1 research outputs found
Moisture Influence Reducing Method for Heavy Metals Detection in Plant Materials Using Laser-Induced Breakdown Spectroscopy: A Case Study for Chromium Content Detection in Rice Leaves
Fast
detection of heavy metals in plant materials is crucial for
environmental remediation and ensuring food safety. However, most
plant materials contain high moisture content, the influence of which
cannot be simply ignored. Hence, we proposed moisture influence reducing
method for fast detection of heavy metals using laser-induced breakdown
spectroscopy (LIBS). First, we investigated the effect of moisture
content on signal intensity, stability, and plasma parameters (temperature
and electron density) and determined the main influential factors
(experimental parameters <i>F</i> and the change of analyte
concentration) on the variations of signal. For chromium content detection,
the rice leaves were performed with a quick drying procedure, and
two strategies were further used to reduce the effect of moisture
content and shot-to-shot fluctuation. An exponential model based on
the intensity of background was used to correct the actual element
concentration in analyte. Also, the ratio of signal-to-background
for univariable calibration and partial least squared regression (PLSR)
for multivariable calibration were used to compensate the prediction
deviations. The PLSR calibration model obtained the best result, with
the correlation coefficient of 0.9669 and root-mean-square error of
4.75 mg/kg in the prediction set. The preliminary results indicated
that the proposed method allowed for the detection of heavy metals
in plant materials using LIBS, and it could be possibly used for element
mapping in future work