Rapid Detection System Research Articles

A Rapid Molecular Detection System for SdhB and SdhC Point Mutations Conferring Differential Succinate Dehydrogenase Inhibitor Resistance in Clarireedia Populations.

Dollar spot, caused by the ascomycete fungus Clarireedia (formerly Sclerotinia), is one of the most resource-demanding diseases on amenity turfgrasses in North America. Differential resistance to the succinate dehydrogenase inhibitor (SDHI) fungicide class, conferred by singular point mutations on the SdhB, SdhC, and SdhD subunits of the succinate dehydrogenase enzyme (SDH), has been reported in dollar spot as well as many other plant-pathogenic fungal diseases. Four unique mutations were previously reported from Clarireedia field isolates collected from two different cool-season golf courses in Japan and Rhode Island: an amino acid substitution H267Y and a silent mutation (CTT to CTC) at codon 181 on the SdhB subunit gene, and amino acid substitutions G91R and G150R on the SdhC subunit gene. To properly diagnose and monitor SDHI resistance in the field, a rapid detection system for known mutations is crucial. As part of this study, additional SDHI-resistant Clarireedia isolates were collected from Rutgers University research plots and in vitro sensitivity to four SDHI active ingredients was assessed. SdhB, SdhC, and SdhD subunits of these isolates were sequenced to reveal an additional mutation on the SdhB subunit gene, H267R, not previously observed in Clarireedia. Cleaved amplified polymorphic sequence (CAPS) and derived CAPS molecular markers were developed to detect five mutations conferring SDHI resistance in Clarireedia isolates and validated using samples from two additional golf courses in Connecticut and Wisconsin experiencing SDHI field failure. This PCR-based molecular detection system will be useful for continued monitoring, assessment, and delay of SDHI resistance in the field.

Handheld Point-of-Care System for Rapid Detection of SARS-CoV-2 Extracted RNA in under 20 min.

The COVID-19 pandemic is a global health emergency characterized by the high rate of transmission and ongoing increase of cases globally. Rapid point-of-care (PoC) diagnostics to detect the causative virus, SARS-CoV-2, are urgently needed to identify and isolate patients, contain its spread and guide clinical management. In this work, we report the development of a rapid PoC diagnostic test (<20 min) based on reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) and semiconductor technology for the detection of SARS-CoV-2 from extracted RNA samples. The developed LAMP assay was tested on a real-time benchtop instrument (RT-qLAMP) showing a lower limit of detection of 10 RNA copies per reaction. It was validated against extracted RNA from 183 clinical samples including 127 positive samples (screened by the CDC RT-qPCR assay). Results showed 91% sensitivity and 100% specificity when compared to RT-qPCR and average positive detection times of 15.45 ± 4.43 min. For validating the incorporation of the RT-LAMP assay onto our PoC platform (RT-eLAMP), a subset of samples was tested (n = 52), showing average detection times of 12.68 ± 2.56 min for positive samples (n = 34), demonstrating a comparable performance to a benchtop commercial instrument. Paired with a smartphone for results visualization and geolocalization, this portable diagnostic platform with secure cloud connectivity will enable real-time case identification and epidemiological surveillance.

Portable multi-channel spectral measurement system for rapid on-site detection

Although current commercial spectrometer can measure and analyze the detecting targets in extremely high spectral resolution, it still suffers from many disadvantages such as complicated system, bulky size and expensive costs, which are difficult to meet the needs of field testing. In order to solve these problems, a portable multi-channel spectral measurement system for rapid on-site detection was designed. Compared to the traditional spectrometer, the proposed design was not only compact, but also had rather high spectral resolution; and additionally, its multi-channel configuration supported simultaneous detection for multiple samples to further improve the sensing efficiency. Proved by the detecting results on Rhodamine 6G and avian influenza virus H7N9 antibodies, the proposed portable multi-channel spectral measurement system could quantitatively measure various samples in high accuracy. Because this system has the advantages of good sensitivity, high resolution and small volume, it is expected to be used in the rapid on-site spectral detection application.

A Rapid Gradation Detection System for Earth and Stone Materials Based on Digital Image

Gradation has an important influence on the mechanical properties of earth and stone materials after compaction, and a reasonable gradation is the key to ensure compaction quality. In this paper, a set of earth and stone materials gradation detection system based on digital image processing was proposed, which was suitable for detection in the construction sites. The system obtained images which were taken from multiple directions of materials transport vehicle, used the thresholding method to segment the images, detected particle contours through edge detection algorithm of Canny to realize the image recognition of particle size, and drew the gradation curve of earth and rock materials finally. It was verified by selecting limestone aggregates of different gradations, and the results showed high accuracy. The system can realize on‐site detection of the earthwork gradation rapidly and accurately at a dam construction site.

A Rapid Multiplex Nucleic Acid Detection System of Airborne Fungi by an Integrated DNA Release Device and Microfluidic Chip

A Rapid Multiplex Nucleic Acid Detection System of Airborne Fungi by an Integrated DNA Release Device and Microfluidic Chip

Review Article The evolution of rapid antigen detection systems and their application for COVID-19 and other serious respiratory infectious diseases

Making the correct diagnosis of a patient seeking medical attention is the ultimate goal of a practicing physician, irrespective of whether the cause of the patient's condition is infectious or non-infectious. Antigen detection tests can be used to aid in the diagnosis of various infectious-related disorders including COVID-19 where it has become especially important due to the serious nature of this disease and its worldwide prevalence. These tests closely mimic one of the earliest prototypes -the urine pregnancy test -and as a result they have gained wide acceptance based on their overall simplicity, low cost and relative accuracy. In some situations, especially as a screening test, they can be used instead of the more technically demanding and complex molecular and serologic assays that are still useful and helpful under many different circumstances. Antigen detection systems are based on finding a particular immunogenic component, typically a protein or polysaccharide molecule, that is both unique and an integral part of the pathogen or other biological entity. Because these tests generally provide only qualitative results, they often need to be supplemented with other and sometimes more sophisticated laboratory-based diagnostic procedures to corroborate the initial test result. In this review, we first describe general background information on antigen-detection methods, including any unique aspects of their overall design, and then follow with an extensive description on the merits and limitations of these tests for detecting COVID-19 and, to a lesser extent, for other serious respiratory diseases caused by three common bacterial pathogens -Streptococcus pyogenes, Streptococcus pneumoniae and Legionella pneumophila.

Development of a novel polymerase spiral reaction (PSR) assay for rapid and visual detection of Clostridium perfringens in meat

C. perfringens is a widespread foodborne pathogen and one of the major concerns in the meat industry. There is a need for a simple, rapid and equipment free detection system for C. perfringens as conventional anaerobic culture method is labour and resource intensive. Here, we applied a novel polymerase spiral reaction phenomenon to develop and evaluate an assay for effortless and visual detection of C. perfringens in meat foods employing pork as a representative model. Specificity of the assay was determined using 51 C perfringens and 20 non- C. perfringens strains. Analytical sensitivity of the developed test was 80 fg DNA per tube indicating 100 times more sensitivity than end-point PCR assay. The detection limits were 980 CFU/g and 9.8 × 104 CFU/g of pork for PSR and PCR assays, respectively. The operation time of the PSR assay including DNA extraction was 120 min. The developed PSR assay was accurate and effective in comparison to culture method, in detecting C. perfringens in 38 of 74 pork samples. Therefore the specificity, sensitivity, negative predictive value, positive predictive value and accuracy rate of the developed PSR assay were 100%. The developed PSR assay is easy to perform, rapid, affordable, permitting sophisticated-equipment free amplification and naked eye interpretation. This is the initial report in which the PSR assay was optimized for the detection of C. perfringens.

Rapid and simple single-chamber nucleic acid detection system prepared through nature-inspired surface engineering.

Background: Nucleic acid (NA)-based diagnostics enable a rapid response to various diseases, but current techniques often require multiple labor-intensive steps, which is a major obstacle to successful translation to a clinical setting.Methods: We report on a surface-engineered single-chamber device for NA extraction and in situ amplification without sample transfer. Our system has two reaction sites: a NA extraction chamber whose surface is patterned with micropillars and a reaction chamber filled with reagents for in situ polymerase-based NA amplification. These two sites are integrated in a single microfluidic device; we applied plastic injection molding for cost-effective, mass-production of the designed device. The micropillars were chemically activated via a nature-inspired silica coating to possess a specific affinity to NA.Results: As a proof-of-concept, a colorimetric pH indicator was coupled to the on-chip analysis of NA for the rapid and convenient detection of pathogens. The NA enrichment efficiency was dependent on the lysate incubation time, as diffusion controls the NA contact with the engineered surface. We could detect down to 1×103 CFU by the naked eye within one hour of the total assay time.Conclusion: We anticipate that the surface engineering technique for NA enrichment could be easily integrated as a part of various types of microfluidic chips for rapid and convenient nucleic acid-based diagnostics.

Monitoring the behaviours of pet cat based on YOLO model and raspberry Pi

ABSTRACT With the progress of the times and the rapid development of science and technology, machine learning and artificial intelligence are increasingly used in transportation, logistics, and homes. In terms of pets, pet monitoring has also become very popular in recent years. In this study, a real-time monitoring system for home pets using raspberry pie is developed. The proposed method consists of a raspberry Pi based YOLOv3-Tiny identification system for rapid detection and better boundary frame prediction of the cat behavior. Based on the YOLOv3-Tiny method, the following fine-tuning is implemented: (1) The images collected using the mobile phones are uniformly cropped to 416*416 pixels. (2) The images were captured in different periods and randomly rotated to plus or minus 20 degrees to make the dataset more robust for training. (3) One thousand four hundred pictures of the cat's movements in the room for marking and training are used. The dataset is also used to train the YOLOv3 model. According to the input image categories, the results of the output are categorized into six cat actions. They were sleeping, eating, sitting down, walking, going to the toilet, and search for a trash can. The average accuracy of both models was 98%. Based on environmental constraints like speed and memory consumption, any one of these models can be used for image recognition. The raspberry Pi system continuously captures the images and instantly sends a message to the registered mobile phone to achieve an instant preventive measure if the cat goes to the toilet for too long or flips through the trash can.

Virus detection using nanoparticles and deep neural network-enabled smartphone system.

Emerging and reemerging infections present an ever-increasing challenge to global health. Here, we report a nanoparticle-enabled smartphone (NES) system for rapid and sensitive virus detection. The virus is captured on a microchip and labeled with specifically designed platinum nanoprobes to induce gas bubble formation in the presence of hydrogen peroxide. The formed bubbles are controlled to make distinct visual patterns, allowing simple and sensitive virus detection using a convolutional neural network (CNN)-enabled smartphone system and without using any optical hardware smartphone attachment. We evaluated the developed CNN-NES for testing viruses such as hepatitis B virus (HBV), HCV, and Zika virus (ZIKV). The CNN-NES was tested with 134 ZIKV- and HBV-spiked and ZIKV- and HCV-infected patient plasma/serum samples. The sensitivity of the system in qualitatively detecting viral-infected samples with a clinically relevant virus concentration threshold of 250 copies/ml was 98.97% with a confidence interval of 94.39 to 99.97%.

Scanning single-molecule counting system for Eprobe with highly simple and effective approach.

Here, we report a rapid and ultra-sensitive detection technique for fluorescent molecules called scanning single molecular counting (SSMC). The method uses a fluorescence-based digital measurement system to count single molecules in a solution. In this technique, noise is reduced by conforming the signal shape to the intensity distribution of the excitation light via a circular scan of the confocal region. This simple technique allows the fluorescent molecules to freely diffuse into the solution through the confocal region and be counted one by one and does not require statistical analysis. Using this technique, 28 to 62 aM fluorescent dye was detected through measurement for 600 s. Furthermore, we achieved a good signal-to-noise ratio (S/N = 2326) under the condition of 100 pM target nucleic acid by only mixing a hybridization-sensitive fluorescent probe, called Eprobe, into the target oligonucleotide solution. Combination of SSMC and Eprobe provides a simple, rapid, amplification-free, and high-sensitive target nucleic acid detection system. This method is promising for future applications to detect particularly difficult to design primers for amplification as miRNAs and other short oligo nucleotide biomarkers by only hybridization with high sensitivity.

Optimization of a hyperspectral imaging system for rapid detection of microplastics down to 100 µm

Plastic pollution has become one of the most emergent issues threating aquatic and terrestrial ecosystems. However, it is still challenging to rapidly detect small microplastics. Here, we present a method to rapidly detect microplastics using hyperspectral imaging in which we optimized a commercially available hyperspectral imaging system (Pika NIR-640, Resonon Inc., USA). The optimizations included: (1) changing the four-lamp assembly to a symmetrical set of converged-light near-infrared lamps that are placed sideways instead of above the sample stage; (2) adopting a macro-photography technique by applying an extension tube between the camera and the lens, and moving the lens of the hyperspectral camera to the imaging target (working distance of ~3 cm); (3) adjusting the exposure and aspect ratio by tuning the frame rate and scan speed of the imaging system. After optimization, the detection resolution of each pixel improved from 250 µm to 14.8 µm. With the optimized system, microplastics down to 100 µm in size were rapidly detected. This result is promising for the application of our new method in the accelerated detection of microplastics and will contribute to a better understanding of the microplastic pollution situation.•A commercially available hyperspectral imaging system is optimized for rapid detection of microplastics.•Infrared lamps are used and macro-photography technique adopted along with adjustments of exposure.•Microplastics down to 100 µm in size could be detected with the optimized system.

Establishing a mass spectrometry-based system for rapid detection of SARS-CoV-2 in large clinical sample cohorts

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is pressing public health systems around the world, and large population testing is a key step to control this pandemic disease. Here, we develop a high-throughput targeted proteomics assay to detect SARS-CoV-2 nucleoprotein peptides directly from nasopharyngeal and oropharyngeal swabs. A modified magnetic particle-based proteomics approach implemented on a robotic liquid handler enables fully automated preparation of 96 samples within 4 hours. A TFC-MS system allows multiplexed analysis of 4 samples within 10 min, enabling the processing of more than 500 samples per day. We validate this method qualitatively (Tier 3) and quantitatively (Tier 1) using 985 specimens previously analyzed by real-time RT-PCR, and detect up to 84% of the positive cases with up to 97% specificity. The presented strategy has high sample stability and should be considered as an option for SARS-CoV-2 testing in large populations.

Implementing the Health Early Warning System based on syndromic and event-based surveillance at the 2019 Hajj.

During the 2019 Hajj, the Ministry of Health in Saudi Arabia implemented for the first time a health early warning system for rapid detection and response to health threats. This study aimed to describe the early warning findings at the Hajj to highlight the pattern of health risks and the potential benefits of the disease surveillance system. Using syndromic surveillance and event-based surveillance data, the health early warning system generated automated alarms for public health events, triggered alerts for rapid epidemiological investigations and facilitated the monitoring of health events. During the deployment period (4 July-31 August 2019), a total of 121 automated alarms were generated, of which 2 events (heat-related illnesses and injuries/trauma) were confirmed by the response teams. The surveillance system potentially improved the timeliness and situational awareness for health events, including non-infectious threats. In the context of the current COVID-19 pandemic, a health early warning system could enhance case detection and facilitate monitoring of the disease geographical spread and the effectiveness of control measures.

Development of an Electrochemical Biosensor of Ubiquitin C-Terminal Hydrolase L1 (UCH-L1) for the Assessment of Traumatic Brain Injury (TBI)

INTRODUCTION Traumatic brain injury (TBI) is defined as a neurotrauma caused by a mechanical force applied to a head. An appropriate clinical therapy after TBI is crucial, or patients may suffer lasting effects in physical and mental abilities [1]. To find TBI, cranial computer tomography (CT) is commonly used, however, CT sometimes fails to find mild TBI (mTBI) even it accounts for majority of all cases of TBI. Recently, a blood-based biomarker, ubiquitin C-terminal hydrolase L1 (UCH-L1) has been regarded a promising marker for mTBI, as it becomes present in serum soon after TBI [2,3]. Actually, it was reported the elevated serum UCH-L1 level was observed with the soldiers exposed to low-level blast [4] and the athletes after sub-concussive dead hits [5]. Enzyme-linked immuno sorbent assay (ELISA) is currently used to detect the biomarker, however, since the ELISA is time-consuming procedure and requires specific laboratory equipment, development of a hand-held device to be used on-site in the field is expected. In this study, we propose and report a novel principle for a rapid, one-step electrochemical detection system for UCH-L1 to be dedicated for mTBI diagnosis. METHOD To detect UCH-L1, we chose an electrochemical method in terms of a rapid response time, miniaturization, and simple instrumentation. We employed a competitive assay combined with square wave voltammetry (SWV) on anti-UCH-L1 antibody immobilized electrode. In this system, a redox probe (phenazine ethosulfate: PES) modified UCH-L1 and the target biomarker is going to competitively bind to the antibody on the electrode, resulting electrochemical signal depend on the concentration of UCH-L1 in a sample. To construct the system, PES modified UCH-L1 was prepared using amine-reactive PES. This molecule has N-hydroxysuccinimide linked to PES, therefore, upon mixing amine-reactive PES with UCH-L1, it will react with amine group of lysine residue and make a covalent bond. After the modification, enzyme-linked immuno sorbent assay (ELISA) was performed to investigate the binding affinity of anti-UCH-L1 antibody against UCHH-L1 and PES modified UCH-L1. To detect UCH-L1, we added UCH-L1 together with PES-modified UCH-L1 on the antibody immobilized gold electrode, and performed SWV at a potential range of -0.5 to 0.1 V with different frequencies of square wave. RESUTLS The result of ELISA showed that the antibody had almost same affinity against non-labeled UCH-L1 and PES-modified UCH-L1. Therefore, the modification of PES did not affect the recognition of antibody. When we incubated the electrode with different concentration of PES-modified UCH-L1, a square wave voltammetry signal was increased in a concentration dependent manner, indicating the PES modified on the UCH-L1 was electrochemically active, and the amount of PES-modified UCH-L1 was detected by SWV when it was captured by anti-UCH-L1 antibody on the electrode. In the competitive assay, the incubation of intact UCH-L1 together with PES-modified UCH-L1 resulted in the SWV signal change depend on the UCH-L1 concentration. With this detection principle, we could detect UCH-L1 within 10 minutes without any washing procedure, which is ideal for the on-site detection of mTBI. CONCLUSION In this study, we developed a rapid electrochemical detection system of TBI biomarker, UCH-L1, based on the competitive of redox probe-modified UCH-L1 with the target. Using this detection system, we were able to observe SWV signal change within 10 minutes upon addition of UCH-L1. This study shows the proof-of-concept of this rapid competitive assay, and we hope it would help future development of TBI detection device. REFERENCES [1] S. W. Hoffman and C. Harrison, Clin. Neuropsychol., 23(8), 1400–1415 (2009).[2] R. Diaz-Arrastia, K. K. W. Wang, L. Papa, M.D. Sorani, J. K. Yue, A. M. Puccio, P. J. McMahon, T. Inoue, E. L. Yuh, H. F. Lingsma, A. I. R. Maas, A. B. Valadka, D. O. Okonkwo, G. T. Manley, TRACK-TBI Investigators, J. Neurotrauma, 31(1), 19–25 (2013).[3] L. Papa, G. M. Brophy, R. D. Welch, L. M. Lewis, C. F. Braga, C. N. Tan, N. J. Ameli, M. A. Lopez, C. A. Haeussler, D. I. M. Giordano, S. Silvestri, P. Giordano, K. D. Weber, C. Hill-Pryor, D. C. Hack, JAMA Neurol., 73(5), 551–560 (2016).[4] C. M. Tate, K. K. W. Wang, S. Eonta, Y. Zhang, W. Carr, F. C. Tortella, R. L. Hayes, G. H. Kamimori, J. Neurotrauma, 30(19), 1620–1630 (2013).[5] V. Puvenna, C. Brennan, G. Shaw, C. Yang, N. Marchi, J. J. Bazarian, K. Merchant-Borna, D. Janigro, PLoS ONE, 9(5), e96296–9 (2015).

An IoT Based Rapid Detection and Response System for Vehicular Collision with Static Road Infrastructure

Road accidents take place everywhere and involve a collision between two dynamic or a dynamic and static object. The proposed project is designed to detect as well as respond to accidents between moving vehicles and stationary road infrastructure such as traffic lights, street lamps etc. The system is consequently installed on this standalone road infrastructure and is powered by a rechargeable lithium-ion battery. The system uses sound, pressure and motion sensors for detection purposes. The sensors are calibrated using the average values observed in previous accidents as their operating threshold values for detection and determination. They are connected with an Arduino and a Wi-Fi module which communicates with nearby medical facilities and highway authorities as a response mechanism in the event of a detected accident. If implemented, it has the potential to effectively reduce the loss of life and limb in the event of road accidents, especially on isolated highway stretches.

Detection of Microbial Nitroreductase Activity by Monitoring Exogenous Volatile Organic Compound Production Using HS-SPME-GC-MS

Development of a rapid approach for universal microbial detection is required in the healthcare, food and environmental sectors to aid with medical intervention, food safety and environmental protection. This research investigates the use of enzymatic hydrolysis of a substrate by a microorganism to generate a volatile organic compound (VOC). One such enzyme activity that can be used in this context is nitroreductase as such activity is prevalent across a range of microorganisms. A study was developed to evaluate a panel of 51 microorganisms of clinical interest for their nitroreductase activity. Two enzyme substrates, nitrobenzene and 1-fluoro-2-nitrobenzene, were evaluated for this purpose with evolution, after incubation, of the VOCs aniline and 2-fluoroaniline, respectively. Detection of the VOCs was done using headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) with obtained limits of quantitation (LOQ) of 0.17 and 0.03 µg/mL for aniline and 2-fluoroaniline, respectively. The results indicated that both enzyme substrates were reduced by the same 84.3% of microorganisms producing the corresponding volatile anilines which were detected using HS-SPME-GC-MS. It was found that nitroreductase activity could be detected after 6–8 h of incubation for the selected pathogenic bacteria investigated. This approach shows promise as a rapid universal microbial detection system.

Turfgrass Disease Diagnosis: Past, Present, and Future.

Turfgrass is a multibillion-dollar industry severely affected by plant pathogens including fungi, bacteria, viruses, and nematodes. Many of the diseases in turfgrass have similar signs and symptoms, making it difficult to diagnose the specific problem pathogen. Incorrect diagnosis leads to the delay of treatment and excessive use of chemicals. To effectively control these diseases, it is important to have rapid and accurate detection systems in the early stages of infection that harbor relatively low pathogen populations. There are many methods for diagnosing pathogens on turfgrass. Traditional methods include symptoms, morphology, and microscopy identification. These have been followed by nucleic acid detection and onsite detection techniques. Many of these methods allow for rapid diagnosis, some even within the field without much expertise. There are several methods that have great potential, such as high-throughput sequencing and remote sensing. Utilization of these techniques for disease diagnosis allows for faster and accurate disease diagnosis and a reduction in damage and cost of control. Understanding of each of these techniques can allow researchers to select which method is best suited for their pathogen of interest. The objective of this article is to provide an overview of the turfgrass diagnostics efforts used and highlight prospects for disease detection.

Deep Learning on chromatographic data for Segmentation and Sensitive Analysis

Lateral flow immunoassay (LFIA) is one of the most common methods in point-of-care testing, which is widely applied in some conditions for various applications. Image segmentation is an increasingly popular experimental paradigm to efficiently test the target area in LFIA. However, due to process pollution, and problems related to the experimental operation and irregular structure of the background of the reaction, currently available tools cannot be used to extract correct signals from these images, which affects the accuracy of detection. Machine learning has significantly improved modern biochemical analysis by pushing the limits of traditional techniques for the recognition and processing of images. In this paper, the U-Net, a variant of the convolutional neural network (CNN) is used for the quantitative analysis of LFIA images for the accurate quantification of single- and multi-target images. By graying, binarizing, and labeling different concentrations of test strips, the target area of LFIA images containing the T-/C-lines is extracted and obtained. Then it provides updated trends and directions for the development of LFIA technology. Several indicators are introduced to evaluate the proposed U-Net structure to verify the feasibility and effectiveness of its image processing capability. When the trained U-Net model was used to process images, the peak signal-to-noise ratio was 22.4 dB, significantly higher than prevalent methods in the area that have reported only a 4 dB improvement in the quality of the graphics. The intersection-over-union between samples also increased to above 93%. Our results show that the proposed method has significant potential for detecting a segmented target in an LFIA area, especially weak positive signals and multichannel detection. With other modifications, this deep learning method can be applied as a powerful tool to study rapid detection devices, systems, and biological images.

A disc-chip based high-throughput acute toxicity detection system

Acute toxicity assay presents vital significance in modern environmental monitoring, including online detection and in-situ assay for emergency events. Although photobacteria related detection methods were established and verified in the past decades with combination of photomultiplier tube (PMT), the price and size of PMT sensor hampered application of rapid acute toxicity assay and detection system miniaturization, especially in the resource-limited occasions. Wide application of smartphones with great low-light performance cameras could be used in photobacteria-based toxicity assay instead of the PMT methods. Herein a box-type portable detection system had been successfully established, including a disc-chip for detection, detection device, and smartphones with a high-performance camera. The system performed well showing stable temperature and rotation control. Results captured by CMOS-based camera presented a linear relationship with PMT-based detection method. An image progress algorithm was also established and tested by series diluted zinc sulfate solution as a reference substance. The system also performed well for toxicity analysis for real Atmospheric particle matter sample. The system could be used in some environmental monitoring scenarios as an alternative solution.