Publication: Elemental imaging and classifying rice grains by using laser ablation inductively coupled plasma mass spectrometry and linear discriminant analysis
Issued Date
2016-09-01
Resource Type
ISSN
10959963
07335210
07335210
Other identifier(s)
2-s2.0-84989892641
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Cereal Science. Vol.71, (2016), 198-203
Suggested Citation
J. Promchan, D. Günther, A. Siripinyanond, J. Shiowatana Elemental imaging and classifying rice grains by using laser ablation inductively coupled plasma mass spectrometry and linear discriminant analysis. Journal of Cereal Science. Vol.71, (2016), 198-203. doi:10.1016/j.jcs.2016.08.017 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/43487
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Elemental imaging and classifying rice grains by using laser ablation inductively coupled plasma mass spectrometry and linear discriminant analysis
Author(s)
Other Contributor(s)
Abstract
© 2016 Elsevier Ltd This study aims to investigate elemental imaging in a longitudinal section of single rice grain using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and to classify rice according to their origins and types of 16 samples using LA-ICP-MS with linear discriminant analysis (LDA). The distributions of 8 essential elements (Ca, Cu, Fe, K, Mg, Mn, P and Zn) in a single rice grain were visualized as elemental images. Investigation of the elemental imaging of rice grain showed that essential elements were presented in large amounts in embryo and elevated level in endosperm. The elemental distributions of rice grain were not uniform. In addition, the concentration of 20 elements distributed in core endosperm was evaluated and used as chemical indicator to discriminate the origin and type of rice samples. The LDA can successfully differentiate rice samples according to their regions of origin (Northeast or South regions of Thailand) and types. Satisfied classifications are obtained with overall correct classification and cross-validation of 93.8% and 91.1% for origin classification and 100% and 97.9% for type classification.