Pumpkin seed oil (PSO), rice bran oil (RBO), sesame oil (SEO) are considered as
functional oils due to its biological activities which are beneficial to human health, as a
consequence, these oils had the higher price. This attracted unethical players to blend
these oils with lower price oils, therefore, its authentication by analysis of purity levels of
oils is very important. This study highlighted the potential application of FTIR
spectroscopy and multivariate calibrations for analysis of PSO, RBO, and SEO in ternary
mixtures. Individual FTIR spectra of studied oils as well as in ternary mixtures with
certain compositions were scanned and pre-processed. Two multivariate calibrations of
principle component regression (PCR) and partial least square regression (PLSR) were
compared and used to build the prediction models at optimized FTIR spectra regions. The
selection of multivariate calibrations, wavenumbers region, and FTIR spectra modes was
based on the statistical parameters of highest R2
and lowest values of root mean square
error of calibration (RMSEC) and root mean square error of prediction (RMSEP). The
results showed that PLSR using second derivative FTIR spectra at wavenumbers region of
3100-2750 and 1500-663 cm-1 was used to predict the levels of PSO in ternary mixtures
with RBO and SEO with R2
> 0.99 in calibration and validation models along with
RMSEC value of 0.0054% and RMSEP of 0.0179%. FTIR spectra using the second and
first derivatives at wavenumbers of 3100-650 cm-1 were used for prediction of RBO and
SEO in ternary mixture with PSO, respectively. It can be concluded that FTIR spectra
combined with PLSR at certain wavenumbers region are accurate as indicated by high R2
values and precise as indicated by low values of RMSEC and RMSEP for analysis of
PSO, RBO and SEO in ternary mixtures.
Enzymatic Degumming of Rice Bran Oil Using Different Commercial Phospholipases and Their Cocktails