Luthria Devanand L, Mukhopadhyay Sudarsan, 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 Jul 23;56(14):5457-62. doi: 10.1021/jf0734572. Epub 2008 Jun 24.
UV spectral fingerprints, in combination with analysis of variance-principal components analysis (ANOVA-PCA), can differentiate between cultivars and growing conditions (or treatments) and can be used to identify sources of variance. Broccoli samples, composed of two cultivars, were grown under seven different conditions or treatments (four levels of Se-enriched irrigation waters, organic farming, and conventional farming with 100 and 80% irrigation based on crop evaporation and transpiration rate). Freeze-dried powdered samples were extracted with methanol-water (60:40, v/v) and analyzed with no prior separation. Spectral fingerprints were acquired for the UV region (220-380 nm) using a 50-fold dilution of the extract. ANOVA-PCA was used to construct subset matrices that permitted easy verification of the hypothesis that cultivar and treatment contributed to a difference in the chemical expression of the broccoli. The sums of the squares of the same matrices were used to show that cultivar, treatment, and analytical repeatability contributed 30.5, 68.3, and 1.2% of the variance, respectively.
紫外光谱指纹图谱结合方差分析-主成分分析(ANOVA-PCA),可以区分不同品种以及生长条件(或处理方式),并可用于识别变异来源。西兰花样本由两个品种组成,在七种不同条件或处理方式下种植(四种富硒灌溉水水平以及有机种植、根据作物蒸发蒸腾速率进行100%和80%灌溉的传统种植)。冻干后的粉末样本用甲醇-水(60:40,v/v)萃取,未经预先分离直接进行分析。使用50倍稀释的提取物获取紫外区域(220-380nm)的光谱指纹图谱。ANOVA-PCA用于构建子集矩阵,以便轻松验证品种和处理方式导致西兰花化学表达差异这一假设。相同矩阵的平方和用于表明品种、处理方式和分析重复性分别导致了30.5%、68.3%和1.2%的变异。