Artificial neural network-facilitated V2C MNs-based colorimetric/fluorescence dual-channel biosensor for highly sensitive detection of AFB1 in peanut

Progress of machine learning-based biosensors for the monitoring of food safety: A review

Rapid urbanization and growing food demand caused people to be concerned about food safety. Biosensors have gained considerable attention for assessing food safety due to selectivity, and sensitivity but poor stability inherently limits their application. The emergence of machine learning (ML) has enhanced the efficiency of different sensors for food safety assessment. The ML combined with various noninvasive biosensors has been implemented efficiently to monitor food safety by considering the stability of bio-recognition molecules. This review comprehensively summarizes the application of ML-powered biosensors to investigate food safety. Initially, different detector-based biosensors using biological molecules with their advantages and disadvantages and biosensor-related various ML algorithms for food safety monitoring have been discussed. Next, the application of ML-powered biosensors to detect antibiotics, foodborne microorganisms, mycotoxins, pesticides, heavy metals, anions, and persistent organic pollutants has been highlighted for the last five years. The challenges and prospects have also been deliberated. This review provides a new prospect in developing various biosensors for multi-food contaminants powered by suitable ML algorithms to monitor in-situ food safety.

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

A dual-mode composite nanozyme-based cascade colorimetric-fluorescence aptasensor for Salmonella Typhimurium detection

BACKGROUND

Salmonella Typhimurium poses a serious threat to human health worldwide, necessitating the development of a rapid, sensitive, and convenient method for S. Typhimurium detection. Nanozymes are considered ideal signal report elements, which are extensively used for developing colorimetric methods. However, single-component nanozymes display low catalytic activity, and colorimetric methods are susceptible to environmental interference, reducing the sensitivity and accuracy of detection results. To address these drawbacks, this study constructed a dual-mode composite nanozyme-based cascade colorimetric-fluorescence aptasensor for S. Typhimurium detection in food.

RESULTS

In this study, the composite Fe3O4@MIL-100(Fe) nanozymes were successful synthesized and demonstrated substantial peroxide-like activity, with 4.76-fold higher specificity activity (SA) than that of Fe3O4 nanozymes. Then, a glucose oxidase (GOx)-Fe3O4@MIL-100(Fe) cascade reaction was developed for colorimetric detection via an aptamer to facilitate the formation of Fe3O4@MIL-100(Fe)/S. Typhimurium/carboxylated g-C3N4 (CCN)-GOx sandwich complexes. Meanwhile, the fluorescence mode was achieved by measuring the fluorescence intensity of the sandwich complexes. In optimum conditions, the dual-mode detection limits (LOD) were 1.8 CFU/mL (colorimetric mode) and 1.2 CFU/mL (fluorescence mode), respectively, with the S. Typhimurium concentration ranging from 10 CFU/mL to 107 CFU/mL. Finally, the feasibility of the dual-mode colorimetric-fluorescence method was validated via three actual samples, yielding recovery rates of 77.32 % to91.17 % and 82.17 % to 103.7 %, respectively.

SIGNIFICANCE AND NOVELTY

This study successfully develops a composite nanozyme-based cascade colorimetric and fluorescence dual-mode aptasensor for S. Typhimurium detection. It presents several distinct benefits, such as a broader linear range (10-107 CFU/mL), a lower LOD value (1.2 CFU/mL), and more accurate results. More importantly, the proposed dual-mode method displays a low LOD in colorimetric mode, demonstrating considerable potential for S. Typhimurium on-site detection in food.

The potential of MXene-based materials in fluorescence-based sensing/biosensing of ionic and organic contaminants in environment and food samples: Recent advancements and challenges

MXenes belong to one of the recently developed advanced materials with tremendous potential for diverse sensing applications. To date, various types of MXene-based materials have been developed to generate direct/indirect ultrasensitive sensing signals against various forms of analytes via fluorescence quenching or enhancement. In this work, the fluorescence sensing/biosensing capabilities of the MXene-based materials have been explored and evaluated against a list of ionic/emerging pollutants in environment and food matrices. The suitability of an MXene-based sensing approach is also validated through the assessment of the performance based on the basic quality assurance parameters, e.g., limit of detection (LOD), sensing range, and response time. Accordingly, the best performing MXene-based materials are selected and recommended for the given target(s) to help facilitate their scalable applications under real-world conditions.

Overview of the Design and Application of Dual-Signal Immunoassays

Immunoassays have been widely used for the determination of various analytes in the fields of disease diagnosis, food safety, and environmental monitoring. Dual-signal immunoassays are now advanced and integrated detection technologies with excellent self-correction and self-validation capabilities. In this work, we summarize the recent advances in the development of optical and electrochemical dual-signal immunoassays, including colorimetric, fluorescence, surface-enhanced Raman spectroscopy (SERS), electrochemical, electrochemiluminescence, and photoelectrochemical methods. This review particularly emphasizes the working principle of diverse dual-signal immunoassays and the utilization of dual-functional molecules and nanomaterials. It also outlines the challenges and prospects of future research on dual-signal immunoassays.

Decentralized food safety and authentication on cellulose paper-based analytical platform: A review

Food safety and authenticity analysis play a pivotal role in guaranteeing food quality, safeguarding public health, and upholding consumer trust. In recent years, significant social progress has presented fresh challenges in the realm of food analysis, underscoring the imperative requirement to devise innovative and expedient approaches for conducting on-site assessments. Consequently, cellulose paper-based devices (PADs) have come into the spotlight due to their characteristics of microchannels and inherent capillary action. This review summarizes the recent advances in cellulose PADs in various food products, comprising various fabrication strategies, detection methods such as mass spectrometry and multi-mode detection, sampling and processing considerations, as well as applications in screening food safety factors and assessing food authenticity developed in the past 3 years. According to the above studies, cellulose PADs face challenges such as limited sample processing, inadequate multiplexing capabilities, and the requirement for workflow integration, while emerging innovations, comprising the use of simplified sample pretreatment techniques, the integration of advanced nanomaterials, and advanced instruments such as portable mass spectrometer and the innovation of multimodal detection methods, offer potential solutions and are highlighted as promising directions. This review underscores the significant potential of cellulose PADs in facilitating decentralized, cost-effective, and simplified testing methodologies to maintain food safety standards. With the progression of interdisciplinary research, cellulose PADs are expected to become essential platforms for on-site food safety and authentication analysis, thereby significantly enhancing global food safety for consumers.

Bifunctional MOF-Encapsulated Cobalt-Doped Carbon Dots Nanozyme-Powered Chemiluminescence/Fluorescence Dual-Mode Detection of Aflatoxin B1

A novel bifunctional MOF-encapsulated cobalt-doped carbon dots nanozyme (Co-CD/PMOF) with excellent peroxidase-mimic catalytic activity and fluorescence property was synthesized and employed to fabricate a chemiluminescence/fluorescence (CL/FL) dual-mode immunosensor for AFB1 detection. Co-CD/PMOF could catalyze the luminol/H2O2 system to generate robust and long-lasting CL signals due to the slow diffusion effect and continuous generation of •OH, O2•-, and 1O2 species. Differing from traditional flash-type CL emissions, this glow-type CL emission is helpful to fabricate a sensitive and accurate CL sensing platform. Then the CL/FL dual-mode detection of AFB1 was developed using antibody-functionalized Co-CD/PMOF as the signal-amplifying nanoprobe. The CL mode assay based on indirect competitive immune principle was carried out on a chemiluminescence optical fiber platform, where the AFB1-OVA-functionalized optical fiber probe was employed for biorecognition, separation, and signal conducting. The AFB1 detection range and LOD were 0.63-69.36 ng/mL and 0.217 ng/mL, respectively. Using AFB1 antibody-functionalized immunomagnetic beads for capturing and separation, the FL mode detection of AFB1 was established based on the sandwich immune principle. A linear range of 0.54-51.91 ng/mL and a LOD of 0.027 ng/mL were obtained. This work designed a sensitive, rapid, and reliable nanozyme-powered dual-mode assay strategy and provided technical support in the field of environmental monitoring and food safety.

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the "State-of-the-Art and Gaps Extracted from Results and Discussions" section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the "Literature Review Guided by Artificial Intelligence" section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.