Single-Probe-Based Colorimetric and Photothermal Dual-Mode Identification of Multiple Bacteria

Advancements in magnetic nanomaterial-assisted sensitive detection of foodborne bacteria: Dual-recognition strategies, functionalities, and multiplexing applications

Foodborne bacterial diseases are a major cause of human death. Sensitively quantifying those bacteria in foodstuffs is crucial for effective prevention. Yet, the matrix effect from abundant food interferents challenges this goal. Magnetic nanomaterials have been extensively utilized as effective sample pretreatment agents to facilitate the sensitive detection of bacterial pathogens, benefiting from their contribution to mitigating interference in food matrices. The advancement of magnetic scaffold-based biosensors in monitoring foodborne bacteria is reviewed in this work. This review highlights the dual-recognition strategies, which contribute to superior affordability and applicability in bacteria monitoring. The functionalities of magnetic nanoscaffolds in constructing pathogen-targeted biosensors are cataloged into three sections: magnetic separation mediators, signal generation probes, and agents for inactivating bacterial pathogens. Additionally, magnetic nanocomposite-driven multiplexing determination is critically discussed, with different detection approaches are highlighted. Further perspectives regarding superior multifunctional magnetic probes, tunable selection of bioreceptor, portable detection devices, smart identification, and differentiation of bacteria mixtures are introduced.

Magnetic Prussian blue nanoparticles to enhance dual-readout lateral flow immunoassay for Salmonella typhimurium detection

BACKGROUND

Traditional immunochromatographic assay (ICA) test strips typically utilize colloidal gold nanoparticles as colorimetric signal reporters, but they often suffer from compromised sensitivity and quantitative accuracy, making them inadequate for the stringent demands of trace-level screening. In addition, the heterogeneous nature of food matrices, such as dairy products, meat, and ready-to-eat fruits and vegetables, complicates the accuracy and reliability of ICA for detecting foodborne pathogens. Therefore, enhancing the detection accuracy of ICA is critical to meet the stringent requirements for pathogen detection in complex food samples.

RESULTS

Here, we synthesized a novel Prussian blue-coated Fe3O4 nanoparticle (Fe3O4@PB) via a simple in-situ growth method. The synthesized Fe3O4@PB nanoparticles demonstrate a high magnetization strength and excellent photothermal conversion efficiency, enabling their application as efficient probes in ICA. Leveraging these properties, we developed a dual-readout ICA incorporating both colorimetric and photothermal modalities for the sensitive detection of Salmonella typhimurium in milk and lettuce samples. The detection limit (LOD) for Fe3O4@PB-ICA reached 2.5 × 103 CFU/mL in colorimetric formats and 1.04 × 103 CFU/mL in photothermal formats, representing approximately 4-fold and 10-fold improvements compared to AuNP-ICA (LOD = 1 × 104 CFU/mL). Furthermore, the average recovery rates ranged from 89.1 % to 111.9 %, while the coefficients of variation below 13.7 %, demonstrating excellent accuracy and precision.

SIGNIFICANCE

The proposed Fe3O4@PB serves as effective probe for purifying and enriching target analytes, significantly improving the accuracy and sensitivity of colorimetric and photothermal immunochromatographic analysis applications. By leveraging the magnetic and photothermal capabilities of Fe3O4@PB, the developed Fe3O4@PB-ICA represents a promising rapid diagnostic platform for the sensitive detection of foodborne pathogens and other analytes.

Identification and Quantification of Multiple Pathogenic Escherichia coli Strains Based on a Plasmonic Sensor Array

Pathogenic Escherichia coli (E. coli) is a widespread and clinically significant foodborne pathogen. Due to its high mutation rates and phenotypic diversity, rapid identification of its subtypes remains challenging and prone to false positives when detecting single strains. In this study, we developed a plasmonic sensor array with high-dimensional signal readouts (ζ-potential, dynamic light-scattering (DLS), surface-enhanced Raman scattering (SERS), and ultraviolet-visible (UV-vis) absorption spectra) for the selective discrimination of pathogenic E. coli, integrated with bacterial culture methods. The plasmonic sensor units demonstrated strong encoding capabilities, facilitating the differentiation of subtle variations among various E. coli strains and showing excellent anti-interference performance. The array realized different pathogenic E. coli strains, bacterial mixture identification, and even quantitative detection. Remarkably, the working concentration for the sensor array was notably low, at 104 CFU/mL. Finally, by incorporating bacterial isolation culture, the designed sensor array obtained 100% accuracy in detecting E. coli in real food samples. These findings highlight the sensor array's potential for applications in food safety monitoring and clinical diagnostics, offering a sensitive, rapid, and reliable tool for pathogen detection in complex samples.

Frost-resistant hydrogel colorimetric sensing array for accurate detection of foodborne pathogens in cold chain systems

The rapid and precise identification of foodborne pathogens in low-temperature environments is critically important yet challenging, particularly within the cold chain system. This study introduces a frost-resistant colorimetric sensing array (FR-CSA), based on polyvinyl alcohol/polyacrylamide/lithium chloride (PVA/PAM/LiCl) double network (DN) hydrogels, designed for the detecting and classifying foodborne pathogens at 4 °C and -20 °C. The integration of LiCl into the PVA/PAM DN hydrogels results in a dense 3D nano-network that significantly lowers the freezing point, enhancing the sensing functionality at subzero temperatures, addressing a critical gap where conventional CSAs fail to perform. The FR-CSA demonstrates high performance, accurately responding to twelve common volatile organic compounds (VOCs) emitted by pathogens and generating distinctive color response patterns. Employing principal component analysis (PCA) and linear discriminant analysis (LDA), the FR-CSA effectively identifies four prevalent low-temperature foodborne pathogens: Staphylococcus aureus, Listeria monocytogenes, E. coli O157:H7, and Salmonella. Additionally, the FR-CSA has been successfully applied to a chicken breast meat model, confirming its efficacy across the tested temperature range. This work presents an innovative approach for pathogen detection in critical low-temperature settings of cold storage, offering significant potential contributions to food preservation within the cold chain system.

Atomically Fe(Ⅲ) anchored metal-organic frameworks-based fluorescent nanozyme for smartphone-adopted chemiluminescence-fluorescence dual-mode analysis of Uric acid

BACKGROUND

Chemiluminescence (CL) analysis is a promising analytical method with advantages including easy operation, high sensitivity and simple instrument. However, the single CL mode usually suffered from poor stability and reproducibility as a result of the flash-type nature of luminescent molecules, leading to false positive or negative results in practical applications. Dual-mode detection is an advanced sensing methodology that identifies analytes through independent output signals. This approach has the ability to circumvent the inherent constraints of individual sensing modes while integrating their respective strengths, thereby yielding a synergistic enhancement in the detection system.

RESULTS

Herein, a chemiluminescence-fluorescence (FL) dual-mode analysis and imaging system is designed by constructing an atomically Fe(Ⅲ) anchored PCN-224 peroxidase-mimicking nanozyme (PCN-224/Fe(Ⅲ)) and achieve an ultrasensitive detection of Uric Acid (UA). The multifunctional PCN-224/Fe(Ⅲ) serves as a high-efficiency co-reaction promoter in the generation of reactive oxygen species (ROS) in both the CL and FL system, while also demonstrating exceptional capabilities as fluorescent nanoprobes. Ultimately, a smartphone-adopted CL imaging device was developed to achieve a visual CL detection through the design of portable paper-based chips. Besides, with the assistance of the TMB-mediated fluorescence energy resonance transfer, the fluorescent PCN-224/Fe(Ⅲ) nanoprobes exhibited good fluorescence detection performance for UA. The limit of detection was achieved as low as 2.45 × 10-10 M and 1.99 × 10-9 M in the CL and FL mode, respectively.

SIGNIFICANCE

This study engineered an atomically Fe(Ⅲ)- MOF-based multifunctional nanozyme and developed an innovative approach for creating CL-FL dual-mode analysis and imaging detection for UA. The proposed CL-FL dual-mode detection system not only provided a portable and sensitive method for UA detection but also offered valuable insights into the mechanism of the co-reaction promoter enhanced CL and FL analysis.

The Identification of Oral Cariogenic Bacteria through Colorimetric Sensor Array Based on Single-Atom Nanozymes

Effective identification of multiple cariogenic bacteria in saliva samples is important for oral disease prevention and treatment. Here, a simple colorimetric sensor array is developed for the identification of cariogenic bacteria using single-atom nanozymes (SANs) assisted by machine learning. Interestingly, cariogenic bacteria can increase oxidase-like activity of iron (Fe)─nitrogen (N)─carbon (C) SANs by accelerating electron transfer, and inversely reduce the activity of Fe─N─C further reconstruction with urea. Through machine-learning-assisted sensor array, colorimetric responses are developed as "fingerprints" of cariogenic bacteria. Multiple cariogenic bacteria can be well distinguished by linear discriminant analysis and bacteria at different genera can also be distinguished by hierarchical cluster analysis. Furthermore, colorimetric sensor array has demonstrated excellent performance for the identification of mixed cariogenic bacteria in artificial saliva samples. In view of convenience, precise, and high-throughput discrimination, the developed colorimetric sensor array based on SANs assisted by machine learning, has great potential for the identification of oral cariogenic bacteria so as to serve for oral disease prevention and treatment.

Digitalization of Colorimetric Sensor Technologies for Food Safety

Colorimetric sensors play a crucial role in promoting on-site testing, enabling the detection and/or quantification of various analytes based on changes in color. These sensors offer several advantages, such as simplicity, cost-effectiveness, and visual readouts, making them suitable for a wide range of applications, including food safety and monitoring. A critical component in portable colorimetric sensors involves their integration with color models for effective analysis and interpretation of output signals. The most commonly used models include CIELAB (Commission Internationale de l'Eclairage), RGB (Red, Green, Blue), and HSV (Hue, Saturation, Value). This review outlines the use of color models via digitalization in sensing applications within the food safety and monitoring field. Additionally, challenges, future directions, and considerations are discussed, highlighting a significant gap in integrating a comparative analysis toward determining the color model that results in the highest sensor performance. The aim of this review is to underline the potential of this integration in mitigating the global impact of food spoilage and contamination on health and the economy, proposing a multidisciplinary approach to harness the full capabilities of colorimetric sensors in ensuring food safety.

Portable hydrogel kit driven by bimetallic carbon dots nanozyme for H2O2-self-supplying dual-modal monitoring of atmospheric CH3SH

Due to the typical volatility of gaseous pollutant methyl mercaptan (CH3SH), the development of a facile, reliable, and accurate onsite environmental surveillance of highly toxic CH3SH faces many challenges, but it is critical to environmental atmosphere assessment and safeguarding public health. Here, we prepared a novel bimetallic carbon dots (Fe&Cu@CDs) nanozyme with high peroxidase-mimicking activity to design a portable hydrogel kit for onsite visual H2O2-self-supplying enzymatic cascade catalytic colorimetric and photothermal signal synergistic amplification dual-modal monitoring of CH3SH in atmospheric environment. Assisted by alcohol oxidase (AOX), CH3SH could be specifically converted into H2O2 for oxidizing chromogenic substrate 3,3',5,5'-tetramethylbenzidine (TMB) catalyzed by Fe&Cu@CDs to produce dark blue ox-TMB with absorption at 652 nm and photothermal characters. Consequently, a CH3SH concentration-dependent change both in naked-eye color and photothermal effect-triggered temperature were observed. By hybridizing AOX-assisted Fe&Cu@CDs + TMB with agarose, a H2O2-self-supplying colorimetric and photothermal signal synergistic amplification sensory hydrogel kit integrated with Color Picker APP-installed smartphone and 660 nm laser-equipped handheld thermal imager for CH3SH was proposed with acceptable results in atmospheric environment around wastepile (e.g., solid waste and food waste piles), which exhibited great potentials to further develop commercial onsite monitoring platforms in warning-early abnormal atmospheric CH3SH for safeguarding environmental health.

Multiple-Emitting Luminescent Metal-Organic Framework as an Array-on-a-MOF for Rapid Screening and Discrimination of Nitroaromatics

Fluorescence array technologies have attracted great interest in the sensing field because of their high sensitivity, low cost, and capability of multitarget detection. However, traditional array sensing relies on multiple independent sensors and thus often requires time-consuming and laborious measurement processes. Herein, we introduce a novel fluorescence array strategy of the array-on-a-metal-organic framework (MOF), which integrates multiple array elements into a single MOF matrix to achieve facile sensing and discrimination of multiple target analytes. As a proof-of-concept system, we constructed a luminescent MOF containing three different emitting channels, including a lanthanide ion (europium/Eu3+, red emission), a fluorescent dye (7-hydroxycoumarin-4-acetic acid/HCAA, blue emission), and the MOF itself (UiO-66-type MOF, blue-violet emission). Five structurally similar nitroaromatic compounds (NACs) were chosen as the targets. All three channels of the array-on-a-MOF displayed rapid and stable fluorescence quenching responses to NACs (response equilibrium achieved within 30 s). Different responses were generated for each channel against each NAC due to the various quenching mechanisms, including photoinduced electron transfer, energy competition, and the inner filter effect. Using linear discriminant analysis, the array-on-a-MOF successfully distinguished the five NACs and their mixtures at varying concentrations and demonstrated good sensitivity to quantify individual NACs (detect limit below the advisory concentration in drinking water). Moreover, the array also showed feasibility in the sensing and discrimination of multiple NACs in real water samples. The proposed "array-on-a-MOF" strategy simplifies multitarget discrimination procedures and holds great promise for various sensing applications.

Hydrogen Bonding-Induced Aggregation of Chiral Functionalized AuNS@Ag NPs for Photothermal Enantioanalysis

Toward the challenges of signaling transduction amplified in enantioselective recognition, we herein devised an innovative strategy for highly selective recognition of amino acids and their derivatives, leveraging photothermal effects. In this approach, bifunctional l-ascorbic acid is employed to reduce silver ions in situ on Au nanostars. Simultaneously, its oxidate (l-dehydroascorbic acid) is bonded to the silver shell as a chiral selector to prepare chiral nanoparticles (C-AuNS@Ag NPs) with the ability to recognize stereoisomers and sensitively modulate the photothermal effect. l-Dehydroascorbic acid can selectively capture one of the enantiomers of the two forms through hydrogen bonding and drive aggregation of the nanoparticles, which sharply enhances the photothermal effect. Consequently, the two forms of the system exhibit a significant temperature difference, which enables the discrimination and quantification of enantiomers. Our strategy verifies that six chiral amino acids and their derivatives can be discriminated with enantioselective response values of up to 79. Additionally, the chiral recognition mechanism was revealed through density functional theory (DFT) calculations, providing a paradigm shift in the development of enantiomeric recognition strategies.

Glucose-metabolism-triggered colorimetric sensor array for point-of-care differentiation and antibiotic susceptibility testing of bacteria

Simple and sensitive discrimination of multiple bacteria and antimicrobial susceptibility test (AST) are significant for food safety, clinical diagnosis and treatment. Herein, based on different metabolic ability of bacteria on glucose, we presented a colorimetric sensor array for point-of-care testing (POCT) of multiple bacteria with methyl red (MER), bromothymol blue (BTB) and bromocresol green (BCG) as probes. Different bacteria resulted in different color changes of three probes, which was converted to RGB (Red (R)/Green (G)/Blue (B)) signals by the color recognizer APP loaded on smartphone. The sensor array performed differentiation of eleven species of bacteria, achieving the quantitative analysis of individual bacteria in tap water and differentiation of bacterial mixtures. Interestingly, the sensor array can be used for AST and evaluating minimal inhibitory concentration (MIC) of antibiotics to bacteria. The research provided meaningful guidance for distinguishing multiple bacteria and evaluating MIC, presenting great potential in practical application.

Double phage displayed peptides co-targeting-based biosensor with signal enhancement activity for colorimetric detection of Staphylococcus aureus

The development of simple, fast, sensitive, and specific strategies for the detection of foodborne pathogenic bacteria is crucial for ensuring food safety and promoting human health. Currently, detection methods for Staphylococcus aureus still suffer from issues such as low specificity and low sensitivity. To address this problem, we proposed a sensitivity enhancement strategy based on double phage-displayed peptides (PDPs) co-targeting. Firstly, we screened two PDPs and analyzed their binding mechanisms through fluorescent localization, pull-down assay, and molecular docking. The two PDPs target S. aureus by binding to specific proteins on its outer membrane. Based on this phenomenon, a convenient and sensitive double PDPs colorimetric biosensor was developed. Double thiol-modified phage-displayed peptides (PDP-SH) enhance the aggregation of gold nanoparticles (AuNPs), whereas the specific interaction between the double PDPs and bacteria inhibits the aggregation of AuNPs, resulting in an increased visible color change before and after the addition of bacteria. This one-step colorimetric approach displayed a high sensitivity of 2.35 CFU/mL and a wide detection range from 10-2 × 108 CFU/mL. The combination with smartphone-based image analysis improved the portability of this method. This strategy achieves the straightforward, highly sensitive and portable detection of pathogenic bacteria.

A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification

Biosensors have been widely used for bacteria determination with great success. However, the "lock-and-key" methodology used by biosensors to identify bacteria has a significant limitation: it can only detect one species of bacteria. In recent years, optical (fluorescent and colorimetric) sensor arrays are gradually gaining attention from researchers as a new type of biosensor. They can acquire multiple features of a target simultaneously, form a feature pattern, and determine the bacteria species with the help of pattern recognition/machine learning algorithms. Previous reviews in this area have focused on the interaction between the sensor array and bacteria or the materials used to make the sensors. This review, on the other hand, will provide researchers with a better understanding of the field by discussing fluorescent and colorimetric sensor arrays based on the mechanism of optical signal generation. These sensor arrays will be compared based on the identified species. Finally, we will discuss the limitations of these sensor arrays and explore possible solutions.

Tesla valve-assisted biosensor for dual-mode and dual-target simultaneous determination of foodborne pathogens based on phage/DNAzyme co-modified zeolitic imidazolate framework-encoded probes

Sensitive and accurate detection of multiplex foodborne pathogens is crucial for food safety. In this work, a dual-mode and dual-target biosensor regulated by a Tesla valve was established for simultaneously determining Escherichia coli O157:H7 (E. coli) and Salmonella typhimurium (S. T). Two zeolitic imidazolate framework (ZIF-8) signal probes decorated with electroactive materials (ferrocene or methylene blue), DNAzyme, and different phages were synthesized to specifically recognize the targets and generate fluorescent/electrochemical dual-mode signals. In the presence of bacteria, they were captured and enriched on two individual working electrodes through the modified 4-mercaptophenylboric acid. The encoded signal probes added on different working electrodes could be conjugated with the corresponding target bacteria depending on the specificity of phages. Under the acidic condition, the DNAzyme could catalyze click chemistry for fluorescent signals. Simultaneously, the released ferrocene and methylene blue from ZIF-8 could generate electrochemical signals at different potentials. Benefiting from the flow regulation feature of the Tesla valve, the triggered fluorescent and electrochemical signals in the two individual electrodes would not influence each other, achieving simultaneous dual-mode and dual-target determination of foodborne pathogens. It depicted good linearity ranged 10-10⁷ CFU mL^-1. And the corresponding detection of limits were 5 CFU mL^-1 and 8 CFU mL^-1 for two bacteria, respectively. A low false positive was realized through the dual-mode strategy. The proposed biosensor can not only on-site, specifically, and sensitively determine E. coli and S. T, but also provide the wide prospect in rapid screening of other foodborne pathogens.

Sensing of Antibiotic-Bacteria Interactions

Sensing of antibiotic-bacteria interactions is an important area of research that has gained significant attention in recent years. Antibiotic resistance is a major public health concern, and it is essential to develop new strategies for detecting and monitoring bacterial responses to antibiotics in order to maintain effective antibiotic development and antibacterial treatment. This review summarizes recent advances in sensing strategies for antibiotic-bacteria interactions, which are divided into two main parts: studies on the mechanism of action for sensitive bacteria and interrogation of the defense mechanisms for resistant ones. In conclusion, this review provides an overview of the present research landscape concerning antibiotic-bacteria interactions, emphasizing the potential for method adaptation and the integration of machine learning techniques in data analysis, which could potentially lead to a transformative impact on mechanistic studies within the field.

Engineering Entropy-Driven Nanomachine-Mediated Morphological Evolution of Anisotropic Silver Triangular Nanoplates for Colorimetric and Photothermal Biosensing

A DNA/RNA biosensor capable of single nucleotide variation (SNV) resolution is highly desirable for drug design and disease diagnosis. To meet the point-of-care demand, rapid, cost-effective, and accurate SNV detection is of great significance but still suffers from a challenge. In this work, a unique nonenzymatic dual-modal (multicolorimetric and photothermal) visualization DNA biosensor is first proposed for SNV identification on the basis of an entropy-driven nanomachine with double output DNAs and coordination etching of anisotropic silver triangular nanoplates (Ag TNPs). When the target initiates the DNA nanomachine, the liberated multiple output DNAs can be utilized as a bridge to produce a superparamagnetic sandwich complex. The incoming poly-C DNA can coordinate and etch highly active Ag+ ions at the tips of Ag TNPs, causing a shift in the plasmon peak of Ag TNPs from 808 to 613 nm. The more target DNAs are introduced, the more output DNAs are released and thus the more Ag+ ions are etched. The noticeable color changes of anisotropic Ag TNPs can be differentiated by "naked eye" and accurate temperature reading. The programmable DNA nanotechnology and magnetic extraction grant the high specificity. Also, the SNV detection results can be self-verified by the two-signal readouts. Moreover, the dual-modal biosensor has the advantages of portability, cost-effectiveness, and simplicity. Particularly, the exclusive entropy-driven amplifier liberates double output DNAs to bridge more poly-C DNAs, enabling the dual-modal visualization DNA biosensor with improved sensitivity.

Dual enzyme induced colorimetric sensor for simultaneous identifying multiple pathogens

Rapid and accurate identification of foodborne pathogens improves public health. Currently employed methods are time-consuming, sensitive to environmental factors, and complex. This study develops a colorimetric sensor for detecting multiple bacteria with one probe using double-enzyme-induced colorimetry. Based on alkaline phosphatase (ALP) in bacteria decomposes L-ascorbic acid 2-magnesium phosphate salt hydrate into ascorbic acid (AA). Manganese dioxide flowers (MnO₂ NFs) can oxidize TMB to etch gold nanorods (Au NRs), which can be inhibited by AA reduction to produce rich colors. Bacteria with varying ALP levels can be identified based on color changes and plasmon resonance wavelength signals produced from Au NRs. Furthermore, the conversion of RGB signals to digital signals and the use of linear discriminant analysis (LDA) allowed 99.57% accuracy in identifying multiple bacteria. It can simultaneously identify five foodborne pathogens across diverse environments (shrimp, meat, milk, etc.). This method may be useful for the rapid and simple identification of foodborne illnesses.