Laboratório de Automação e Instrumentação em Química Analítica e Quimiometria (LAQA), Universidade Federal da Paraíba, CCEN, Departamento de Química, Caixa Postal 5093, CEP 58051-970, João Pessoa, PB, Brazil.
Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral-CONICET, Ciudad Universitaria, 3000 Santa Fe, Argentina.
Anal Chim Acta. 2014 Feb 6;811:13-22. doi: 10.1016/j.aca.2013.12.022. Epub 2013 Dec 27.
In this work the Successive Projection Algorithm is presented for intervals selection in N-PLS for three-way data modeling. The proposed algorithm combines noise-reduction properties of PLS with the possibility of discarding uninformative variables in SPA. In addition, second-order advantage can be achieved by the residual bilinearization (RBL) procedure when an unexpected constituent is present in a test sample. For this purpose, SPA was modified in order to select intervals for use in trilinear PLS. The ability of the proposed algorithm, namely iSPA-N-PLS, was evaluated on one simulated and two experimental data sets, comparing the results to those obtained by N-PLS. In the simulated system, two analytes were quantitated in two test sets, with and without unexpected constituent. In the first experimental system, the determination of the four fluorophores (l-phenylalanine; l-3,4-dihydroxyphenylalanine; 1,4-dihydroxybenzene and l-tryptophan) was conducted with excitation-emission data matrices. In the second experimental system, quantitation of ofloxacin was performed in water samples containing two other uncalibrated quinolones (ciprofloxacin and danofloxacin) by high performance liquid chromatography with UV-vis diode array detector. For comparison purpose, a GA algorithm coupled with N-PLS/RBL was also used in this work. In most of the studied cases iSPA-N-PLS proved to be a promising tool for selection of variables in second-order calibration, generating models with smaller RMSEP, when compared to both the global model using all of the sensors in two dimensions and GA-NPLS/RBL.
在这项工作中，提出了连续投影算法（SPA）用于三向数据建模的 N-PLS 区间选择。所提出的算法将 PLS 的降噪特性与 SPA 中丢弃无信息变量的可能性相结合。此外，当测试样本中存在意外成分时，可以通过残差双线性化（RBL）过程实现二阶优势。为此，对 SPA 进行了修改，以便为三线性 PLS 选择区间。通过比较 N-PLS 的结果，评估了所提出的算法（即 iSPA-N-PLS）在一个模拟数据集和两个实验数据集上的能力。在所模拟的系统中，在两个测试集中对两个分析物进行了定量分析，其中一个测试集有意外成分，另一个没有。在第一个实验系统中，通过激发-发射数据矩阵进行了四个荧光团（L-苯丙氨酸；L-3,4-二羟基苯丙氨酸；1,4-二羟基苯和 L-色氨酸）的测定。在第二个实验系统中，通过高效液相色谱法与紫外-可见二极管阵列检测器在含有两种未校准喹诺酮类药物（环丙沙星和丹诺沙星）的水样中进行了氧氟沙星的定量分析。为了比较目的，在这项工作中还使用了 GA 算法与 N-PLS/RBL 相结合。在所研究的大多数情况下，iSPA-N-PLS 被证明是二阶校准中选择变量的有前途的工具，与在二维中使用所有传感器的全局模型和 GA-NPLS/RBL 相比，生成的模型具有更小的 RMSEP。
