蚁群算法在近红外光谱定量分析中的应用研究

蚁群算法是新近发展的基于群体智能的仿生优化算法,它模拟蚂蚁的觅食行为来解决复杂的组合优化问题.蚁群算法的优点是智能搜索、全局优化、鲁棒性、分布式计算和容易与其他算法相结合等.近红外光谱定量分析技术在很多领域得到广泛的应用,而其关键技术环节之一是建立近红外光谱测量数据的多元校正模型.文章将蚁群算法应用于近红外光谱定量分析中,建立了谷物样品的傅里叶变换近红外漫反射光谱和谷物中蛋白质含量的定量分析模型,得到了较好的结果.校准集的相关系数与相对标准偏差分别为0.943和3.41%,预测集的相关系数与相对标准偏差分别为0.913和4.67%.

Application Study of Ant Colony Algorithm in Near Infrared Spectroscopy Quantitative Analysis

Ant colony algorithm is a novel bio-inspired optimization algorithm,which simulates the foraging behavior of ants for solving various complex combinatorial optimization problems.The advantages of ant colony algorithm are intelligent search,global optimization,robustness,distributed computation and easy combination with other heuristic method.Near infrared spectroscopy quantitative analysis has been applied in many fields,whereas the key step is building the calibration model of measured data.In the present paper,ant colony algorithm was used to build the quantitative analysis model of Fourier transform near infrared diffuse spectroscopy for protein in cereal.Satisfied results were obtained.For calibration set,the correlation coefficient and relative standard deviation were 0.943 and 3.41%,respectively,while for prediction set,the correlation coefficient and relative standard deviation were 0.913 and 4.67%,respectively.