A robust method to improve the regression accuracy of LIBS data: determination of heavy metal Cu in Tegillarca granosa

• Published in: Analytical methods Vol. 15; no. 46; pp. 6460 - 6467
• Main Authors: Huang, Jie, Chen, Xiaojing, Xie, Zhonghao, Ali, Shujat, Chen, Xi, Yuan, Leiming, Jiang, Chengxi, Huang, Guangzao, Shi, Wen
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 30.11.2023
• Subjects: Accuracy; Algorithms; Calibration; Contamination; Copper; Data analysis; Health risks; Heavy metals; Laser induced breakdown spectroscopy; Least squares; Metal concentrations; Outliers (statistics); Predictions; Robustness (mathematics); Spectroscopy; Spectrum analysis; Tegillarca granosa
• ISSN: 1759-9660; 1759-9679; 1759-9679
• DOI: 10.1039/D3AY01411H

Tegillarca granosa ( T. granosa ) is susceptible to contamination by heavy metals, which poses potential health risks for consumers. Laser-induced breakdown spectroscopy (LIBS) combined with the classical partial least squares (PLS) model has shown promise in determining heavy metal concentrations in T. granosa . However, the presence of outliers during calibration can compromise the model's integrity and diminish its predictive capabilities. To address this issue, we propose using a robust method for partial least squares, RSIMPLS, to improve the accuracy of Cu prediction in T. granosa . The RSIMPLS algorithm was employed to analyze and process the high-dimensional LIBS data and utilized diagnostic plots to identify various types of outliers. By selectively eliminating certain outliers, a robust calibration method was achieved. The results showed that LIBS spectroscopy has the potential to predict Cu in T. granosa , with a coefficient of determination ( R p 2 ) of 0.79 and a root mean square error of prediction (RMSEP) of 11.28. RSIMPLS significantly improved the prediction accuracy of Cu concentrations with a 43% decrease in RMSEP compared to the PLS. These findings validated the effectiveness of combining LIBS data with the RSIMPLS algorithm for the prediction of Cu concentrations in T. granosa .