Cheese Adulteration Research Articles

Portable near-infrared (NIR) spectrometer and chemometrics for rapid identification of butter cheese adulteration

Artisanal cheeses are highly valued around the world for their distinct sensory characteristics, thus being prone to adulteration by substituting authentic material for cheaper products, such as vegetable oil. In this work, we developed a method based on a portable NIR spectrometer as a non-destructive and low-cost alternative to identify adulteration in butter cheese. Dataset consisted of authentic and intentionally adulterated cheeses in the laboratory and commercial cheeses, which were identified as authentic and adulterated with vegetable oil after analysis of the fatty acid profile. PLS-DA classification models identified adulterated samples with an accuracy of 94.44%. PLS prediction models showed excellent performance (RPD > 3.0) to predict the adulterant level. These results demonstrate that NIR spectra can be used to identify the replacement of authentic fat by soybean oil in butter cheese and that the developed models can be used to identify adulteration in external samples with good performance.

Infrared spectroscopy-based rapid determination of adulteration in commercial sheep's milk cheese via n-hexane and ethanolic extraction

This study aimed to authenticate a commercial cheese using FT-IR spectroscopy combined with chemometrics via ethanol and n-hexane extraction. Erzincan Tulum cheese (ETC) is one of the most produced and consumed cheeses in Türkiye. With an increase in production and consumption, adulteration has become a concern. Authentic ETC (n = 24) and non-authentic cheese samples (n = 22) were purchased from local markets. Ethanol and n-hexane extracts of the ETC were used for FT-IR analysis. Aliquots of the extracts were placed on a zinc selenide crystal, dried, and scanned in the mid-infrared region (4000–650 cm−1). Partial least squares-discriminant analysis (PLS-DA), soft independent modelling of class analogy (SIMCA), and conditional entropy (CE) were used as chemometric tools. PLS-DA and SIMCA provided 99% accuracy, followed by CE as low as 93.5%. Despite some disadvantages, overall, FT-IR spectroscopy with chemometrics is simple, rapid, and an inexpensive tool to detect cheese adulteration.

Determination of grated hard cheese adulteration by digital image analysis and multivariate analysis

Hard-cheeses are foods with high commercial value, thus fraudulent practices are frequent and difficult to detect, which reduce hard-cheese quality and consequently, decreases nutritional value. This work presents a novel and feasible tool to identify the adulteration of grated-cheese with higher levels of additives than those permitted (cellulose and silicon dioxide), and foreign substances used to increase weight and volume (wheat-flour, wheat-semolina and sawdust). The methodology is based on the use of colour histograms (obtained from digital images) and multivariate classification analysis. Satisfactory results were obtained with support vector machines with average values of accuracy and sensitivity above 82%, as adulterated samples were discriminated from original (unadulterated) samples. The proposed screening methodology represents a fast, simple, reliable and affordable way to perform quality control of grated-cheeses to avoid food fraud and nutritional detriments of worldwide consumption foods such as hard-cheeses.

MALDI-TOF-MS integrated workflow for food authenticity investigations: An untargeted protein-based approach for rapid detection of PDO feta cheese adulteration

Advances in Matrix-assisted Laser Desorption/Ionization -Time-Of-Flight Mass Spectrometry (MALDI-TOF-MS) have led to its supremacy for complex assessment of food authenticity studies, like dairy products fraud, holding promise for the discovery of potential authenticity (bio)markers. In this study, an integrated untargeted protein-based workflow in combination with advanced chemometrics is presented, to address authenticity challenges in PDO feta cheese which is legally manufactured by the mixture of sheep/goat milk. Potential markers attributed to specific animal origin were found from protein profiles acquired for authentic feta and white cheeses (prepared from cow milk), belonging to 4 kDa–18.5 kDa mass area. Rapid detection of feta cheese adulteration from cow milk was also achieved down to 1% adulteration level. The discriminative models showed high predictive ability for feta cheese authenticity (Q2 = 0.920, RMSEE = 0.053) and its adulteration (Q2 = 0.835, RMSEE = 0.121), introducing a reliable approach in routine analysis. The methodology was successfully applied in detection of cow milk in sheep yoghurt.

3D printed microcell featuring a disposable nanocomposite Sb/Sn immunosensor for quantum dot-based electrochemical determination of adulteration of ewe/goat’s cheese with cow’s milk

Abstract This work describes a new type of electrochemical immunosensing device which consists of a 3D printed mini cell integrating two thermoplastic electrodes and a disposable graphite screen-printed working electrode modified with Sb2O5/SnO2. The applicability of the immunodevice to the analysis of complex matrices is demonstrated through the assessment of adulteration of cheese produced from ewe/goat’s milk with cow’s milk. The operation of the device is based on: (i) the development of a competitive immunoassay onto the surface of the screen-printed working electrode using biotinylated anti-bovine k-casein labeled with streptavidin-conjugated CdSe/ZnS quantum dots (QDs) and (ii) the stripping voltammetric detection of Cd(II) released after the acidic dissolution of Cd-based QDs. Determination is carried out at the screen-printed working electrode covered with Sb/Sn nanoparticles formed in-situ by reduction of the embedded precursors (Sb2O5/SnO2) simultaneously with the deposition of Cd on the surface of the working electrode. Under the selected experimental variables, the limit of detection was 0.07 % (v/v) cow’s milk in ewe/goat’s cheese. The synergistic combination of 3D printing, QD labeling and in-situ formation of nanostructured sensing surface, establishes the proposed sensor as an easy-constructed and ultrasensitive immunodetection device with wide scope of applicability to other QDs-based bioassays.

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.

NIRS to assess chemical composition of sheep and goat cheese

NIRS to assess chemical composition of sheep and goat cheese

Determination of grated hard cheeses adulteration by near infrared spectroscopy (NIR) and multivariate analysis

Cheese is a dairy product of massive consumption and is commonly commercialised as grated cheese. Adulteration of cheese reduces its quality and consequently, decreases its nutritional value. This work proposes an analytical tool to determine the adulteration of cheeses with higher levels of additives than those permitted (cellulose and silicon dioxide) and non-authentic substances used to increase weight and volume such as wheat-flour, wheat-semolina and sawdust. The proposed methodology is based on near infrared spectroscopy (NIR) and multivariate classification analysis. Excellent classification results were obtained with partial least squares discriminant analysis (PLS-DA) as adulterated samples were discriminated from original (unadulterated) samples. The prediction ability was assessed with adulterated commercial grated cheeses. The screening tool represents a fast, reliable and affordable way to perform quality control of grated cheeses to avoid consumer deception based on nutritional detriment and misleading labelling.

A simultaneous triplex TaqMan real-time PCR approach for authentication of caprine and bovine meat, milk and cheese

Goat foodstuffs are considered as healthy foods with high nutritional value. This study demonstrated the development and validation of a triplex real-time PCR on the basis of species-specific and species-conservative TaqMan probes for the simultaneous identification of caprine and bovine DNA in meats, milk and cheeses with a prerequisite designed endogenous control. In this research, caprine and bovine meat, milk and cheese were specifically identified via developed primers and probes, and the limits of detection of this methodology were 0.005 and 0.01 ng DNA of milk and cheese from goat, and 0.01 and 0.05 ng DNA of milk and cheese from cow. Taken together, this approach was elaborated to address dairy adulteration issues to eliminate the fraud of economically motivated goat milk and cheese adulteration by adding cow milk.

Use of quantitative descriptive analysis (QDA) coupled with multivariate statistical methods to detection and discrimination of adulterated fresh cheeses

AbstractThe most recurrent adulteration of cheeses consists of adding vegetable fat of lower cost than milk fat. Several analytical techniques are used to detect adulteration of cheeses but do not offer sensory perception information. The objective of this study was to detect and discriminate fresh cheeses adulterated with known concentrations of vegetable fat using sensory analysis coupled with multivariate techniques. The panel generated eight attributes through Quantitative Descriptive Analysis. The results showed that the power of discrimination was high (p &lt; .05) in each panelist and panel level in all attributes evaluated. Principal Components Analysis explained the use of average value of the attributes, about 85% of the variability of the cheeses in two main components. Using dendrogram and confidence ellipses, the adulterated cheeses with a concentration of 15 g/L or higher of vegetable fat were identified. The discrimination of adulterated cheeses by this method was 92.5%.Practical applicationsThe findings demonstrated the high sensitivity of the sensory panel, which aligned well with analytical statistical techniques for the differentiation and detection of cheeses, which may be applied well to other adulterated food products. The proposed methodology could be implemented in companies, research centers, and associations of producers with economic restrictions that do not allow for sophisticated analytical equipment for the determination of adulterated food products.

Differential scanning calorimetry coupled with chemometric tools for determining adulteration with vegetable fat in fresh cheeses

The adulteration of cheeses is a problem that has increased due to the high value of butterfat. Analytical methods for identifying adulteration are costly and time-consuming. Differential scanning calorimetry (DSC) has been effective for studying the thermal properties of milk fat. The aim of this study was to determine the thermal properties of cheese with added vegetable fat, relate them to the lipid profile and couple the DSC data with cluster analysis to identify adulterated samples. Two endothermic peaks (11.1 and 47.2 °C) in cheeses were found; the first peak could be related to saturated fatty acids and the second to unsaturated fatty acids. It was possible to discriminate 100% of genuine cheeses from adulterated cheeses with a vegetable fat minimum content of 5 g/L using DSC results coupled with cluster analysis; therefore, DSC coupled with cluster analysis can be a tool to determine the authenticity of fresh cheeses.

Research And Practice: Quantification Of Raw And Heat-Treated Cow Milk in Sheep Milk, Cheese And Bryndza By ELISA Method

The aim of this study was to test the reliability of commercial ELISA tests (RC-bovino) within raw and heat treated cow milk detection in sheep milk and cheese in order to obtain a high-quality, reliable and economically beneficial method suitable for routine application in practice. These tests were subsequently used for quantification of cow milk in commercial "Bryndza". Raw sheep milk, cow milk and heat-treated cow milk (pasteurisation at 72 °C for 15 sec or at 85 °C for 3 sec) were mixed in precisely defined proportions (0 - 100% cow milk in sheep milk). The milk mixtures were sampled to detect adulteration and subsequently cheese was made. By ELISA tests was possible to determine these amounts of raw cow milk in sheep milk: 0.5% (0.2%), 5 % (4.81%), 50% (42.08%) and 75% (56.52%). The pasteurized samples in different combinations gave lower optical density responses than those prepared from raw milk (by approximately 60%). In context with the above mentioned, the relationship between the real and detected amount of cow milk (%) in different production stages (milk, cheese) using a regression analysis was examined. However, a lower reliability of the detection was indicated by R2 values, which ranged from 0.4058 (cheese) to 0.5175 (milk). In practice this means that although individual percentage (%) of cow milk in the sample can be detected, but in the unknown sample it can not be clearly confirm whether the cow milk was raw or heat-treated. In this context, the results can be inaccurate and may not correspond to the real situation. Within monitoring phase of this research, 9 samples of bryndza were analysed with the results of detected cow milk ranged from 11.56% to 14.3%. The obtained results confirm that the appropriate selection of ELISA tests can become an important factor in the setting of analytical capabilities for the detection of milk and cheese adulteration.

Mass spectrometry detection of fraudulent use of cow whey in water buffalo, sheep, or goat Italian ricotta cheese

Ricotta cheese is a typical Italian product, made with whey from various species, including cow, buffalo, sheep, and goat. Ricotta cheese nominally manufactured from the last three species may be fraudulently produced using the comparatively cheaper cow whey. Exposing such food frauds requires a reliable analytical method. Despite the extensive similarities shared by whey proteins of the four species, a mass spectrometry-based analytical method was developed that exploits three species-specific peptides derived from β-lactoglobulin and α-lactalbumin. This method can detect as little as 0.5% bovine whey in ricotta cheese from the other three species. Furthermore, a tight correlation was found (R2>0.99) between cow whey percentages and mass spectrometry measurements throughout the 1–50% range. Thus, this method can be used for forensic detection of ricotta cheese adulteration and, if properly validated, to provide quantitative evaluations.

Detection of plant oil addition to cheese by synchronous fluorescence spectroscopy.

The fraudulent addition of plant oils during the manufacturing of hard cheeses is a real issue for the dairy industry. Considering the importance of monitoring adulterations of genuine cheeses, the potential of fluorescence spectroscopy for the detection of cheese adulteration with plant oils was investigated. Synchronous fluorescence spectra were collected within the range of 240 to 700 nm with different wavelength intervals. The lowest detection limits of adulteration, 3.0 and 4.4%, respectively, were observed for the application of wavelength intervals of 60 and 80 nm. Multiple linear regression models were used to calculate the level of adulteration, with the lowest root mean square error of prediction and root mean square error of cross validation equalling 1.5 and 1.8%, respectively, for the measurement acquired at the wavelength interval of 60 nm. Lower classification errors were obtained for the successive projections algorithm-linear discriminant analysis (SPA–LDA) rather than for the principal component analysis (PCA)–LDA method. The lowest classification error rates equalled 3.8% (∆λ = 10 and 30 nm) and 0.0% (∆λ = 60 nm) for the PCA–LDA and SPA–LDA classification methods, respectively. The applied technique is useful for detecting the addition of plant fat to hard cheese.

Preliminary Discrimination of Cheese Adulteration by FT-IR Spectroscopy

The present work describes a preliminary study to compare some traditional Romanian cheeses and adulterated cheeses using Attenuated Total Reflectance-Fourier transform infrared spectroscopy (ATR-FTIR). For PLS model calibration (6 concentration levels) and validation (5 concentration levels) sets were prepared from commercial Dalia Cheese from different manufacturers by spiking it with palm oil at concentrations ranging 2-50 % and 5-40 %, respectively. Fifteen Dalia Cheese were evaluated as external set. The spectra of each sample, after homogenization, were acquired in triplicate using a FTIR Shimatsu Prestige 21 Spectrophotometer, with a horizontal diamond ATR accessory in the MIR region 4000-600 cm -1 . Statistical methods as PLS were applied using MVC1 routines written for Matlab R2010a. As first step the optimal condition for PLS model were obtained using cross-validation on the Calibration set. Spectral region in 3873-652 cm -1 , and 3 PLS-factors were stated as the best conditions and showed an R 2 value of 0.9338 and a relative error in the calibration of 17.2%. Then validation set was evaluated, obtaining good recovery rates (108%) and acceptable dispersion of the data (20%). The curve of actual vs. predicted values shows slope near to 1 and origin close to 0, with an R 2 of 0.9695. When the external sample set was evaluated, samples F19, F21, F22 and F24, showed detectable levels of palm fats. The results proved that FTIR-PLS is a reliable non-destructive technique for a rapid quantification the level of adulteration in cheese. The spectroscopic methods could assist the quality control authority, traders and the producers to discriminate the adulterated cheeses with palm oil.

A duplex polymerase chain reaction for the quantitative detection of cows’ milk in goats’ milk cheese

A duplex polymerase chain reaction (PCR) has been applied for the detection of both cows’ and goats’ milk in goats’ milk cheeses using primers targeting the mitochondrial 12S rRNA genes. By means of a linear normalised calibration curve between the log of the ratio of the cows’ band intensity and the sum of cows’ and goats’ intensities vs. the log of cows’ milk percentage, it was possible to quantify cheese adulteration with cows’ milk in the range of 1–60%. The duplex PCR technique allowed the detection of 0.1% of cows’ milk in goats’ milk cheese. The proposed method was successfully applied to cheeses with defined amounts of cows’ milk and to 15 of a total of 17 commercial cheese samples. The results showed the fraudulent addition of cows’ milk in three samples labelled as pure goats’ milk cheeses and the omission of goats’ milk mentioned on the label of two cheeses containing mixed milk.

Detection of Adulteration in Grated Cheese by Using Calcium, Phosphorus, Magnesium, and Lactose Indices

Abstract Calcium, phosphorus, and magnesium levels and calcium/phosphorus and calcium/magnesium ratios were determined for 74 authentic cheeses. Lactose was determined for 40 authentic grating cheeses. Phosphorus was measured by an AOAC method, calcium and magnesium by a modified AOAC atomic absorption method, and lactose by liquid chromatography. The average calcium/phosphorus and calcium/magnesium ratios were 1.55 and 23.3, respectively, and were used with lactose data as criteria for evaluating grated cheese adulteration.

Detection of Cow's Milk in Manchego Cheese

Analysis of caseins by electrophoresis and fatty acids by gas liquid chromatography for detection of cow's milk in Manchego cheese was studied. Polyacrylamide gel electrophoresis was more sensitive and gave better resolution than cellulose acetate. By polyacrylamide gel electrophoresis 5% of cow's milk in sheep's milk was measurable. Detection of cow's milk in cheese was more difficult, although it was possible to detect 10% by a band similar to αs1I (degradation product from bovine αs1 casein). Quantitation was troubled by the proteolytic process during ripening. When milk fat was used for adulteration of cheese, gas chromatography was capable of detecting 7% of bovine milk fat when composition of the original milk fat was known. If unknown, the limit of detection was higher.