Food Composition and Methods Development Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, Maryland 20705-3000, USA.
Appl Spectrosc. 2011 Mar;65(3):250-9. doi: 10.1366/10-06109.
Spectral fingerprinting, as a method of discriminating between plant cultivars and growing treatments for a common set of broccoli samples, was compared for six analytical instruments. Spectra were acquired for finely powdered solid samples using Fourier transform infrared (FT-IR) and Fourier transform near-infrared (NIR) spectrometry. Spectra were also acquired for unfractionated aqueous methanol extracts of the powders using molecular absorption in the ultraviolet (UV) and visible (VIS) regions and mass spectrometry with negative (MS-) and positive (MS+) ionization. The spectra were analyzed using nested one-way analysis of variance (ANOVA) and principal component analysis (PCA) to statistically evaluate the quality of discrimination. All six methods showed statistically significant differences between the cultivars and treatments. The significance of the statistical tests was improved by the judicious selection of spectral regions (IR and NIR), masses (MS+ and MS-), and derivatives (IR, NIR, UV, and VIS).
光谱指纹图谱作为一种区分植物品种和生长处理的方法，对一组常见的西兰花样品的六种分析仪器进行了比较。使用傅里叶变换红外（FT-IR）和傅里叶变换近红外（NIR）光谱法对细粉固体样品进行了光谱采集。还对粉末的未分级甲醇水提物进行了分子吸收在紫外（UV）和可见（VIS）区域和质谱分析，采用负（MS-）和正（MS+）离子化。使用嵌套单向方差分析（ANOVA）和主成分分析（PCA）对光谱进行分析，以统计评估区分质量。所有六种方法均显示品种和处理之间存在统计学上的显著差异。通过明智地选择光谱区域（IR 和 NIR）、质量（MS+和 MS-）和导数（IR、NIR、UV 和 VIS），提高了统计检验的显著性。
