Department of Applied Chemistry, East China Institute of Technology, Fuzhou 344000, China.
Anal Bioanal Chem. 2010 Jun;397(4):1549-56. doi: 10.1007/s00216-010-3693-9. Epub 2010 Apr 23.
The development of analytical techniques suitable for sensitive, high-throughput, and nondestructive food analysis has been of increasing interest in recent years. In this study, mass-spectral fingerprints of various cheese products were rapidly recorded in the mass range of m/z 50-300 Da without any sample pretreatment, using neutral desorption extractive electrospray ionization mass spectrometry (ND-EESI-MS) in negative ion mode. The results demonstrate that both volatile and nonvolatile analytes on greasy cheese surfaces can be directly sampled by a neutral desorption gas beam. The influence of the neutral desorption gas flow on the analyte signal was systematically investigated. Under optimized experimental conditions, reproducible results were obtained using ND-EESI-MS. Principal component analysis was applied to differentiate a total of 49 individual cheese samples (four different types), which were purchased from three different supermarkets. All samples were successfully classified according to their types; but distributors and sensory properties were not distinguishable from the spectra data. The principal components 2, 3, and 4 scores showed an excellent capacity of distinguishing types of cheese. Molecular markers of interest can be identified using tandem mass spectrometry and matching the data with those from reference compounds. The experimental data show that ND-EESI-MS is able to sensitively and directly detect analytes on greasy surfaces without chemical contamination, providing a convenient method for high-throughput food analysis with a high degree of safety.
近年来，开发适用于敏感、高通量和非破坏性食品分析的分析技术一直受到越来越多的关注。在这项研究中，使用中性解吸提取电喷雾电离质谱（ND-EESI-MS）在负离子模式下，无需任何样品预处理，快速记录了各种奶酪产品在 m/z 50-300 Da 质量范围内的质谱指纹图谱。结果表明，中性解吸气体束可以直接对油腻奶酪表面的挥发性和非挥发性分析物进行采样。系统研究了中性解吸气流对分析物信号的影响。在优化的实验条件下，使用 ND-EESI-MS 可获得可重复的结果。主成分分析用于区分总共 49 个从三个不同超市购买的单个奶酪样品（四种不同类型）。根据类型对所有样品进行了成功分类；但分销商和感官特性无法从光谱数据中区分出来。主成分 2、3 和 4 的得分显示出极好的区分奶酪类型的能力。可以使用串联质谱法识别感兴趣的分子标记物，并将数据与参考化合物的数据进行匹配。实验数据表明，ND-EESI-MS 能够灵敏且直接地检测油腻表面上的分析物，而不会造成化学污染，为具有高度安全性的高通量食品分析提供了一种便捷的方法。
