Discriminant analysis of vegetable oils by near‐infrared reflectance spectroscopy

Abstract

Discriminant analysis of four vegetable oil types (cotton‐seed, peanut, soybean and canola) was performed by near‐infrared reflectance spectroscopy. The objective of this study was to provide an alternate method to differentiate vegetable oil types and to classify unknown oil samples. Second derivative spectra of the vegetable oils were subjected to discriminate analysis with Mahalanobis distances principles. A four‐wavelength (1704, 1802, 1816 and 2110 nm) equation was derived, which produced a sum of inverse squared distance of 0.0548. Although all four groups were successfully separated with a chi square of 18.9, the soybean oil group is more dispersed in space than the other three groups. Iodine values of the soybean oil samples suggest that this group may have a wide range of hydrogenation states. Discriminant analysis can be successfully used to differentiate vegetable oil types and possibly could also be used to differentiate degree of hydrogenation and oxidative states of oils.

The Detection and Quantification of Adulteration in Olive Oil by Near-Infrared Spectroscopy and Chemometrics

“…The correlation between the NIR profiles and the quality parameters 17,19 clearly show that the quality parameters of oils can be used to detect adulteration in olive oil. However, this has not yet been studied.…”

“…Bewig et al and Chen et al 17,19 used the NIR spectral profiles to predict quality parameters of vegetable oils. In another application, Bewig et al 18 used a four-wavelength calibration for classifying four different vegetable oils.…”

Development of a Qualitative near Infrared Screening Method for Quality Consistency Assessment of Virgin Sunflower Oil

“…Among qualitative methods, it is Mahalanobis distance method that is used for classifying samples in near infrared analysis [5]. In Mahalanobis distance (M-distance), samples with distance less than 3 standard deviations are considered to be members of the same group as those used to develop the model, while those that have M-distance greater than 3 standard deviations are considered to be non-members [6]. Another advantage of using the Mahalanobis measurement for discrimination is that the distances are calculated in units of standard deviation from the group mean [6] [7] [8].…”

“…In Mahalanobis distance (M-distance), samples with distance less than 3 standard deviations are considered to be members of the same group as those used to develop the model, while those that have M-distance greater than 3 standard deviations are considered to be non-members [6]. Another advantage of using the Mahalanobis measurement for discrimination is that the distances are calculated in units of standard deviation from the group mean [6] [7] [8]. Therefore, the calculated circumscribing ellipse formed around the cluster actually defines the one sigma or one standard deviation boundary of that group [9].…”

“…Discriminant analysis has been applied in the analysis of vegetable oils by Near-Infrared reflectance spectroscopy using second derivative spectra of four different vegetable oils for discriminant analysis by using Mahalanobis distance principles [6]. Various other methods like multivariate modeling have been applied to predict butter fat content of spreads in adulteration of butter fats with cheaper vegetable oils.…”