Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion

• Published in: Food chemistry Vol. 339; p. 128125
• Main Authors: Pérez-Rodríguez, Michael, Dirchwolf, Pamela Maia, Rodríguez-Negrín, Zenaida, Pellerano, Roberto Gerardo
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-03-2021
• Subjects: Adulteration; Flour - analysis; Food Analysis - methods; Food Analysis - statistics & numerical data; Food Contamination - analysis; Food Contamination - statistics & numerical data; LDA; Mineral profiles; Minerals - analysis; Oryza - chemistry; PCA based data fusion; Principal Component Analysis; Rice flour; LDA; PCA based data fusion; Rice flour; Mineral profiles; Adulteration
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2020.128125

•Mineral profiles measured by ICP OES were used to identify fraud in rice flour.•LDA models were fitted to assess the authenticity of the different flour types analyzed.•Models based on elemental features achieved correct predictions ranging from 72 to 88%.•PCA based data fusion approach allowed to detect rice flour adulteration with 100% success rate.•The proposed method is reliable to distinguish among adulterated and unadulterated flour samples.
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.