A Fluorescent Biosensor for Sensitive Detection of Salmonella Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network

The Application of Multi-Parameter Multi-Modal Technology Integrating Biological Sensors and Artificial Intelligence in the Rapid Detection of Food Contaminants

Against the backdrop of continuous socio-economic development, there is a growing concern among people about food quality and safety. Individuals are increasingly realizing the critical importance of healthy eating for bodily health; hence the continuous rise in demand for detecting food pollution. Simultaneously, the rapid expansion of global food trade has made people's pursuit of high-quality food more urgent. However, traditional methods of food analysis have certain limitations, mainly manifested in the high degree of reliance on personal subjective judgment for assessing food quality. In this context, the emergence of artificial intelligence and biosensors has provided new possibilities for the evaluation of food quality. This paper proposes a comprehensive approach that involves aggregating data relevant to food quality indices and developing corresponding evaluation models to highlight the effectiveness and comprehensiveness of artificial intelligence and biosensors in food quality evaluation. The potential prospects and challenges of this method in the field of food safety are comprehensively discussed, aiming to provide valuable references for future research and practice.

A novel nanoparticle-based fluorescent sandwich immunoassay for specific detection of Salmonella Typhimurium

The diseases caused by foodborne pathogens have a serious impact on human health and social stability. Conventional detection methods can involve long assay times and complex pretreatment steps, making them unsuitable for rapid, large-scale analysis of food samples. We constructed a novel nano-fluorescence sandwich immunosorbent immunoassay (nano-FSIA) to rapidly detect Salmonella Typhimurium in food, based on strong covalent binding between streptavidin and biotin. We used antibodies coupled to large particle-size fluorescent microspheres as fluorescent probes for direct quantitative analysis of S. typhimurium in milk. The optimized parameters were determined, and specificity and sensitivity were validated in phosphate-buffered saline (PBS) and milk. The results demonstrated a wide dynamic detection range for S. typhimurium (103-108 colony forming units [CFU]/mL), with the limit of detection in PBS and milk at 234 and 346 CFU/mL, respectively. The results of nano-FSIA were consistent with those of plate counts and enzyme-linked immunosorbent assays, providing an effective and promising single-bacterium counting method for the rapid detection of Salmonella.

A low-background and wash-free signal amplification F-CRISPR biosensor for sensitive quantitative and visible qualitative detection of Salmonella Typhimurium

In traditional CRISPR-based biosensors, the cleavage-induced signal generation is insufficient because only a signals is generated at a CRISPR-induced cleavage. Herein, we developed an improved CRISPR/Cas12a-based biosensor with an enlarged signal generation which integrated the hybridization chain reaction (HCR) and low-background Förster Resonance Energy Transfer (FRET) signal output mode. The HCR with nucleic acid self-assembly capability was used as a signal carrier to load more signaling molecules. To get the best signal amplification, three different fluorescence signal output modes (fluorescence recovery, FRET and low-background FRET) generated by two fluoresceins, FAM and Cy5, were fully investigated and compared. The results indicated that the low-background FRET signal output mode with the strictest signal generation conditions yielded the highest signal-to-noise ratio (S/N) (19.17) and the most obvious fluorescence color change (from red to yellow). In optimal conditions, the proposed biosensor was successfully applied for Salmonella Typhimurium (S. Typhimurium) detection with 6 h (including 4 h for sample pre-treatment) from the initial target processing to the final detection result. The qualitative sensitivity, reliant on color changes, was 103 CFU/mL. The quantitative sensitivity, calculated by the fluorescence value, were 1.62 × 101 CFU/mL, 3.72 × 102 CFU/mL, and 8.71 × 102 CFU/mL in buffer solution, S. Typhimurium-spiked milk samples, and S.Typhimurium-spiked chicken samples, respectively. The excellent detection performance of the proposed biosensor endowed its great application potential in food and environment safety monitoring.

Machine learning assisted biosensing technology: An emerging powerful tool for improving the intelligence of food safety detection

Recently, the application of biosensors in food safety assessment has gained considerable research attention. Nevertheless, the evaluation of biosensors' sensitivity, accuracy, and efficiency is still ongoing. The advent of machine learning has enhanced the application of biosensors in food security assessment, yielding improved results. Machine learning has been preliminarily applied in combination with different biosensors in food safety assessment, with positive results. This review offers a comprehensive summary of the diverse machine learning methods employed in biosensors for food safety. Initially, the primary machine learning methods were outlined, and the integrated application of biosensors and machine learning in food safety was thoroughly examined. Lastly, the challenges and limitations of machine learning and biosensors in the realm of food safety were underscored, and potential solutions were explored. The review's findings demonstrated that algorithms grounded in machine learning can aid in the early detection of food safety issues. Furthermore, preliminary research suggests that biosensors could be optimized through machine learning for real-time, multifaceted analyses of food safety variables and their interactions. The potential of machine learning and biosensors in real-time monitoring of food quality has been discussed.

Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0

Food safety is closely related to human health. However, the regulation and testing processes for food safety are intricate and resource-intensive. Therefore, it is necessary to address food safety risks using a combination of deep learning, the Internet of Things, smartphones, quick response codes, smart packaging, and other smart technologies. Intelligent designs that combine digital systems and advanced functionalities with biosensors hold great promise for revolutionizing current food safety practices. This review introduces the concept of Food Safety 4.0, and discusses the impact of intelligent biosensors, which offer attractive smarter solutions, including real-time monitoring, predictive analytics, enhanced traceability, and consumer empowerment, helping improve risk management and ensure the highest standards of food safety.

Development of a Dual Mode UCNPs-MB Biosensor in Combination with PCR for Sensitive Detection of Salmonella

In recent years, the high prevalence of Salmonella has emerged as a serious threat to public safety, prompting attempts to utilize accurate, rapid, and direct methods to ensure food safety. In this study, a multifunctional platform featuring dual-mode detection channels (colorimetric-fluorescence) combined with polymer chain reaction (PCR) was proposed for the sensitive and rapid detection of Salmonella. Additionally, the colorimetric measurements were achieved by color changes induced by methylene blue (MB) insertion into the double-stranded DNA, and the fluorescence measurements were performed by internal filter effect (IFE)-induced fluorescence quenching of upconversion nanoparticles (UCNPs) by MB. The results showed that the IFE and PCR amplification processes improved the sensitivity of the sensor towards Salmonella detection, with a limit of detection (LOD) of 21.8 CFU/mL. Moreover, this colorimetric-fluorescence dual-mode PCR biosensor was applied to determine Salmonella in food samples, such as chicken, egg, and fish, which produced satisfactory results. Overall, the present study results demonstrate the potential for combining PCR amplification with IFE to develop an efficient and reliable dual-mode analysis platform to safeguard food security.

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.