采用激光诱导击穿光谱(LIBS)技术对大豆油中的铁(Fe)含量进行定量检测。 实验中用一系列不同Fe浓度的大豆油样品, 采用二通道高精度光谱仪采集其LIBS光谱信号。 根据样品LIBS谱线图, 确定了Fe的两个特征谱线404.58和406.36 nm, 并应用不同的一元回归方法对两个特征谱线分别建立一元指数回归定量分析模型、 一元线性回归定量分析模型和一元二次回归定量分析模型。 研究结果表明, Fe Ⅰ 404.58及Fe Ⅰ 406.36的一元指数、 一元线性及一元二次回归模型的预测平均相对误差分别为29.49%, 8.93%, 8.70%和28.95%, 8.63%, 8.44%。 Fe Ⅰ 406.36建立的回归模型预测结果优于Fe Ⅰ 404.58, 三个回归模型中一元二次回归模型性能最优。 由此可见, LIBS技术检测大豆油中的Fe元素具有一定的可行性, 一元二次回归定量分析模型可以有效提高Fe元素预测浓度的精度。

LIBS (laser-induced breakdown spectroscopy) was used to detect Fe element content in soybean oil quantitatively. In this experiment, a series of soybean oil samples with different concentrations of Fe were used; LIBS spectra were collected with a two-channel high precision spectrometer. According to the LIBS spectrum of samples, two characteristic wavelength of Fe (404.58 and 406.36 nm) were determined, and different simple regression methods (exponential regression, linear regression and quadratic regression) were used to establish the quantitative analysis models of Fe content using each characteristic spectral line. The results indicate that the average relative error of Fe Ⅰ 404.58 and Fe Ⅰ 406.36 in simple exponential regression, linear regression and quadratic regression models were 29.49%, 8.93%, 8.70% and 28.95%, 8.63%, 8.44%, respectively. The results of Fe Ⅰ 406.36 regression models is better than that of Fe Ⅰ 404.58, and the quadratic regression model is optimal among the three regression models. According to these results, LIBS technology has certain feasibility for detecting Fe in soybean oil; the quadratic linear regression model can improve the prediction accuracy of Fe element effectively.