ArticleSensors (Basel, Switzerland)2018
Comparative Study of the Detection of Chromium Content in Rice Leaves by 532 nm and 1064 nm Laser-Induced Breakdown Spectroscopy.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Optical imaging spectroscopy coupled with machine learning for detecting heavy metal of plants: A review.Frontiers in plant science · 2022Review
- Nondestructive detection of apple crispness via optical fiber spectroscopy based on effective wavelengths.Food science & nutrition · 2019Article
- High-Sensitivity Determination of Nutrient Elements inMolecules (Basel, Switzerland) · 2019Article
- Rapid Identification of Kudzu Powder of Different Origins Using Laser-Induced Breakdown Spectroscopy.Sensors (Basel, Switzerland) · 2019Article
- Rapid Determination of Cadmium Contamination in Lettuce Using Laser-Induced Breakdown Spectroscopy.Molecules (Basel, Switzerland) · 2018Article
- Quantitative Determination of Cd in Soil Using Laser-Induced Breakdown Spectroscopy in Air and Ar Conditions.Molecules (Basel, Switzerland) · 2018Article
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Authors and funding
8 authors.
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Abstract
Fast detection of toxic metals in crops is important for monitoring pollution and ensuring food safety. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the chromium content in rice leaves. We investigated the influence of laser wavelength (532 nm and 1064 nm excitation), along with the variations of delay time, pulse energy, and lens-to-sample distance (LTSD), on the signal (sensitivity and stability) and plasma features (temperature and electron density). With the optimized experimental parameters, univariate analysis was used for quantifying the chromium content, and several preprocessing methods (including background normalization, area normalization, multiplicative scatter correction (MSC) transformation and standardized normal variate (SNV) transformation were used to further improve the analytical performance. The results indicated that 532 nm excitation showed better sensitivity than 1064 nm excitation, with a detection limit around two times lower. However, the prediction accuracy for both excitation wavelengths was similar. The best result, with a correlation coefficient of 0.9849, root-mean-square error of 3.89 mg/kg and detection limit of 2.72 mg/kg, was obtained using the SNV transformed signal (Cr I 425.43 nm) induced by 532 nm excitation. The results indicate the inspiring capability of LIBS for toxic metals detection in plant materials.
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