Generalization of Reference System for Calculating the Second Dimension Retention Index in GC × GC–MS

Prodhan, Aminul Islam; Sleman, Ahmed A.; Kim, Seongho; McClain, Craig J.; Zhang, Xiang

doi:10.1007/s41664-018-0074-4

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…Despite the excellent instrumental capability, the identification accuracy of current mass spectrum matching method is only about 79.6% [31][32][33] . Therefore, additional information such as retention index must be used to reduce the rate of false identifications 34,35 . On average, retention index matching removed about 8.9% of false identifications generated by mass spectrum matching (Table 1).…”

Section: Metabolite Coverage By Gc×gc-msmentioning

confidence: 99%

Integrating comprehensive two-dimensional gas chromatography mass spectrometry and parallel two-dimensional liquid chromatography mass spectrometry for untargeted metabolomics

Prodhan

Shi

Song

et al. 2019

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Section: Metabolite Coverage By Gc×gc-msmentioning

confidence: 99%

Integrating comprehensive two-dimensional gas chromatography mass spectrometry and parallel two-dimensional liquid chromatography mass spectrometry for untargeted metabolomics

Prodhan

Shi

Song

et al. 2019

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“…Retention times and total percentages offer important details regarding the makeup and characteristics of the substances under study. The retention time, which is a measure of how long a compound stays in a chromatographic column (Prodhan et al, 2018), varies from 14.1 to 19.68 minutes for each component. Due to the various chemical characteristics of each compound, there are variations in the way that each one interacts with the material in the column.…”

Section: Gas Chromatography-mass Spectrometry (Gc-ms)mentioning

confidence: 99%

Medicinal Assessment of Kenaf (Hibiscus cannabinus L.): Antibacterial, Phytochemical, and Nutritional Profiling

Sulaiman,

Abubakar,

Muhammad

et al. 2024

Preprint

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While pharmaceutical drugs often come with detrimental side effects such as liver damage and addiction, the Hibiscus cannabinus L. (kenaf) plant has proven to be a promising traditional medicinal alternative. However, there are extremely few studies to investigate the variability of medicinal and nutritional parameters in Kenaf tissues. The phytochemicals, proximate composition, mineral content, amino acid content, and antibacterial activity of kenaf tissues have been studied and compared using advanced techniques. UPLC analysis reveals that leaves contain the highest concentration of caffeic acid (76.4 mg/100 g), which is also present in stem bark (51.3 mg/100 g). GC-MS analysis shows linoleic acid is predominant in seeds (46.23%) and stem bark (40.7%), while E-Phytol is mostly in leaves (33.5%) and hexadecanoic acid in flowers. Phenolic analysis indicates water as the most effective extraction solvent, with leaves showing the highest phenolic content (592.1 mg/100 g). Water remains the best solvent for flavonoid extraction, with flowers and leaves having the highest flavonoid concentrations (722.1 mg/100 g and 552.2 mg/100 g, respectively). Seed is the most nutritious, containing the highest amount of crude protein and nitrogen-free extract. Stem bark is rich in calcium (883 mg/kg) and potassium (3093 mg/kg). Amino acid analysis shows seeds are high in glutamic acid and aspartic acid, while proline is predominant in stem bark. Leaf tissue exhibits the strongest antibacterial activity against E. coli and S. aureus, suggesting its potential use in antibacterial applications. This demonstrates the research's contributions to possible uses of Kenaf in health and nutrition.

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“…33,129,130 Therefore, additional information such as retention index must be used to reduce the rate of false identifications. 131,132 On average, retention index matching removed about 8.9% of false identifications generated by mass spectrum matching (Table 5.2). Overall, only about However, the percentage of assignment was dramatically reduced to only 1.1% to 1.8%…”

Section: Metabolite Coverage By Gc×gc-msmentioning

confidence: 99%

GC×GC-MS and parallel 2DLC-MS based untargeted metabolomics.

Prodhan¹

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Untargeted metabolomics aims to analyze as many metabolites as possible in a biological system. Due to the vast complexity of the metabolome, bioanalytical platforms have been developed for untargeted metabolomics. While comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) has been used in different scientific fields, its use in untargeted metabolomics is limited due to several challenges, such as complicated data analysis and limited accuracy of metabolite identification. Twodimensional liquid chromatography mass spectrometry (2DLC-MS) has also been employed in untargeted metabolomics, but with limited metabolite coverage. This dissertation describes the efforts in developing new data analysis algorithms to improve the accuracy of metabolite identification in GC×GC-MS as well as integration of multiple analytical techniques for increased metabolite coverage and high confidence of metabolite quantification and pathway assignment. This dissertation is divided into six chapters. Chapter One provides the overview of the current methodologies for untargeted metabolomics. Chapters Two, Three and Four describe the methods developed for the calculation of the second dimension retention index (2) in GC×GC-MS. Specifically, Chapter Two introduces a surface fitting approach to viii 2 calculation using n-alkanes as reference compounds and considering the second dimension separation as in pseudo-isothermal mode. Chapter Three describes an improved method for calculation of 2 by considering the second dimension separations in its actuality, i.e., temperature-programmed mode. Chapter Four introduces a universal reference system for 2 calculation to improve retention map coverage that allows the use of any compounds as reference compounds while keeping retention scale in conventional n-alkane scale. Chapter Five introduces a method that integrates the GC×GC-MS and parallel 2DLC-MS platforms for untargeted metabolomics to achieve higher metabolite coverage and accurate metabolite quantification. Chapter Six summarizes the overall scientific contribution of this dissertation.

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Generalization of Reference System for Calculating the Second Dimension Retention Index in GC × GC–MS

Cited by 5 publications

References 36 publications

Integrating comprehensive two-dimensional gas chromatography mass spectrometry and parallel two-dimensional liquid chromatography mass spectrometry for untargeted metabolomics

Integrating comprehensive two-dimensional gas chromatography mass spectrometry and parallel two-dimensional liquid chromatography mass spectrometry for untargeted metabolomics

Medicinal Assessment of Kenaf (Hibiscus cannabinus L.): Antibacterial, Phytochemical, and Nutritional Profiling

GC×GC-MS and parallel 2DLC-MS based untargeted metabolomics.

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