Differentiation of bovine and porcine gelatins in processed products via sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and principal component analysis (PCA) techniques

Gelatin is widely used in food and pharmaceutical products. However, the addition of gelatin especially in food products becomes a controversial issue among Muslims due to its animal origin. Thus, the present study was aimed to detect and differentiate the origin of gelatin added in processed foods using a combination method of sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and Principal Component Analysis (PCA). Porcine gelatin had exhibited 11 prominent polypeptides compared to bovine gelatin with 2 prominent polypeptides. Polypeptides of both gelatin sources at molecular weight ranged from 53 to 220 kDa can be used to differentiate between porcine and bovine gelatins using PCA. The efficiency in extracting gelatin from processed foods by different solutions was also evaluated. Extraction of gelatin in processed foods by cold acetone and deionised water had exhibited a similar polypeptide patterns, suggesting both solutions are suitable. The study indicated that approach of a simple gelatin extraction combined with SDS-PAGE and PCA, may provide robust information for gelatin species differentiation of processed foods

FTIR-ATR spectroscopy based metabolite fingerprinting as a direct determination of butter adulterated with lard

Adulteration of butter with cheaper animal fats, such as lard, has become an issue in recent years. A simple and rapid analytical method of attenuated total reflectance in Fourier transform infrared spectroscopy was developed in order to determine the lard content in butter. The multivariate calibration of partial least square model for the prediction of adulterant was developed for quantitative measurement. The model yielded the highest regression with the correlation coefficient (R2) = 0.999, its lowest root mean square error estimation = 0.0947, and its root mean square error prediction = 0.0687, respectively. Cross validation testing evaluates the predictive power of the model. Partial least square model to be effective as their intercept of R2Y and Q2Y were 0.08 and-0.34, respectivel