Discriminating between Cultivars and Treatments of Broccoli Using Mass Spectral Fingerprinting and Analysis of Variance-Principal Component Analysis
Discriminating between Cultivars and Treatments of Broccoli Using Mass Spectral Fingerprinting and Analysis of Variance-Principal Component Analysis
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DOI：
10.1021/jf801606x
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发表时间：
2008-11-12
影响因子：
6.1
通讯作者：
Harnly, James M.
中科院分区：
文献类型：
--
作者：
Luthria, Devanand L.;Lin, Long-Ze;Harnly, James M.
代谢物指纹图谱，获得与直接注射质谱（MS）与正离子和负离子，方差-主成分分析（ANOVA-PCA）的分析来区分品种和生长处理的青花菜。样本集包括两个品种的西兰花，雄伟和遗产，第一种生长有四个不同水平的硒和第二种有机和常规的两种灌溉率。在两个品种和七个处理的化学成分差异产生的图案，视觉和统计上可区分使用方差分析-主成分分析。PCA加载允许提供最显着的化学差异的分子和碎片离子的识别。酚类化合物的标准化分析方法表明，重要的鉴别离子不是酚类化合物。鉴别离子的洗脱时间和先前的结果表明，它们是常见的糖和有机酸。方差分析计算的正离子和负离子MS指纹图谱表明，33%的方差来自品种，59%来自生长处理，8%来自分析不确定性。虽然正负离子化指纹图谱差异显著，但方差分布无差异。个体质量与品种或生长处理的高方差与高PCA负荷相关。ANOVA数据表明，只有分析不确定度方差较大的变量才应删除。所有其他变量代表允许样品相对于栽培品种和处理分离的鉴别质量。
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.