified by comparing their mass spectra with mass spectrometry libraries of NIST 14 and Wiley 8.0 and only the compounds with a mass matching index greater than 800 will be confirmed. Furthermore, the Kovats indices (KI) of volatile components were calculated based on the retention time of C7-C30 n-alkanes according to the previous study (Stultz & Dorman, 2020). The quantitative method of the components was carried out according to the formula of previous research (Huyan et al, 2019).…”

Section: Determination Of Macadamia Nuts' Volatile Componentsmentioning

confidence: 99%

Flavor and compositional analysis of macadamia nuts during long‐term storage

Liu

Fan

Tang

2022

Food Processing Preservation

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The production of unpleasant flavor of macadamia nuts during long-term storage is a very common phenomenon in nut industry. Changes in volatile components were monitored by HS-SPME/GC-MS throughout a 9-month storage period to expound the unpleasant flavor of macadamia nuts. Aldehydes (primarily hexanal, heptanal, octanal, (E)-2-octenal, (2E)-2-nonenal, trans-2-decenal, nonanal, decanal, (E)-2-heptenal, and pentanal), and acids (mainly hexanoic and acetic acid) composed the main unpleasant flavor of macadamia nuts based on their concentrations, olfactory characteristics, and thresholds. Compositional analysis showed total lipid, fatty acids, and amino acids decreased by 6.35 g/100 g, 137, and 4.165 mg/g, respectively, after 9 months of storage. Besides, there was no kernel discoloration, while the AV and PV increased by 1.89 mg/g and 8.13 meqO 2 /kgOil, respectively. The flavor deterioration was induced by lipid oxidation and had little correlation with protein oxidation. Moreover, results of correlation and PLSR analysis demonstrated protein oxidation in macadamia nuts during storage were related to lipid oxidation. Novelty impact statement:1. Unpleasant flavor composition was first identified based on sensory analysis and ROAVs of volatiles.2. Lipid oxidation of macadamia nuts induced flavor deterioration during storage.3. Changes in amino acids were closely related to lipid oxidation in macadamia nuts.

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“…On this basis, the retention method proposed by Lee et al is widely used in the representation of retention data of PAHs (Babushok & Linstrom, 2004). At present, the only feasible and widely used method for the determination of Lee-RI data is temperature-programmed gas chromatography (GC; Stultz & Dorman, 2020). Due to the extremely complex process of experimental determination, high fault tolerance rate and low efficiency, quantitative structure-property relationship (QSPR) model has become a trend to predict Lee-RI data.…”

Section: Introductionmentioning

confidence: 99%

Computational prediction of Lee retention indices of polycyclic aromatic hydrocarbons by using machine learning

et al. 2022

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Given the difficult of experimental determination, quantitative structure–property relationship (QSPR) and deep learning (DL) provide an important tool to predict physicochemical property of chemical compounds. In this paper, partial least squares (PLS), genetic function approximation (GFA), and deep neural network (DNN) were used to predict the Lee retention index (Lee‐RI) of PAHs in SE‐52 and DB‐5 stationary phases. Four molecular descriptors, molecular weight (MW), quantitative estimate of drug‐likeness (QED), atomic charge weighted negative surface area (Jurs_PNSA_3), and relative negative charge (Jurs_RNCG) were selected to construct regression models based on genetic algorithm. For SE‐52, PLS model showed best prediction power, followed by DNN and GFA. The relative error (RE), root mean square error (RMSE), and regression coefficient (R2) of best PLS regression model are 1.228%, 5.407, and 0.980. For DB‐5, DNN model showed best prediction power, followed by GFA and PLS. The RE, RMSE and R2 of best DNN regression model for DB‐5‐1 and DB‐5‐2 are 1.058%, 4.325%, 0.976%, 0.821%, 3.795%, and 0.970%, respectively. The three regression models not only show good predictive ability, but also highlight the stability and ductility of the models.

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Characterization of 9 gas chromatography columns by Kovats and Lee retention indices for dioxin analysis

Cited by 7 publications

References 23 publications

Comprehensive Two-Dimensional Gas Chromatography Advances in Technology and Applications: Biennial Update

Comprehensive Two-Dimensional Gas Chromatography Advances in Technology and Applications: Biennial Update

Flavor and compositional analysis of macadamia nuts during long‐term storage

Computational prediction of Lee retention indices of polycyclic aromatic hydrocarbons by using machine learning

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