FEAST of biosensors: Food, environmental and agricultural sensing technologies (FEAST) in North America

Classification techniques of ion selective electrode arrays in agriculture: a review

Agriculture has a substantial demand for classification, and each agricultural product exhibits a unique ion signal. This paper summarizes the classification techniques of ion-selective electrode arrays in agriculture. Initially, data sample collection methods based on ion-selective electrode arrays are summarized. The paper then discusses the current state of classification algorithms from the perspectives of machine learning, artificial neural networks, extreme learning machines, and deep learning, along with their existing research in ion-selective electrodes and related fields. Then, the potential applications in crop and livestock growth status classification, soil classification, agricultural product quality classification, and agricultural product type classification are discussed. Ultimately, the future challenges of ion-selective electrode research are discussed from the perspectives of the sensor itself and algorithms combined with sensor arrays, which also positively impact the promotion of their application in agriculture. This work will advance the application of classification techniques combined with ion-selective electrode arrays in agriculture.

Central Countries' and Brazil's Contributions to Nanotechnology

Abstract:

Nanotechnology is a cornerstone of the scientific advances witnessed over the past few years. Nanotechnology applications are extensively broad, and an overview of the main trends worldwide can give an insight into the most researched areas and gaps to be covered. This document presents an overview of the trend topics of the three leading countries studying in this area, as well as Brazil for comparison. The data mining was made from the Scopus database and analyzed using the VOSviewer and Voyant Tools software. More than 44.000 indexed articles published from 2010 to 2020 revealed that the countries responsible for the highest number of published articles are The United States, China, and India, while Brazil is in the fifteenth position. Thematic global networks revealed that the standing-out research topics are health science, energy, wastewater treatment, and electronics. In a temporal observation, the primary topics of research are: India (2020), which was devoted to facing SARS-COV 2; Brazil (2019), which is developing promising strategies to combat cancer; China (2018), whit research on nanomedicine and triboelectric nanogenerators; the United States (2017) and the Global tendencies (2018) are also related to the development of triboelectric nanogenerators. The collected data are available on GitHub. This study demonstrates the innovative use of data-mining technologies to gain a comprehensive understanding of nanotechnology's contributions and trends and highlights the diverse priorities of nations in this cutting-edge field.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

Organic Electronics in Biosensing: A Promising Frontier for Medical and Environmental Applications

The promising field of organic electronics has ushered in a new era of biosensing technology, thus offering a promising frontier for applications in both medical diagnostics and environmental monitoring. This review paper provides a comprehensive overview of organic electronics' remarkable progress and potential in biosensing applications. It explores the multifaceted aspects of organic materials and devices, thereby highlighting their unique advantages, such as flexibility, biocompatibility, and low-cost fabrication. The paper delves into the diverse range of biosensors enabled by organic electronics, including electrochemical, optical, piezoelectric, and thermal sensors, thus showcasing their versatility in detecting biomolecules, pathogens, and environmental pollutants. Furthermore, integrating organic biosensors into wearable devices and the Internet of Things (IoT) ecosystem is discussed, wherein they offer real-time, remote, and personalized monitoring solutions. The review also addresses the current challenges and future prospects of organic biosensing, thus emphasizing the potential for breakthroughs in personalized medicine, environmental sustainability, and the advancement of human health and well-being.

Non-destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground-breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non-destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non-destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry.

Machine Learning for Improved Detection of Pathogenic E. coli in Hydroponic Irrigation Water Using Impedimetric Aptasensors: A Comparative Study

Reuse of alternative water sources for irrigation (e.g., untreated surface water) is a sustainable approach that has the potential to reduce water gaps, while increasing food production. However, when growing fresh produce, this practice increases the risk of bacterial contamination. Thus, rapid and accurate identification of pathogenic organisms such as Shiga-toxin producing Escherichia coli (STEC) is crucial for resource management when using alternative water(s). Although many biosensors exist for monitoring pathogens in food systems, there is an urgent need for data analysis methodologies that can be applied to accurately predict bacteria concentrations in complex matrices such as untreated surface water. In this work, we applied an impedimetric electrochemical aptasensor based on gold interdigitated electrodes for measuring E. coliO157:H7 in surface water for hydroponic lettuce irrigation. We developed a statistical machine-learning (SML) framework for assessing different existing SML methods to predict the E. coliO157:H7 concentration. In this study, three classes of statistical models were evaluated for optimizing prediction accuracy. The SML framework developed here facilitates selection of the most appropriate analytical approach for a given application. In the case of E. coliO157:H7 prediction in untreated surface water, selection of the optimum SML technique led to a reduction of test set RMSE by at least 20% when compared with the classic analytical technique. The statistical framework and code (open source) include a portfolio of SML models, an approach which can be used by other researchers using electrochemical biosensors to measure pathogens in hydroponic irrigation water for rapid decision support.

A Connected World: System-Level Support Through Biosensors

The goal of protecting the health of future generations is a blueprint for future biosensor design. Systems-level decision support requires that biosensors provide meaningful service to society. In this review, we summarize recent developments in cyber physical systems and biosensors connected with decision support. We identify key processes and practices that may guide the establishment of connections between user needs and biosensor engineering using an informatics approach. We call for data science and decision science to be formally connected with sensor science for understanding system complexity and realizing the ambition of biosensors-as-a-service. This review calls for a focus on quality of service early in the design process as a means to improve the meaningful value of a given biosensor. We close by noting that technology development, including biosensors and decision support systems, is a cautionary tale. The economics of scale govern the success, or failure, of any biosensor system.

Genetic engineering of bacteriophages: Key concepts, strategies, and applications

Bacteriophages are the most abundant biological entity in the world and hold a tremendous amount of unexplored genetic information. Since their discovery, phages have drawn a great deal of attention from researchers despite their small size. The development of advanced strategies to modify their genomes and produce engineered phages with desired traits has opened new avenues for their applications. This review presents advanced strategies for developing engineered phages and their potential antibacterial applications in phage therapy, disruption of biofilm, delivery of antimicrobials, use of endolysin as an antibacterial agent, and altering the phage host range. Similarly, engineered phages find applications in eukaryotes as a shuttle for delivering genes and drugs to the targeted cells, and are used in the development of vaccines and facilitating tissue engineering. The use of phage display-based specific peptides for vaccine development, diagnostic tools, and targeted drug delivery is also discussed in this review. The engineered phage-mediated industrial food processing and biocontrol, advanced wastewater treatment, phage-based nano-medicines, and their use as a bio-recognition element for the detection of bacterial pathogens are also part of this review. The genetic engineering approaches hold great potential to accelerate translational phages and research. Overall, this review provides a deep understanding of the ingenious knowledge of phage engineering to move them beyond their innate ability for potential applications.

Rapid and label-free Listeria monocytogenes detection based on stimuli-responsive alginate-platinum thiomer nanobrushes

In this work, we demonstrate the development of a rapid and label-free electrochemical biosensor to detect Listeria monocytogenes using a novel stimulus-response thiomer nanobrush material. Nanobrushes were developed via one-step simultaneous co-deposition of nanoplatinum (Pt) and alginate thiomers (ALG-thiomer). ALG-thiomer/Pt nanobrush platform significantly increased the average electroactive surface area of electrodes by 7 folds and maintained the actuation properties (pH-stimulated osmotic swelling) of the alginate. Dielectric behavior during brush actuation was characterized with positively, neutral, and negatively charged redox probes above and below the isoelectric point of alginate, indicating ALG-thiomer surface charge plays an important role in signal acquisition. The ALG-thiomer platform was biofunctionalized with an aptamer selective for the internalin A protein on Listeria for biosensing applications. Aptamer loading was optimized and various cell capture strategies were investigated (brush extended versus collapsed). Maximum cell capture occurs when the ALG-thiomer/aptamer is in the extended conformation (pH > 3.5), followed by impedance measurement in the collapsed conformation (pH < 3.5). Low concentrations of bacteria (5 CFU mL-1) were sensed from a complex food matrix (chicken broth) and selectivity testing against other Gram-positive bacteria (Staphylococcus aureus) indicate the aptamer affinity is maintained, even at these pH values. The new hybrid soft material is among the most efficient and fastest (17 min) for L. monocytogenes biosensing to date, and does not require sample pretreatment, constituting a promising new material platform for sensing small molecules or cells.

An Experimental Framework for Developing Point-of-Need Biosensors: Connecting Bio-Layer Interferometry and Electrochemical Impedance Spectroscopy

Biolayer interferometry (BLI) is a well-established laboratory technique for studying biomolecular interactions important for applications such as drug development. Currently, there are interesting opportunities for expanding the use of BLI in other fields, including the development of rapid diagnostic tools. To date, there are no detailed frameworks for implementing BLI in target-recognition studies that are pivotal for developing point-of-need biosensors. Here, we attempt to bridge these domains by providing a framework that connects output(s) of molecular interaction studies with key performance indicators used in the development of point-of-need biosensors. First, we briefly review the governing theory for protein-ligand interactions, and we then summarize the approach for real-time kinetic quantification using various techniques. The 2020 PRISMA guideline was used for all governing theory reviews and meta-analyses. Using the information from the meta-analysis, we introduce an experimental framework for connecting outcomes from BLI experiments (KD, kon, koff) with electrochemical (capacitive) biosensor design. As a first step in the development of a larger framework, we specifically focus on mapping BLI outcomes to five biosensor key performance indicators (sensitivity, selectivity, response time, hysteresis, operating range). The applicability of our framework was demonstrated in a study of case based on published literature related to SARS-CoV-2 spike protein to show the development of a capacitive biosensor based on truncated angiotensin-converting enzyme 2 (ACE2) as the receptor. The case study focuses on non-specific binding and selectivity as research goals. The proposed framework proved to be an important first step toward modeling/simulation efforts that map molecular interactions to sensor design.

Development of a Biosensor Based on Angiotensin-Converting Enzyme II for Severe Acute Respiratory Syndrome Coronavirus 2 Detection in Human Saliva

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel coronavirus responsible for COVID-19. Infection in humans requires angiotensin-converting enzyme II (hACE2) as the point of entry for SARS-CoV-2. PCR testing is generally definitive but expensive, although it is highly sensitive and accurate. Biosensor-based monitoring could be a low-cost, accurate, and non-invasive approach to improve testing capacity. We develop a capacitive hACE2 biosensor for intact SARS-CoV-2 detection in saliva. Laser-induced graphene (LIG) electrodes were modified with platinum nanoparticles. The quality control of LIG electrodes was performed using cyclic voltammetry. Truncated hACE2 was used as a biorecognition element and attached to the electrode surface by streptavidin-biotin coupling. Biolayer interferometry was used for qualitative interaction screening of hACE2 with UV-attenuated virions. Electrochemical impedance spectroscopy (EIS) was used for signal transduction. Truncated hACE2 binds wild-type SARS-CoV-2 and its variants with greater avidity than human coronavirus (common cold virus). The limit of detection (LoD) is estimated to be 2,960 copies/ml. The detection process usually takes less than 30 min. The strength of these features makes the hACE2 biosensor a potentially low-cost approach for screening SARS-CoV-2 in non-clinical settings with high demand for rapid testing (for example, schools and airports).

Bioanalytical approaches for the detection, characterization, and risk assessment of micro/nanoplastics in agriculture and food systems

This review discusses the most recent literature (mostly since 2019) on the presence and impact of microplastics (MPs, particle size of 1 μm to 5 mm) and nanoplastics (NPs, particle size of 1 to 1000 nm) throughout the agricultural and food supply chain, focusing on the methods and technologies for the detection and characterization of these materials at key entry points. Methods for the detection of M/NPs include electron and atomic force microscopy, vibrational spectroscopy (FTIR and Raman), hyperspectral (bright field and dark field) and fluorescence imaging, and pyrolysis-gas chromatography coupled to mass spectrometry. Microfluidic biosensors and risk assessment assays of MP/NP for in vitro, in vivo, and in silico models have also been used. Advantages and limitations of each method or approach in specific application scenarios are discussed to highlight the scientific and technological obstacles to be overcome in future research. Although progress in recent years has increased our understanding of the mechanisms and the extent to which MP/NP affects health and the environment, many challenges remain largely due to the lack of standardized and reliable detection and characterization methods. Most of the methods available today are low-throughput, which limits their practical application to food and agricultural samples. Development of rapid and high-throughput field-deployable methods for onsite screening of MP/NPs is therefore a high priority. Based on the current literature, we conclude that detecting the presence and understanding the impact of MP/NP throughout the agricultural and food supply chain require the development of novel deployable analytical methods and sensors, the combination of high-precision lab analysis with rapid onsite screening, and a data hub(s) that hosts and curates data for future analysis.

Context-Aware Diagnostic Specificity (CADS)

Rapid detection of proteins is critical in a vast array of diagnostic or monitoring applications [...].

Substrate Materials for Biomolecular Immobilization within Electrochemical Biosensors

Electrochemical biosensors have potential applications for agriculture, food safety, environmental monitoring, sports medicine, biomedicine, and other fields. One of the primary challenges in this field is the immobilization of biomolecular probes atop a solid substrate material with adequate stability, storage lifetime, and reproducibility. This review summarizes the current state of the art for covalent bonding of biomolecules onto solid substrate materials. Early research focused on the use of Au electrodes, with immobilization of biomolecules through ω-functionalized Au-thiol self-assembled monolayers (SAMs), but stability is usually inadequate due to the weak Au-S bond strength. Other noble substrates such as C, Pt, and Si have also been studied. While their nobility has the advantage of ensuring biocompatibility, it also has the disadvantage of making them relatively unreactive towards covalent bond formation. With the exception of Sn-doped In2O3 (indium tin oxide, ITO), most metal oxides are not electrically conductive enough for use within electrochemical biosensors. Recent research has focused on transition metal dichalcogenides (TMDs) such as MoS2 and on electrically conductive polymers such as polyaniline, polypyrrole, and polythiophene. In addition, the deposition of functionalized thin films from aryldiazonium cations has attracted significant attention as a substrate-independent method for biofunctionalization.