摘要
采用激光诱导击穿光谱（LIBS）技术对大豆油中的铬（Cr）含量进行检测研究。以一系列Cr含量不同的大豆油为样本,采用Ava Spec双通道高精度光谱仪在206.28～481.77 nm波段范围内采集LIBS光谱。根据样本的LIBS谱线图,确定Cr元素的主要特征谱线,并对Cr元素主要特征谱线应用线性回归或最小二乘支持向量机（LS-SVM）方法建立其单变量、二变量及多变量校正模型。利用建立的校正模型对样本Cr含量进行预测。研究结果表明,二变量及多变量校正模型的性能优于单变量校正模型,LS-SVM建立的多变量校正模型性能最优。对于单变量及二变量校正模型,预测样本的平均相对误差（RE）分别为14.16%和11.58%;而对于线性回归及LS-SVM建立的多变量校正模型,预测样本的平均RE分别为10.95%和4.97%。由此可见,LIBS技术检测大豆油中的重金属Cr含量具有一定的可行性,LS-SVM方法可以有效提高校正模型的预测精度。
Laser-induced breakdown spectroscopy（LIBS） is used to detect chromium content in soybean oil. A series of soybean oil samples with different chromium concentrations are used, and an Ava Spec two-channel spectrometer is used to acquire spectra of samples in the wavelength range of 206.28～481.77 nm. According to the LIBS spectra,several primary characteristic spectral lines of the Cr element are confirmed, then linear regression or least squares support vector machine（LS-SVM） method is used to develop univariate, bivariate and multivariate calibration models.Cr content of the samples is predicted by these calibration models. The results indicate that the performance of bivariate and multivariate calibration models is superior to that of the univariate calibration model, and the performance of the multivariate calibration model developed by LS-SVM is the best. The average relative error（RE）of sample prediction results in univariate and bivariate calibration models is 14.16% and 11.58%, respectively. The average RE of sample prediction in multivariate calibration models developed by linear regression and LS-SVM is10.95% and 4.97%, respectively. According to these results, the LIBS technique has some feasibility to detect Cr content in soybean oil, and the LS-SVM method can improve the prediction accuracy of calibration models effectively.
出处
《激光与光电子学进展》
CSCD
北大核心
2016年第4期227-233,共7页
Laser & Optoelectronics Progress
基金
国家自然科学基金青年基金(31401278)
江西省自然科学基金(20132BAB214010)
关键词
光谱学
激光诱导击穿光谱
大豆油
铬含量
最小二乘支持向量机
spectroscopy
laser-induced breakdown spectroscopy
soybean oil
chromium content
least squares support vector machine
