A robust method to improve the regression accuracy of LIBS data: determination of heavy metal Cu in Tegillarca granosa .
Jie HuangXiaojing ChenZhonghao XieShujat AliXiaojing ChenLeiming YuanChengxi JiangGuangzao HuangWen ShiPublished in: Analytical methods : advancing methods and applications (2023)
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 .
Keyphrases
- heavy metals
- risk assessment
- health risk
- big data
- health risk assessment
- electronic health record
- aqueous solution
- machine learning
- human health
- randomized controlled trial
- deep learning
- single molecule
- systematic review
- solid phase extraction
- drinking water
- magnetic resonance imaging
- neural network
- computed tomography
- mass spectrometry