Authenticity Assessment of Dairy Products

A high-performance chiral 19F-labeled probe with an increased structural twisting

We developed a new strategy to enhance the chiral discrimination capability of 19F-labeled probes by tuning the torsion angle of the probe's backbone, allowing for the resolution of challenging analytes. Its versatility is demonstrated through the superior performance and the wide analyte scope.

Application of Microsatellites to Trace the Dairy Products Back to the Farm of Origin

The increasing number of food frauds, mainly targeting high quality products, is a rising concern among producers and authorities appointed to food controls. Therefore, the development or implementation of methods to reveal frauds is desired. The genetic traceability of traditional or high-quality dairy products (i.e., products of protected designation of origin, PDO) represents a challenging issue due to the technical problems that arise. The aim of the study was to set up a genetic tool for the origin traceability of dairy products. We investigated the use of Short Tandem Repeats (STRs) to assign milk and cheese to the corresponding producer. Two farms were included in the study, and the blood of the cows, bulk milk, and derived cheese were sampled monthly for one year. Twenty STRs were selected and Polymerase Chain Reactions for each locus were carried out. The results showed that bulk milk and derived cheese express an STR profile composed of a subset of STRs of the lactating animals. A bioinformatics tool was used for the exclusion analysis. The study allowed the identification of a panel of 20 markers useful for the traceability of milk and cheeses, and its effectiveness in the traceability of dairy products obtained from small producers was demonstrated.

19 F-Labeled Probes for Recognition-Enabled Chromatographic 19 F NMR

The NMR technique is among the most powerful analytical methods for molecular structural elucidation, process monitoring, and mechanistic investigations; however, the direct analysis of complex real-world samples is often hampered by crowded NMR spectra that are difficult to interpret. The combination of fluorine chemistry and supramolecular interactions leads to a unique detection method named recognition-enabled chromatographic (REC) 19 F NMR, where interactions between analytes and 19 F-labeled probes are transduced into chromatogram-like 19 F NMR signals of discrete chemical shifts. In this account, we summarize our endeavor to develop novel 19 F-labeled probes tailored for separation-free multicomponent analysis. The strategies to achieve chiral discrimination, sensitivity enhancement, and automated analyte identification will be covered. The account will also provide a detailed discussion of the underlying principles for the design of molecular probes for REC 19 F NMR where appropriate.

An overview of nutritional profiling in foods: Bioanalytical techniques and useful protocols

Maintaining a nutritious diet is essential for humans if they want to live a healthier life. Several food businesses and food safety organizations play a significant role and offer useful ways for improving nutritional quality that assists consumers in making informed selections. Making poor food choices and consuming unhealthy meals are the main causes of non-communicable diseases (NCDs). Nutritional profiling (NP) models are developed to evaluate the nutritional value, calorie content, and the amount of micronutrients and macronutrients contained in a given food accompanied by additional details on the nutritional anomaly provided by published standard nutrients and nutritional databases. To construct an ideal nutritional model that can facilitate food consumption, bioanalytical methods such as chromatography, microscopic techniques, molecular assays, and metabolomics can be applied. With the use of these technologies, one can learn more about the health advantages of nutrition and how to prevent disease. A wider element of NP is also provided by the developing technologies in the area of nutrition research, such as nanotechnology, proteomics, and microarray technology. In this review, we are focusing on the different bioanalytical techniques and the various protocols of NP and their application and refinement of the models. We have evaluated various NP techniques currently used in the food industry for the detection of different components present in food items.

Detection and quantification of adulteration in milk and dairy products: A novel and sensitive qPCR-based method

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A novel qPCR method was able to detect and quantify cow, buffalo, goat and sheep DNA in milk and dairy products.

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Established detection limit was 0.016 ng for the four species.

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The limit of detection of cow DNA in buffalo, goat and sheep DNA samples was 0.1% (0.01 ng)

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This method is able to detect and quantify adulteration between cows, buffaloes, goats and sheep dairy samples.

Species identification in dairy products has a notable importance in food traceability and adulteration control and consequently has a significant effect on the final economic value of foods. In the present study, we developed a method based on real-time quantitative PCR (qPCR) for detection and quantification of cow DNA in DNA samples from milk and dairy products from buffaloes, goats, and sheep. The qPCR reactions showed high specificity, and the amplifications only occurred to species-specific primers. The calibration curves allowed for the quantification of the amount of DNA of each species-specific primer, and the established detection limit was 0.016 ng for the four species. The detection limit of cow DNA in buffalo, goat and sheep DNA samples was 0.1% (0.01 ng). Although the present study aimed to detect and quantify cow DNA in buffalo, goat, and sheep dairy products, we believe that the qPCR assays can also be directed to differentiate and quantify goat × sheep, and/or buffalo × goat/sheep.

Recognition-Enabled Automated Analyte Identification via 19F NMR

Nuclear magnetic resonance (NMR) is an indispensable tool for structural elucidation and noninvasive analysis. Automated identification of analytes with NMR is highly pursued in metabolism research and disease diagnosis; however, this process is often complicated by the signal overlap and the sample matrix. We herein report a detection scheme based on ¹⁹F NMR spectroscopy and dynamic recognition, which effectively simplifies the detection signal and mitigates the influence of the matrix on the detection. It is demonstrated that this approach can not only detect and differentiate capsaicin and dihydrocapsaicin in complex real-world samples but also quantify the ibuprofen content in sustained-release capsules. Based on the ¹⁹F signals obtained in the detection using a set of three ¹⁹F probes, automated analyte identification is achieved, effectively reducing the odds of misrecognition caused by structural similarity.

Animal Species Authentication in Dairy Products

Milk is one of the most important nutritious foods, widely consumed worldwide, either in its natural form or via dairy products. Currently, several economic, health and ethical issues emphasize the need for a more frequent and rigorous quality control of dairy products and the importance of detecting adulterations in these products. For this reason, several conventional and advanced techniques have been proposed, aiming at detecting and quantifying eventual adulterations, preferentially in a rapid, cost-effective, easy to implement, sensitive and specific way. They have relied mostly on electrophoretic, chromatographic and immunoenzymatic techniques. More recently, mass spectrometry, spectroscopic methods (near infrared (NIR), mid infrared (MIR), nuclear magnetic resonance (NMR) and front face fluorescence coupled to chemometrics), DNA analysis (real-time PCR, high-resolution melting analysis, next generation sequencing and droplet digital PCR) and biosensors have been advanced as innovative tools for dairy product authentication. Milk substitution from high-valued species with lower-cost bovine milk is one of the most frequent adulteration practices. Therefore, this review intends to describe the most relevant developments regarding the current and advanced analytical methodologies applied to species authentication of milk and dairy products.

Near-infrared techniques for fraud detection in dairy products: A review

The dairy products sector is an important part of the food industry, and their consumption is expected to grow in the next 10 years. Therefore, the authentication of these products in a faster and precise way is required for the sake of public health. This review proposes the use of near-infrared techniques for the detection of food fraud in dairy products as they are faster, nondestructive, environmentally friendly, do not require sample preparation, and allow multiconstituent analysis. First, we have described frequent forms of food fraud in dairy products and the application of traditional techniques for their detection, highlighting gaps and counterproductive characteristics for the actual global food chain, as longer sample preparation time and use of reagents. Then, the application of near-infrared spectroscopy and hyperspectral imaging for the detection of food fraud mainly in cheese, butter, and yogurt are described. As these techniques depend on model development, the coverage of different dairy products by the literature will promote the identification of food fraud in a faster and reliable way.

Simultaneous analysis of multiple adulterants in milk using microfluidic paper-based analytical devices

This study reports the simultaneous colorimetric detection of urea, H₂O₂, and pH in milk samples using microfluidic paper-based analytical devices (μPADs) fabricated through a craft cutter printer. Paper-based devices were designed to contain three detection zones interconnected to a sampling zone by microfluidic channels. Colorimetric analysis was performed using images digitalized through an office scanner. The volumes of chromogenic and sample solutions were optimized, and the best colorimetric performance was achieved by adding 0.5 and 10 μL into detection and sampling zones, respectively. Simultaneous assays were then carried out, and the recorded responses revealed a linear behavior in the concentration ranges from 0-30.0 mmol L^-1, 0-10.0 mmol L^-1 and 6.0-9.0 for urea, H₂O₂ and pH, respectively. The limit of detection values obtained for urea and H₂O₂ were 2.4 mmol L^-1 and 0.1 mmol L^-1, respectively. For pH measurements, colorimetric assay allowed the monitoring of solution pH with a resolution of 0.25 units. The use of μPADs to detect target adulterants exhibited suitable reproducibility (RSD ≤ 6.0%), accuracy (91-102%) and no cross-reaction occurrence. When compared to reference techniques, colorimetric assays did not reveal a significant difference at a confidence level of 95%. As a proof-of-concept, the feasibility of the proposed approach was successfully demonstrated through the analysis of potential adulterants in sixteen milk samples, which were tested without any pretreatment requirement. Based on the achievements, μPADs in conjunction with colorimetric measurements emerge as a powerful tool for rapid screening of potential adulterants in milk.

Biochemical, Physicochemical and Sensory Properties of Yoghurts Made from Mixing Milks of Different Mammalian Species

Among developed countries, bovine milk production makes a major contribution towards the economy. Elevating consumer demand for functional foods has triggered a niche for non-bovine milk-based products. Mixing milks from different species can be a strategy to increase the consumption of non-bovine milk and enable consumers and dairy companies to benefit from their nutritional and technological advantages. Thus, this review aimed to gather the most important research on yoghurts derived from processing mixtures of milks of different species. We discuss the impact of milk mixtures (i.e., species and milk ratio) on nutritional, physicochemical, sensory, rheological and microbiological properties of yoghurts. More specifically, this paper only highlights studies that have provided a clear comparison between yoghurts processed from a mixture of two milk species and yoghurts processed from a single species of milk. Finally, certain limitations and future trends are discussed, and some recommendations are suggested for future research.

Discrimination of milk species using Raman spectroscopy coupled with partial least squares discriminant analysis in raw and pasteurized milk

BACKGROUND

Heat treatment is the most common practice for the microbiological safety of milk; hence, determination of the heat treatment of milk is essential. Also, mislabeling or adulteration of expensive milk samples, like ewe or goat milk, with cow's milk is a growing problem in the dairy market. Thus, the determination of the authenticity of milk samples has crucial importance for both producers and consumers. The aim of this study was to discriminate milk samples using Raman spectroscopy with partial least squares discriminant analysis (PLS-DA), first with regard to whether the milk was heat-treated or not, and second with regard to species (cow, goat, ewe, mixture (adulterated)) in both raw and pasteurized milk.

RESULTS

First, discrimination of milk samples as raw or pasteurized was achieved using PLS-DA. Both in calibration and prediction models, high sensitivity and specificity values were obtained for raw and pasteurized milk samples. Second, the proposed method also discriminated milk samples according to their species (cow, goat, ewe, and mixture) for both raw and pasteurized milk. In both calibration and prediction models, the sensitivity and specificity values were above 0.857 and 0.897 respectively. Also, the accuracy values were above 0.915. The results obtained denote satisfactory accurate classification of the samples.

CONCLUSION

The results suggest that Raman spectroscopy coupled with PLS-DA can be successfully used to discriminate milk samples according to heat treatment (raw/pasteurized) and their species within 20 s per sample. It was seen that Raman spectra provide valuable information to be used especially for discrimination of milk samples according to their origin. © 2020 Society of Chemical Industry.

Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples

Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near-infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825-1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000-4,000 cm-1, using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm-1. Principal components analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and partial least-squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS-DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R-square and root mean square error (RMSE) values obtained for the PLS-DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave-one-out cross-validation procedure was used as an internal cross-validation criteria and the R-square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples.

Milk Authentication: Stable Isotope Composition of Hydrogen and Oxygen in Milks and Their Constituents

This paper summarises the isotopic characteristics, i.e., oxygen and hydrogen isotopes, of Slovenian milk and its major constituents: water, casein, and lactose. In parallel, the stable oxygen isotope ratios of cow, sheep, and goat's milk were compared. Oxygen stable isotope ratios in milk water show seasonal variability and are also 18O enriched in relation to animal drinking water. The δ18Owater values were higher in sheep and goat's milk when compared to cow milk, reflecting the isotopic composition of drinking water source and the effect of differences in the animal's thermoregulatory physiologies. The relationship between δ18Omilk and δ18Olactose is an indication that even at lower amounts (>7%) of added water to milk can be determined. This procedure once validated on an international scale could become a reference method for the determination of milk adulteration with water.

Rapid Liquid AP-MALDI MS Profiling of Lipids and Proteins from Goat and Sheep Milk for Speciation and Colostrum Analysis

Rapid profiling of the biomolecular components of milk can be useful for food quality assessment and for food fraud detection. Differences in commercial value and availability of milk from specific species are often the reasons for the illicit and fraudulent sale of milk whose species origin is wrongly declared. In this study, a fast, MS-based speciation method is presented to distinguish sheep from goat milk and sheep colostrum at different phases. Using liquid atmospheric pressure (AP)-matrix-assisted laser desorption/ionisation (MALDI) MS, it was possible to classify samples of goat and sheep milk with 100% accuracy in one minute of data acquisition per sample. Moreover, an accuracy of 98% was achieved in classifying pure sheep milk samples and sheep milk samples containing 10% goat milk. Evaluating colostrum quality and postnatal stages represents another possible application of this technology. Classification of sheep colostrum samples that were collected within 6 hours after parturition and 48 hours later was achieved with an accuracy of 84.4%. Our data show that substantial changes in the lipid profile can account for the accurate classification of colostrum collected at the early and late time points. This method applied to the analysis of protein orthologs of different species can, as in this case, allow unequivocal speciation analysis.

Determination of milk fat authenticity in ultra-filtered white cheese by using Raman spectroscopy with multivariate data analysis

Cheese is one of the most widely consumed food products in the world. However, the increasing demand for nutritionally enhanced or functional products by the cheese industry has created new approaches that partially or fully replace milk fat. With this, new methods of adulteration have also been noted, potentially leading to these fully/partially-replaced products being offered as cheese. In this study, Raman spectroscopy was used to determine origins of fats in margarine, corn, and palm oils present in white and ultra-filtered cheese samples. Raman spectra were evaluated with partial least square-discriminant (PLS-DA) and PLS to identify fat/oil origins and adulteration ratios. The coefficients of determination and limits of detection for margarine, and corn and palm oil adulteration were found to be 0.990, 0.993, 0.991 and 3.38%, 3.36% and 3.59%, respectively.

Opportunities for fraudsters: When would profitable milk adulterations go unnoticed by common, standardized FTIR measurements?

Milk is regarded as one of the top food products susceptible to adulteration where its valuable components are specifically identified as high-risk indicators for milk fraud. The current study explores the impact of common milk adulterants on the apparent compositional parameters of milk from the Dutch market as measured by standardized Fourier transform infrared (FTIR) spectroscopy. More precisely, it examines the detectability of these adulterants at various concentration levels using the compositional parameters individually, in a univariate manner, and together in a multivariate approach. In this study we used measured boundaries but also more practical variance-adjusted boundaries to set thresholds for detection of adulteration. The potential economic impact of these adulterations under a milk payment scheme is also evaluated. Twenty-four substances were used to produce various categories of milk adulterations, each at four concentration levels. These substances comprised five protein-rich adulterants, five nitrogen-based adulterants, seven carbohydrate-based adulterants, six preservatives and water, resulting in a set of 360 samples to be analysed. The results showed that the addition of protein-rich adulterants, as well as dicyandiamide and melamine, increased the apparent protein content, while the addition of carbohydrate-based adulterants, whey protein isolate, and skimmed milk powder, increased the apparent lactose content. When considering the compositional parameters univariately, especially protein- and nitrogen-based adulterants did not raise a flag of unusual apparent concentrations at lower concentration levels. Addition of preservatives also went unnoticed. The multivariate approach did not improve the level of detection. Regarding the potential profit of milk adulteration, whey protein and corn starch seem particularly interesting. Combining the artificial inflation of valuable components, the resulting potential profit, and the gaps in detection, it appears that the whey protein isolates deserve particular attention when thinking like a criminal.

Evaluation of the quality of local butters: A new approach based on Raman spectroscopy and supported by the classical pycnometer method

In this study, the quality of the local Romanian butters was investigated using the classical pycnometer and optic microscopy methods, combined with Raman spectroscopy. We used a pool of 10 samples with different characteristics, and analyzed them by the three aforementioned methods. Pycnometric measurements showed a direct correlation between the fat content and the density values of the samples. Raman spectroscopy validated the results from the pycnometric measurements and the optical microscopy and indicated several other properties, such as protein content, hydration, saturation level of the polycarbonate chains, as well as the total cis isomer content and the type of arrangement preferred by the aliphatic chains (polymorphic transition). The methods employed in the present study have a strong potential to become analytical tools for the food industry and food safety agencies in order to assess the quality of butters and margarines, in a fast and cost-effective manner.

Seasonal Variation in Stable Isotope Ratios of Cow Milk in Vilnius Region, Lithuania

Various studies have shown that stable isotope analysis has the potential to verify the geographic origin of foods and drinks. However, stable isotope composition is not always constant in the environment and can even change in the same area. Dairy products are of particular interest as a group of foods that play an important role in feeding the population. The composition of milk is fundamentally dependent on the feeding of the cows, and thereby on a particular environment. To better understand the amount of variation in δ18O, δ13C, and δ15N values in the milk from the same area, we measured stable isotope ratios in cow milk water, artesian water, and precipitation (δ18O) as well as in bulk milk samples (δ13C and δ15N) collected in 2014⁻2016. Different water and food sources were available during the winter (artesian water only and dry grass) and summer (artesian water and fresh grass), and spring and autumn seasons reflected transitional periods. Oxygen stable isotope ratios in milk water were relatively lower in winter and transitional seasons and higher in summer, showing the dependence on the main water source. δ13C values reflected particular food sources. This study shows the applicability of the stable isotope ratio method in linking cow milk to specific environments and reveals the amount of variation in stable isotope ratios in the same area. These results could be valuable for other studies on geographical origin determination of dairy products.

Tackling Fraudsters with Global Strategies to Expose Fraud in the Food Chain

Deliberate adulteration of food products is as old as food processing and production systems. Food adulteration is occurring increasingly often today. With globalization and complex distribution systems, adulteration may have a far-reaching impact and even adverse consequences on well-being. The means of the international community to confront and solve food fraud today are scattered and largely ineffective. A collective approach is needed to identify all stakeholders in the food supply chain, certify and qualify them, exclude those failing to meet applicable standards, and track food in a real time. This review provides some background into the drivers of fraudulent practices (economically motivated adulteration, food-industry perspectives, and consumers' perceptions of fraud) and discusses a wide range of the currently available technologies for detecting food adulteration followed by multivariate pattern recognition tools. Food chain integrity policies are discussed. Future directions in research, concerned not only with food adulterers but also with food safety and climate change, may be useful for researchers in developing interdisciplinary approaches to contemporary problems.

Raman spectroscopy coupled with chemometric methods for the discrimination of foreign fats and oils in cream and yogurt

The adulteration of milk fat in dairy products with cheaper non-milk based fats or oils is frequently encountered in the dairy industry. In this study, Raman spectroscopy with chemometric was used for the discrimination of foreign fats and oils in milk cream and yogurt. Firstly, binary mixtures of cream and oils (corn and sunflower oil), and vegetable fat blends which are potentially or currently used by the dairy industry were prepared. All fat or oil samples and their binary mixtures were examined by using Raman spectroscopy. Then, fat content of skim milk was adjusted to 3% (w/w) by the milk fat, external oils or fats, and binary mixtures, and was used in yogurt production. The lipid fraction of yogurt was extracted and characterized by Raman spectroscopy. The spectral data were then pre-processed and principal component analysis (PCA) was performed. Raman spectral data showed successful discrimination for about the source of the fats or oils. Temperature effect was also studied at six different temperatures (25, 30, 40, 50, 60 and 70 °C) in order to obtain the best spectral information. Raman spectra collected at higher temperatures were more intense. Obtained results showed that the performance of Raman spectroscopy with PCA was very promising and can be expected to provide a simple and quick way for the discrimination of foreign fats and oils in both milk cream and yogurt. Fermentation and yogurt processing affected clustering of fat samples by PCA, probably depending on some lipolysis or production of new products that can affect the Raman scattering. However, those changes did not affect differentiation of samples by Raman spectroscopy.

Modern analytical methods for the detection of food fraud and adulteration by food category

This review provides current information on the analytical methods used to identify food adulteration in the six most adulterated food categories: animal origin and seafood, oils and fats, beverages, spices and sweet foods (e.g. honey), grain-based food, and others (organic food and dietary supplements). The analytical techniques (both conventional and emerging) used to identify adulteration in these six food categories involve sensory, physicochemical, DNA-based, chromatographic and spectroscopic methods, and have been combined with chemometrics, making these techniques more convenient and effective for the analysis of a broad variety of food products. Despite recent advances, the need remains for suitably sensitive and widely applicable methodologies that encompass all the various aspects of food adulteration. © 2017 Society of Chemical Industry.

Feasibility of discrimination of dairy creams and cream-like analogues using Raman spectroscopy and chemometric analysis

Dairy cream and its analogues with sunflower oil, coconut oil and palm oil in different milk fat/vegetable fat ratios were prepared and analysed using Raman spectroscopy. The linear discriminant analysis was conducted in order to classify the samples. Samples were well separated and displayed distinguishing linear arrangement along the principal component that expressed the variation in lipid unsaturation. Good separation of sunflower oil and milk fat samples was obtained in contrast to the samples with coconut and palm oil, where the substantial overlapping occurred. The method permitted classifying of the samples in terms of the type of fat used. Calibrated model was extremely sensitive (100%) for dairy cream. The results indicated that it is possible to consider the Raman spectroscopy coupled with chemometric analysis as a rapid way for the detection of dairy cream adulteration with sunflower, coconut and palm oils.

Detection of Adulterants and Contaminants in Liquid Foods-A Review

Milk and fruit juices have paramount importance in human diet. Increasing demand of these liquid foods has made them vulnerable to economic adulteration during processing and in supply chain. Adulterants are difficult to detect by consumers and thus necessitating the requirement of rapid, accurate and sensitive detection. The potential adulterants in milk and fruit juices and their limits set by different regulatory bodies have been briefly described in this review. Potential advantages and limitations of various techniques such as physicochemical methods, chromatography, immunoassays, molecular, electrical, spectroscopy with chemometrics, electronic nose, and biosensors have been described. Spectroscopy in combination with chemometrics has shown potential for rapid, precise, and sensitive detection of potential adulterants in these liquid foods.

Rapid Analysis Procedures for Triglycerides and Fatty Acids as Pentyl and Phenethyl Esters for the Detection of Butter Adulteration Using Chromatographic Techniques

This paper presents the development of three methods for quality control, fraud detection, and authentication of butter fat and other oils/fats using chromatographic techniques, with one method for triglycerides and two methods for fatty acids (FAs). The procedure for the analysis of triglycerides requires only dissolution of the sample inn-hexane and gas chromatography (GC) analysis using a capillary column. The second method is based on the transesterification of triglycerides as pentyl esters in a single-step reaction using sodium pentanoate in pentanol. The reaction proceeds at room temperature and is similar to the potassium hydroxide-catalysed transesterification of triglycerides with methanol and even more similar to the sodium methoxide method and sodium butanoate method. The advantage of using pentyl esters includes reducing the volatility of short-chain FAs, and substantial recoveries were obtained compared with methyl ester analysis. The third method involves the transesterification of triglycerides in fat through reaction with 2-phenylethanol in a single step; 2-phenylethanol possesses a chromophore, and the phenethyl esters formed are analysed by high-performance liquid chromatography (HPLC) with UV detection.

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.

Detection of fraudulent addition of bovine whey in water buffalo ricotta cheese by isoelectric focusing

BACKGROUND

Prevention of food fraud in the dairy field is a difficult issue for researchers, industries and policy makers, both for commercial and health reasons. Currently, no analytical method allows detection of the addition of bovine whey to water buffalo ricotta, so this fraudulent practice cannot be prevented. The authors' aim was to develop such a method.

RESULTS

The conditions for extraction and purification of denatured ricotta whey proteins, which are unfolded and coagulated by heating during the production process, were optimized. The optimal composition of the polyacrylamide gel (pH range, type and concentration of chemical separator) was first evaluated and then the best conditions to perform the separation by isoelectric focusing were established. The performance of the method (precision, selectivity, robustness, sensibility) was determined.

CONCLUSIONS

The method was shown to be reliable and robust for detection of the presence of bovine whey added to water buffalo Ricotta at percentages above 5% (v/v). The results suggest that the differences observed between bovine and water buffalo electrophoretic profiles are due to bovine β-lactoglobulin isoform A, which is never detected in water buffalo samples.

A control method to inspect the compositional authenticity of Minas Frescal cheese by gel electrophoresis

This study introduces a qualitative method to inspect the compositional authenticity of white nonripened cheeses like Minas Frescal, a typical Brazilian cheese, especially when irregular replacement of milk by whey is suspected. A sodium dodecyl sulfate gel electrophoresis (SDS-PAGE) method, followed by image densitometry, was validated. Cheeses were freeze-dried to electrophoresis, and β-lactoglobulin (β-LG) was chosen as the adulteration marker. In gel trypsin digestion followed by matrix assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry provided its identification. Cheeses with a minimum of 14 mg·g(-1) of β-LG are considered to be adulterated. The method shows satisfactory precision with a detection limit of 7 mg·g(-1). Forty-two commercial samples from inspected establishments were then assessed and subjected to cluster analysis. Compliant and noncompliant groups were set with 24 (57%) authentic samples and 18 (43%) adulterated samples, respectively, showing that proper analytical monitoring is required to inhibit this practice.

Determination of the Authenticity of Dairy Products on the Basis of Fatty Acids and Triacylglycerols Content using GC Analysis

Milk fat is an important food component, and plays a significant role in the economics, functional nutrition, and chemical properties of dairy products. Dairy products also contain nutritional resources and essential fatty acids (FAs). Because of the increasing demand for dairy products, milk fat is a common target in economic fraud. Specifically, milk fat is often replaced with cheaper or readily available vegetable oils or animal fats. In this study, a method for the discrimination of milk fat was developed, using FAs profiles, and triacylglycerols (TGs) profiles. A total of 11 samples were evaluated: four milk fats (MK), four vegetable oils (VG), two pork lards (PL), and one beef tallow (BT). Gas chromathgraphy analysis were performed, to monitor the FAs content and TGs composition in MK, VG, PL, and BT. The result showed that qualitative determination of the MK of samples adulterated with different vegetable oils and animal fats was possible by a visual comparision of FAs, using C14:0, C16:0, C18:1n9c, C18:0, and C18:2n6c, and of TGs, using C36, C38, C40, C50, C52, and C54 profiles. Overall, the objective of this study was to evaluate the potential of the use of FAs and TGs in the detection of adulterated milk fat, and accordingly characterize the samples by the adulterant oil source, and level of adulteration. Also, based on this preliminary investigation, the usefulness of this approach could be tested for other oils in the future.