Luthria Devanand L, Lin Long-Ze, Robbins Rebecca J, Finley John W, Banuelos Gary S, Harnly James M
Food Composition and Methods Development Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, Maryland 20705, USA.
J Agric Food Chem. 2008 Nov 12;56(21):9819-27. doi: 10.1021/jf801606x. Epub 2008 Oct 9.
Metabolite fingerprints, obtained with direct injection mass spectrometry (MS) with both positive and negative ionization, were used with analysis of variance-principal components analysis (ANOVA-PCA) to discriminate between cultivars and growing treatments of broccoli. The sample set consisted of two cultivars of broccoli, Majestic and Legacy, the first grown with four different levels of Se and the second grown organically and conventionally with two rates of irrigation. Chemical composition differences in the two cultivars and seven treatments produced patterns that were visually and statistically distinguishable using ANOVA-PCA. PCA loadings allowed identification of the molecular and fragment ions that provided the most significant chemical differences. A standardized profiling method for phenolic compounds showed that important discriminating ions were not phenolic compounds. The elution times of the discriminating ions and previous results suggest that they were common sugars and organic acids. ANOVA calculations of the positive and negative ionization MS fingerprints showed that 33% of the variance came from the cultivar, 59% from the growing treatment, and 8% from analytical uncertainty. Although the positive and negative ionization fingerprints differed significantly, there was no difference in the distribution of variance. High variance of individual masses with cultivars or growing treatment was correlated with high PCA loadings. The ANOVA data suggest that only variables with high variance for analytical uncertainty should be deleted. All other variables represent discriminating masses that allow separation of the samples with respect to cultivar and treatment.
通过直接进样质谱法（MS）在正离子化和负离子化模式下获得的代谢物指纹图谱，结合方差分析-主成分分析（ANOVA-PCA）用于区分西兰花的品种和种植处理方式。样本集包括两个西兰花品种，即“Majestic”和“Legacy”，前者在四种不同硒水平下种植，后者采用有机和传统两种灌溉速率种植。两个品种和七种处理方式之间的化学成分差异产生了可通过ANOVA-PCA在视觉和统计上区分的模式。PCA载荷使得能够识别出提供最显著化学差异的分子离子和碎片离子。一种针对酚类化合物的标准化分析方法表明，重要的区分离子并非酚类化合物。区分离子洗脱时间及先前结果表明它们是常见的糖类和有机酸。对正离子化和负离子化MS指纹图谱进行的ANOVA计算表明，33%的方差来自品种，59%来自种植处理方式，8%来自分析不确定性。尽管正离子化和负离子化指纹图谱存在显著差异，但方差分布并无差异。单个质量数随品种或种植处理方式的高方差与高PCA载荷相关。ANOVA数据表明，仅应删除分析不确定性方面具有高方差的变量。所有其他变量均代表区分质量数，可实现样本在品种和处理方式方面的分离。
