Detection Of Milk Adulteration Research Articles

Separation and determination of denatured α s1-, α s2-, β- and κ-caseins by hydrophobic interaction chromatography in cows’, ewes’ and goats’ milk, milk mixtures and cheeses

Caseins α s1-, α s2-, β- and κ- from raw cows’, ewes’ and goats’ milk were separated and determined by hydrophobic interaction chromatography (HIC) by using a Propyl column (Eichrom) in the presence of 8.0 M urea in the mobile phase. The method is based on fast and easy solubilization of real raw samples by 4.0 M guanidine thiocyanate followed by the HIC analysis, without any preliminary precipitation or separation of the casein fraction. Elution conditions have been optimized by analyzing commercial single bovine standard caseins and their mixture. In the optimized chromatographic conditions the four casein fractions were separated in less than 45 min. A linear relationship between the concentration of casein and peak area (UV absorbance detector at 280 nm) has been obtained over the concentration range of 0.5 to 40 μ M. The detection limit for α-, β- and κ-caseins ranged between 0.35 and 0.70 μ M. The precision of the method was evaluated, the coefficient of variation for α-, β- and κ-casein determination ranging between 3.0 and 6.0%. The method has been validated by the analysis of reference skim milk powder (BCR-063R) certificated for total nitrogen content. The method was applied to commercial casein mixture and to the qualitative and quantitative analysis of casein fractions in unprocessed, raw cows’, goats’ and ewes’ milk (10 samples analyzed for each species), in one sample of unprocessed buffalos’ milk and in commercial cheeses (mozzarella, robiola, ricotta and stracchino). Binary mixtures of milk (cow/goat and cow/ewe) were also analyzed and the ratio between casein peak areas (α s1/κ, α s2/β, β/κ and α s2/α s1) of the HIC chromatograms was proposed and discussed in order to evaluate a possible application of this method to detect milk adulteration.

Separation and quantification of the major casein fractions by reverse-phase high-performance liquid chromatography and urea–polyacrylamide gel electrophoresis: Detection of milk adulterations

The separation and quantification of bovine κ-, α- and β-caseins by HPLC–UV using an RP column which contained polystyrene–divinilbenzene copolymer based packing was optimized and validated. Gradient elution was carried out at a flow-rate of 1 ml/min and a temperature of 46 °C, using a mixture of two solvents. Solvent A was 0.1% trifluoroacetic acid in water and solvent B was acetonitrile–water–trifluoroacetic acid (95:5:0.1). The effluent was monitored by a UV detector at 280 nm. The determinations were performed in the linear range of 0.038–0.377 mg/ml for κ-casein, 0.188–1.883 mg/ml for α-casein and 0.151–1.506 mg/ml for β-casein. The detection limits were 0.006, 0.019 and 0.015 mg/ml for κ-casein, α-casein and β-casein, respectively. The validity of the method was verified. The recoveries ranged from 91 to 100% for bovine milk. The precision of the method was also evaluated, the RSD being less than 3.67%. The same HPLC procedure was used for the separation of caprine and ovine caseins. Different chromatographic profiles were obtained for bovine, ovine and caprine milks, although it was only possible to detect and quantify additions of 5% or more of bovine milk to caprine milk. With respect to detection of milk adulterations, electrophoresis using urea–polyacrylamide gel electrophoresis (PAGE) analysis was more sensitive. The evolution of casein proteolysis in cheeses made from bovine milk and cheeses made from ovine milk, during 30 days of ripening was followed by HPLC–UV and urea–PAGE methodologies. The results obtained by these techniques were similar.

A multiprobe miniature Hall effect system for measurement of magnetic field profiles

Differential scanning calorimetry (DSC) coupled with machine-learning tools (random forest, gradient boosting machine, and multilayer perceptron, RF, GBM, MLP) were used to detect adulteration of raw bovine milk (formaldehyde, whey, urea, and starch). Adulterated samples presented a different DSC profile from raw milk. GBM and MLP were able to classify 100% of adulterated samples, whereas RF showed optimal performance with recognition and prediction capability of 100% and 88.5%, respectively. Overall, peak temperature of crystallization was the most important discriminating predictor for GBM and RF models, whereas peak temperature of boiling followed by onset temperature of crystallization and onset temperature of boiling were the most important predictors for MLP model. The detection of adulteration in milk has a multidimensional approach and DSC associated with machine-learning methods present an interesting perspective with practical potential to be adopted by the dairy industry.