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Advancements in food science and technology have paved the way for the development of natural antimicrobial compounds to ensure the safety and quality of meat and meat products. Among these compounds, bacteriocin produced by lactic acid bacteria has gained considerable scientific attention for its ability to preserve the healthy properties of meat while preventing spoilage. This natural preservative is seen as a pioneering tool and a potent alternative to chemical preservatives and heat treatment, which can have harmful effects on the nutritional and sensory qualities of meat. Bacteriocin produced by lactic acid bacteria can be used in various forms, including as starter/protective cultures for fermented meats, purified or partially purified forms, loaded in active films/coatings, or established in encapsulate systems. This review delves into the downstream purification schemes of LAB bacteriocin, the elucidation of their characteristics, and their modes of action. Additionally, the application of LAB bacteriocins in meat and meat products is examined in detail. Overall, the use of LAB bacteriocins holds immense potential to inspire innovation in the meat industry, reducing the dependence on harmful chemical additives and minimizing the adverse effects of heat treatment on nutritional and sensory qualities. This review provides a comprehensive understanding of the potential of bacteriocin produced by lactic acid bacteria as a natural and effective meat preservative. [ABSTRACT FROM AUTHOR]

Research on medicinal plants is developing each day due to inborn phytochemicals, which can encourage the progress of novel drugs. Most plant-based phytochemicals have valuable effects on well-being. Among them, beetroot leaves (BL) are known for their therapeutic properties. Here, three solvents, namely, acetonitrile, ethanol, and water, and their combinations were developed for BL extraction and simultaneous assessment of phytochemical compounds and antioxidant and antifoodborne pathogen bacteria activities. By using the augmented simplex-centroid mixture design, 40.40% acetonitrile diluted in water at 38.74% and ethanol at 20.86% favored the recovery of 49.28 mg GAE/mL (total phenolic content (TPC)) and 0.314 mg QE/mL (total flavonoid content (TFC)), respectively. Acetonitrile diluted in water at 50% guarantees the best antioxidant activity, whereas the optimal predicted mixture for the highest antibacterial activity matches 24.58, 50.17, and 25.25% of acetonitrile, ethanol, and water, respectively. These extraction conditions ensured inhibition of Staphylococcus aureus, Salmonella enterica, and Escherichia coli, respectively, at 0.402, 0.497, and 0.207 mg/mL. Under optimized conditions, at three concentrations of BL, minimal inhibitory concentration (MIC), 2 × MIC, and 4 × MIC, a linear model was employed to investigate the inhibition behavior against the three tested bacteria. The early logarithmic growth phase of these bacteria illustrated the bactericidal effect of optimized extracted BL with a logarithmic growth phase inferior to 6 h. Therefore, BL extract at 4 × MIC, which corresponds to 1.608, 1.988, and 0.828 mg/mL, was more efficient against S. aureus, S. enterica, and E. coli. [ABSTRACT FROM AUTHOR]

The purpose of this work was to study the effect of selenium nanoparticles (Se NPs) on the biological and morphofunctional parameters of barley seeds (Hordéum vulgáre L.) We used seeds of Hordéum vulgáre L. with reduced morphofunctional characteristics. For the experiment, Se NPs were synthesized and stabilized with didecyldimethylammonium chloride. It was found that Se NPs have a spherical shape and a diameter of about 50 nm. According to dynamic light scattering data, the average hydrodynamic radius of the particles was 28 ± 8 nm. It is observed that the nanoparticles have a positive ζ-potential (+ 27.3 mV). For the experiment, we treated Hordéum vulgáre L. seeds with Se NPs (1, 5, 10 and 20 mg/L). The experiment showed that treatment of Hordéum vulgáre L. seeds with Se NPs has the best effect on the length of roots and sprout at concentration of 5 mg/L and on the number and thickness of roots at 10 mg/L. Germinability and germination energy of Hordéum vulgáre L. seeds were higher in group treated with 5 mg/L Se NPs. Analysis of macrophotographs of samples, histological sections of roots and 3D visualization of seeds by microcomputing tomography confirmed the best effect at 5 mg/L Se NPs. Moreover, no local destructions were detected at concentrations > 5 mg/L, which is most likely due to the inhibition of regulatory and catalytic processes in the germinating seeds. the treatment of Hordéum vulgáre L. seeds with > 5 mg/L Se NPs caused significant stress, coupled with intensive formation of reactive oxygen species, leading to a reorientation of root system growth towards thickening. Based on the results obtained, it was concluded that Se NPs at concentrations > 5 mg/L had a toxic effect. The treatment of barley seeds with 5% Se NPs showed maximum efficiency in the experiment, which allows us to further consider Se NPs as a stimulator for the growth and development of crop seeds under stress and reduced morphofunctional characteristics. [ABSTRACT FROM AUTHOR]

Grape pomace is a rich source of bioactive compounds and dietary fiber. This study aims to valorize the grape pomace by microwave-vacuum-assisted drying and extraction, which is a novel, green, and clean label technology. The drying and extraction of bioactive compounds from the grape pomace was optimized using response surface methodology. Box-Behnken design was used for three process variables, i.e., time, power, and vacuum levels. The highest drying rate was observed (5.53 g/100 g min after 10 min of drying) at the combination of 80 W and 20 inHg. This combination significantly reduced the drying time (25%) and resulted in the highest yield (64.5%) of bioactive compounds. Equally, changes in moisture ratio behavior were rapid under these processing conditions. Furthermore, Midilli model (R2 = 0.999, RMSE = 0.002, SSE = 3.71 × 10−6) was the best to justify the fitness of experimental values with predicted values. In addition, the diffusion coefficient, activation energy, and extraction yield were increased with increase in power and pressure. The concentration of bioactive components was higher in dried pomace compared to the extract. The extraction was successfully achieved without the use of solvent and the characteristics of extracted phenolics remained unaltered. Based on these findings, the microwave-vacuum-assisted drying and extraction process can be claimed as a sustainable approach. [ABSTRACT FROM AUTHOR]

In this work, selenium nanoparticles (Se NPs) stabilized with cocamidopropyl betaine were synthesized for the first time. It was observed that Se NPs synthesized in excess of selenic acid had a negative charge with ζ-potential of −21.86 mV, and in excess of cocamidopropyl betaine—a positive charge with ξ = + 22.71 mV. The resulting Se NPs with positive and negative charges had a spherical shape with an average size of about 20–30 nm and 40–50 nm, respectively. According to the data of TEM, HAADF-TEM using EDS, IR spectroscopy and quantum chemical modeling, positively charged selenium nanoparticles have a cocamidopropylbetaine shell while the potential- forming layer of negatively charged selenium nanoparticles is formed by SeO32− ions. The influence of various ions on the sol stability of Se NPs showed that SO42− and PO43− ions had an effect on the positive Se NPs, and Ba2+ and Fe3+ ions had an effect on negative Se NPs, which corresponded with the Schulze-Hardy rule. The mechanism of coagulating action of various ions on positive and negative Se NPs was also presented. Also, influence of the active acidity of the medium on the stability of Se NPs solutions was investigated. Positive and negative sols of Se NPs had high levels of stability in the considered range of active acidity of the medium in the range of 1.21–11.98. Stability of synthesized Se NPs stability has been confirmed in real system (liquid soap). An experiment with the addition of Se NPs stabilized with cocamidopropyl betaine to liquid soap showed that the particles of dispersed phases retain their initial distributions, which revealed the stability of synthesized Se NPs. [ABSTRACT FROM AUTHOR]

Food allergies impose a significant health concern on the community. A small number of certain food items can cause an allergic reaction within the human body. The symptoms can range from mild hives or itchiness to life-threatening anaphylaxis. In most cases, such reactions can be prevented by simply being aware of the allergen-based food items and avoiding the consumption of the same. We are among the first research attempts to train a deep learning–based object detection model to detect the presence of such food items within an image. We introduce our Allergen30 dataset, which hosts more than 6,000 annotated images of 30 commonly used food items that can trigger an adverse reaction. We report the comparison of multiple variants of the current state-of-art object detection methods, YOLOv5 and YOLOR. Furthermore, we qualitatively analyzed the performance of these methods by surveying the predictions made on the test dataset images. [ABSTRACT FROM AUTHOR]

Tindora or ivy gourd (Coccinia indica) is a tropical vegetable and has the potential to become popular worldwide due to its rich nutritional characteristics, adaptability to adverse environmental condition and fast fruiting nature. The fresh samples are bright green (good) in colour; it turns yellow (intermediate quality) and ultimately turns bright red (bad or inedible as vegetable). In this paper, tindora is classified using two different colour representations of the image matrix, namely RGB and HSV. In the RGB representation, intensity of the green layer versus intensity of the red layer for each pixel position is plotted. The shape of the plot enables us to identify the tindora quality. In the case of HSV representation, the hue layer is used for classification purposes. The hue layer matrix is vectorized and the Poincare plot is computed. The distribution of the Poincare plot is analysed using Cartesian quadrant system. Based on the analysis, tindora quality is determined. The accuracy of the tindora classification using RGB representation is 68.67 and HSV representation is 94.33. The classifier used in both the representations after feature extraction is primarily threshold based and hence has low computational burden. The low computational burden makes the method ideal for incorporation into smartphones in the form of an app. [ABSTRACT FROM AUTHOR]

Dairy cheese ball (Rasgulla) is one of the most commercially acclaimed dairy-based sweetmeat products in the Indian subcontinent. The main constrain with rasgulla export is the prediction of its shelf-life period, as there are several factors upon which the perishability of the product is dependent. Twenty-five nodes or concepts were chosen after a thorough consultation with 36 numbers of experts and stakeholders sweetmeat producers, shopkeepers, chhana producers, experts from packaging industries and an experienced tasting panel. The proposed fuzzy cognitive model (FCM) was constructed based on the linguistic variables assigned by the experts. For the defuzzification process centre of gravity, the method was used. There were 137 numbers of interconnections (within the 25 concepts) to build the FCM-based shelf-life model. Steady-state and dynamic-state FCM analysis was done considering the values of each concept to 1 and 0.01 respectively. The two most influential concepts namely freshness of chhana and moisture content were assigned to 0.01 for a better understanding of the rasgulla shelf life with varying values of the concepts. The major challenge related with the application of FCM in food science or food processing is the validation of the proposed model. In this paper, a validation scheme has been proposed to overcome this challenge. [ABSTRACT FROM AUTHOR]

Hyperlipidemia, the most common form of dyslipidemia, is the main source of cardiovascular disorders, characterized by elevated level of total cholesterol (TC), triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) with high-density lipoprotein cholesterol (HDL-C) in peripheral blood. It is caused by a defect in lipid metabolism in the surface of Apoprotein C-II or a defect in lipoprotein lipase activity as well as reported in genetic, dietary and environmental factors. Several electronic databases were investigated as information sources, including Google Scholar, PubMed, Web of Science, Scopus, ScienceDirect, SpringerLink, Semantic Scholar, MEDLINE and CNKI Scholar. The current review focused on the risk factors of dyslipidemia, synthetic medication with their side effects and different types of medicinal plants having significant potential for the management of hyperlipidemia. The management of hyperlipidemia mostly involves a constant decrease in lipid level using different remedial drugs like statin, fibrate, bile acid sequestrates and niacin. However, this extensive review suggested that the consequences of these drugs are arguable, due to their numerous adverse effects. The selected parts of herb plants are used intact or their extracts containing active phytoconstituents to regulate the lipids in blood level. It was also noted that the Chinese herbal medicine and combination therapy is promising for the lowering of hyperlipidemia. This review intends to provide a scientific base for future endeavors, such as in-depth biological and chemical investigations into previously researched topics. [ABSTRACT FROM AUTHOR]

In this paper, we have illustrated a simple and effective method of assessing the fresh and deteriorated oyster mushroom samples. Analysis of correlation coefficients is done very effectively here to identify the interrelationship of different color layers of the RGB and HSV color map of the samples, as this degrades progressively. These correlation features are further analyzed using two supervised learning models incorporating support vector machine (SVM) and artificial neural network (ANN) to develop a simple classifier model to identify the two different classes of the mushroom images. The proposed work is simple as it includes a simple computational supervised model, and, more importantly, it is very effective as it explores the interrelation between the different layers of two different color maps of the image samples. The highest classifier accuracy achieved using ANN and SVM models exceeds 95% and 98% respectively; and the mean accuracy level from fifty such observations stands 94.7% and 93.4%, which are high considering contemporary researches. High accuracy of classification, simplicity of analysis and image acquisition through smartphones makes the proposed work suitable for implementing in application-based software for smartphone users which would further enhance the wide applicability of the proposed mushroom classifier scheme. [ABSTRACT FROM AUTHOR]

Automatic assessment of the quality of fruits and vegetables is a growing field of research in this modern era in order to enable faster processing of good quality foods. In this work, we have analyzed ten major colour variant features of two sets of oyster mushrooms in terms of histograms of each layer of the red–green–blue colourmap, hue-saturation-vital component colourmap, luminance-chrominance colourmap and the greyscale image. Besides, texture analysis has been carried out using entropy window filtering. Apart from that, five other minor features, such as mean, standard deviation, entropy, kurtosis and skewness of each of these layers, and four other greyscale features, such as contrast, correlation, energy and homogeneity are analyzed in this work. Two different freshness assessment models employing statistical methods like principal component analysis (PCA) and supervised learning algorithms such as artificial neural network (ANN) have been used here to investigate the different features of the mushroom images and classify the same into fresh and deteriorated classes. Analysis revealed that the ANN classifier outperforms the PCA threshold classifier with almost all the features. The highest classifier accuracy is obtained as 94.4% using the ANN model and 93.3% using the PCA threshold freshness detector. Most importantly, the use of smartphones ensures portability, as well as the possibility of widespread application of the proposed models. [ABSTRACT FROM AUTHOR]

Aegle marmelos is a sacred fruit with several medicinal properties, though availability is limited in a certain period of the year. It is therefore preserved in the form of leather through dehydration. In total, 16 concepts or nodes were selected after an interview with 27 stakeholders from different segments of food industries (packaging technology, food safety, certified industry auditor, food chemist, experienced sensory evaluation panellists, and food industry stakeholders) and a thorough review of shelf-life determining factors for leather-like products. Linguistic variables were assigned to find the rules governing the proposed model. The centre of gravity method was employed to defuzzify the linguistic variables with triangular membership function. A fuzzy cognitive map (FCM) was constructed with 106 numbers of connections to study the uncertainties in shelf-life modelling. Steady-state FCM analysis was conducted with considering the value of each concept equal to 1. Dynamic-state FCM was conducted considering the moisture content and moisture permeability with 0.1 as a case example. Different dynamic state infers how the shelf life of the leather product be influenced by different concepts/nodes/issues in comparison to steady-state FCM. FCM modelling is feasible for storage study of any type of food, though in this study bael leather is chosen. [ABSTRACT FROM AUTHOR]

Androsace foliosa, also known as rock jasmine and a member of the Primulaceae family, is an important medicinal plant with a wide range of applications such as antioxidant, antidiabetic, hepatoprotective, abortifacient, antibacterial, antifungal, anticancerous, anti-inflammatory as well as a flavor enhancer, as insecticidal, as a spice and commonly found in Pakistan. The current study was planned to identify the phytochemical constituents of A. foliosa roots and leaves extracts using high-performance liquid chromatography (HPLC) and gas chromatography/mass spectrometry (GC–MS). HPLC results showed the presence of important phenolic and flavonoids such as gallic acid, rutin and quercetin in A. foliosa leaves and roots crude methanolic extracts. GC–MS analysis of A. foliosa n-hexane leaves fraction revealed the presence of nine pharmacologically active compounds such as hydroxylamine-O-decyl, butanoic acid heptafluoro, sulfurous acid 2-propyl heptyl ester, E-15-heptadecenal, eicosane 9-cyclohexyl, (Z,E)-tetradeca-9,12-dienyl acetate, octadecyl-propane-1,3-diol, di-isobutyl phthalates, aspidofractinine-3-methanol, 17-methoxy-, acetate (ester). Besides, E-15-heptadecenal (34%) and hydroxylamine-O-decyl (30%) are major compounds occurring in A. foliosa n-hexane leaves fraction. As phytochemical constituents present in A. foliosa extracts have been identified, it could be an important medicinal plant to isolate the pharmacologically active compounds to manage different diseases and A. foliosa might play an important role in clinical science, particularly in drug development. [ABSTRACT FROM AUTHOR]