Adulteration Of Dairy Products Research Articles

Identification of pasteurized mare milk and powder adulteration with bovine milk using quantitative proteomics and metabolomics approaches

Adulteration in dairy products presents food safety challenges, driven by economic factors. Processing may change specific biomarkers, thus affecting their effectiveness in detection. In this study, proteomics and metabolomics approaches were to investigate the detection of bovine milk (BM) constituents adulteration in pasteurized mare milk (PMM) and mare milk powder (MMP). Several bovine proteins and metabolites were identified, with their abundances in PMM and MMP increasing upon addition of BM. Proteins like osteopontin (OPN) and serotransferrin (TF) detected adulteration down to 1 % in PMM, whereas these proteins in MMP were utilized to identify 10 % adulteration. Biotin and N6-Me-adenosine were effective in detecting adulteration in PMM as low as 10 % and 1 % respectively, while in MMP, their detection limits extend down to 0.1 %. These findings offer insights for authenticating mare milk products and underscore the influence of processing methods on biomarker levels, stressing the need to consider these effects in milk product authentication.

Adulteration in dairy products and their risk to human life: On the verge of crying over spoiled milk

Adulteration in dairy products and their risk to human life: On the verge of crying over spoiled milk

Mitochondrial DNA D-Loop Amplification and Sequencing for Species Differentiation in Milk.

Adulteration of dairy products, mainly through the substitution of high-quality milk for lower-quality milk, results in the production of low-value products, raising health, social, and economic concerns. As such, the development of methods to ensure dairy products' safety and quality is of great concern for governments and consumers. Although several methods have been developed for species differentiation in dairy products, their application and the establishment of reliable molecular markers for authentication purposes still need to be improved. In this chapter, we describe a low-cost, sensitive, fast, and reliable PCR-based method for mitochondrial D-loop DNA amplification for efficient detection of cattle milk in binary mixtures with sheep milk, thereby allowing the authentication of processed dairy products.

Innovation Techniques to Assess Adulterated Ghee: A Review

Adulteration in dairy products is not only serious trouble to mortal health but also causes profitable losses. Ghee, the most precious fat, is vented at a high price over other fats and oil. Unethical vendors take advantage of this by mixing ghee with inexpensive fats or oils. Adulteration of ghee could be estimated through its physical-chemical constants such as Butyro-Refractometer reading, Reichert-Meissl value, Polenske value, Iodine value, saponification value, which are time-consuming, Therefore, researchers nowadays adopted novel techniques. The addition of coconut oil in ghee is detected using Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and chemometrics. Analysis of the adulteration in cow ghee with soybean oil by olfactory machine system, electronic nose (E-nose) tool is used as a rapid technique. Analysis of adulteration in cow ghee with vanaspati by using an image analysis technique called particle analysis and colour measurement and a rapid and accessible protocol based on β-sitosterol using Reversed-phase thin layer chromatography (RP-TLC) is developed to check the purity of milk fat. DPPH (2, 2-diphenyl-1-picrylhydrazyl) based chromogenic assay is a rapid method that has been used to identify the presence of palm oil in ghee.

Detection of Adulteration in Infant Formula Based on Ensemble Convolutional Neural Network and Near-Infrared Spectroscopy.

Adulteration in dairy products has received world-wide attention, and at the same time, near infrared (NIR) spectroscopy has proven to be a promising tool for adulteration detection given its advantages of real-time response and non-destructive analysis. Regardless, the accurate and robust NIR model for adulteration detection is hard to achieve in practice. Convolutional neural network (CNN), as a promising deep learning architecture, is difficult to apply to such chemometrics tasks due to the high risk of overfitting, despite the breakthroughs it has made in other fields. In this paper, the ensemble learning method based on CNN estimators was developed to address the overfitting and random initialization problems of CNN and applied to the determination of two infant formula adulterants, namely hydrolyzed leather protein (HLP) and melamine. Moreover, a probabilistic wavelength selection method based on the attention mechanism was proposed for the purpose of finding the best trade-off between the accuracy and the diversity of the sub-models in ensemble learning. The overall results demonstrate that the proposed method yielded superiority regression performance over the comparison methods for both studied data sets, and determination coefficients (R2) of 0.961 and 0.995 were obtained for the HLP and the melamine data sets, respectively.

Detection of Milk Fat Adulteration in Commercial Butter and Sour Cream

Background and Objective: Adulteration of dairy products by substitution of milk fat by vegetable oil is common in the Eurasian Economic Union. The objective of the paper is to investigate the potential adulteration of the fat and to test the more convenient methods of detection, i.e., determination of fatty acids or sterols profiles in commercial butter and cream. Materials and Methods: Ten samples of commercial butter and 8 samples of commercial sour cream were collected on the national market of Kazakhstan. The analyses involved the original sour cream and butter without any modification (deep-freezing) and were achieved within the shelf-life period. The fatty acid composition was analyzed by GS-FID and Sterol fractions were analyzed by GS-MS. Statistical analysis was achieved by principal components analysis (PCA), Pearson types, Kruskal-Wallis test. Results: Sixty percent of the butter samples contained traces of phytosterols and one sample contained up to 78% $-sitosterol. In sour cream samples, only three contained 100% cholesterol while two contained more than 60% sitosterol. The detection of fat adulteration by analyzing the fatty acids patterns is convenient in case of massive substitution of milk fat, but a discrete substitution does not modify the fatty acids profiles leading to misinterpretation. Conclusion: The results exhort to give preference to sterol profile determination as an official method to detect fat adulteration in dairy products. This is even more important as the current standard used in Central Asia based on some fatty acids ratios can lead to incorrect conclusions.

Chemometric analysis combined with FTIR spectroscopy of milk and Halloumi cheese samples according to species' origin.

Food adulteration is an issue of major concern, as numerous foodstuffs and beverages do not follow their labeling. Our research interest is in the field of authenticity of dairy products and particularly cheese. Adulteration of dairy products is a well‐known phenomenon, and there are numerous published studies specifically on the authenticity of cheese. In fact, substitution of a portion of fat and/or proteins, adulteration with milk of other species’ origin, and mislabeling of ingredients are some of the main issues that the science of dairy products’ authenticity is regularly facing. Discrimination of dairy products can be determined through several chemical or microbiological methods as presented in the literature. In addition, chemometric analysis is an important tool for interpretation of a huge load of measurements. The aim of this study is to discriminate between various milk samples, which is the primary ingredient of dairy products. Milk samples with different trademarks were analyzed. That data was combined with Halloumi cheese samples for chemometric discrimination of species’ origin. The innovative point of this study is the fact that it is the first time that a research study related to dairy products includes Halloumi cheese which is a traditional Cypriot cheese, not well‐studied until now. The first step of the methodology was the freeze‐drying via lyophilization of the samples. Fourier transformed infrared spectroscopy (FTIR) was chosen for their chemical characterization. Moreover, interpretation of the measurements was carried out by chemometric analysis using SIMCA software. For this study, FTIR data combined with chemometrics have given a very good discrimination of the samples according to their species’ origin. Chemometric methods such as PCA and OPLS‐DA have been used with great success. In the future, this model will be studied regarding geographical origin of the samples.

Graphene nanoplatelet/graphitized nanodiamond-based nanocomposite for mediator-free electrochemical sensing of urea

Urea is well-known to offer tremendous scope for sensing/diagnosing such as adulteration in dairy products or diseases in human body. This study was organized to describe and validate a new mediator-free, unsophisticated, and direct current voltage (IV)-based sensor for facile detection of urea using nanocomposites made of urease-immobilized graphene nanoplatelets and graphitized nanodiamonds. This nanocomposite displayed sensitive and direct signal in the form of current at 0 V without the need of any complex chemical reaction. This platform was highly sensitive (limit of detection of 5 μg/mL) far superior to the comparable systems introduced recently. The incorporation of graphitized nanodiamonds within the graphene nanoplatelets layers helped improve the sensitivity by a factor of three (up to 806.3 µA (mg mL−1)−1 cm−2) with 20 s response time. As such, the use of this nanocomposite was helpful in improving sensing performances with enhanced enzyme loading capacity.

Purity detection of goat milk based on electronic tongue and improved artificial fish swarm optimized extreme learning machine

The nutritional value of goat milk is higher than that of cow milk, which is scarce and expensive. Therefore, many bad credit enterprises mix cow’s milk into goat milk and sell it as pure goat milk, which infringes on the rights and interests of consumers. The adulteration of dairy products has a long history. Many techniques have been applied for the detection of dairy products’ adulteration, but no obvious results have been achieved. The electronic tongue system, based on a virtual instrument, can provide fast detection of goat milk’s quality. The qualitative identification of different purities of goat milk by kernel principal component analysis (KPCA) has achieved 100% accuracy. By comparing the four algorithms of the optimal extreme learning machine (ELM), it was determined that the quantitative model for the prediction of adulterated goat milk, established by the improved artificial fish swarm optimized ELM method, had high prediction accuracy. The prediction set coefficient R2 was 0.998, the average absolute error (MAE) was 0.083, and the root mean square error (MSE) was 0.011. According to its unique advantages, the electronic tongue provides a new idea and method for the detection of food adulteration. As a modern intelligent sensory instrument, the electronic tongue has great potential for brand identification as well as the identification of goat milk and goat milk powder adulteration.

Screening method for simultaneous detection of elaidic and vaccenic trans fatty acid isomers by capillary zone electrophoresis

A novel screening method for the simultaneous detection of elaidic and vaccenic trans fatty acid isomers by CZE-UV was proposed and applied to brazilian spreadable cheese analysis. Optimized background electrolyte was composed by 10 mmol L−1 heptakis (2, 3, 6-tri-o-methyl)-beta cyclodextrin, 24 mmol L−1 sodium tetraborate buffer solution, 45% methanol and 15% acetonitrile. Sample preparation comprised only a saponification reaction and dilution of sample solution before analysis. The method proved to be simple, environmental friendly and sensitive (LOD calculated for elaidic and vaccenic acid were 0.0163 mmol L−1 and 0.0169 mmol L−1, respectively) and may be used to detect adulteration in dairy products, where the addition of hydrogenated vegetable fat to food is prohibited.

Detection of the adulteration in pure cow ghee by electronic nose method (case study: sunflower oil and cow body fat)

ABSTRACTCow ghee is very used in some regions of Iran, such as Kermanshah province. Cow ghee is a natural source that contains high-quality nutrients which are needed for the human body. Adulteration in dairy products is not only a serious threat to human health but also it causes economic losses. Diagnosis of foodstuff cheating and its estimation is one of the key concerns in recent years. The aim of this study was the detection of the adulteration in cow ghee by olfactory machine system. Therefore, an electronic nose system was used for the different levels of sunflower oil and cow body fat mixed with pure cow ghee (10%, 20%, 30%, 40%, and 50%). The principal components analysis (PCA) and artificial neural networks (ANNs) methods were used to achieve this goal. Based on the results, the accuracy of the principal components analysis of sunflower oil and cow body fat were 96% and 97% of the data variance, respectively. According to the results, artificial neural networks identified the adulteration with sunflower oil and cow body fat with an accuracy of 91.3% and 82.5%, respectively.

A quadruplex PCR (qxPCR) assay for adulteration in dairy products

This study describes the development of a quadruplex quantitative Real Time PCR (qxPCR) based on SYBR®GreenER chemistry, for rapid identification of DNA of cow, goat, sheep and buffalo in dairy products, and for quantification of cow DNA in these products. The platform was applied to: (i) mixes of milks at fixed percentages; (ii) cheeses prepared with the same mixes; (iii) commercial dairy products. The methodology enabled the detection of DNA from cow in mixes of milk and cheeses with a limit of detection (LOD) of 0.1%. When applied to commercial dairy products the qxPCR gave results comparable with each single-plex Real Time PCR.A good correlation (R2>0.9) between peaks’ area of derivative of melting curves of amplicons and percentages of cow milk in milk mixes and cheeses, allows for an estimation of cow DNA in a dynamic range varying from 0.1–5% to 1–25%.

PCR-based detection of cow and goat milk in sheep milk and dairy products marketed in Mashhad city of Iran

BACKGROUND: The extensive consumption of milk and dairy products makes these foodstuffs targets for potential adulteration with financial gains for unscrupulous producers. OBJECTIVES: The aim of this study was using PCR assay to detect cow milk in labeled sheep milk, sheep yoghurt, and Lighvan cheese (a traditional ripened cheese produced from sheep's milk). METHODS: The assay utilized primers targeting the mitochondrial 12s and 16s rRNA gene. In this study, 35 samples of sheep milk, 35 samples of sheep yoghurt, and 35 samples of Lighvan cheese were purchased from different supermarkets in Mashhad city with different batch numbers. RESULTS: The results showed only 21 out of 105 (20%) samples contained pure sheep milk. Undeclared presence of cow and goat milk was detected in 33(31.5%) and 68(65%) of the 105 samples, respectively. CONCLUSIONS: It seems the PCR based analytical method is an applicable technique to monitor adulteration in dairy products.

Complex chromatographic determination of the adulteration of dairy products: A new approach

A new approach to complex chromatographic determination of the adulteration of dairy products was proposed, based on the determination of not only the total fatty acid composition but also the composition of the sterol fraction and the concentration of trans-isomers of fatty acids. Procedures for the chromatographic identification of low-fat dairy products were developed, including the extraction of a water-milkalcohol emulsion with a hexane-ether mixture followed by the chromatographic determination of the sterol fraction and trans-isomers.

Application of immunological methods for the detection of species adulteration in dairy products

SummaryA number of enzyme‐linked immunosorbent assays (ELISAs) have been developed for the detection of milk adulteration in dairy products. Target antigens have been caseins, lactoglobulins, immunoglobulins and other whey proteins. Polyclonal and monoclonal antibodies have been used in a variety of formats including direct, indirect, competitive and sandwich ELISAs. ELISAs have been successfully applied to the detection of cows' milk adulteration of sheep, goat and buffalo milk. Goat milk adulteration of sheep milk has also been detected. A number of ELISAs have also been applied to cheese. It is recommended that ELISA should be used in combination with PCR to ensure compliance with current legislation.

Detection of Foreign Fat Adulteration of Milk Fat by Near Infrared Spectroscopic Method

A near infrared spectroscopic method for the detection of foreign fat adulteration in dairy products was examined using the extracted fats from samples of butter and margarine mixtures and from milk and soymilk mixtures. Butter and margarine mixtures were also analyzed. The near infrared absorptions at 1164, 1660, 2144, and 2176nm are due to cis unsaturation of fatty acid moieties, the ratio of which is intrinsic to each oil. We suggest that the near infrared spectra around these wavelengths could be used as a simple index for fatty acid profiles in oil. As for the original near infrared spectra, the difference of spectral data at log 1164, 1660, 2144, or 2176nm from 2124nm correlated very well with the mixing ratios. As for their second derivative spectra, spectral data at log 1164, 1660, 2144, or 2176nm correlated with the mixing ratios very well, and further, the correlation proved to be better by employing the difference between these values and those at 2124nm. The application of near infrared method to dairy products indicated that the adulteration of the fat with as little as 3% foreign fat could be detected simply, rapidly, and “nondestructively” for butter and margarine mixtures.