Laser induced breakdown spectroscopy (LIBS) is an emerging tool with rapid, nondestructive, green characteristics in qualitative or quantitative analyses of composition in materials. But LIBS has its shortcomings in detect limit and sensitivity. In this work, heavy metal Cu in Gannan Navel Orange, which is one of famous fruits from Jiangxi of China, was analyzed. In view of LIBS's limit, it is difficult to determinate heavy metals in natural fruits. In this work, nine orange samples were pretreated in 50-500 µg/mL Cu solution, respectively. Another one orange sample was chosen as a control group without any pollution treatment. Previous researchers observed that the content of heavy metals is much higher in peel than in pulp. So, the content in pulp can be reflected by detecting peel. The real concentrations of Cu in peels were acquired by atomic absorption spectrophotometer (AAS). A calibration model of Cu I 324.7 and Cu I 327.4 was constructed between LIBS intensity and AAS concentration by six samples. The correlation coefficient of the two models is also 0.95. All of the samples were used to verify the accuracy of the model. The results show that the relative error (RE) between predicted and real concentration is less than 6.5%, and Cu I 324.7 line has smaller RE than Cu I 327.4. The analysis demonstrated that different characteristic lines decided different accuracy. The results prove the feasibility of detecting heavy metals in fruits by LIBS. But the results are limited in treated samples. The next work will focus on direct analysis of heavy metals in natural fruits without any pretreatment. This work is helpful to explore the distribution of heavy metals between pulp and peel.
Laser-induced breakdown spectroscopy (LIBS) coupled with the linear multivariate regression method was utilized to analyze chromium (Cr) quantitatively in potatoes. The plasma was generated using a Nd:YAG laser, and the spectra were acquired by an Andor spectrometer integrated with an ICCD detector. The models between intensity of LIBS characteristic line(s) and concentration of Cr were constructed to predict quantitatively the content of target. The unary, binary, ternary, and quaternary variables were chosen for verifying the accuracy of linear regression calibration curves. The intensity of characteristic lines Cr (CrI: 425.43, 427.48, 428.97 nm) and Ca (CaI: 422.67, 428.30, 430.25, 430.77, 431.86 nm) were used as input data for the multivariate calculations. According to the results of linear regression, the model of quaternary linear regression was established better in comparing with the other three models. A good agreement was observed between the actual content provided by atomic absorption spectrometry and the predicted value obtained by the quaternary linear regression model. And the relative error was below 5.5% for validation samples S1 and S2. The result showed that the multivariate approach can obtain better predicted accuracy than the univariate ones. The result also suggested that the LIBS technique coupled with the linear multivariate calibration method could be a great tool to predict heavy metals in farm products in a rapid manner even though samples have similar elemental compositions.
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