Food Analytical Methods最新文献

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.

Graphical Abstract

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.

Early detection and control of aflatoxin B1 (AFB1) contamination are essential for ensuring food safety. However, conventional analytical methods frequently encounter technical limitations including operational complexity and insufficient sensitivity. To overcome these limitations, an ultrasensitive fluorescent immunoassay detection of AFB1 was developed based on SiO₂ nanospheres. These highly dispersible nanospheres were utilized as carriers to co-load CdSe/ZnS quantum dots (QDs) and AFB1-specific aptamers (Apt), resulting in the construction of SiO₂@QDs@Apt fluorescent probes with enhanced signal amplification (7.51 (times) 10³-fold improvement in sensitivity compared to the QDs@Apt method without SiO₂ nanospheres). The assay achieved an ultra-low detection limit of 5.06 (times) 10^–7 ng/mL and a broad linear range of 10⁻⁷~10 ng/mL. Validation through standard spiking experiments and real sample analysis confirmed the method’s accuracy and reliability. This strategy demonstrated higher sensitivity than the commercial ELISA kit tested, suggesting its potential utility for trace AFB1 monitoring in certain food matrices. Overall, this simple, specific, and cost-effective strategy could serve as a sensitive analytical platform for AFB1 monitoring in food safety laboratories.

Traditional refining is a crucial process used to remove undesired components such as free fatty acids, phospholipids, and oxidation products from crude oil. However, it also results in the loss of some beneficial minor components, including tocopherols and phenolic compounds. Nutritionally important minor components can be retained through alternative refining approaches like minimal refining that minimize processing intensity and reduce the number of refining stages. Calcium hydroxide has been used as an alternative to sodium hydroxide to reduce neutralization loss since it is cheaper and weaker than sodium hydroxide. The research focused on obtaining neutralized corn oil by calcium hydroxide Ca(OH)₂ instead of sodium hydroxide (NaOH), which is typically used in traditional neutralization. Response surface methodology was employed to optimize the process for producing corn oil via reduced FFA and retained α-tocopherol levels. The elucidated optimum values for the neutralization of corn oil by Ca(OH)₂ were at 0.44%, 47.4 °C, and 14.4 min. FFA values for crude, traditional, and minimal neutralized corn oils were 0.21%, 0.20%, and 0.13%, respectively. The α-tocopherol level of the crude, traditional, and minimal neutralized corn oils were 126.43, 100.56, and 125.04 mg/kg oil, respectively.

A reliable analytical method for detecting adulteration in ginger powder (GP) is essential to ensure product quality and maintain consumer trust. In this study, attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy was employed to provide a rapid and simple method for detecting GP adulterated with white powder fillers, namely corn starch (CS), potato starch (PS), rice flour (RF), tapioca starch (TS), and wheat flour (WF). Two chemometric approaches were applied for the classification of the mid-infrared spectra (800–650 cm⁻¹): class-modelling and discriminant approaches. Data-driven soft independent modelling of class analogy (DD-SIMCA) was utilized to model only the authentic GP class, while partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were employed as binary classifiers using both authentic and adulterated calibration samples. All models demonstrated good classification performance on the prediction set within their respective frameworks. The discriminant models showed high sensitivity and specificity for the detection of adulterated samples, with efficiency values of 0.980 for PLS-DA and 1.000 for SVM. Although DD-SIMCA yielded a lower efficiency value of 0.919, it successfully classified the authentic GP samples, with a sensitivity value of 0.959, and demonstrated its suitability for one-class authentication tasks. Overall, the results highlight the potential of ATR-FTIR spectroscopy combined with appropriate chemometric strategies for the detection of adulteration in ginger powder.

This study aimed to evaluate the accuracy of food rapid test kits based on colloidal gold immunochromatographic assays (GICA) for detecting chloramphenicol, enrofloxacin, and sulfonamides in livestock, poultry, and aquatic products, and to provide a reference for their practical application. Rapid test kits from different manufacturers were used to detect the above targets in pork, chicken, fish, shrimp, and other samples. The interference resistance, linearity, and sensitivity of the test kits were analyzed by setting up a multi-concentration gradient combined with the interference experiment of flavoring matrix. Test kits D and E showed strong anti-interference ability and specificity for chloramphenicol, though test kit E yielded false positives at 0.05 µg/kg. For enrofloxacin, polynomial fitting correlation coefficients (0.9991, 0.9966, 0.9993) outperformed linear regression. Compared to test kits using sulfadiazine or sulfamethoxazole as the benchmark, those based on sulfamethazine demonstrated superior accuracy. Test kit B achieved an overall accuracy rate of 96.6%. In contrast, test kits using sulfamethoxazole as the benchmark showed no response to sulfadiazine, sulfamerazine, or sulfamethazine, resulting in a false negative rate as high as 100%. The GICA is effective in screening for chloramphenicol in seasoned meat and fish samples; for enrofloxacin, it exhibits a good linear relationship and is suitable for preliminary screening in the circulation chain. The sensitivity for sulfonamides depends heavily on the benchmark substance used in test kit design. We recommend selecting rapid tests developed with a structurally representative benchmark, like sulfamethazine, and validating them across matrices before deployment. Confirm with traditional methods if needed.