文献类型： 外文期刊

作者： Li Wen-bing ¹ ; Yao Lin-tao ² ; Liu Mu-hua ¹ ; Huang Lin ¹ ; Yao Ming-yin ¹ ; Chen Tian-bing ¹ ; He Xiu-wen ¹ ; Yang Ping ¹ ;

作者机构： 1.Jiangxi Agr Univ, Coll Engn, Key Lab Opt Elect Applicat Biomat, Nanchang 330045, Peoples R China

2.Jiangxi Acad Agr Sci, Nanchang 330200, Peoples R China

关键词： Laser induced breakdown spectroscopy;PLS;Data pretreatment;Quantitative model

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2015 年 35 卷 5 期

页码：

收录情况： SCI

摘要： Cu in navel orange was detected rapidly by laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) for quantitative analysis, then the effect on the detection accuracy of the model with different spectral data pretreatment methods was explored. Spectral data for the 52 Gannan navel orange samples were pretreated by different data smoothing, mean centralized and standard normal variable transform. Then 319 similar to 338 nm wavelength section containing characteristic spectral lines of Cu was selected to build PLS models, the main evaluation indexes of models such as regression coefficient (r), root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were compared and analyzed. Three indicators of PLS model after 13 points smoothing and processing of the mean center were found reaching 0.992 8, 3.43 and 3.4 respectively, the average relative error of prediction model is only 5.55%, and in one word, the quality of calibration and prediction of this model are the best results. The results show that selecting the appropriate data pre-processing method, the prediction accuracy of PLS quantitative model of fruits and vegetables detected by LIBS can be improved effectively, providing a new method for fast and accurate detection of fruits and vegetables by LIES.