In this study, sesame oil have been analyzed by near infrared spectroscopy (NIRS). Sesame oil was mixed with the other edible oil. NIR transmittance spectra of sesame oil mixtures were acquired by using a dip probe. Partial least squares regression (P...
In this study, sesame oil have been analyzed by near infrared spectroscopy (NIRS). Sesame oil was mixed with the other edible oil. NIR transmittance spectra of sesame oil mixtures were acquired by using a dip probe. Partial least squares regression (PLSR) was applied to develop a calibration model over the spectral range 1100-1750nm. The calibration model predicted the content of sesame oil for validation set with a standard errors of prediction (SEP) of 0.3871%. Soft Independent Modeling of Class Analogy (SIMCA) was used to identify sesame oil and adulteration. This model identified the sesame oil and adulteration for validation set with 100% accuracy. This study showed that the rapid and non-destructive the determination of the quantitative analysis of sesame oil, the identification of sesame oil and adulteration was successfully performed by portable NIR system.