Food Analytical Methods最新文献

The article focuses on the importance of sea bass, which is preferred by consumers in Turkey and worldwide. However, seafood can deteriorate rapidly under unfavorable conditions during storage due to their nutrient content, water content, and weakness in connective tissues. Temperature changes, inappropriate processing methods during transportation, and temperature changes during storage in markets are reported to cause losses in seafood quality. The deterioration of seafood, especially in seafood stored under inappropriate conditions because of temperature, causes changes contrary to consumer preferences because of the rapid growth of microorganisms, especially odor changes in seafood. This study examines the models related to the discipline of predictive microbiology, which are stated to provide an accurate shelf life prediction of the rate of microbiological spoilage and emphasize the importance of mathematical predictions of these models for seafood. Furthermore, the paper observes that machine learning algorithms such as Random Forest, Decision Tree, k-Nearest Neighbors, AdaBoost, Gradient Tree Boosting, Random Forest, Decision Tree, k-Nearest Neighbors, AdaBoost, and Gradient Tree Boosting have been used to predict the shelf life of seafood products. Finally, how to augment the limited data in a laboratory study to evaluate the shelf life of sea bass stored at different temperatures, how to prove the consistency of the augmented data with the original data, and how to optimize successful machine learning methods for robust problem-solving processes between different engineering fields are explained in detail. The results show that the optimized Extra Tree algorithm is the most successful for Pseudomonas quantity estimation with an R² metric value of 0.9940 and TVC quantity estimation with an R² metric value of 0.9910, while the other algorithms are less successful than this algorithm. These results show that machine learning methods can be a rapid, powerful, and effective tool for shelf life prediction of sea bass. Additionally, it should be emphasized that the number of input parameters (temperature, number of the bacteria) are of utmost significant for augmentation of the data for development and application of the machine learning algorithms.

Environmental pollution, agricultural practices, climate change, and the various stages of edible oil production are responsible for oil contamination with various chemicals. Among vegetable fats, olive pomace oils (OPOs) have higher polycyclic aromatic hydrocarbon (PAH) contents, exceeding the limits in some cases. Several methods for the determination of PAHs in animal and vegetable fats and oils have been published over the years, but they have often failed to eliminate matrix-specific interferences in OPO. The few methods proposed or applied for the specific analysis of PAHs in OPO over the past 20 years are mainly based on two different analytical approaches, namely liquid chromatography-fluorescence detector (LC-FLD) and gas chromatography-mass spectrometry (GC–MS). In the case of the LC-FLD approaches, liquid–liquid extraction with appropriate solvents and one or more purification steps on stationary phases of different compositions are performed. In the case of GC techniques, on the other hand, the most commonly used sample preparation is liquid–liquid partitioning. Due to widespread public concern about PAH contamination, several studies have been conducted to explore ways to mitigate the presence of PAHs in OPOs (i.e., refining processes).

Plastics are remarkable due to their chemical and physical properties and are used in many fields, especially food packaging. To produce the packages, during the manufacturing processes, an array of additives such as plasticisers, stabilisers, antioxidants, lubricants, and pigments is required. Unfortunately, most additives can migrate and contaminate the food they are intended to hold and preserve. Identifying potential migrants is likely the only way to gain knowledge, improve the manufacturing processes, and assess potential health risks related to the migration of additives and their degradation products to food media. Therefore, in this study, commercial polyethylene packages were investigated using validated liquid chromatography with tandem mass spectrometry (LC–MS/MS) method to reliably quantify the content of the antioxidants Irganox 1010 and Irgafos 168-ox and their migration levels to food simulants representing foods having hydrophilic and lipophilic properties. The results revealed the highly varying amount of antioxidants in different types of polyethylene food contact materials.

This study aimed to develop and optimize an ion chromatography methodology for the determination of inulin in aqueous extracts of roasted açaí (Euterpe oleracea Mart.) seeds belonging to the Arecaceae family. Additionally, the optimization of inulin extraction conditions was sought using an experimental design with temperature and solid-to-solvent ratio as independent variables. Ion chromatography analysis was adjusted by varying flow conditions, eluent composition, and column oven temperature. The optimized chromatographic conditions were as follows: a flow rate of 1 mL min⁻¹, eluent containing 2 mmol L⁻¹ NaOH and 5 mmol L⁻¹ sodium acetate in an isocratic mode, column oven temperature of 20 °C, injection volume of 20 µL, and a runtime of 40 min. The optimal extraction condition was determined using a temperature of 94.1 °C and a solid-to-solvent ratio of 10/135.25 (g mL⁻¹), resulting in an inulin content of 16% (w w⁻¹). The developed methodology met the criteria and guidelines of the Association of Official Analytical Chemists (AOAC) validation guide, including linearity, precision, accuracy, repeatability, reproducibility, matrix effect, and recovery. The results demonstrate the efficiency and applicability of the methodology, which is essential for identifying the best ways to utilize açaí seeds in an environmentally responsible and economically viable manner. This study promotes innovation and sustainable development in the Amazon region, aligning with the principles of the green concept.

This study employs network toxicology, molecular docking, and molecular dynamics simulations to assess the characteristics and potential molecular mechanisms of aspartame-induced hepatocellular carcinoma (HCC). Utilizing ChEMBL, STITCH, and SwissTargetPrediction databases, potential target proteins associated with aspartame are identified. HCC-related targets are determined through bioinformatics and weighted gene co-expression network analysis (WGCNA). Gene enrichment analysis explores the signaling pathways related to aspartame-induced HCC. Further refinement using the STRING database and Cytoscape software highlights 15 key targets. Molecular docking, conducted using Autodock Vina, assesses the relationships between aspartame and each key target. Molecular dynamics simulations evaluate the binding capabilities of aspartame with core targets obtained through molecular docking. The results indicate that aspartame may induce HCC by modulating apoptosis and proliferation of liver cancer cells, affecting inflammatory signaling pathways, and regulating estrogen metabolism, posing to the occurrence and development of liver toxicity and associated inflammation, thereby leading to a risk of hepatocarcinogenesis. This study provides a theoretical foundation for understanding the molecular mechanisms underlying aspartame-induced HCC. Additionally, our network toxicology approach accelerates the elucidation of toxic pathways for uncharacterized food additives.

Graphical Abstract

In this research, the determination of the number of products in closed (sealed) and bulk-type packages has been investigated using “X-ray imaging” and “image processing” techniques. A custom image processing program was developed to analyze X-ray images captured at a resolution of 784 × 1024 pixels. The program assigns numerical values ranging from absolute white to absolute black to each pixel in the image on a scale of 0–255. The number of products that belong to separate groups within each package was calculated by dividing the total grayness value of the group by the average grayness value of a single unit of the product. To minimize fluctuations, we employed moving average grayness values. The grayness values represent the overall intensity or darkness of the pixels in the image. The error rate of the multi-head weighers tested in determining the number of products in closed packages was determined to be 4%, the error rate of the product counting systems was 1.25%, and the error rate of the developed X-ray imaging method was 0.25%.

Aluminium (Al) is the third most common element in the Earth’s crust and occurs naturally in drinking water and agricultural products, and humans are consequently exposed to the element from dietary sources. A tolerable weekly intake of 1 mg/kg has been established by the European Food Safety Authority (EFSA); however, no maximum levels for aluminium in foodstuffs have so far been established in the European Union (EU) legislation. Official food control requires validated methods for the determination of aluminium. Acid digestion assisted by microwaves is the main sample preparation technique used for the determination of aluminium, usually in combination with atomic spectrometry for quantification. In the present study, different parameters in the digestion step were investigated including test portion, digestion temperature, the reagent used and duration of the digestion to assess the aluminium extraction. The presented work is following up on an observation from a proficiency test (PT) on trace elements (including aluminium) in cocoa powder organised in 2020 by the European Union Reference Laboratory for metals and nitrogenous compounds in feed and food (EURL-MN), where the participant results for aluminium showed an unexpectedly large variation. In addition to the PT material, different certified reference materials were included in the present study, and the results highlighted that the temperature and reagent used are the most critical parameters to obtain a satisfactory sample digestion prior to aluminium determination. Based on the obtained results, it is recommended to digest food samples with a mix of ultrapure water and nitric acid for 25 min at a temperature of at least 240 °C with a mix of HNO₃ and H₂O to achieve satisfactory microwave-assisted digestion.

Knowing the geographical origins of chili papers produced in specific areas is crucial because the geographical origins of various varieties of chili powder have a significant impact on their quality and price. In this research, for the first time, NIR (near-infrared) spectroscopy was used for the identification and classification of the geographical origin of chili powder of 6 different varieties, combining the method of PCA (principal component analysis) to extract relevant spectral features from the spectral data and segregate visible cluster trends, SIMCA (soft independent modeling of class analogy) statistically based classification model, and the four machine learning (ML) classifiers, including K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), were applied for supervised classification. It was found that the SVM classifier, with a C value of 4013.0 and γ of 0.04125, delivered the highest cross-validation accuracy of 98.41% and prediction accuracy of 97.22%. The optimization process, guided by a detailed 3D contour plot, led to a model that not only generalized well but also offered remarkable precision, as confirmed by confusion matrices. The classification accuracy of the SIMCA model was 94.04% for the calibration set and 84.74% for the prediction set. The nonlinear SVM technique of classification outperformed the linear SIMCA model and other ML models. In general, the results indicated that chili powder from various geographic origins could be discriminated by the use of NIR spectroscopy combined with the SVM model quickly, nondestructively, and reliably.

This paper aimed to demonstrate the production of edible extracts from red jambo flowers, cultivated in northeastern Brazil, and investigate their bioactive properties. For this purpose, a multivariate optimization of the extraction process was performed, by solid–liquid extraction, where it was observed that the presence of ethanol, acetone, and polysorbate in the extracting solution had the most significant influence on the extraction process, as opposed to temperature, time, volume of extracting solution, and the use of ultrasound. After the mixture system optimization, the best extraction condition was achieved when the extracting solution was composed of 25% ethanol, 25% acetone, and 50% polysorbate 0.25% in water, resulting in an extract containing 27.11 mg of anthocyanins, 457.69 mg of total carotenoids, and 198.09 mg of, total flavonoids, per 100 g of dried flower. The reducing capacity was 466.8 mg GAE per 100 g of dried flower, and the antioxidant activity was 17.25% against the DPPH (2,2-diphenyl-1-picrylhydrazyl) radical. Through chromatographic analysis, it was possible to identify 10 compounds with bioactive properties (ferulic acid dihexoside, pedunculagin, methyl-dihydroquercetin dihexoside, dimethyl-dihydromyricetin diglucoside, kaempferol-3-O-hexosyl-rutinoside-7-O-rhamnoside, quercetin-O-hexoside-O-hexoside, ellagic acid, quercetin-O-hexoside, hesperetin-O-rutinoside, and diosmetin-O-rhamnoside), with a high prevalence of flavonoids. The extract showed no toxicity in an in vivo model of Galleria mellonella when administered at up to 1.6 g kg⁻¹ of body mass. The extract exhibited inhibition of Staphylococcus aureus (23 mm), Salmonella Typhimurium (12 mm), and Escherichia coli (12 mm), with inhibition zones close to that of gentamicin for the latter two. This study highlights the promising potential of red jambo flower extract as a valuable source of bioactive compounds with significant antioxidant, antimicrobial, and non-toxic properties. The optimized extraction process yielded extracts rich in bioactive compounds, demonstrating its suitability for various applications in the food industry. Further research is warranted to explore the full range of applications and potential health benefits of this natural extract.