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A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models.
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2020-07-10 , DOI: 10.1155/2020/7686724
Quanxi Feng 1, 2 , Huazhou Chen 1, 2 , Hai Xie 1 , Ken Cai 3 , Bin Lin 1, 2 , Lili Xu 4
Affiliation  

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.

中文翻译:

基于遗传算法的新型优化框架,用于改进近红外定量校准模型。

全球鱼粉生产用于动物饲料,蛋白质是向动物提供营养的主要成分。为了监视和控制畜牧业的营养供应,近红外(NIR)技术用于快速检测鱼粉样品中的蛋白质含量。NIR定量校准的目的是增强模型预测能力,而化学计量学算法的研究不可避免地需要。在这项工作中,构建了用于NIR校准的GSMW-LPC-GA的新型优化框架。在该框架中,通过网格搜索移动窗口(GSMW)策略选择了一些有用的NIR波段,然后将该波段中的变量/波长转换为潜在主成分(LPC)作为遗传算法(GA)优化的输入。GA作为NIR波段的二次优化的实现而迭代运行。在变量总体演化的步骤中,研究了参数缩放模式,以最佳确定交叉概率和变异算子。在GSMW-LPC-GA框架下,对鱼粉蛋白质的NIR预测效果在实验上优于仅采用移动窗口校准模型的效果。结果表明,该框架适用于鱼粉蛋白的近红外定量分析。GA最终被认为是一种可实施的方法,为提高NIR校准模型的性能提供了有效的策略。该框架有望为分析各种肥料的某些未知变化和影响提供有效策略。
更新日期:2020-07-10
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