Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR

Abstract

Adulteration of canola oil with four potential edible oils was analysed using FT‐IR and chemometric methods. The adulterants (corn, peanut, soybean, and sunflower oils) were studied in four different proportions (canola oil + adulterant oils: 90+10, 95+5, 98+2 and 99+1 in volume). Excellent classification results were obtained when multi‐class approaches were performed with a maximum error of 3%, using 1630 or 16 wavenumbers as variables. In the case of one‐class approaches, the selection of variables (16 wavenumbers) was necessary, improving the classification error to 5%. The differences observed using the different methods were related to the nature of each model depending on how the boundaries are set in each of them, responding either to a PCA‐based or PLS‐based algorithm.