Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
Research Cluster for Cannabis and its Natural Substances, Chulalongkorn University, Bangkok 10330, Thailand; Department of Pharmacognosy and Pharmaceutical Botany, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok 10330, Thailand.
J Chromatogr A. 2022 Aug 30;1679:463394. doi: 10.1016/j.chroma.2022.463394. Epub 2022 Jul 31.
In this work, first and second dimensional retention index (I and I) based calculation approach is established to simulate peak retention times (t and t) of samples for the given sets of volatile compounds in comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS). For the result without t and t data of alkane references (t and t), the following steps were applied: (1) curve fitting based on van den Dool and Kratz relationship in order to simulate t using a training set of volatile compounds in a sample with their experimental t data, and (2) simulation of t at different t to construct their isovolatility curves based on a nonlinear equation with p-p parameters and a constant (within the ranges of -0.0052 to 0.0049, -0.6181 to -0.0230, -26.4775 to -0.2698, 0.0050 to 9.6259, -7.2976 to -3.9524 and 0.9157 to 4.0779, respectively). These parameters were obtained by performing curve fitting according to the experimental t data of the same training set with the least square values ranging from 4.58×10 to 32.55. Simulation of t and t of target analytes (t and t) with known I and I were performed using t and the simulated isovolatility curves. All the calculations and curve fittings were carried out by using Solver in Microsoft Excel. The approach was applied to simulate results for 542 compounds in several samples including analysis of saffron (Crocus sativas L.), Boswellia papyrifera, acacia honey and incense powder/smoke, perfume and cannabis either reported from literature or from the experiments in this work using different experimental approaches. These were compared with the experimental data showing correlation with the R ranges of 0.98-1.00 and 0.80-0.97 for t and t, respectively. This approach was then applied to propose 6 compounds which may be incorrectly identified based on the differences of >2 times of the standard deviations between t and the experimental t in both residue and leave-one-out analyses.
在这项工作中,建立了基于一维和二维保留指数 (I 和 I) 的计算方法,以模拟给定挥发性化合物集在全二维气相色谱-质谱联用 (GC×GC-MS) 中的样品峰保留时间 (t 和 t)。对于没有烷烃参考物 (t 和 t) 的 t 和 t 数据的结果,应用了以下步骤:(1) 基于范登杜尔和克拉茨关系的曲线拟合,以便使用具有实验 t 数据的样品中的挥发性化合物训练集模拟 t,以及 (2) 在不同 t 下模拟 t 以构建它们的等挥发曲线基于具有 p-p 参数和常数的非线性方程 (在 -0.0052 至 0.0049、-0.6181 至 -0.0230、-26.4775 至 -0.2698、0.0050 至 9.6259、-7.2976 至 -3.9524 和 0.9157 至 4.0779 的范围内)。这些参数是通过根据具有最小平方值 4.58×10 至 32.55 的实验 t 数据对同一训练集进行曲线拟合获得的。使用 t 和模拟的等挥发曲线对具有已知 I 和 I 的目标分析物 (t 和 t) 的 t 和 t 进行模拟。所有计算和曲线拟合都是在 Microsoft Excel 中的 Solver 中进行的。该方法应用于模拟包括从文献或本工作中的不同实验方法报告的番红花 (Crocus sativas L.)、乳香、阿拉伯胶蜂蜜和熏香/烟雾、香水和大麻等几种样品中 542 种化合物的结果,与实验数据的相关性 R 范围分别为 0.98-1.00 和 0.80-0.97。然后,该方法应用于提出 6 种化合物,根据在残差和留一法分析中 t 和实验 t 之间的标准偏差差异大于 2 倍,可能会错误识别这些化合物。