Food Analytical Methods最新文献

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.

A core–shell structured magnetic molecularly imprinted nanoparticle (MMINP) was developed, capable of forming a photonic crystal (PC) sensor in the presence of a magnetic field for the detection of sulfamethazine (SM2) residues in food. The preparation conditions were meticulously optimized, and the adsorption performance of MMINPs was comprehensively characterized through Adsorption Kinetics Experiments and Equilibrium Binding Experiments. As the concentration of SM2 varied, the structural colors were systematically characterized under a designated magnetic field strength. The experiments revealed that within the range of SM2 concentrations from 10⁻¹ μg/L to 10³ μg/L, there was a shift in the reflected wavelength from 570 to 610 nm, and the color changed from green to red–orange. The sensor achieved a minimum detection limit of 2.75 μg/L, a maximum adsorption capacity of 3.86 mg/g, an imprint factor of 1.70, and was reused for at least five adsorption-resolution cycles. The recoveries ranged from 69.97% to 102.68% and RSD < 8.73%. The sensor introduces an innovative approach for the rapid visual colorimetric detection of SM2 in complex food matrices.

In the detection of quinolone residues in freshwater shrimp across various regions of China, more samples were found to contain residues of the banned drug norfloxacin using liquid chromatography-quadrupole tandem mass spectrometry (LC-QQQ). To further validate these results, three qualitative methods were developed: optimized LC-QQQ, liquid chromatography-quadrupole linear ion trap tandem mass spectrometry (LC-Qtrap), and liquid chromatography-quadrupole electrostatic field orbital trap tandem mass spectrometry (LC-Q Exactive). The optimized LC-QQQ involved adjusting the mobile phase gradient and monitoring specific ions, while LC-Qtrap utilized enhanced ion scanning (EPI) mode. For LC-Q Exactive, the full MS-dd-MS² mode was applied. After review, all suspected samples were excluded due to the potential interference from an endogenous substance in the shrimp that has a similar mass to norfloxacin. Each method has distinct features and can be employed as a qualitative method in different laboratory settings, offering robust technical support for food safety oversight.

Determination of progesterone in livestock and milk samples in a simple and minimally invasive way can be a challenging task in animal breeding systems; thus, a voltametric screen-printed electrode (SPE) enhanced with manganese oxide nanoparticles anchored on oxide graphene derivatives (GO/MnO₂), denominated SPE/GO/MnO₂, was successfully optimized for the indirect determination of progesterone by derivatization with hydrochloric phenylhydrazine and then directly tested in livestock and milk samples. Manganese oxide nanoparticles were obtained by a direct single synthesis through the Hummers’ reaction modified. Nanocomposites were characterized by HR-TEM, Raman spectroscopy, XPS, and electrochemical impedance spectroscopy. The combination of MnO₂ and GO nanosheet resulted in a highly sensitive material with superior electrochemical activity regarding the indirect oxidation of P4 when compared to clean disposable (SPE) sensors, and previous works concerning the determination of P4. The GO/MnO₂ nanocomposite promoted an outstanding increase towards peak signal for P4, while also exhibiting an excellent linear range for P4 (0.05–3.0 µmol L⁻¹) and a detection limit (LOD) of 0.001 µmol L⁻¹. The modified sensor also demonstrated a good recovery rate for P4 in cattle serum samples and pasteurized cow’s milk samples, which ranged from 98 to 104%. Interference studies demonstrated that the variation among peak current (Δip) of all possible interferents evaluated in this work did not exceed ± 3%. The newly developed sensor not only stands out for its distinguished sensitivity but also for its rapid and simple way to be produced, providing a direct, practical approach for determining P4 in livestock specimens.

Graphical Abstract

The novel grape cultivar BRS Carmem (Vitis vinifera L.) is highly productive and rich in anthocyanins, yet its potential for natural colorant production remains underexplored at that time. This study optimized a green extraction method using a 2³ multivariate experimental design, evaluating temperature, solvent volume, and extraction time. Citric acid (2% m/v in water) was selected as a non-toxic alternative that best extracted the pigments from the grape. The optimized conditions (64 °C, 90 mL solution, and 5 min) yielded 1061.68 mg of cyanidin-3-glucoside per 100 g of peel. The extract exhibited high reducing capacity (3312.76 mg citric acid eq./100 g of peel) and significant antimicrobial activity against Staphylococcus aureus, Salmonella spp., and Escherichia coli. Toxicity assessment using Galleria mellonella indicated an LD₅₀ of 4.8 g/kg body weight. This cost-effective, eco-friendly method produces an anthocyanin-rich extract with strong bioactivity, reinforcing its potential for natural colorant production and functional food applications.

Triazines are widely utilized in agriculture to control weeds and have been found in food frequently, posing a risk to human health. The analysis of trace contaminants in milk samples generally involves the process of protein precipitation. In this work, a lab-made semi-automated solid-phase extraction (SPE) strategy was developed for the rapid extraction of six triazine herbicides in milk samples. Six triazines were directly extracted with macroporous resin-based SPE followed by high-performance liquid chromatography detection. Some parameters affecting the extraction efficiency, including the type and amount of macroporous resin, salt concentration, pH, flow rate, extraction time, type, and volume of elution solvent, were investigated and optimized. The working curves exhibited good linearity in the concentration range of 0.78–100.00 µg L⁻¹ (R² ≥ 0.9996). LODs and LOQs were in the range of 0.15–0.31 µg L⁻¹ and 0.50–1.05 µg L⁻¹, respectively. The relative standard deviations (RSDs) for intra-day and inter-day were 1.91–3.85% and 0.59–3.78%, respectively. The recoveries and RSDs for five spiked milk samples were 91.5–107.8% and 0.15–6.77%, respectively. The proposed method eliminated the commonly used protein precipitation procedure and could improve the sample throughput, which was a simple, efficient, and economical sample pretreatment with potential applications in complicated matrix pesticide residue analysis.

Herbal teas are a key component of phytotherapy, known for their health benefits due to bioactive compounds like polyphenols and flavonoids. This study focused on optimizing an antioxidant herbal tea formulated from olive buds and leaves, enriched with fennel seeds, lemon zest, and oregano. Olive buds showed the highest polyphenol (78.79 mg GAE/g DM) and flavonoid (56 mg EC/g DM) contents, while olive leaves also contained significant levels (38.66 mg GAE/g DM polyphenols; 39.74 mg EC/g DM flavonoids). HPLC analysis confirmed the predominance of oleuropein (up to 2314 µg/mL) and hydroxytyrosol (1765 µg/mL). Antioxidant assays revealed strong activity, with low IC₅₀ values in DPPH (21–30 µg/mL for oregano, buds, and leaves) and high reducing power in FRAP (EC₅₀ ≈ 101–104 µg/mL for leaves and buds). Sensory optimization identified a balanced formulation (81% buds, 9% leaves, 5.1% fennel, 4.2% oregano, 0.7% lemon), while percolation proved the most effective extraction method, yielding 21.5 mg GAE/100 mL polyphenols and 26.5 mg EC/100 mL flavonoids. These results highlight the potential of olive by-products to develop eco-friendly, functional herbal teas combining health-promoting properties with consumer acceptability.