Search Results (1,029)

Honey is one of the most valued natural food products, yet it remains highly vulnerable to fraud through mislabeling and adulteration, practices that mislead consumers and compromise food safety. We develop a low-cost and portable electronic nose (e-nose) system for classifying locally available honey brands in the Philippines. The system integrates an array of eight MQ gas sensors to detect volatile organic compounds (VOCs), with an Arduino Mega 2560 handling data acquisition and a Raspberry Pi 5 executing data processing and classification. An Extreme Gradient-Boosted Decision Tree (XGBoost) algorithm was applied to analyze the VOC profiles of three honey brands, each with 38 samples, resulting in a total dataset of 114 samples. The dataset was divided into training, testing, and validation sets to assess the system’s classifying and predictive performance, with accuracy evaluated using a 3 × 3 confusion matrix. The results showed that the system effectively distinguished between honey brands, achieving a validation accuracy of 87.50%, corresponding to 21 out of 24 correctly identified validation trials. Full article

Xinhui Citri Reticulatae Pericarpium (CRP, “Chenpi”) is highly valued but is frequently challenged by origin-related adulteration and mislabeling. In this study, a rapid fingerprinting strategy based on direct analysis in real-time quadrupole time-of-flight mass spectrometry (DART-QTOF-MS) coupled with chemometric analysis was developed for the geographical characterization of CRP. DART-QTOF-MS enabled fast acquisition of mass spectral fingerprints with minimal sample preparation, and characteristic compounds were tentatively assigned on the basis of accurate mass and library matching. Comparative analysis showed that the high-mass region was dominated by polymethoxylated flavones and exhibited relatively limited between-region variation. In contrast, the low-mass region contained more evident origin-related differences and provided more informative variables for classification. Among the low-molecular-weight compounds, methyl N-methylanthranilate was markedly enriched in Xinhui samples, and 2-indolinone was identified as a promising candidate marker and further confirmed in CRP extracts by UPLC–MS/MS. OPLS-DA based on selected low-molecular-weight markers supported the discrimination of core Xinhui CRP and non-core Xinhui CRP. Overall, these results demonstrate the potential of DART-QTOF-MS as a screening tool for CRP origin authentication and highlight the value of low-molecular-weight markers for future quality-control applications. Full article

Background/Objectives: Beeswax, a complex natural secretion primarily derived from Apis mellifera and Apis cerana, has evolved from an ancient remedy into a multifunctional excipient and bioactive material in modern pharmaceutical sciences. This review evaluates its physicochemical properties, pharmaceutical applications, and emerging biomedical potential, while addressing current quality and regulatory challenges. Methods: A narrative review was conducted by analyzing literature on the chemical composition, functional properties, conventional uses, advanced drug delivery applications, pharmacological activities, and quality control of beeswax, emphasizing structural characteristics, formulation roles, and integration into innovative delivery technologies. Results: Beeswax is a lipid-based matrix composed of over 300 constituents, including wax esters, hydrocarbons, and free fatty acids, conferring thermoplasticity, biocompatibility, and structural stability. Traditionally, it functions as a stiffening agent, viscosity modifier, and emulsion stabilizer in topical formulations, forming an occlusive barrier that enhances skin hydration. In advanced systems, it serves as a solid lipid matrix in nanostructured lipid carriers (NLCs), microspheres, and 3D-printed tablets, enabling controlled drug release and improved bioavailability of lipophilic compounds. It also exhibits antimicrobial, anti-inflammatory, and wound-healing activities, while beeswax-derived policosanols show potential cardiovascular and gastroprotective benefits. However, concerns regarding paraffin adulteration and pesticide contamination highlight the need for stringent analytical and regulatory oversight. Conclusions: With rigorous quality control and sustainable sourcing, beeswax remains a versatile, eco-friendly material bridging traditional medicine and advanced pharmaceutical innovation. Full article

The comprehensive quality assessment of raw milk from small-scale producers remains essential for improving dairy sector competitiveness. This study employed a multivariate approach to correlate the physicochemical, colorimetric, and sensory profiles of raw milk from eleven producers in the town of Supe, Barranca, Lima, Peru. Milk samples were analyzed using a Lactoscan MCC ultrasonic analyzer, CIEL*a*b* colorimetry, and the Flash Profile sensory method. Data integration and interpretation were performed using Analysis of Variance (ANOVA), Generalized Procrustes Analysis (GPA) and Hierarchical Multiple Factor Analysis (HMFA). The results revealed significant heterogeneity, identifying two distinct producer groups. A high-quality group (DF7, DF10, DF11) presented adequate physicochemical parameters: high fat content (>3.77%), total solids (>12.06%), normal freezing point (≈−0.53 °C), creamy color (high L* and b*), and positive sensory attributes (“fatty”, “creamy”). In contrast, a low-quality group (DF4, DF5, DF8, DF9) showed evidence of water adulteration (12–16%), reflected in an elevated freezing point (up to −0.44 °C), low solids-not-fat, and defective sensory profiles (“tasteless”, “salty”). The HMFA demonstrated a strong concordance between instrumental and sensory data sets, identifying water adulteration and fat content as the primary drivers of quality variation. This integrated methodology provides a robust diagnostic tool for quality-based payment systems and targeted technical assistance, offering a replicable model for enhancing quality control and valorizing raw milk in smallholder dairy systems. Full article

Food fraud has emerged as a significant and under-recognised public health threat, with documented global incidents resulting in severe illness, hospitalisations, and fatalities. International estimates suggest that up to 9% of the global food trade is adulterated. In South Africa, evidence of mislabelling, substitution, counterfeit products, illicit trade, and the use of unauthorised additives continues to surface, yet the national burden and regulatory response remain insufficiently characterised. This review synthesised peer-reviewed literature and articles from reputable South African media sources published from 2015 to December 2025, focusing on food fraud within the South African context. Searches were conducted across Web of Science (WoS), Scopus, and PubMed, supplemented by Google Scholar and the EU Food Fraud Database, with emphasis on studies reporting fraud associated with South African food products. Standard PRISMA procedures guided the final selection of fifteen (14) eligible articles. These studies reveal widespread food fraud driven mainly by economic gain. Common practices include substituting high-value products, mislabelling meat and seafood, altering dates on expired goods, and producing counterfeits with unauthorised additives and packaging. Collectively, these factors compromise consumer health, undermine industry integrity, and impede effective surveillance. Strengthening South Africa’s food fraud prevention ecosystem will require coordinated multisectoral engagement, targeted investment in detection technologies, and robust regulatory reforms. Full article

Extra-virgin olive oil (EVOO) is a chemically complex lipid matrix whose minor constituents—especially phenolic secoiridoids—drive sensory quality, oxidative stability, and health benefits. However, these bioactives are vulnerable to heat, light, oxygen, and pro-oxidant metals during processing and distribution, while the high cost of EVOO often makes it a target for adulteration and mislabeling. This review critically assesses nano-enabled, food-grade strategies that (i) preserve phenolics and aroma compounds through nanoencapsulation, inclusion complexes, Pickering stabilization, and structured lipid systems; (ii) control their release and bioaccessibility during digestion; and (iii) enhance authenticity verification via sensor-ready packaging, spectroscopy/chemometrics, and digital traceability systems (IoT, machine learning, blockchain). We align these innovations with the “product identity constraints” of the EVOO category and with official quality standards used in routine control (IOC/EU). Finally, we explore circular valorization of olive-mill by-products within food-centered biorefineries, outlining pathways to convert biomass into ingredients, materials, and energy, thus reducing environmental impacts. Research priorities are proposed to develop scalable, regulation-compliant nanotechnologies that extend shelf life and increase consumer trust without compromising EVOO category standards. Full article

Honey authenticity control remains analytically challenging due to the complexity of its matrix and the increasing sophistication of adulteration practices. While chromatographic, spectrometric, and isotopic methods provide high confirmatory accuracy, their routine application is constrained by cost, time, and infrastructure requirements. In this context, fluorescence spectroscopy has emerged as a rapid, non-destructive, and cost-efficient screening approach capable of capturing subtle matrix-level compositional variations. This review critically evaluates the application of steady-state and excitation–emission matrix (EEM) fluorescence in honey quality and authenticity assessment. Fluorescence is positioned within tiered analytical frameworks as a first-line or intermediate screening tool preceding confirmatory chromatographic or NMR-based analyses. Emphasis is placed on intrinsic fluorophore domains, excitation–emission measurement strategies, and chemometric interpretation, including multiway analysis and supervised classification models. Recent developments in portable LED-based systems, laser-induced fluorescence, nanoparticle-based probes, and data-fusion strategies are discussed alongside key limitations related to matrix effects, spectral overlap, reproducibility, and model transferability. The review provides a structured framework for the strategic integration of fluorescence spectroscopy into contemporary honey authentication workflows. Full article

Ground clove bud adulteration with cheaper materials, such as clove stem and soil, poses a significant threat to spice quality and consumer trust. This study introduces a novel, alternative analytical method for the authentication and detection of adulteration in ground clove bud samples. The approach combines voltammetric fingerprinting using a multi-walled carbon nanotube-modified electrode with robust chemometric analysis. Cyclic voltammetry of clove bud samples revealed anodic peaks above +0.5 V and a smaller cathodic peak between +0.5 and −0.3 V vs. Ag/AgCl, suggesting the presence of electroactive compounds. Voltammograms were obtained for authentic clove bud samples sourced from three major Indonesian production regions (South Sulawesi, North Maluku, and East Java), showing varying redox peak intensities. Chemometric analysis, specifically Partial Least Squares Discriminant Analysis (PLS-DA), was successfully employed to differentiate clove bud samples by geographical origin, and Principal Component Analysis (PCA) was used to discriminate authentic clove bud samples from adulterants. Furthermore, Partial Least Squares Regression (PLSR) was utilized to quantify adulteration levels, predicting adulterant concentration (10–100% w/w) using electrochemical signal intensities. The PLSR method exhibited strong linearity between observed and predicted values, confirming its robustness. This proposed method offers a simple, portable, and practical approach for the quality control of ground clove bud. The combination of rapid voltammetric measurement and chemometric modelling provides a valuable and practical tool to prevent fraud and ensure the integrity of the spice trade. Full article

Indian senna (Senna alexandrina Mill.), a perennial medicinal species belonging to the family Fabaceae, holds significant therapeutic and commercial importance owing to its rich content of sennosides and rhein derivatives, which confer well-established laxative properties. Its high market demand, however, renders the species vulnerable to deliberate or inadvertent adulteration. While previous investigations have utilized functional marker systems such as SCoT (Start Codon Targeted Polymorphism)- and CBDP (CAAT Box Derived Polymorphism)-derived SCAR (Sequence Characterised Amplified Region) markers for genetic characterization, the present study is the first to report the development of sequence-specific RAPD- and ISSR-based SCAR markers consolidated into a single-tube multiplex PCR assay. Genomic DNA isolated from young leaves of S. alexandrina and its commonly encountered adulterant species was amplified using RAPD primer OPI-02 and ISSR primer UBC-835. Polymorphic amplicons were cloned, sequenced, and employed for the design of SCAR primers, which were rigorously validated for specificity. Species-specific SCAR markers were successfully integrated into a single multiplex reaction, enabling precise and unequivocal identification of S. alexandrina, Cassia fistula and Senna sophera. The multiplex amplification profiles were entirely consistent with corresponding uniplex assays, endorsing the method’s robustness and reproducibility. This streamlined, one-tube multiplex SCAR-PCR system represents a significant advancement toward reliable, high-throughput molecular authentication of Indian senna and its closely related medicinal plant species (adulterants). Full article

Melissopalynological analysis is essential for determining the botanical origin of honey, corbicular pollen and bee bread, as well as detecting adulteration. However, it traditionally relies on labor-intensive and subjective manual pollen identification. As a proof-of-concept preceding full honey analysis, this study evaluates artificial intelligence methods for automated pollen grain recognition under controlled conditions. Hazel (Corylus avellana L.) and dandelion (Taraxacum officinale F.H. Wigg.) were used as model taxa to validate the proposed approach before its application to real varietal honey samples. This study introduces a novel three-stage pipeline that decouples object detection from feature extraction, utilizing YOLOv12m for region-of-interest generation and, for the first time in melissopalynology, DINOv3 ConvNeXt-B for deep feature representation. Microscopic images acquired at 400× magnification yielded 2498 dandelion and 1941 hazel pollen grains. The detector achieved an mAP@0.5 of 0.936 with an F1 score of 0.88, while the classifier reached 98.1% accuracy with good class separability (Silhouette coefficient: 0.407). The primary technical contribution is the systematic optimization of the detection-to-classification interface. Context-aware bounding box expansion (12%) and an optimized IoU-NMS threshold (0.65) significantly improve the stability of morphological feature extraction, as confirmed by ablation studies. Computational cost reporting further supports reproducible, deployment-oriented comparison. The results confirm the feasibility of this AI-based framework as an intermediate step toward automated melissopalynological analysis, with future work focusing on standardized microscopy protocols and expanded pollen databases for varietal honey authentication. Full article

Kratom (Mitragyna speciosa Korth.) has gained increasing global attention due to its traditional use, psychoactive properties, and emerging therapeutic potential; however, concerns regarding adulteration, substitution, and inconsistent quality of commercial products necessitate robust authentication strategies. This study aimed to integrate DNA barcoding and comprehensive chemical profiling to authenticate kratom variants and discriminate them from closely allied Mitragyna species for quality control and forensic applications. Nine DNA barcoding regions were analyzed, alongside chemical characterization using UHPLC, GC–MS, and LC–MS/QTOF. Among the tested loci, the internal transcribed spacer (ITS) and ITS2 regions exhibited the highest interspecific variation and effectively distinguished kratom from allied species. UHPLC and GC–MS analyses confirmed that mitragynine was exclusively detected in kratom variants, with Kan Khiao exhibiting the highest content (94.33 ± 0.14 mg/g) when quantified against the mitragynine standard using UHPLC analysis. LC–MS/QTOF profiling revealed an alkaloid-rich chemotype in kratom dominated by mitragynine and 7-hydroxymitragynine, whereas M. diversifolia, M. hirsuta, and M. rotundifolia showed distinct profiles enriched in phenolic acids and flavonoid glycosides. Multivariate analyses further identified procyanidin B1, datiscetin-3-O-rutinoside, mitragynine, and 7-hydroxymitragynine as key discriminatory markers. Overall, the combined molecular and chemical workflow provides a robust framework for kratom authentication, supporting regulatory monitoring, quality assurance, and forensic identification of kratom materials. Full article

The adulteration of wheat flour with borax poses a serious food safety risk, yet conventional rapid non-destructive screening methods remain limited. This study developed a machine learning-based microwave non-destructive semi-quantitative detection method for identifying borax adulteration in wheat flour. Using a proprietary microwave detection system, which acquires broadband frequency-domain amplitude attenuation and phase shift responses in the 2.5–11.5 GHz band, amplitude attenuation spectra and dimensional phase offset spectra were obtained from 155 samples prepared at three adulteration levels (0%, 0.1–0.9%, 1–5%). These samples simulated real-world adulteration scenarios. To address high-dimensionality and class imbalance, a hybrid Random Forest-Whale Optimization Algorithm (RF-WOA) was employed to synergistically optimize feature selection and model hyperparameters. Through hierarchical repeated validation and macro-level metric evaluation, this approach achieved an overall classification accuracy of 94.6% and a macro F1 score of 0.95 while compressing the original 1800-dimensional feature space to approximately 200 effective features. Confusion matrix analysis indicates 100% recall for undiluted samples, with misclassifications primarily occurring between adjacent adulteration levels and no false negatives introduced for adulterated samples. These results demonstrate that microwave sensing combined with the RF-WOA provides a rapid, non-destructive, and robust preliminary screening and grading evaluation strategy for borax adulteration in wheat flour, exhibiting significant potential in food safety monitoring and regulatory inspection. Full article

The global edible oil market is consistently at risk of economically motivated adulteration, underscoring the necessity of robust analytical methods essential for authentication. Among various phytochemicals, phytosterols have emerged as powerful diagnostic markers and compositional indicators for verifying the botanical origin, purity, and quality of edible oils. This review summarizes recent advancements in phytosterol analysis, highlighting its application in detecting adulteration in high-value oils such as olive oil, tea seed oil, and sesame oil. We discuss the approaches of multiple chromatographic and mass spectrometry techniques (GC-MS, LC-MS) with chemometric analysis of novel markers like fatty acyl sterol esters and sterol degradation products. Furthermore, we discuss significant challenges, including the need for comprehensive databases, the identification of complex sterol compositional profiles, and the limitations of current standardized methods. The advancement of phytosterol-based authentication increasingly depends on the development of rapid, high-throughput, and non-targeted sterol profiling approaches, supported by artificial intelligence and bioinformatics, to ensure vegetable oil authenticity and safeguard market integrity. Full article

The high market demand for Sidr honey, known for its nutritional and therapeutic properties, makes it susceptible to adulteration with sugar syrups, compromising authenticity and consumer safety. This study employed physicochemical tests and liquid chromatography–mass spectrometry (LC–MS) sugar profiling to analyze the impact of adulteration with corn, date, and agave syrups (5–35% w/w) on Kuwaiti Ziziphus spina-christi (Sidr) honey samples. Authentic Sidr honey exhibited pH values within 3.4–6.1, free acidity (FA) of <50 mEq kg⁻¹, high electrical conductivity (mean EC: 1066.21 ± 353 µS cm⁻¹), and moisture content <20%. Adulteration did not significantly affect pH or moisture (p > 0.05). FA significantly changed only in corn syrup-adulterated samples (p < 0.05). Electrical conductivity varied significantly with syrup type (p < 0.05). LC–MS was used to quantify the fructose (F) and glucose (G) contents, their ratio (F/G), and the total sugar content (F + G). For the authentic samples, F/G = 1.10–1.35, consistent with reported ranges. Corn syrup reduced F + G and F/G, date syrup raised both sugar contents, modestly changing F/G, while agave syrup, markedly increased both F/G and F + G. This integrated approach of physicochemical characterization and targeted sugar profiling effectively detects syrup adulteration, enhancing honey authentication, consumer protection, and market transparency. Full article

Conventional immunochromatographic assays (ICAs) face limitations in sensitivity and dynamic range, hindering their application in on-site, class-specific screening of sulfonylurea (SU) adulteration in functional foods. To address this, a signal amplification strategy was developed by engineering high-density platinum nanozymes on a mesoporous metal–organic framework (PCN-224). The mesoporous architecture of PCN-224 facilitated high-density and stable loading of catalytically active Pt sites. The established PCN-224@Pt-based ICA achieved detection limits of 0.52–7.94 μg/kg in tea and 0.69–7.02 μg/kg in capsules, with linear ranges of 1.69–513.01 μg/kg and 2.05–716.47 μg/kg, respectively. Compared with traditional colloidal gold immunochromatographic assays (CG-ICAs), sensitivity was improved by up to 57-fold, while the linear detection range was expanded by over 5-fold relative to the previously reported PCN-224@PDA- ICA. The method demonstrated recovery rates of 81.8–119.8% and coefficients of variation between 2.5% and 11.4%. Validation against LC-MS/MS using 20 real samples showed excellent agreement (R² > 0.99). This work not only provides a sensitive and rapid tool for the surveillance of SU adulteration in functional foods but also establishes a generalizable nanozyme design strategy applicable to enhancing the performance of a wide range of ICA-based detection platforms. Full article