A distinction of cow and buffalo ghee using principal component analysis of triglyceride composition

Abstract

Triglyceride (TG) profiling was explored to distinguish cow ghee from buffalo ghee to check their admixing. Cow and buffalo butter was clarified at 110, 130 and 150°C to obtain ghee. Temperature of clarification did not show any significant effect on TG profile. Cow ghee showed maxima at TG C38 and C52. Buffalo ghee exhibited maxima at C38 and C50 unlike cow ghee. Cow ghee samples showed a higher TG content of C42 to C54, whereas buffalo ghee samples were associated with a greater content of C26 to C38. Principal component analysis (PCA) and hierarchical clustering showed two ghee types as distinct separate clusters.

Instrumental research methods in sustainable food production as the guarantor of the Vologda Butter brand

“…It is reliably known that the best criterion for the authenticity of milk fat is its fatty acid composition, determined by the instrumental method of gas chromatography in accordance with GOST 32915-2014 "Milk and dairy products. Determination of the fatty acid composition of the fatty phase by gas chromatography" [3][4][5][6].…”

Predicting adulteration of grated Parmigiano Reggiano cheese with Ricotta using electrophoresis, multivariate nonlinear regression and computational intelligence methods

“…Farah et al (2021) showed the practical potential of machine-learning algorithms to the dairy industry in detecting adulteration of raw bovine milk with starch, formaldehyde, whey and urea. Other classifiers such as principal component analysis have shown excellent performance in the rapid discrimination of cheeses according to their origin and in the distinction of cow and buffalo ghee, showing high classification accuracy and robustness (Pathania et al 2021;Silva et al 2021).…”

“…Other classifiers such as principal component analysis have shown excellent performance in the rapid discrimination of cheeses according to their origin and in the distinction of cow and buffalo ghee, showing high classification accuracy and robustness (Pathania et al . 2021; Silva et al . 2021).…”