Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Instituto de Química de Rosario (IQUIR-CONICET), Rosario, Argentina.
Anal Chim Acta. 2011 Aug 5;699(1):18-25. doi: 10.1016/j.aca.2011.04.061. Epub 2011 May 11.
A new variable selection algorithm is described, based on ant colony optimization (ACO). The algorithm aim is to choose, from a large number of available spectral wavelengths, those relevant to the estimation of analyte concentrations or sample properties when spectroscopic analysis is combined with multivariate calibration techniques such as partial least-squares (PLS) regression. The new algorithm employs the concept of cooperative pheromone accumulation, which is typical of ACO selection methods, and optimizes PLS models using a pre-defined number of variables, employing a Monte Carlo approach to discard irrelevant sensors. The performance has been tested on a simulated system, where it shows a significant superiority over other commonly employed selection methods, such as genetic algorithms. Several near infrared spectroscopic experimental data sets have been subjected to the present ACO algorithm, with PLS leading to improved analytical figures of merit upon wavelength selection. The method could be helpful in other chemometric activities such as classification or quantitative structure-activity relationship (QSAR) problems.
描述了一种新的变量选择算法，该算法基于蚁群优化（ACO）。当光谱分析与多元校正技术（如偏最小二乘（PLS）回归）结合使用时，该算法旨在从大量可用的光谱波长中选择与分析物浓度或样品性质估计相关的波长。新算法采用了合作信息素积累的概念，这是 ACO 选择方法的典型特征，并使用预定义数量的变量优化 PLS 模型，采用蒙特卡罗方法丢弃不相关的传感器。该性能已在模拟系统中进行了测试，结果表明它明显优于其他常用的选择方法，如遗传算法。将当前的 ACO 算法应用于几个近红外光谱实验数据集，结果表明，在波长选择后，PLS 可提高分析性能。该方法可在其他化学计量学活动（如分类或定量结构-活性关系（QSAR）问题）中提供帮助。
