Yamamoto Atsushi, Tokai Naoji, Kakehashi Rie, Saigusa Daisuke
Faculty of Environment, Tottori University of Environmental Studies, 1-1-1 Wakabadaikita, Tottori, Tottori 689-1111, Japan.
Forefront Research Center, Graduate School of Science, Osaka University, 1-1 Machikaneyama-cho, Toyonaka, Osaka 560-0043, Japan.
Mass Spectrom (Tokyo). 2024;13(1):A0158. doi: 10.5702/massspectrometry.A0158. Epub 2024 Dec 5.
This study investigates the mass spectrometric analysis of 10 novel amidoamine oxide compounds, which are innovative hydrogelators for polar solvents. This research aims to identify characteristic fragment patterns for these amide compounds using high-resolution mass spectrometry. Methanol solutions of the compounds were analyzed in positive and negative ion modes, and MS1 and MS2 spectra at 6 collision energy levels were obtained via electrospray ionization and hybrid tandem mass spectrometry. The importance of low-intensity peaks in structure elucidation was emphasized because low-intensity fragments could provide crucial structural information, especially for compounds with similar structures. Chain-length-dependent fragmentation patterns were observed, which could aid in predicting the structures of related compounds. This research highlights the challenges of balancing informative low-intensity peaks with accurate spectral matching in databases. Based on our results, combining mass spectrometry with separation techniques, such as liquid chromatography, could enhance structural elucidation for unknown compounds. This study contributes to the broader field of mass spectrometry and structural chemistry, particularly in the analysis of amide compounds, and future directions are proposed for developing robust algorithms for selecting and interpreting low-intensity peaks to improve compound identification in complex mixtures.
本研究调查了10种新型氨基胺氧化物化合物的质谱分析，这些化合物是用于极性溶剂的新型水凝胶剂。本研究旨在利用高分辨率质谱法确定这些酰胺化合物的特征碎片模式。以正离子和负离子模式分析了这些化合物的甲醇溶液，并通过电喷雾电离和混合串联质谱法获得了6个碰撞能量水平下的MS1和MS2光谱。强调了低强度峰在结构解析中的重要性，因为低强度碎片可以提供关键的结构信息，特别是对于结构相似的化合物。观察到了链长依赖性的碎片模式，这有助于预测相关化合物的结构。本研究突出了在数据库中平衡信息丰富的低强度峰与准确的光谱匹配所面临的挑战。根据我们的结果，将质谱法与液相色谱等分离技术相结合，可以增强对未知化合物的结构解析。本研究为更广泛的质谱和结构化学领域做出了贡献，特别是在酰胺化合物的分析方面，并提出了未来的发展方向，即开发强大的算法来选择和解释低强度峰，以改善复杂混合物中化合物的鉴定。
