Departamento de Química Analítica, Nutrición y Bromatología. Facultad de Ciencias Químicas, Universidad de Salamanca, 37008, Salamanca, Spain.
Departamento de Química Analítica, Nutrición y Bromatología. Facultad de Ciencias Químicas, Universidad de Salamanca, 37008, Salamanca, Spain.
Talanta. 2020 Aug 15;216:120811. doi: 10.1016/j.talanta.2020.120811. Epub 2020 Feb 6.
Herein we propose, for the first time, a rapid method based on flow injection analysis, electrospray ionization-tandem mass spectrometry (FIA-ESI-MS/MS) and multivariate calibration for the determination of l-leucine, l-isoleucine and L-allo-isoleucine in saliva. As far as we know, multivariate calibration has never been applied to the data from this non-separative approach. The possibilities of its use were explored and the results obtained were compared with the corresponding ones when using univariate calibration. Partial least square regression (PLS1) multivariate calibration models were built for each analyte by analyzing different saliva samples, and were subsequently applied to the analysis of another set of samples which had not been used in any calibration step. For Leu, the model worked satisfactorily with root mean square errors in the prediction step of 17%. This error can be considered acceptable and is common in methodologies that do not include a separation step. Results were compared with those obtained when univariate calibration was used, using the m/z transition 132.1 → 43.0 as the quantitation variable. In this case, the obtained results were not acceptable, with RMSEP of 236%, due to the fact that saliva samples contained another compound, different to the target analytes, which also shared the same transition. Ile and aIle have the same fragmentation patterns, so quantification of the sum of both compounds was performed, with RMSEP of 14% using a PLS1 model. Similar results were obtained when a univariate calibration model using the m/z transition 132.1 → 69.0 was employed. However, the use of this transition should be carefully examined when other compounds present in the matrix contribute to the analytical signal. The method increases sample throughput more than one order of magnitude compared to the corresponding LC-ESI-MS/MS method and is especially suitable as screening. When abnormally high or low concentrations of the analytes studied are obtained, the use of the method that includes separation is recommended to confirm the results.
在此,我们首次提出了一种基于流动注射分析-电喷雾串联质谱(FIA-ESI-MS/MS)和多元校正的快速方法,用于测定唾液中的 L-亮氨酸、L-异亮氨酸和 L-allo-异亮氨酸。据我们所知,多元校正从未应用于这种非分离方法的数据。我们探索了其使用的可能性,并将所得结果与使用单变量校正时的相应结果进行了比较。通过分析不同的唾液样本,为每个分析物建立了偏最小二乘回归(PLS1)多元校正模型,然后将这些模型应用于另一组未在任何校正步骤中使用的样本的分析。对于 Leu,该模型在预测步骤中的均方根误差为 17%,工作效果令人满意。这个误差可以认为是可以接受的,在不包括分离步骤的方法中很常见。将结果与使用单变量校正时的结果进行了比较,使用 m/z 跃迁 132.1 → 43.0 作为定量变量。在这种情况下,由于唾液样本中含有与目标分析物不同的另一种化合物,也共享相同的跃迁,因此得到的结果是不可接受的,RMSEP 为 236%。Ile 和 aIle 具有相同的碎片模式,因此对两种化合物的总和进行定量,使用 PLS1 模型的 RMSEP 为 14%。使用 m/z 跃迁 132.1 → 69.0 的单变量校正模型也得到了相似的结果。然而,当基质中存在的其他化合物对分析信号有贡献时,应仔细检查使用此跃迁的情况。与相应的 LC-ESI-MS/MS 方法相比,该方法将样品通量提高了一个数量级以上,特别适合作为筛选方法。当获得研究分析物的异常高或低浓度时,建议使用包括分离的方法来确认结果。