Development and perspectives of rapid detection technology in food and environment

A ratiometric fluorescent "off-on" sensor for acrylamide detection in toast based on red-emitting copper nanoclusters stabilized by bovine serum albumin

Acrylamide, as a Class 2A carcinogen, poses serious threats to human health. To achieve rapid and accurate determination of acrylamide in food, a ratiometric fluorescent "off-on" sensor was designed by incorporating red-emitting copper nanoclusters and glutathione. Copper nanoclusters with bimodal emission at 395 nm and 650 nm (excited at 310 nm) were synthesized by using bovine serum albumin as the ligand and ascorbic acid as the reductant. With glutathione addition, the fluorescence intensity at 650 nm gradually decreased, while the case at 395 nm slightly increased. The quenched fluorescence at 650 nm was subsequently restored by acrylamide through thiol-ene Michael addition reaction between acrylamide and glutathione. The constructed sensor showed excellent performance towards acrylamide detection in the range of 5-300 μM with a detection limit of 1.48 μM, and was further applied to real-sample detection of acrylamide in toast and exhibited good recoveries (90.29-101.30 %), indicating potential applications of this sensor.

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.

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

One-step fabrication of flexible polyamide@Ag-dodecanethiol membranes for highly sensitive SERS detection of thiram

The surface-enhanced Raman scattering (SERS) is an effective spectral technology based on Raman scattering, but in practice, the commonly used SERS substrates suffer from low sensitivity and poor stability. In order to overcome these limitations, the SERS substrates were prepared from hydrophobic modification of dodecanethiol (C12) coupled with a flexible substrate, which was then used for pesticides detection in water. A flexible PA@Ag-C12 substrate with surface functionalization has been obtained. This work aims to investigate the self-assembly of Ag NPs modified with C12 onto polyamide (PA) membranes. Initially, transmission electron microscopy and scanning electron microscopy were used to analyze the substrate's morphology. Then with the help of an energy-dispersive spectrometer, sulfur content of C12-modified Ag NPs was analyzed. In order to determine the hydrophobicity of the modified Ag NPs, the contact angle was used. The results indicate that the gap between Ag NPs on PA membrane can be effectively controlled in order to prevent Ag NPs from aggregating. Furthermore, the finite-difference time-domain analysis indicated that the PA@Ag-C12 substrate exhibited a stronger electromagnetic enhancement effect than the PA@Ag substrate. By reducing NPs gaps on the PA membrane, the number of 'hot spots' increased, and the SERS performance of the substrate was improved as a result. According to the results of this study, this method can greatly reduce the manufacturing costs and time costs of the SERS substrate while maintaining the original uniformity. The SERS performance of PA@Ag-C12 was found to be three orders of magnitude better than that of PA@Ag direct self-assembled substrate, and the detection limit for Rhodamine 6G (R6G) was approximately 8.47 × 10-14M. On the basis of the PA@Ag-C12 substrate, thiram is detectable at a detection limit of 5.88 × 10-11M with a high degree of sensitivity and repeatability.

Smart and Multifunctional Nanomaterials and Applications for Food Safety

Due to growing concerns about food safety and public health, the contaminants or residues of various harmful substances in food have received much attention in recent years [...].

Development of nucleotide signatures for common poisonous organisms provides a new strategy for food poisoning diagnosis

DNA barcoding is widely used in toxic species authentication, but due to serious DNA degradation of forensic materials, the application of full-length barcode sequences in food poisoning diagnosis is greatly limited. Nucleotide signature, a shorter specific molecular marker, derived from traditional DNA barcoding has been proposed as an emerging tool of toxic species detection in deeply processed materials. In this study, to resolve the frequent food poisoning accidents with unknown origin, we envisioned developing a nucleotide signature data set of common poisonous organisms and combining high-throughput sequencing (HTS) to reveal the poisoning cause. Ninety-three individuals and 1093 DNA barcode sequences of twelve common poisonous plants, fish, mushrooms and their related species were collected. Through sequence alignment and screening, the nucleotide signatures were respectively developed and validated as their specific molecular markers. The sequence length varied from 19 bp to 38 bp. These fragments were conserved within the same species or genera, and the specificity between related species has been also demonstrated. To further evaluate the application potential of nucleotide signature in forensic diagnosis, simulated forensic specimens (SFS) containing different poisonous ingredients were sequenced by HTS with PCR-free libraries. As a result, the nucleotide signature was successfully captured from original HTS data without assembly and annotation, accompanied by a high detection sensitivity of 0.1 ng/µl in mixture system. Therefore, this method was suitable for the assay of forensic materials with serious DNA degradation. The present study undoubtedly provides a new perspective and strong support for the detection of toxic ingredients and the diagnosis of food poisoning.

Exploitation of In Vivo-Emulated In Vitro System in Advanced Food Science

Reasonable model construction contributes to the accuracy of experimental results. Multiple in vivo models offer reliable choices for effective evaluation, whereas their applications are hampered due to adverse features including high time-consumption, high cost and ethical contradictions. In vivo-emulated in vitro systems (IVE systems) have experienced rapid development and have been brought into food science for about two decades. IVE systems' flexibly gathers the strengths of in vitro and in vivo models into one, reflecting the results in an efficient, systematic and interacted manner. In this review, we comprehensively reviewed the current research progress of IVE systems based on the literature published in the recent two decades. By categorizing the IVE systems into 2D coculture models, spheroids and organoids, their applications were systematically summarized and typically exemplified. The pros and cons of IVE systems were also thoroughly discussed, drawing attention to present challenges and inspiring potential orientation and future perspectives. The wide applicability and multiple possibilities suggest IVE systems as an effective and persuasive platform in the future of advanced food science.

N, N'-methylene bisacrylamide/divinyl benzene based-highly cross-linked hybrid monolithic column: Production and its applications for powerful capture of four chlorophenols

In this paper, the role of the halogen bond in capillary monolithic column microextraction was explored for the first time. Benzene-1,3,5-tricarbohydrazide (BTH) was synthesized as a functional monomer, N, N'-methylene bisacrylamide (MBA) and divinyl benzene (DVB) were used as cross-linking agents, the hybrid monolithic column of poly (BTH-co-DVB-co-MBA) was prepared using methanol and polyethylene glycol as pore-forming agents and azodiisobutyronitrile as the initiator. Due to the existence of BTH, a large number of nitrogen atoms (Lewis base) were introduced into the monolithic column, which could form a halogen bond with chlorine-containing organic pollutants and enhance its adsorption performance. Based on the monolithic column, a sensitive and environment-friendly solid-phase microextraction technology was studied. The monolithic column was integrated with high-performance liquid chromatography (HPLC) to extract and detect four kinds of chlorophenol in real water samples. Under best conditions, the method showed excellent extraction ability and linearity, with a linear correlation coefficient of 0.9958-0.9987, a low detection limit (LOD) of 0.04-0.23 μg L^-1 (S/N = 3), and relative standard deviation (RSD) less than 3.09%. The recovery rate was kept between 87.30% and 123.00%, and the RSD was less than 3.83%, which indicated that the column had powerful capture performance, high precision, and strong anti-matrix interference ability in the real sample, and had potential application value in practical work.

Magnetic microbead enzyme-linked immunoassay based on phage encoded protein RBP 41-mediated for rapid and sensitive detection of Salmonella in food matrices

Rapid and sensitive quantitative detection methods are required to monitor and detect Salmonella throughout the food supply chain and early prevention of foodborne disease outbreaks. In this study, a magnetic microbead enzyme-linked immunoassay (MELISA) based on phage receptor binding protein was developed for rapid enrichment and detection of Salmonella in complex food matrices. RBP 41 from phage T102 acted as a species-specific recognition element for Salmonella by exploiting its strong binding capacity to Salmonella surface receptors. RBP 41-MBs were prepared by coupling recombinant RBP 41 with MBs and used to separate and enrich Salmonella cells from spiked food samples. The captured complexes were further integrated with ELISA procedures by HRP-labeled anti-Salmonella antibody for rapid and accurate detection of Salmonella. The whole method took <1.5 h and the detection limit was 10 CFU/mL. Therefore, MELISA was successfully developed for the detection of Salmonella in various spiked food samples (skim milk, lettuce, and chicken breast). The ELISA reaction process of this method was carried out on magnetic beads. It simplified the process of the traditional ELISA method and reduces the reaction time. This study expanded the use of phage-associated proteins and demonstrated the promising prospects for practical applications in the detection of foodborne pathogens.

Research progress on detection techniques for point-of-care testing of foodborne pathogens

The global burden of foodborne disease is enormous and foodborne pathogens are the leading cause of human illnesses. The detection of foodborne pathogenic bacteria has become a research hotspot in recent years. Rapid detection methods based on immunoassay, molecular biology, microfluidic chip, metabolism, biosensor, and mass spectrometry have developed rapidly and become the main methods for the detection of foodborne pathogens. This study reviewed a variety of rapid detection methods in recent years. The research advances are introduced based on the above technical methods for the rapid detection of foodborne pathogenic bacteria. The study also discusses the limitations of existing methods and their advantages and future development direction, to form an overall understanding of the detection methods, and for point-of-care testing (POCT) applications to accurately and rapidly diagnose and control diseases.

CRISPR-Cas-based detection for food safety problems: Current status, challenges, and opportunities

Food safety is one of the biggest public issues occurring around the world. Microbiological, chemical, and physical hazards can lead to food safety issues, which may occur at all stages of the supply chain. In order to tackle food safety issues and safeguard consumer health, rapid, accurate, specific, and field-deployable detection methods meeting diverse requirements are one of the imperative measures for food safety assurance. CRISPR-Cas system, a newly emerging technology, has been successfully repurposed in biosensing and has demonstrated huge potential to establish conceptually novel detection methods with high sensitivity and specificity. This review focuses on CRISPR-Cas-based detection and its current status and huge potential specifically for food safety inspection. We firstly illustrate the pending problems in food safety and summarize the popular detection methods. We then describe the potential applications of CRISPR-Cas-based detection in food safety inspection. Finally, the challenges and futuristic opportunities are proposed and discussed. Generally speaking, the current food safety detection methods are still unsatisfactory in some ways such as being time-consuming, displaying unmet sensitivity and specificity standards, and there is a comparative paucity of multiplexed testing and POCT. Recent studies have shown that CRISPR-Cas-based biosensing is an innovative and fast-expanding technology, which could make up for the shortcomings of the existing methods or even replace them. To sum up, the implementation of CRISPR-Cas and the integration of CRISPR-Cas with other techniques is promising and desirable, which is expected to provide "customized" and "smart" detection methods for food safety inspection in the coming future.