Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States.
Anal Chem. 2012 Jul 17;84(14):6225-32. doi: 10.1021/ac3012335. Epub 2012 Jun 29.
Laser electrospray mass spectrometry (LEMS) coupled with offline multivariate statistical analysis is used to discriminate eight phenotypes from a single plant organ class and to find potential biomarkers. Direct analysis of the molecules from the flower petal is enabled by interfacing intense (10(13) W/cm(2)), nonresonant, femtosecond laser vaporization at ambient pressure with electrospray ionization for postionization of the vaporized analytes. The observed mass spectral signatures allowed for the discrimination of various phenotypes using principal component analysis (PCA) and either linear discriminant analysis (LDA) or K-nearest neighbor (KNN) classifiers. Cross-validation was performed using multiple training sets to evaluate the predictive ability of the classifiers, which showed 93.7% and 96.8% overall accuracies for the LDA and KNN classifiers, respectively. Linear combinations of significant mass spectral features were extracted from the PCA loading plots, demonstrating the capability to discover potential biomarkers from the direct analysis of tissue samples.
激光电喷雾质谱联用（LEMS）与离线多元统计分析相结合，用于从单个植物器官类别中区分八种表型，并寻找潜在的生物标志物。通过在环境压力下将强（10（13）W/cm（2））、非共振、飞秒激光汽化与电喷雾电离相连接，从花瓣中直接分析分子，对汽化的分析物进行后电离。观察到的质谱特征允许使用主成分分析（PCA）以及线性判别分析（LDA）或 K-最近邻（KNN）分类器对各种表型进行区分。使用多个训练集进行交叉验证，以评估分类器的预测能力，LDA 和 KNN 分类器的总准确率分别为 93.7%和 96.8%。从 PCA 加载图中提取出显著质谱特征的线性组合，证明了从组织样本的直接分析中发现潜在生物标志物的能力。
