Elemental analysis of rice using laser-ablation sampling: Determination of rice-polishing degree
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In this study, laser-induced breakdown spectroscopy (LIBS) was used to estimate the degree of ricepolishing. As-threshed rice seeds were dehusked and polished for different times, and the resulting grains wereanalyzed using LIBS. Various atomic, ionic, and molecular emissions were identified in the LIBS spectra. Theircorrelation with the amount of polished-off matter was investigated. Na I and Rb I emission line intensitiesshowed linear sensitivity in the widest range of polished-off-matter amount. Thus, univariate models based onthose lines were developed to predict the weight percent of polished-off matter and showed 3-5 % accuracyperformances. Partial least squares-regression (PLS-R) was also applied to develop a multivariate model usingSi I, Mg I, Ca I, Na I, K I, and Rb I emission lines. It outperformed the univariate models in prediction accuracy(2 %). Our results suggest that LIBS can be a reliable tool for authenticating the degree of rice polishing, whichis closed related to nutrition, shelf life, appearance, and commercial value of rice products.
Abstract
In this study, laser-induced breakdown spectroscopy (LIBS) was used to estimate the degree of ricepolishing. As-threshed rice seeds were dehusked and polished for different times, and the resulting grains wereanalyzed using LIBS. Various atomic, ionic, and molecular emissions were identified in the LIBS spectra. Theircorrelation with the amount of polished-off matter was investigated. Na I and Rb I emission line intensitiesshowed linear sensitivity in the widest range of polished-off-matter amount. Thus, univariate models based onthose lines were developed to predict the weight percent of polished-off matter and showed 3-5 % accuracyperformances. Partial least squares-regression (PLS-R) was also applied to develop a multivariate model usingSi I, Mg I, Ca I, Na I, K I, and Rb I emission lines. It outperformed the univariate models in prediction accuracy(2 %). Our results suggest that LIBS can be a reliable tool for authenticating the degree of rice polishing, whichis closed related to nutrition, shelf life, appearance, and commercial value of rice products.
참고문헌 (36)
- R. E. Russo, , Anal. Chem, 85(13), pp.6162~6177 (2013)[DOI]
- J. Merten, , Spectrochim. Acta Part B : Atom. Spectrosc, 189, pp.106358~ (2022)[DOI]
- M. Miyabe, , J. Anal. At. Spectrom, 38(2), pp.347~358 (2023)[DOI]
- F. J. Fortes, , Anal. Chem, 85(2), pp.640~669 (2013)[DOI]
- D. W. Hahn, , Appl. Spectrosc, 66(4), pp.347~419 (2012)[DOI]
- J. P. Singh, "Laser-Induced Breakdown Spectroscopy" (2007), Elsevier Science B. V
- T. A. Alrebdi, , Molecules, 27(15), pp.5048~ (2022)[DOI]
- V. F. Lebedev, , J. Anal. At. Spectrom, 33(2), pp.240~250 (2018)[DOI]
- A. K. Rai, , Molecules, 25(4), pp.984~ (2020)[DOI]
- E. J. Kautz, , J. Anal. At. Spectrom, 34(11), pp.2236~2243 (2019)[DOI]
- M. Martinez, , J. Anal. At. Spectrom, 38(2), pp.303~314 (2022)[DOI]
- L. J. Fernández-Menéndez, , Spectrochim. Acta Part B : Atom. Spectrosc, 190, pp.106390~ (2022)[DOI]
- A. W. Miziolek, "Laser-Induced Breakdown Spectroscopy (LIBS) Fundamentals and Applications" (2006), Cambridge University Press
- H. Kim, , Spectrochim. Acta Part B : Atom. Spectrosc, 184, pp.106282~ (2021)[DOI]
- L. Brunnbauer, , TrAC Trends Anal. Chem, 159, pp.116859~ (2023)[DOI]
- S. Jung, , Mater. Today Commun, 33, pp.104867~ (2022)[DOI]
- G. S. Khush, , Plant Mol. Biol, 35, pp.25~34 (1997)[DOI]
- "Corporate Statistical Database (FAOSTAT)"[URL]
- G. Kim, , J. Agric. Food Chem, 60(3), pp.718~724 (2012)[DOI]
- F. Liu, , Sensors, 18(3), pp.705~ (2018)[DOI]
- P. Yang, , Food Chem, 272, pp.323~328 (2019)[DOI]
- T. -J. Jiang, , Electrochim. Acta, 216, pp.188~195 (2016)[DOI]
- N. Sharma, , Appl. Phys. B : Lasers Opt, 126, pp.122~ (2020)[DOI]
- P. Yang, , J. Cereal Sci, 80, pp.111~118 (2018)[DOI]
- M. Pérez-Rodríguez, , Food Chem, 297, pp.124960~ (2019)[DOI]
- M. Pérez-Rodríguez, , Food Chem, 331, pp.127051~ (2020)[DOI]
- P. Yang, , Appl. Opt, 57(28), pp.8297~8302 (2018)[DOI]
- Z. Luo, , Appl. Opt, 58(7), pp.1631~1638 (2019)[DOI]
- S. H. Choi, , J. Anal. At. Spectrom, 29(7), pp.1233~1237 (2014)[DOI]
- B. -M. Yao, , Food Sci. Nutr, 8(2), pp.982~992 (2020)[DOI]
- G. Jo, , Food Chem, 289, pp.299~307 (2019)[DOI]
- "NIST Atomic Spectra Database"[URL]
- "Agricultural Research Service"[URL]
- S. Abdelli-Messaci, , Spectrochim. Acta Part B: Atom. Spectrosc, 60(7-8), pp.955~959 (2005)[DOI]
- D. Choi, , Appl. Spectrosc, 68(2), pp.198~212 (2014)[DOI]
- S. Wold, , Chemom. Intell. Lab. Syst, 58(2), pp.109~130 (2001)[DOI]