Sequential Detection Research Articles

Introducing a deep learning method for brain tumor classification using MRI data towards better performance

Magnetic resonance imaging (MRI) is widely used to detect brain tumors, but its accuracy depends on the physician's expertise and often requires biopsy confirmation. Deep learning, especially in the field of computer vision, has revolutionized the diagnosis and classification of brain tumors using MRI. This study aims to design a sequential brain tumor detection and classification model based on deep learning and using fully convolutional neural networks. The proposed model consists of two steps: distinguishing non-neoplastic brain from neoplastic brain and determining the tumor type of the latter. Two models were trained using the Brain Tumor MRI Dataset. Four optimizers are studied for three classification tasks (Adam, Nesterov momentum, root-mean-square propagation, and adaptive gradient) to achieve the best results. Adam performed best at distinguishing tumor from non-tumor brains, with 100 % training accuracy and 98 % validation and test accuracy. Nesterov Momentum performed best at differentiating the three tumor types, with 100 % training accuracy and 92 % validation and testing accuracy. Nesterov also performed best on the third classification task, with 100 % training accuracy and 95 % validation and test accuracy. Nesterov-based sequence models show significant results compared to literature works. The proposed Nesterov momentum-based sequence model achieves high accuracy in MRI brain tumor detection and classification.

A tert-Butyldiphenylsilyl-Containing Polyimide-Based Chemosensor for Sequential Detection of Fluoride Ions and Trace Water in Organic Solvents.

A tert-butyldiphenylsilyl-containing polyimide (PI-OSi) has been established as a colorimetric and ratiometric chemosensor for rapid detecting fluoride ions (F-). The UV-vis absorbance ratio value (A322/A288) of PI-OSi in a DMF solution displays a wide linear range change to F- concentrations with a detection limit (DL) value of 2.13 μM. Additionally, adding incremental amounts of F- to a DMF solution of PI-OSi shows an immediate color change to yellow and finally to green from colorless. More interestingly, the resulting PI-OSi plus F- system (PI-OSi·F) could detect trace water in DMF. The A292/A322 value of PI-OSi·F almost linearly increases with low water content, which suggests convenient quantitative sensing of trace water content in DMF. The DL value of PI-OSi·F for sensing water in DMF is determined to be 0.00149% (v/v). The solution color of PI-OSi·F returns to colorless when the water content increases, indicating that PI-OSi·F can conveniently estimate water content in DMF by naked-eye detection. The detection mechanisms confirmed by an 1H NMR study and a DFT calculation involve a F--induced desilylation reaction of PI-OSi to form phenolate anion followed by protonation with trace water. Finally, PI-OSi film was fabricated for the colorimetric detection of F- and water in CH3CN.

Peptide-based probe for colorimetric and fluorescent detection of Cu2+ and S2- in environmental and biological systems

Pollution caused by Copper and hydrogen sulfide pollution has severe adverse effects on the environment and organisms. Real-time, fast and accurate monitoring of Cu2+ and S2- faces serious challenges. In this study, we designed a novel biosensor and synthesized it by mimicking the structure of the main Cu(II)-binding site on bovine serum albumin. As a peptide-based sensor, FGGH (FITC-Gly-Gly-His-NH2) can perform the sequential detection of Cu2+ and S2– by fluorescence and colorimetry. The high water solubility and selectivity make it suitable for monitoring Cu2+ and S2– in environmental water samples with high sensitivity; its limit of detection (LOD) is as low as 1.42 nM for Cu2+ and 22.2 nM for S2–. The paper-based sensing platform of this probe was found to be a promising tool for the on-site visualization of real-time quantitative analysis of Cu2+ and S2– due to its rapid response and recyclable detection characteristics. Additionally, FGGH was successfully used to image Cu2+ and S2– in living cells and zebrafish models with adequate fluorescence stability and low cytotoxicity, providing the first visual evidence of the effect of the interactions between Cu2+ and S2– on the redox homeostasis of organisms.

Sequential Anomaly Detection Under Sampling Constraints

The problem of sequential anomaly detection is considered, where multiple data sources are monitored in real time and the goal is to identify the “anomalous” ones among them, when it is not possible to sample all sources at all times. A detection scheme in this context requires specifying not only when to stop sampling and which sources to identify as anomalous upon stopping, but also which sources to sample at each time instance until stopping. A novel formulation for this problem is proposed, in which the number of anomalous sources is not necessarily known in advance and the number of sampled sources per time instance is not necessarily fixed. Instead, an arbitrary lower bound and an arbitrary upper bound are assumed on the number of anomalous sources, and the fraction of the expected number of samples over the expected time until stopping is required to not exceed an arbitrary, user-specified level. In addition to this sampling constraint, the probabilities of at least one false alarm and at least one missed detection are controlled below user-specified tolerance levels. A general criterion is established for a policy to achieve the minimum expected time until stopping to a first-order asymptotic approximation as the two familywise error rates go to zero. Moreover, the asymptotic optimality is established of a family of policies that sample each source at each time instance with a probability that depends on past observations only through the current estimate of the subset of anomalous sources. This family includes, in particular, a novel policy that requires minimal computation under any setup of the problem.

Boric acid group-functional ruthenium complex as a novel fluorescence probe for robust detection of propyl gallate and tert-butyl hydroquinone by tuning the pH.

This study reported a ruthenium complex-based fluorescence probe, achieving rapid and sequential detection of propyl gallate (PG) and tert-butyl hydroquinone (TBHQ) for the first time by tuning pH only. Under 480nm excitation, probe exhibited intensive emission at 620nm, which was selectively quenched by PG at pH 7.0 due to the covalent binding between the boric acid of probe and o-diphenol hydroxyl of PG. Then pH was tuned to 7.4, the emission was significantly quenched by TBHQ because of the π-π stacking between aromatic rings of probe and paraquinone of TBHQ. This probe realized specific and sensitive detection of PG and TBHQ with wide range and low detection limit (0.26µM for PG and 0.66µM for TBHQ). Furthermore, a portable visual test paper detection platform was built based on this probe for rapid and sensitive detection of antioxidants in food, which was of great significance for market regulation.

Bayesian safety surveillance with adaptive bias correction.

Postmarket safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveillance is challenged by sequential multiple testing and by biases induced by residual confounding in observational data. The current standard approach based on the maximized sequential probability ratio test (MaxSPRT) fails to satisfactorily address these practical challenges and it remains a rigid framework that requires prespecification of the surveillance schedule. We develop an alternative Bayesian surveillance procedure that addresses both aforementioned challenges using a more flexible framework. To mitigate bias, we jointly analyze a large set of negative control outcomes that are adverse events with no known association with the vaccines in order to inform an empirical bias distribution, which we then incorporate into estimating the effect of vaccine exposure on the adverse event of interest through a Bayesian hierarchical model. To address multiple testing and improve on flexibility, at each analysis timepoint, we update a posterior probability in favor of the alternative hypothesis that vaccination induces higher risks of adverse events, and then use it for sequential detection of safety signals. Through an empirical evaluation using six US observational healthcare databases covering more than 360 million patients, we benchmark the proposed procedure against MaxSPRT on testing errors and estimation accuracy, under two epidemiological designs, the historical comparator and the self-controlled case series. We demonstrate that our procedure substantially reduces Type 1 error rates, maintains high statistical power and fast signal detection, and provides considerably more accurate estimation than MaxSPRT. Given the extensiveness of the empirical study which yields more than 7 million sets of results, we present all results in a public R ShinyApp. As an effort to promote open science, we provide full implementation of our method in the open-source R package EvidenceSynthesis.

A fluorescence turn “on-off” imaging probe for sequential detection of Al3+ and L-Cysteine in HeLa cells

At this “Aluminum Age”, exposure to aluminum (metallic or ionic form) is inevitable and inestimable. The presence of aluminum in biological systems is evident but more often aluminum toxicity is less understood. Therefore, the presence of biologically reactive aluminum needs to be identified and quantified. Alongside metals, L-cysteine, an essential amino acid, plays a pivotal role in the homeostasis of cellular oxidative and reductive stress. However, excess (<7g) could be lethal and can lead to death. Thus, in-situ selective detection of aluminum and L-cysteine is of larger interest. Here we report a fluorogenic probe (R) for the sequential selective detection and quantification of Al3+ and L-cysteine in a semi-aqueous medium (3:7; water: DMSO). The probe (R) was synthesized by a one-step acid-mediated condensation reaction between pyridine-3,4-diamine and 2-hydroxy-1-napthaldehyde. The synthesized probe was characterized using 1H and 13C NMR, and HR-Mass spectroscopic techniques. The probe (R) is non-emissive in nature, but on recognition of Al3+, the probe R showed “turn-on” emission (bright yellow colour) showing two emission maxima (522 nm and 547 nm), and no naked eye observable color change. Other competing cations do not show any noticeable fluorescence outcome. The R + Al3+ ensemble can specifically detect L-cysteine among all the essential amino acids by showing a fluorescence “turn-off” response. The sensing mechanism of Al3+ is obeying the chelation-enhanced fluorescence (CHEF) effect. The binding constant of R + Al3+ is 0.3 × 104 M−1. The limit of detection (LoD) for Al3+ and L-cysteine are 2.02 × 10-7 M and 0.5 × 10-5 M respectively. The probe (R) can show maximum efficiency within the pH range (7.0–10.0). The probe is found non-toxic (>80 % cell viability with 15 µM concentration) and employed for the in-vitro fluorescence imaging in the HeLa cell.

Data-adaptive symmetric CUSUM for sequential change detection

Detecting change points sequentially in a streaming setting, especially when both the mean and the variance of the signal can change, is often a challenging task. A key difficulty in this context often involves setting an appropriate detection threshold, which for many standard change statistics may need to be tuned depending on the prechange and postchange distributions. This presents a challenge in a sequential change detection setting when a signal switches between multiple distributions. Unfortunately, change point detection schemes that use the log-likelihood ratio, such as cumulative sum (CUSUM) and the generalized log-likelihood ratio (GLR), are quick to react to changes but are not symmetric when both the mean and the variance of the signal change. This makes it difficult to set a single threshold to detect multiple change points sequentially in a streaming setting. We propose a modified version of CUSUM that we call data-adaptive symmetric CUSUM (DAS-CUSUM). The DAS-CUSUM procedure is symmetric for changes between distributions, making it suitable to set a single threshold to detect multiple change points sequentially in a streaming setting. We provide results that relate the expected detection delay and average run length for our proposed procedure when both prechange and postchange distributions are normally distributed. Experiments on simulated and real-world data show the utility of DAS-CUSUM.

A novel reversible fluorescent probe for sequential detection of Al3+ and HPO42− based on caffeic acid and its applicability in cell imaging

In this work, we focused on synthesizing a new fluorescent probe HBA based on caffeic acid for detecting Al3+/HPO42− by the mode of “off-on-off”. HBA alone exhibited a weak fluorescence emission in aqueous solution. When Al3+ was added into the HBA solution, a significant green fluorescence was found at 498 nm. However, the introduction of HPO42− to the solution of [HBA-Al3+] complex induced the disappearance of green fluorescence. The detection limit of HBA was calculated as low as 41.7 nM for Al3+ and 62.1 nM for HPO42−, respectively. Probe HBA exhibited good cell-membrane penetrability and had the potential to trace Al3+/HPO42− in biological systems.

Application of learning analytics for sequential patterns detection associated with moments of distraction in students in e‐learning platforms

AbstractOwing to the positive impact of technology on education, in recent years, e‐learning platforms have increased in popularity and implementation. However, different authors agree that, despite the benefits of using these platforms, one of the main problems is the distraction during the interaction between the student and the E‐Learning platform, since the students visit web resources as social networks, which is not related to their learning process. to identify patterns associated with moments of distraction that students can present on these platforms. Following the above, this study aims to apply Learning Analytics using the Density‐based spatial clustering of applications with noise (DBSCAN) clustering technique and the PrefixSpan algorithm, to identify frequent sequential patterns of interaction, before a distraction event of the student from their interaction with a virtual learning platform. From this, clustering with a Silhouette coefficient close to 0.75 is obtained, presenting good separation, high cohesion, and compaction in the generated groups. Additionally, a slight characterization of each group is achieved based on the most frequent sequential patterns of interaction. Finally, in this study, a successful integration between DBSCAN and PrefixSpan is achieved, to identify sequential interaction patterns associated with moments of distraction in E‐Learning platforms, which generates a starting point for later studies to make the sequential recommendation of educational resources from these patterns.

Sequential Recognition of Bisulfate and Acetate by a Ruthenium(II) Complex: Experimental and Theoretical Studies

AbstractNew complex [RuLCl2(PPh3)] (L=N,N‐bis(2‐hydroxy‐5‐nitrobenzaldehyde)‐2,2’‐diaminodiethylamine) was prepared and characterized analytically and was employed as a luminescent chemosensor for the detection of anions. The results show that [RuLCl2(PPh3)] can detect HSO4− and AcO− selectively and in sequential order in H2O‐CH3CN (8 : 2, v/v) at pH 7.0. The spectral binding, titration, and interference analyses reveal that the addition of HSO4− to [RuLCl2(PPh3)] results in an intense fluorescence emission (IF/I0=6.55). This shows that HSO4− interacts suitably with the complex to switch ON the fluorescence which could be explained by inhibition of a PET mechanism as the above addition forms [RuLCl(HSO4)(PPh3)] in the excited state. Selectivity interaction of complex [RuLCl2(PPh3)] with HSO4− results in the formation of [RuLCl(HSO4)(PPh3)] in water but the system exhibits negligible change in its emission except for AcO−, which enhances fluorescence intensity. For the addition of AcO− to [RuLCl2(PPh3)], the system [RuLCl(AcO)(PPh3)] is formed, however, the adding of HSO4− to [RuLCl(AcO)(PPh3)] does not show any change in the intensity, suggesting that there exists a logic gate function for the addition of HSO4− followed by AcO− to [RuLCl2(PPh3)]. This finding is interesting because [RuLCl2(PPh3)] can act as a fast selective chemosensor for the sequential detection of HSO4− and AcO−.

A note on online change point detection

We investigate sequential change point estimation and detection in univariate nonparametric settings, where a stream of independent observations from sub-Gaussian distributions with a common variance factor and piecewise-constant but otherwise unknown means are collected. We develop a simple CUSUM-based methodology that provably control the probability of false alarms or the average run length while minimizing, in a minimax sense, the detection delay. We allow for all the model parameters to vary in order to capture a broad range of levels of statistical hardness for the problem at hand. We further show how our methodology is applicable to the case in which multiple change points are to be estimated sequentially.

Adaptive Sampling and Quick Anomaly Detection in Large Networks

The monitoring of data streams with a network structure have drawn increasing attention due to its wide applications in modern process control. In these applications, high-dimensional sensor nodes are interconnected with an underlying network topology. In such a case, abnormalities occurring to any node may propagate dynamically across the network and cause changes of other nodes over time. Furthermore, high dimensionality of such data significantly increased the cost of resources for data transmission and computation, such that only partial observations can be transmitted or processed in practice. Overall, how to quickly detect abnormalities in such large networks with resource constraints remains a challenge, especially due to the sampling uncertainty under the dynamic anomaly occurrences and network-based patterns. In this paper, we incorporate network structure information into the monitoring and adaptive sampling methodologies for quick anomaly detection in large networks where only partial observations are available. We develop a general monitoring and adaptive sampling method and further extend it to the case with memory constraints, both of which exploit network distance and centrality information for better process monitoring and identification of abnormalities. Theoretical investigations of the proposed methods demonstrate their sampling efficiency on balancing between exploration and exploitation, as well as the detection performance guarantee. Numerical simulations and a case study on power network have demonstrated the superiority of the proposed methods in detecting various types of shifts. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Continuous monitoring of networks for anomalous events is critical for a large number of applications involving power networks, computer networks, epidemiological surveillance, social networks, etc. This paper aims at addressing the challenges in monitoring large networks in cases where monitoring resources are limited such that only a subset of nodes in the network is observable. Specifically, we integrate network structure information of nodes for constructing sequential detection methods via effective data augmentation, and for designing adaptive sampling algorithms to observe suspicious nodes that are likely to be abnormal. Then, the method is further generalized to the case that the memory of the computation is also constrained due to the network size. The developed method is greatly beneficial and effective for various anomaly patterns, especially when the initial anomaly randomly occurs to nodes in the network. The proposed methods are demonstrated to be capable of quickly detecting changes in the network and dynamically changes the sampling priority based on online observations in various cases, as shown in the theoretical investigation, simulations and case studies.

A mitochondria-targeted phosphorescent probe for sequentially detecting Cu2+ and cysteine and its imaging in living cells and in vivo

This study reported a unique mitochondria-targetable phosphorescent 'on-off-on' probe, Ir-DPA, based on an iridium(III) complex derivative for the sequential and specific detection of Cu2+ and Cys in vitro and in vivo. Probe Ir-DPA was constructed by a lipophilic cation unit as the chromophore as well as the mitochondria biomarker and a dipicolylamine (DPA) group as the recognition unit for Cu2+, followed by removal of Cu2+ specifically in the presence of Cys. The probe itself was strongly emissive at 570 nm. Upon addition of Cu2+, marked quenching occurred as result of the binding of Cu2+ with DPA moiety of this probe, accompanied by a distinct naked-eye luminescence color change from orange yellow to colorless. Additionally, further addition of Cys, the displacement of Cu2+ from in situ generated Ir-DPA-Cu ensemble because of the large nucleophilicity of cysteine toward Cu2+ led to the release of probe, giving rise to the luminescence recovery, as well as a relevant chromogenic change from colorless to orange yellow. Ir-DPA showed a large Stokes shift of 200 nm and high selectivity and sensitivity for the rapid detection of Cu2+ and Cys under physiological conditions, with low detection limits of 0.04 μM and 1.21 μM for Cu2+ and Cys, respectively. More importantly, the probe successfully targeted mitochondria, sensitively detected Cu2+ and exogenously or endogenously generated Cys both in mitochondria of living cells and zebrafish. Finally, Ir-DPA was capable of quantifying Cu2+ level in water samples and the in situ generated ensemble identifying Cys in human serum. These results indicated the great potential of Ir-DPA for monitoring Cu2+ and Cys in environmental, biological systems, and mitochondrial processes.

Selected aspects of measurement data processing in electronic warfare devices

Electronic warfare (Polish acronym WRE ‒ walka radioelektroniczna) is a set of militaryactions based on the use of the electromagnetic spectrum and it includes:— reception and identification of electromagnetic emissions,— reducing the effectiveness of the enemy’s electronic devices,— enabling effective use of the spectrum by own forces.The task of the WRE in peacetime is to obtain information about radio-electronic devices, especiallyradars. These tasks are performed by ELINT (Electronic Intelligence) and/or ESM (Electronic WarfareSupport Measures) devices. They operate passively in the 0.5÷18 GHz band, their detection is difficultand the information they provide allows to detect a threat to own forces and to take appropriatecountermeasures. Radar recognition is a complex process that involves the following stages. The firststage: gathering measurements and their grouping into the so-called packets and graphical display ofthe received pulse trains. The measurements are represented by the so-called measurement vectors(descriptors) containing signal parameters, including the TOA (Time of Arrival) pulse with nanosecondaccuracy. The second stage: associating pulse packets with logical sources in order to obtainrepresentative data strings. This stage is signal sorting and deinterleaving. The third stage: estimationof the WS signal vector based on the associated pulse packets. The final stage of data processing is thecomparison of the obtained signal descriptor parameters WS with Database (BD) radar patterns. Thereconnaissance result allows for the assessment of the threat resulting from the operation of this radar.Radar recognition requires precise determination of many parameters of its signal. For pulsed radars,important parameters are the Pulse Repetition Interval (PRI) and the type of PRI modulation. Radarsare characterised by a high complexity of PRI changes. This provides great utility and makes it possibleto distinguish even radar units and changes in their locations. This allows for the detection and fullidentification of objects that use radars (e.g. ships) with the given object completely out of sight; it alsoallows to predict intentions of the target. The basic inter-pulse PRI modulations include: stagger, dwelland switch, jitter or sliding. PRI changes in radar signals may be periodic with long cycles, difficultto identify in cluttered conditions. Unfortunately, the received pulse trains are usually distorted. Thismakes the WS estimation process difficult ‒ especially in automatic mode. PRI analysis has receivedmuch attention in the literature. The methods are computationally complex and have numerouslimitations. This article presents ASWC algorithm (Sequential Cycle Detection Algorithm) of PRIproprietary, with relatively low computational complexity. Examples of test results confirming its higheffectiveness in automatic PRI analysis performed under interference conditions were also presented.Keywords: radars, electronic warfare, radar recognition, ELINT, ESM, pulse descriptor, PRI

Facile synthesis of NS-doped carbon dots as sensitive “ON-OFF-ON” fluorescent sensor for Cu2+ and GSH detection

In this paper, a novel nitrogen and sulfur co-doped carbon quantum dots (NS-CQDs) were successfully prepared by a dehydration exothermic carbonization method. The NS-CQDs exhibited uniform size distribution, splendid photostability, and bright fluorescence emission with a fluorescence quantum yield of 24.1 %. It was found that Cu2+ could quench the fluorescence at 467 nm based on the static quenching effect when Cu2+ was added to the NS-CQDs. At this time, the fluorescence sensor changed from the “ON” state to the “OFF” state. When glutathione (GSH) was further introduced into the NS-CQDs/Cu2+ system, the fluorescence intensity of NS-CQDs was amazingly restored through the coordination reaction between GSH and Cu2+. The fluorescence sensor changed from the “OFF” state to the “ON” state. Therefore, NS-CQDs as an “ON-OFF-ON” fluorescence sensor was designed for sequential detection of Cu2+ and GSH. Furthermore, this study successfully demonstrated the sensor’s ability to selectively detect Cu2+ and GSH within a wide concentration range. Specifically, the detection range for Cu2+ was 0.1 μM−200.0 μM with a detection limit of 0.07 μM, while the range for GSH was 0.6 μM−180.0 μM with a detection limit of 0.1 μM. Most importantly, the NS-CQDs nanosensor could reliably monitor Cu2+ and GSH levels in human serum samples, with significant potential for practical applications.

Online Simulator-Based Experimental Design for Cognitive Model Selection

The problem of model selection with a limited number of experimental trials has received considerable attention in cognitive science, where the role of experiments is to discriminate between theories expressed as computational models. Research on this subject has mostly been restricted to optimal experiment design with analytically tractable models. However, cognitive models of increasing complexity with intractable likelihoods are becoming more commonplace. In this paper, we propose BOSMOS, an approach to experimental design that can select between computational models without tractable likelihoods. It does so in a data-efficient manner by sequentially and adaptively generating informative experiments. In contrast to previous approaches, we introduce a novel simulator-based utility objective for design selection and a new approximation of the model likelihood for model selection. In simulated experiments, we demonstrate that the proposed BOSMOS technique can accurately select models in up to two orders of magnitude less time than existing LFI alternatives for three cognitive science tasks: memory retention, sequential signal detection, and risky choice.

A novel “off-on-off” sensor for consecutive detection of Al3+ and [formula omitted] as well as application in food and living cells

A novel julolidine based fluorescent sensor JBP was designed and synthesized for the sequential detection of Al3+ and H2PO4- in aqueous media. JBP exhibited high selectivity towards Al3+ with a Stokes shift of 132 nm. The fluorescence of the JBP-Al3+ complex could be quenched upon the addition of H2PO4-. The sequential detection of Al3+ and H2PO4- could be repeated up to three times. The detection limits for Al3+ and H2PO4- were calculated to be 5.7 × 10−8 M and 1.48 × 10−7 M, respectively. Additionally, JBP was effectively employed to detect Al3+ in environmentally relevant water samples, herbal teas, and various food samples (including edible alum, baking powder, fritters, and jellyfish) using only a smartphone. Importantly, JBP exhibited low toxicity and was capable of traversing cell membranes to detect Al3+ and H2PO4- within living cells.

An efficient off–on fluorescence probe based on EDA functionalized Ti3C2 MXene quantum dots for sequential detection of Fe3+ and histamine

MXene quantum dots (MQDs) exhibit vast application prospection the field of fluorometric sensing, but how to efficiently utilize MQDs for the selective detection of two components still remains a challenge. Herein, an off–on fluorescence sensor based on ethylenediamine-functionalized Ti3C2 MQDs (EDA-MQDs) was designed to selectively and sequentially detect Fe3+ and histamine. EDA-MQDs were synthesized using a facile ultrasonic-hydrothermal method and exhibited bright blue fluorescence with a quantum yield up to 21.12 %. Inner filter effect occurred between Fe3+ and EDA-MQDs, resulting in fluorescence quenching at 460 nm. Meanwhile, on account of the strong chelation between imidazole group on histamine and Fe3+, the Fe3+ concentration in the system was reduced, so the fluorescence of EDA-MQDs was recovered. Thus, the as-designed fluorescence probe was used for dual-detection of Fe3+ and histamine in real samples with satisfactory sensitivity and selectivity. The proposed sensing method was verified to possess good linear response in the range of 1–100 μM and 1–60 μM with detection limits of 0.11 μM and 46 nM for Fe3+ and histamine, respectively. This work provided a highly effective tool for selective detecting histamine and Fe3+ as well as tendered a novel strategy for functionalized Ti3C2 MQDs in food analysis.

Biogenic Carbon Dot-Embedded Chitosan Hydrogels as a Two-Stage Fluorescence OFF–ON Probe for Sequential and Ratiometric Detection of Fe(III) and Glutathione

Biogenic Carbon Dot-Embedded Chitosan Hydrogels as a Two-Stage Fluorescence OFF–ON Probe for Sequential and Ratiometric Detection of Fe(III) and Glutathione