Graham Daniel J, Castner David G
National ESCA and Surface Analysis Center for Biomedical Problems.
National ESCA and Surface Analysis Center for Biomedical Problems ; Chemical Engineering University of Washington.
Mass Spectrom (Tokyo). 2013;2(Spec Iss):S0014. doi: 10.5702/massspectrometry.S0014. Epub 2013 Apr 15.
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) instruments can rapidly produce large complex data sets. Within each spectrum, there can be hundreds of peaks. A typical 256×256 pixel image contains 65,536 spectra. If this is extended to a 3D image, the number of spectra in a given data set can reach the millions. The challenge becomes how to process these large data sets while taking into account the changes and differences between all the peaks in the spectra. This is particularly challenging for biological materials that all contain the same types of proteins and lipids, just in varying concentrations and spatial distributions. This data analysis challenge is further complicated by the limitations in the ion yield of higher mass, more chemically specific species, and potentially by the processing power of typical computers. Herein we briefly discuss analysis methodologies including univariate analysis, multivariate analysis (MVA) methods, and some of the limitations of ToF-SIMS analysis of biological materials.
飞行时间二次离子质谱（ToF-SIMS）仪器能够快速生成大量复杂的数据集。在每个光谱中，可能会有数百个峰。一幅典型的256×256像素图像包含65,536个光谱。如果将其扩展为三维图像，给定数据集中的光谱数量可达数百万。挑战在于如何处理这些大数据集，同时考虑光谱中所有峰之间的变化和差异。对于所有都包含相同类型蛋白质和脂质、只是浓度和空间分布不同的生物材料而言，这一挑战尤为艰巨。更高质量、化学特异性更强的物种的离子产率限制，以及典型计算机的处理能力，可能会使这一数据分析挑战进一步复杂化。在此，我们简要讨论分析方法，包括单变量分析、多变量分析（MVA）方法，以及ToF-SIMS对生物材料分析的一些局限性。
