Loading...

Evaluation of FT-IR spectroscopy combined with SIMCA and PLS‑DA for detection of adulterants in pistachio butter

Khanban, F ; Sharif University of Technology | 2022

155 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.infrared.2022.104369
  3. Publisher: Elsevier B.V , 2022
  4. Abstract:
  5. This work scrutinized the adulteration of pistachio butter with three potential edible oils using Fourier transform infrared spectroscopy (FT-IR) and multivariate classification methods. Each of the classes, including non-adulterated samples and adulterated samples consisting of pistachio butter mixed with various concentrations of peanut oil, corn oil and sunflower oil, were classified. For this purpose, multivariate methods, including soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA), were applied to classify the FTIR data. After evaluating the model on unknown samples, the results indicated that PLS-DA was better than the SIMCA model. The classification efficiency of 98% was obtained using the PLS-DA algorithm compared to the SIMCA model with 91% efficiency for prediction. According to the results FTIR spectroscopy, accompanied by multivariate classification methods, can be used as a rapid and reliable method for classifying and predicting adulteration in pistachio butter with edible oils. © 2022
  6. Keywords:
  7. Adulteration ; Classification methods ; Edible oils ; Multivariate analysis ; Pistachio butter ; Root mean square error of calibration (RMSEC) ; Spectroscopy ; Discriminant analysis ; Efficiency ; Least squares approximations ; Mean square error ; Multivariant analysis ; Spectrum analysis ; Sunflower oil ; Adulteration ; Classification methods ; Multi variate analysis ; Multivariate classification ; Partial least squares discriminant analyses (PLSDA) ; Pistachio butter ; Root mean square error of calibration ; Root mean square error of calibrations ; Soft independent modeling of class analogies ; Spectroscopy:spectroscopy ; Fourier transform infrared spectroscopy
  8. Source: Infrared Physics and Technology ; Volume 127 , 2022 ; 13504495 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S1350449522003504