Departamento de Química Analitica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario and Instituto de Química Rosario (IQUIR-CONICET), Suipacha 531, Rosario (S2002LRK), Argentina.
Anal Chem. 2010 Jun 1;82(11):4510-9. doi: 10.1021/ac100424d.
Analyte quantitation can be achieved from second-order data in the presence of uncalibrated components using multivariate calibration methods such as partial least-squares with residual bilinearization. However, the latter fails under conditions of identical profiles for interfering agents and calibrated components in one of the data dimensions. To overcome this problem, a new residual bilinearization procedure for linear dependency is here introduced. Simulated data show that the new model can conveniently handle the studied analytical problem, with a success comparable to multivariate curve resolution-alternating least-squares and also comparable to a version of parallel factor analysis adapted to cope with linear dependencies. The new approach has also been applied to two experimental examples involving the determination of the antibiotic ciprofloxacin in (1) urine samples from lanthanide-sensitized excitation-time decay matrixes and (2) serum samples from a novel second-order signal based on the time evolution of chemiluminescence emission. The results indicate good analytical performance of the new procedure toward the analyte in comparison with the classical approaches.
可以使用多元校准方法（如带有残差双线性化的偏最小二乘法）从存在未校准成分的二阶数据中定量分析分析物。然而，当数据维度之一中的干扰剂和校准成分的轮廓相同时，后者会失败。为了克服这个问题，这里引入了一种新的用于线性相关性的残差双线性化程序。模拟数据表明，新模型可以方便地处理所研究的分析问题，其成功率可与多元曲线分辨率交替最小二乘法相媲美，也可与适应处理线性相关性的并行因子分析的一个版本相媲美。该新方法还应用于两个实验实例，涉及（1）从镧系元素敏化激发时间衰减矩阵中的尿液样本中测定抗生素环丙沙星，以及（2）基于化学发光发射时间演变的新型二阶信号的血清样本。结果表明，与经典方法相比，该新程序对分析物具有良好的分析性能。
