Food Analytical Methods最新文献

Fusarium toxins, as significant pollutants threatening global food security, have garnered increasing attention due to their mixed contamination issues. Among them, the co-contamination rate of Zearalenone (ZEN) and Fumonisin B (FB) in grains reaches up to 37.6%, and both exhibit synergistic toxicity, are difficult to remove, and pose serious threats to animal and human health. Therefore, developing rapid and sensitive multi-target detection technologies is of great importance. However, traditional colloidal gold-based colorimetric immunochromatographic assay (ICA) for multi-detection suffers from drawbacks such as signal overlap and insufficient sensitivity. In this study, we synthesized a signal probe Zr-MOF@PtNPs by leveraging the high molar extinction coefficient of PtNPs and the high specific surface area of Zr-MOFs. Zr-MOF stabilizes and disperses PtNPs, enabling them to exhibit excellent colorimetric performance with superior recognition under different light backgrounds, significantly enhancing the detection sensitivity and anti-background interference capability of traditional multi-detection colorimetric ICA. In this research, a dual immunochromatographic method using Zr-MOF@PtNPs as signal labels was established for the simultaneous detection of ZEN and FB in corn, which is simple to operate and achieves detection ranges of 0.15–5 ng/mL for ZEN and 0.6–10 ng/mL for FB, with LODs of 0.27 ng/mL and 0.21 ng/mL, respectively. This improves the detection sensitivity of multi-detection colorimetric immunochromatographic assay strips for synchronous detection of ZEN and FB, providing new methodological support for the simultaneous detection of multiple mycotoxins.

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In the study, 1,4-phenylene diisocyanate (PPDI) as a neutral linking monomer is applied as direct synthesis of sulfonated covalent organic polymers (COPs) with 2,5-diaminobenzenesulfonic acid (DBS) under moderate and economic circumstances, and the Fe₃O₄ nanoparticles were further decorated on the surface of COPs. The prepared PPDI-DBS@Fe₃O₄ with abundant active sulfonic acid groups showed good adsorption performance for β-agonists. The PPDI-DBS@Fe₃O₄ was applied in magnetic solid-phase extraction (MSPE) for the enrichment and purification of β-agonists. Combined with liquid chromatography–tandem mass spectrometry, the proposed method showed low detection limits of 0.03–0.05 µg kg⁻¹ in food samples. The spiked recoveries were 93.5%–102.3% with the relative standard deviations of 2.6%–5.0%.

The geographical provenance of agricultural products is a key determinant of quality and economic value. An untargeted metabolomics-based ML pipeline was established to authenticate the origin of Yongfu Luohan Guo. LC-QTOF/MS profiling of samples from multiple origins generated comprehensive metabolite datasets, which were analyzed using supervised ML algorithms. Multivariate analysis revealed distinct metabolic signatures differentiating Yongfu and non-Yongfu Luohan Guo. The CNN algorithm outperformed other models, achieving 100% classification accuracy using 8 key metabolites These biomarkers include two flavonoids: 5-hydroxy-3-(5-hydroxy-2,4-dimethoxyphenyl)-6-methoxy-7-[3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxychromen-4-one and glabrol; three terpenoids: rhodojaponin III, lapidin, and acetylcimigenol arabinoside; and three alkaloids: corynoxeine, indirubin, and hydroquinidine. Region-specific metabolite accumulation trends were observed in Yongfu Luohan Guo, correlating with fruit quality attributes. This metabolomics-ML integration strategy presents a scalable solution for geographical indication authentication of diverse agricultural products.

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In this study, a new fluorescent sensor was occurred from pyrene-substituted calix[4]arene derivatives and its usability in the detection of Cu²⁺ ions and triazole-based pesticides was investigated. Calix[4]arene derivatives formed complexes that exhibited an "on–off-on" fluorescence response by interacting with pesticides containing triazole groups and showed significant changes in their optical properties. This innovative sensor plays an effective binding role especially for Cu²⁺ ions and the change in the fluorescence properties of the complexes formed with these ions can be used for the detection of pesticides. In the study, "turn-on–off-on" fluorescence responses were observed as a result of the interaction of calix[4]arene-pyrene complex with triazole-based pesticides (tebuconazole, tetraconazole, penconazole and difenoconazole). In addition, it is emphasized that the sensor is a potential tool for the rapid and sensitive detection of pesticides, which are important in terms of environmental pollution and food safety. The results obtained show that calix[4]arene derivatives have great potential in the detection of environmental pollutants and in the development of optical sensors sensitive to these pollutants.

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Food safety is critically linked to human health, necessitating advanced monitoring of biogenic amines (BAs), key biomarkers of food freshness. Here, a supramolecular fluorescence probe was developed using sulfobutyl ether-β-cyclodextrin (SBE-β-CD) encapsulated nitro-spiropyran (SP), for ultra-sensitive sensing of histamine. Hydrophobic interactions drive the closed-loop SP into the SBE-β-CD cavity, forming a non-fluorescent complex (SBE-β-CD@SP). Upon histamine introduction, competitive displacement ejects SP from the cavity. The liberated SP undergoes rapid ring-opening under 365-nm UV excitation to form fluorescent merocyanine (MC), emitting the fluorescence signal. This turn-on response enables quantitative detection of histamine, exhibiting a linear range of 10–70 nM (R² = 0.996) with a low detection limit of 0.069 nM. The probe demonstrated high selectivity over interfering amines and achieved 94.2–97.9% recovery in spiked fish samples, validating real-world utility. This work establishes a host–guest displacement-based sensing strategy via histamine-triggered MC activation, which exhibits potential applicability to fish freshness assessment. By integrating supramolecular chemistry with nanomaterial design, this probe offers a novel approach for food safety monitoring.

N-nitrosamines (NAs) have recently been identified as contaminants in various matrices, including processed meat products. These compounds exhibit high carcinogenic potential, raising significant concerns regarding their presence. In this context, the present study proposes the use of solid-phase microextraction (SPME) in the Headspace (HS), a technique that offers higher efficiency and a greener profile compared to conventional extraction methods, enabling the achievement of low detection limits when combined with gas chromatography coupled to flame ionization detection (GC/FID). Instrumental parameters were optimized to ensure suitable separation and detection of N-nitrosodiethylamine (NDEA) and N-nitrosodimethylamine (NDMA). The optimization of the HS-SPME parameters revealed that the PDMS/Car/DVB fiber, sample solution at pH 7, ionic strength adjusted to 30% NaCl, extraction temperature of 35 °C, and extraction time of 60 min were optimal conditions. The analytical parameters of the developed method were evaluated, yielding excellent selectivity and limits of detection of 4 and 38 µg kg⁻¹ for NDEA and NDMA, respectively. The method exhibited precision with RSDs from 2.6% to 14.4%, with the higher value attributed to the manual nature of the SPME process for NDMA, and accuracy, with recoveries between 92.3% and 111.5%, thereby confirming the effectiveness of the developed method for the determination of NAs. The proposed approach provides an efficient and sustainable alternative to more costly conventional methods. The developed method was applied to two commercial sausage samples purchased from different local markets, and no detectable levels of the evaluated NAs were found. The proposed approach provides an efficient and sustainable alternative to more costly conventional methods.

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Basidiomycota secrete a wide range of enzymes allowing them to break down structural cell-wall carbohydrates present in water-insoluble side streams of the food industry. Fermentation of such side streams by edible fungi represents an alluring option, but methods for quantifying the fungal biomass are required to assess the success of fermentation, as parts of the side streams may still be present at the end of the fermentation. Current methods for quantifying chitin as a marker compound are usually based on photometric assays requiring complex sample preparation and the use of toxic chemicals; therefore, a modified version of the Smith and Gilkerson photometric assay was compared with a semi-automated method that detects the nitrogen compounds remaining after protein hydrolysis using the Kjeldahl method. Mixtures containing fruiting bodies of P. ostreatus and mycelia of L. squarrosulus in defined amounts as well as mycelial samples obtained from bioreactors were analysed by both methods. Comparable chitin contents were obtained above 20–40% fungal content with both methods, and the quantified fungal contents were well reproducible. The semi-automated method has a higher detection limit but is suitable for fungal mycelia and may replace the photometric assay in routine analysis with fungal content above 20–40%.

Confectionery products such as chocolates and biscuits are widely consumed worldwide; however, the potential presence of hidden non-halal fats such as lard remains a significant concern for halal-observant consumers. This study employed gas chromatography with flame ionization detection (GC–FID) combined with principal component analysis (PCA) to detect porcine fatty acid biomarkers in imported chocolates and biscuits. Total fat content ranged from 11.5 to 32.5%, with palm kernel-based chocolates enriched in lauric (42–52%) and myristic acids (18–20%), while other chocolates were dominated by palmitic, stearic, and oleic acids. Biscuits contained high proportions of palmitic and oleic acids (> 75%). PCA of the complete fatty acid dataset (PC1 = 46–49%, PC2 = 28–34%) clearly separated lard-adulterated samples, with PM-2 and PMO-2 clustering with the lard reference. Targeted PCA using porcine biomarkers palmitic-to-oleic acid ratio (C16:0/C18:1) and eicosadienoic acid (C20:2) confirmed this clustering. The method’s sensitivity was demonstrated by LOD and LOQ values of 0.96% and 2.80% for the C16:0/C18:1 ratio, and 0.017% and 0.039% for C20:2, respectively. Calibration using simulated lard–palm oil mixtures (0–15% w/w; five replicates per level) enabled quantitative estimation of lard at 12.58% and 11.49% in PM-2 and PMO-2. All biscuits clustered separately from lard, confirming authenticity. These findings demonstrate that the detection of porcine biomarkers using GC–FID combined with PCA provides a robust, sensitive, and quantitative approach for halal authentication of imported chocolates and biscuits, offering a practical platform for regulatory monitoring and consumer protection.

This study presents a systematic investigation of hybrid MetaFormer-based deep learning architectures for the classification of olive fruit diseases caused by olive fly (Bactrocera oleae) damage and fungal infections, which frequently co-occur in real agricultural settings. Olive production, which holds strategic economic, ecological, and cultural importance in Mediterranean regions, is increasingly threatened by these factors, leading to significant yield and quality losses. Early and accurate disease detection is therefore essential for effective disease management and sustainable olive production. Building upon the MetaFormer framework, this work proposes a modular and task-adaptive architectural paradigm in which heterogeneous token-mixing blocks—identity mapping, random mixing, separable convolution, and self-attention—are systematically combined and sequentially ordered to form hybrid architectures. Rather than introducing a single fixed model, six different MetaFormer-based hybrid configurations are developed to explore how block composition and ordering influence performance, robustness, and computational efficiency. All proposed models are trained from scratch under identical experimental conditions and compared against established baseline architectures, including CAFormer-S18, ConvFormer-S18, and PoolFormerV2-S12. Experimental results demonstrate that several hybrid configurations achieve strong classification performance, with accuracies up to 97.98% and macro F1-scores approaching 0.98, outperforming or matching baseline models while using substantially fewer parameters. In addition to standard evaluation, robustness under realistic domain shifts—such as blur, illumination changes, and colour distortions—is explicitly assessed, revealing that certain block orderings provide improved generalization stability under distribution shifts. Furthermore, a comprehensive resource-efficiency analysis shows that the proposed models offer a favourable trade-off between accuracy and computational cost, operating with significantly lower parameter counts and competitive inference latency. The ability to achieve high performance and robustness without relying on transfer learning highlights the effectiveness of task-adaptive MetaFormer designs in limited-data scenarios. Overall, this study demonstrates that treating MetaFormer as a configurable block-composition framework enables the development of lightweight, robust, and explainable architectures suitable for real-world agricultural applications. The findings provide valuable insights into architectural design strategies for data-constrained visual recognition tasks and lay the foundation for future research on task-driven and adaptive MetaFormer-based systems.

Sacha inchi (Plukenetia volubilis) is a nutrient-rich oilseed valued for its high content of polyunsaturated fatty acids, particularly omega-3 and linoleic acid. Conventional extraction techniques such as steam distillation (SD) and hydro distillation (HD) are limited by low yields and high energy demands, reducing both efficiency and sustainability. This study evaluates ohmic-heated extraction (OHE), an electrically driven green technology, as an alternative method for oil recovery from sacha inchi seeds. Response surface methodology (RSM) was employed to optimize key parameters: reaction time, voltage, and solvent-to-solid ratio for maximizing oil yield. OHE performance was compared with SD and HD based on oil yield, energy efficiency, and physicochemical, antimicrobial, and morphological properties. The optimal OHE conditions were determined to be 70 V, 30 min, and a solvent-to-solid ratio of 7.06:1, resulting in a yield of 23.16%, significantly higher than that obtained by HD (10.6%) and SD (0.04%). Under these conditions, OHE produced a substantially higher oil efficiency of 225.89 mL/kWh with energy consumption of only 0.141 kWh, representing up to 91.21% savings compared with SD and HD. The oil obtained through OHE exhibited a light-yellow color and a refractive index of 1.48, comparable to conventionally extracted oils, although its specific gravity was slightly lower. Gas chromatography–mass spectrometry (GC–MS) analysis identified 17 chemical constituents, with linoleic acid methyl ester, hexadecanoic acid methyl ester, and methyl stearate as the dominant components across all extraction methods, reflecting the characteristic fatty acid composition of sacha inchi oil. Scanning electron microscopy further revealed pronounced structural disruption of seed tissues, indicative of enhanced cell wall breakdown and oil release. Overall, OHE demonstrates high efficiency, significant energy savings, and the ability to produce high-quality sacha inchi oil, highlighting its potential for sustainable applications in the food and cosmetic industries.