No abstract

Fast and reliable techniques for detection of food adulteration are indispensable to verify food authenticity. In this study, the detection of lard against different types of animal fats based oil using dielectric spectroscopy technique subjected to middle frequency range of 100Hz-100KHz is investigated. The animal fats were extracted and mixed with hexane solvent for different sample concentration levels. Analysis of variance (ANOVA) technique was applied to the collected data for statistical data analysis. The experimental results indicate that the dielectric value is not a function of frequencies but a function of sample's concentration levels. It is statistically shown that there is significant difference between type of animal fats with respect to their dielectric values at different frequencies and concentrations illustrating the ability of the proposed technique on lard detection objective. Furthermore, the principal component analysis (PCA) was used to classify lard and other animal fats. Results show that lard can be distinguished clearly from other animal fat sample group.

No abstract

A dielectric spectroscopy method was applied to determine major fatty acids composition in vegetable oils. Dielectric constants of vegetable oils were measured in the frequency range of 5–30 MHz. After data pre-treatment, prediction models were constructed using partial least squares (PLS) regression between dielectric spectral values and the fatty acids compositions measured by gas chromatography. Generally, the root means square error of validation (RMSECV) was less than 11.23% in the prediction of individual fatty acids. The determination coefficient (R2) between predicted and measured oleic, linoleic, mono-unsaturated, and poly-unsaturated fatty acids were 0.84, 0.77, 0.84, and 0.84, respectively. These results indicated that dielectric spectroscopy coupled with PLS regression could be a promising method for predicting major fatty acid composition in vegetable oils and has the potential to be used for in-situ monitoring systems of daily consumption of dietary fatty acids.

The study focused on application of spectral permittivity technique subjected to high frequency range of 8.2-12.1 GHz at the temperature of 25°C to identify animal fats from vegetable oils. Analysis of Variance (ANOVA) technique was used as a statistical data analysis to determine whether the samples are statistically distinctive. Principal Component Analysis (PCA) was used to classify animal fats and vegetable oils on their permittivity spectral. ANOVA analysis results showed that there is a significant difference between animal fats and vegetable oils with respect to their spectral permittivity at different frequencies. PCA classification plots showed that vegetable oil could be grouped into different clusters from the animal fats. From the results obtained in this study, spectral permittivity technique could be used to distinguish animal fats and vegetable oils.