Fast Detection Method Research Articles

Feasibility of Nondestructive Soluble Sugar Monitoring in Tomato: Quantified and Sorted through ATR-FTIR Coupled with Chemometrics

As a fast detection method, Fourier transform infrared attenuated total reflection (ATR-FTIR) spectroscopy is seldom used for monitoring soluble sugars in crops. This study aimed to demonstrate the feasibility of leveraging ATR-FTIR coupled with chemometrics to quantify and sort the contents of soluble sugar in tomatoes. Firstly, 192 tomato samples were scanned using ATR-FTIR; subsequently, a quantitative model was developed using PLSR with selected wavelength variables as inputs. Finally, a classification model was estimated through probabilistic neural network (PNN) to determine the samples. The results indicated that ATR-FTIR had successfully captured the spectra from the cellular layers of tomatoes, resulting in a robust PLSR model created by 468 selected variables with a R² value of 0.86, a RMSEP of 0.71%, a ratio of performance to relative percent deviation (RPD) of 1.87, and a ratio of prediction to interquartile range (RPIQ) of 2.1. Meanwhile, the PNN model demonstrated a high rate correct (RC) of 92.17% in identifying whether the samples with a higher soluble sugar content than the limit of detection (LOD at 2.1%). Overall, ATR-FTIR coupled with chemometrics has proven effective for non-destructive determination of soluble sugars in tomatoes, offering new insights into internal monitoring techniques for crop quality assurance.

A study on fast detection methods for tetracycline antibiotic residues based on magnetic nanoparticles and electrochemical aptasensors

This study presents the development of novel electrochemical aptasensors utilizing magnetic nanomaterials for rapid and sensitive detection of tetracycline (TET) antibiotic residues. Two types of nanocomposites, Cu2O@Au octahedral nanoparticles and rGO-Fe3O4, were synthesized and characterized for aptasensor fabrication. The Cu2O@Au-based aptasensor demonstrated a linear detection range from 1 nM to 1 μM, with a limit of detection (LOD) of 0.3 nM. The rGO-Fe3O4/sodium alginate-based aptasensor exhibited a linear range of 5 nM to 500 nM, with an LOD of 1.5 nM. Both aptasensors showed excellent specificity towards TET, good reproducibility (RSD < 5.6 %), and stability (>90 % response after 2 weeks). The practical applicability of the aptasensors was validated through TET detection in spiked milk samples, achieving recoveries between 88.6 % and 107.8 % with RSDs below 7.2 %. These aptasensors offer advantages such as rapid response, simple fabrication, and potential for on-site detection, making them promising tools for food safety monitoring and environmental surveillance.

Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection

Apple core browning not only affects the nutritional quality of apples, but also poses a health risk to consumers. Therefore, there is an urgent need to develop a fast and reliable non-destructive detection method for apple core browning. To deal with the challenges of the long incubation period, strong infectivity, and difficulty in the prevention and control of apple core browning, a novel non-destructive detection method for apple core browning has been developed through combining hyperspectral imaging and dielectric techniques. To reduce the computational complexity of high-dimensional multi-view data, canonical correlation analysis is employed for feature dimensionality reduction. Then, the two low-dimensional vectors extracted from two different sensors are concatenated into one united feature vector; therefore, the information contained in the hyperspectral and dielectric data is fused to improve the detection accuracy of the non-destructive method. At last, five traditional classifiers, such as k-Nearest Neighbors, a support vector machine with radial basis function kernel and polynomial kernel, Decision Tree, and neural network, are trained on the fused feature vectors to discriminate apple core browning. The experimental results on our own constructed dataset have shown that the sensitivity, specificity, and precision of SVM with RBF kernel based on concatenated 70-dimensional feature vectors extracted via canonical correlation analysis reached 99.98%, 99.70%, and 99.70%, respectively, which achieved better results than other models. This study can provide theoretical assurance and technical support for further development of higher accuracy and lower-cost non-destructive detection devices for apple core browning.

Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method

It is crucial to appropriately maintain automatic ticket gates (ATGs) to keep transportation operating smoothly in urban areas. Although the average failure rate of new ATGs is extremely low, continuous operation for many years might lead to unstable performance due to deterioration, and the need for periodic maintenance to avoid fatal faults might halt operations for extended periods. To detect anomalies at an early stage, “anomaly signs” can be utilized to flag ATGs for maintenance by service engineers before anomalies occur. In addition, to minimize the cost of ATG monitoring, the necessary computing resources should be minimized, which means using only light-weight statistical methods rather than deep learning or machine learning. In this paper, we focus on the automatic separation modules inside ATGs that separate multiple tickets by complicated mechatronic controls because this module is the major cause of maintenance calls from station attendants. We propose a simple anomaly sign detection, called the histogram limitation method (HLM). We evaluated the anomaly sign scores over time with maintenance timing and compared them with the conventional fast unsupervised anomaly detection method, Histogram-Based Outlier Score (HBOS) widely used in various domains. The experimental results using real field ATG monitoring data show that HLM successfully detected anomaly signs before a maintenance call was necessary, which is better performance compared with HBOS. Despite being a simple modification based on HBOS, HLM also provides anomaly sign scores that agree adequately with assessments by maintenance service engineers.

Fast error detection method for additive manufacturing process monitoring using structured light three dimensional imaging technique

This paper presents a novel method to speed up error detection in an additive manufacturing (AM) process by minimizing the necessary three-dimensional (3D) reconstruction and comparison. We develop a structured light 3D imaging technique that has native pixel-by-pixel mapping between the captured two-dimensional (2D) image and the reconstructed 3D point cloud. This 3D imaging technique allows error detection to be performed in the 2D image domain prior to 3D point cloud generation, which drastically reduces complexity and computational time. Compared to an existing AM error detection method based on 3D reconstruction and point cloud processing, experimental results from a material extrusion (MEX) AM process demonstrate that our proposed method significantly increases the error detection speed.

The power spectrum indicator, a new and fast method for the early detection of chaos

The power spectrum indicator, a new and fast method for the early detection of chaos

A mixed Ce/Eu metal-organic framework for ratiometric detection of Al3+ ion.

As a heavy metal ion, excessive aluminum ions pose a serious threat to human health and the ecological environment. Developing a simple, efficient, and fast detection method to detect the content of aluminum ions is of great significance, especially for ensuring human health and ecological safety. Herein, the mixed rare earth metal-organic framework (Ce0.74Eu0.26TPTC and Ce0.62Eu0.38TPTC) were prepared based on simple ligand 1,1':4',1″-Terphenyl-2',4,4″,5'-tetracarboxylic acid (H4TPTC). The Ce0.74Eu0.26TPTC and Ce0.62Eu0.38TPTC have dual luminescence centers, which can be used as ratio fluorescent probes to detect Al3+ ions, making the detection results more accurate and reliable. Therefore, this work can promote the further development of rare earth-based MOFs in the detection of heavy metal ions.

Visual Detection of Dopamine with CdS/ZnS Quantum Dots Bearing by ZIF-8 and Nanofiber Membranes.

Dopamine (DA) is a widely present, calcium cholinergic neurotransmitter in the body, playing important roles in the central nervous system and cardiovascular system. Developing fast and sensitive DA detection methods is of great significance. Fluorescence-based methods have attracted much attention due to their advantages of easy operation, a fast response speed, and high sensitivity. This study prepared hydrophilic and high-performance CdS/ZnS quantum dots (QDs) for DA detection. The waterborne CdS/ZnS QDs were synthesized in one step using the amphiphilic polymer PEI-g-C14, obtained by grafting tetradecane (C14) to polyethyleneimine (PEI), as a template. The polyacrylonitrile nanofiber membrane (PAN-NFM) was prepared by electrospinning (e-spinning), and a metal organic frame (ZIF-8) was deposited in situ on the surface of the PAN-NFM. The CdS/ZnS QDs were loaded onto this substrate (ZIF-8@PAN-NFM). The results showed that after the deposition of ZIF-8, the water contact angle of the hydrophobic PAN-NFM decreased to within 40°. The nanofiber membrane loaded with QDs also exhibited significant changes in fluorescence in the presence of DA at different concentrations, which could be applied as a fast detection method of DA with high sensitivity. Meanwhile, the fluorescence on this PAN-NFM could be visually observed as it transitioned from a blue-green color to colorless, making it suitable for the real-time detection of DA.

A dendrimer-based platform integrating surface-enhanced Raman scattering and class-incremental learning for rapidly detecting four pathogenic bacteria

Rapid monitoring of pathogens is crucial for preventing foodborne diseases, thus making it an urgent need to develop efficient, fast, and simple methods for on-site detection of multiple pathogens. With advances in SERS-based label-free biosensors and machine learning, progress is evident, yet challenges such as complex substrates and limited model interpretability persist. In this work, we reported a novel dendrimer-based platform that integrated surface-enhanced Raman scattering (SERS) with class-incremental learning (CIL) for quick and simultaneous detection of four pathogenic bacteria, namely Escherichia coli, Salmonella Paratyphi B, Pseudomonas aeruginosa, and Staphylococcus aureus. First, the poly(amidoamine) (PAMAM)-based gold nanoassemblies on silicon wafer (PGNAs/Si) was designed as a highly active SERS substrate with an easy fabrication process. Compared with naked gold nanoparticles, both sensitivity and repeatability were improved by introducing a controllable dendritic nanostructure with ultra-small internal nanogaps. The detection limits of four pathogens in water, food, and dietary supplement matrices were all on the order of 10 CFU/mL. Subsequently, to analyze the complex SERS spectra and distinguish samples with different pathogens, CIL models were established using the light gradient boosting machine (LightGBM) algorithm. Notably, the discriminative models demonstrated excellent classification performance with an accuracy of over 93.44 %. Further, the SHapley Additive exPlanations (SHAP) method was utilized to identify the key SERS features in accurately discriminating different pathogens. Together, these results demonstrate that the dendrimer-based platform, by integrating SERS with CIL, can rapidly detect four pathogenic bacteria, underscoring its potential for food safety testing or quality monitoring in medical supplies manufacturing.

A fast modularity hardware Trojan detection technique for large scale gate-level netlists

Hardware Trojans (HTs) are a kind of malicious circuit implanted by adversaries and induce malfunction under rare situations. Attackers may insert HTs into untrusted third-party intellectual properties (3PIPs), thus severely threatening the hardware security of ICs. To overcome this issue, state-of-art HT detection techniques are proposed based on feature extraction of gate-level netlists (GLNs). However, these techniques may take a long time to extract HT signals for large scale GLNs. In this paper, we propose a fast modularity HT detection (FMTD) method for large scale GLNs. The GLN modularity algorithm can divide the whole GLN into several small modules with the boundaries of D flip-flops (DFFs) of each module. By analyzing the transition rate of critical signals, preserving suspicious DFFs, and repairing the ring circuit, we can ensure the integrity of HT circuits during the GLN modularity process. Then, the calculation of the testability of each module is conducted in parallel with our self-designed tool. In the self-designed tool, we repair the ring circuit, calculate the testability values, and calibrate the testability values of module boundary signals. Compared with the EDA tools, our self-designed tool has no upper limit of testability values. Then, the testability values are sent to the unsupervised K-means clustering simultaneously to diagnose the HT signals. Facilitated by the modularity of the GLN, the detection time of 105 order signals sample is reduced by up to 90 % when compared to the traditional COTD method, while our MFTD method shows a similar HT detection performance to that of the traditional COTD method. For all 20 kinds of GLN samples in Trust-hub, our FMTD method can obtain a detection accuracy of 100 %, and signal diagnosis precision of more than 93 % with a diagnosis false positive rate lower than 1 %.

Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods

Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of traditional chemometrics and deep learning methods in identifying and quantifying adulterated CAO were compared from a statistical perspective, and no significant difference were founded in the identification of CAO at different levels of adulteration. The recognition rate of pure and adulterated CAO was 100 %, but there were misclassifications among different adulterated CAOs. The deep learning models outperformed chemometrics methods in quantitative prediction of adulteration level, with RP2, RMSEP, and RPD of the optimal ConvLSTM model achieved 0.999, 0.9 % and 31.5, respectively. The classifiers and models developed in this study based on deep learning have wide applicability and reliability, and provide a fast and accurate method for adulteration detection in CAO.

A fast detection method for metering anomalies of three-phase energy meters based on sliding filter and decision tree

In the actual measurement of three-phase electricity meters, the presence of electromagnetic interference, environmental temperature, and other factors increases the noise in the measurement data, which affects the accuracy of anomaly detection. In this regard, a fast detection method for three-phase meter measurement anomalies based on sliding filters and decision trees is studied. Firstly, sliding filtering and sliding window matrix methods are used to reduce the dimensionality and filter the measurement data of three-phase electric meters. Then, a cascaded random forest is constructed using a CART decision tree, and the filtered three-phase meter measurement data is input into the cascaded random forest. The CART decision tree uses a binary method to partition the nodes. Finally, using the Gini coefficient as a measurement indicator, a cascaded structure is formed through layered stacking to output rapid detection results of outliers in three-phase electricity meter measurements. The experimental results show that this method can quickly detect measurement abnormalities of three-phase energy meters, timely detect meter flying anomalies and unexpected mutations, and improve the fault handling efficiency of three-phase energy meters.

Target localization and defect detection of distribution insulators based on ECA‐SqueezeNet and CVAE‐GAN

AbstractInsulators, as typical equipment for distribution networks, provide good electrical insulation between live conductors and earth. Timely and accurate detection is essential for insulator detection issues. However, as the complexity of neural networks increases, the detection efficiency is often lower. Therefore, this paper proposes a fast insulator positioning and defect detection method. Firstly, for insulator target localization, the SqueezeNet network is improved using ECA attention mechanism. In addition, to address the issue of low defect detection accuracy, a joint algorithm has been proposed. The integration of convolutional variational autoencoder (CVAE) and generative adversarial network (GAN) solve their own shortcomings due to different image focus angles. The target localization accuracy reaches 94.30%, and the defect detection accuracy reaches 89.60%. It solves the problems of difficulty in locating small targets in a large field of view and inaccurate detection due to a small number of abnormal samples. This method has been tried and tested in practical distribution network systems.

Facial detection of formaldehyde by using acidichromic carbon dots and the reaction between formaldehyde and ammonium chloride

Herein we report the facial detection of formaldehyde (FA) by using an interesting red acidichromic carbon dots (ACDs) which turns blue when pH gradually decreases. The color change was attributed to the conversion between the double bonds (C=N) and single bonds (C-N) on the surface of the ACDs. Inspired by the reaction between FA and ammonium chloride that produces H+ and methenamine and decrease the pH value of the solution, a fast and simple visual detection method for FA was found with a minimum discriminated concentration of 0.04 mol/L. A fluorescence detection method for FA was also found with LOD of 0.029 mol/L and FA in real sample, e.g., shredded squid was successfully analyzed. This work provides a new idea of developing fast visual detection method for daily monitor or in-site semiquantitative assessment on FA.

Development of the novel gene chip and restriction fragment length polymorphism (RFLP) methods for rapid detection of Mycobacterium tuberculosis complex in broth culture

BackgroundTuberculosis (TB) is a major global public health issue. Prompt and accurate TB diagnosis is crucial for starting appropriate treatments and preventing the disease's spread. Current diagnostic techniques are either slow or expensive. This study aimed to create and evaluate a new, fast, highly reliable, and cost-effective TB detection method using a gene chip and Restriction Fragment Length Polymorphism (RFLP) analysis on Mycobacteria Growth Indicator Tubes (MGIT) specimens. MethodsWe assessed the effectiveness of a novel gene chip and RFLP methods targeting the 16S rRNA gene of Mycobacterium tuberculosis in 2000 MGIT culture-positive specimens. RFLP analysis identified the AfeI restriction site within the M. tuberculosis complex (MTBC) genome. Discrepancies were investigated through extensive sequencing and Cobas TaqMan PCR analysis, along with reviewing patient profiles. ResultsBoth methods showed high efficacy in detecting MTBC in broth cultures, with the gene chip method achieving a sensitivity of 99.27 %, specificity of 98.35 %, and the RFLP method showing a sensitivity of 98.18 %, specificity of 99.31 %. False negatives in two isolates were due to a mutation in the AfeI site. Additionally, five cases showed MTBC presence when nontuberculous Mycobacterium species grew in cultures. ConclusionOur novel gene chip and RFLP methods are effective for rapid highly-reliable and cost-effective M. tuberculosis detection in MGIT specimens. Both gene chip and RFLP methods are suitable for resource-limited settings, offering an economical advantage. These methods have significant potential to improve clinical TB diagnosis.

A fast collision detection method based on point clouds and stretched primitives for manipulator obstacle-avoidance motion planning

A fast collision detection method based on point clouds and stretched primitives for manipulator obstacle-avoidance motion planning

Comparison of commercial DNA kits for allergen detection of celery in food matrices

For correct allergen risk management by industry, retail and food safety authorities, sensitive and reliable fast allergen detection methods are required, even more when precautionary allergen labelling based on reference doses will be implemented in legislation.This study aimed to perform a comparative assessment of three commercially available quantitative or qualitative test kits, for DNA analysis of celery in food products. Five product groups, representing different sectors of the AOAC food-matrix triangle, being (plant-based) meat products, snacks, sauces, dried herbs and spices, and smoothies, were identified to potentially contain celery. From each group, blank and incurred (labelled to contain celery) food products were selected, of which the blank food products were additionally spiked with low protein levels of celery prior to qPCR assessment.Results show that the assessed test kits perform according to their specifications, however, a clear influence of the matrix on the detection ability of celery was observed. In addition, quantification of the amount of celery in the different food products showed to be challenging in all food product groups using the two quantification kits.

Establishment and methodological evaluation of a rapid detection method for Cryptococcus neoformans and Cryptococcus gattii species complexes based on CRISPR-Cas12a technology

PurposeThe opportunistic pathogens causing Cryptococcal meningitis are Cryptococcus neoformans and Cryptococcus gattii species complexes. At present, clinical detection methods for this condition include culture, ink staining, and cryptococcal antigen detection. In addition, enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), and real-time quantitative PCR (qPCR) can be applied for the detection of Cryptococcus. Nevertheless, these methods cannot achieve point-of-care detection (POCT); thus, there is a pressing need to establish a fast, sensitive, and effective detection method. MethodsRecombinase polymerase amplification (RPA) and clustered regularly spaced short palindromic repeat (CRISPR) techniques are effective tools for achieving rapid POCT. In this study, RPA was combined with CRISPR-Cas12a to establish a fast, sensitive, and specific detection method for cryptococcal meningitis. ResultsThis study included RPA-Cas12a fluorescence detection and RPA-Cas12a immunochromatographic detection, which can be performed within 50 min. Moreover, the detection limit was as low as 102 copies/μL. Interestingly, the developed method demonstrated satisfactory specificity and no cross-reactivity with other fungi and bacteria. 36 clinical samples were tested, and the consistency between the test results and those obtained using the commonly used clinical culture method was 100 %. ConclusionIn this study, a rapid detection method for Cryptococcus neoformans and Cryptococcus gattii species complexes was developed based on CRISPR-Cas12a technology, characterized by its high sensitivity and specificity, ease of use, and cost-effectiveness, making it suitable for on-site detection.

Developing an off-on fluorescence sensor based on red copper nanoclusters wrapped by sulfhydryl and polymer double ligands for sensitive detection of N-acetyl-L-cysteine

N-acetyl-L-cysteine (NAC) as a class of thiols is commonly used in the treatment of lung diseases, detoxification and prevention of liver damage. In this paper, 4-mercaptobenzoic acid (4-MBA) coated and polyvinylpyrrolidone (PVP) attached copper nanoclusters (4-MBA@PVP-CuNCs) were successfully synthesized using a simple one-pot method with an absolute quantum yield of 10.98 %, and its synthetic conditions (like effects of single/double ligands and temperature) were studied intensively. Then Hg2+ could quench the fluorescence of the 4-MBA@PVP-CuNCs and its fluorescence was restored with the addition of NAC. Based on the above principles, an off–on switching system was established to detect NAC. That is, the 4-MBA@PVP-CuNCs-Hg probe was prepared by adding Hg2+ to switch off the fluorescence of the CuNCs by static quenching, and then NAC was added to switch on the fluorescence of the probe based on the chelation of NAC and Hg2+. Moreover, the effects of metal ion types and mercury ion doses for the probe construction were also further discussed. The method showed excellent linearity in the range of 0.05–1.25 µM and low detection limit of 16 nM. Meanwhile, good recoveries in real urine, tablets and pellets were observed, which proved the reliability of the method and provided a convenient, fast and sensitive method for NAC detection.

A Fast and High-Accuracy Foreign Object Detection Method for Belt Conveyor Coal Flow Images with Target Occlusion.

Foreign objects in coal flow easily cause damage to conveyor belts, and most foreign objects are often occluded, making them difficult to detect. Aiming at solving the problems of low accuracy and efficiency in the detection of occluded targets in a low-illumination and dust fog environment, an image detection method for foreign objects is proposed. Firstly, YOLOv5s back-end processing is optimized by soft non-maximum suppression to reduce the influence of dense objects. Secondly, SimOTA label allocation is used to reduce the influence of ambiguous samples under dense occlusion. Then, Slide Loss is used to excavate difficult samples, and Inner-SIoU is used to optimize the bounding box regression loss. Finally, Group-Taylor pruning is used to compress the model. The experimental results show that the proposed method has only 4.20 × 105 parameters, a computational amount of 1.00 × 109, a model size of 1.20 MB, and an mAP0.5 of up to 91.30% on the self-built dataset. The detection speed on the different computing devices is as high as 66.31, 41.90, and 33.03 FPS. This proves that the proposed method achieves fast and high-accuracy detection of multi-layer occluded coal flow foreign objects.