Interpretation of static time-of-flight secondary ion mass spectra of adsorbed protein films by multivariate pattern recognition.
Interpretation of static time-of-flight secondary ion mass spectra of adsorbed protein films by multivariate pattern recognition.
复制标题
通过多变量模式识别解释吸附蛋白膜的静态飞行时间二次离子质谱。
DOI：
10.1021/ac0111311
复制
发表时间：
2002
影响因子：
7.4
通讯作者：
Castner,DavidG
中科院分区：
文献类型：
--
作者：
Wagner,MS;Tyler,BJ;Castner,DavidG
多变量分析在多维光谱数据分析中越来越普遍。我们之前已经证明了多元分析技术主成分分析（PCA）是解释吸附蛋白膜的静态飞行时间二次离子质谱（TOF-SIMS）光谱的一种很好的方法。PCA是一种无监督的模式识别技术，当更多的蛋白质被添加到数据集中时，由于组内变化很大，它会失去不同蛋白质光谱之间的分辨率。利用云母和聚四氟乙烯基材上吸附蛋白质的TOF-SIMS光谱数据集，将监督模式识别技术判别主成分分析（discriminant principal component analysis， DPCA）和线性判别分析（linear discriminant analysis， LDA）与主成分分析（PCA）进行了比较，前者旨在控制组内变化，同时最大化组间分离以增强组间区分。DPCA和LDA在数量上提高了组间的区分，并提供了与PCA不同的数据信息。LDA能够对未知样本进行分类，其误分类率低于PCA或DPCA。无监督和有监督模式识别技术对吸附蛋白膜的静态TOF-SIMS光谱的解释和分类都是有用的。
Multivariate analysis has become increasingly common in the analysis of multidimensional spectral data. We previously showed that the multivariate analysis technique principal component analysis (PCA) is an excellent method for interpreting the static time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of adsorbed protein films. PCA is an unsupervised pattern recognition technique that loses resolution between spectra of different proteins as more proteins are added to the data set due to large within-group variation. The supervised pattern recognition techniques discriminant principal component analysis (DPCA) and linear discriminant analysis (LDA), which aim to control within-group variation while maximizing between-group separation to enhance discrimination between groups, were compared with PCA using data sets of TOF-SIMS spectra of proteins adsorbed onto mica and PTFE substrates. DPCA and LDA quantitatively improved discrimination between groups and provided different information about the data than PCA. LDA was able to classify unknown samples with a misclassification rate lower than PCA or DPCA. Both unsupervised and supervised pattern recognition techniques are useful for the interpretation and classification of static TOF-SIMS spectra of adsorbed protein films.