In a paper published in the journal Foods, researchers have comprehensively examined the present research landscape of intelligent packaging on a global scale, offering a distinct categorization of intelligent packaging technology. The study addresses concerns regarding food safety and its direct impact on human well-being.
Study: Advancements in AI-powered Intelligent Packaging for Food Safety. Image credit: Trong Nguyen/Shutterstock
Despite its potential benefits in terms of cost-effectiveness and efficiency, this technology remains underutilized. The article also evaluates the pros and cons of smart packaging or detecting food freshness. It provides a comprehensive overview of recent progress in integrating AI-based technologies into intelligent packaging systems for detecting food freshness. Notably, recent advancements in AI-driven food freshness detection within smart packaging are highlighted, offering insights into potential future developments in the field.
Background
The interconnection between food safety, nutrition, and security is evident. The World Health Organization reports that around 10% of the global population (60 billion people) experience foodborne illnesses yearly, leading to 4.2 million deaths and 33 million disability-adjusted life years. Preventing such illnesses is possible, and the efforts of WHO in establishing robust food safety systems are crucial.
Packaging is key to guaranteeing food security, quality, and reducing waste. Traditional and active packaging methods play roles, with the latter enhancing protection using additives. Yet, real-time monitoring of freshness is lacking. Intelligent packaging, an emerging method, offers real-time tracking, data exchange, and rapid freshness detection, improving food safety. Advanced techniques like pattern recognition and deep learning aid accurate freshness detection.
This review summarizes AI's recent strides in intelligent packaging, particularly in freshness detection. It outlines global research, categorizes intelligent packaging, discusses its pros and cons for freshness detection, and highlights AI's role in enhancing these processes. Limitations are acknowledged, and future directions for AI's involvement in intelligent packaging's freshness detection are anticipated. This insight will guide the field's future growth.
Evolution of Intelligent Packaging
A new concept of intelligent packaging, defining it as a system with functions like detection, perception, tracking, and communication, was introduced by Yam. This approach extends shelf life, enhances safety, and improves product quality. This comprehensive definition is widely adopted. Notably, several intelligent packaging products exist today, such as those from UC Berkeley, Stanford, Germany's Kuhne, and Zhejiang University. These products advance food freshness testing but still have room for improvement, addressing limitations like cost and speed.
Intelligent packaging enhances product protection, value, and shelf life while aligning with Industry 4.0 trends through technology integration, offering fresh business prospects. Intelligent packaging technology encompasses diverse disciplines, like material and information technologies, automating product protection, control, quality monitoring, anti-counterfeiting, and automation in production. It forms a quality control system reliant on complementary intelligent technologies, underpinning the creation of intelligent packaging products. In China, two classification methods for intelligent packaging exist: (1) based on indicators, sensors, and information technology and (2) based on functional materials, structures, and information technology. While these products have indeed brought about advancements in current food freshness testing technology, addressing limitations such as high costs and slow speed, there remains a need for further refinement and enhancement in various aspects.
Freshness Assessment via Intelligent Packaging
As living standards improve, food quality assessment includes freshness evaluation, influenced by food type, microbes, storage, and packaging. Freshness is often gauged through metabolite-indicator relationships, with oxygen and carbon dioxide key for fruits and vegetables. Modified atmosphere packaging (MAP) regulates these gases for extended shelf life. In animals, freshness is linked to Adenosine Triphosphate (ATP) cycle activity, affecting decay. Dairy products use many indicators for microbial growth detection. Packaging choice considers food characteristics, safety, and sensitivity to light, oxygen, and temperature. Intelligent packaging for freshness aims for cost-effective non-destructive methods.
Direct factor detection includes metabolite-based assessment involving sensors, indicators, and spectrometry for real-time analysis. While piezoelectric sensors, fluorescent probes, and multispectral imaging aid in detecting metabolite changes, maturity monitoring tracks maturation's impact on food quality. Time-temperature indicators (TTi) monitor time-temperature history, and leak indicators assess package integrity by detecting gas changes due to leakage.
AI-Driven Solutions for Extended Freshness and Quality
Indirect factor detection employs indicators like barcodes and RFID technology. Barcodes provide real-time stock, inventory, and safety data. RFID tags and sensors enhance cold chain management by monitoring temperature, humidity, and more. Augmented Reality (AR) superimposes virtual images onto real environments, enhancing consumer engagement. Companies like Blippar and Skywell Software use AR to create interactive packaging experiences. Intelligent packaging's evolution seeks cost-effective, non-invasive solutions for ensuring food freshness and safety throughout the supply chain.
Prioritizing the development of intelligent packaging is crucial for extending food shelf life and reducing losses from spoilage. However, current research is limited, with few sensors meeting fast and accurate detection needs, and expensive sensors hindering their application. AI, based on data and expert systems, enhances food freshness monitoring through data analysis, pattern recognition, and classification. Deep learning and computer vision techniques, like convolutional neural networks (CNN) and supervised learning, are employed for odor assessment and image-based freshness detection in various food products, promising to revolutionize the field.
Conclusion
The application of artificial intelligence (AI) technology in food freshness testing has gained global traction, despite certain shortcomings. Limited sensor types and unstable performance hinder effective freshness detection. AI algorithms’ computation time for processing vast data needs optimization, especially for 3D and multidimensional data. Current food freshness algorithms, including neural networks and support vector machines, require substantial training samples and can exhibit prediction errors. Image processing and chemical signal conversion present slow information processing and prolonged response times.
AI-assisted food freshness testing is still experimental, lacking comprehensive studies on intelligent packaging, stability, and integrated systems. Future research should explore the integration of AI with intelligent packaging, establish evaluation methods, enhance detection efficiency, and facilitate accurate food freshness assessment.
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