基于主成分分析和改进支持向量机的猪肉品质识别

目的:剔除近红外光谱存在大量冗余信息以及提高猪肉品质识别的精度,建立猪肉品质快速识别方法。方法:运用主成分分析对猪肉光谱数据进行降维处理,筛选出猪肉光谱特征波长;运用樽海鞘群算法进行支持向量机(SVM)模型的参数优化,提出一种基于樽海鞘算法改进支持向量机的猪肉品质识别模型。结果:与灰狼算法(GWO)优化SVM(GWO-SVM)、网格搜索算法改进SVM(Grid-SVM)、粒子群算法改进SVM(PSO-SVM)和SVM相比,基于SSA-SVM的猪肉品质识别模型的精度最高。结论:融合主成分分析和樽海鞘算法优化SVM模型的猪肉品质识别模型可以有效提高SVM模型的猪肉品质识别精度。

Pork quality identification based on principal component analysis and improved support vector machine

Objective:In order to eliminate the large amount of redundant information in near-infrared spectroscopy and to improve the accuracy of pork quality identification, and to establish a method for rapid identification of pork quality.Methods:Principal component analysis was used to reduce the dimensionality of pork spectrum data and the characteristic wavelengths of pork spectrum were selected. The parameters of the support vector machine (SVM) model were optimized by the salp swarm algorithm. Pork quality recognition model was proposed based on an improved support vector machine optimized by salp swarm algorithm.Results:compared with grey wolf optimization algorithm improved SVM (GWO-SVM), grid search algorithm improved SVM (Grid-SVM), particle swarm optimization algorithm improved SVM (PSO-SVM) and SVM, the pork quality recognition model based on SSA-SVM had the highest precision.Conclusion:Pork quality identification model based on PCA and SVM optimized by salp swarm algorithm can effectively improve the accuracy of pork quality identification.