Strehmel Nadine, Hummel Jan, Erban Alexander, Strassburg Katrin, Kopka Joachim
Max Planck Institute of Molecular Plant Physiology, Department Prof. L. Willmitzer, Am Muehlenberg 1, D-14476 Potsdam-Golm, Germany.
J Chromatogr B Analyt Technol Biomed Life Sci. 2008 Aug 15;871(2):182-90. doi: 10.1016/j.jchromb.2008.04.042. Epub 2008 May 8.
The generation of retention index (RI) libraries is an expensive and time-consuming effort. Procedures for the transfer of RI properties between chromatography variants are, therefore, highly relevant for a shared use. The precision of RI determination and accuracy of RI transfer between 8 method variants employing 5%-phenyl-95%-dimethylpolysiloxane capillary columns was investigated using a series of 9 n-alkanes (C(10)-C(36)). The precision of the RI determination of 13 exemplary fatty acid methyl esters (C(8) ME-C(30) ME) was 0.22-0.33 standard deviation (S.D.) expressed in RI units in low complexity samples. In the presence of complex biological matrices this precision may deteriorate to 0.75-1.11. Application of the previously proposed Kováts, van den Dool or 3rd-5th order polynomial regression algorithms resulted in similar precision of RI calculation. For transfer of empirical van den Dool-RI properties between the chromatography variants 3rd order regression was found to represent the minimal necessary assumption. The range of typical regression coefficients was r(2)=0.9988-0.9998 and accuracy of RI prediction between chromatography variants varied between 5.1 and 19.8 (0.29-0.69%) S.D. of residual RI error, RI(predicted)-RI(determined) (n>64). Accuracy of prediction was enhanced when subsets of chemically similar compound classes were used for regression, for example organic acids and sugars exhibited 0.78 (n=29) and 3.74 (n=37) S.D. of residual RI error, respectively. In conclusion, we suggest use of percent RI error rather than absolute RI units for the definition of matching thresholds. Thresholds of 0.5-1.0% may apply to most transfers between chromatography variants. These thresholds will not solve all matching ambiguities in complex samples. Therefore, we recommend co-analysis of reference substances with each GC-MS profiling experiment. Composition of these defined reference mixtures may best approximate or mimic the quantitative and qualitative composition of the biological matrix under investigation.
保留指数(RI)库的生成是一项昂贵且耗时的工作。因此,色谱变体之间RI属性的转移程序对于共享使用至关重要。使用一系列9种正构烷烃(C(10)-C(36))研究了采用5%-苯基-95%-二甲基聚硅氧烷毛细管柱的8种方法变体之间RI测定的精密度以及RI转移的准确性。在低复杂度样品中,13种示例性脂肪酸甲酯(C(8) ME-C(30) ME)的RI测定精密度为0.22 - 0.33标准偏差(S.D.),以RI单位表示。在复杂生物基质存在的情况下,这种精密度可能会降至0.75 - 1.11。应用先前提出的科瓦茨、范登杜尔或三阶至五阶多项式回归算法,RI计算的精密度相似。对于色谱变体之间经验性范登杜尔-RI属性的转移,发现三阶回归代表了最小必要假设。典型回归系数的范围为r(2)=0.9988 - 0.9998,色谱变体之间RI预测的准确性在5.1至19.8(0.29 - 0.69%)S.D.的残余RI误差(RI(预测)-RI(测定),n>6X)之间变化。当使用化学性质相似的化合物类别的子集进行回归时,预测准确性会提高,例如有机酸和糖的残余RI误差的S.D.分别为0.78(n = 29)和3.74(n = 37)。总之,我们建议使用RI误差百分比而非绝对RI单位来定义匹配阈值。0.5 - 1.0%的阈值可能适用于色谱变体之间的大多数转移。这些阈值并不能解决复杂样品中的所有匹配模糊性问题。因此,我们建议在每次气相色谱 - 质谱分析实验中共同分析参考物质。这些定义的参考混合物的组成可能最接近或模拟所研究生物基质的定量和定性组成。