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最小二乘支持向量机的核桃露饮品中脂肪成分的定量分析 被引量:8

Determination of Fat in Walnut Beverage Based on Least Squares Support Vector Machine
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摘要 利用近红外光谱对核桃露中的重要指标脂肪含量进行定量分析,同时进行建模变量优化、建模方法比较以优选最佳模型。为消除散射对光谱造成的影响,采用标准正态变换(SNV)方法对数据进行预处理,采用遗传算法(GA)结合向后间隔偏最小二乘法(BiPLS)优选的特征波长分别作为偏最小二乘法(PLS)及最小二乘支持向量机(LS-SVM)的输入变量,建立核桃露中脂肪含量的近红外定量模型,采用决定系数(R 2)、预测标准偏差(RMSEP)以及性能偏差比(RPD)对各模型进行评价,探究光谱波段选择方法对于核桃露中脂肪指标模型构建的影响,同时确定最佳建模方法。结果表明:进行变量筛选能够对模型起到优化作用,BiPLS及GA-BiPLS方法分别选择了150及30个变量点,占全光谱的10%及2%,对应了核桃露样品中脂肪成分的特征吸收峰,使得PLS模型的RMSEP值从0.049分别下降到0.043和0.040,同时模型的相关系数R 2从0.964提高到0.973及0.974,性能偏差比RPD从4.88增长到5.62及6.00,主成分数也有不同程度的减少,降低模型复杂程度的同时提高了模型准确性。相比于PLS模型,核桃露脂肪指标的LS-SVM模型的R 2,RMSEP及RPD值均表现出了更好的效果,分别达到0.986,0.036及6.52。说明基于最小二乘支持向量机建立的分析模型有较高的准确度及稳定性,可能是由于PLS作为一种经典的线性建模方法,在建立模型的过程中忽略了样品数据集中的非线性因素,而核桃露样品光谱测量过程中噪声、背景等因素的干扰,以及各指标成分间的相互影响,使得脂肪含量与近红外光谱信息间存在复杂非线性的变化关系,LS-SVM方法能够更为有效地对其进行描述,增强了光谱变量与指标浓度间的相关性,使得建立的模型有着更好的准确度以及普适性,说明了在实际生产中,LS-SVM方法具备优良的可行性,体现了其在核桃露饮品品质分析方面的巨大潜力。基于最小二乘支持向量机方法所建立的核桃露脂肪含量的定量分析模型,具有准确、稳定的特点,能够为核桃露生产的质量监控提供技术借鉴,同时为饮品品质的分析方法研究提供了新的思路。 Near-infrared spectroscopy was used to quantitatively analyze the fat content of walnut beverage.At the same time,modeling variables were optimized and modeling methods were compared to optimize the best model.In order to eliminate the influence of scattering on the spectrum,the data are preprocessed by the standard normal transformation(SNV)method.The preferred characteristic wavelengths of genetic algorithms(GA)combined with backward interval partial least squares(BiPLS)were used as input variables of partial least squares(PLS)and least squares support vector machine(LS-SVM)respectively to establish model of fat content in walnut beverage.The R^2,RMSEP and RPD were used to evaluate the effect of spectral band selection method on the construction of fat index model in walnut beverage and determine the best modeling method.The results showed that the variable selection could optimize the model.150 and 30 variable points corresponding to the characteristic absorption peaks of the fat components in walnut beverage samples were selected by BiPLS and GA-BiPLS methods,respectively,accounting for 10%and 2%of the full spectrum.The RMSEP value of the PLS model decreased from 0.049 to 0.043 and 0.040,respectively,and the R 2 increased from 0.964 to 0.973 and 0.974.The range error ratio RPD increased from 4.88 to 5.62 and 6.00,and the principal component number also decreased to varying degrees.The method of variable selection could reduce model dimensions and improve model accuracy.Compared with the PLS model,the R^2,RMSEP and RPD values of the LS-SVM model showed better results,reaching 0.986,0.036 and 6.52,respectively.The LS-SVM model has higher accuracy and stability than the PLS model.Since PLS is a classic linear modeling method,the nonlinear factors in the sample data set are ignored in the process of building the model.However,there was a complex nonlinear relationship between fat content and near-infrared spectral information,which is due to the interference of noise,background and other factors in the spectral measurement process of walnut beverage samples and the interaction between various indicators.The LS-SVM method could enhance the correlation between spectral variables and index concentration,so that the established model has better accuracy and universality.It shows that in the actual production,the LS-SVM method has excellent feasibility,which reflects its great potential in the analysis of the quality of walnut beverage.Based on the LS-SVM method,the quantitative analysis model of walnut fat content has accurate and stable characteristics,which can provide technical reference for the quality monitoring of walnut beverage production,and provide a new idea for the analysis of beverage quality.
作者 李子文 李宗朋 买书魁 盛晓慧 尹建军 刘国荣 王成涛 张海红 辛立斌 王健 LI Zi-wen;LI Zong-peng;MAI Shu-kui;SHENG Xiao-hui;YIN Jian-jun;LIU Guo-rong;WANG Cheng-tao;ZHANG Hai-hong;XIN Li-bin;WANG Jian(China National Research Institute of Food and Fermentation Industries Corporation,Beijing100015,China;Beijing Engineering and Technology Research Center of Food Additives,Beijing Technology and Business University,Beijing 100048,China;College of Agriculture,Ningxia University,Yinchuan 750021,China;Shanghai Precise Packaging Corporation,Shanghai 201514,China)
出处 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2019年第12期3916-3920,共5页 Spectroscopy and Spectral Analysis
基金 国家重点研发计划项目(2018YFD0400905) 国家自然科学基金项目(31671937)资助
关键词 核桃露 近红外光谱技术 最小二乘支持向量机 波段筛选 Walnut beverage Near-infrared spectroscopy Least squares support vector machines(LS-SVM) Band selection
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