Search Results (90)

Aroma and flavor are central to consumer perception, product acceptance, and market positioning of animal-derived foods such as meat, milk, and eggs. These sensory traits arise from volatile organic compounds (VOCs) formed via lipid oxidation (e.g., hexanal, nonanal), Maillard/Strecker chemistry (e.g., pyrazines, furans), thiamine degradation (e.g., 2-methyl-3-furanthiol, thiazoles), and microbial metabolism, and are modulated by species, diet, husbandry, and post-harvest processing. Despite extensive research on food volatiles, there is still no unified framework spanning meat, milk, and eggs that connects production factors with VOC pathways and links them to sensory traits and consumer behavior. This review explores how production systems, feeding strategies, and processing shape VOC profiles, creating distinct aroma “fingerprints” in meat, milk, and eggs, and assesses their value as markers of quality, authenticity, and traceability. We have also summarized the advances in analytical techniques for aroma fingerprinting, with emphasis on GC–MS, GC–IMS, and electronic-nose approaches, and discuss links between key VOCs and sensory patterns (e.g., grassy, nutty, buttery, rancid) that influence consumer perception and willingness-to-pay. These patterns reflect differences in production and processing and can support regulatory claims, provenance verification, and label integrity. In practice, such markers can help producers tailor feeding and processing for flavor outcomes, assist regulators in verifying claims such as “organic” or “free-range,” and enable consumers to make informed choices. Integrating VOC profiling with production data and chemometric/machine learning pipelines can enable robust traceability tools and sensory-driven product differentiation, supporting transparent, value-added livestock products. Thus, this review integrates production variables, biochemical pathways, and analytical platforms to outline a research agenda toward standardized, transferable VOC-based tools for authentication and label integrity. Full article

Dairy products harbor complex and dynamic microbial communities that contribute to their sensory properties, safety, and cultural distinctiveness. Raw milk contains a diverse microbiota shaped by seasonality, storage conditions, lactation stage, animal health, farm management, and genetics, serving as a variable starting point for further processing. Fermentation, whether spontaneous or starter driven, selects for subsets of lactic acid bacteria (LAB), yeasts, and molds, resulting in microbial succession that underpins both artisanal and industrial products such as kefir and cheese. Kefir represents a balanced LAB–yeast symbiosis, with species composition influenced by grain origin, milk type, and processing parameters, whereas the cheese microbiota reflects the interplay of starter and non-starter LAB, coagulants, ripening conditions, and “house microbiota”. Methodological factors—including DNA extraction, sequencing platform, and bioinformatic pipelines—further impact the reported microbial profiles, highlighting the need for standardization across studies. This review synthesizes current knowledge on raw milk, kefir, and cheese microbiomes, emphasizing the biological, technological, environmental, and methodological factors shaping microbial diversity. A holistic understanding of these drivers is essential to preserve product authenticity, ensure safety, and harness microbial resources for innovation in dairy biotechnology. Full article

The increasing demand for sustainably produced food has intensified interest in fermented milk products, such as yoghurt, which combine nutritional value with environmental and ethical considerations. However, the authenticity of sustainability claims in this sector remains contested, raising concerns about consumer trust and regulatory clarity. This review examines the role of certification and labelling in verifying and communicating the sustainability of fermented milk products. The analysis covers regulatory frameworks, consumer perceptions, and the potential of digital tools to improve transparency. Findings highlight inconsistencies in defining key terms such as organic, probiotic, and carbon-neutral, which hinder certification harmonization. Complex labels and allergen declarations can reduce clarity and trust, while overlapping or vague eco-labels risk contributing to consumer confusion and skepticism. Despite this, credible certifications still enhance purchase intent. Modern technologies, including blockchain traceability, interactive QR codes, and digital product passports, offer new ways to reinforce trust, though implementation costs and regulatory gaps remain barriers. This review concludes that effective sustainability communication must integrate robust certification schemes with simplified, transparent messaging. Harmonized standards, improved label design, and consumer education are essential to support informed choices and foster trust in sustainable dairy. Full article

Cheese products worldwide have gained protected designation of origin status in many instances, yet this food group also has the highest reported fraud rates. Quesillo Caquetá is the first Colombian cheese to acquire a protected designation of origin, but still there is a lack of information regarding its composition. In this study, a compositional analysis was performed to establish a set of characteristic parameters to aid the identification of the authenticity of Quesillo Caquetá. Physicochemical analysis, mineral composition determination, carbohydrate, fatty acid, and peptide profiles were conducted on 29 samples of Quesillo Caquetá made with milk from the northern, southern, and central regions of the province of Caquetá. The results revealed 7 minerals, 3 carbohydrates, 19 fatty acids, and 45 peptides (21 peptides from bovine αs1-casein and 24 peptides from bovine β-casein). This suggests that Quesillo Caquetá is a significant source of sodium, calcium, phosphorus, and monounsaturated fatty acids such as oleic acid, omega-3, and omega-6, as well as some peptides that match sequences with antihypertensive, immunomodulatory, antioxidant, and antimicrobial activity reported in the literature. The specificity of the fatty acid and peptide profiles can become a valuable tool for identifying the authenticity of Quesillo Caquetá against possible imitations in the market. Full article

Halloumi cheese, a traditional Cypriot dairy product with Protected Designation of Origin (PDO) status, is renowned for its unique texture and high melting point. PDO certification is crucial for Halloumi cheese as it ensures the product’s authenticity, protects its traditional production methods and geographical origin, and safeguards consumers and producers against fraud and mislabeling. However, concerns over adulteration, particularly through the addition of skim milk powder, pose challenges to its authenticity and quality control. This study is the first to analyze Halloumi cheese using Brunauer–Emmett–Teller (BET) analysis and Fourier Transform Infrared (FTIR) spectroscopy, providing a novel approach to assessing its composition and authenticity. Furthermore, it marks the first time Halloumi samples have been examined in the context of PDO certification. Alongside PDO-certified Halloumi, two additional sample sets were produced following PDO specifications for moisture, fat, and salt content, with the controlled incorporation of skim milk powder as an adulterant at concentrations of 1% and 5%. Principal component analysis (PCA) was employed to visualize and interpret the spectral data, revealing promising results. Chemometric analysis showed that the specific surface area from BET measurements and the FTIR spectral subregion between 1650 and 1100 cm⁻¹ were key factors, and they were retained for model construction. These findings could play a crucial role in establishing official food fraud detection methodologies, particularly for the Cyprus and EU markets. While this study serves as an initial investigation, additional samples will be tested in future studies to validate these preliminary results and to assess the potential of applying these techniques in real-world food fraud detection scenarios. 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

Milk is a nutritionally rich food and a frequent target of economically motivated adulteration, particularly through substitution with lower-cost milk types. Over the past decade, significant progress has been made in the authentication of milk using advanced proteomic and chemometric approaches, with a focus on the discovery and application of protein and peptide biomarkers for species differentiation and fraud detection. Recent innovations in both top-down and bottom-up proteomics have markedly improved the sensitivity and specificity of detecting key molecular targets, including caseins and whey proteins. Peptide-based methods are especially valuable in processed dairy products due to their thermal stability and resilience to harsh treatment, although their species specificity may be limited when sequences are conserved across related species. Robust chemometric approaches are increasingly integrated with proteomic pipelines to handle high-dimensional datasets and enhance classification performance. Multivariate techniques, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are frequently employed to extract discriminatory features and model adulteration scenarios. Despite these advances, key challenges persist, including the lack of standardized protocols, variability in sample preparation, and the need for broader validation across breeds, geographies, and production systems. Future progress will depend on the convergence of high-resolution proteomics with multi-omics integration, structured data fusion, and machine learning frameworks, enabling scalable, specific, and robust solutions for milk authentication in increasingly complex food systems. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

Sheep and goat milk authenticity is of great importance, especially for countries like Greece, where these products are connected to the country’s rural economy and cultural heritage. The aim of the study is to evaluate the effectiveness of Fourier Transform Infrared Attenuated Total Reflectance (ATR-FTIR) spectroscopy in combination with chemometric techniques for the classification of cow, sheep, and goat milk and consequently support fraud identification. A total of 178 cow, sheep and goat milk samples were collected from livestock farms in Thessaly, Greece. Sheep and goat milk samples were confirmed as authentic by applying a validated Enzyme Linked Immunosorbent Assay (ELISA), while all samples were analyzed using ATR-FTIR spectroscopy in both raw and freeze-dried form. Freeze-dried samples exhibited clearer spectral characteristics, particularly enhancing the signals from triglycerides, proteins, and carbohydrates. Partial Least Squares Discriminant Analysis (PLS-DA) delivered robust discrimination. By using the spectral range between 600 and 1800 cm⁻¹, 100% correct classification of all milk types was achieved. These findings highlight the potential of FTIR spectroscopy as a fast, non-destructive, and cost-effective tool for milk identification and species differentiation. This method is particularly suitable for industrial and regulatory applications, offering high efficiency. 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

This study introduces a novel method for classifying whole and lactose-free milk and the detection of water adulteration through analyzing dried droplets. The key innovation is the addition of NaCl, which modulates crystallization to enhance structural differentiation and facilitate the classification of milk types and detection of adulteration. Dried droplets of milk containing NaCl concentrations of $0\%$, $2\%$, and $4\%$ were analyzed, revealing distinct morphologies, including amorphous, cross-shaped, and dendritic crystals. These structures were quantitatively characterized using lacunarity to assess their discriminatory power. Two classification approaches were evaluated: one based on lacunarity analysis alone and another incorporating deep learning. Both methods yielded high classification accuracies, with lacunarity achieving $95.04\%\pm 6.66\%$, while deep learning reached $95.22\%\pm 4.47\%$. Notably, the highest performance was obtained with $2\%$ NaCl, where lacunarity reached $97.08\%\pm 2.27\%$ and deep learning $96.88\%\pm 2.8\%$, indicating improved precision and stability. While deep learning demonstrated more consistent performance across test cases, lacunarity alone captured highly discriminative structural features, making it a valuable complementary tool. The integration of NaCl and lacunarity analysis offers a robust and interpretable methodology for ensuring the quality and authenticity of dairy products, particularly in detecting adulteration, where morphological contrast is less evident. 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

The global demand for animal protein presents significant challenges in the production of nutritionally rich foods, such as milk and meat. Traditionally, the quality of these products is assessed using physicochemical, microbiological, and sensory methods. Although effective, these techniques are constrained by time limiting their widespread application. Furthermore, growing concerns regarding sustainability, animal welfare, and transparency have driven the development of technologies to enhance the rapid and precise assessment of food quality. In this context, omics technologies have transformed the characterization of animal-origin food by providing in-depth molecular understanding of their composition and quality. These tools enable the identification of biomarkers, adulteration detection, optimization of nutritional profiles, and enhancement of authentication and traceability, facilitating the development of functional foods. Despite their potential, several barriers persist, including high implementation cost, the need for specialized infrastructure, and the complexity of integrating multi-omics data. The main aim of this review was to provide information on advances in the application of omics technologies in dairy and meat production systems and studies that use them in food quality, authentication, and sustainability. It also outlines opportunities in areas such as fraud prevention and functional product development to support the transition to safer, healthier, and more transparent food systems. Full article

Milk’s biological origin determination, including its adulteration and authenticity, presents serious limitations, highlighting the need for innovative advanced solutions. The utilisation of proteomic technologies combined with personalised algorithms creates great potential for a more comprehensive approach to analysing milk samples effectively. The current study presents an innovative approach utilising proteomics and neural networks to classify and distinguish bovine, ovine and caprine milk samples by employing advanced machine learning techniques; we developed a precise and reliable model capable of distinguishing the unique mass spectral signatures associated with each species. Our dataset includes a diverse range of mass spectra collected from milk samples after MALDI-TOF MS (Matrix-assisted laser desorption/ionization-time of flight mass spectrometry) analysis, which were used to train, validate, and test the neural network model. The results indicate a high level of accuracy in species identification, underscoring the model’s potential applications in dairy product authentication, quality assurance, and food safety. The current research offers a significant contribution to agricultural science, providing a cutting-edge method for species-specific classification through mass spectrometry. The dataset comprises 648, 1554, and 2392 spectra, represented by 16,018, 38,394, and 55,055 eight-dimensional vectors from bovine, caprine, and ovine milk, respectively. Full article

With the growing interest in health and fitness, whey protein supplements are becoming increasingly popular among fitness enthusiasts and athletes. The surge in demand for whey protein supplements highlights the need for cost-effective methods to characterise product quality throughout the food supply chain. This study presents a rapid and low-cost method for authenticating sports whey protein supplements using smartphone video imaging (SVI) combined with machine learning. A gradient of colours ranging from purple to red is displayed on the front screen of a smartphone to illuminate the sample. The colour change on the sample surface is captured in a short video by the front-facing camera. Then, the video is split into frames, decomposed into RGB colour channels, and converted into spectral data. The relationship between video data and sample labels is established using machine learning models. The proposed method is tested on five tasks, including identifying 15 brands of whey protein concentrate (WPC), quantifying fat content and energy levels, detecting three types of adulterants, and quantifying adulterant levels. Moreover, the performance of SVI was compared to that of hyperspectral imaging (HSI), which has an equipment cost of around 80 times that of SVI. The proposed method achieves accuracies of 0.933 and 0.96 in WPC brand identification and adulterant detection, respectively, which are only around 0.05 lower than those of HSI. It obtains coefficients of determination of 0.897, 0.906 and 0.963 for the quantification of fat content, energy levels and milk powder adulteration, respectively. Such results demonstrate that the combination of smartphones and machine learning offers a low-cost and viable preliminary screening tool for verifying the authenticity of whey protein supplements. Full article