Arroyo David, Ortiz M Cruz, Sarabia Luis A, Palacios Francisco
Department of Chemistry, Faculty of Sciences, University of Burgos, Pza. Misael Bañuelos s/n, 09001 Burgos, Spain.
J Chromatogr A. 2008 Apr 11;1187(1-2):1-10. doi: 10.1016/j.chroma.2008.02.040. Epub 2008 Feb 15.
This paper reports the properties and advantages of the three-way calibration models based on parallel factor analysis (PARAFAC) in the simultaneous determination of malachite green (MG) and its metabolite (leucomalachite green, LMG) in trout. A recently method proposed by community reference laboratory AFSSA-LERMVD (Fougères, France) has been used. The method is based on liquid chromatography-triple quadrupole mass spectrometry (LC-MS/MS) in multiple reaction monitoring (MRM) mode. The validation of the method has been carried out taking into account the Decision 2002/657/EC. The figures of merit for PARAFAC and univariate calibration models of six non-consecutive days analyzed during a month were evaluated. With the samples of the first 3 days, calibration models were built and the fish fortification samples of the other days were predicted. Decision limits (CCalpha, alpha=0.01), detection capabilities (CCbeta, beta=0.05) and mean relative errors in absolute value (in calibration and with test samples) obtained with PARAFAC calibrations were more homogeneous than the ones obtained with the univariate calibrations, especially in LMG. These figures of merit were in the range of 0.2-0.83 microg kg(-1) (CCbeta) and 0.2-0.49 microg kg(-1) (CCalpha), whereas mean relative errors in absolute value were in the range of 1.1-7.4% in calibration and 3-12% in test samples for MG and LMG with PARAFAC calibrations. The PARAFAC calibrations allow detecting the test samples which are not similar to the calibration samples and in this way their wrong quantification is avoided.
本文报道了基于平行因子分析（PARAFAC）的三向校准模型在同时测定鳟鱼中孔雀石绿（MG）及其代谢物（无色孔雀石绿，LMG）时的特性和优势。采用了法国富热尔社区参考实验室AFSSA-LERMVD最近提出的一种方法。该方法基于液相色谱-三重四极杆质谱联用仪（LC-MS/MS）的多反应监测（MRM）模式。已根据2002/657/EC号指令对该方法进行了验证。评估了一个月内分析的六个非连续日的PARAFAC和单变量校准模型的品质因数。利用前3天的样品建立校准模型，并对其他日期的鱼强化样品进行预测。PARAFAC校准获得的决策限（CCalpha，alpha = 0.01）、检测能力（CCbeta，beta = 0.05）和绝对值平均相对误差（在校准和测试样品中）比单变量校准获得的更为均匀，尤其是在LMG方面。这些品质因数在0.2 - 0.83微克·千克⁻¹（CCbeta）和0.2 - 0.49微克·千克⁻¹（CCalpha）范围内，而PARAFAC校准的MG和LMG在校准中的绝对值平均相对误差在1.1 - 7.4%范围内，在测试样品中为3 - 12%。PARAFAC校准能够检测出与校准样品不相似的测试样品，从而避免对其进行错误定量。
