Combining multivariate image analysis with high-performance thin-layer chromatography for development of a reliable tool for saffron authentication and adulteration detection

Amirvaresi, A ; Sharif University of Technology | 2020

1064 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.chroma.2020.461461
  3. Publisher: Elsevier B.V , 2020
  4. Abstract:
  5. In this work, high-performance thin-layer chromatography (HPTLC) coupled with multivariate image analysis (MIA) is proposed as a fast and reliable tool for authentication and adulteration detection of Iranian saffron samples based on their HPTLC fingerprints. At first, the secondary metabolites of saffron were extracted using ultrasonic-assisted solvent extraction (UASE) which was optimized using central composite design (CCD). Next, the RGB coordinates of HPTLC images were used for estimation of saffron origin based on principal component analysis (PCA). The PCA scores plot showed that saffron samples were clustered into two clear-cut groups which was 92% matched with the geographical origins of the samples. In the next step, common plant-derived adulterants of saffron including safflower, saffron style, calendula, and rubia were investigated with MIA analysis of HPTLC images using partial least squares-discriminant analysis (PLS-DA) at 5–35% (w/w) levels. The PLS-DA results showed proper classification of saffron and adulterants with sensitivity 99.14%, specificity 96.94%, error rate 1.96% and accuracy 98.04. Also, the effect of HPTLC injection volume on the performance of the proposed strategy was evaluated. The ability of the proposed method was then investigated by analyzing two additional sample sets including mixed samples of four plant-derived adulterants and adulterated commercial samples. Sensitivity and specificity of this model were 100% which confirmed its validity. © 2020 Elsevier B.V
  6. Keywords:
  7. Adulteration ; Chemometrics ; High-performance thin layer chromatography ; Partial least squares-discriminant analysis ; Saffron ; Authentication ; Discriminant analysis ; Food additives ; Least squares approximations ; Metabolites ; Multivariant analysis ; Solvent extraction ; Thin layer chromatography ; Ultrasonic applications ; Additional samples ; Central composite designs ; Geographical origins ; High performance thin layer chromatography ; Multivariate image analysis ; Partial least squares discriminant analyses (PLSDA) ; Secondary metabolites ; Sensitivity and specificity ; Image analysis ; Article ; Calendula ; Case report ; Clinical article ; Diagnostic test accuracy study ; Nonhuman ; Partial least squares regression ; Rubia ; Safflower ; Ultrasound ; Validity ; Chemistry ; Image processing ; Least square analysis ; Procedures ; Chromatography, Thin Layer ; Crocus ; Drug Contamination ; Image Processing, Computer-Assisted ; Iran ; Least-Squares Analysis ; Multivariate analysis ; Principal component analysis
  8. Source: Journal of Chromatography A ; Volume 1628 , 2020
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0021967320307378