基于近红外的松子蛋白质品质分类处理
    点此下载全文
引用本文:蒋大鹏,张冬妍,李丹丹,曹 军.基于近红外的松子蛋白质品质分类处理[J].计算技术与自动化,2018,(3):180-184
摘要点击次数: 1289
全文下载次数: 0
作者单位
蒋大鹏,张冬妍,李丹丹,曹 军 (东北林业大学 机电学院黑龙江 哈尔滨 150040) 
中文摘要:为了探索松子基于近红外光谱的无损品质分类。建立松子蛋白质品质的分类数学模型。采用近红外测量获取松子光谱数据,运用SMO-SVM、Pegasos-SVM与LS-SVM方法建立松子蛋白质分类相关性模型,并对相应验证集上的数据进行预测验证。实验结果表明支持向量机精准率略高,但耗费时间比LS-SVM与Pegasos-SVM多。研究中所建模型均能达到一定程度上的良好分类,精准度均达到80%以上,可有效实现依据近红外光谱数据预测松子蛋白质含量等级的目的。此模型对于其他干果类食品的等级品质分类具有一定的实践指导意义与应用价值。
中文关键词:松子  近红外  支持向量机  蛋白质
 
Classification of Pine Nut Protein Quality Based on Near Infrared
Abstract:In order to explore the non-destructive quality classification of pine nuts based on near-infrared.Establishment of classification mathematical model of protein quality.Measuring the near infrared spectrum data acquired pine nuts.The support vector machine and the least squares support vector machine were used to establish the correlation model of pine nut protein classification,and the data on the corresponding verification set were predicted and verified.The experimental results of SMO-SVM show that the precision of support vector machine was slightly higher,but it takes more than one third of the least squares support vector machine.The accuracy of the model was 80% or more,which can effectively achieve the purpose of predicting the protein content of pine nuts based on near infrared spectroscopy data.This model has certain practical significance and application value for the grade quality classification of other dried fruit.
keywords:pine nuts  near infrared  support vector machine  protein
查看全文   查看/发表评论   下载pdf阅读器