Khoya (khoa or mawa) is a traditional dairy product, prepared by heating and concentrating milk, which is widely used in preparation of indigenous milk sweets. But, challenges such as process variability, quality deterioration, microbial contamination, adulteration, limited shelf life and inefficient supply chain management hinder its production and distribution. New solutions to these challenges are available across the khoya value chain due to recent advancements in artificial intelligence (AI) and Industry 4.0 technologies. This review highlights the applications of AI in khoya processing, packaging, transportation, distribution and quality management. The role of machine learning, deep learning, computer vision, Internet of Things (IoT), digital twins, smart sensors, and blockchain in process optimization, automated quality inspection, adulteration detection, shelf-life prediction, intelligent packaging, cold-chain monitoring, logistics optimization and demand forecasting is explored. We also review AI-enabled analytical tools for rapid and non-destructive quality assessment, such as hyperspectral imaging, electronic nose, and electronic tongue. The review also discusses the contribution of AI to improving food safety, traceability, sustainability and operational efficiency, as well as to reducing post-harvest losses and environmental impacts. Finally, the paper discusses the existing challenges, future research directions, and prospects of AI-enabled smart dairy manufacturing. The review finds that AI can play a significant role in improving the quality, safety, efficiency, and sustainability of the khoya industry and helping its transition to intelligent and data-driven dairy processing.
Meeting the global demand for fresh, minimally processed food requires us to rethink how we monitor food safety. Traditional laboratory methods are often too slow, labor-intensive, and impractical for real-time applications. To overcome these delays, biosensors have emerged as a rapid, highly sensitive, and cost-effective alternative. This study explores how biosensing technology accurately detects pathogens, chemical contaminants like heavy metals and pesticides, and spoilage indicators across dairy, meat, produce, and packaged foods. What makes these tools truly transformative is their seamless integration with modern digital infrastructure. By combining biosensors with the Internet of Things (IoT), artificial intelligence (AI), nanotechnology, edge computing, and blockchain, we can create intelligent, continuous monitoring systems. These interconnected frameworks allow for real-time, farm-to-fork traceability, enabling early hazard detection, extending shelf life, and significantly reducing food waste through data-driven decisions. Despite this immense potential, bringing smart biosensors to the commercial market involves overcoming distinct practical hurdles. We examine current technical barriers, including biofouling, long-term sensor stability, power management, and high manufacturing costs. More importantly, we highlight the emerging innovations actively solving these bottlenecks, such as biodegradable materials, battery-free platforms, advanced printed electronics, and smart packaging technologies.
Chowdhury Sanat Anjum Reem, Md Anamul Hasan Chowdhury, Md. Ashrafudoulla, Sang‐Do Ha
Biofilm formation in food processing environments significantly threatens food safety and quality due to its resistance to conventional cleaning and disinfection methods. These resilient microbial communities contribute to contamination, spoilage and foodborne illnesses, highlighting the need for innovative and technology-driven control strategies. Emerging digital tools, particularly blockchain technology and artificial intelligence (AI), offer new opportunities for enhancing biofilm management. Blockchain ensures secure, real-time traceability of hygiene records, contamination events and compliance activities across the supply chain. Complementing this, AI technologies such as machine learning and sensor-based analytics support early detection of microbial growth, anomaly identification and predictive risk assessment. Together, these tools promote data-driven decision-making and more proactive contamination prevention. While pilot applications show promise in improving transparency and sanitation outcomes, challenges remain, including data integration, implementation costs and regulatory barriers. Addressing these issues will require interdisciplinary collaboration and supportive policy frameworks. This review summarizes the current and potential roles of blockchain and AI in biofilm control and outlines future directions for research and industrial application.
This study introduces an enhanced anomaly detection framework integrating Time-[Formula: see text]-Variational Autoencoders (Time-[Formula: see text]-VAE) and Transformer architectures for blockchain-based carbon trading markets. Against intensifying global climate challenges, ensuring carbon market integrity is critical. While blockchain technology enhances transparency, it simultaneously introduces novel regulatory complexities in detecting sophisticated anomalies. Our improved hybrid model, trained on raw transaction records of Moss Carbon Credit (MCO2) tokens sourced via Ethereum blockchain APIs, demonstrates significant efficacy in identifying critical anomalies including smart contract-driven token distribution and fake liquidity attacks through empirical case analysis. The research establishes a scientific framework for blockchain deployment and supervision in carbon markets.
The technical architecture establishes a multi-node data certification platform via consortium blockchain, integrates the InterPlanetary File System (IPFS) for distributed encrypted storage of experimental data, and automates ethical reviews, experimental protocol supervision, and resource allocation through smart contracts. Innovatively, the system mints NFT-based digital identity certificates with unique digital fingerprints to comprehensively document genetic profiles, experimental histories, and medical records of individual mice, ensuring verifiability and tamper-resistance of full-lifecycle data.At the governance level, a token-based economic model and decentralized autonomous organization (DAO) framework are introduced. Dynamic incentive mechanisms promote secure cross-institutional research data sharing, while on-chain voting protocols enable decentralized scientific decision-making, effectively balancing open data access with privacy protection requirements. This system establishes a trusted data infrastructure spanning "biological individuals-experimental processes-research outcomes," providing a scalable Web 3.0 paradigm for digital transformation in life sciences. It drives the evolution of laboratory animal management toward intelligent, standardized, and ethics-compliant practices.
Because every work of art deteriorates over time, cultural heritage protection and conservation is a subject of significant relevance. The type of material, the impact of external climatic conditions, and human variables, all play a role in this degradation. In general, works of art should be conserved in controlled and stable climatic scenarios that need to be recorded and monitored. The goal of this study is to offer a system for data security and monitorization of the elements that influence artefact degradation, while still maintaining a pleasant museum climate for visitors. The study was started in the project MUSEION, where the preservation of historical artefacts was ensured by monitoring pollution levels in museums. Humidity, temperature, vibrations, air pollutants (CO, CO2, NO2, SO2), and volatile organic compounds are the key factors analyzed. The suggested IoT system will be a cloud-based solution that would attempt to provide a wide range of features, including individual material analysis (paintings, metals, textiles, etc.). The prototype will include different components: monitoring stations, data acquisition and administration server, visualization, Cloud database, security aspects using Blockchain technology and alerting platform. Blockchain is a distributed ledger technology and can be used to control the management, ownership and shared display of artefacts and to discourage the sale of heritage objects. The integration of the sensors in the Cloud, which can offer real-time data in case of nominal value exceedances, is a significant characteristic of the technical solution. The results of continuous monitoring over a long period of time reveal the main reasons for art object deterioration in museums. Furthermore, with the support of the entire system, instant decisions for artefact conservation can be established. By minimizing the number of events induced by physical and chemical processes that lead to artefact degradation, the research demonstrated the efficiency, reliability, security and scalability of the pilot system.
Chunghwan Jung, Soo-Jung Kim, Jaehyuck Jang, Joo Hwan Ko · 9 authors
The development of real-time and sensitive humidity sensors is in great demand from smart home automation and modern public health. We hereby proposed an ultrafast and full-color colorimetric humidity sensor that consists of chitosan hydrogel sandwiched by a disordered metal nanoparticle layer and reflecting substrate. This hydrogel-based resonator changes its resonant frequency to external humidity conditions because the chitosan hydrogels are swollen under wet state and contracted under dry state. The response time of the sensor is ~10 4 faster than that of the conventional Fabry-Pérot design. The origins of fast gas permeation are membrane pores created by gaps between the metal nanoparticles. Such instantaneous and tunable response of a new hydrogel resonator is then exploited for colorimetric sensors, anti-counterfeiting applications, and high-resolution displays.
Accurate assessment of fish quality is difficult in practice due to the lack of trusted fish provenance and quality tracking information. Working with Sydney Fish Market (SFM), we develop a Blockchain-enabled fish provenance and quality tracking (BeFAQT) system. A multilayer Blockchain architecture based on attribute-based encryption (ABE) is proposed to tackle the privacy issue caused by applying Blockchain to secure supply chain data and achieve trusted and confidential data sharing among parties in fish supply chains. An Internet-of-Things (IoT) chain saves encrypted fish provenance and quality tracking data, and an ABE chain is specifically designed for the access control to the data in the IoT chain. Latest IoT and artificial intelligence (AI) technologies, including NarrowBand-IoT, image processing, and biosensing, are developed for fish origin proof, supply chain tracking, and objective fish quality assessment. As proven by field trials with SFM and a local fish supply chain, the BeFAQT is able to provide trusted and comprehensive fish provenance and quality tracking information in real time.
Ronan D. Mendonça, Otávio Santos Gomes, Luiz F. M. Vieira, Alex Borges Vieira · 6 authors
In this paper, we propose a blockchain-based cold chain technology for vaccine cooling track. The COVID-19 pandemic has caused the death of millions of people. An important step towards ending the pandemic is vaccination. Vaccines must be kept under control temperature during the whole process, from fabrication to the hands of the health professionals who will immunize the population. However, there are numerous reports of vaccine loss due to temperature variations, and, currently, people getting vaccinated have no control if their vaccine was kept safe. Blockchain is a technology solution that can provide public and verifiable records. We review the World Health Organization (WHO) cool chain and Blockchain technology. Moreover, we describe current IoT temperature monitoring devices and propose Blockcoldchain to track vaccine cold chain using blockchain, thus proving an unalterable vaccine temperature history. Our experimental results using smart contracts demonstrate the system's feasibility.
Dixon Vimalajeewa, Subhasis Thakur, John G. Breslin, D.P. Berry · 5 authors
The use of Internet of Things (IoT) with the Internet of Nano Things (IoNT) can further expand decision making systems (DMS) to improve reliability as it provides a new spectrum of more granular level data to make decisions. However, growing concerns such as data security, transparency and processing capability challenge their use in real-world applications. DMS integrated with Block Chain (BC) technology can contribute immensely to overcome such challenges. The use of IoNT and IoT along with BC for making DMS has not yet been investigated. This study proposes a BC-powered IoNT (BC-IoNT) system for sensing chemicals level in the context of farm management. This is a critical application for smart farming, which aims to improve sustainable farm practices through controlled delivery of chemicals. BC-IoNT system includes a novel machine learning model formed by using the Langmuir molecular binding model and the Bayesian theory, and is used as a smart contract for sensing the level of the chemicals. A credit model is used to quantify the traceability and credibility of farms to determine if they are compliant with the chemical standards. The accuracy of detecting the chemicals of the distributed BC-IoNT approach was >90% and the centralized approach was <80%. Also, the efficiency of sensing the level of chemicals depends on the sampling frequency and variability in chemical level among farms.
IoT-enable monitoring can provide valuable information for the shellfish quality evaluation during cold storage condition. However, IoT based information storage relies on the centralized platform, it is possible to tamper. In this paper, we establish blockchain based multi-sensors (WSN) monitoring system to collect quality parameters and verify captured information for improving transparency and trust during cold storage. The implementation of the K-means and SVM algorithms were used in quality evaluation applications to classify and predict the quality loss of frozen shellfish. The results show blockchain based WSN monitoring can achieve the dynamic indicators continuous monitoring and ensures the data security and reliability. The proportion of the training set and the test set in the allowable deviation range is 88.89% and 87.17%. The root mean square error (RMSE) of training set and test set are 0.1502 and 0.1793 by SVM model. The performance of the K-means and SVM model has higher accuracy than BP model. This paper could help to reduce the risk of food losses and improve quality and safety management of frozen shellfish during cold storage.
Shengjing Sun, Xiaochen Zheng, Javier Villalba-Díez, Joaquín Ordieres‐Meré
Indoor air pollution has been ranked among the top five environmental risks to public health. Indoor Air Quality (IAQ) is proven to have significant impacts on people's comfort, health, and performance. Through a systematic literature review in the area of IAQ, two gaps have been identified by this study: short-term monitoring bias and IAQ data-monitoring solution challenges. The study addresses those gaps by proposing an Internet of Things (IoT) and Distributed Ledger Technologies (DLT)-based IAQ data-monitoring system. The developed data-monitoring solution allows for the possibility of low-cost, long-term, real-time, and summarized IAQ information benefiting all stakeholders contributing to define a rich context for Industry 4.0. The solution helps the penetration of Industrial Internet of Things (IIoT)-based monitoring strategies in the specific case of Occupational Safety Health (OSH). The study discussed the corresponding benefits OSH regulation, IAQ managerial, and transparency perspectives based on two case studies conducted in Spain.
Antonio Arena, Alessio Bianchini, Pericle Perazzo, Carlo Vallati · 5 authors
Urban population is expected to continuously grow in size. The smart city concepts allows to handle the new challenges and issues created by this growth by applying a wide range of technologies that can provide citizens with a better living environment. Smart agriculture will play an important part of smart cities, as a sustainable and high quality food supply chain is crucial to facilitate the grow of human agglomerates. In this context, European laws imposes very strict requirements in the food industry, in order to ensure that food provenance is always guaranteed. Such fine-grained traceability can be only achieved by applying state-of-the-art technologies. In this paper, we present BRUSCHETTA, a blockchain-based application for the traceability and the certification of the Extra Virgin Olive Oil (EVOO) supply chain. EVOO is an emblematic food product for Italy, but it is also one of the most falsified ones. BRUSCHETTA provides a blockchain-based system to enforce the certification of this product by tracing its entire supply chain: from the plantation to the shops. The goal is to enable the final customer to access a tamper-proof history of the product, including the farming, harvesting, production, packaging, conservation, and transportation processes. BRUSCHETTA leverages Internet of Things (IoT) technologies in order to interconnect sensors dedicated to EVOO quality control, and to let them operate on the blockchain. We also provide a support for the correct tailoring of the BRUSCHETTA blockchain system, and we propose a mechanism for its dynamic auto-tuning to optimize it in case of high loads.