Determination and quantification of heavy metals in sediments through laser-induced breakdown spectroscopy and partial least squares regression

Sangmi Yoon, Jaeseung Choi, Seung Jae Moon, Jung Hyun Choi

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7 Scopus citations


Conventional analysis techniques and sample preprocessing methods for identifying trace metals in soil and sediment samples are costly and time-consuming. This study investigated the determination and quantification of heavy metals in sediments by using a Laser-Induced Breakdown Spectroscopy (LIBS) system and multivariate chemometric analysis. Principle Component Analysis (PCA) was conducted on the LIBS spectra at the emission lines of 11 selected elements (Al, Ca, Cd, Cr, Fe, K, Mg, Na, Ni, Pb, and Si). The results showed apparent clustering of four types of sediment samples, suggesting the possibility of application of the LIBS technique for distinguishing different types of sediments. Mainly, the Cd, Cr, and Pb concentrations in the sediments were analyzed. A data-smoothing method—namely, the Savitzky–Golay (SG) derivative—was used to enhance the performance of the Partial Least Squares Regression (PLSR) model. The performance of the PLSR model was evaluated in terms of the coefficient of determination (R2), Root Mean Square Error of Calibration (RMSEC), and Root Mean Square Error of Cross Validation (RMSECV). The results obtained using the PLSR with the SG derivative were improved in terms of the R2 and RMSECV, except for Cr. In particular, the results for Cd obtained with the SG derivative showed a decrease of 25% in the RMSECV value. This demonstrated that the PLSR model with the SG derivative is suitable for the quantitative analysis of metal components in sediment samples and can play a significant role in controlling and managing the water quality of rivers.

Original languageEnglish
Article number7154
JournalApplied Sciences (Switzerland)
Issue number15
StatePublished - 1 Aug 2021

Bibliographical note

Funding Information:
Funding: This work was supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea entitled “NRF-2016R1D1A1B04934910”.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Data processing
  • Heavy metal
  • LIBS
  • PLSR
  • Sediment analysis


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