Goicoechea Héctor C, Olivieri Alejandro C
Cátedra de Química Analítica, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe S3000 CC. 242, Argentina.
Appl Spectrosc. 2005 Jul;59(7):926-33. doi: 10.1366/0003702054411643.
A new second-order multivariate method has been developed for the analysis of spectral-pH matrix data, based on a bilinear least-squares (BLLS) model achieving the second-order advantage and handling multiple calibration standards. A simulated Monte Carlo study of synthetic absorbance-pH data allowed comparison of the newly proposed BLLS methodology with constrained parallel factor analysis (PARAFAC) and with the combination multivariate curve resolution-alternating least-squares (MCR-ALS) technique under different conditions of sample-to-sample pH mismatch and analyte-background ratio. The results indicate an improved prediction ability for the new method. Experimental data generated by measuring absorption spectra of several calibration standards of ascorbic acid and samples of orange juice were subjected to second-order calibration analysis with PARAFAC, MCR-ALS, and the new BLLS method. The results indicate that the latter method provides the best analytical results in regard to analyte recovery in samples of complex composition requiring strict adherence to the second-order advantage. Linear dependencies appear when multivariate data are produced by using the pH or a reaction time as one of the data dimensions, posing a challenge to classical multivariate calibration models. The presently discussed algorithm is useful for these latter systems.
基于实现二阶优势并处理多个校准标准的双线性最小二乘（BLLS）模型，开发了一种用于分析光谱 - pH矩阵数据的新二阶多元方法。对合成吸光度 - pH数据进行的模拟蒙特卡罗研究，使得在不同样本间pH不匹配和分析物 - 背景比率条件下，能够将新提出的BLLS方法与约束平行因子分析（PARAFAC）以及多元曲线分辨 - 交替最小二乘（MCR - ALS）技术组合进行比较。结果表明新方法的预测能力有所提高。通过测量几种抗坏血酸校准标准品和橙汁样品的吸收光谱生成的实验数据，采用PARAFAC、MCR - ALS和新的BLLS方法进行二阶校准分析。结果表明，对于需要严格遵循二阶优势的复杂成分样品中的分析物回收率而言，后一种方法提供了最佳分析结果。当使用pH或反应时间作为数据维度之一生成多元数据时，会出现线性相关性，这对经典多元校准模型构成挑战。目前讨论的算法对后一种系统很有用。
