研究目的
To rapidly identify heavy metal pollution in Tegillarca granosa using laser-induced breakdown spectrometry (LIBS) coupled with linear regression classification (LRC).
研究成果
LIBS combined with LRC is effective and feasible for detecting heavy metal contamination in T. granosa, achieving an accuracy of 90.67%. The method provides a rapid and reliable approach for food safety monitoring.
研究不足
The study did not explore the impact of varying laser parameters on the classification accuracy. The method's performance under different environmental conditions was not tested.
1：Experimental Design and Method Selection:
The study used LIBS coupled with LRC to classify five types of T. granosa samples.
2：Sample Selection and Data Sources:
Five groups of T. granosa samples were prepared, including Cd-, Zn-, Pb-contaminated, mixed contaminated, and control samples.
3：List of Experimental Equipment and Materials:
LIBS system with Nd:YAG laser, spectrometer, and CCD.
4：Experimental Procedures and Operational Workflow:
Samples were measured five times at different spots, and each spectrum was an average of these measurements.
5：Data Analysis Methods:
Threshold method was used for feature selection, and LRC was used for classification.
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