| Abstract: | Tegillarca granosa (T. granosa) is susceptible to heavy metals, which may pose a threat to
consumer health. Thus, healthy and polluted T. granosa should be distinguished quickly. This
study aimed to rapidly identify heavy metal pollution by using laser-induced breakdown
spectroscopy (LIBS) coupled with linear regression classification (LRC). Five types of T.
granosa were studied, namely, Cd-, Zn-, Pb-contaminated, mixed contaminated, and control
samples. Threshold method was applied to extract the significant variables from LIBS spectra.
Then, LRC was used to classify the different types of T. granosa. Other classification models
and feature selection methods were used for comparison. LRC was the best model, achieving an
accuracy of 90.67%. Results indicated that LIBS combined with LRC is effective and feasible
for T. granosa heavy metal detection. |
