Nanomaterial-enhanced analytical methods have emerged as transformative platforms in forensic science, addressing critical sensitivity, selectivity, and operational limitations of conventional techniques across trace evidence, toxicology, biological fluid analysis, and nucleic acid profiling. Gold nanoparticles, quantum dots, carbon-based nanomaterials, and magnetic nanoparticles have collectively enabled detection capabilities at femtomolar to attomolar concentrations, multiplexed immunoassay formats, magnetically assisted sample preparation from degraded biological matrices, and enhanced PCR amplification from inhibitor-rich forensic specimens. Surface-enhanced Raman scattering, fluorescence-based transduction, and electrochemical sensing at nanocomposite electrode surfaces have each demonstrated performance profiles that substantially exceed conventional forensic analytical benchmarks. Despite these advances, the translation of nanomaterial platforms into accredited forensic casework remains constrained by nanoparticle aggregation instability, batch-to-batch synthesis variability, matrix-dependent signal suppression, and the absence of universally adopted validation frameworks governing limit of detection determination, measurement uncertainty quantification, and proficiency testing for nanomaterial-specific analytical modalities. Emerging innovations including portable handheld SERS devices, blockchain-integrated chain-of-custody architectures, molecularly imprinted polymer nanoparticle probes, and AI-augmented chemometric classification frameworks are collectively advancing the field toward real-time, field-deployable forensic analysis. Sustained progress requires parallel investment in international standardization, ethical governance of ultra-sensitive biological surveillance capabilities, and equitable access infrastructure ensuring that nanomaterial-enabled forensic precision serves justice systems globally.
Muhammad Tauseef Tariq Kisana, Imran Ul Haq Imran Ul Haq
The pre- and post-harvest interface represents a critical control zone in the food supply chain where microbial contamination can compromise the safety, quality, and shelf-life. Although numerous studies have examined pre-harvest contamination and post-harvest disease management separately, limited reviews have comprehensively addressed the critical interface linking these stages. This review synthesizes current knowledge on contamination pathways, major pathogens, monitoring approaches, and integrated control strategies associated with fresh fruits and vegetables. Relevant peer-reviewed literature was critically evaluated to provide an overview of food safety risks and management options across the production chain. Fresh produce may become contaminated through soil, irrigation water, organic amendments, wildlife, harvesting equipment, storage environments, and human handling. These pathways facilitate the introduction and dissemination of bacterial, fungal, and viral pathogens. Major bacterial hazards include Salmonella enterica, Escherichia coli O157, and Listeria monocytogenes, while fungal pathogens such as Aspergillus, Fusarium, and Penicillium species contribute to spoilage and mycotoxin production. Viral pathogens, particularly norovirus and hepatitis A virus, are also important causes of produce-associated outbreaks. Environmental stressors, including drought, heavy rainfall, temperature fluctuations, and crop injuries, further increase contamination risks. The review highlights integrated management strategies, including Good Agricultural Practices (GAPs), sanitation programs, rapid cooling, biological control agents, and emerging decontamination technologies. Among these, ozone and cold plasma show strong antimicrobial potential, although their large-scale adoption is constrained by economic and technical limitations. Environmental Monitoring Programs (EMPs), molecular detection tools such as polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), and next-generation sequencing (NGS), together with blockchain-based traceability systems, support rapid pathogen detection and outbreak prevention. Integrating the Food Safety Objective (FSO) framework with One Health principles provides a sustainable approach for reducing contamination risks throughout the supply chain. Future research should focus on improving the cost-effectiveness, scalability, and practical implementation of emerging monitoring and intervention technologies. Keywords: Pre-harvest, post-harvest, food safety, fresh produce, microbial contamination, mycotoxins, biofilms, traceability, good agricultural practices, environmental monitoring programs.
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.
The book&s;s emphasis is shifted to the future in Chapter 8 , âEmerging Trends and Advanced Topics,â which offers a survey of the cutting-edge ideas and revolutionary technologies that are set to completely alter the logistics industry. The chapter makes the case that logistics is developing into a hyperconnected, intelligent, and autonomous ecosystem rather than a collection of distinct tasks. The convergence of multiple important technological domains, each supported by complex mathematical and engineering principles, is what is driving this evolution. In order to enable strong predictive analytics, demand forecasting, and real-time optimization, artificial intelligence and machine learning are positioned as the brains of logistics operations in the future. This is where the story starts. The idea of âdigital twins,â which produce virtual representations of entire supply chain networks in real time, expands on this theme. These virtual models, mathematically grounded in dynamical systems and Markov Decision Processes, allow companies to simulate complex scenarios, assess risks, and test optimization strategies in a virtual environment before physical implementation. From optimization, the chapter moves to the critical issues of trust and security, highlighting Blockchain for Supply Chain Transparency. It explains that blockchain&s;s function will transcend simple record-keeping, creating a decentralized and tamper-proof ledger for all transactions. The text delves into the mathematical foundations securing this trust, including cryptographic hash functions, Zero-Knowledge Proofs (ZKP), and secure consensus algorithms. The physical implementation of these trends in warehouse automation and robotics is finally examined in this chapter. Multi-agent reinforcement learning and graph-based optimization models are used to coordinate swarms of autonomous mobile robots and cooperative âcobotsâ in hyper-automated warehouses of the future, achieving previously unheard-of levels of efficiency and adaptability. This idea is expanded to the scale of advanced manufacturing and smart factories, where material flows are coordinated in real-time to satisfy changing manufacturing demands and logistics becomes a deeply integrated, cyber-physical part of the production system itself. According to the chapter&s;s conclusion, the combination of these technologies will result in intelligent, self-adjusting, and extremely resilient logistics networks, offering businesses that adopt this technological change a major competitive edge.
Chowdhury Sanat Anjum Reem, Md Anamul Hasan Chowdhury, Md. Ashrafudoulla, SangâDo 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 work aims to build on URIâs autonomous Lab on Paper (LoP) platform technology, which was used to conduct enzyme-linked immunosorbent assays (ELISAs), to conducting enzymatic activity assays. This requires new optimizations and designs to accommodate the test. In this work we use alkaline phosphatase (ALP) enzyme as the model analyte for the proof-of-concept. To accompany the new device, new MATLAB analytical tools were written and tested, to aid in robust colorimetric signal analysis. The test fixture was designed to be reusable, when a new paper circuit and reagent vials are supplied, however, it was assumed that an eventual final product will be a single use system. The circuit was built with modularity in mind to facilitate the development of future tests. Flow speed tests were shown to follow the form of Washburnâs equation. The housing was optimized for ease of use. The proof of concept was proven; achieved however it demonstrated a lack in the precision of the measurements. The signal followed the expected form of a linear curve, however, overlapping in test results means that the precision of the measurement is weak. Several challenges limited the success of the platform and should be investigated in future work. The most important is a full examination and mitigation of the âcoffee ringâ effect in order to produce a uniform image in the detection zone.
Andi Arniaty Arsyad, Irawan Widi Widayat, Mario Köppen
For more than a decade, various farm-specific models have been developed by collaborating and integrating sensing technologies as a step toward successful data-farm documentation and effective decision-making. However, the stored and gathered data continues to rely on cloud infrastructure or centralized platform control, which is particularly vulnerable to threats such as data tampering, data distortion, confidentiality, and manipulation, which caused the farm product data difficult to trace to its provenance. In this paper, we propose a farm transaction model by demonstrating a flow of farm transaction simulation implicated by MBC sensing instrument with an array of sensors, controllers, networking hardware, computing equipment, and internal memory functions to enhance data integrity and security farm object. Based on the proposed model, a proof-of-concept experimental system called Encapsulating Block Mesh (EBM) integrates blockchain technology with the specific application case of cocoa production has been implemented. Results have shown that farm objects represented by MBC take turn recording information on the process of generating, transacting, and consuming a farm product and encrypting it into a block was validated and linked in the EBM with the hash of transaction data that connected to each cocoa farm object in a simulation environment.
Salmonella represents one of the major causes of foodborne diseases in humans, in addition to provoking important economic losses in the agri-food sector worldwide. Therefore, the surveillance and control of this human pathogenic bacterium in foodstuffs and biological fluids are necessary in order to prevent and diagnose the disease. Molecular methods based on the detection of DNA sequences specific to pathogenic species are an appealing alternative to traditional culture-based methods that require 5 to 6 days to obtain a definitive result. Among them, and because of its easy miniaturization, electrochemical genosensors are a suitable option for decentralized genetic testing [1-2]; however, they often require a set of sample pretreatment steps before genetic DNA analysis, thus making their implementation at the point of need more difficult. Herein, we report the integration of a nucleic acid-based sensor and an isothermal DNA amplification technique, helicase-dependent amplification or HDA, onto indium tin oxide (ITO) surfaces for the detection of a DNA sequence specific for the typA gene of Salmonella. DNA amplification process occurs at 65 ºC with short oligonucleotides flanking the target sequence, which act as primers. The reversed primer is covalently bound to the ITO surface through a thiol group present at its 5’ terminus, whereas forward fluorescein-tagged primer is incorporated in solution. As a result of the isothermal elongation step, fluorescein-tagged DNA duplexes are attached to the ITO surface and their enzymatic labelling is achieved via Fab fragments directed against fluorescein, conjugated with the redox enzyme alkaline phosphatase. Then, α-naphthyl phosphate is enzymatically dephosphorylated into an electroactive derivate α-naphthol whose amount, directly related to the Salmonella present in the sample, is measured by differential pulse voltammetry. This developed integrated sensing platform allows the detection of Salmonella down to 10 genomes in just over 2 hours [3], the same detection limit as that achieved by real-time PCR but without need of high-end benchtop instrumentation. Furthermore, the sensing layer built onto ITO surfaces maintains its performance even after 9 months storage, and possesses a great potential to be extended to the in-situ, fast and reliable detection of other pathogens. References: [1] D. Mabey, R.W. Peeling, A. Ustianowski and M.D. Perkins, Nat. Rev Microbiol., 2004, 2, 231-240. [2] A.S. Patterson, K. Hsieh, H.T. Soh and K.W. Plaxco, Trends Biotechnol., 2013, 31, 704-712. [3] S. Barreda-García, R. Miranda-Castro, N. de-los-Santos-Álvarez, A.J. Miranda-Ordieres, M.J. Lobo-Castañón, Chem. Comm., 2017, 53, 9721-9724. Acknowledgments: This work has been supported by the Spanish Ministerio de Economía y Competitividad (CTQ2015-63567-R), the Principado de Asturias government (FC-15-GRUPIN14-025), and co-financed by FEDER funds.
Digital PCR (dPCR) exploits limiting dilution of a template into an array of PCR reactions. From this array the number of reactions that contain at least one (as opposed to zero) initial template is determined, allowing inferring the original template concentration. Here we present a novel protocol to efficiently infer the concentration of a sample and its optimal dilution for dPCR from few targeted qPCR assays. By taking advantage of the real-time amplification feature of qPCR as opposed to relying on endpoint PCR assessment as in standard dPCR prior knowledge of template concentration is not necessary. This eliminates the need for serial dilutions in a separate titration and reduces the number of necessary reactions. We describe the theory underlying our approach and discuss experimental moments that contribute to uncertainty. We present data from a controlled experiment where the initial template concentration is known as proof of principle and apply our method on directly monitoring transcript level change during cell differentiation as well as gauging amplicon numbers in cDNA samples after pre-amplification.
Open access
Innovative Microfluidic and Catalytic Techniques Innovation