Adulteration Of Goat Milk Research Articles

Comparative Analysis of Simplex and Duplex PCR for Detection of Adulteration of Goat Milk and Its Heat-Treated Products

Comparative Analysis of Simplex and Duplex PCR for Detection of Adulteration of Goat Milk and Its Heat-Treated Products

Analysis of cow and goat milk by spr: adulteration detection and differences in protein adsorption

The article discusses using Surface Plasmon Resonance (SPR) to detect adulteration of goat milk with cow milk. The SPR technique is based on changes in the refractive index and can be used to monitor the adsorption of proteins on the sensor surface. The SPR technique can differentiate between pure cow milk and pure goat milk by measuring the initial refractive index, which is higher for cow milk. The article then describes the changes in the refractive index that occur when goat milk is adulterated with cow milk and how adding a bovine casein film on the sensor surface can make it more selective for cow milk. The differences in the chemical compositions and molecular structures of cow and goat milk and how these differences affect the adsorption of proteins and fatty acids on the sensor surface. Finally, other proteins, such as α-lactalbumin and β-lactoglobulin, are used as indicators of adulteration.

Identification and quantification of goat milk adulteration using mid-infrared spectroscopy and chemometrics

The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics.

Raman spectroscopy-based authentication of powder goat milk adulteration with cow milk

This study introduces, for the first time, the utilization of a handheld Raman spectrometer in conjunction with Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) for the authentication of powdered goat milk (target class) against adulteration with powdered cow milk. Since the representativeness of the target class is the first critical step to construct a model without overfitting or underfitting, especially when dealing with a few numbers of target samples, three distinct validation strategies (approaches 1, 2, and 3, respectively), incorporating no partitioning of the target samples, Procrustes cross-validation, and Kennard-Stone-based partitioning, were assessed. Moreover, the spectral data underwent preprocessing techniques including offset correction (OFF), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), and Savitzky-Golay derivative (SGD). Achieving a correct classification rate of 100% for all studied samples using MSC for approaches 1 and 2, and SGD for approach 3, underscores the robustness of Raman spectroscopy in furnishing pertinent analytical insights for the authentication of pure powdered goat milk, with a significance level of 0.01. This methodology furnishes distinct spectral signatures associated with the macromolecular composition, particularly fats, proteins, carbohydrates, and carotenoids, facilitating the detection of adulterations as low as 1% w/w of cow milk added to powdered goat milk. Thus, the proposed method offers a rapid and reliable on-site screening tool, consonant with the principles of green food analysis, especially advantageous in situations where identifying the type and/or nature of the adulterants is not feasible.

Machine learning and electrochemistry techniques for detecting adulteration of goat milk with cow milk

Machine learning and electrochemistry techniques for detecting adulteration of goat milk with cow milk

Visualized detection of goat milk adulteration using golden nanoparticles (AuNPs) and the dilution sensitive-PCR based on supercoiled plasmids and dUTP incorporation

This work aimed to establish a strategy that can identify goat milk adulteration by any form of liquid substances. To realize this, supercoiled pUC-19 plasmid was added to pure goat milk for PCR amplification. The superhelical structure of pUC-19 impeded the progression of DNA polymerase, and the incorporation of dUTP which is not the natural substrate of the polymerase further reduced PCR efficiency. Therefore, a slight change of conditions such as lowering plasmid concentration as the result of adulteration, would lead to failed PCR amplifications (no products). Pure goat milk that was adulterated by four liquids were successfully detected by the PCR-based strategy. Moreover, the authentication can be visualized using ssDNA-functionalized AuNPs that could be re-dispersed by the PCR products from blocked primer. Goat milk adulteration by different adulterants can be detected by the established strategy, providing a point-of-care method for authorities to prevent food fraud concerning milk products.

Protein Stability in Pasteurized Cow and Goat Milk using Protein Gel Electrophoresis

Milk is a nutritious beverage that contains whey, casein, and milk fat globule membrane (MFGM) proteins. Pasteurization kills pathogenic microorganisms in milk, making it safe to drink. A high heat load during processing can affect the protein quality of milk. In this study, protein gel electrophoresis (Native and SDS-PAGE) was used to view the protein profiles of retail cow and goat milk samples pasteurized at different temperatures (145, 165, or 280 °F) compared to commercially spray dried milk (356–482 °F). SDS-PAGE provided better resolution of the milk proteins compared to Native-PAGE. There were clear species differences in the MFGM proteins. This could be used to spot adulteration of goat milk with cow milk, however none of the samples showed signs of adulteration. There were no overt patterns of protein degradation with increasing pasteurization temperature. However drying reduced band intensity, especially MFGM and whey proteins in the goat milk sample. Whereas most samples came from different herds where genetics and environmental conditions varied, two of the goat milk samples were from the same herd, one fresh and one spray dried. It was evident from these two samples that spray drying can alter proteins in goat milk. Even so, if fresh milk is unavailable or unaffordable, dry milk remains a valuable protein source.

Detection of goat milk adulteration in horse milk using LC-HRMS-based non-targeted metabolomics and chemometrics

Detection of goat milk adulteration in horse milk using LC-HRMS-based non-targeted metabolomics and chemometrics

N-glycan profiles as a tool in qualitative and quantitative analysis of goat milk adulteration

Goat milk is closer to human milk in some respects than cow milk, and therefore preferred by many consumers. Because of the short lactation period and consequently less milk production of goats, the price of goat milk is often higher than that of cow milk, so that adulteration of goat milk is common. N-glycans have stability and thus have a good potential for acting as a new biomarker for identifying dairy adulteration. In this study, the N-glycan structures of goat milk and cow milk were analyzed by Ultra-high performance liquid chromatography (UPLC) and MALDI-TOF-MS. Based on the high species specificity of N-glycans, a method for identifying goat milk mixed with cow milk was established. The adulteration content of 5% cow milk in goat milk could be qualitatively and quantitatively detected. A prediction model of adulteration in goat milk was established by using partial least squares (PLS).

A Rapid RPA-CRISPR/Cas12a Detection Method for Adulteration of Goat Milk Powder.

Because of the serious adulteration of goat milk, the rapid on-site detection of goat milk powder adulteration is needed. In this study, the CRISPR/Cas12a detection system combined with recombinase polymerase amplification (RPA) was employed to qualitatively detect the adulteration of goat milk powder with cattle-derived components. Specific primers and crRNA were designed and screened. After the optimization of RPA and the Cas system, the RPA-CRISPR/Cas12a detection method was established. The detection can complete the rapid identification of cattle-derived components in 45 min, without the assistant of large equipment. The absolute detectability of the RPA-CRISPR/Cas12a assay could reach 10-2 ng/μL for cattle genomic DNA, and 1% (w/w) for cattle milk powder, which is suitable to meet the testing requirements for on-site detection. In total, 55 commercial goat milk powder products were collected for blind testing. The results showed that 27.3% of the samples were adulterated with cattle ingredients, revealing a serious adulteration situation in goat milk powder market. The RPA-CRISPR/Cas12a assay established in this research exhibited its potential for practical use of on-site detection to detect cow milk powder in goat milk powder and can provide reliable technical reference for combating food fraud of adulteration of goat milk products.

The detection of goat milk adulteration with cow milk using a combination of voltammetric fingerprints and chemometrics analysis

In this study, a novel analytical approach was developed for detecting and predicting adulteration of goat milk with cow milk using a combination of voltammetric fingerprints and chemometrics analysis. The fresh milk samples were obtained from local farmers and analyzed using cyclic voltammetry technique using a glassy carbon electrode as the working electrode and KClO4 as the supporting electrolyte. The voltammetric fingerprint was obtained from both milk samples and showed an anodic peak between a potential range of 0.40–0.75 V versus Ag/AgCl. This anodic peak is mainly attributed to several electroactive species contained in both milk samples. The current intensities at the potential range of 0 to + 1 V versus Ag/AgCl were further selected due to the majority of electroactive components in the milk samples having their oxidation potential in this potential range. The current intensities were further pre-treated using maximum normalization and submitted to the chemometric tools for multivariate analysis. Orthogonal partial least square-discriminant analysis provided clear discrimination between goat and cow milk. Meanwhile, the prediction of goat milk adulteration with cow milk was achieved using partial least squares regression analysis. This multivariate analysis enabled a satisfactory discrimination and successful model to predict the percentage of cow milk as adulterants in goat milk samples. The demonstrated results revealed that a combination of voltammetric fingerprints and chemometrics tools might offer a low-cost, simple, and rapid analysis which might be possible as a promising method to be developed further for the detection of adulterants.

Goat milk-derived short chain peptides: Peptide LPYV as species-specific characteristic and their versatility bioactivities by MOF@Fe3O4@GO mesoporous magnetic-based peptidomics

Goat milk as an ideal substitute for human milk has not been sufficiently explored. An in-situ synthesized MOF@Fe3O4@GO was demonstrated as a magnetic mesoporous adsorbent for efficiently enriching short chain peptides (SCPs) in milk compared with the routine solid phase extraction approach with graphite carbon black or C18 as the packing material in terms of the number of enriched SCPs and data stability. A total of 61 and 126 SCPs were identified and quantified in bovine milk (0.09–89.34 μg L−1) and goat milk (10.5–1267.06 μg L−1), respectively, and peptide LPYV can be used as a potential marker for adulteration of goat milk. Relative high expression of chymotrypsin and pepsin by EnzymePredictor analysis could partially elaborate the reason of the abundance of SCPs in goat milk. Compared with bovine milk, further bioinformatics analysis indicated that goat milk could own higher nutritional value because of relative higher concentrations (>1 mg/L) of SCPs (LLV, FL, LVYP) with confirmed bioactivities including angiotensin-converting enzyme (ACE) inhibitor, antioxidant, dipeptidylpeptidase (DPP) III and DPP IV inhibitor, etc. Overall, this study opened a novel avenue for understanding versatility benefit of dairy products from a perspective of SCPs by using a developed MOF@Fe3O4@GO mesoporous magnetic-based peptidomics.

Detection of adulteration of goat milk sold in the Turkish market by real-time Polymerase Chain reaction

Detection of milk adulteration is important to maintaining the quality of milk and milk products regarding pricing and consumer protection. Thus, in the current study, we investigated the adulteration of goat milk sold in the market and labeled it as “100% Goat Milk” with cows and sheep milk by real-Time PCR reaction (RT-PCR). This study is the first investigation on goat milk in Turkey and has particular importance as it was carried out using TaqMan probe RT-PCR. In total, 60 milk samples sold in the market and labeled as “100% Goat Milk” were collected from 12 different provinces of Turkey. The RT-PCR TaqMan probe detected the adulteration of goat milk with cow and sheep milk. In the study, 18 (30%) samples were found to be compatible with the label. It was determined that 42 (70%) samples did not comply with the “100% Goat Milk” statement on the label. It was determined that 6 (10%) of the samples contained only sheep milk, 18 (30%) contained only cow milk, another 6 (10%) contained goat and cow milk, 12 (20%) contained sheep and cow milk, and 36 (60%) did not contain any goat milk. The results of this study revealed high levels of adulteration in goat milk products. Therefore, careful continuous monitoring of these products’ production and sales is necessary regarding deception of consumers and public health.

Rapid quantification of goat milk adulteration with cow milk using Raman spectroscopy and chemometrics.

As goat milk has a higher economic value compared to cow milk, the phenomenon of adulterating goat milk with cow milk appears in the market. In this study, the potential of Raman spectroscopy along with chemometrics was investigated for the authentication and quantitation of liquid goat milk adulterated with cow milk. First, the results of principal component analysis (PCA) showed that there were differences between the Raman spectra of cow and goat milk, which made quantitative experiments possible. For quantification, three different brands of cow milk and goat milk were selected randomly and adulterated goat milk with cow milk at the proportion of 5-95%. 342 samples were used for the construction of the partial least squares regression (PLSR) model with 80% for the calibration set and 20% for the prediction set. The PLSR model showed excellent performance in quantifying the level of adulteration, for the prediction set, with a coefficient of determination (R2) of 0.9781, root mean square error (RMSE) of 3.82%, and a ratio of prediction to deviation (RPD) of 6.8. The results demonstrated the potential of Raman spectroscopy as a rapid, low cost and non-destructive analytical tool for detecting adulteration in goat milk.

Rapid detection of adulteration of goat milk and goat infant formulas using near-infrared spectroscopy fingerprints

This study aimed to establish near-infrared spectroscopy (NIRS) fingerprints of goat milk and goat infant formulas to detect their adulteration, and thus protect infants from allergic reactions to bovine milk proteins. Principal component analysis combined with the fingerprint method can discriminate adulterated goat milk from goat milk, and desalted bovine whey powder from desalted goat whey powder in goat infant formulas, as well as differentiate Stage 1, Stage 2, and Stage 3 goat infant formulas. The NIRS fingerprint combined with the PCA method can quickly distinguish goat milk, goat infant formulas, and their adulteration, then achieve quality control.

Goat milk authentication based on amino acid ratio and chemometric analysis

This study aimed to determine the authenticity of goat milk by differentiating pure goat milk from goat milk adulterated with cow milk using orthogonal partial least square discriminant analysis (OPLS-DA). In comparison with authentication based on amino acid content, the OPLS-DA model based on amino acid ratio exhibited a substantially lower coefficient of variation, and remarkable goodness-of-fit parameters (R2X = 0.99; R2Y = 0.80; Q2 = 0.65). Unadulterated goat milk was distinguished from goat milk adulterated with cow milk with 100% classification accuracy. Additionally, the model validation confirmed the excellent predictive ability of the OPLS-DA model. Lys, Glu, Pro, and Val were proposed as potential markers between goat milk and adulterants. The procedure was successfully established to detect commercial goat milk adulteration.

A high sensitivity method of closed-tube loop-mediated isothermal amplification developed for visual and rapid detection of cow milk adulteration

A novel closed-tube loop-mediated isothermal amplification (LAMP) was developed to detect goat milk adulterated with cow milk. The reaction results of the LAMP assay developed, identified using both visual observation and agarose gel electrophoresis, were completely consistent, indicating that visual observation was suitable and efficient to judge the final LAMP results. The specificity of LAMP assay was evaluated using DNA of related species namely goat, pork, chicken, duck, dog, which revealed only a specific amplification of cow. The sensitivity of LAMP assay was as low as 10 fg. The limit of detection (LOD) of LAMP assay reached 0.01%, 10 times higher than PCR assay. In addition, this assay successfully amplified and identified 0.01% of cow commercial milk products in goat milk. Therefore, our results provided a simple, specific, sensitive, rapid, and cost-effective method to detect goat milk adulteration.

The use of paired comparison test on the goat's milk adulteration detection (whole and semiskimmed)

AbstractDairy goat products are in high demand in both traditional and new emerging markets. Compared to cow milk, goat milk and its products are increasingly preferred for their health and nutritional benefits such as greater digestibility and lipid metabolism. As a result, it appears that goat's milk consumers and manufacturers encounter the problem of goat's milk falsification with cow's milk. In this study, paired comparison tests were performed by adding varying amounts of cow's milk to the whole and semiskimmed goat's milk to determine to what extent the consumer can detect its falsification. As the amount of cow's milk reaches 50%, the detection of goat's milk adulteration becomes visible. This identification of goat milk adulteration by cow's milk is more marked in semiskimmed milk than in whole milk. This nondetection of whole goat milk adulteration by small amounts of cow's milk may be due to the milk's unique organoleptic characteristics, including an intense smell and a strong flavor. Furthermore, no parameter tested in our study had any effect on detecting potential adulteration of goat's milk.Practical applicationsThis work highlighted a practical and rapid method for the detection of possible goat's milk adulteration (whole and semiskimmed) by cow's milk using a paired comparison test.

A Randomized Combined Channel Approach for the Quantification of Color- and Intensity-Based Assays with Smartphones.

Quantification of colorimetric assays with smartphones is being increasingly reported. However, a complete characterization of the performance of existing color spaces and single-color channels for optimum color/intensity change quantification is absent. Moreover, it has not been ascertained if it is necessary to utilize existing color spaces to reach optimal assay quantification. In this study, a randomized channel approach was adapted utilizing all single channels from RGB, HSV, and CieLab color space and all nonrepeating random combinations of two and three channels of these color spaces. Assays based on color or intensity change using pH strips and gold or carbon black nanoparticle-containing paper strips were optimized using this approach. Several novel channel combinations showed great promise, in terms of prediction error and interphone variation reduction, outperforming RGB, HSV, and CieLab color spaces. These novel combinations were used in a custom-developed smartphone application that performed automated background subtraction and polynomial regression for the quantification of a lateral flow assay for the detection of goat milk adulteration with cow milk and for pH prediction in soil. For the lateral flow assay the channel combination BSA was found optimum (mean average error = 36% ± 6%; R2 = 0.97). For the soil pH assay the channel combination RLC was found optimum (mean average error = 1.31% ± 0.02%; R2 = 0.997). The study has shown that nonclassical channel combinations for colorimetric quantification of specific assays are very promising and should be considered for smartphone-based analysis.

Simultaneous determination of goat milk adulteration with cow milk and their fat and protein contents using NIR spectroscopy and PLS algorithms

This work employed NIR spectroscopy and PLS algorithms for the identification and quantification of goat milk adulteration by adding cow milk, besides the determination of their fat and protein contents. Since cow milk can represent a health risk to allergic consumers regardless its amount, PLS-DA was able to identify cow milk additions in goat milk as low as 1.0154 g/100g, likewise the non-adulterated goat and cow milk samples, achieving a 100% of correct classification. For quantification purposes, the Successive Projections Algorithm for interval selection in PLS (iSPA-PLS) provided the best results for the determination of both adulteration and fat contents, while PLS gave better results for the protein quantification. Despite the great similarity of both natural dairy matrices and their intrinsic variability, the prediction results provided suitable values with high correlation coefficients and low RMSEP and REP values, with RPD values higher than 3. Therefore, the proposed methodology proved to be a useful, fast and non-destructive tool for screening the quality of goat milk in terms of its adulteration with cow milk, in addition to the quantification of its fat and protein contents.