拉曼光谱结合PSO-LSSVM算法检测三组分食用调和油含量

提出了一种将拉曼光谱和基于粒子群的最小二乘支持向量机(PSO-LSSVM)算法相结合快速定量检测三组分食用调和油含量的方法。以三组分的食用调和油为研究对象，对拉曼光谱分四步进行了预处理，进而准确提取拉曼光谱的特征峰强度。以训练集样本的特征峰强度和调和油样品的百分比含量作为回归预测模型的输入值和输出值，建立LSSVM和PSO-LSSVM数学模型，通过测试集样本的相关系数和均方误差对模型的预测能力进行分析。非线性建模的最小二乘支持向量机(LSSVM)算法的核函数参数σ和正则化参数γ对模型的学习和泛化能力影响很大，导致模型的预测精度和泛化能力过度依赖于参数--在优化步长过小时耗时较长，过大时又无法得到全局最优值。提出的PSO-LSSVM算法，利用粒子群全局优化能力和收敛速度快的特点对LSSVM的模型参数σ和γ进行优化，从而克服LSSVM算法中耗时与盲目性的问题。分析结果表明，PSO-LSSVM算法对三组分食用调和油中大豆油、花生油和葵花仁油定量预测模型的测试集相关系数分别为0.967 7，0.997 2，0.995 3；均方误差分别为0.054 9，0.009 2，0.047 1。与LSSVM算法相比，PSO-LSSVM模型的预测精度更高。因此，该方法可以快速、准确地检测三组分食用调和油的含量。

Raman Spectra Combined with PSO-LSSVM Algorithm for Detecting the Components in Ternary Blended Edible Oil

The paper presents a method which combines the Raman spectrum and the least square support vector machine(LSSVM)based on particle swarm optimization(PSO)to detect the content of three components of edible blend oil rapidly and quantitatively.In this paper,three components of edible oil were investigated.The characteristic peak intensity of Raman spectra was extracted by four pretreatments of the spectra.Then in the training samples,the characteristic peak intensity and the percentage of mixed oil samples were used as the input values and the output values of the regression analysis model.The mathematical models of LSSVM and PSO-LSSVM were established after different pretreatments.The predictive ability of the model was analyzed by the correlation coefficient and mean square error in the test samples.The traditional LSSVM algorithm for nonlinear modeling has many issues,such as its kernel parameter σ and the regularization parameter γ have great influences on the learning model and generalization ability.The fitting precision and generalization ability of the model are dependent on its related parameters,and the time consuming is too long while the optimal step size is too little;however,the global optimal values are hardly to get while the optimization step size is large.Yet,the PSO-LSSVM algorithm has the PSO algorithm advantages of fast convergence and global search capability,which can overcome the problems of time consuming and blindness in LSSVM algorithm.So the kernel parameters σ and γ of LSSVM algorithm are optimized by Global optimization ability and fast convergence characteristics of PSO algorithm.In the quantitative analysis of the three components of edible blend oil,the validation set correlation coefficients of the model for soybean oil,peanut oil and sunflower kernel oil were 0.967 7,0.997 2 and 0.995 3,respectively;in addition,the mean square errors were 0.054 9,0.009 2 and 0.047 1,respectively.Compared with the LSSVM algorithm,the prediction accuracy of PSO-LSSVM model is higher and the convergence rate is faster which has been verified by the experiments.Thus,the method can detect the content of the three components of edible oil accurately.