Search Results (70)

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

Traditional clustering methods are often ineffective in extracting relevant features from high-dimensional, nonlinear near-infrared (NIR) spectra, resulting in poor accuracy of detecting lactose-free milk adulteration. In this paper, we introduce a clustering model based on Gram angular field and convolutional depth manifold (GAF-ConvDuc). The Gram angular field accentuates variations in spectral absorption peaks, while convolution depth manifold clustering captures local features between adjacent wavelengths, reducing the influence of noise and enhancing clustering accuracy. Experiments were performed on samples from 2250 milk spectra using the GAF-ConvDuc model. Compared to K-means, the silhouette coefficient (SC) increased from 0.109 to 0.571, standardized mutual information index (NMI) increased from 0.696 to 0.921, the Adjusted Randindex (ARI) increased from 0.543 to 0.836, and accuracy (ACC) increased from 67.2% to 88.9%. Experimental results indicate that our method is superior to K-means, Variational Autoencoder (VAE) clustering, and other approaches. Without requiring pre-labeled data, the model achieves higher inter-cluster separation and more distinct clustering boundaries. These findings offer a robust solution for detecting lactose-free milk adulteration, crucial for food safety oversight. 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

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

NIR spectroscopy has become one of the most prominent techniques in the food industry due to its easy and fast use. Coupled with PLS, it is a well-established method for determining nutrients, contaminants, or adulterants in foods. Nevertheless, it is not common when calculating the capability of detection or discrimination given a target/permitted value, providing probabilities of false non-compliance (α) or false compliance (β). That is exactly the main purpose of this work, where a single procedure using the accuracy line to evaluate these figures of merit by generalizing ISO 11843 when using NIR-PLS in real scenarios in agri-food industries is shown. Nevertheless, it is a completely general procedure and can be used in any analytical context in which a PLS calibration is applied. As an example of its versatility, several analytical determinations were performed using different common food matrices in the agri-food industry (butter, flour, milk, yogurt, oil, and olives) for the quantification of protein, fat, salt, and two agrochemicals. Some results were a detection capability of 5.2% of fat in milk, 1.20 mg kg⁻¹ for diflufenican, and 2.34 mg kg⁻¹ for piretrin in olives when maximum limits were established at 5%, 0.6 mg kg⁻¹, and 0.5 mg kg⁻¹ respectively. Also, 1.02% for salt in butter and 11.45%, 3.78%, and 2.65% for protein in flour, milk, and yogurt, respectively, were obtained when minimum limits were established at 1.2%, 12%, 4%, and 3% respectively. In all cases α = β = 0.05. 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

Ensuring the authenticity of Mozzarella di Bufala Campana (MdBC), a Protected Designation of Origin (PDO) cheese, is essential for regulatory enforcement and consumer protection. This study evaluates a multi-technology analytical platform developed to detect adulteration due to the addition of non-buffalo milk or non-PDO buffalo milk in PDO dairy buffalo products. Peripheral laboratories use gel electrophoresis combined with polyclonal antipeptide antibodies for initial screening, enabling the detection of foreign caseins, including those originating outside the PDO-designated regions. For more precise identification, Matrix-Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS) differentiates species by detecting proteotypic peptides. In cases requiring confirmation, nano-liquid chromatography coupled to electrospray tandem mass spectrometry (nano-LC-ESI-MS/MS) is used in central state laboratories for the highly sensitive detection of extraneous milk proteins in PDO buffalo MdBC cheese. On the other hand, analysis of the pH 4.6 soluble fraction from buffalo blue cheese identified 2828 buffalo-derived peptides and several bovine specific peptides, confirming milk adulteration. Despite a lower detection extent in the pH 4.6 insoluble fraction following tryptic hydrolysis, the presence of bovine peptides was still sufficient to verify fraud. This integrated proteomic approach, which combines electrophoresis and mass spectrometry technologies, significantly improves milk adulteration detection, providing a robust tool to face increasingly sophisticated fraudulent practices. Full article

The camel milk market’s rapid expansion necessitates strategies that ensure raw milk quality and safety, particularly in small-scale production. This study examines smallholder farmers in Haixi, Qinghai Province, China, where traditional practices intersect with modern standards. Analyzing 80 raw camel milk samples, the study assessed risks like adulteration, microbial contamination, and nutritional variability. DNA testing and microbial assays revealed that 66.67% of hand-milked samples were adulterated with cow milk, a significantly higher rate than mechanically processed samples (p < 0.05). Manual milking also showed higher microbial counts (up to 2.05 × 10⁴ CFU/mL) and somatic cell levels, indicating hygiene issues. Nutritional analysis found that grazing systems yielded milk with more vitamin A, B2, and potassium, while semi-intensive systems had higher ash content. A quality evaluation framework was developed, combining pastoralist knowledge with rapid diagnostic tools, focusing on mechanization, cold-chain efficiency, and community training. This framework provides strategies to reduce adulteration, ensure nutritional consistency, and align small-scale production with international standards. The study proposes culturally adaptive quality control methods to protect consumer health, support rural livelihoods, and standardize the camel milk market. Full article

This study critically examines the limitations of the official Italian methodology used for detecting bovine adulteration milk in Protected Designation of Origin (PDO) Mozzarella di Bufala Campana (MdBC). This method focuses on the whey fraction of cheese samples, which comprises about 1% of total MdBC proteins, and is based on a high-performance liquid chromatography (HPLC) quantification of the bovine β-lactoglobulin A (β-Lg A) as a marker. Here, we have demonstrated that this official methodology suffers from measurement inconsistencies due to its reliance on raw bovine whey standards, which fail to account for β-Lg genetic polymorphisms in real MdBC samples and protein thermal modifications during cheesemaking. To overcome these limitations, we propose a dual proteomics-based approach using matrix-assisted laser desorption ionization (MALDI-TOF) mass spectrometry (MS) and nano-HPLC-electrospray (ESI)−tandem mass spectrometry (MS/MS) analysis of MdBC extracted whey. MALDI-TOF-MS focused on identifying proteotypic peptides specific to bovine and buffalo β-Lg and α-lactalbumin (α-La), enabling high specificity for distinguishing the two animal species at adulteration levels as low as 1%. Complementing this, nano-HPLC-ESI-MS/MS provided a comprehensive profile by identifying over 100 bovine-specific peptide markers from β-Lg, α-La, albumin, lactoferrin, and osteopontin. Both methods ensured precise detection and quantification of bovine milk adulteration in complex matrices like pasta filata cheeses, achieving high sensitivity even at minimal adulteration levels. Accordingly, the proposed dual proteomics-based approach overcomes challenges associated with whey protein polymorphism, heat treatment, and processing variability, and complements casein-based methodologies already validated under European standards. This integrated framework of analyses focused on whey and casein fraction enhances the reliability of adulteration detection and safeguards the authenticity of PDO buffalo mozzarella, upholding its unique quality and integrity. Full article

The popularity of Greek-style yogurt (made from cow, ewe, and goat milk) has grown significantly in recent years thanks to its high protein content, nutritional value, and unique creamy texture, making it vulnerable to illegal practices, such as adulteration. In the present work, a fast and reliable matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based methodology was developed for the detection of yogurt adulteration with cow milk powder, exploiting the intact protein profile. An integrated protein-based workflow was established for the detection of as little as 1% cow milk powder addition into cow and goat milk yogurt. Simultaneously, markers for yogurt classification based on their animal origin (cow, ewe, or goat), type (traditional or strained), and thermal treatment of milk were revealed for the first time. Statistical analysis using chemometric tools, such as unsupervised principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA) recognition techniques, were implemented for the discrimination/classification of the yogurt samples. Full article

Food adulteration remains a pressing issue, with serious implications for public health and economic fairness. Electroanalytical techniques have emerged as promising tools for detecting food adulteration due to their high sensitivity, cost-effectiveness, and adaptability to field conditions. This review delves into the application of these techniques across various food matrices, including olive oil, honey, milk, alcoholic beverages, fruit juices, and coffee. By leveraging methodologies such as voltammetry and chemometric data processing, significant advancements have been achieved in identifying both specific and non-specific adulterants. This review highlights novel electrodes, such as carbon-based electrodes modified with nanoparticles, metal oxides, and organic substrates, which enhance sensitivity and selectivity. Additionally, electronic tongues employing multivariate analysis have shown promise in distinguishing authentic products from adulterated ones. The integration of machine learning and miniaturization offers potential for on-site testing, making these techniques accessible to non-experts. Despite challenges such as matrix complexity and the need for robust validation, electroanalytical methods represent a transformative approach to food authentication. These findings underscore the importance of continuous innovation to address emerging adulteration threats and ensure compliance with quality standards. Full article

Milk adulteration may cause losses or quality problems in the dairy industry, with implications regarding consumers’ health. The addition of water is one of the most common adulterations, which can be detected by determining the freezing point of milk. A proficiency testing (PT) program is in place in Chile concerning the determination of the freezing point of raw milk. A limited number of laboratories participate regularly to assure the validity of their measurements. The reference values were obtained independently through a calibration test, as recommended by ISO 13528:2022 in those cases where a small number of participants are evaluated. The uncertainty of the reference values assigned to nine PT rounds that included three PT items was investigated. A modification has been proposed for their calculations, which consists of the incorporation of the CRM uncertainty. This involves assessing the participants’ performance using the z’-score across all laboratories utilizing the thermistor method, which presents a slightly higher likelihood of inaccurate evaluation (2.9% of total participations). The assigned values demonstrated compatibility with the participants’ results. It is recommended that the same standard deviation for proficiency assessment be utilized for evaluating the performance of participants. Full article

The present study proposes an L-shaped coplanar strip dipole antenna for sensing the presence of adulterants in liquid food samples. The proposed antenna dimensions are optimized using ANSYS HFSS, and a prototype is fabricated and validated. The sensing region is optimized based on the current distribution and measured reflection coefficients. Adulterant detection is performed by monitoring the variation in the reflection coefficient and resonance frequency of the antenna sensor. To verify the effectiveness of the proposed planar dipole as a sensor, an adulterant, which is hydrogen peroxide, is added to various liquid samples – milk, pineapple juice, and mango juice. The reflection coefficient of the antenna sensor is found to vary with various concentrations of the samples in the study. The sensitivity analysis of the antenna sensor and the repeatability of the results is also analyzed in the work. The experimental analysis assures the use of the proposed antenna as a sensor for the detection of adulterants in liquid food samples. Full article

Surface-enhanced Raman scattering (SERS) is a powerful optical sensing platform that amplifies the target signals by Raman scattering. Despite SERS enabling a meager detection limit, even at the single-molecule level, SERS also tends to equally enhance unwanted molecules due to the non-specific binding of noise molecules in clinical samples, which complicates its use in complex samples such as bodily fluids, environmental water, or food matrices. To address this, we developed a novel non-fouling biomimetic SERS sensor by self-assembling an anti-adhesive, anti-fouling, and size-selective Lubricin (LUB) coating on gold nanoparticle (AuNP) functionalized glass slide surfaces via a simple drop-casting method. Compared to a conventional AuNPs-SERS substrate, the biomimetic SERS meets the requirements of simple preparation and enables direct droplet detection without any sample pre-treatment. Atomic force microscopy was used to confirm the self-assembled Lubricin coating on the AuNP surface, acting as an anti-fouling and size-selective protection layer. A series of Raman spectra were collected using melamine as the target analyte, which was spiked into 150 mM NaCl solution or undiluted milk. It was demonstrated that the LUB coating effectively prevents the detrimental fouling generated by the proteins and fats in milk, ensuring the clear detection of melamine. Our sensor showed high selectivity and could detect melamine in milk at concentrations as low as 1 ppm. Given that the EU/US legal limit for melamine in food is 2.5 ppm, this sensor offers a promising, cost-effective solution for routine screening and has potential applications for detecting food adulteration in the food safety, environmental monitoring, aquaculture, and biomedical fields. Full article