Ni Yongnian, Huang Chunfang, Kokot Serge
Department of Chemistry, Nanchang University, Nanchang, Jiangxi 330047, China.
Anal Chim Acta. 2007 Sep 19;599(2):209-18. doi: 10.1016/j.aca.2007.08.005. Epub 2007 Aug 6.
A differential kinetic spectrophotometric method was researched and developed for the simultaneous determination of iron and aluminium in food samples. It was based on the direct reaction kinetics and spectrophotometry of these two metal ions with Chrome Azurol S (CAS) in ethylenediamine-hydrochloric acid buffer (pH 6.3). The results were interpreted with the use of chemometrics. The kinetic runs and the visible spectra of the complex formation reaction were studied between 540 and 750 nm every 30 s over a total period of 285 s. A set of synthetic metal mixture samples was used to build calibrations models. These were based on the spectral and kinetic two-way data matrices, which were processed separately by the radial basis function-artificial neural network (global RBF-ANN) method. The prediction performance of these models was poorer than that from the combined kinetic-spectral three-way array, which was similarly processed by the same method (% relative prediction error (RPE(T))=5.6). These results demonstrate that improved predictions can be obtained from the data array, which has more information, and that appropriate chemometrics methods can enhance analytical performance of simple techniques such as spectrophotometry. Other chemometrics models were then applied: N-way partial least squares (NPLS), parallel factor analysis (PARAFAC), back propagation-artificial neural network (BP-ANN), single radial basis function-artificial neural network (RBF-ANN), and principal component neural network (PC-RBF-ANN). There was no substantial difference between the methods with the overall %RPE(T) range being 5.0-5.8. These two values corresponded to the NPLS and BP-ANN models, respectively. The proposed method was applied for the determination of iron and aluminium in some commercial food samples with satisfactory results.
研究并开发了一种微分动力学分光光度法，用于同时测定食品样品中的铁和铝。该方法基于这两种金属离子与铬天青S（CAS）在乙二胺 - 盐酸缓冲液（pH 6.3）中的直接反应动力学和分光光度法。利用化学计量学对结果进行解释。在540至750 nm波长范围内，每隔30 s研究络合物形成反应的动力学过程和可见光谱，共持续285 s。使用一组合成金属混合物样品建立校准模型。这些模型基于光谱和动力学双向数据矩阵，分别采用径向基函数 - 人工神经网络（全局RBF - ANN）方法进行处理。这些模型的预测性能比用相同方法处理的动力学 - 光谱组合三向阵列的预测性能差（相对预测误差（RPE(T)）=5.6%）。这些结果表明，从具有更多信息的数据阵列中可以获得更好的预测，并且适当的化学计量学方法可以提高诸如分光光度法等简单技术的分析性能。然后应用了其他化学计量学模型：N向偏最小二乘法（NPLS）、平行因子分析（PARAFAC）、反向传播 - 人工神经网络（BP - ANN）、单径向基函数 - 人工神经网络（RBF - ANN）和主成分神经网络（PC - RBF - ANN）。这些方法之间没有实质性差异，总体RPE(T)范围为5.0 - 5.8%。这两个值分别对应NPLS和BP - ANN模型。所提出的方法应用于一些商业食品样品中铁和铝的测定，结果令人满意。
