Compound-Specific 14 N/ 15 N Analysis of Amino Acid Trimethylsilylated Derivatives from Plant Seed Proteins.
Jean-Baptiste DomergueJulie LalandeCyril AbadieGuillaume TcherkezPublished in: International journal of molecular sciences (2022)
Isotopic analyses of plant samples are now of considerable importance for food certification and plant physiology. In fact, the natural nitrogen isotope composition (δ 15 N) is extremely useful to examine metabolic pathways of N nutrition involving isotope fractionations. However, δ 15 N analysis of amino acids is not straightforward and involves specific derivatization procedures to yield volatile derivatives that can be analysed by gas chromatography coupled to isotope ratio mass spectrometry (GC-C-IRMS). Derivatizations other than trimethylsilylation are commonly used since they are believed to be more reliable and accurate. Their major drawback is that they are not associated with metabolite databases allowing identification of derivatives and by-products. Here, we revisit the potential of trimethylsilylated derivatives via concurrent analysis of δ 15 N and exact mass GC-MS of plant seed protein samples, allowing facile identification of derivatives using a database used for metabolomics. When multiple silylated derivatives of several amino acids are accounted for, there is a good agreement between theoretical and observed N mole fractions, and δ 15 N values are satisfactory, with little fractionation during derivatization. Overall, this technique may be suitable for compound-specific δ 15 N analysis, with pros and cons.
Keyphrases
- gas chromatography
- mass spectrometry
- amino acid
- tandem mass spectrometry
- liquid chromatography
- high resolution mass spectrometry
- gas chromatography mass spectrometry
- structure activity relationship
- high performance liquid chromatography
- high resolution
- capillary electrophoresis
- solid phase extraction
- simultaneous determination
- ms ms
- emergency department
- squamous cell carcinoma
- machine learning
- quantum dots
- deep learning
- locally advanced