ArticleInternational journal of environmental research and public health2018
Rapid Screening of Cadmium in Rice and Identification of Geographical Origins by Spectral Method.
Article in International journal of environmental research and public health, 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.
- Eco-friendly colorimetric detection of lead and mercury using l-cysteine-functionalized gold nanoparticles: a step towards greening the exposome.RSC advances · 2025Article
- Constructing an origin discrimination model of japonica rice in Heilongjiang Province based on confocal microscopy Raman spectroscopy technology.Scientific reports · 2025Article
- Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches.Foods (Basel, Switzerland) · 2023Review
- Evaluation of quantitative synchrotron radiation micro-X-ray fluorescence in rice grain.Journal of synchrotron radiation · 2023Article
- Raman Molecular Fingerprints of Rice Nutritional Quality and the Concept of Raman Barcode.Frontiers in nutrition · 2021Article
- Identification of Promising Genotypes Through Systematic Evaluation for Arsenic Tolerance and Exclusion in Rice (Frontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accuracy, repeatability and detection limits of the energy-dispersive X-ray fluorescence (XRF) spectrometer used in this study were tested to verify its suitability for rapid screening of cadmium in samples. Concentrations of cadmium in rice grain samples were tested by the XRF spectrometer. The results showed that the apparatus had good precision around the national limit value (0.2 mg/kg). Raman spectroscopy has been analyzed in the discrimination of rice grain samples from different geographical origins within China. Scanning time has been discussed in order to obtain better Raman features of rice samples. A total of 31 rice samples were analyzed. After spectral data pre-treatment, principal component analysis (PCA), K-means clustering (KMC), hierarchical clustering (HC) and support vector machine (SVM) were performed to discriminate origins of rice samples. The results showed that the geographical origins of rice could be classified using Raman spectroscopy combined with multivariate analysis.
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