Artículo
Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion
Pérez Rodríguez, Michael
; Dirchwolf, Pamela Maia
; Rodríguez Negrín, Zenaida; Pellerano, Roberto Gerardo
Fecha de publicación:
03/2021
Editorial:
Elsevier
Revista:
Food Chemistry
ISSN:
0308-8146
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
The present work proposes to detect adulteration in rice flour using mineral profiles. Eighty-seven flour samples from two rice kinds (Indica and Japonica) plus thirty adulterated flour samples were analyzed by ICP OES. After obtaining the quantitative elemental fingerprint of the samples, PCA and LDA were applied. Binary and multiclass associations were considered to assess rice flour authenticity through fraud identification. Models based on element predictors showed accuracies ranging from 72 to 88% to distinguish adulterated and unadulterated samples. The fusion of the mineral features with the principal components (PCs) obtained from PCA provided classification rates of 100% in training samples, and 91–100% in test samples. The proposed method proved to be a useful tool for quality control in the rice industry since a perfect success rate was achieved for rice flour fraud detection.
Palabras clave:
ADULTERATION
,
LDA
,
MINERAL PROFILES
,
PCA BASED DATA FUSION
,
RICE FLOUR
Archivos asociados
Licencia
Identificadores
Colecciones
Articulos(IQUIBA-NEA)
Articulos de INSTITUTO DE QUIMICA BASICA Y APLICADA DEL NORDESTE ARGENTINO
Articulos de INSTITUTO DE QUIMICA BASICA Y APLICADA DEL NORDESTE ARGENTINO
Citación
Pérez Rodríguez, Michael; Dirchwolf, Pamela Maia; Rodríguez Negrín, Zenaida; Pellerano, Roberto Gerardo; Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion; Elsevier; Food Chemistry; 339; 3-2021; 1-7
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