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荧光光谱结合支持向量机测定食用色素纯度
引用本文:张毅,陈国庆,朱纯,朱焯炜,徐瑞煜.荧光光谱结合支持向量机测定食用色素纯度[J].光谱学与光谱分析,2016,36(12):3978-3985.
作者姓名:张毅  陈国庆  朱纯  朱焯炜  徐瑞煜
作者单位:1. 江南大学理学院,江苏 无锡 214122
2. 江苏省轻工光电工程技术研究中心,江苏 无锡 214000
基金项目:国家自然科学基金项目(61178032;61378037),中央高校基本科研业务费专项资金项目(JUSRP51517)
摘    要:采用一种由原点矩法改造所得的特征压缩算法对荧光光谱数据进行预处理,将处理后的数据与加权最小二乘支持向量机(WLS-SVM)算法结合,建立鲁棒回归模型,用以预测实际食用色素粉末的纯度。以亮蓝和胭脂红这2种色素为例论述该方法对实际食用色素粉末纯度的预测效果。首先,利用FLS920荧光光谱仪测量获得两种色素的标准样本和实际样本在最佳激发波长下的荧光发射光谱数据,利用由原点矩法改造所得的特征压缩算法对获取的荧光光谱数据进行压缩和变换,一方面缩短了算法的运算时间,另一方面也提高了模型的预测精度。将预处理后的荧光光谱数据输入加权最小二乘支持向量机中建立浓度预测模型,该模型对亮蓝、胭脂红实际样本溶液给出的预测光谱与它们的实测光谱吻合程度好,半高峰宽区间内的平均决定系数分别为0.662和0.931。所有亮蓝、胭脂红溶液的预测浓度和标称浓度之间具有良好的线性关系,相关系数分别为0.997和0.992。由此通过多项式拟合得到的亮蓝、胭脂红粉末的预测纯度分别为61.0%和72.3%。

关 键 词:光谱学  合成食用色素  纯度软测量  加权最小二乘支持向量机    
收稿时间:2015-09-03

Soft Measurement of the Purity of the Synthetic Edible Pigment Powder Using Fluorescence Spectroscopy Combined with SVM
ZHANG Yi,CHEN Guo-qing,ZHU Chun,ZHU Zhuo-wei,XU Rui-yu.Soft Measurement of the Purity of the Synthetic Edible Pigment Powder Using Fluorescence Spectroscopy Combined with SVM[J].Spectroscopy and Spectral Analysis,2016,36(12):3978-3985.
Authors:ZHANG Yi  CHEN Guo-qing  ZHU Chun  ZHU Zhuo-wei  XU Rui-yu
Institution:1. School of Science,Jiangnan University,Wuxi 214122,China2. Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology,Wuxi 214000,China
Abstract:The feature compression algorithm which was reformed from the original Moment method was used for the pre-processing of the fluorescence spectral data,then combined the data and the Weighted Least Squares Support Vector Machine (WLS-SVM)algorithm to establish a robust regression model,which is used for forecasting the purity of edible pigment pow-der.In this paper,brilliant blue and ponceau 4R served as an example to discuss the method of forecasting effect of edible pig-ment powder purity.The emission fluorescence spectra of two edible pigment at the optimal excitation wavelength were measured by FLS920 fluorescence spectrometer.The compression and transformation of the fluorescence spectral data was acquired by the feature compression algorithm reformed from the Original Moment method.On the one hand the feature compression algorithm shortened the operation time,on the other hand it improved the prediction accuracy of the model.Then,the concentration pre-diction model was established after inputting the fluorescence spectral data pre-processed into the Weighted Least Squares Sup-port Vector Machine.The model gave anastomotic predicted spectral data with the actual experiments of the brilliant blue and ponceau 4R,and the average coefficient of determination in the half peak width was 0.700 and 0.930 respectively.There was a good linear relationship between the predicted and the nominal concentration of the brilliant blue and ponceau 4R,and the corre-lation coefficients were 0.997 and 0.992 respectively.It can be concluded that,the predicted concentration of the brilliant blue and ponceau 4R powder were got the results of 61.0% and 72.3% respectively.
Keywords:Spectroscopy  Synthetic edible pigment  Soft measurement of purity  Weighted least squares support vector machine
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