From Farms to Labs: The New Trend of Sustainable Meat Alternatives

Food Science of Animal Resources, Год журнала: 2024, Номер 45(1), С. 13 - 30

Опубликована: Ноя. 28, 2024

Meat analogs or meat alternatives mimic conventional by using non-meat ingredients. There are several reasons for the rising interest in alternatives, e.g., health-consciousness, environmental concerns, and growing demand sustainable diets. Factors like low-calorie foods, low-fat, efforts to reduce greenhouse gas emissions, flexitarian lifestyles also contributing this change (conventional analogs). Numerous substitutes presently being launched alternative markets. Plant-based meat, restructured cultured hybrid insect protein-based prevalent among alternatives. The scope of including plant-based insect-based protein products, is expanding due advances food technology. Innovation technology plays a crucial role production. Still, there some challenges market consumer acceptance, appearance cost Innovative approaches, such as advanced technologies awareness consumer, required deal with these challenges. This review briefly examines technological advances, regulatory requirements, pros cons, trends finding highlights importance resource food. Moreover, can fulfill increasing decrease impact. Additionally, explores ways improve overall scenario

Язык: Английский

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Predictive AI Models for Food Spoilage and Shelf-Life Estimation

Опубликована: Янв. 1, 2025

Food spoilage is a global problem which causes food waste, economic loss and foodborne illness. The shelf life estimation of traditionally done with fixed expiration dates this leads to disposal still eatable or eating spoiled food. Recently, the development Artificial Intelligence (AI), predictive models have been developed better evaluate based on such factors as temperature, humidity, microbial activities gas emissions. This paper discusses part played by AI in prediction spoilage, while also outlining various machine learning deep (regression, classification, convolutional neural network – CNN hybrid AI). powered relies multiple sources data including IoT enabled sensors, Spectroscopy well real time environmental monitoring. practical use industry driven context applications smart packaging, quality supply chains, retail inventory product optimization. However, adoption field limited scarce low quality, accuracy, ethical concerns exist, implementation expensive. In review, potential for transforming highlighted could be achieved working obtaining greater scalability, model different sectors. role enhancing security, sustainability efficient resources, waste reduction increasing accessibility good perishables every consumer will gain feasibility improvement AI.

Язык: Английский

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Computer Vision for Food Quality Assessment: Advances and Challenges

Опубликована: Фев. 19, 2025

One of the transformative technologies in food quality assessment provided by computer vision is an automated, precise and efficient product evaluation. In this review, progress, difficult issues, future way applied control are discussed. Advances imaging technology, artificial intelligence, deep learning have improved inspection accuracy to real time defect detection, ripeness estimation contamination detection. Feature extraction classification using hyper spectral neural networks, Convolutional Neural Networks (CNNs) Generative Adversarial (GANs) been design robust schemes for assessment. Although these breakthroughs made, problems like variability, dependence on large annotated databases, high implementation costs, processing limitations hold back common use. The complexity system integration industrial production still remains a concern, especially small or medium size enterprises. Future research aimed at integrating IoT edge computing monitoring, explainable AI transparent decision making, multimodal data fusion accurate would address above mentioned challenges. Moreover, creation sustainable low cost solutions will be crucial ensuring availability different industry sectors.

Язык: Английский

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Role of AI in Food Production and Preservation

Опубликована: Фев. 1, 2025

Язык: Английский

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AI-Powered Food Contaminant Detection: A Review of Machine Learning Approaches

Опубликована: Фев. 1, 2025

Food safety is being transformed by artificial intelligence (AI), which boosting contamination detection, real time monitoring and transparency of food supply chain. AI based techniques like machine learning, deep learning computer vision help to detect chemical, microbial physical contaminants in more accurately efficiently. These advancements have led processes be automated, minimize the impact human error facilitate better decision taking. Other innovations include rapid, automated detection traceability using driven spectroscopy, sensor block chain integration. Challenges adopting AI, however, fragmented proprietary data, lack model interpretability, sheer implementation costs, regulatory hurdles. Implementing has cost technical challenges for small medium sized businesses. Also, models must explainable FMV compliant provide necessary reliability. Future research will consist building upon developed this thesis, incorporation with IoT edge computing as well setting up ethical frameworks. Trust standardized regulations, unbiased predictions, data privacy protections. Although presents some hurdles, it power contribute a much safer, efficient transparent global

Язык: Английский

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Modern Trends in Food Production: The Role of AI in Smart Food Factories

Опубликована: Фев. 1, 2025

The integration of Artificial Intelligence (AI) in food production is revolutionizing the industry by enhancing efficiency, improving safety, and driving sustainability. Smart factories powered AI are optimizing processes through automation, predictive maintenance, real-time quality control. AI-driven supply chain management reducing waste, ensuring better resource allocation, streamlining logistics. Furthermore, playing a crucial role developing personalized nutrition alternative protein sources, catering to evolving consumer demands. Despite its numerous benefits, adoption manufacturing faces challenges such as high implementation costs, data privacy concerns, workforce displacement. Overcoming these obstacles requires investment training, regulatory frameworks, ethical deployment. Looking ahead, advancements robotics, block integration, AI-powered 3D printing will further shape future production. By addressing leveraging responsibly, can create safer, more efficient, sustainable systems for future.

Язык: Английский

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Meat replacers as part of sustainable food systems in Romania: A Causal Network Model

Journal of Agriculture and Food Research, Год журнала: 2025, Номер unknown, С. 101803 - 101803

Опубликована: Март 1, 2025

Язык: Английский

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Computer Vision for Food Quality Assessment: Advances and Challenges

SSRN Electronic Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Overcoming risk of bacterial biofilm challenges in cultured meat production: Innovative strategies for safety and sustainability

Trends in Food Science & Technology, Год журнала: 2025, Номер unknown, С. 105086 - 105086

Опубликована: Май 1, 2025

Язык: Английский

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