Rapid Detection Technology Research Articles

Nondestructive TVB‐N Detection in Packaged Beef Using Hyperspectral Imaging

ABSTRACTHyperspectral imaging (HSI) technology has shown potential in monitoring fresh meat. It is necessary to investigate the rapid detection technology of packaged fresh meat in order to ensure food quality and safety. In this study, hyperspectral images of beef in the presence of packaging film were collected, and the spectra were optimized using spectral pretreatment, band selection and film radiation correction methods to predict the total volatile basic nitrogen (TVB‐N) of beef in the presence of packaging film. The results of the competitive adaptive reweighted sampling (CARS) combined with least squares support vector machines (CARS‐LSSVM) model constructed by using the corrected reflectance spectra were = 0.8687, RMSEP = 3.1970 mg/100 g, which is higher than that obtained before the correction ( was 0.8168, and RMSEP was 3.0449 mg/100 g). The results reveal that HSI with radiation correction has the potential in monitoring the TVB‐N content of beef through packaging film.

Microfluidic biosensors for rapid detection of foodborne pathogenic bacteria: recent advances and future perspectives

Rapid detection of foodborne pathogenic bacteria is critical for ensuring food safety and preventing foodborne disease outbreaks. Traditional detection methods, while accurate, are often time-consuming and labor-intensive, making rapid detection technologies a pressing need. Microfluidic biosensors have emerged as a powerful solution, offering high sensitivity, specificity, and rapid analysis with minimal sample volume. In this review, we summarize recent advances in microfluidic biosensor technology, highlighting innovations in detection techniques such as electrochemical and optical microfluidic biosensors. We have also introduced microfluidic components, which are crucial for the implementation of microfluidic biosensors. Based on the current state of this technology development, we finally provide several most important recommendations for future research directions in this emerging research area, which may enable widespread commercialization and adoption in the food industry.

Development of a rapid detection technology system for noxious plants based on a novel isothermal amplification technique.

Noxious plants pose a significant threat to human and livestock health, as well as to the safety of agricultural and forestry ecosystems. Accurate and rapid identification of these plants is crucial for risk prevention. This paper explores for the first time the development and application of a rapid detection technology for noxious plants based on a novel isothermal amplification technique. We targeted the seeds, leaves, and grain impurities of four major noxious weeds: Amaranthus palmeri, the A.tuberculatus complex, Rhaponticum repens, and Euphrosyne xanthiifolia, we designed and screened primers and probes suitable for this isothermal amplification method, determined their limit of detection, optimized the genomic DNA extraction methods, and verified the method. We developed genomic DNA extraction methods for single tissue components of plant seeds and leaves, as well as for mixed tissue components. Ultimately, we established standardized detection protocols for different tissue forms of each species, significantly enhancing detection efficiency. This study enables the detection positive samples in seeds or leaves within 10 to 15 minutes and positive samples from mixtures within 12 to 18 minutes. The entire process, from sample collection, genomic DNA preparation to reaction completion, takes approximately 35 minutes. This detection technology, which marks the first development of an isothermal amplification-based method for noxious plants, meets the needs for on-site rapid testing, aiding in the timely identification of risks and the implementation of corresponding prevention and control measures.

A Simple and Sensitive RT-qPCR Technology for Rapid Detection of Porcine Reproductive and Respiratory Syndrome Virus.

To establish a rapid and sensitive detection method for the porcine reproductive and respiratory syndrome virus (PRRSV), gene-specific primers and a TaqMan probe were designed based on the M gene of PRRSV, and a new stable fully pre-mixed reverse transcription real-time fluorescence quantitative PCR (RT-qPCR) reaction mixture was developed. A simple and rapid RT-qPCR detection method for PRRSV was developed by optimizing nucleic acid amplification conditions. The results showed that the method was able to specifically detect PRRSV without cross-reactivity with the other 11 porcine susceptible viruses. The sensitivities of the assay were 3.12 × 100 copies/μL and 100 TCID50/μL for M gene and virus, respectively, and the repeatability and reproducibility (relative standard deviation, CV) of the assay were less than 2.5%. Based on the new fullly pre-mixed RT-qPCR reaction mixture, the RT-qPCR detection method may provide a new, simple, and rapid method for accurately detecting PRRSV.

Resilient sustainable current and emerging technologies for foodborne pathogen detection.

Foodborne pathogens such as Salmonella, Escherichia coli and Listeria pose significant risks to human health. The World Health Organization estimates that 2.2 million deaths per year are directly caused by foodborne and waterborne bacterial diseases worldwide. Accordingly, detecting pathogens in food is essential to ensure that our food is safe. This review explores the critical role of novel technologies in enhancing food safety practices whilst delving into adopting and integrating innovative, resilient and sustainable approaches in the food supply chain. Further, applying novel, emerging advanced analytical techniques such as Raman spectroscopy and nanotechnology based biosensors in food contamination detection is discussed. These advanced technologies show the promise of real-time monitoring, traceability, and predictive analytics to identify and mitigate potential hazards before they reach consumers. They can provide rapid and accurate results and ensure the integrity of food products. Furthermore, the herein-highlighted synergistic integration of these technologies offers a promising path toward a safer and more transparent food system, thereby addressing the challenges of today's globalised food market and laying the platform for developing multimodal technologies for affordable, sensitive and rapid pathogen detection along the different stages of the food chain, from "farm to fork".

Advancements in electrochemical sensingTechnology for Heavy Metal Ions Detection.

Most heavy metal ions are carcinogenic and non-biodegradable, posing threats to ecological balance and human health in trace amount. Therefore, there is a pressing demand for rapid and dependable detection technologies. Electrochemical sensing technology distinguishes itself with its ease of use and swiftness, rendering it perfect for the expeditious detection of heavy metal elements. This review examines various electrochemical detection techniques for on-site real-time monitoring of heavy metal ions. Advanced methods using innovative electrochemical sensor technologies are explored, highlighting the importance of sensing strategies for the quick and easy monitoring of metal levels in different environments. Additionally, the role of nanotechnology and electrochemical techniques in enhancing the sensitivity and selectivity of sensors for better detection of heavy metals is discussed. Finally, the future direction of sensor development is addressed, focusing on integrating new materials and technologies to improve the performance of sensor in environmental monitoring, food safety and public health.

A rapid household mite detection and classification technology based on artificial intelligence-enhanced scanned images

A rapid household mite detection and classification technology based on artificial intelligence-enhanced scanned images

Recent developments in isothermal amplification technology for rapid detection of SARS-CoV-2.

Coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally, posing a significant threat to human health. Rapid and accurate detection of infectious disease pathogens is of crucial practical significance for early screening, timely intervention, and outbreak prevention. However, conventional diagnostic methods are increasingly unable to meet clinical demands. Recently developed isothermal analysis methods offer mild reaction conditions and reduce dependence on specialized instruments. These convenient, fast, and reliable methods show great promise for diagnosing infectious pathogens, especially for on-site detection in areas without laboratories or with limited resources. Among them, loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA), which integrate various widely used detection techniques, stand out as rapidly advancing and relatively mature isothermal nucleic acid amplification technologies. This review outlines several representative isothermal amplification technologies and associated detection methods. We summarize the latest advancements in LAMP and RPA technologies for the rapid detection of SARS-CoV-2 and discuss the future prospects of isothermal amplification in diversified testing.

Defective UIO66 metal-organic framework nanoparticles assisted by cascade isothermal amplification technology for the detection of aflatoxin B1.

Aflatoxin B1 (AFB1) exhibits significant toxicity and pose a serious threat to food safety, environmental hygiene, and public health even in trace amounts. Hence, the development of a rapid, accurate, and sensitive detection technology has become a pivotal aspect of ensuring control standards. In this study, we introduce the UIO66 and two defective dichloroacetic acid@UIO66 (DCA@UIO66, DU) metal-organic framework nanoparticles, named DU1 and DU2, characterized by different defect levels. It is noteworthy that DU1 exhibits superior DNA sensing capability compared to UIO66 and DU2. With a fluorescence quenching efficiency of 92.66% and a recovery efficiency of 1256.75%, DU1 demonstrates the substantial potential in the detection field. Furthermore, we employ cascade isothermal amplification to assist DU1-mediated fluorescence sensors in detecting AFB1. AFB1 is efficiently identified through an aptamer competition process facilitated by magnetic nanoparticles, which initiates the exponential amplification triggered rolling circle amplification reaction, and converts trace amounts of toxin signal into a large number of long single-stranded DNA molecules. Upon recognition of the amplification product by the fluorescent probe on DU1, a more stable double-stranded DNA is formed and leaves the surface of DU1, leading to a significant change in fluorescence intensity. This method exhibits acceptable sensitivity, with a detection limit of 0.09pgmL-1 and a wide detection range spanning from 0.2pgmL-1 to 20pgmL-1. Additionally, this assay exhibits satisfactory specificity and high accuracy in practical sample applications. Our proposed method offers a solid theoretical framework and technical backing, thereby facilitating the establishment of a new generation of mycotoxin detection standards.

Au Ordered Array Substrate for Rapid Detection and Precise Identification of Etomidate in E-Liquid Through Surface-Enhanced Raman Spectroscopy.

Etomidate (ET), a medical anesthetic, is increasingly being incorporated into e-liquids for consumption and abuse as a new psychoactive substance (NPS), leading to significant social issues. In this work, large-area Au micro- and nano-structured ordered arrays were engineered as surface-enhanced Raman spectroscopy (SERS) substrates for fast detection and precise identification of ET and its metabolites. This ordered array, characterized by abundant electromagnetic enhancement hotspots and structural uniformity, imparts unique properties to the SERS substrate, including ultra-sensitivity, spectral signal reproducibility, and precise quantitative capabilities. Furthermore, it effectively mitigates interference from the complex matrix of e-liquids, facilitating the rapid detection of trace amounts of ET molecules. This SERS rapid detection technology can act as a preliminary screening method for gold-standard spectroscopic analysis, facilitating the on-site rapid screening of suspicious samples and thereby enabling efficient detection and precise verification.

PORTECT: Porcine detection kit using CRISPR-CAS method as new innovation for rapid and accurate halal detection

The PORTECT Porcine Detection Kit is an innovative solution leveraging CRISPR-Cas technology for rapid and accurate detection of porcine DNA in food products, addressing the growing demand for halal certification. This system uses a guide RNA (gRNA) designed specifically to recognize and target porcine DNA sequences, triggering Cas12 enzymes to cleave the DNA and a reporter compound for a visual color change. This process allows for precise, fast, and efficient detection of non-halal substances. Unlike traditional methods like PCR and ELISA, which require complex procedures, the PORTECT kit offers a more accessible and time-saving approach to halal verification, ensuring consumer trust and regulatory compliance. The technology is developed with bioinformatics tools and produces highly specific gRNA to target the porcine gene. The kit demonstrates significant potential in both the halal food industry and the general market for DNA detection tools, but future developments aim to lower production costs and expand detection capabilities beyond porcine DNA.

Rapid non-destructive detection technology for traditional Chinese medicine preparations based on machine learning: a review

In recent years, with the increasing societal focus on drug quality and safety, quality issues have become a major challenge faced by the pharmaceutical industry, directly impacting consumer health and market trust. By combining multispectral imaging technology with machine learning, it is possible to achieve rapid, non-destructive, and precise detection of traditional Chinese medicine(TCM) preparations, thereby revolutionizing traditional detection methods and developing more convenient and automated solutions. This paper provides a comprehensive review of the current applications of rapid, non-destructive detection techniques based on machine learning algorithms in the field of TCM preparations. It analyzed the principles and advantages of commonly used rapid, non-destructive detection techniques, offering a reference for the application and promotion of these technologies in TCM preparation detection. Additionally, this paper explored various data preprocessing techniques, operational processes, and machine learning algorithms to enhance data utilization efficiency. Finally, it focused on the challenges of applying machine learning in TCM preparation detection and offered corresponding recommendations, providing guidance for the future integration of machine learning with rapid, non-destructive detection techniques in practical production.

Research on infrared spectral characteristics and rapid detection technology of industrial hemp

Objective: The aim of this study was to research the infrared spectroscopic features of various industrial hemp samples, establish a rapid infrared detection method for industrial hemp based on cannabidiol (CBD) content, and utilize infrared spectroscopy to differentiate industrial hemp flowers and leaves from different varieties and origins. Methods: Appropriate preprocessing was conducted on the industrial hemp samples based on their inherent characteristics. The samples were then scanned using the FTIR spectrometer, with careful subtraction of the background spectrum. A semi-quantitative rapid detection method for flowers and leaves using infrared spectroscopy was developed through partial least squares discriminant analysis. Data processing software was used to conduct principal component analysis and cluster analysis on the transmittance values of infrared characteristic absorption peaks, aiding in the identification and differentiation of samples from different varieties, origins, and harvest dates. Results: A semi-quantitative rapid detection method for industrial hemp flowers and leaves based on CBD content was successfully developed. Principal component analysis was able to clearly distinguish industrial hemp of different varieties and origins, effectively clustering samples of the same variety. This underscores the significant impact of mid-infrared spectroscopy in the identification and analysis of industrial hemp, providing an innovative approach to quickly identifying different hemp varieties.

Rapid detection of poultry meat quality using S-band to KU-band radio-frequency waves combined with machine learning-A proof of concept.

Rapid changes in consumer preferences for high-quality animal-based protein have driven the poultry industry to identify non-invasive, in-line processing technologies for rapid detection of muscle meat quality defects. At production plants, technologies like radio-frequency waves (RF waves) can identify and separate myopathy-conditioned meat, reducing misclassification errors due to human fatigue and inexperience. Previous studies have shown that advanced diagnostic tools combined with complex data analytics, such as support vector machines (SVMs) and backpropagation neural networks (BPNNs), can classify chicken breast myopathies post-deboning. This study demonstrates RF wave use for myopathy detection at four processing stages. Using 107 (48-day old) broilers, RF wave data in amplitude and phase were collected from live birds, pre-chilled without giblets (WOGs), post-chilled WOGs, and freshly deboned fillets (3-3.5h post-slaughter) and examined by hand-palpation for woody breast categories (1-normal; 2-moderate; 3-severe). Data preprocessing involved false discovery rate and predictor analysis to identify specific signature frequencies and develop classification models using supervised machine learning (ML) algorithms. Variable clustering analysis identified seven to eight different frequencies at various processing stages. Preprocessed data with identified signature frequencies were used to develop classification models using BPNN and SVM. BPNN demonstrated superior classification accuracy compared to SVM, with accuracy ranges from 90.0% to 96.1% for live birds, 78.9% to 97.1% for pre-chilled WOGs, 82.1% to 95.9% for post-chilled WOGs, and 94.2% to 98.2% for deboned fillets. Integrating specific RF range devices or sensors with supervised ML algorithms like SVM and BPNN in poultry processing can effectively detect muscle myopathies at different processing steps during in-line processing.

Biosensor‐Based Microfluidic Platforms for Rapid Clinical Detection of Pathogenic Bacteria

AbstractPathogenic bacteria are commonly found in food, water, and soil, posing significant public health challenges globally. Therefore, early, rapid, and highly sensitive strategies for monitoring the bacterial proliferation are crucial for ensuring public health, medical diagnosis, and food safety. Compared to traditional techniques, microfluidic platforms provide powerful detective tools characterized by high integration, high throughput, ease of operation, low reagent consumption, and high sensitivity. Driven by substantial commercial demand, research and development in microfluidic‐based rapid detection methods and technologies has progressed significantly derived by the interdisciplinary integration of multiple disciplines. In this review, progress in clinical detection of pathogenic bacteria with microfluidic biosensors, including microfluidic devices for point‐of‐care (POC) testing, is summarized. Strategies for pathogenic bacteria detection, containing their advantages and disadvantages are discussed in detail. Advanced platforms for capturing and detecting pathogenic bacteria, such as microchannels, microarrays, digital microfluidics (DMF) and paper‐based platforms, are highlighted. The accomplishments and shortcomings of these microfluidic devices are also summarized. Additionally, case studies of biosensor‑based microfluidic devices used for detecting diseases caused by bacterial imbalances are listed. Finally, possible research perspectives for further development in highly effective biosensor‑based microfluidics for clinical detection of pathogenic bacteria are proposed.

Highly sensitive quad-ray shaped polarization insensitive THz meta-biosensor

Abstract Metasurface-based sensors are now becoming crucial for label-free and rapid-detection technologies in biomedical applications, leading to a growing demand for new highly sensitive meta-biosensors. This paper demonstrates a perfectly symmetrical quad-ray X-shaped THz meta-absorber (QRXMA), enabling narrowband and polarization insensitivity performance. The theoretical modelling and performance investigation focuses on its potential for improved refractive index (RI) sensing for multiple biological samples. The simulation outcomes reveal that the proposed optimal QRXMA achieves an absorption rate of 99.98% at 1.3 THz for both TE and TM polarized incidences. Furthermore, we highlight the adjustability of absorptive characteristics of the proposed device by altering the different geometric parameters. These findings underscore the QRXMA potential in RI sensing applications, achieving a sensitivity of approximately 116.8 GHz RIU−1 and a figure of Merit (FoM) of about 2 with an 8 μm thick non-destructive analyte layer. This research makes a significant contribution to the development of highly efficient meta-biosensors with promising applications in THz detection, sensing, and imaging.

Microfluidic colorimetric biosensor for rapid and sensitive detection of Shewanella oneidensis MR‑1

Extracellular respiratory bacteria (ERB) assume a significant role in biological and environmental remediation, so the rapid detection of ERB is essential to monitor its biological repair process. This study developed a microfluidic colorimetric biosensor for rapid and sensitive detection of ERB model strain Shewanella oneidensis MR-1. In the process of separation and enrichment of bacteria based on immune magnetic nanoparticles (MNPs), the biological signal is significantly amplified by the excellent peroxidase activity of flower-like CoOOH (FL-CoOOH). Firstly, immune MNPs, samples and immune FL-CoOOH were mixed and incubated within the microfluidic chip’s channel. Magnetic capture in the separation chamber yielded MNP-bacteria-CoOOH complex. Then, 3, 3′, 5, 5′-tetramethylbenzidine (TMB) substrate was added to make the FL-CoOOH nanozyme catalyze the formation of yellow product. Finally, the target bacteria in the microplate were quantitatively detected. Based on the excellent simulated enzyme activity and stability of FL-CoOOH, the detection can be completed within 45 min after the introduction of the microfluidic chip. The detection was 5×101∼5×106 CFU/mL. Compared with the previously reported colorimetric sensors for MR-1, the sensitivity was increased by 99.54 % to 23 CFU/mL. This study uniquely integrates optical sensors and microfluidic technology for rapid ERB detection with enhanced portability. The design of biosensor can facilitate further investigations into extracellular respiration mechanisms and rapid ecological environment restoration applications.

Development of a colloidal gold immunochromatographic test strip for detecting the smooth Brucella

SummaryBrucellosis, caused by Gram-negative Brucella, spreads in human and animal populations through contact with infected animals and products. Developing a rapid and sensitive detection technology for pathogen is crucial to reduce the risk of this disease transmitting between animal populations and to humans. We produced a monoclonal antibody LPS-6B5, which shows high affinity to LPS and limited cross-reactivity with other bacteria. Based on LPS-6B5, a colloidal gold immunochromatographic assay (GICA) was developed which demonstrates high sensitivity and specificity in detecting cultured B. melitensis, B. abortus and B. suis. The Gold Immunochromatographic Assay (GICA) strips exhibited the most sensitive detection limits, with a value of 7.8125 × 105 CFU/mL for Brucella melitensis, surpassing the sensitivity levels observed for Brucella abortus and Brucella suis. It is also suitable for clinical and field samples, providing a cost-effective and user-friendly alternative to traditional methods.

Recent Advances in the CRISPR/Cas-Based Nucleic Acid Biosensor for Food Analysis: A Review.

Food safety is a major public health issue of global concern. In recent years, the CRISPR/Cas system has shown promise in the field of molecular detection. The system has been coupled with various nucleic acid amplification methods and combined with different signal output systems to develop a new generation of CRISPR/Cas-based nucleic acid biosensor technology. This review describes the design concept of the CRISPR/Cas-based nucleic acid biosensor and its application in food analysis. A detailed overview of different CRISPR/Cas systems, signal amplification methods, and signal output strategies is provided. CRISPR/Cas-based nucleic acid biosensors have the advantages of high sensitivity, strong specificity, and timeliness, achieving fast analysis of a variety of targets, including bacteria, toxins, metal ions, pesticides, veterinary drugs, and adulteration, promoting the development of rapid food safety detection technology. At the end, we also provide our outlook for the future development of CRISPR/Cas-based nucleic acid biosensors.

Fabrication of Surface-Enhanced Raman Scattering (SERS) Substrates in Analytical Science by Natural-inspired Materials: A Review

Surface-Enhanced Raman Scattering (SERS) spectroscopy, as a novel rapid detection technology, offers molecular fingerprinting capabilities that achieve trace-level detection. The key to optimizing SERS sensitivity and reliability lies in the precise control of the nanostructures of SERS substrates. Nature, through billions of years of evolution, has served as a masterful creator, developing organisms with remarkable abilities based on micro/nanostructures, such as the superhydrophobicity of lotus leaves and the strong adhesive forces of gecko feet. This review categorizes the recent developments in SERS substrates inspired by natural materials into three main types: plant-based, animal-based, and virus-based. Each category is explored in detail, describing how their unique nanoarchitectures inspire the development of highly sensitive SERS substrates, along with their fabrication methods and applications in analytical science. Additionally, the review identifies current challenges, such as the uniformity and scalability of naturally inspired SERS substrates and suggests future directions, including the integration of hybrid biomimetic structures and advanced manufacturing techniques. By fostering a deeper understanding of these nature-inspired materials, we aim to enhance the practical application of SERS in analytical science in the future.