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Accurate Identification and Quantification of Chinese Yam Powder Adulteration Using Laser-Induced Breakdown Spectroscopy.
Zhao, Zhifang; Wang, Qianqian; Xu, Xiangjun; Chen, Feng; Teng, Geer; Wei, Kai; Chen, Guoyan; Cai, Yu; Guo, Lianbo.
Afiliación
  • Zhao Z; School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
  • Wang Q; Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China.
  • Xu X; School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
  • Chen F; Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China.
  • Teng G; Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314033, China.
  • Wei K; School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
  • Chen G; Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China.
  • Cai Y; Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314033, China.
  • Guo L; Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Foods ; 11(9)2022 Apr 22.
Article en En | MEDLINE | ID: mdl-35563939
ABSTRACT
As a popular food, Chinese yam (CY) powder is widely used for healthy and commercial purposes. Detecting adulteration of CY powder has become essential. In this work, chemometric methods combined with laser-induced breakdown spectroscopy (LIBS) were developed for identification and quantification of CY powder adulteration. Pure powders (CY, rhizome of winged yam (RY) and cassava (CS)) and adulterated powders (CY adulterated with CS) were pressed into pellets to obtain LIBS spectra for identification and quantification experiments, respectively. After variable number optimization by principal component analysis and random forest (RF), the best model random forest-support vector machine (RF-SVM) decreased 48.57% of the input variables and improved the accuracy to 100% in identification. Following the better feature extraction method RF, the Gaussian process regression (GPR) method performed the best in the prediction of the adulteration rate, with a correlation coefficient of prediction (Rp2) of 0.9570 and a root-mean-square error of prediction (RMSEP) of 7.6243%. Besides, the variable importance of metal elements analyzed by RF revealed that Na and K were significant due to the high metabolic activity and maximum metal content of CY powder, respectively. These results demonstrated that chemometric methods combined with LIBS can identify and quantify CY powder adulteration accurately.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Foods Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Foods Año: 2022 Tipo del documento: Article País de afiliación: China