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- W3140175787 abstract "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 319~338nm 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 reaching0.992 8,3.43 and 3.4respectively,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 LIBS." @default.
- W3140175787 created "2021-04-13" @default.
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- W3140175787 date "2015-01-01" @default.
- W3140175787 modified "2023-09-26" @default.
- W3140175787 title "Influence of Spectral Pre-Processing on PLS Quantitative Model of Detecting Cu in Navel Orange by LIBS" @default.
- W3140175787 hasPublicationYear "2015" @default.
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