Food Safety Applications Research Articles

Advances in High-Performance Liquid Chromatography (HPLC) and Ultra-Performance Liquid Chromatography (UPLC)

High-Performance Liquid Chromatography (HPLC) and Ultra-Performance Liquid Chromatography (UPLC) are fundamental analytical techniques that have revolutionized chemical analysis across various fields. These chromatographic methods offer exceptional separation capabilities, high resolution, and precise quantification of complex mixtures. HPLC has evolved significantly since its inception, with improvements in column technology, detection systems, and automation. UPLC, introduced as an advancement to conventional HPLC, operates at significantly higher pressures using sub-2-μm particle sizes, resulting in enhanced resolution, speed, and sensitivity. Both techniques have found extensive applications in pharmaceutical analysis, environmental monitoring, food safety, clinical diagnostics, and biomedical research. The implementation of various detection methods, including UV-visible, fluorescence, mass spectrometry, and electrochemical detection, has expanded their analytical capabilities. Recent developments in column technology, such as core-shell particles and monolithic columns, have further improved separation efficiency. The integration of artificial intelligence and machine learning has enhanced method development and data analysis. This review discusses the fundamental principles, technological advancements, and diverse applications of HPLC and UPLC. Additionally, it discusses current challenges, emerging trends, and future prospects in chromatographic science, including green chromatography initiatives and miniaturization efforts. The continuous evolution of these techniques contributes significantly to analytical chemistry's advancement, promising even more sophisticated applications in various scientific disciplines.

Uniformly aligned Ag NPs/graphene paper for enhanced SERS detection of pesticide residue

The surface-enhanced Raman scattering (SERS) technique provides a quick and reliable method for detecting pesticide residues. In this study, flexible substrates, composed of orderly arranged silver nanospheres (Ag NPs) films on graphene paper, were fabricated through a simple, low-cost Ag NP self-assembly process at a liquid–liquid interface, followed by transfer of the films onto the graphene paper. The SERS performance of the fabricated substrates was evaluated using a portable Raman spectrometer, with rhodamine 6G (R6G) serving as the probe molecule. The results indicate that the bilayer Ag NP films-covered graphene paper exhibits optimal overall performance, characterized by high sensitivity and high uniformity. The limit of detection (LOD) for the R6G molecule is as low as 8.73 × 10−9 M, demonstrating the strong signal amplification capability of the SERS substrate. Moreover, the relative standard deviation (RSD) of the Raman intensity at 1508 cm−1 for different selected points on the substrate is 5.018 %, indicating high uniformity of the SERS substrate. Finally, the performance of the SERS substrate was further evaluated by detecting thiram in fresh orange juice, demonstrating the capability to detect concentrations as low as 10−6 M. This result highlights the significant potential of the developed SERS substrate for practical applications in food safety and quality control.

Synthesis and characterization of a copper-based metal–organic framework and its application in microextraction and determination of chlorpyrifos by ion mobility spectrometry

This study describes an innovative method for the microextraction and determination of chlorpyrifos, utilizing a newly synthesized copper-based metal–organic framework (MOF) with 3,3ʹ-thiodipropionic acid as the ligand. This approach offers a sensitive and reliable solution for the ultra-trace determination of chlorpyrifos in various matrices, highlighting its potential for broader environmental and food safety applications. To minimize environmental impact, a hydrothermal method was chosen for MOF synthesis. The MOF was fully characterized using FT-IR, X-ray diffraction, FESEM-EDX, BET, zeta potential analysis, and XPS; and integrated into a dispersive solid-phase microextraction (DSPME) technique, paired with ion mobility spectrometry (IMS) for chlorpyrifos detection. The DSPME procedure has been optimized for extraction efficiency, minimal solvent use, and low absorbent consumption. A low detection limit of 0.65 ng mL−1 was observed and validated along with a linear detection range of 1.0–400.0 ng mL−1 as the result of this optimization. Experiments with water, pear fruit, and soil samples demonstrated the method’s practicality and superior performance compared to existing techniques, showcasing its potential for real-world applications.

Bacterial biofilm inactivation by plasma activated nanobubble water

This research developed novel plasma activated nanobubble water (PANBW) by integrating atmospheric cold plasma and nanobubble water (NBW) technologies. Mixing of plasma reactive species with NBW to generate the PANBW makes it an effective solution for microbial biofilm inactivation and water treatment, possibly by leveraging the benefits of both technologies. Selected properties of PANBW, including the concentrations of reactive oxygen and nitrogen species (RONS) were characterized, and the stability of RONS during storage for 7 days were evaluated. The combination of argon and air as feed gases was used to determine the influence of feed gas type on RONS production and the effect of the generated PANBW on biofilm reduction. The effectiveness of PANBW in inactivating mixed-species bacterial biofilms was assessed against NBW, plasma activated water (PAW), and their combinations. This comparison involved treating biofilms of Salmonella enterica Typhimurium ATCC 13311 and Aeromonas australiensis, that were grown on stainless steel coupons by these solutions. The PANBW treatment was most effective in the inactivation of the tested mixed species biofilms with a reduction of >2 log CFU/cm2 in the biofilm population. The confocal laser scanning microscopy analysis was consistent with the bacterial inactivation results. This study highlights the potential of atmospheric cold plasma when combined with nanobubble technology, as a novel and efficient method for biofilm control and food safety applications.

Construction of a N-CDs/AuNCs@ZIF-8-assisted ratiometric fluorescent nanosensor for glyphosate detection in edible and medicinal malt

Glyphosate (Gly) is a widely-used herbicide in food production, while, the residue of which due to the long-term and excessive overspray poses serious threats to public health. The development of reliable methods for its sensitive detection is of great urgency. In this study, a novel ratiometric fluorescent nanosensor by encapsulating N-doped carbon dots (N-CDs) and gold nanoclusters (AuNCs) in zeolitic imidazole framework-8 (ZIF-8) as the dual-emissive fluorescence probes at 410 and 650 nm, respectively, was constructed for Gly detection. Due to the adsorption property of ZIF-8, the N-CDs/AuNCs@ZIF-8 nanoprobes accumulated Cu2+ to quench the red fluorescence of AuNCs, and the blue fluorescence of N-CDs was stable. While thiocholine, a product of acetylthiocholine, hydrolyzed by acetylcholinesterase could coordinate with Cu2+, resulting in significant fluorescence recovery of AuNCs. This phenomenon was utilized for the quantitation of Gly, due to its inhibitory effect on acetylcholinesterase activity. By calculating the fluorescence intensity ratio (I650/I410), Gly in real sample could be accurately determined in a concentration range of 2–100 ng/mL with a limit of detection of 1.92 ng/mL because of the anti-interference and the self-correction ability of the two fluorescence signals. The N-CDs/AuNCs@ZIF-8 nanoprobes-assisted ratiometric fluorescent nanosensor exhibited unique merits of rapid construction, simple operation, high specificity, and good accuracy for Gly in edible and medicinal malt samples with recoveries of 93.7–108.2 %. This study presents a multiple tool for the versatile sensing of trace pesticides in more food matrices, which can be extended to a full range of environmental and food safety applications.

Carboxymethylcellulose-based aggregation-induced emission antibacterial material for multifunctional applications.

Polysaccharides are ubiquitous in nature, typically harmless, and highly compatible with various tissues in biomedical contexts. These properties make them attractive for use in multifunctional materials. In this study, the aggregation-induced emission (AIE) antibacterial material (PLOCMC) was successfully synthesized by carboxymethylcellulose (CMC) and ε-Poly-Lysine (ε-PL). PLOCMC exhibits not only the AIE property but also a room temperature phosphorescent (RTP) phenomenon. This dual emission behavior enhances its potential applications in chemical sensing and anti-counterfeiting. Notably, PLOCMC shows low cytotoxicity and exhibits antibacterial activity against typical Gram-positive and Gram-negative bacteria, making it a potent agent against a variety of bacterial strains. Additionally, PLOCMC demonstrates specific responsiveness to Fe3+ ions and nitrite, indicating its potential utility in food safety and monitoring applications.

A brief review on recent high-performance platforms for electrochemical sensing of azo dye Allura Red (E129): food safety and pharmaceutical applications

Artificial dyes are increasingly widespread, especially in foodstuffs and pharmaceutical products as an industry marketing strategy to attract consumers. Consumption of the synthetic azo dye Allura Red (AR) has potential risks to human health and can cause several adverse health effects such as genotoxicity, carcinogenicity, attention deficit hyperactivity disorder, allergic and asthmatic diseases in children. The limited number of electrochemical sensing platforms for AR successfully applied for safety assessment of foods, beverages, and drug formulations testifies that the food and pharmaceutical matrices present significant analytical challenges and there is still a need to amplify the analytical performances of these systems. The authors intensively reviewed recent papers (mainly since 2019) emphasizing electrode engineering, strategies for electrochemical signal amplification, and analytical applications in food and drug control. A critical discussion on the latest interesting innovations and perspectives of the most promising electrochemical tools for AR electro­analysis is presented. The challenges and limitations in the design of electrochemical sensors for AR analysis are also discussed with a view to providing new directions for future research and development of sustainable sensing devices, paving the way for advancements in this field.

Real-Time Milk Quality Control Using Multi-Spectral Sensing and Edge Computing: Advancing On-Site Detection of Milk Components with XGBoost

This study explores the use of edge computing technologies to enhance the quality control processes in the dairy industry. Traditional milk quality control methods can be time-consuming and sometimes inadequate, whereas this new approach offers real-time data processing and rapid decision-making capabilities. The objective of the study is to assess the effectiveness of evaluating various spectral characteristics of milk in predicting critical parameters such as protein and fat content. In this research, a multi-channel sensor capable of collecting spectral data at various wavelengths was utilized. The collected data were processed using advanced machine learning models, where XGBoost and other regression models were assessed for their accuracy in predicting protein and fat content. The findings demonstrate the suitability of this technology for quality control in the dairy industry. The results reveal that edge computing-based systems can determine milk quality more quickly and accurately. This technology holds significant potential for overcoming the challenges faced in milk quality control, particularly in developing countries. This study provides valuable insights into how the use of edge computing can enhance operational efficiency and ensure product quality in the dairy industry. This research represents an important step towards developing more effective quality control mechanisms in the dairy industry and aims to establish a robust foundation for future studies. Recommendations focus on the adaptation of this technology to other food safety applications and its diversification for widespread industrial use.

Automation for lateral flow rapid tests: Protocol for an open-source fluid handler and applications to dengue and African swine fever tests.

Lateral flow rapid diagnostic tests (RDTs, RTs) are cost-effective with low infrastructure requirements for limited-resource settings, and in any setting can represent a bridge between early disease monitoring at outbreak onset and fully-scaled molecular testing for human or animal diseases. However, the potential of RTs to handle higher throughput testing is hampered by the need for manual processing. Here we review dengue virus and African swine fever virus rapid tests, and present a novel protocol that employs an open-source fluid handler to automate the execution of up to 42 RTs per run. A publicly accessible website, rtWIZARD.lji.org, provides printouts for correctly spacing cassettes, worksheets for sample organization, and test-specific fluid handler protocols to accurately deliver samples from a 48-tube rack to each cassette's sample and running buffer wells. An optional QR-coded sheet allows for de-identified sample-to-result traceability by producing a unique printable label for each cassette, enabling results to be entered via a scanner. This work describes a highly cost-effective model for increasing outbreak diagnostic efficiency and of increasing RT throughput for other applications including workplace testing, food safety, environmental testing, and defense applications.

Assessment of the ability of Lactococcus lactis 537 to bind aflatoxin M1 in the presence of inulin and Terminalia ferdinandiana (Kakadu plum)

One promising method for the detoxification of Aflatoxin M1 (AFM1) involves the use of probiotic bacteria combined with prebiotics. This approach is both safe and cost-effective, while also offering additional health benefits. The objective of this study was to investigate the capacity of Lactococcus lactis 537 (Lc. Lactis 537) to bind AFM1 in milk and phosphate-buffered saline (PBS), either in the presence or absence of inulin or Kakadu plum fruit powder (KP), which are prebiotic substances. Lc. Lactis 537 was incubated for 0, 1, 2, and 24 h at 30 °C, with or without inulin or KP, to assess its ability to bind and reduce AFM1 levels. The concentration of AFM1 was determined using liquid chromatography-mass spectrometry (LC-MS9) during the different incubation periods. The results demonstrated that Lc. Lactis 537 significantly reduced the initial concentration of AFM1 in both milk and PBS after 24 h of incubation. Moreover, the presence of inulin or KP enhanced the binding and reduction capacity of Lc. Lactis 537. This suggests that the synbiotic effect where probiotic bacteria like Lc. Lactis work in synergy with prebiotics such as inulin or KP could play a critical role in the removal of AFM1 from contaminated substances. In conclusion, the synbiotic of Lc. Lactis 537 with inulin or KP represents a promising biological approach for AFM1 detoxification, and further research is warranted to explore its potential applications in food safety.

Characterization of two Campylobacter jejuni phages and evaluation of their antibacterial efficacy with EDTA.

Campylobacter jejuni is a leading cause of foodborne illness worldwide. The application of bacteriophages offers a promising approach to specifically target and reduce C. jejuni contamination in food products. In this study, two C. jejuni phages were characterized, and their ability to inhibit bacterial growth in combination with ethylenediaminetetraacetic acid (EDTA) was investigated. Both phages exhibited tolerance to a wide range of temperature (4-60°C) and pH (3-9). Phage vB_CjeM-PC10 and vB_CjeM-PC22 were found to have a latent period of 30 min and 20 min and a burst size of 7 and 35 PFU/cell, respectively. Phage vB_CjeM-PC10 has a linear double-stranded DNA (dsDNA) genome of 51,148 bp with 77 ORFs and 29% GC content. Phage vB_CjeM-PC22 has a circular dsDNA genome of 32,543 bp with 56 ORFs and 28% GC content. At 42°C, the combination of these phages (MOI = 10) and EDTA decreased the count of viable C. jejuni by 5.2 log10 and inhibited the regrowth of resistant cells for 48 h. At 4°C, phage vB_CjeM-PC10 alone (MOI = 1000) reduced the count of viable C. jejuni by 3 log10 in brain heart infusion (BHI) broth and 2 log10 on chicken skin after incubation for 48 h. Although these phages were effective against C. jejuni, they cannot be utilized directly for food safety applications because they are lysogenic. Nevertheless, these findings expand the genome library of C. jejuni phages and enrich data resources by highlighting potential strategies for controlling C. jejuni infections.

Deep-Eutectic-Solvent-Decorated Metal-Organic Framework for Food and Environmental Sample Preparation.

Deep eutectic solvent (DES) is distinguished by its unique solvent properties, chemical stability, and eco-friendly nature, which are pivotal in a spectrum of chemical processes. It enhances the sample preparation process by increasing efficiency and minimizing the environmental impact. Metal-organic frameworks (MOFs), which are porous structures formed through coordination bonds between metal ions and organic ligands, are defined by their adjustable pore dimensions, extensive surface areas, and customizable architectures. The integration of DES within MOF to create DES@MOF capitalizes on the beneficial attributes of both materials, augmenting MOFs' stability and versatility while providing a multifunctional carrier for DES. This composite material is both highly stable and readily tunable, establishing it as a leading contender for applications in sample preparation for food and environmental samples. This comprehensive review explores the application of DES-decorated MOF in food and environmental sample preparation and highlights the expansive potential of DES@MOF in diverse fields. We provide a detailed analysis of the characteristics of DES@MOF and its individual components, methods for decorating MOFs with DES, the advantages of these composite materials in sample pretreatment, and their specific applications in food safety and environmental monitoring. DESs are employed to modify MOFs, offering a multitude of benefits that can substantially improve the overall performance and applicability of MOFs. The review also discusses current challenges and future directions in this field, offering valuable insights for further research and development. The synergistic effects of DES and MOFs offer new opportunities for applications in food safety and other areas, leading to the development of more efficient, sensitive, and environmentally friendly analytical methods. This collaboration paves the way for sustainable technologies and innovative solutions to complex challenges.

Chemical Composition, Biological Activity, and Application of Rosa damascena Essential Oil as an Antimicrobial Agent in Minimally Processed Eggplant Inoculated with Salmonella enterica.

Rosa damascena is mostly grown for its usage in the food, medical, and perfume industries, while it is also used as an attractive plant in parks, gardens, and homes. The use of R. damascena essential oil may yield new results in relation to the antimicrobial activity of essential oils and their use mainly in extending the shelf life of foods. This study investigates the chemical composition and antimicrobial properties of Rosa damascena essential oil (RDEO) using gas chromatography-mass spectrometry (GC-MS) and various bioassays to explore its potential applications in food preservation and microorganism growth control. The GC-MS analysis revealed that RDEO is predominantly composed of phenylethyl alcohol (70%), which is known for its antimicrobial and aromatic properties. Additionally, other significant constituents were identified, including nerol, citronellol, and geraniol, which may contribute to the EOs overall bioactivity. The antimicrobial activity was assessed through the minimal inhibition concentration against five Candida yeast strains, four Gram-positive, and four Gram-negative bacteria, including biofilm-forming Salmonella enterica. Determination of minimum inhibitory concentrations (MIC) revealed the strongest effects of RDEO's on Gram-negative species, with MIC50 values as low as 0.250 mg/mL for S. enterica. Moreover, an in situ assessment utilizing fruit and vegetable models demonstrated that the vapor phase of RDEO significantly suppressed microbial growth, with the most substantial reductions observed on kiwi and banana models. As a result of our study, the antimicrobial effect of RDEO on the microbiota of sous vide processed eggplant was detected, as well as an inhibitory effect on S. enterica during storage. The insecticidal activity against Megabruchidius dorsalis Fahreus, 1839, was also studied in this work and the best insecticidal activity was found at the highest concentrations. These results suggest that RDEO has the potential to serve as a natural antimicrobial agent in food preservation and safety applications, providing an alternative to synthetic preservatives.

Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy

Conventional detection methods require the isolation and enrichment of bacteria, followed by molecular, biochemical, or culture-based analysis. To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (EEM) fluorescence data generated based on bacteriophage T7 and Escherichia coli interactions for in-situ detection of live bacteria in the presence of fresh produce homogenate. We trained classification models using various ML algorithms based on the 3-D EEM data generated with bacteria and their interactions with a T7 phage. These ML algorithms, including linear Support Vector Classifier (SVC) and Random Forest (RF), demonstrate high accuracy (>0.85) for detecting E. coli at 102 CFU/ml concentration within 6 h. Additionally, these ML models can differentiate among different E. coli concentration levels. For example, the Gaussian Process model achieved an accuracy of 92% in detecting different concentration levels of live E. coli. Application of these ML methods to detect E. coli in spinach homogenate yielded an accuracy of 89% using the linear-SVC model. Furthermore, feature selection techniques were employed to reduce the dimensionality of the data, revealing that only six features were necessary for achieving classification accuracy (>0.85) of spinach homogenate samples containing 102 CFU/ml of E. coli. These findings highlight the potential of this novel bacterial detection methodology, offering rapid, specific, and efficient solutions for applications in food safety and environmental monitoring.

ENGINEERED CHITOSAN NANOPARTICLES FOR ENCAPSULATION OF THYMOL

This study successfully obtained chitosan thymol nanoparticles using an electrohydrodynamic technique, which is a simple one-step procedure. The morphological and physical characterization, antioxidant, and antimicrobial activity assessments of electrosprayed thymol-loaded chitosan nanoparticles (CTNPs) were carried out. The ABTS assay and the agar well diffusion test were used to determine the antioxidant and antimicrobial activities of the CTNP samples, respectively. The results showed that CTNPs possessed efficient antimicrobial capacity against B. cereus, S. aureus, E. coli, and S. typhimurium. CTNPs indicated a radical scavenging activity of 90% regarding the ABTS assay. CTNPs with biological activities could be an effective alternative for practical food safety and health applications. In this study, the use of electrohydrodynamic atomization technique to produce biopolymer nanoparticles present a novel approach for encapsulating thymol-like volatile active agents.

Revolutionizing agri-food technology: Development and validation of the Portable Intelligent Oil Recognition System (PIORS)

The increasing demand for high-quality vegetable oils has amplified the need for efficient, and accurate, oil recognition systems. This paper introduces the Portable Intelligent Oil Recognition System (PIORS), a pioneering advancement in the field. PIORS stands as the first-ever portable, AI-powered device tailored for rapid and accurate oil type identification, capable of delivering results within a mere five seconds. Using advanced machine learning, PIORS can distinguish between various oil types such as sesame, black seed, flaxseed, and almond oils, ensuring the quality and safety of the final product. The device's innovative design integrates an advanced sensor array with seamless Bluetooth connectivity, offering real-time data synchronization with mobile applications. Several Machine learning models, including Support Vector Machines (SVM), AdaBoost, Random Forest, K-Nearest Neighbors (K-NN), Gradient-Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) were implemented and thoroughly validated to obtain correct categorization. The results show that the XGBoost instantaneous classification algorithm continuously beat the competition, obtaining an astounding success rate of 97 percent in predicting and differentiating between the four oil classes. PIORS not only sets a new standard for speed and efficiency in oil quality assessment but also paves the way for groundbreaking applications in food safety, environmental monitoring, and quality control processes. This paper details the development, implementation, and validation of PIORS, showcasing its potential to revolutionize the agri-food industry with its cutting-edge, AI-driven approach to oil recognition.

A rapid and sensitive paper-based sensor for sulfide ion detection in Chinese liquors

The accurate monitoring of sulfide ion concentrations is vital for ensuring food safety, but current detection methods are often cumbersome and confined to laboratories. This study introduces an innovative paper-based colorimetric sensor that leverages the peroxidase-like activity of cobalt tetrasulfonate phthalocyanine (CoTsPc) for rapid, sensitive, and selective sulfide ion quantification. The underlying mechanism relies on the interaction between cobalt and sulfide ions, modulating the catalytic efficiency of CoTsPc and triggering a visible color change when 3,3′,5,5′-tetramethylbenzidine (TMB) and hydrogen peroxide are present. By controlling solvent evaporation, the reaction kinetics are standardized, enabling reproducible and accurate colorimetric measurements. Experimental results show that adding sulfide ions to the TMB-H₂O₂ reaction system causes a gradual decrease in color intensity on the paper substrate. This change verifies effectiveness of the sensor, highlighting its potential for reliable sulfide ion detection in food safety applications, while providing a simple and portable alternative to conventional methods.

Electrochemical detection of p-nitrophenol using glassy carbon electrode modified using high-entropy oxide nanoparticles

The electrochemical detection of p-nitrophenol (PNP) has gained significant attention due to its potential application in environmental monitoring and food safety. In this study, a novel electrochemical sensor based on a (CeGdHfPrZr)O2 (HEO) nanoparticle-modified glassy carbon electrode (GCE) has been developed for the detection of PNP. Hydrothermal synthesis was employed to prepare a single-phase HEO nanoparticle. Various characterization techniques were employed to investigate the prepared HEO nanoparticles. The GCE was modified with HEO nanoparticles to enhance its electrocatalytic activity for the detection of PNP. As a result, the oxidation peak current of PNP was greatly enhanced compared to unmodified GCE, indicating an improved electrode reaction kinetics. The sensor showed high sensitivity (0.1517 μA μM−1 cm−2) and selectivity towards PNP, with a detection limit as low as 0.321 μM. The developed electrochemical sensor exhibited excellent reproducibility and selectivity towards PNP over other interfering species. The reusability and stability of the sensor were also investigated. The modified GCE exhibited good long-term stability and could be reused for at least 50 cycles without significant loss of sensitivity.

Biosensing technology for detection and assessment of pathogenic microorganisms.

At present, the prevalence of infectious diseases is rising annually, making it an important risk factor for human health that should not be neglected. Consequently, infection control and prevention have become even more important. The key to determining and designing the most effective anti-infectious medication depends upon the immediate and accurate identification of the causative agent. The standard techniques used for routine infection screening and surveillance tests are shifting toward biosensors. Furthermore, biosensors are projected to be employed for microbiological detection to satisfy the higher accuracy required for clinical diagnosis. This is because of their compact size, real-time monitoring and ability to analyze large sample numbers with less sophistication and manpower requirement, which have allowed them to develop quickly with extensive uses. Biosensors have multiple applications in food safety, environmental surveillance, drug sensing and national security because they offer several advantages such as quick response, outstanding sensitivity, remarkable selectivity, high degree of accuracy and precision, ease of use and affordable price. This review highlights the performance aspects of recently developed biosensors for the detection of infectious bacteria and viruses in biological and environmental samples and emphasizes the significance of nanotechnology in signal amplification for enhanced biosensor performance and dependability.

Simple strategy for dual-responsive ratio electrochemical-colorimetric detection of nitrite in food and environment.

A dual-responsive ratio electrochemical-colorimetric method for nitrite (NO2-) is established based on the combination of nanoenzyme (Mn3O4) catalysis with diazotization reactions. The Mn3O4 can oxidize colorless 3,3',5,5'-tetramethylbenzidine (TMB) into blue TMBox. The NO2- induces the diazotization reaction of TMBox, leading to a decrease of the signal at 652nm and the generation of a new signal from diazotized TMBox at 445nm. Furthermore, the presence of NO2- reduces the electrochemical oxidation signal of TMB and simultaneously provides its electrochemical signal. Compared withtraditional single-mode detection, dual-mode detection offers higher sensitivity, lower detection limits, and better interference resistance. The inherent advantages of this method make it feasible to detect NO2- in real samples, offering broad prospects for applications in food safety and environmental monitoring.