ArticleFrontiers in plant science2020
Application of Laser-Induced Breakdown Spectroscopy in Detection of Cadmium Content in Rice Stems.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 35 citations in OpenAlex.
- Application of Portable Near-Infrared Spectroscopy for Quantitative Prediction of Protein Content inFoods (Basel, Switzerland) · 2025Article
- Assessing the Sensitivity and Efficiency of Laser-Induced Breakdown Spectroscopy (LIBS) for High-Concentration Cadmium Detection in Cocoa Powder.Sensors (Basel, Switzerland) · 2025Article
- Exploring the potential of microscopic hyperspectral, Raman, and LIBS for nondestructive quality assessment of diverse rice samples.Plant methods · 2025Article
- Quantitative Prediction of Acid Value of Camellia Seed Oil Based on Hyperspectral Imaging Technology Fusing Spectral and Image Features.Foods (Basel, Switzerland) · 2024Article
- Fusion of Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy for Mineral Identification Based on Machine Learning.Molecules (Basel, Switzerland) · 2024Article
- Application and Research Progress of Laser-Induced Breakdown Spectroscopy in Agricultural Product Inspection.ACS omega · 2024Review
- Heavy Metal Detection inFoods (Basel, Switzerland) · 2023Article
- DNA-Based Tools to Certify Authenticity of Rice Varieties-An Overview.Foods (Basel, Switzerland) · 2022Review
- Novel Latex Microsphere Immunochromatographic Assay for Rapid Detection of Cadmium Ion in Asparagus.Foods (Basel, Switzerland) · 2021Article
- Fast Identification of Soybean Seed Varieties Using Laser-Induced Breakdown Spectroscopy Combined With Convolutional Neural Network.Frontiers in plant science · 2021Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The presence of cadmium in rice stems is a limiting factor that restricts its function as biomass. In order to prevent potential risks of heavy metals in rice straws, this study introduced a fast detection method of cadmium in rice stems based on laser induced breakdown spectroscopy (LIBS) and chemometrics. The wavelet transform (WT), area normalization and median absolute deviation (MAD) were used to preprocess raw spectra to improve spectral stability. Principal component analysis (PCA) was used for cluster analysis. The classification models were established to distinguish cadmium stress degree of stems, of which extreme learning machine (ELM) had the best effect, with 91.11% of calibration accuracy and 93.33% of prediction accuracy. In addition, multivariate models were established for quantitative detection of cadmium. It can be found that ELM model had the best prediction effects with prediction correlation coefficient of 0.995. The results show that LIBS provides an effective method for detection of cadmium in rice stems. The combination of LIBS technology and chemometrics can quickly detect the presence of cadmium in rice stems, and accurately realize qualitative and quantitative analysis of cadmium, which could be of great significance to promote the development of new energy industry.
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