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From azo dyes to natural colors: evaluating safety and regulatory standards

Azo dyes are synthetic chemicals added to food products to enhance color and increase consumer appeal. Compounds such as Tartrazine, Sunset Yellow, and Carmoisine fall into this category, offering no nutritional benefits, health advantages, or preservative qualities. These dyes are favored in the food industry for their cost-effectiveness, stability, and availability, despite ongoing safety concerns. Health risks associated with azo dyes primarily stem from the breakdown of the azo bond, which can release aromatic amines, which are compounds known to be carcinogenic and allergenic. This review provides an extensive analysis of azo dyes, their classification, and the current regulations and toxicity issues associated with them. It also investigates potential natural alternatives that could effectively replace azo dyes, such as anthocyanins, curcumin, red beet betalains, and lycopene, which offer safer and often nutritionally beneficial options. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic review was conducted using PubMed and ScienceDirect with the search terms “Azo dyes,” “Food dyes,” and “Food safety,” without restricting publication years but focusing on the most recent references available. The review aims to present aunderstanding of the implications of azo dye use in the food industry and to highlight viable natural alternatives that ensure consumer safety without compromising product appeal.

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Enhancing fruit and vegetable shelf-life by applying edible coatings: towards a more sustainable packaging system

Currently, the Food Industry is confronted with significant challenges due to the detrimental impact of plastic usage and food waste on sustainability. Consumers are becoming increasingly aware of these issues and are demanding eco-friendly packaging solutions that preserve the quality of food products. This demand is particularly challenging when dealing with perishable items such as fruits and vegetables. In this context, edible coatings emerge as a viable alternative to traditional packaging. These coatings, comprising thin layers of biopolymers, are applied to the surface of food products, providing protection by inhibiting microbial growth, preventing mechanical damage and the oxidation of some nutrients such as polyunsaturated fatty acids, and reducing water loss, among other benefits. Furthermore, the biopolymers used in the formation of edible coatings can be enhanced with additional compounds such as nanoparticles, essential oils, and nanoemulsions, thereby improving the physicochemical properties of the coating and enhancing product preservation, which ultimately leads to the reduction of food waste. This review consolidates current data on edible coatings applied to both fruits and vegetables, offering a comprehensive overview of the formulation process and various methods for enhancing the coatings. Additionally, this review considers the principles of circular economy, noting that several by-products from the food industry can be utilized in the formation of edible coatings.

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Harnessing Rosmarinus officinalis: A Dual Approach to Antioxidant and Antifungal Activity in Food Preservation
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Food is an essential part of human life, serving as a fundamental source of nutrition and energy. But has limited shelf life, so in order to increase the the latest and maintain it’s quality certain preservatives are used , they prevent poisoning, illnesses, increase the shelf life and maintain its nutritional values[1]. As much as synthetic preservatives are effective they have significant disadvantages. Health risks include allergic reactions, hormonal disruptions, and potential carcinogenic effects. Environmentally, they are often non-biodegradable, contributing to pollution and ecotoxicity, harming wildlife and ecosystems. Extracts from medicinal plants are promising alternative natural preservatives cause of Their biological properties (antioxidant and antimicrobial) due to the presence of several active aromatic compounds [2]In this work, the antimicrobial capacities of extracts from Rosmarinus officinalis are measured. They were chosen for their beneficial effects for human health and their notable properties and richness with phenolic compounds. The extract was obtained by the maceration method with a yield equal 14.34%, measuring the antifungal activity of Rosmarinus officinalis extract using three different concentration 250,200 and 150 mg/L, the extract showed activity against FOL Fusarium oxysporum f. sp. lycopersici varies depending on the concentration, it showed highest inhibition percentage at 250 mg/L with τ=30.5%, as for the antioxidant activity the total phenolic compounds was measured , and several test been made ,by 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2-Azinobis (3-ethylbenzothiazolin) -6-sulphonic acid (ABTS), Ferric Reducing Antioxidant Power (FRAP), and phenanthroline, the results showed that Rosmarinus officinalis extract exhibits an excellent antioxidant capacity.

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A novel machine learning approach for revolutionizing orthodontic care
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Anticipating the type of orthodontic treatment needed holds significant weight in patients' decision-making processes. In recent times, several deep learning techniques have shown impressive results in several computer vision tasks, especially in image segmentation, image classification, image identification, etc., using convolutional neural networks. This study aimed to develop a model using convolutional neural networks for predicting orthodontic malocclusion types, which are very difficult to work on using traditional image processing techniques. Currently, proposals to use orthodontic software in dental clinics have been put forward; however, they lack the ability to comprehensively analyze complex patient data, including genetic factors, craniofacial features, and treatment outcomes, potentially leading to sub-optimal treatment decisions. Therefore, the use of machine learning in orthodontics remains largely unexplored due to it being a very vast field. In this study, dental images of three thousand six hundred (3600) patients were used to predict the type of malocclusion. These included images both before and after the treatment. The results of this study demonstrate the effectiveness of convolutional neural networks in accurately classifying different types of orthodontic treatment. The findings reveal the potential for machine learning to assist orthodontists in treatment planning decisions, providing valuable decision support to orthodontists and improving patient outcomes, thus opening avenues for further advancements in orthodontic care.

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Transforming Citrus Peel Waste: Innovative Green Extraction and Multi-Functional Applications of Pectin and Essential Oils

The present study focuses on the green extraction methods and the evaluation of the biological properties of pectin and essential oils derived from citrus peel waste, aiming to both add value to these byproducts and manage waste effectively. Citrus peels are abundant in bioactive compounds, rendering them an innovative resource for transforming waste into value-added products. Pectin, a non-starch polysaccharide, and essential oils, consisting of volatile aromatic compounds, present opportunities for diverse industrial applications, depending on the extraction methods and raw material sources. This research integrates recent advancements in sustainable extraction technologies with a comprehensive review of peer-reviewed literature to compare the physicochemical properties and biological activities of pectin and essential oils from diverse citrus species. Key aspects investigated include the efficiency of different green extraction methods, the impact of raw material variation on product quality, and the potential for novel applications in functional foods, natural preservatives, and therapeutic agents. The systematic review highlights how the synergy between the physicochemical attributes and biological activities of citrus peel derivatives can drive their applications in functional foods, natural preservatives, therapeutic agents, and other industrial applications. By integrating recent advancements in sustainable extraction technologies, the importance of leveraging waste-derived compounds to achieve both economic and environmental goals, thus supporting the development of environmentally sustainable practices within the citrus industry.

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Image Enhancement Using Generative Adversarial Networks in Computer Vision
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Image enhancement serves as a critical function in the field of computer vision, improving the quality and clarity of images for various applications. In this study, we present an advanced approach to image enhancement by leveraging the power of Generative Adversarial Networks (GANs). Our method employs a sophisticated GAN architecture, specifically tailored for image enhancement tasks. The GAN model comprises two primary components: a generator and a discriminator. The generator is responsible for producing enhanced images from the input data, while the discriminator evaluates the authenticity of these images, distinguishing between real, high-quality images and the ones generated by the model. Initially, the generator utilizes a deep convolutional neural network (DCNN) to process the input image. It aims to enhance the image by reducing noise, improving resolution, and refining details. The generator is trained to learn the mapping from low-quality images to high-quality counterparts through a series of convolutional and deconvolutional layers, incorporating techniques such as residual learning and attention mechanisms to optimize the enhancement process. Parallelly, the discriminator functions as a binary classifier, assessing the quality of the generated images against real, high-resolution images. The discriminator's feedback is crucial, as it guides the generator to produce more realistic and high-quality images through an adversarial learning process. This dynamic interplay between the generator and discriminator forms the crux of the GAN framework, driving continuous improvement in image quality. Our approach was rigorously evaluated on several challenging datasets, including medical and low-light image datasets. The results underscore the superior performance of our GAN-based method compared to traditional image enhancement techniques. Key achievements include significant improvements in image clarity, reductions in artifacts, and enhanced resolution, all achieved with efficient computational performance. These compelling findings not only validate the effectiveness of our proposed method but also highlight its potential applications in various fields.

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Nutritious horticulture crops for malnutrition alleviation

Malnutrition, including undernutrition, micronutrient deficiencies, and the rising incidence of overweight and obesity, remains a significant global health challenge. Horticulture crops, such as fruits, vegetables, roots, tubers, and legumes, have the potential to alleviate various forms of malnutrition through their nutrient-dense profiles. This investigation studies the nutritional compositions and health benefits of selected horticulture crops and their role in combating malnutrition. The data show that horticulture crops are rich sources of essential vitamins, minerals, dietary fiber, and phytochemicals. For example, spinach and kale are excellent sources of vitamins A (472 μg RAE and 565 μg RAE, respectively), C (28.1 mg and 93.4 mg), and K, as well as folate (179 μg) and iron (2.7 mg and 1.1 mg). Sweet potatoes are particularly high in vitamin A (835 μg RAE), while legumes like lentils provide substantial amounts of protein (9.0 g), fiber (7.9 g), folate (179 μg), iron (3.3 mg), and zinc (1.1 mg). Horticulture crops have demonstrated their ability to alleviate micronutrient deficiencies, reduce the risk of chronic diseases, and improve maternal and child health. However, challenges such as access, affordability, seasonality, and knowledge gaps must be addressed. Leveraging opportunities like biofortification, home/community gardening, value chain development, and nutrition education can transform the nutritious bounty of horticulture crops into sustainable solutions for combating malnutrition globally.

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A Novel Approach for Sketch Colorization Using Generative AI
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The colorization of sketches is a key aspect in computer vision, with applications ranging across the art, design and entertainment realms. True conversion of a monochromatic sketch into color is an invaluable skill for creative people and enthusiasts. Machine learning techniques, especially deep learning, have shown significantly better performance in various computer vision tasks, particularly for image segmentation, object classification, and other tasks. Although these methods are very promising, the literature still lacks concrete examples of GAN+autoencoder techniques and an in-depth discussion of their integration. Our research adopts a novel method, which integrates GANs and autoencoders to fill this gap. The intent of this integration is to improve the accuracy and effectiveness of color images generated from sketches. But our tests show that the proposed model is superior to existing techniques in terms of capturing fine details and creating a pleasing image. These results have significance in that they represent a breakthrough for state-of-the art sketch colorization techniques, and enable artists and designers to create realistic images of their sketches. This research not only highlights the lack of integration among GANs and autoencoders; it also offers an entirely new approach that helps both to improve the accuracy and degree of realism in colorized images. This represents a major breakthrough for sketch colorization techniques overall.

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Repurposing of natural compound derivatives for control of foodborne fungi and mycotoxins

Controlling agricultural or foodborne fungal pathogens is becoming increasingly problematic since effective agents available for antifungal treatment are often very limited. The development of new antifungal agents is expensive and time-consuming; therefore, an alternative approach, termed antifungal drug or compound repurposing (ADCR), has recently been employed. ADCR is the repositioning of already marketed chemicals approved for treating other diseases or for use as food additives (flavorings, antioxidants, etc.) to control fungi. One of the merits of ADCR is that the mechanisms of action, cellular targets and safety of the chemicals have already been characterized.

We investigated the antifungal efficacy of the natural product salicylaldehyde (SA), a generally-recognized-as-safe (GRAS) compound, in corn and pistachio kernels. SA has been utilized as a food flavoring agent or an intermediate for synthesizing pharmaceuticals. Since SA emits volatiles, test crops were remotely exposed to SA (0.1 to 1.0 mM) for 1, 2 and 3 days (Petri plates, raised bed containers). This transient exposure of crops to SA would allow for minimal deposition of chemicals on crop surfaces. The results showed that SA treatment (0.5 mM or higher) for 3 days almost completely inhibited the growth of fungi (aflatoxigenic Aspergillus flavus, seedborne filamentous fungi). We also studied the antifungal activity of 4-isopropyl-3-methylphenol (4I3M), a derivative of the natural products thymol and carvacrol, against A. flavus and seedborne fungi, and determined that 4I3M possesses potent antifungal and anti-aflatoxigenic activity against A. flavus, while it exhibites less antifungal efficacy against seedborne filamentous fungi compared to SA.

We concluded that natural compounds that do not have any significant environmental impact are a potent source of antifungal/anti-mycotoxigenic agents, either in their nascent form or as lead structures for more effective derivatives.

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VigilantAI: Real-time detection of anomalous activity from a video stream using deep learning
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In an era where artificial intelligence (AI) solutions are increasingly integrated into various sectors, this research delves into leveraging AI for enhancing public safety through real-time detection of illegal activities such as robberies and threats at gunpoint using CCTV footage.
With the advancement in deep learning in object detection, the study focuses on deploying the YoloV5 model, trained on a custom dataset compiled from diverse CCTV sources and movies, to identify specific criminal actions. This dataset, enriched through augmentation techniques and annotated with bounding boxes, allows for the precise detection of threats, achieving an accuracy rate of 85\%. Our system stands out by not only spotting robbery and gun point activities but also by instantly alerting security personnel, facilitating a rapid response to potentially dangerous situations. This capability is important for law enforcement agencies worldwide, offering them an advanced tool to act swiftly and prevent crimes, thereby enhancing public security. The essence of our work demonstrates the practical application and significant impact of AI in strengthening security measures, providing a solid foundation for future enhancements in the field. Through this initiative, we aim to foster a safer environment in public spaces, reducing crime rates and increasing the general public's sense of safety.

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