Feng Quanxi, Chen Huazhou, Xie Hai, Cai Ken, Lin Bin, Xu Lili
College of Science, Guilin University of Technology, Guilin 541004, China.
Center for Data Analysis and Algorithm Technology, Guilin University of Technology, Guilin 541004, China.
Comput Intell Neurosci. 2020 Jul 10;2020:7686724. doi: 10.1155/2020/7686724. eCollection 2020.
The global fishmeal production is used for animal feed, and protein is the main component that provides nutrition to animals. In order to monitor and control the nutrition supply to animal husbandry, near-infrared (NIR) technology was utilized for rapid detection of protein contents in fishmeal samples. The aim of the NIR quantitative calibration is to enhance the model prediction ability, where the study of chemometric algorithms is inevitably on demand. In this work, a novel optimization framework of GSMW-LPC-GA was constructed for NIR calibration. In the framework, some informative NIR wavebands were selected by grid search moving window (GSMW) strategy, and then the variables/wavelengths in the waveband were transformed to latent principal components (LPCs) as the inputs for genetic algorithm (GA) optimization. GA operates in iterations as implementation for the secondary optimization of NIR wavebands. In steps of the variable's population evolution, the parametric scaling mode was investigated for the optimal determination of the crossover probability and the mutation operator. With the GSMW-LPC-GA framework, the NIR prediction effect on fishmeal protein was experimentally better than the effect by simply adopting the moving window calibration model. The results demonstrate that the proposed framework is suitable for NIR quantitative determination of fishmeal protein. GA was eventually regarded as an implementable method providing an efficient strategy for improving the performance of NIR calibration models. The framework is expected to provide an efficient strategy for analyzing some unknown changes and influence of various fertilizers.
全球鱼粉产量用于动物饲料，蛋白质是为动物提供营养的主要成分。为了监测和控制畜牧业的营养供应，利用近红外（NIR）技术快速检测鱼粉样品中的蛋白质含量。近红外定量校准的目的是提高模型预测能力，因此化学计量学算法的研究必不可少。在这项工作中，构建了一种新颖的GSMW-LPC-GA优化框架用于近红外校准。在该框架中，通过网格搜索移动窗口（GSMW）策略选择一些信息丰富的近红外波段，然后将波段中的变量/波长转换为潜在主成分（LPC）作为遗传算法（GA）优化的输入。GA以迭代方式运行，作为近红外波段二次优化的实现。在变量种群进化步骤中，研究了参数缩放模式以优化确定交叉概率和变异算子。使用GSMW-LPC-GA框架，对鱼粉蛋白质的近红外预测效果在实验上优于简单采用移动窗口校准模型的效果。结果表明，所提出的框架适用于鱼粉蛋白质的近红外定量测定。GA最终被视为一种可实施的方法，为提高近红外校准模型的性能提供了有效策略。该框架有望为分析各种肥料的一些未知变化和影响提供有效策略。
