Food Analytical Methods最新文献

Genistein (GEN) is a typical phytoestrogen that is widely distributed in legumes and other plant-derived foods, possessing significant biological activities such as potent antioxidant capacity and anti-cancer properties. However, excessive dietary intake of GEN may cause endocrine system disruption and increase the risk of cancer development, which highlights the urgent need for establishing a sensitive, rapid and reliable method for GEN detection. In this study, a novel electrochemical sensor dedicated to GEN detection was constructed, with metal–organic framework/reduced graphene oxide (MIL-100/RGO) as the core sensitive material. Reduced graphene oxide (RGO) was first synthesized by the modified Hummers method, followed by the fabrication of MIL-100/RGO nanocomposite via a facile hydrothermal method. MIL-100, with its unique porous structure, provides a large specific surface area and excellent redox activity, while RGO effectively enhances electron transfer efficiency of the composite. The two components exert a synergistic effect, significantly improving the overall electrochemical performance of the sensor. Results showed that the constructed sensor exhibits excellent electrochemical activity and strong anti-interference capability against common coexisting substances in food. It has a linear detection range of 2 ~ 150 μM with two linear segments: for 2 ~ 40 μM, the regression equation is ΔI = 0.09245C (μM) + 0.01877 (R² = 0.9942) with a sensitivity of 1.3086 μM⁻¹ cm⁻²; for 40 ~ 150 μM, the regression equation is ΔI = 0.0084C (μM) + 3.2841 (R² = 0.9931) with a sensitivity of 0.1185 μM⁻¹ cm⁻², and a low limit of detection (LOD) of 0.93 μM. Moreover, the sensor achieved satisfactory sensitivity and accuracy when applied to real food sample detection, indicating great potential for practical application in GEN analysis.

Maturity assessment is a key link in the precise grading and sorting of edamames with pod. Traditional maturity assessment of edamames with pod relies on manual visual inspection by agricultural producers, which suffers from high labor intensity, strong subjectivity, and high error rates. This study employed visible-near infrared hyperspectral imaging technology to perform non-destructive, precise detection on edamames with pod samples representing four maturity stages. In this study, linear partial least-squares discriminant analysis (PLS-DA), least-squares support vector machine (LS-SVM), and k-nearest neighbors (KNN) were applied to discriminate the maturity of edamames with pod. In addition, EMSTFormer-SpecNet of maturity assessment model for edamames with pod was proposed in this study, which included a channel attention mechanism, a multi-scale feature extraction module, and a transformer module. The result of this study demonstrates that the EMSTFormer-SpecNet model outperforms machine learning models and common deep learning models in processing one-dimensional hyperspectral reflectance data of edamames in pod with different maturity levels. The classification accuracy rate of the test set reached 97.82%, which was 9.93% higher than that of the optimal machine learning model SNV-CARS-PLS-DA, and 7% and 4.38% higher than that of the deep learning models 1DCNN and 1D-ResNet, respectively. The result of this study demonstrates that hyperspectral imaging technology combined with deep learning methods has great potential in the maturity assessment of edamames with pod, providing a theoretical basis and technical support for the accurate grading and sorting of podded food.

Magnetic mixed cation exchange material (MMCX) was utilized as an efficient adsorbent in magnetic solid-phase extraction (MSPE) for the determination and identification of 30 β-agonists in animal-derived foods. This was coupled with ultrahigh performance liquid chromatography quadrupole high-field high-resolution mass spectrometry following sample pretreatment by enzymatic probe sonication (EPS) and MSPE. The purification procedures for both EPS and MSPE were appropriately optimized. Under the optimized conditions, the method demonstrated limits of detection (LODs) and quantification (LOQs) in the ranges of 0.03 ~ 0.30 μg/kg and 0.1 ~ 1.0 μg/kg, respectively, which are superior to those reported in existing methods. Satisfactory recoveries ranged from 84.3% to 105.8%, with relative standard deviations below 15%. Matrix effects were observed as signal suppression, ranging from 5.1% to 29.3%. The established method also exhibited good linearity with correlation coefficients (r) greater than 0.994. This methodology allows sample processing to be completed within 20 min, significantly reducing both detection time and cost. The method has been validated using positive samples obtained from animals administered β-agonists and has been successfully applied to residue monitoring of β-agonists in animal-derived foods.

The increasing demand for dairy products and the growing attention of health organizations have highlighted the need for reliable techniques to detect chemical contaminants in milk. Vibrational spectroscopy has emerged as an effective analytical approach for identifying and quantifying a wide range of residues in dairy matrices. This review focuses on the application of FTIR and Raman spectroscopy as non-invasive, rapid, and accurate methods for detecting residual antibiotics in bovine milk. Particular emphasis is placed on the detection of Tetracycline and β-lactam antibiotics, which are among the most widely used drug classes in livestock production. A comprehensive evaluation of recent studies demonstrates that Raman spectroscopy offers superior sensitivity for Tetracycline detection, while FTIR, especially MIR (FT-MIR), consistently provides higher correlation coefficients than NIR (FT-NIR) in antibiotic quantification. These findings underscore the strong potential of vibrational spectroscopy for routine monitoring of milk quality. Furthermore, advancements in Artificial Intelligence and machine-learning-based modeling are expected to significantly enhance the accuracy, robustness, and industrial applicability of these optical methods. Overall, the combined use of Raman, MIR, and NIR spectroscopy, supported by intelligent data-driven algorithms, represents a promising pathway toward reliable antibiotic residue detection and improved food safety in the dairy supply chain.

Vitamin K2, consisting of various menaquinones (MK-n), provides health benefits beyond the coagulation role of vitamin K1 and is primarily produced by bacteria in fermented foods or the human gut. Although Bacillus and some lactic acid bacteria are established producers, staphylococcal synthesis remains poorly characterized. This study optimized an extraction procedure for vitamin K2 from staphylococcal biomass and developed a rapid, 10-min ultra-high-performance liquid chromatography method with diode array detection (UHPLC-DAD) to quantify MK-7, the most bioavailable form. The method was validated for linearity, accuracy, and intra- and inter-day precision using Staphylococcus carnosus IMDO-S10, Staphylococcus shinii IMDO-S216, and Staphylococcus equorum IMDO-S257. Staphylococcus carnosus IMDO-S10 produced the highest MK-7 levels (1688 nmol/g cell dry mass), while MK-7 production by S. shinii IMDO-S216 decreased under high aerobic conditions. The validated UHPLC-DAD method enables screening of staphylococcal strains and monitoring of MK-7 production during growth, supporting applications in food fermentation.

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This study used paper as the base material and black wolfberry (Lycium ruthenicum) anthocyanins as the colorimetric agent to fabricate paper-based colorimetric indicators via immersion, casting, and electrospinning. The indicators were evaluated for pH sensitivity and applied to monitor pork freshness under air packaging (AP) and modified atmosphere packaging (MAP) at 5 °C to identify an optimal intelligent colorimetric paper. Among the four indicators (A, B, C, and D), the immersion-prepared anthocyanin-PVA/paper indicator (indicator B) exhibited the most distinct and stable color change within the critical pH range of 6–8 (ΔE > 5 within 30 min) and showed good structural integrity. The FTIR, DTG, and SEM analyses confirmed its uniform and stable structure. In application tests, the pork under AP exceeded the microbiological acceptability limit (TVC > 6 log CFU/g) and the TVB-N threshold of 15 mg/100 g by day 3, while MAP effectively delayed these changes. The color transition of indicator B correlated well with the variations in TVC, TVB-N, and pH, providing a clear visual signal of spoilage under AP and remaining acceptable under MAP. Overall, indicator B was identified as a practical and reliable intelligent freshness indicator for chilled pork.

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For the first time, a novel electrochemical sensing platform has been developed for the ratiometric and simultaneous detection of butylated hydroxyanisole (BHA) and tert-butylhydroquinone (TBHQ). To achieve this, the ZIF-8 nanostructure was first synthesized and then functionalized with methylene blue (MB) and carboxylated multiwalled carbon nanotubes (MWCNT-COOHs). The resulting ZIF-8, ZIF-8-MB, and ZIF-8-MB-MWCNT nanostructures were analyzed using characterization techniques such as XRD, FT-IR, and FE-SEM. Differential pulse voltammetry (DPV) and cyclic voltammetry (CV) were used to assess the electrochemical performance of the ZIF-8-MB-MWCNT nanocomposite. The sensor exhibited a wide linear detection range of 1 to 1000 μM and an ultra-low detection limit of 0.055 µM for TBHQ and 0.065 µM for BHA, showcasing its exceptional analytical capabilities. TBHQ and BHA were successfully detected in real food samples, with recovery rates ranging from 97.60% to 103.00% proving the sensor’s usefulness and guaranteeing accuracy and dependability.

Water content represents a critical quality parameter in all food products, yet its precise quantification remains challenging due to the potential loss of volatile compounds during heating and the risk of sample degradation or chemical transformation. Among various analytical techniques, Karl Fischer (KF) titration is one of the most widely accepted methods owing to its high specificity toward water based on a well-established redox reaction. Nevertheless, even KF titration may produce biased results in complex food matrices containing both carbohydrates and proteins. In such systems, the Maillard reaction—a non-enzymatic reaction between reducing sugars and amino groups—can occur upon heating. This reaction not only generates solid brown reaction products that may hinder water release but also produces water as a byproduct during its initial stages. Consequently, distinguishing between inherent moisture and water formed during heating becomes challenging. In this study, vaporization–coulometric Karl Fischer titration (vap-C-KF) was employed and compared with the conventional oven-drying method. Model systems were prepared by mixing maltodextrin and whey proteins in various ratios (1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2, 9:1; w/w). A total of 330 measurements were performed, with each mixture analyzed in five replicates at five different temperatures: 125 °C, 145 °C, 155 °C, 165 °C, and 185 °C. The results were evaluated using descriptive statistics (maximum, minimum, median), standard deviation (SD), relative standard deviation (RSD), F-test, and ANOVA. Deviations from the optimal temperature significantly affected both the measured water content and extraction time. Furthermore, depending on the protein-to-maltodextrin ratio, it was estimated that between 0.34% and 0.80% of the total measured water content originated from the Maillard reaction.

The leaves of Syzygium nervosum DC., traditionally used across Southeast Asia, are widely available, low-cost, and rich in diverse bioactive constituents that confer notable antioxidant, anti-inflammatory, antimicrobial, antibacterial, and pharmacological properties, underscoring their potential for nutraceutical and therapeutic applications. This study aimed to optimize the ultrasound-assisted extraction (UAE) of bioactive compounds from Syzygium nervosum DC. leaves using water as an environmentally friendly solvent. Water’s high polarity, non-toxicity, and low cost make it ideal for extracting polar phytochemicals. A Box–Behnken design within the response surface methodology framework was applied to evaluate extraction yield, total phenolic content (TPC), total flavonoid content (TFC), and antioxidant capacity, considering extraction time (2–6 min), ultrasonic power (20–40 W), and duty cycle (60–100%). The optimal conditions—4.775-min extraction time, 31.797 W power, and 79.363% duty cycle—produced a yield of 29.156%, with TPC and TFC values of 205.993 mg GA/g and 82.295 mg Q/g, respectively. The extract exhibited strong antioxidant activity with an IC₅₀ of 65.236 mg/L and showed antibacterial effects, with minimum inhibitory concentration (MIC) values of 200 mg/L for Escherichia coli and 400 mg/L for Staphylococcus aureus. Using water as a green solvent highlights the sustainability of the UAE process, demonstrating high efficiency in recovering phenolic and flavonoid compounds. This eco-friendly approach provides a safe alternative to organic solvents and confirms that systematic optimization of ultrasonic parameters can markedly enhance phytochemical recovery, supporting the development of natural antioxidant-rich formulations for biomedical applications.

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Yak milk is rich in nutrients and holds significant economic value. However, its premium quality and market value are frequently undermined by economically motivated adulteration practices, posing risks to consumer trust and industry sustainability. This study focused on detecting yak milk adulterated with varying proportions of water, Holstein milk, and cattle yak milk, aiming to develop a rapid, accurate, and non-invasive detection method by integrating electronic nose (E-nose) data with machine learning algorithms and multivariate statistical analysis. The results demonstrated that both principal component analysis (PCA) and canonical discriminant analysis (CDA) could clearly distinguish between different types of raw milk. For adulterated samples, both methods effectively differentiated water-adulterated yak milk. Validated via tenfold cross-validation for robustness, the random forest (RF) model yielded an overall area under the curve (AUC) > 0.99, with perfect classification (AUC = 1.00) for the water-adulterated group. The multilayer perceptron (MLP) attained an average accuracy above 96% across all three adulterated categories, reaching as high as 99.8% for yak milk adulterated with Holstein milk. In quantitative prediction, multiple linear regression (MLR) demonstrated strong capability for predicting water content (determination coefficient (R_c²) = 0.9888), whereas MLP achieved the highest R_c² (0.9998) for Holstein milk-adulterated yak milk. These findings demonstrated that E-nose coupled with CDA provides superior qualitative discrimination of adulterated yak milk, and MLP exhibited exceptional performance in quantifying adulterant concentrations. This study provided a rapid, low-cost, and non-destructive analytical solution, with significant potential for on-site market surveillance of high-value specialty dairy products like yak milk.