A highly sensitive sandwich ELISA for the determination of glycomacropeptide to detect liquid whey in raw milk

Biological, functional and nutritional properties of caseinomacropeptide from sweet whey

Bioactive peptides derived from bovine milk proteins have gained much attention due to their health promoting functions. All over the world, cheese industry generates high volumes of sweet whey that could be used as an alternative source of bioactive peptide in nutraceuticals and food industry. Caseinomacropeptide (CMP) is a bioactive peptide derived from κ-casein by the action of chymosin during cheese manufacturing. CMP consist of two forms which are glycosylated (gCMP) and non-glycosylated (aCMP). The predominant carbohydrate in gCMP is N-acetylneuraminic (sialic acid) which gives functional and biological properties to gCMP. Due to its unique composition and technological characteristics such as wide pH range solubility, emulsifying, gelling, and foaming ability, CMP has received special attention. Therefore, there is an increased interest in researches for isolation and concentration of CMP. However, the isolation and purification methods are not cost-effective. It would be easier to optimize the conditions for isolation, purification, and utilization of CMP in nutraceuticals and food industry through deeper understanding of the effective factors. In this review, the structure of CMP, biological activities, isolation, and purification methods, the factors affecting functional properties and application areas of CMP in food industry are discussed.

Rapid detection of mozzarella and feta cheese adulteration with cow milk through a silicon photonic immunosensor

Mozzarella di Bufala Campana and Feta are two cheeses with Protected Designation of Origin the fraudulent adulteration of which with bovine milk must be routinely checked to ensure that consumers actually buy these high-end products and avoid health issues related to bovine milk allergy. Here, we employed, for the first time, a silicon-based photonic immunosensor for the detection of mozzarella and feta adulteration with bovine milk. The photonic immunosensor used relies on Mach-Zehnder interferometers monolithically integrated along with their respective light sources on a silicon chip. A rabbit polyclonal antiserum raised against bovine κ-casein was used for the development of a competitive immunoassay realized in three steps, including a reaction with the antiserum, a biotinylated anti-rabbit IgG antibody, and streptavidin. The implementation of this assay configuration significantly reduced the non-specific signal due to the cheese matrix, and allowed completion of the assay in ∼9 min. After optimization of all assay conditions, bovine cheese could be quantified in mozzarella or feta at concentrations as low as 0.5 and 0.25% (w/w), respectively; both quantification limits were below the maximum allowable content of bovine milk in mozzarella and feta (1% w/w) according to the EU regulations. Equally important, the assays were reproducible with intra- and inter-assay coefficients of variation <10%, and exhibited a wide linear dynamic range that extended up to 50 and 25% (w/w) for mozzarella and feta, respectively. Taking into account its performance, the proposed immunosensor may be transformed to a new tool against fraudulent activities in the dairy industry.

Assessment of pregnancy-associated glycoprotein profile in milk for early pregnancy diagnosis in goats

Objective

This study was conducted to assess the level of pregnancy-associated glycoprotein (PAG) in whole and skim milk samples, and its suitability for early pregnancy diagnosis in goats.

Methods

A two-step sandwich enzyme-linked immunosorbent assay (ELISA) system for estimation of milk PAG was developed and validated, which employed caprine-PAG specific polyclonal antisera. Whole and skim milk samples (n = 210 each) from fifteen multiparous goats were collected on alternate days from d 10 to d 30, and thereafter weekly till d 51 post-mating. PAG levels in milk samples were estimated by ELISA and the pregnancies were confirmed at d40 post-mating by transrectal ultrasonography (TRUS).

Results

The level of PAG in whole and skim milk samples of both pregnant and non-pregnant goats remained below the threshold values until d 24 after mating. Thereafter, PAG concentration in whole and skim milk increased steadily in pregnant goats, whereas it continued below the threshold in non-pregnant does. The PAG profiles in whole and skim milk of pregnant goats were almost similar and exhibited strong positive relationship (r = 0.891; p<0.001). Day 26 post-mating was identified as the first time-point for significantly (p<0.05) higher milk PAG concentration in pregnant goats than to non-pregnant goats. When compared to TRUS examination for pregnancy diagnosis, the accuracy and specificity of PAG ELISA using whole and skim milk samples were 94.5% and 95.4%; and 95.3% and 100%, respectively. The high values of area-under-curve (0.904 [whole milk] and 0.922 [skim milk]), demonstrate outstanding discrimination ability of the milk assays. Among the sampling dates chosen, d 37 post-mating was identified as the best suitable time point for collection of milk samples to detect pregnancy in goats.

Conclusion

The PAG concentration in whole and skim milk of goats collected between days 26 and 51 post-breeding can be used for the accurate prediction of pregnancy and may be useful for assisting management decisions in goat flocks.

Evaluation of human breastmilk adulteration by combining Fourier transform infrared spectroscopy and partial least square modeling

A two-step chemometric procedure was developed on the attenuated total reflection-Fourier transform infrared data of human breastmilk to detect adulteration by water or cow milk. The samples, collected from a Milk Bank, were analyzed before and after adulteration with whole, skimmed, semi-skimmed cow milk and water. A preliminary clustering via principal component analysis distinguished three classes: pure milk, milk adulterated with water, and milk adulterated with cow milk. A first partial least square-discriminant analysis (PLS-DA) classification model was built and then applied on new samples to identify the specific adulterants. The external validation on this model reached 100% of the correct identification of pure milk and 90% of the type of adulterants. In the following step, four PLS calibration models were built to quantify the amount of the adulterant detected in the classification analysis. The prediction performance of these models on new samples showed satisfactory parameters with root mean square error of prediction and percentage relative error lower than 1.38% and 3.31%, respectively.

Determination of whey adulteration in milk powder by using laser induced breakdown spectroscopy

A rapid and in situ method has been developed to detect and quantify adulterated milk powder through adding whey powder by using laser induced breakdown spectroscopy (LIBS). The methodology is based on elemental composition differences between milk and whey products. Milk powder, sweet and acid whey powders were produced as standard samples, and milk powder was adulterated with whey powders. Based on LIBS spectra of standard samples and commercial products, species was identified using principle component analysis (PCA) method, and discrimination rate of milk and whey powders was found as 80.5%. Calibration curves were obtained with partial least squares regression (PLS). Correlation coefficient (R(2)) and limit of detection (LOD) values were 0.981 and 1.55% for adulteration with sweet whey powder, and 0.985 and 0.55% for adulteration with acid whey powder, respectively. The results were found to be consistent with the data from inductively coupled plasma - mass spectrometer (ICP-MS) method.

Quantification of whey in fluid milk using confocal Raman microscopy and artificial neural network

In this work, we assessed the use of confocal Raman microscopy and artificial neural network as a practical method to assess and quantify adulteration of fluid milk by addition of whey. Milk samples with added whey (from 0 to 100%) were prepared, simulating different levels of fraudulent adulteration. All analyses were carried out by direct inspection at the light microscope after depositing drops from each sample on a microscope slide and drying them at room temperature. No pre- or posttreatment (e.g., sample preparation or spectral correction) was required in the analyses. Quantitative determination of adulteration was performed through a feed-forward artificial neural network (ANN). Different ANN configurations were evaluated based on their coefficient of determination (R2) and root mean square error values, which were criteria for selecting the best predictor model. In the selected model, we observed that data from both training and validation subsets presented R2>99.99%, indicating that the combination of confocal Raman microscopy and ANN is a rapid, simple, and efficient method to quantify milk adulteration by whey. Because sample preparation and postprocessing of spectra were not required, the method has potential applications in health surveillance and food quality monitoring.

Assessment of goat milk adulteration with a label-free monolithically integrated optoelectronic biosensor

The label-free detection of bovine milk in goat milk through a miniaturized optical biosensor is presented. The biosensor consists of ten planar silicon nitride waveguide Broad-Band Mach-Zehnder interferometers (BB-MZIs) monolithically integrated and self-aligned with their respective silicon LEDs on the same Si chip. The BB-MZIs were transformed to biosensing transducers by functionalizing their sensing arm with bovine k-casein. Measurements were performed by continuously recording the transmission spectra of each interferometer through an external spectrometer. The amount of bovine milk in goat milk was determined through a competitive immunoassay by passing over the sensor mixtures of anti-k-casein antibodies with the calibrators or the samples. The output spectra of each BB-MZI recorded during the reaction were subjected to Discrete Fourier Transform in order to convert the observed spectral shifts to phase shifts in the wavenumber domain. The method had a detection limit of 0.04 % (v/v) bovine milk in goat milk, dynamic range 0.1-1.0 % (v/v), recoveries 93-110 %, and intra- and inter-assay coefficients of variation less than 12 and 15 %, respectively. The proposed biosensor compared well in terms of analytical performance with a competitive ELISA developed using the same monoclonal antibodies. Nevertheless, the duration of the biosensor assay was 10 min whereas the ELISA required 2 h. Thus, the fast and sensitive determinations along with the small size of the sensor make it ideal for incorporation into portable devices for assessment of goat or ewe's milk adulteration with bovine milk at the point-of-need.

Luminescent solid phase for sialic acid determination: a promising sensor for milk-adulterated samples

This article presents the synthesis, characterization and spectroscopic study of silica modified with thenoyltrifluoroacetonate (SilTTA) and coordinated to an europium (III) ion, for the determination of sialic acid (NANA). Elemental analysis and infrared spectroscopy suggest silica functionalization, as well as coordination of beta-diketone to the lanthanide ion. The emission spectra of compound-free and coordinated Eu-SilTTA to NANA showed significant changes with respect to the maximum emission and spectral profile, suggesting that the NANA ion is coordinated to the Eu(III). The values of the phenomenological intensity parameters show an increase in polarizability around the Eu(III) in the case of Eu-SilTTA coordinated to NANA, as expected, since water molecules are less polarizable than sialic acid. The results of the batch assay showed that luminescent silica can be used for sialic acid determination in milk-adulterated samples, with a correlation coefficient of 0.9992; and a detection limit of 0.4 mg/L; relative standard deviation (RSD%) = 0.0028.

Chemical and functional properties of glycomacropeptide (GMP) and its role in the detection of cheese whey adulteration in milk: a review

Glycomacropeptide (GMP) is a C-terminal part (f 106–169) of kappa-casein which is released in whey during cheese making by the action of chymosin. GMP being a biologically active component has gained much attention in the past decade. It also has unique chemical and functional properties. Many of the biological properties have been ascribed to the carbohydrate moieties attached to the peptide. The unique set of amino acids in GMP makes it a sought-after ingredient with nutraceutical properties. Besides its biological activity, GMP has several interesting techno-functional properties such as wide pH range solubility, emulsifying properties as well as foaming abilities which are shown to be promising for applications in food and nutrition industry. These properties of GMP have given new dimension for the profitable utilization of cheese whey to the dairy industry. A number of protocols for isolation of GMP from cheese whey have been reported. Moreover, its role in detection of sweet/rennet whey adulteration in milk and milk products has also attracted attention of various researchers, and many GMP-specific analytical methods have been proposed. This review discusses the chemico-functional properties of GMP and its role in the detection methods for checking cheese or sweet whey adulteration in milk. Recent concepts used in the isolation of GMP from cheese whey have also been discussed.