Search Results (116)

Food fraud represents a growing global challenge with significant implications for public health, market integrity, sustainability, and consumer trust. Beyond economic losses, fraudulent practices undermine the environmental and social sustainability of food systems by distorting markets, misusing natural resources, and weakening incentives for authentic and responsible production. Despite the establishment of harmonized frameworks of the European Union for official controls, the increasing complexity of food supply chains has exposed persistent gaps in fraud detection, particularly for high-value products such as those with PDO (Protected Designation of Origin) and PGI (Protected Geographical Ιndication) Certification. This study investigates the perceptions, attitudes, and experiences of frontline inspectors in Greece to assess current challenges and opportunities for strengthening official food fraud controls. Data were collected through a structured questionnaire, validated by experts and administered nationwide, involving 122 participants representing all major national food inspection authorities. Statistical analysis revealed significant institutional differences in perceptions of fraud prevalence, with mislabeling of origin, misleading organic claims, ingredient substitution, and documentation irregularities identified as the most common fraudulent practices. Olive oil, honey, meat, and dairy emerged as the most vulnerable product categories. Inspectors reported relying primarily on consumer complaints and institutional databases as key tools for identifying fraud risks. Food fraud was perceived to contribute strongly to losses in consumer trust in food safety and product authenticity, as well as to the erosion of sustainable production models that depend on transparency, fair competition, and responsible resource use. Overall, the findings highlight detection gaps, uneven resources across authorities, and the need for improved coordination and capacity-building to support more efficient, transparent, and sustainability-oriented food fraud control in Greece. Full article

Fermented meat products rely on complex microbial ecosystems in which lactic acid bacteria (LAB) play a central role in safety, quality, and sensory development. In recent years, increasing demand for reduced-nitrite formulations, clean-label products, and improved risk management have driven renewed interest in microbial control strategies beyond traditional fermentation practices. This systematic review aims to synthesize current knowledge on the microbiological aspects of meat fermentation, spanning traditional spontaneous processes and modern approaches to microflora control, including starter cultures, biocontrol strategies, and omics-based tools. A systematic literature search was conducted in PubMed, Web of Science, Scopus, and Google Scholar, with the final search performed on 15 May 2025. After screening and eligibility assessment following PRISMA 2020 guidelines, 141 studies were included in the qualitative synthesis. The review integrates evidence on microbial succession, metabolic functions, pathogen inhibition, biogenic amine control, and flavour formation, with particular emphasis on advances in metagenomics, metabolomics, and predictive microbiology. Across studies, LAB-dominated ecosystems—particularly those involving Latilactobacillus sakei, Latilactobacillus curvatus, and Lactiplantibacillus plantarum—consistently emerge as the primary drivers of fermentation stability and safety. The strongest evidence supports the use of selected starter and protective cultures, bacteriocinogenic LAB, and omics-guided predictive control to enhance process reliability, support reduced-nitrite strategies, and mitigate microbiological risks without compromising product quality. Overall, the integration of traditional fermentation knowledge with data-driven microbial management provides a robust framework for developing safe, authentic, and sustainable fermented meat products. Full article

The growing demand for robust food authentication methods has driven the establishment of fast, sensitive, and field-based detection systems for identifying meat species. This study presents a colorimetric-based PSR approach for identifying yak (Bos grunniens) meat within fresh, thermally processed, and blended meat samples. Targeting the mitochondrial D-loop locus, the assay incorporates a simple alkaline lysis (AL) procedure for efficient DNA extraction, eliminating the requirement for specialized instrumentation. The PSR assay demonstrated high specificity, showing no evidence of cross-reactivity with closely associated food animals such as buffalo, cattle, goat, sheep, mithun, and pig. Sensitivity assessment revealed the assay’s capability to detect 1 pg of yak DNA, with reliable performance in samples exposed to thermal conditions up to 121 °C. Additionally, the technique detected yak meat down to a concentration of 0.1% in binary beef mixtures. This method provides a significant improvement in sensitivity over end-point PCR and is particularly well-suited for field applications due to its practical simplicity, affordability, as well as no reliance on sophisticated instrument. This is, to the best of our understanding, the first reported PSR-based approach developed for the identification of yak meat, offering a robust tool for food origin verification, regulatory enforcement, and product integrity monitoring. Full article

The rising price of sika deer meat is increasing the risk of economic adulteration, highlighting the need for rapid and reliable authentication methods to protect both market integrity and consumers. This work presents a novel countermeasure: a polymerase chain reaction (PCR)-based nucleic acid test strip designed for the specific and visual identification of sika deer meat. Our approach commenced with the design of specific primers targeting the cytochrome C oxidase subunit I (COI) gene. To guarantee the reliability of the assay, a DNA standard plasmid was constructed to serve as an unambiguous positive control for the PCR. Under optimized conditions, results showed that authentic sika deer meat generated both test and control lines on the strip, while adulterated and negative samples produced only the control line. The assay demonstrated flawless specificity and a detection sensitivity of 1.0 ng·μL⁻¹ for target DNA, representing a tenfold enhancement over gel electrophoresis. Furthermore, the method demonstrated a detection limit of 1% for sika deer meat in admixed samples, with a faint but visible signal observed down to 0.1% under optimized conditions. In conclusion, the developed test strip method is not only specific and sensitive but also user-friendly, positioning it as a practical and powerful tool for rapid, on-site meat authentication. Full article

From the first spontaneous fermentations of early civilizations to the precision of modern biotechnology, natural starter cultures have remained at the heart of fermented food and beverage production. Composed of complex microbial communities of lactic acid bacteria, yeasts, and filamentous fungi, these starters transform raw materials into products with distinctive sensory qualities, extended shelf life, and enhanced nutritional value. Their high microbial diversity underpins both their functional resilience and their cultural significance, yet also introduces variability and safety challenges. This review traces the historical development of natural starters, surveys their global applications across cereals, legumes, dairy, vegetables, beverages, seafood, and meats, and contrasts them with commercial starter cultures designed for consistency, scalability, and safety. Within the context of organic food production, natural starters offer opportunities to align fermentation with principles of sustainability, biodiversity conservation, and minimal processing, but regulatory frameworks—currently focused largely on yeasts—pose both challenges and opportunities for broader certification. Emerging innovations, including omics-driven strain selection, synthetic biology, valorization of agro-industrial byproducts, and automation, offer new pathways to improve safety, stability, and functionality without eroding the authenticity of natural starter cultures. By bridging traditional artisanal knowledge with advanced science and sustainable practices, natural starters can play a pivotal role in shaping the next generation of organic and eco-conscious fermented products. Full article

This study aimed to establish a multiplex PCR identification system capable of rapidly detecting adulteration in sheep and goat meat, while qualitatively identifying common adulterant meats (pork, chicken, and duck). Species-specific primers targeting mitochondrial DNA sequences were designed after screening for gene fragments with intraspecies conservation and interspecies specificity across five target species. The multiplex PCR conditions and system were systematically optimized and evaluated for specificity, reproducibility, sensitivity, and practical applicability using simulated mixed samples and heat-treated products. The results demonstrated that the system could successfully identify sheep meat, goat meat, and adulterant meat components in randomly combined target meat template DNAs with excellent reproducibility. The system maintained a high sensitivity, detecting target species even at low DNA template concentrations and in samples with low adulteration ratios. Moreover, target meat components remained detectable in heat-treated products, confirming the system’s robustness under realistic market conditions. This multiplex PCR identification system demonstrates strong specificity, good reproducibility, high sensitivity, and broad applicability. It provides an important tool for effectively monitoring sheep and goat meat adulteration and offers crucial technical support for ensuring the authenticity of sheep and goat meat. Full article

Background: Meat adulteration poses serious public health, economic, and religious concerns, particularly in the Middle East and North Africa (MENA) and Gulf Cooperation Council (GCC) regions where halal authenticity is essential. While isolated studies have reported undeclared species in meat products, a comprehensive regional synthesis of prevalence, detection technologies, and regulatory responses has been lacking. Methods: This scoping review followed PRISMA-ScR guidelines. A systematic search of PubMed, Scopus, and Web of Science from database inception to 15 September 2025 was conducted using controlled vocabulary (MeSH) and free-text terms. Eligible studies included laboratory-based investigations of meat adulteration in MENA and GCC countries. Data were charted on study characteristics, adulteration types, detection methods, and regulatory context. Results: Out of 50 records screened, 35 studies were included, covering 27 MENA/GCC countries. Prevalence of adulteration varied widely, from 5% in UAE surveillance studies to 66.7% in Egyptian native sausages. Undeclared species most frequently detected were poultry, donkey, equine, pig, and dog. Molecular methods, particularly PCR and qPCR, were most widely applied, followed by ELISA and spectroscopy. Recent studies introduced biosensors, AI-assisted spectroscopy, and blockchain traceability, but adoption in regulatory practice remains limited. Conclusions: Meat adulteration in the MENA and GCC regions is localized and product-specific rather than uniformly widespread. Detection technologies are advancing, yet regulatory enforcement and halal-sensitive verification remain fragmented. Strengthening laboratory capacity, harmonizing regional standards, and investing in portable biosensors, AI-enhanced spectral tools, and blockchain-based traceability are critical for consumer trust, halal integrity, and food safety. Full article

DNA barcoding technology, as a species identification method based on specific DNA sequence variations, has been widely applied in meat product authentication in recent years. This paper reviews the technical principles, current applications, and comparative advantages of DNA barcoding in meat identification, particularly in contrast to traditional authentication methods. It further highlights the critical role of DNA barcoding in ensuring meat authenticity, enhancing food safety, and contributing to biodiversity conservation efforts. Furthermore, the paper explores the strategic implications and future trends of DNA barcoding in food regulation and ecological protection, demonstrating its practical feasibility and broad prospects in meat products. By highlighting its applications in detecting food adulteration and verifying species origin, this review aims to promote the safety and sustainable development of the meat industry while providing valuable insights for related fields. Ultimately, the implementation of DNA barcoding technology serves as a crucial safeguard for public food safety and health, aligning with the growing demand for improved food control systems. Full article

Ensuring the authenticity of meat products is a critical issue for consumer protection, regulatory compliance, and the integrity of local food systems. In this study, attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy combined with chemometric and machine learning models was applied to differentiate Maltese from non-Maltese pork. Spectral datasets were subjected to a range of preprocessing techniques, including Savitzky–Golay first and second derivatives, detrending, orthogonal signal correction (OSC), and standard normal variate (SNV). Linear methods such as principal component analysis–linear discriminant analysis (PCA-LDA), the soft independent modeling of class analogy (SIMCA), and partial least squares regression (PLSR) were compared against nonlinear approaches, namely support vector machine regression (SVMR) and artificial neural networks (ANNs). The results revealed that derivative preprocessing consistently enhanced spectral resolution and model robustness, with the fingerprint region (1800–600 cm⁻¹) yielding the highest discriminative power. While PCA-LDA, SIMCA, and PLSR achieved high accuracy, SVMR and ANN models provided a superior predictive performance, with accuracies exceeding 0.99 and lower misclassification rates under external validation. These findings highlight the potential of FTIR spectroscopy combined with nonlinear chemometrics as a rapid, non-destructive, and cost-effective strategy for meat authentication, supporting both consumer safety and sustainable food supply chains. Full article

Seafood fraud, such as mislabeling low-cost rainbow trout as premium salmon, poses serious food safety risks and damages consumer rights. To address this growing concern, this study develops a deep learning-based, smartphone-compatible sensing system for fish meat identification and salmon freshness grading. By providing consumers with real-time, image-based verification tools, the system supports informed purchasing decisions and enhances food safety. The system adopts a two-stage design: first classifying fish meat types, then grading salmon freshness into three levels based on visual cues. An improved DenseNet121 architecture, enhanced with global average pooling, dropout layers, and a customized output layer, improves accuracy and reduces overfitting, while transfer learning with partial layer freezing enhances efficiency by reducing training time without significant accuracy loss. Experimental results show that the two-stage method outperforms the one-stage approach and several baseline models, achieving robust accuracy in both classification and grading tasks. Sensitivity analysis demonstrates resilience to blur and camera tilt, though real-world adaptability under diverse lighting and packaging conditions remains a challenge. Overall, the proposed system represents a practical, consumer-oriented tool for seafood authentication and freshness evaluation, with potential to enhance food safety and consumer protection. Full article

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

This study investigates the effects of species, geographical origin, and processing on the proximate composition of frog leg meat, with a focus on developing predictive models for processing status. Data were systematically compiled from 18 published studies, yielding 32 entries across 10 edible frog species and multiple processing methods. Proximate composition parameters (moisture, protein, fat, ash) were compared between processed and unprocessed samples, and classification models were trained using moisture content as the primary predictor. Logistic regression and several machine learning algorithms, including Stochastic Gradient Descent, Support Vector Machine, Random Forest, and Decision Tree, were benchmarked under a Leave-One-Study-Out (LOSO) cross-validation framework. Results demonstrated that moisture content alone was sufficient to accurately distinguish processing status, with a critical threshold of ~73% separating processed from unprocessed frog legs. Logistic regression achieved perfect specificity and precision (100%) with an overall accuracy of 96.8%, while other classifiers also performed strongly (>90% accuracy). These findings confirm moisture as a species- and origin-independent marker of processing, offering a simple, rapid, and cost-effective tool for authenticity verification and quality control in frog meat and potentially other niche protein products. Future work should expand sample coverage, validate thresholds across processing types, and integrate biochemical and sensory quality assessments. Full article

Pigeon meat is gaining increasing popularity due to its high nutritional value and desirable sensory qualities. This study aimed to comprehensively evaluate the quality-related components of pigeon meat by analyzing conventional nutritional indicators—including amino acids, fatty acids, and flavor nucleotides—in combination with multi-omics approaches. The results indicated that pigeon meat contains high levels of arginine (Arg), alanine (Ala), linoleic acid, and glycerophospholipids (GPs), which contribute significantly to its flavor profile. Additionally, several lipids, namely, PS (18:0/20:4), PE (16:2; O/2:0), HexCer (9:0;2O/42:11), Hex2Cer (38:1;2O), PS (16:0; O/21:0), and PE (42:9), were identified as potential characteristic markers of pigeon meat. A comparative analysis among three breeds—White King, Shiqi, and Tarim pigeons—revealed breed-specific differences in endogenous compounds, with each breed exhibiting distinct compositional traits. This study provides a comprehensive dataset for quality assessment and offers critical insights for the authenticity verification of pigeon meat. Full article

Food fraud in meat products presents serious economic and public health challenges, underscoring the need for rapid and reliable detection methods. This study investigates the potential of Raman spectroscopy combined with machine learning to accurately discriminate between pure and mixed minced meat preparations. We evaluated three classification algorithms: Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Random Forests (RFs). Raman spectra were collected from 19 distinct samples consisting of different ratios of pork, beef, and lamb minced meat. Our findings suggest that homogenization markedly enhances spectral consistency and classification accuracy. In the pure meat samples case, all three models (SVM, ANN, and RF) achieved notable increases in classification accuracies (from 0.50–0.70 to above 0.85), a dramatic improvement over unhomogenized samples. In more complex homogenized mixtures, SVM delivered the highest performance, achieving an accuracy of up to 0.88 for 50:50 mixtures and 0.86 for multi-ratio samples, often outperforming both ANN and RF. While the underlying interpretation of the classification models remains complex, the findings consistently underscore the critical role of homogenization on model performance. This work demonstrates the robust potential of the Raman spectroscopy-coupled machine learning approach for the rapid and accurate identification of minced meat species. Full article

Amidst growing demand for meat products, concerns regarding their authenticity and safety have intensified, primarily due to potential fraudulent substitutions of cheaper meats, which are not accurately labeled. This study presents a novel strategy for the rapid screening and validation of target peptides for accurate quantitative analysis using high-resolution mass spectrometry (HRMS) coupled with multivariate statistical analysis. By integrating hierarchical clustering analysis (HCA) with parallel reaction monitoring (PRM), five species-specific peptides were validated as reliable biomarkers for pork quantification. These peptides demonstrated accurate quantification in simulated meat products with known accurate contents, achieving recoveries of 78–128%, with RSD less than 12%. This methodology markedly enhances screening efficiency by excluding 80% of non-quantitative peptides, providing a robust solution for meat authenticity verification. Full article