Characterization of Long-Chain Fatty Acid as N-(4-Aminomethylphenyl) Pyridinium Derivative by MALDI LIFT-TOF/TOF Mass Spectrometry.
Cheryl FrankfaterXuntian JiangFong-Fu HsuPublished in: Journal of the American Society for Mass Spectrometry (2018)
Charge remote fragmentation (CRF) elimination of CnH2n+2 residues along the aliphatic tail of long chain fatty acid is hall mark of keV high-energy CID fragmentation process. It is an important fragmentation pathway leading to structural characterization of biomolecules by CID tandem mass spectrometry. In this report, we describe MALDI LIFT TOF-TOF mass spectrometric approach to study a wide variety of fatty acids (FAs), which were derivatized to N-(4-aminomethylphenyl) pyridinium (AMPP) derivative, and desorbed as M+ ions by laser with or without matrix. The high-energy MALDI LIFT TOF-TOF mass spectra of FA-AMPP contain fragment ions mainly deriving from CRF cleavages of CnH2n+2 residues, as expected. These ions together with ions from specific cleavages of the bond(s) involving the functional group within the molecule provide more complete structural identification than those produced by low-energy CID/HCD using a linear ion-trap instrument. However, this LIFT TOF-TOF mass spectrometric approach inherits low sensitivity, a typical feature of high-energy CID tandem mass spectrometry. Because of the lack of unit mass precursor ion selection with sufficient sensitivity of the current LIFT TOF-TOF technology, product ion spectra from same chain length fatty acids with difference in one or two double bonds in a mixture are not distinguishable. Graphical Abstract ᅟ.
Keyphrases
- mass spectrometry
- liquid chromatography
- gas chromatography
- tandem mass spectrometry
- high performance liquid chromatography
- fatty acid
- high resolution mass spectrometry
- ms ms
- high resolution
- ultra high performance liquid chromatography
- capillary electrophoresis
- simultaneous determination
- quantum dots
- solid phase extraction
- water soluble
- machine learning
- aqueous solution
- deep learning
- magnetic resonance