Search Results (70)

Ionizing radiation is a non-thermal sanitization technique used in the food field to eliminate bacteria, molds, insects and other microbes, resulting in delayed spoilage and extended shelf life. In this work, mozzarella cheese was irradiated with X-rays at a dose of 3.0 kGy, and irradiation-induced lipid modifications were evaluated through a comprehensive analysis of the mozzarella lipid fingerprint. To this aim, an optimized microwave-assisted extraction method associated with UHPLC-Q-Orbitrap-MS analysis was used for reliable and accurate lipid identification in the controls and in irradiated samples. The outcomes demonstrated that the X-ray dose employed in this investigation did not cause the formation of new lipid molecules. However, lipidomic chemometric modeling, including partial least squares-discriminant analysis, enabled the discrimination of irradiated versus non-irradiated samples and the selection of five ceramides, eight hexosyl ceramides, four sphingomyelins, one phosphatidylethanolamine, one cholesterol ester, ten oxidized triacylglycerols, and one oxidized diacylglycerol as potential markers of treatment. Finally, an artificial neural network was developed to accurately model the entire pattern in omics data in relation to the treatment. This developed analytical workflow allows for expanding knowledge on the effects of this technology and could have interesting applications in food safety traceability and control plans. Full article

Background/Objectives: Metabolites are low-molecular-weight organic compounds (<1 kDa) that act as intermediates and end products of cellular metabolism. Their characterization provides valuable information on the nutritional quality, functionality, and potential health impacts of food products. In the dairy sector, proton nuclear magnetic resonance (¹H NMR) spectroscopy has emerged as a powerful tool for metabolite profiling, enabling the simultaneous identification and quantification of diverse compounds. In this study, ¹H NMR was applied to characterize and compare the metabolic composition of whey, a major by-product of cheese and yogurt production, and whey protein concentrate (WPC-80), a whey derivative containing approximately 80% protein by weight and rich in essential amino acids. Methods: Five whey and four WPC-80 samples from a single Parmigiano Reggiano dairy plant were collected, each representing a biologically independent sample. Statistical evaluation was performed using Mann–Whitney U tests to identify significantly different metabolites between groups, while principal component analysis and partial least squares discriminant analysis were employed to assess group separation and determine discriminant metabolites. Results: The results revealed marked compositional differences: whey was higher in dimethyl sulfone, succinate, orotate, fumarate, and lactose (p < 0.05), whereas WPC-80 contained significantly higher levels of histidine, formate, glucose + glucose-6-phosphate, acetate, and choline (p < 0.05). Moreover, metabolites such as hippurate, valine, lactate + threonine, and uracil were exclusively found on whey and not in WPC-80, likely due to processing steps such as ultrafiltration. Conclusions: These findings highlight the metabolic distinctions introduced by WPC-80 processing from Parmigiano Reggiano whey and provide insights into the nutritional and functional characteristics of whey-derived products. Such knowledge can inform the design of innovative dairy ingredients and functional foods, with potential benefits for both industry applications and consumer health. Full article

The main objective of this research consists in finding a rapid method for cheese lipidomics based on NMR data. This study plays an important role in differentiation and characterization of cheese samples in accordance with fat composition, especially in the case of fat substitution with exogenous animal or vegetal fat. Our findings play an important role in relation to religious requirements regarding non-allowed foods (pork fat, for example, in some cultures) and in the correct characterization of foods according to their lipidic profile. The approach consists in establishing a fingerprint region (0.86–0.93 ppm from ¹H-NMR spectra) and then creating a database of the results obtained. The evaluation of the long-chain saturated fatty acids and the saturated short-chain fatty acids (C4 to C8) was established with a newly developed set of equations that make the computation possible even when mixtures of fats from different sources are present. This was accomplished by developing a new method for quantification of the fatty acid composition of different types of cheese, based on ¹H-NMR spectroscopy. Principal component analysis (PCA) was applied to 40 cheese samples with varying degrees (0%, 5%, 12%, or 15%) of milk fat substitution (pork fat, vegetable fat, hydrogenated oils) and different clotting agents (calcium chloride or citric acid). The best sample discrimination was achieved using fatty acid profiles estimated from ¹H-NMR data (using a total of six variables), explaining 89.7% of the total variance. Clear separation was observed between samples containing only milk fat and those with added fats. These results demonstrate that the integration of ¹H-NMR spectroscopy with principal component analysis (PCA) provides a reliable approach for discriminating cheese samples according to their fat composition. Full article

The authenticity of Protected Designation of Origin (PDO) Feta cheese is critical for consumer confidence and market integrity, particularly in light of widespread concerns over economically motivated adulteration. This study evaluated the potential of Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modeling to differentiate authentic Feta from non-Feta white brined cheeses. A total of 90 cheese samples, consisting of verified Feta and cow milk cheeses, were analyzed in both freeze-dried and fresh forms. Spectral data from raw, first derivative, and second derivative spectra were analyzed using principal component analysis–linear discriminant analysis (PCA-LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) to distinguish authentic Feta from non-Feta cheese samples. Derivative processing significantly improved classification accuracy. All classification models performed relatively well, but the PLS-DA model applied to second derivative spectra of freeze-dried samples achieved the best results, with 95.8% accuracy, 100% sensitivity, and 90.9% specificity. The most consistently highlighted discriminatory regions across models included ~2920 cm⁻¹ (C–H stretching in lipids), ~1650 cm⁻¹ (Amide I band, corresponding to C=O stretching in proteins), and the 1300–900 cm⁻¹ range, which is associated with carbohydrate-related bands. These findings support ATR-FTIR spectroscopy as a rapid, non-destructive tool for routine Feta authentication. The approach offers promise for enhancing traceability and quality assurance in high-value dairy products. Full article

The assessment of milk coagulation properties (MCPs) is crucial for enhancing goat cheese production and quality. In this study, 501 bulk goat milk samples were collected from various farms to evaluate the MCPs. Traditionally, cheesemaking aptitude is evaluated using lactodynamographic analysis, a reliable but time-consuming laboratory method. Mid-infrared spectroscopy (MIRS) offers a promising alternative for the large-scale prediction of goat milk’s technological traits. Reference MCP measurements were paired with mid-infrared spectra, and prediction models were developed using partial least squares regression, with accuracy evaluated through cross- and external validation. The ability of MIRS to classify milk samples by coagulation aptitude was evaluated using partial least squares discriminant analysis. Only the model for rennet coagulation time obtained sufficient accuracy to be applied for screening (R²_CrV = 0.68; R²_Ext = 0.66; RPD = 2.05). Lower performance was observed for curd-firming time (R²_CrV = 0.33; R²_Ext = 0.27; RPD = 1.42) and curd firmness (R²_CrV = 0.55; R²_Ext = 0.43; RPD = 1.35). Classification of high coagulation aptitude achieved balanced accuracy values of 0.81 (calibration) and 0.74 (validation). With further model refinement and larger calibration datasets, MIRS may become a resource for the dairy-goat sector to monitor and improve milk suitability for cheesemaking. Full article

The production of Fiore Sardo cheese is regulated by the specification of the Protected Designation of Origin (PDO), which aims to guarantee the specific area of production, the know-how of local producers, and the specific use of raw milk from Sarda sheep. The thermization of milk is a sub-pasteurization process that is commonly used in cheese-making to lower the bacterial load and increase the shelf life of the product; it is therefore a cause of non-compliance with the PDO specification of Fiore Sardo cheese, allowing producers to gain practical and economic advantages. In this work, NIR spectroscopy coupled with multivariate discriminant analysis was used to identify the thermal treatment of milk in Fiore Sardo cheese samples. Cheeses were produced using raw milk (38 °C), low-thermized milk (57 °C for 30 s), and high-thermized milk (68 °C for 30 s). The NIR spectra of the cheeses were used to build discriminant models for individuating the thermal treatment of the processed milk. The obtained discriminant models were able to correctly classify about 90% of the Fiore Sardo cheese samples. This method could be suitable as a screening technique to authenticate Fiore Sardo PDO cheese. Full article

Using a multi-analytical approach, this investigation characterized Parmigiano Reggiano PDO cheese produced with milk from dairy cows fed different diets. Ten samples of Parmigiano Reggiano PDO cheese, aged for 24 months, were produced with milk from dairy cows fed only dry hay (P-DH; N = 6) or a diet with part of the dry hay replaced with fresh herbage (P-FF; N = 4). Instrumental (Flash GC-FID) analysis of the volatile fraction, image analyses, and sensory quantitative descriptive analysis (QDA^®) were carried out. The Parmigiano Reggiano cheese belonging to the P-FF group showed a higher intensity of yellow than P-DH for both sensory and image analyses. Regarding the volatile profiles, no differences were observed related to the two experimental groups, while sensory analyses allowed for some discrimination, in particular color and aroma attributes. Instrumental and sensory characterization can be used to obtain a unique analytical profile for Parmigiano Reggiano PDO cheeses produced with milk from dairy cows fed different forage sources and help to define the quality and authenticity of this typical high-value food product. Full article

Serpa cheese, a Portuguese Protected of Denomination Origin (PDO) cheese, known for its unique sensory attributes, is made from the raw milk of native sheep. In this preliminary work, ten samples of Serpa cheese were submitted for a sensory evaluation performed by an expert panel in a sensory laboratory accredited according to ISO 17025 for Serpa cheese parameters, and the panelists classified the cheeses based on texture, taste and odor scores, in accordance with the specifications for the classification of this type of cheese. All cheeses were analyzed by SPME-GC-MS. Following an exploratory unsupervised multivariate analysis, the supervised multivariate analysis by partial least squares—discriminant analysis (PLS-DA), associated the relative percent area of the identified volatiles with the classification of cheeses attributed by the sensory panel. Among the 144 compounds putatively identified, there was a pattern of compound distribution of some of them, such as acetoin, diacetyl, and 2,3-butanediol, leaning toward the cheese samples with high taste and odor scores, while other compounds, such as ethyl caprate, capric acid, and 3-methylindole, were more associated with the cheese samples rated with a low score. Despite the reduced number of samples that may have imposed some restrictions on the conclusions drawn, there was a clear trend in the volatiles’ distribution, allowing us to identify, based on the higher correlation loadings, potential candidates for the Serpa cheese sensory quality. This preliminary study presents, for the first time, an overview of the volatiles that are present in Serpa PDO cheese that may be responsible for the positive or negative sensory evaluation of this PDO cheese. Full article

The aim of this study was to investigate the effect of the addition of wild garlic leaves on the sensory quality, volatiles, color, and texture of sheep milk soft rennet-curd cheese. The sensory evaluation of color, appearance, texture, odor, and taste was performed using a 5-point scale. The intensity of selected taste and odor discriminants was also assessed. Volatiles were analyzed by the GC-MS method. Color and textural characteristics were determined instrumentally. The wild garlic addition had no effect on the sensory characteristics of the cheese (p > 0.05). However, cheese with herbs exhibited a less intensive sour odor (p ≤ 0.05), sheep’s milk odor, and taste (p ≤ 0.01). (E)-7-methyl-4-decene, dichloroacetic acid undecyl ester, and 3,5-dimethyl-octane, described as creamy, acetic, and acid pungent in smell, were not detected in the cheese with wild garlic while they were present in the natural one. Moreover, herbal cheese was more piquant (p ≤ 0.01). PCA showed that the differences in volatiles resulted both from the use of wild garlic and the time of storage. Herbal addition affected almost all color characteristics, except for the hue angle (h), but caused an increase only in hardness and chewiness. In conclusion, wild garlic leaves can be recommended as an additive in the production of soft sheep’s milk rennet-curd cheese. Full article

Food poisoning outbreaks frequently involve staphylococcal enterotoxins (SEs). SEs include 33 distinct types and multiple sequence variants per SE type. Various mass spectrometry methods have been reported for the detection of SEs using a conventional bottom-up approach. However, the bottom-up approach cannot differentiate between all sequence variants due to partial sequence coverage, and it requires a long trypsin digestion time. While the alternative top-down approach can theoretically identify any sequence modifications, it generally provides lower sensitivity. In this study, we optimized top-down mass spectrometry conditions and incorporated a fully ¹⁵N-labeled SEA spiked early in the protocol to achieve sensitivity and repeatability comparable to bottom-up approaches. After robust immunoaffinity purification of the SEA, mass spectrometry signals were acquired on a Q-Orbitrap instrument operated in full-scan mode and targeted acquisition by parallel reaction monitoring (PRM), enabling the identification of sequence variants and precise quantification of SEA. The protocol was evaluated in liquid and solid dairy products and demonstrated detection limits of 0.5 ng/mL or ng/g in PRM and 1 ng/mL or ng/g in full-scan mode for milk and Roquefort cheese. The top-down method was successfully applied to various dairy products, allowing discrimination of contaminated versus non-contaminated food, quantification of SEA level and identification of the variant involved. Full article

Cheese quality is affected by seasonal variations. These variations can influence several aspects of cheese, including its flavor, texture, nutritional content, and overall sensory qualities. The aim of this study was to assess the performance of near-infrared (NIR) instrumentation in terms of its ability to detect seasonal variations in Halloumi cheese samples when applying limited sample preparation compared to traditional protocols. Therefore, the use of NIR spectroscopy was examined for the determination of seasonal variations in Halloumi cheese samples from Cyprus in combination with chemometrics. Partial Least Squares Discriminant Analysis (PLS-DA) was applied. We found that NIR and chemometrics successfully discriminated the Halloumi cheese samples based on different climate conditions, the four seasons in the year when the milk collection took place. To externally validate the model, the dataset was divided into training and test sets. The innovation of this study is that Halloumi cheese was studied regarding seasonal variations by applying NIR for the first time. The outcome of this preliminary study is positive in terms of the capability of NIR to distinguish seasonal variations in Halloumi cheese, especially those due to differences in fatty acid molecules throughout the year. Future studies will include more samples to increase the current database. Full article

The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into “Caciocavallo Podolico” cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds. Full article

Milk is the most consumed liquid food in the world due to its high nutritional value and relatively low cost, characteristics that make it vulnerable to adulteration. One of the most common types of milk adulteration involves the undeclared addition of cow’s milk to milk from other mammalian species, such as goats, sheep, buffalo or donkeys. The incidence of such adulteration not only causes a crisis in terms of commercial market and consumer uncertainty but also poses a risk to public health, as allergies can be triggered by proteins in undeclared cow’s milk. In this study, a specific qualitative touchdown (TD) PCR method was developed to detect the undeclared addition of cow’s milk in goat and sheep milk based on the discrimination of the peak areas of the melting curves after the modification of bovine-specific primers. The developed methodology has high specificity for the DNA templates of other species, such as buffalos and donkeys, and is able to identify the presence of cow’s milk down to 1%. Repeatability was tested at low bovine concentrations of 5% and 1% and resulted in %RSD values of 1.53–2.04 for the goat–cow assay and 2.49–7.16 for the sheep–cow assay, respectively. The application of this method to commercial goat milk samples indicated a high percentage of noncompliance in terms of labeling (50%), while a comparison of the results to rapid immunochromatographic and ELISA kits validated the excellent sensitivity and applicability of the proposed PCR methodology that was able to trace more adulterated samples. The developed assays offer the advantage of multiple detection in a single run, resulting in a cost- and time-efficient method. Future studies will focus on the applicability of these assays in dairy products such as cheese and yogurt. Full article

The present study deals with the discrimination of various European cheese products based on spectroscopic data and chemometric analysis. It is the first study that includes cheese products from Cyprus along with cheese samples from abroad and several different cheese types. Therefore, forty-nine samples were collected, freeze-dried, and measured by using spectroscopic techniques, such as FTIR (Fourier-Transform Infrared Spectroscopy) and ¹H-NMR (proton nuclear magnetic resonance). Discriminant analysis was applied, particularly OPLS-DA. All data obtained from ¹H-NMR were included, whereas, regarding the FTIR data, only the spectral subregion between 1900 and 400 cm⁻¹ was used in the extracted model. The cheese samples were classified according to the milk species’ origin. In the future, the samples of this study will be enriched for further testing with spectroscopic techniques and chemometrics. Full article

Sardinia, located in Italy, is a significant producer of Protected Designation of Origin (PDO) sheep cheeses. In response to the growing demand for high-quality, safe, and traceable food products, the elemental fingerprints of Pecorino Romano PDO and Pecorino Sardo PDO were determined on 200 samples of cheese using validated, inductively coupled plasma methods. The aim of this study was to collect data for food authentication studies, evaluate nutritional and safety aspects, and verify the influence of cheesemaking technology and seasonality on elemental fingerprints. According to European regulations, one 100 g serving of both cheeses provides over 30% of the recommended dietary allowance for calcium, sodium, zinc, selenium, and phosphorus, and over 15% of the recommended dietary intake for copper and magnesium. Toxic elements, such as Cd, As, Hg, and Pb, were frequently not quantified or measured at concentrations of toxicological interest. Linear discriminant analysis was used to discriminate between the two types of pecorino cheese with an accuracy of over 95%. The cheese-making process affects the elemental fingerprint, which can be used for authentication purposes. Seasonal variations in several elements have been observed and discussed. Full article