摘要
提出了一种运用量子粒子群(quantum-behaved particle swarm optimization,QPSO)算法优化多输出最小二乘支持向量机(multi-output least squares support vector machine,MLSSVM)的新混合优化算法。该算法结合激光拉曼光谱技术可实现对四组分食用调和油中花生油、芝麻油、葵花油和大豆油的快速定量鉴别。采用基线校正去除背景荧光,结合Savitzky-Golay Filters光谱平滑法对原始拉曼光谱进行预处理。构建基于QPSO-MLSSVM混合优化算法的定量分析模型,并采用20个组分组成的预测集对其进行模型校验。实验结果表明,基于QPSO-MLSSVM混合优化算法的定量分析模型对于四组分调和油的预测效果良好,均方差(mean square error,MSE)为0.0241,低于0.05,各油分预测相关系数均高于98%。研究结果充分表明,应用激光拉曼光谱技术结合QPSO-MLSSVM算法,对四组分调和油中各油分进行快速定量检测可行,具备较强的自适应能力和良好的预测精度,可以满足多组分调和油的成分鉴别。
This paper presents a new hybrid optimization algorithm based on the multi-output least squares support vector machine(MLSSVM)which is optimized by quantum-behaved particle swarm optimization(QPSO).The rapid quantitative identification for the peanut oil,sesame oil,sunflower oil and soybean oil in the four-component edible blending oil can be realized with the algorithm combined with laser Raman spectroscopy.The background fluorescence was removed by baseline correction,and Savitzky-Golay filters spectral smoothing method is used for the pretreation of original Raman spectra.The quantitative analysis model based on QPSO-MLSSVM hybrid optimization algorithm is established,and the prediction set composed of 20 components is used to verify the model.The experimentalresult shows that it is effective for the prediction of four-component blendingoil with the quantitative analysis model based on QPSO-MLSSVM hybrid optimization algorithm,and the Mean Square Error(MSE)is 0.024 1,which is less than0.05,the correlation coefficients of each component were above 98%.The results show that it is feasible to detect the content of each oil of four-component blending oil by laser Raman spectroscopy combined with QPSO-MLSSVM algorithm,it has strong adaptive ability and good prediction accuracy that can satisfythe multi-component mixed oil component identification.
作者
张燕君
张芳草
付兴虎
徐金睿
ZHANG Yan-jun;ZHANG Fang-cao;FU Xing-hu;XU Jin-rui(School of Information Science and Engineering,The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province,Yanshan University,Qinhuangdao066004,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2018年第5期1437-1443,共7页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金(11673040,61675176),河北省自然科学基金(F2014203125),燕山大学“新锐工程”人才支持计划项目资助
关键词
拉曼光谱
食用调和油
量子粒子群算法
最小二乘支持向量机
定量检测模型
Raman spectroscopy
Edible blend oil
Quantum particle swarm optimization(QPSO)
Least squares support vector machine(SVM)
Quantitative detection model
