Food Analytical Methods最新文献

In this research, a straightforward sample treatment for multiresidue pesticide evaluation of onion samples was developed using the solid-phase extraction/quick, easy, cheap, effective, rugged, and secure (SPE/QuEChERS) method. The suggested technique is based on acetonitrile liquid-liquid partitioning, then follows dispersive solid-phase extraction with ZrO₂ particles for extract purification. ZrO₂ is synthesized via co precipitation and analyzed via XRD, FTIR, and SEM. ZrO₂ has been demonstrated to be more effective than normal graphitized carbon black at eliminating pigment. Thirty-five pesticides from various chemical classes were examined using gas chromatography and tandem mass spectrometry (GC-MS/MS) to assess the outlined technique. Most analytes had recoveries in the range of 74–105%, with relative standard deviations of less than 14%. The linearity, precision, and accuracy of GC-MS/MS were adequate. The validated technique was applied effectively to onion samples from the farmer’s field.

In this study, a novel gallic acid-based anthocyanin electrospun sensor was developed to monitor the freshness of the shrimp. “The sensor contains blueberry anthocyanin as indicator dye, polyacrylonitrile as polymer, and gallic acid as copigment. The sensor was studied by SEM, FT-IR, color stability, and its response to dimethylamine and trimethylamine. The results showed that the anthocyanin electrospun sensor was copigmented with gallic acid, which improved the color stability during storage (ΔE<5) and sensitivity to dimethylamine and trimethylamine. The color changes were obvious by the naked eye, which proved that the anthocyanin-gallic of the anthocyanin-gallic acid electrospun sensor during shrimp storage over 5 days at 4 °C was positively correlated with the contents of TVB-N (R² = 0.9905) and pH (R² = 0.9906). The color of the sensors ranged from pink to purple to yellow, and they represented the freshness, medium freshness, and spoilage of shrimp. The color changes are obvious to the naked eye. The indicated membrane had good application value in the nondestructive testing of shrimp, as the anthocyanin-gallic acid sensor could evaluate the freshness of the shrimp. This membrane demonstrates significant potential for nondestructive testing of shrimp freshness. The combination of electrospun technology and copigmentation provided a new idea for detecting the freshness of food.

Meat quality has gained ample attention owing to increased consumer awareness and competition among poultry processors to deliver premium quality products. Nevertheless, chicken breast meat is susceptible to microbial spoilage resulting in economic and product losses. Conventional approaches such as organoleptic, aerobic plate count (APC), and molecular methods have been employed for assessing the microbiological quality of meat products but suffer various shortcomings. This study was a proof-of-concept evaluation of emerging structured illumination reflectance imaging (SIRI) as a non-destructive, objective means to evaluate microbial spoilage in chicken breast meat. The experimental chicken breast samples were kept on a retail tray for 1–13 days at 3-day intervals and subjected to image acquisition by broadband SIRI at varied spatial frequencies of sinusoidally-modulated structured illumination (0.05–0.40 cycles mm⁻¹). The chicken samples were categorized into fresh and spoiled classes using the APC threshold of 5 log₁₀ CFU g⁻¹. Acquired pattern images were demodulated into amplitude component (AC) and direct component (DC) images (corresponding to uniform illumination). Three pre-trained deep learning models, including VGG16, EfficientNetB6, and ResNeXt101, were employed to extract the features from the demodulated images, followed by principal component analysis (PCA) to reduce feature redundancy. The selected PCs were used to build classification models using linear discriminant analysis (LDA) and support vector machine (SVM) separately to distinguish between fresh and spoiled samples. AC images consistently outperformed DC images in the resultant classification performance. When the LDA classifier was used, AC images yielded maximum accuracy improvements of 3.6%–6%, depending on feature type and spatial frequency; with the SVM classifier, AC images achieved maximum improvements of 4.4% to 6.4%. The SVM model with the features extracted by ResNeXt101 from AC images at 0.25 cycles mm⁻¹ achieved the best overall classification accuracy of 76% in differentiating fresh and spoiled samples. This study shows that the SIRI technique is effective for enhanced assessment of microbial spoilage in broiler breast meat, but more dedicated efforts are needed to improve both hardware and software for practical application.

Most of the existing methods for wheat mildew detection are biochemical methods, which have the problems of complicated procedures and slow speed. In this paper, a novel wheat mildew detection and classification model is proposed by combining gas chromatography-ion mobility spectrometry (GC-IMS) with a broad learning network (BLN) model. Firstly, the GC-IMS fingerprint spectrums of wheat samples with different degrees of mildew are collected by GC-IMS spectrometer, and then an effective and efficient incremental learning system without the need for deep architecture is constructed to identify these fingerprint spectrums. In the BLN model, ridge regression of the pseudo-inverse is designed to find the desired connection weights, and the new weights can be updated easily by only computing the pseudo-inverse of the corresponding added node. To improve the classification accuracy of the BLN model, incremental learning and the spatial attention mechanism (SAM) are introduced into the model. Experimental results show that the training time of the proposed model is greatly reduced compared to existing deep-learning models. Under the small sample set condition, the mean average accuracy (mAP) of wheat mildew types reaches 90.32%, and the identification precision of early wheat mildew reaches 95.34%. The comprehensive index shows that the neural network model proposed in this paper can be used as an alternative model for deep learning in similar areas of image recognition. The experiment also proved that GC-IMS combined with a broad learning model is an efficient and accurate method for wheat mildew detection.

In the present research, a quick, easy, cheap, effective, rugged, and safe (QuEChERS) method was optimized and validated for the determination of 14 selected PCB congeners in mussel (Mytilus galloprovincialis) and fish samples (Sparus aurata). The optimization included the selection of different QuEChERS procedures, extraction solvent, sample weight, and suitable sorbents for the clean-up step to achieve high sensitivity and minimal matrix interferences simultaneously. The identification and quantification of the selected PCBs were carried out using GC–MS. The method was validated providing in all cases excellent linearity (r² above 0.99). Recoveries were estimated at three different fortification levels (10, 50, and 100 ng g⁻¹) and ranged from 72.5 to 109.5% and 72.1 to 88.4% for mussel and fish samples, respectively. In addition, for both matrices, the LOQs ranged from 1 to 2.5 ng g⁻¹. The matrix effect was in all cases < 29%, while the expanded uncertainty U%, which was estimated at all the fortification levels, was found below 53% in all cases. Eventually, the optimized and validated method was applied to mussel and fish samples acquired from aquacultures in NW Greece, revealing the absence of the selected congeners in all cases.

Per- and polyfluorinated alkyl substances (PFASs) are compounds which exhibit unique chemical and physical properties resulting in bioaccumulation in aquatic and terrestrial food chains. Due to a global concern on the adverse health effects, European Food Safety Authority (EFSA) set tolerable weekly intake and thereafter, to ensure an efficient protection of public health, the European Commission set maximum levels for four priority components, namely perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorohexanesulfonic acid (PFHxS), and perfluorooctanesulfonic acid (PFOS), in certain foodstuffs. This study demonstrates an analytical method based on high-performance liquid chromatography (HPLC) coupled to Orbitrap mass spectrometry (Orbitrap-MS) for the quantitative determination of four priority PFASs. The optimized and validated LC-Orbitrap-MS method fulfils the requirements specified in the “Guidance Document on Analytical Parameters for the Determination of Per- and Polyfluoroalkyl Substances (PFASs) in Food and Feed” and allows a reliable analysis of PFASs in selected food products, fulfilling the requirements of Commission Regulation 915/2023, Commission Regulation (EU) 2022/1428, and Commission Recommendation (EU) 2022/1431. The method was successfully used for the compliance testing of four priority PFASs according to the newly established legislation in food samples (n = 58) represented by eggs, fish, meat, and meat by-products collected in Latvia, providing the occurrence data from the Baltic states.

A smart material based on ferrofluid deep eutectic solvents graphene oxide magnetite (MGO-DES FF) was successfully synthesized by adding choline chloride-thymol DES as carrier solvent onto MGO composite for the determination of ofloxacin in egg and milk samples. The synthesized materials were characterized using Fourier transform-infrared spectroscopy (FTIR), transmission electron microscopy (TEM), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and vibration sample magnetometer (VSM) to evaluate their functional groups, morphological and magnetic properties, respectively. The MGO-DES FF material was used as ferrofluid sorbent for liquid phase microextraction (LPME) of ofloxacin prior to UV–Vis spectrophotometry analysis. Several parameters were optimized including the type of DES, ferrofluid composition, ferrofluid volume, desorption solvent type and volume, pH, and extraction time to study their effects on the recovery percentage of ofloxacin. Under optimum conditions, good linearity was achieved between the range of 1 – 10 mg L⁻¹ for ofloxacin and a correlation coefficient of 0.9963. The LOD and LOQ values recorded were 0.75 and 1.55 µg L⁻¹, respectively. The RSD% for intra-day and inter-day were 2.64 and 7.40, respectively. The recovery percentage for milk and egg samples was ranging from 80.8 to 92.0%. Based on the results obtained, the developed MGO-DES FF LPME method demonstrated excellent sensitivity and efficiency for the extraction of ofloxacin in food samples. It showed great potential as an alternative method for the extraction of pharmaceutically active contaminants in the challenging matrix.

Escherichia coli, as a prevalent foodborne pathogen, can harm people health seriously. Hence, developing the effective methods for E. coli determination is vital. In the paper, a convenient hybridization ratiometric fluorescent probe was constructed for the prompt determination of E. coli based on red emissive gold nanoclusters (AuNCs) and blue carbon dots (bCDs). The relevant experimental conditions were systematically optimized including copper concentration, incubation temperature, and time, in order to obtain the optimal results. There existed a strong linear relationship between the fluorescence intensity ratios (I₆₃₀/I₄₅₀) and the concentrations of E. coli for both AuNCs and bCDs. Based on the determination, it was found that the linear concentration range spanned from 10³ to 10⁷ CFU mL⁻¹, with a low limit of detection (LOD) of 120 CFU mL⁻¹. This method based on the mechanism of copper cocatalysis presents a sensitive and convenient strategy for E. coli determination, and has a promising future in the field of food safety.

Leeches are a well-known animal-derived health supplement commonly used as an anticoagulation and antithrombosis agent; however, adulteration and counterfeiting are often made for illegal profits. To identify leech species, this study developed a rapid, simple, and visualized method based on loop-mediated isothermal amplification (LAMP), which relies on a specific primer set designed according to the mitochondrial DNA control region of the target species. Quantitative polymerase chain reaction (qPCR) was also employed in parallel to compare the sensitivity and confirm the primer specificity. Primer sets with high specificity were successfully screened for LAMP reactions against four common leech species on the market. All of them have produced typical amplification profiles of the target sequences in qPCR reactions with significantly lower amplification sensitivity than LAMP assay. The newly established LAMP method in this study can be accomplished within 1 h, and it could be successfully applied for on-site visual identification of mislabeling and adulteration in the leech market.

Polyphenols are a large group of compounds of natural origin, known for their health-promoting effects on the human body. In plant materials, they can be present in the form of glycosides or aglycones, to a different extent bounded to the sample matrix. Their distribution in different parts of the plant may also vary. The extraction process is one of the most important and difficult stages of sample preparation for the analysis of polyphenol compounds. The main goal is to choose the right extraction method to isolate polyphenols from plant samples with the highest possible efficiency and in unchanged forms. This review summarizes some aspects of different extraction methods for phenolic compounds proposed in the last 5 years. Efforts were made to look critically at each of the described extraction methodologies.