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Vis-NIR hyperspectral imaging coupled with independent component analysis for saffron authentication

Hashemi Nasab, F. S ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1016/j.foodchem.2022.133450
  3. Publisher: Elsevier Ltd , 2022
  4. Abstract:
  5. In the present contribution, visible-near infrared hyperspectral imaging (Vis-NIR-HSI) combined with a novel chemometric approach based on mean-filed independent component analysis (MF-ICA) followed by multivariate classification techniques is proposed for saffron authentication and adulteration detection. First, MF-ICA was used to exploit pure spatial and spectral profiles of the components. Then, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to find patterns of authentic samples based on their distribution maps. Then, detection of five common plant-derived adulterants of saffron including safflower, saffron style, calendula, rubia and turmeric were investigated. For this purpose, partial least squares-discriminant analysis (PLS-DA) for supervised classification to find a boundary between authentic and adulterated saffron samples. Classification accuracies for all models for calibration and prediction sets were 100 %. Finally, a mixed dataset was prepared and analyzed using the proposed strategy which again 100 % of accuracies for calibration and prediction sets were obtained. At the end, data driven soft independent modelling of class analogy (DD-SIMCA) was used to evaluate model for class modeling. Sensitivity was 95% for authentic class and specificities for all adulterants were 100%. © 2022 Elsevier Ltd
  6. Keywords:
  7. Class modelling ; Mean-field independent component analysis ; Partial least squares ; Saffron ; Authentication ; Calibration ; Cluster analysis ; Discriminant analysis ; Hierarchical systems ; Hyperspectral imaging ; Independent component analysis ; Infrared devices ; Spectroscopy ; Chemometric approach ; Class models ; Classification technique ; Independent components analysis ; Mean field independent components analysis ; Multivariate classification ; Near-infrared hyperspectral imaging ; NIR hyperspectral imaging ; Partial least-squares ; Visible near-infrared ; Least squares approximations ; turmeric ; biological product ; Calendula ; Data analysis software ; Hierarchical clustering ; Hyperspectral imaging ; Nonhuman ; Partial least squares regression ; Prediction ; Rubia ; Safflower ; Sensitivity and specificity ; Crocus ; Least square analysis ; Biological products ; Least-Squares analysis ; Principal component analysis
  8. Source: Food Chemistry ; Volume 393 , 2022 ; 03088146 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0308814622014121