Food Analytical Methods最新文献

Honey is a natural sweetener produced by honeybees and is widely appreciated by consumers because of its multiple beneficial properties. Because of its high value, honey is placed as a targeted product for fraudulent practices. In this work, LC-LRMS fingerprinting was employed for classifying honey samples from 10 countries. Good classification and prediction performance were achieved based on a classification decision tree by consecutive paired PLS-DA models using a hierarchical model builder (HMB), obtaining sensitivity and specificity values higher than 83.3% and 92.6%, respectively, except for the case of China versus Japan. Tentative association of some phenolic compounds was accomplished, which provides useful chemical markers for country discrimination. For instance, methoxyphenylacetic acid, previously identified in New Zealander honeys, was tentatively annotated to m/z 165.0, detected in honey from New Zealand and Australia. The prediction of “unknown” samples was successful for most cases, obtaining sensitivity and specificity values of 100% for most countries. Good classification based on the continent of production was also accomplished, obtaining perfect discrimination among samples produced in Oceania and good classification performance was observed in Asian and European samples. Finally, the obtained fingerprints demonstrated to be useful chemical descriptors to quantify, as a proof of concept, adulterated Spanish honey with honey from Italy, China, and Serbia using partial least squares (PLS) regression, obtaining internal and external validation prediction errors lower than 23%.

The extensive use of antibiotics in dairy farming has raised significant concerns regarding the potential presence of their residues in milk. This poses serious health risks, including allergic reactions and the emergence and development of antimicrobial resistance (AMR). The study is aimed at the quantification of antibiotic residues, specifically oxytetracycline, penicillin G, sulfadiazine, and tetracycline, in raw cow milk collected from Adama, Ethiopia. A multiresidue LC-MS/MS analytical method was optimized and validated according to the standards set by the European Commission (EU 2021/808). Sample preparation was performed by solvent extraction that contains McIlvaine buffer (pH = 4) followed by solid phase extraction (SPE) that utilized Oasis® Hydrophile-Lipophile Balance (HLB) cartridges as well as extracting the antibiotic residues by methanol. Excellent performance characteristics of the method were demonstrated in terms of recovery rates and calibration linearity within the range from 0 to 250 μg/kg concentration. A total of 162 raw milk samples randomly collected from intensively and semi-intensively managed dairy farms in Adama, Ethiopia, were analyzed, with 8% of them testing positive above the decision limit for residues. Notably, penicillin G and oxytetracycline were detected in 5.6% and 2.5% of the samples, respectively. The concentrations found range from 13.15 to 142.38 μg/kg, which exceed the maximum residue levels (MRLs) established by EU Commission Regulation no. 37/2010 standards in several samples. The results raised public health concerns, especially with milk samples exceeding MRLs permitted. This is due to the potential health risks earlier highlighted. Therefore, the study findings necessitate the need for strict enforcement of regulatory frameworks and improved veterinary antibiotic use practices. Such measures will mitigate risks to public health and ensure food safety. Moreover, ongoing monitoring and consumer education are essential to effectively address the antibiotic residue issue.

Foodborne diseases caused by bacterial pathogens are of major public health and zoonotic concern. The objective of the present study was to develop real-time TaqMan PCR assays for the detection and quantification of important bacterial foodborne pathogens, including Clostridium perfringens, Staphylococcus aureus, and Salmonella spp. For this purpose, primers and probes were designed from conserved regions of the target genes (cpa for C. perfringens, nuc for S. aureus, and invA for Salmonella spp.) and TaqMan assays were standardized. The analytical sensitivity of the developed real-time TaqMan assays using gel-purified PCR amplicons was determined to be 2.8 copies/μL, 3.5 copies/μL, and 7.0 copies/μL of DNA for C. perfringens, Salmonella spp., and S. aureus, respectively. The analytical sensitivity of the TaqMan assays was 10- to 1000-fold higher than that of conventional endpoint PCR. The standard curves showed good linearity with R² = 0.99 for all the pathogen-specific TaqMan assays developed and the assays were found to be reliable and reproducible. In spiking studies, the limit of detection (LoD) of the developed TaqMan assays under un-enriched conditions was 1.2 × 10⁵ CFU/g, 3.2 × 10⁸ CFU/g and 3.3 × 10⁴ CFU/g of meat for C. perfringens, Salmonella spp., and S. aureus respectively. After 6 h of enrichment, the LoD considerably improved to 1.2 CFU/g, 320 CFU/g, and 3.3 CFU/g of meat, respectively. The study highlights the need and importance of the enrichment step in the detection of FBPs. The developed real-time TaqMan assays may serve as rapid laboratory tools for the detection and quantification of C. perfringens, S. aureus, and Salmonella spp. in meat.

The formation of crack kernels, which compromises rice quality, is influenced by the maximum moisture content gradient (MMCG). Consequently, accurately modeling the MMCG as it relates to drying conditions is essential for optimizing drying processes. However, this gradient demonstrates complex, non-linear variations involving multiple variables, making it challenging and time-consuming to model using traditional methods. Artificial neural networks (ANNs) offer a powerful alternative due to their inherent ability to handle such complexities. This work presents an ANN model for predicting the MMCG within rice kernels during combined hot air and far-infrared drying. The model utilized a three-layer, fully connected feedforward network. The inputs were drying time, inlet air temperature, and far-infrared (FIR) intensity. The outputs predicted the average of moisture content (MC), MC at the short axis of the kernel (MCS), and MC at the kernel center, enabling the prediction of MMCG. The two hidden layers, containing 20 neurons, employed a tan-sigmoid transfer function. The Levenberg-Marquardt algorithm was used to train the network. Training data was generated from a finite element method (FEM) simulation based on Fick’s law of diffusion. The trained ANN was validated and tested using randomly generated data. To prevent overfitting, the training process incorporated an early stopping method. The results demonstrate the network’s ability to accurately predict MC and MMCG behavior, as indicated by root mean square error (RMSE) and R-squared (R²). The ANN model demonstrates high predictive accuracy, confirming its effectiveness in modeling moisture content and MMCG during rice drying.

This study presents the development of an electrochemical sensor based on nanochannels for the sensitive detection of the fungal toxin zearalenone (ZEN). The sensor incorporates gold nanoparticles (AuNPs) and multi-walled carbon nanotubes (cMWCNTs) onto anodic aluminum oxide (AAO), with nickel oxide (NiO) modification on the reverse side of the AAO, thereby forming a NiO-AAO@AuNPs-cMWCNTs/SPCE electrode system. The NiO modification facilitates the initial oxidation of ZEN, resulting in the generation of distinctive electrochemical oxidation peaks. To investigate the reaction mechanism of ZEN oxidation, electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) were employed. In the concentration range of 1 ~ 40 μg/mL, the sensor showed a clear linear correlation between ZEN concentration and the impedance response, expressed by the regression equation: R_{EIS (Ω)} = 40.8 + 2.7 C_{ZEN (μg/mL)} (R² = 0.997, n = 3). The limit of detection (LOD) was determined to be 0.1 μg/mL. This nanochannel-based electrochemical platform provides a reliable and efficient strategy for ZEN detection, demonstrating the potential of nanostructured materials in food safety monitoring.

Health benefits, religion, animal welfare, environmental protection, and food scandals are among the reasons why many people choose a vegetarian or vegan diet. In this study, the usage of low-field NMR spectroscopy at 80 MHz to identify the present of animal-derived thickener gelatin in food, especially in dairy products, was explored. The fingerprint of aromatic and NH_x signals between δ 6.0 and δ 9.0 ppm can be used for identification and quantitative analysis of gelatin in the investigated products. External calibration curve was linear between 5 mg/mL and 25 mg/mL (R² = 0.985). The limit of detection (LOD) and limit of quantification (LOQ) were defined as 0.14mg/g and 0.42mg/g with respect to finished products, respectively. More than 50 samples of vegan and non-vegan products (yoghurt, cream, pudding, mousse, and candies) were successfully investigated. NMR results correspond with the labelling information for all samples. Gelatin was predominately detected in mousse (median 3.3 mg/g), yoghurt (median 2.2 mg/g), and pudding (1.0 mg/g) samples. Gelatin was also detected in non-dairy candy samples with contents between 17 mg/g and 96 mg/g, which is consistent with the information on the packaging. Low-field NMR can be a quicker and cheaper alternative to conventional techniques for verification of animal origin of thickeners in food products.

The presence of veterinary drug residues, particularly enrofloxacin, in food products constitutes a serious public health concern. To address this issue, it is imperative to develop highly sensitive detection methods for accurate identification of enrofloxacin residues. This study introduces a deep learning-assisted immunosensor based on dual-sized microspheres (DLIDM) for the sensitive quantification of enrofloxacin. The sensor employs two types of polystyrene microspheres: 500 μm microspheres (PS₅₀₀) functionalized with enrofloxacin antibodies as separation carriers, and 3 μm particles (PS₃) conjugated with enrofloxacin antigens as the signaling probes. After the immunoreaction, the system quickly separates immunocomplexes from uncaptured signal probes based on their different settling times. The uncaptured probes are then counted using optical microscopy and a YOLOv11-based algorithm. Finally, a quantitative relationship was established between the number of free signal probes and enrofloxacin concentration. The results demonstrate that the DLIDM achieves sensitive detection with a wide linear range (0.5 ng/mL to 1 μg/mL) and a low limit of detection (0.11 ng/mL). In spiked egg samples, the DLIDM enables accurate detection for enrofloxacin with recoveries from 92.1% to 111.4%, and relative standard deviations were 7.96%–12.08%. With its combination of operational simplicity, high sensitivity, and speediness, this immunosensor presents a promising new platform for food safety monitoring.

The rapid and accurate detection of neonicotinoid pesticides is critical for safeguarding food safety and environmental health, particularly in developing regions where excessive pesticide use is a growing concern. Surface-enhanced Raman spectroscopy (SERS) offers a powerful tool for detecting trace-level analytes in complex matrices due to its high sensitivity and molecular specificity. In this study, we demonstrate the effective use of a pegylated gold nanoparticle (AuNP)-based SERS substrate, drop-cast on glass base covered with aluminum foil to give a rigid support to substrate, for the detection of two widely used neonicotinoids: acetamiprid (ACE) and imidacloprid (IMI). The engineered substrate delivers a uniform distribution of AuNPs, enabling consistent signal enhancement across the surface. It exhibits a broad detection range from 100 ppm down to 0.001 ppm, with a remarkable limit of detection (LOD) of 0.001 ppm for both pesticides. The calculated analytical enhancement factors (AEFs) were 7.93 × 10⁶ for ACE and 3.85 × 10⁶ for IMI, underscoring the substrate’s high sensitivity. Furthermore, Principal Component Analysis (PCA) was employed to distinguish spectral fingerprints of the two analytes, enabling clear and reliable differentiation. The straightforward fabrication process, combined with excellent signal reproducibility, long shelf life, and substrate stability, highlights the practical potential of this versatile SERS platform for real-world pesticide monitoring and food safety applications.

Graphical Abstract

The study and characterization of specialty coffees are essential to ensure the superior quality and traceability of these premium products, given growing market demand, the lack of standardized quality, and the risk of fraud. This study investigates the volatile profiles of specialty coffee beans from different producing countries, obtained with various post-harvest processing methods and different roasting levels. To analyze the samples, an integrated approach using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC–MS) and chemometric techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), was developed and optimized. Considering the limited number of samples for each class, the LDA models achieved a 100% correct classification rate in training for all classes, except for Ethiopian green beans, which were classified correctly with a rate above 85%. The results of the validation process are also acceptable, with a minimum correct classification of 50%. Furthermore, some volatile compounds could be considered potential product or process markers, such as furan compounds, mainly associated with the degree of roasting. These compounds also contribute to the flavor of the final beverage. Preliminary results suggest that the optimized method could represent an important tool for authenticity assessment and environmental valorization of specialty coffees, with potential applications in quality control and product or process certification.