Direct determination of oil content in binary mixtures of peanut and canola oils using partial least squares and attenuated total reflectance fourier transform infrared spectroscopy

An attenuated total reflectance Fourier transform infrared (ATR-FT-IR) spectrophotometric and partial least squares (PLS) chemometric method was developed for the direct determination of percent content by mass of peanut and canola oils in binary mixtures. A training set was generated using a full factorial design and was used to predict the concentrations in a testing data set, which was generated using a central composite design. We compared the performance of commonly used signal processing techniques (first derivative, binning, standard normal variate [SNV], and Savitzky-Golay smoothing) prior to PLS analysis. Savitzky-Golay smoothing outperformed the other aforementioned techniques with a root mean square error of calibration (RMSEC) for peanut oil of 2.81 × 10-2 and 2.71 x 10-2 for canola oil. The root mean square error of prediction (RMSEP) was 1.53 × 10-1 for both peanut and canola oils. The relatively small root mean square error (RMSE) values indicated that the differences between the actual and predicted values of both the training and testing set were minimal. An R 2 of 0.999 with a root mean square error of prediction (RMSEP) of 1.53 × 10-1 was obtained for both edible oils, implying that the probe could potentially provide an avenue of method development for the direct determination of oil content in binary mixtures of peanut and canola oils.