Next Article in Journal
A Novel Protein Demonstrating Antibacterial Activity Against Multidrug-Resistant Escherichia coli Purified from Bacillus velezensis CB6
Previous Article in Journal
Screening of Antifungal Lactic Acid Bacteria and Their Impact on the Quality and Shelf Life of Rye Bran Sourdough Bread
Previous Article in Special Issue
Immobilization Techniques for Food-Grade Nuclease P1 and Their Application in Nucleotide Production
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Foods
Volume 14
Issue 7
10.3390/foods14071254
foods-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Research Progress on Nanotechnology-Driven Enzyme Biosensors for Electrochemical Detection of Biological Pollution and Food Contaminants

by
Liang Qu
1,
Xue Zhang
2,
Yanhong Chu
2,
Yuyang Zhang
2,
Zhiyuan Lin
2,
Fanzhuo Kong
2,
Xing Ni
2,
Yani Zhao
2,
Qiongya Lu
2 and
Bin Zou
2,*
1
School of Food and Bioengineering, Wuhu Institute of Technology, Wuhu 241003, China
2
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Foods 2025, 14(7), 1254; https://doi.org/10.3390/foods14071254
Submission received: 17 January 2025 / Revised: 26 February 2025 / Accepted: 2 April 2025 / Published: 3 April 2025
(This article belongs to the Special Issue Food Grade Immobilisation Systems for Enzymes)
Browse Figures
Review Reports Versions Notes

Abstract

:
Electrochemical biosensors have attracted widespread attention from researchers due to their simple and rapid operation. Recent advancements in nanobiotechnology have further enhanced their performance, with nanomaterials like graphene, carbon nanotubes, and metal nanoparticles being widely used as carriers for immobilizing enzymes, cells, and DNA molecules. These materials improve stability, sensitivity, and selectivity, making biosensors more effective. This article reviews the introduction, principles, and classification of enzyme-based electrode sensors, as well as their research and application progress in the detection of food risk factors (including foodborne pathogens, biotoxins, drug residues, food additives, allergens, etc.). It also explores future prospects, including advancements in nanotechnology and enzyme immobilization techniques, highlighting their potential in food safety and beyond.

1. Introduction

Food safety issues, such as drug residues, contamination by foodborne pathogens, the illegal use of food additives, and biotoxins, have garnered widespread attention [1,2]. Conventional food safety detection methods include liquid chromatography, gas chromatography [3,4], and mass spectrometry [5]; however, these methods suffer from drawbacks such as cumbersome pretreatment processes, high costs, and long detection times. Electrode detection technology is suitable for rapid testing in most industries due to its high automation and quick detection. Nevertheless, the complex environment of food safety testing can cause conventional sensors to be easily interfered with by external factors, resulting in weak detection specificity [6,7]. In 1962, Clark et al. first proposed the concept of combining enzymes with electrodes [8]; five years later, Updike developed an enzyme electrode that was successfully applied for the detection of glucose content in food [9]. Enzyme electrodes have become a focus of attention in current food detection research because of their strong specificity, high sensitivity, and reusability. Zhang et al. [10] prepared a novel glutamate oxidase electrode sensor where the reaction produces hydrogen peroxide, which undergoes electron transfer on the electrode surface. The built-in electronic components convert the electrical signal into a digital signal, allowing for the detection of L-sodium glutamate content in soy sauce. This method does not require soy sauce decolorization or derivatization and can be measured repeatedly over 1000 times. Further studies have demonstrated that high-performance enzyme electrode sensors can enhance and sustain enzyme activity by optimizing the microenvironment on the carrier surface, such as charge distribution and pore structure. The key to this approach lies in the rational design and surface modification of the carrier material. Notably, multi-walled carbon nanotubes (MWCNTs), characterized by their porous hollow structure, high specific surface area, and hydrophobic properties, are extensively utilized in electrochemical sensing and enzyme immobilization applications. Zou et al. [11] developed a high-sensitivity biosensor using multi-walled carbon nanotubes (MWCNTs) functionalized with various groups (-SH, -NH2, -Cl, or -OH), alkyl chains, and ionic liquids (-IL1/-IL2) as acetylcholinesterase (AChE) carriers. Figure 1 illustrates the synthesis diagram of the Cl/MWCNTs/AChE/GCE biosensor. Analysis revealed that IL1-modified MWCNTs optimized AChE immobilization, achieving a detection limit of 3.3 × 10−11 M for organophosphorus (OP) under optimal conditions (pH 7.0, 0.25 U AChE, 14 min inhibition). The IL1-MWCNTs/AChE/GCE sensor maintained 98.5% reactivity over two weeks and demonstrated 90–104% recovery in vegetable samples, proving its effectiveness for rapid, accurate OP detection. The gradual industrial application of enzyme electrode rapid detection technology has opened up a new chapter in the field of food safety detection.

2. Principle of Enzyme Electrode Sensors

The enzyme gel or suspension is immobilized on the surface of the sensitive membrane of the electrode. Compared to free enzymes, immobilized enzymes not only exhibit significantly improved stability but also reduce costs due to their reusable catalytic properties [12,13]. Enzymes are commonly immobilized on electrodes using adsorption, crosslinking, covalent binding, entrapment, layer-by-layer self-assembly techniques, or combinations thereof in order to fully enhance enzymatic catalytic efficiency. Upadhyay et al. [14] prepared a dual-enzyme electrode sensor by immobilizing AChE and choline oxidase using the crosslinking method to detect diisopropyl fluorophosphate and carbofuran in milk, with detection limits of 2.0 × 10−4 and 2.0 × 10−4 mol/L, respectively. Karra et al. [15] immobilized the enzyme on the electrode using a perfluorosulfonic acid membrane, with the enzyme being covalently linked to carbon nanotubes and chitosan. The detection current of the newly prepared enzyme electrode sensor increased by 1000%. Based on Xia’s research [16], it is known that chloride ionic liquid groups are the optimal carrier for AChE, and functionalizing MWCNTs enhances electron transfer and improves the catalytic environment of AChE. The introduction of horseradish peroxidase (HRP) increases the sensitivity of choline oxidation. The novel dual-enzyme sensor Cl/MWCNTs/HRP/AChE/GCE is used to detect OP residues in vegetables. Figure 2 illustrates the synthesis diagram of the Cl/MWCNTs/HRP/AChE/GCE biosensor. The detection range is 1.0 × 10−11–1.0 × 10−7 mol/L, with a low detection limit of 4.5 × 10−12 mol/L. The reproducibility RSD is 5.6%, the repeatability RSD is 3.0%, the storage stability is above 96.5% within four weeks, and the recovery rate is 83–104%.
The analyte diffuses into the active center of the immobilized enzyme to participate in the catalytic reaction, leading to the generation or reduction of specific active substances. Sensors are classified into amperometric and potentiometric types, both of which measure the content of a specific substance and indirectly reflect the content of another substance [17]. Hatad et al. [18] successfully prepared an amperometric fructose-amino acid oxidase electrode sensor using ruthenium(III) hexammine chloride as the carrier material. Since the response current of the sensor is correlated with the content of glycated albumin, it can measure the content of glycated albumin in actual samples.
The sensor is highly stable and rapid in detection, capable of measuring the content of glycated albumin in protease-digested samples (1.3 μL) within 1 min, with a validity period of up to three months. Potentiometric enzyme electrodes are relatively more complex, and their response current is influenced by environmental temperature, pressure, analyte concentration, and the electrode itself, among other conditions. Hsu et al. [19] prepared a potentiometric sensor based on a urease/bovine serum albumin/Pt carrier material for urea detection. The detected value output between the reference electrode and the working electrode corresponds to the substrate concentration according to the Nernst equation, with a sensitivity of 15.2 mV/dec for urea detection. The sensor exhibits strong specificity for urea detection and has a validity period of 70 days. Amperometric sensors offer a simpler and more intuitive effect compared to potentiometric sensors.

3. Classification of Enzyme Electrode Sensors

Enzyme electrodes are typically classified based on the transducer principle and the type of enzymatic catalytic reaction. They are categorized into electrochemical, optical, thermal, piezoelectric, semiconductor, and acoustic enzyme sensors according to different transducer principles; among these categories, research on optical, thermal, and piezoelectric sensors has been limited due to issues with signal stability or the complexity of equipment construction. In contrast, electrochemical enzyme electrode sensors have emerged as the most extensively studied and deeply investigated subject in this field, owing to their superior sensitivity, excellent repeatability, and remarkable selectivity. Shervedani et al. [20] studied a laccase electrode derived from Agaricus bisporus using voltammetry and electrochemical impedance spectroscopy to detect dopamine in plasma and pharmaceutical samples. The results showed that the linear detection ranges were 5.0 × 10−10 to 1.3 × 10−8 mol/L and 4.7 × 10−8 to 4.3 × 10−7 mol/L, with correlation coefficients of 0.999 and 0.989, and a detection limit of 2.9 × 10−8 mol/L. Ertek et al. [21] made a photoelectric sensor based on the graphite electrode modified by dehydrogenase to detect glucose, with a detection limit of 5.0 × 10−5 mol/L; the sensor had excellent sensitivity, repeatability, and selectivity.
Based on the types of enzymatic catalytic reactions, enzyme electrodes can be classified into oxidoreductase electrodes, hydrolase electrodes, isomerase electrodes, transferase electrodes, ligase electrodes, and synthase electrodes. As shown in Table 1, the detection objects, enzyme action mechanism, detection methods, and performance characteristics of enzyme electrodes has been systematically compared according to the types of enzyme-catalyzed reactions. Among these, oxidase/reductase electrodes are the most commonly used. Cui et al. [22] developed a sensor based on multi-walled carbon nanotubes and xanthine oxidase for the rapid and accurate monitoring of intracellular purine content. Under the action of xanthine oxidase, this sensor can provide more information about purine metabolism within cells, opening up a new path for the precise detection of intracellular purines. Kang et al. [23] prepared an enzyme electrode sensor based on acyl-CoA synthetase and oxidase; they found that the electrode detection signal exhibited a good linear relationship with fatty acid concentration, thus enabling the detection of fatty acid concentration.

4. Research and Application of Enzyme Electrode Sensors in Food Safety Detection

This article reviews the research progress of enzyme electrode sensors in the safety detection of food and drug residues, biotoxins, food additives, foodborne pathogens, and allergens in recent years [28,29,30,31,32].

4.1. Drug Residue Detection

Agricultural product drug residues are classified into veterinary drug residues and pesticide residues. The long-term consumption of meat products with excessive veterinary drug residues can lead to the accumulation of these drugs in the body, causing various acute and chronic poisoning effects. Pesticide residues on the surface of fruits and vegetables often disrupt the balance of hormones in the human body, leading to immune deficiencies and potentially causing cancer, birth defects, and mutations [33,34,35,36,37]. Pesticides are mainly classified into three categories based on their chemical structures: organophosphates, carbamates, and organochlorines.
As organophosphorus pesticides with high toxicity and persistence in agricultural products pose a serious threat to human health and safety, the development of rapid and efficient methods for detecting organophosphorus compounds has gradually attracted attention. Common organophosphorus pesticides on the market include chlorpyrifos, dichlorvos, and trichlorfon, among others. Organophosphorus compounds are a class of acetylcholinesterase inhibitors that readily cause the irreversible phosphorylation of AChE, rendering it inactive. Therefore, it can be inferred that the content of organophosphorus compounds can be indirectly estimated by monitoring the activity of AChE.
Zheng et al. [38] used antibacterial silver nanoparticles and biocompatible chitosan as carrier materials to prepare an AChE electrode for detecting organophosphorus pesticides in agricultural products, with a detection limit for paraoxon of only 1.45 × 10−5 mol/L. Miao et al. [39] utilized platinum metal and multi-walled carbon nanotubes as carrier materials to prepare an enzyme electrode for detecting organophosphorus pesticides in agricultural products. The detection limits for malathion, methyl parathion, and chlorpyrifos were 1.6 × 10−10, 9.0 × 10−11, and 8.0 × 10−11 mol/L, respectively. For chlorfenvinphos, the linear range was 5.0 × 10−8–5.0 × 10−7 mol/L, with a detection limit of 2.97 × 10−8 mol/L. This enzyme electrode sensor demonstrated excellent applicability for detecting organophosphorus content in agricultural products.
Itoh et al. [40] prepared a dual-enzyme electrode sensor with AChE and choline oxidase using a gelatin entrapment method. This amperometric enzyme electrode showed a maximum response to acetylcholine chloride at pH 6.8 and 37 °C, with a detection time of only 10 min. The detection limit for trichlorfon was 1 × 10−10 mol/L. The enzyme electrode had a shelf life of 30 days, with an average relative deviation of the measured values of 2.18%. Zou et al. [41] combined the advantages of MWCNTs and ionic liquids to construct a dual-enzyme sensor with a Cl/FePP-modified MWCNTs/AChE/glassy carbon electrode. Ferriporphyrin (FePP) was covalently bound via ionic liquids to enhance the sensitivity and stability of the sensor. As shown in Figure 3, MWCNTs are synthesized in situ to obtain Cl-MWCNTs, which are then covalently bound to FePP to generate FePP-MWCNTs and chlorinated ionic liquid. The addition of AChE results in the fabrication of a biomimetic dual-enzyme sensor, where the ionic liquid serves as both an adhesive and an electronic conductor, and FePP catalyzes the oxidation of choline to enhance the sensitivity. When OP inhibits AChE, the inhibition rate can be determined by measuring the current generated during the oxidation process of acetylcholine, enabling the rapid detection of OP residues. The detection limit of this sensor for monocrotophos is 3.2 × 10−11 mol/L, with a recovery rate of 89–104%. After 5 weeks of storage, the oxidation current remained at 97.8% of its original value. This biosensor exhibits high stability and sensitivity, making it a promising device for food safety monitoring.
JI et al. [42] developed a multienzyme reaction-mediated electrochemical (MRMEC) biosensor for the sensitive, rapid, and interference-resistant detection of ethephon (ETH). Fe3O4@Au-Pt and GN-Au NPs were prepared as catalysts and signal amplifiers. Fe3O4@Au-Pt catalyzes the oxidation of tetramethvlbenzidine (TMB), resulting in a decrease in current. Based on the inhibition of AChE by ETH, differential pulse voltammetry (DPV) was used to detect ETH through the TMB signal, with a limit of detection (LOD) of 2.01 nmol/L. MRMEC effectively analyzed ETH in mangoes, showing satisfactory precision (coefficient of variation: 2.88–15.97%) and recovery rates (92.18–110.72%). As shown in Figure 4, an electrochemical biosensor, which was designed based on nanoenzyme catalysis and cascade enzyme reactions, is used to detect ETH in the aforementioned experiment. This sensor holds promise for detecting various organophosphorus pesticides in food samples.
Carbamate pesticides, first extracted from lentils, are highly toxic and have been extensively synthesized for use on crops due to their effective insecticidal and acaricidal properties to control pest infestations. Currently, major market products include sevin and carbofuran. Zheng et al. [43] prepared an enzyme electrode sensor using ionic liquid-functionalized graphene and gelatin as modifying materials to detect pesticide residues in agricultural products, with a detection limit for sevin of 5.3 × 10−15 mol/L.
The sensor retained 95.2% of its initial current after 15 days, demonstrating a strong stability, high sensitivity, and low cost. Carbosulfan, a carbamate pesticide, is widely used in rice production for its ability to control pest growth. Nesakumar et al. [44] employed nano-zinc oxide and platinum-modified AChE enzyme electrodes to measure carbosulfan levels in agricultural products, achieving a detection limit of 2.4 × 10−10 mol/L under optimal conditions, with an electrode recovery rate ranging from 99.06% to 100.96%.
Magar et al. [45] designed a selective nanostructured electrochemical biosensing system using six mutants of the EST2 protein from A, with acidocaldarius as the OP-specific biological receptor. The EST2 mutant enzymes were immobilized on disposable screen-printed electrodes modified with Al2O3/Cu nanocomposites. Comparative studies of the inhibition percentages of the six mutant proteins were conducted over a wide range of pesticide concentrations, achieving a wide dynamic inhibition range with a limit of detection (LOD) of 0.01 nM and a limit of quantification (LOQ) of 0.05 nM for oxon toxicity. The newly developed EST-based biosensor selectively determines paraoxon in different spiked food samples.
Atrazine is a highly effective organochlorine herbicide that inhibits tyrosinase [46]. Based on this principle, Han et al. [47] prepared a tyrosinase electrode sensor using poly(diallyldimethylammonium chloride)-functionalized carbon nanotubes as the carrier material for the detection of atrazine. The detection limit was as low as 1.8 × 10−8 mol/L, with a linear range of 4.6 × 10−8 to 3.7 × 10−6 mol/L. The experiment optimized the inhibition time and the thickness of the electrode’s sensitive surface. This detection method is rapid, convenient, and easy to operate, enabling the efficient detection of atrazine residues in actual samples.
The main detection methods for conventional drug residues include gas chromatography, liquid chromatography, mass spectrometry, and enzyme-linked immunosorbent assay (ELISA). The detection instruments are bulky and complex, and the pre-treatment before detection is cumbersome. Although ELISA is rapid and efficient, it has high detection costs and cannot be used for the real-time, on-site testing of batch products. Enzyme electrode sensors, on the other hand, offer high sensitivity, rapid detection, strong specificity, and require no special pre-treatment of samples, enabling the testing of large batches of samples. In recent years, with continuous exploration by researchers, the performance of enzyme electrodes has gradually been optimized, and there have been numerous research applications for drug residues in agricultural products.

4.2. Biotoxin Detection

Toxic substances produced by animals, plants, and microorganisms are referred to as biotoxins. Common biotoxins include the neurotoxin found in pufferfish, gossypol in cottonseeds, and the endotoxin produced by Clostridium botulinum [48]. As biotoxins increasingly threaten human health and safety, enzyme electrode sensors are playing an increasingly important role in biotoxin detection due to their excellent detection sensitivity and accuracy [49,50,51,52,53].
Zhang et al. [54] developed a novel Love Wave sensor-based biosensor using HepG2 cells for the real-time and sensitive detection of okadaic acid (OA), a toxin that causes diarrhetic shellfish poisoning. The sensor unit is detachable and equipped with a miniaturized 8-channel recorder, facilitating experiment preparation and signal measurement. The sensor operates at a synchronous frequency of approximately 160 MHz and is manufactured using ST-cut quartz substrates. HepG2 cells are cultured on the sensor surface as sensing elements in order to record the cell attachment process. The results indicate that the sensor can monitor cell adhesion in real time, with the response signal being correlated to the initial cell seeding density. When the cell-based Love Wave sensor is treated with OA toxin, it exhibits a good performance across various OA concentrations, with a wide linear detection range (10–100 μg/L). Based on the ultrasensitive acoustic wave platform, this biosensor holds promise as a real-time and convenient tool for OA screening.
After a period of time following the death of an organism, highly toxic substances such as histamine, putrescine, and cadaverine, which are produced by microbial decomposition, can pose serious health risks when ingested by humans. Leonardo et al. [55] developed an enzyme electrode sensor based on the combination of diamine oxidase and magnetic beads to detect the levels of histamine, putrescine, and cadaverine, with detection limits within the micromolar range. The sensor exhibits excellent reproducibility (variability below 10%) and repeatability (up to eight consecutive measurements), and has a linear range spanning two orders of magnitude (1.0 × 10−5 to 1.0 × 10−3 mol/L). Young et al. [56] prepared an amperometric sensor using coenzyme pyrroloquinoline quinone as a modifying material for the detection of histamine. The sensor demonstrates good stability, maintaining no signal loss for up to a month. Under optimal conditions, the detection range is 3.60 × 10−4 to 1.53 × 10−3 mol/L, with a detection limit of 3.42 × 10−4 mol/L. This method is highly practical and can be applied to the detection of food spoilage.
Aflatoxins typically grow in moldy foods with a high starch content, such as nuts and grains. Aflatoxins are highly toxic and carcinogenic. Uludag et al. [57] developed a novel aflatoxin enzyme sensor to detect aflatoxins in wheat spike samples, with a detection limit as low as 6.03 × 10−9 mol/L and a recovery rate of 94%. This new biosensor is rapid, sensitive, fully automated, and miniaturized, holding promise for significant economic impact on the food industry in the future. Chen et al. [58] extracted aflatoxin oxidase from Armillaria mellea, which was physically adsorbed onto a gold electrode modified with chitosan and single-walled carbon nanotubes. The prepared enzyme electrode exhibited a significant electrochemical response to sterigmatocystin, with a linear detection range of 10–1480 ng/mL and a detection limit of 3 ng/mL.
Gaelle et al. [59] immobilized a novel recombinant enzyme in polyvinyl alcohol and attached it to a cobalt–phthalocyanine-modified screen-printed electrode, preparing an enzyme electrode for the detection of microcystin content in aquatic products. The results showed a detection limit of 9 × 10−4 mol/L; the sensor not only exhibited high sensitivity but also demonstrated superior analytical performance.
β-imidazole naturally occurs in plants (such as Peganum harmala), fungi (such as Claviceps purpurea), and tobacco, and is an inhibitor of monoamine oxidase. An excessive intake of β-imidazole from different sources may lead to neurological dysfunction and other diseases. Radulescu et al. [60] immobilized the enzyme on a screen-printed electrode using a copper-containing Prussian blue stable film, using benzylamine as the substrate for the enzymatic reaction, developing a rapid analysis method for β-imidazole based on the inhibition of monoamine oxidase. The results showed detection limits of 5.0 × 10−6 mol/L for ergotamine and 2.5 × 10−6 mol/L for harmaline. This sensor enables the rapid analysis and detection of food. Given the significant harm that toxins pose to humans in various stages of food production, with the continuous optimization of enzyme electrode sensor performance, it is expected to be widely used in the detection of toxins in agricultural products as well.

4.3. Food Additive Testing

Food additives are classified into two major categories based on their origin—natural and synthetic. They can improve the color, aroma, taste, preservation, and freshness of food. The excessive or illegal use of food additives can seriously endanger human health and safety. Common food additives include antioxidants, antibiotics, and others [61,62,63,64,65].
Antioxidants are widely used in food, pharmaceutical, and commercial products, and the excessive supplementation of antioxidants can lead to toxic effects. Natural substances, such as catechins, can boost metabolism and slow aging. However, catechins can also be combined with iron, and their excessive intake will reduce the absorption of iron by the body. Sadeghi et al. [66] used Fe3O4 polyaniline and chitosan as carrier materials to prepare laccase electrode sensors to detect the catechol content in tea. The results showed that the detection range was 5.0 × 10−7 to 8.0 × 10−5 mol/L and the detection limit was 4.0 × 10−7 mol/L. The optimal response time of the enzyme electrode at pH 5, 40 °C was only 8 s. Wang et al. [67] developed a new laccase electrode for the detection of catechol, with a detection limit of 2.9 × 10−7 mol/L. The enzyme electrode has good reproducibility, selectivity, and stability. Oil-soluble antioxidants have the ability to cut off the chain reaction of oil oxidation and hinder the continued oxidation of oil; because of their own toxicity, the state stipulates that their use in food should not exceed 0.2 g/kg. Long et al. [68] prepared horseradish peroxidase electrode sensors using gold–platinum nanotubes and graphene as modification materials to detect the content of two antioxidants. The results showed that the detection limits of butylated hydroxyanisole and propyl gallate were 2.55 × 10−7 mol/L and 1.13 × 10−7 mol/L, respectively. The detection ranges were 1.66 × 10−5–2.77 × 10−4 mol/L and 4.71 × 10−7–4.71 × 10−4 mol/L, respectively.
The excessive use of antibiotics can cause a decline in the body’s own resistance. With the increasing demand for quantitative analysis and the detection of antibiotics in agricultural products, researchers continue to explore it. Antibiotics are usually divided into aminoglycosides, tetracycline, macrolides (erythromycin), beta-lactam (penicillin), etc. Taghdisi et al. [69] invented a novel exonuclide I sensor, whose detection limits for tetracycline antibiotics in milk and serum samples were 7.4 × 10−10 and 7.1 × 10−10 mol/L, respectively. The antibiotic kanamycin belongs to the aminoglycoside family and has a strong antibacterial effect on many enterobacteriaceae bacteria. Song et al. [70] developed a horseradish peroxidase sensor to detect kanamycin in milk, and the results showed that the detection range was 1.72 × 10−11–2.57 × 10−7 mol/L and the detection limit was 8.58 × 10−12 mol/L. The sensor has a strong specificity, sensitivity, and selectivity, and can be applied to the detection of kanamycin in dairy products. Gan et al. [71] used a bare pencil graphite electrode as the working electrode, using imipenem as the substrate, to establish an electrochemical method for measuring the activity of New Delhi metallo-β-lactamase (NDM-1) inhibitors. Upon validation, the IC50 values for EDTA and L-captopril were found to be 1.22 ± 0.20 μM and 22.92 ± 1.19 μM, respectively, which are consistent with the results obtained by the UV–visible spectrophotometric method. The steady-state kinetic studies of the typical competitive inhibitors of L-captopril revealed that the K values measured using the two methods were similar, at 41.5 ± 3.8 μM (DPV-PGE) and 52.5 ± 2.5 μM (UV–vis), respectively.
The rational use of synthetic additives has enriched food production and promoted human health, but after all, they are not natural substances and if used improperly or excessively, will seriously harm human health. The enzyme electrode sensor achieves the highly sensitive and rapid detection of food additives and contributes to human health and safety.

4.4. Detection of Foodborne Pathogenic Bacteria

Agricultural products are rich in nutrients and are easily contaminated by microorganisms, especially pathogenic microorganisms. The traditional detection methods of food-borne pathogens are time-consuming and complicated, which makes it particularly important to explore a rapid and convenient detection method. Vegetables, meat, dairy products, and seafood are easily contaminated by listeria, which can cause infections of human brain tissue and blood in severe cases [72,73,74,75,76].
Chen et al. [77] invented a urease biosensor for the rapid detection of listeria in milk, with a detection limit of 300 CFU/mL. Salmonella is one of the most serious pathogens that can infect produce. Alkaline phosphatase can catalyze L-ascorbate-2-phosphate to produce L-ascorbic acid, while triphosphine can promote L-ascorbic acid regeneration. Wang et al. [78] designed an alkaline phosphatase electrode sensor based on this REDOX cycle to detect Salmonella. The detection limits of phosphate buffer and agricultural water were 7.6 × 102 CFU/mL and 6.0 × 102 CFU/mL, respectively. Cyanobacteria can produce certain protease inhibitors, causing water pollution. lcer et al. [79] adopted a novel nucleic acid biosensor to achieve the rapid and efficient detection of cyanobacteria in drinking water. The sensor was composed of two sets of gold electrode arrays, including gold nanoparticles modified by antibiotin and protease; the results showed that the detection limit of cyanobacteria was only 6 × 10−12 mol/L. Traditional pathogenic bacteria detection is inefficient and slow, such as microbial culture, plate counting, etc. The detection of the enzyme electrode sensor is rapid, efficient, stable, convenient, and low cost; it has great application prospects in the detection of foodborne pathogens.

4.5. Food Allergen Detection

Allergens are the antigens that can sensitize the human body, and the body will have an allergic reaction after exposure to these allergens. Allergic reactions usually occur in the body in the form of skin, respiratory, or digestive allergies, and, in severe cases, as systemic allergies and even death. Since the 21st century, there have been more and more cases of allergic diseases, which have become one of the most common global diseases affecting human health [80,81,82,83,84].
Sun et al. [85] invented a new horseradish peroxidase sensor to detect allergens in peanuts, and the results showed that the detection limit was as low as 1.3 × 10−17 mol/L. Ruiz et al. [86] invented an electrochemical magnetic bead immune sensor platform for the determination of the allergen β-lactoglobulin in dairy products, horseradine peroxidase-labeled antibody, using antibody modification fixed on the magnetic bead on the surface of the disposable carbon screen-printed electrode; they measured at a potential of −0.20 V, before adding hydroquinone as the electron transfer medium and hydrogen peroxide as the enzyme substrate. With a linear detection range of 2.8–100 ng/mL, the magnetic immunosensor platform has been successfully applied to the detection of β-lactoglobulin in different types of milk.
Amaya et al. [87] determined the content of sensitive gluten in gluten-free foods using electrochemistry, labeled the aptamer on the magnetic bead with streptavitin and peroxidase, and measured the enzyme activity using the time–current method. The activity was inversely proportional to the target concentration in the test solution, and the results showed that when the aptamer had the strongest affinity for the target, the sensor has a detection limit as low as 0.5 mg/L and has an excellent analytical performance.

5. Application of Nanomaterials in Enzyme Electrode Sensors

5.1. Nanomaterials

Nanomaterials are fine materials whose grain size reaches the nanometer level. When a material reaches the nanometer size, the physical and chemical properties of the material change significantly due to the interaction between the atoms of the material itself. The peculiar effects exhibited by nano-sized substances mainly include interface effects, small-size effects, and macroscopic quantum tunneling effects [88,89,90,91,92].
There are three main stages in the development of nanomaterials. The initial stage is only limited to a single particle powder or single-phase materials. In the second stage, when single-phase nanomaterials gradually cannot meet the demand, researchers began to study a variety of composite nanomaterials. In the third stage, with the increasing development of nanotechnology, researchers began to focus on the study of nano self-assembly and nanoarray systems. Due to their unique physical and chemical properties, nanomaterials have been widely used in the field of electrochemical sensing analysis [29,93,94,95,96]. Based on Zou’s study [11], the functionalization of MWCNTs with ionic liquids after modification with different groups is shown in Figure 5. MWCNTs serve as the carrier material, modified with functional groups including -NH2, -OH, -SH, and -Cl, as well as alkyl groups ranging from -CH3 to -(CH2)15CH3 and ionic liquids such as Cl− and BF4−. Subsequently, MWCNTs functionalized with -SH, -(CH2)7CH3, and chloride ionic liquid are used as novel modifiers to improve the performance of AChE.

5.2. Nanomaterials Promote the Performance of Enzyme Electrode Sensors

Over the past few decades, nanomaterials have found extensive applications in the field of electrochemical biosensing due to their unique electrochemical properties, significantly enhancing various aspects of the performance of electrochemical biosensors [97,98,99,100,101,102]. Nanomaterials, with their interface effect and small size effect, can significantly improve various performance indicators of biosensors, such as stability, reproducibility, and sensitivity [103]. Moreover, due to the differences in the composition, morphology, and size of various nanomaterials, their roles in the construction process of biosensors are also distinct [104,105,106].

5.2.1. Carbon Nanomaterials

Carbon nanomaterials are derivatives of carbon and are widely used as carrier materials in electrochemical sensing for analytical detection. Examples include carbon nanotubes (CNTs), multi-walled carbon nanotubes (MWCNTs), graphene, mesoporous carbon spheres, crystalline diamond, and diamond-like carbon [107,108,109,110,111,112]. We have described and compared all the carbon nanomaterials mentioned and have presented them in Table 2.
In terms of sensors, compared with the other carbon nanomaterials mentioned in Table 2 above, MWCNTs have the ability to enhance the electrochemical reactivity of important biomolecules and facilitate electron transfer in proteases. Due to the presence of adsorbates, MWCNTs exhibit significant sensitivity to changes in surface conductivity, making them widely used as modification materials in high-sensitivity sensors. Mohammed et al. [116] achieved the precise detection of diclofenac using a novel enzyme electrode based on functionalized MWCNTs and gold–platinum bimetallic nanoparticles (Au-PtNPs). The results showed a detection range of 5 × 10−10–1 × 10−9 mol/L and a detection limit of 3 × 10−10 mol/L. With the assistance of MWCNTs, the sensor exhibited excellent selectivity, strong interference resistance, good reproducibility, repeatability, and stability.
Kaur et al. [117] found that nanocomposites composed of poly3, 4-ethylenedioxyethiophene, and functionalized MWCNTs, as covalently fixed AChE modification materials, could effectively maintain the activity and stability of AChE, and the detection limit of chlorpyrifos, which was prepared based on this biosensor, was as low as 1 × 10−12 mol/L. The detection range was 1 × 10−12–5 × 10−8 mol/L, and the recovery range of realistic samples was 95–97%. Dan et al. [118] found that MWCNTs and gold nanoparticle composites could provide an extremely hydrophilic surface for biomolecular adhesion, and the immobilized AChE showed a good affinity to the substrate; its Km was as low as 2.68 × 10−11 mol/L and the detection range for malathion was 3 × 10−9–3 × 10−6 mol/L. The detection limit was 1.8 × 10−9 mol/L.

5.2.2. Other Nanomaterials

In addition to carbon nanomaterials, other nanomaterials are also used in electrochemical biosensors. These materials not only share the common properties of nanomaterials but also exhibit their unique properties due to their different compositions [119,120,121]. For example, nano-metal materials, such as Au, Ag, Pt, etc., have excellent electron transfer ability. Metal oxides, such as Fe3O4, have magnetic effects, and TiO2 has a photoelectric effect. These unique properties will produce unique effects when applied to sensors [122,123,124,125,126]. In addition, nanometallic materials such as Au, Ag, and Pt have excellent electron transfer capabilities. Among metal oxides, Fe3O4 possesses magnetic effects, while TiO2 exhibits photoelectric effects. These unique properties, when applied to sensors, can produce distinct effects [127].

6. Conclusions and Outlooks

With the rapid and steady development of agriculture, many problems related to agricultural products have followed, among which pesticide residues are the most concerned. As far as the safety of agricultural products is concerned, the pesticide residues that are more threatening to the human body generally have a huge biological toxicity, such as monocrotophos and methamidophos. AChE is a catalytic enzyme in the animal nervous system, which is involved in the development and maturation of cells and can promote the development of neurons and the regeneration of nerves. When the human body consumes too much OP, it will cause the irreversible phosphorylation of esterase in the body; therefore, the enzyme loses the ability to catalyze the hydrolysis of acetylcholine and gradually ages, so that the transport of the neurotransmitter choline is hindered, thus affecting the function of the central nervous system. By combining enzyme-catalyzed reactions and electrochemical detection principles, such sensors can achieve the highly sensitive and highly selective monitoring of food contaminants, including the real-time detection of pesticide residues, pathogens, food additives, heavy metals, and other harmful substances. Its advantages, such as its convenience, speed, low cost, and strong adaptability, make the enzyme electrode sensor an important technological innovation in the field of food detection.
Although enzyme electrode sensors have been widely used in research on the composition detection of agricultural products, overcoming many limitations of traditional detection methods such as long-time consumption, high costs, and the inability to process in batches, how to rapidly detect specific contaminants using enzyme electrode biosensors has become a major issue in current research in the field of agricultural product safety detection technology. It is also a key factor hindering the industrial application of enzyme electrodes in the agricultural sector. Therefore, it is necessary to further improve the specific recognition of substrates via enzyme electrodes, optimize the stability of enzyme electrodes in use, and overcome the current problem of the rapid inactivation of enzyme electrodes due to environmental influences. In addition, the production process of enzyme electrodes needs to be further simplified to facilitate mechanized production. The poor reproducibility of electrodes during the complex physicochemical preparation process necessitates the further optimization and simplification of the enzyme electrode manufacturing process, while ensuring the sensitivity and service life of the enzyme electrodes.

Author Contributions

L.Q.: conceptualization, methodology, validation, formal analysis, investigation, writing—original draft, and writing—review and editing. X.Z.: investigation, resources, and writing—review and editing. Y.C.: investigation, resources, and writing—review and editing. Y.Z. (Yuyang Zhang): investigation, resources, and writing—review and editing. Z.L.: investigation, resources, and writing—review and editing. F.K.: investigation, resources, and writing—review and editing. X.N.: investigation, resources, and writing—review and diting. Q.L.: investigation, resources, and writing—review and editing. Y.Z. (Yani Zhao): investigation, resources, and writing—review and editing. B.Z.: conceptualization, supervision, methodology, investigation, resources, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the Scientific Research Startup Funding Project for Introduced Talents of Wuhu Institute of Technology (No. wzyrc202403); the Wuhu Science and Technology Innovation Strategy Research Special Project (No. 2024swwzx07); the Wuhu Institute of Technology Food Nutrition and Health Innovation Team (No. 2023jxtd06); and the “Double-Teacher and Dual-Ability” Teacher Team Construction Project of Anhui Provincial Department of Education (No. [2024]117).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Park, D.; Han, S.B.; Shin, H.C.; Choi, Y.S.; Kim, Y.R.; Kim, J.Y.; Jang, G.H.; Eom, M. Validation of analytical methods for newly regulated veterinary drug residues in livestock and fishery products with maximum residue limits in Republic of Korea. Food Chem. 2024, 460, 140705. [Google Scholar] [CrossRef] [PubMed]
  2. Yu, X.; Yang, Y.; Shen, Q.; Sun, Y.; Kang, Q.; Shen, D. A novel differential ratiometric molecularly imprinted electrochemical sensor for determination of sulfadiazine in food samples. Food Chem. 2024, 434, 137461. [Google Scholar] [PubMed]
  3. Nilghaz, A.; Mousavi, S.M.; Amiri, A.; Tian, J.; Cao, R.; Wang, X. Surface-Enhanced Raman Spectroscopy Substrates for Food Safety and Quality Analysis. J. Agric. Food Chem. 2022, 70, 5463–5476. [Google Scholar]
  4. Chen, C.; Tian, T.; Yu, H.; Yuan, H.; Wang, B.; Xu, Z.; Tian, H. Characterisation of the key volatile compounds of commercial Gouda cheeses and their contribution to aromas according to Chinese consumers’ preferences. Food Chem.-X 2022, 15, 100416. [Google Scholar] [CrossRef]
  5. Zhang, H.; Chang, Q.; Chen, H.; Xie, Y.; Bai, Y.; Wang, X.; Li, L.; Pang, G. Qualitative and quantitative analysis of pesticide isomers based on collision-induced dissociation of high-resolution mass spectrometry. Microchem. J. 2024, 200, 110338. [Google Scholar]
  6. Cesewski, E.; Johnson, B.N. Electrochemical biosensors for pathogen detection. Biosens. Bioelectron. 2020, 159, 112214. [Google Scholar]
  7. Zhu, H.; Li, M.; Cheng, C.; Han, Y.; Fu, S.; Li, R.; Cao, G.; Liu, M.; Cui, C.; Liu, J.; et al. Recent Advances in and Applications of Electrochemical Sensors Based on Covalent Organic Frameworks for Food Safety Analysis. Foods 2023, 12, 4274. [Google Scholar] [CrossRef]
  8. Clark, L.C.; Lyons, C. Electrode systems for continuous monitoring in cardiovascular surgery. Ann. N. Y. Acad. Sci. 1962, 102, 29–45. [Google Scholar]
  9. Updike, S.J.; Hicks, G.P. The Enzyme Electrode. Nature 1967, 214, 986. [Google Scholar]
  10. Zhang, J.L.; Bi, C.Y.; Li, J.-L. A New Technology for Detecting L-Glutamic Acid Sodium in Soy Sauce; China Condiment: Harbin, China, 2014. [Google Scholar]
  11. Zou, B.; Chu, Y.; Xia, J.; Yao, J. Acetylcholinesterase biosensor based on functionalized surface of carbon nanotubes for monocrotophos detection. Anal. Biochem. 2018, 560, 12–18. [Google Scholar]
  12. Smart, A.; Crew, A.; Pemberton, R.; Hughes, G.; Doran, O.; Hart, J.P. Screen-printed carbon based biosensors and their applications in agri-food safety—ScienceDirect. TrAC Trends Anal. Chem. 2020, 127, 115898. [Google Scholar] [CrossRef]
  13. Bu, L.; Guo, L.; Xie, J. An in situ assay of nerve agents enabled by a self-assembled bienzymatic electrochemical biosensor. New J. Chem. 2020, 44, 7460–7466. [Google Scholar] [CrossRef]
  14. Upadhyay, S.; Sharma, M.K.; Rao, G.R.; Bhattacharya, B.K.; Rao, V.K.; Vijayaraghavan, R. Application of bimetallic nanoparticles modified screen printed electrode for the detection of organophosphate compounds using an enzyme inhibition approach. Anal. Methods 2011, 3, 2246–2253. [Google Scholar] [CrossRef]
  15. Karra, S.; Gorski, W. Signal Amplification in Enzyme-Based Amperometric Biosensors. Anal. Chem. 2013, 85, 10573–10580. [Google Scholar] [CrossRef]
  16. Xia, J.J.; Bin, Z.; Wang, P.Y.; Qing, L.; Xin, S.; Rui, J. Acetylcholinesterase biosensors based on ionic liquid functionalized carbon nanotubes and horseradish peroxidase for monocrotophos determination. Bioprocess Biosyst. Eng. 2020, 43, 293–301. [Google Scholar]
  17. Probst, D.; Lee, I.; Sode, K. The development of micro-sized enzyme sensor based on direct electron transfer type open circuit potential sensing principle. Electrochim. Acta 2022, 426, 140798. [Google Scholar] [CrossRef]
  18. Hatada, M.; Pavlidis, S.; Sode, K. Development of a glycated albumin sensor employing dual aptamer-based extended gate field effect transistors. Biosens. Bioelectron. 2024, 251, 116118. [Google Scholar] [CrossRef]
  19. Hsu, C.H.; Hsu, Y.W.; Weng, Y.C. A novel potentiometric sensor based on urease/bovine serum albumin-poly(3,4-ethylenedioxythiophene)/Pt for urea detection. Z. Für Naturforschung B 2016, 71, 277–282. [Google Scholar] [CrossRef]
  20. Shervedani, R.K.; Amini, A. Direct electrochemistry of dopamine on gold-Agaricus bisporus laccase enzyme electrode: Characterization and quantitative detection. Bioelectrochemistry 2012, 84, 25–31. [Google Scholar] [CrossRef]
  21. Ertek, B.; Akgül, C.; Dilgin, Y. Photoelectrochemical glucose biosensor based on a dehydrogenase enzyme and NAD+/NADH redox couple using a quantum dot modified pencil graphite electrode. RSC Adv. 2016, 6, 20058–20066. [Google Scholar] [CrossRef]
  22. Cui, J.; Hou, T.; Wang, Q.; Gao, G.; Bi, S.; Zhou, K.; Li, J.; Wu, D. An Enzyme Assisted Electrochemical Detection System of Purine Intracellular Utilizing MWCNTs-IL Modified Glassy Carbon Electrode. Electrochim. Acta 2015, 180, 360–365. [Google Scholar]
  23. Kang, J.; Hussain, A.T.; Catt, M.; Trenell, M.; Haggett, B.; Yu, E.H. Electrochemical detection of non-esterified fatty acid by layer-by-layer assembled enzyme electrodes. Sens. Actuators B Chem. 2014, 190, 535–541. [Google Scholar]
  24. Zhao, Y.; Zhang, W.; Lin, Y.; Du, D. The vital function of Fe3O4@Au nanocomposites for hydrolase biosensor design and its application in detection of methyl parathion. Nanoscale 2013, 5, 1121–1126. [Google Scholar] [CrossRef] [PubMed]
  25. Marx, S.; Jose, M.V.; Andersen, J.D.; Russell, A.J. Electrospun gold nanofiber electrodes for biosensors. Biosens. Bioelectron. 2011, 26, 2981–2986. [Google Scholar] [CrossRef]
  26. Goebel, G.; Talke, A.; Ahnert, U.; Lisdat, F. Electrochemical Activity Determination of Catechol-O-methyl Transferase by Selective Dopamine Detection. Chemelectrochem 2019, 6, 4533–4540. [Google Scholar] [CrossRef]
  27. Taghdisi, S.M.; Danesh, N.M.; Ramezani, M.; Alibolandi, M.; Nameghi, M.A.; Gerayelou, G.; Abnous, K. A novel electrochemical aptasensor for ochratoxin a sensing in spiked food using strand-displacement polymerase reaction. Talanta 2021, 223, 121705. [Google Scholar] [CrossRef]
  28. Chu, Y.-H.; Zou, B.; Chen, X.-S.; Xia, J.-J.; Huo, S.-H. Research progress in electrochemical enymze electrode sensor in detection of food safety. Sci. Technol. Food Ind. 2017, 17, 335–340. [Google Scholar]
  29. Liu, S.; Meng, S.; Wang, M.; Li, W.; Dong, N.; Liu, D.; Li, Y.; You, T. In-depth interpretation of aptamer-based sensing on electrode: Dual-mode electrochemical-photoelectrochemical sensor for the ratiometric detection of patulin. Food Chem. 2023, 410, 135450. [Google Scholar] [CrossRef]
  30. Zhu, L.; Dong, X.X.; Gao, C.B.; Gai, Z.; He, Y.X.; Qian, Z.J.; Liu, Y.; Lei, H.T.; Sun, Y.M.; Xu, Z.L. Development of a highly sensitive and selective electrochemical immunosensor for controlling of rhodamine B abuse in food samples. Food Control 2022, 133, 108662. [Google Scholar]
  31. Mustapha, A.T.; Zhou, C. Novel assisted/unassisted ultrasound treatment: Effect on respiration rate, ethylene production, enzymes activity, volatile composition, and odor of cherry tomato. LWT-Food Sci. Technol. 2021, 149, 111779. [Google Scholar]
  32. Lu, B.; Han, F.; Aheto, J.H.; Rashed, M.M.A.; Pan, Z. Artificial bionic taste sensors coupled with chemometrics for rapid detection of beef adulteration. Food Sci. Nutr. 2021, 9, 5220–5228. [Google Scholar] [CrossRef] [PubMed]
  33. Chen, Z.; Sun, Y.; Shi, J.; Zhang, W.; Zhang, X.; Hang, X.; Li, Z.; Zou, X. Convenient self-assembled PDADMAC/PSS/Au@Ag NRs filter paper for swift SERS evaluate of non-systemic pesticides on fruit and vegetable surfaces. Food Chem. 2023, 424, 136232. [Google Scholar] [PubMed]
  34. Chen, Z.; Sun, Y.; Zhang, X.; Shen, Y.; Huang, X.; Shi, J.; Li, Z.; Zou, X.; Khalifa, S.A.M. Green and sustainable self-cleaning flexible SERS base: Utilized for cyclic-detection of residues on apple surface. Food Chem. 2024, 441, 138345. [Google Scholar] [PubMed]
  35. Yu, Y.; Wang, Y.; Okonkwo, C.E.; Chen, L.; Zhou, C. Multimode ultrasonic-assisted decontamination of fruits and vegetables: A review. Food Chem. 2024, 450, 139356. [Google Scholar]
  36. Xu, Y.; Zhang, W.; Shi, J.; Li, Z.; Huang, X.; Zou, X.; Tan, W.; Zhang, X.; Hu, X.; Wang, X.; et al. Impedimetric aptasensor based on highly porous gold for sensitive detection of acetamiprid in fruits and vegetables. Food Chem. 2020, 322, 126762. [Google Scholar]
  37. Azam, S.R.; Ma, H.; Xu, B.; Devi, S.; Siddique, M.A.B.; Stanley, S.L.; Bhandari, B.; Zhu, J. Efficacy of ultrasound treatment in the removal of pesticide residues from fresh vegetables: A review. Trends Food Sci. Technol. 2020, 97, 417–432. [Google Scholar]
  38. Zheng, Q.; Yu, Y.; Fan, K.; Ji, F.; Wu, J.; Ying, Y. A nano-silver enzyme electrode for organophosphorus pesticide detection. Anal. Bioanal. Chem. 2016, 408, 5819–5827. [Google Scholar]
  39. Miao, S.S.; Wu, M.S.; Ma, L.Y.; He, X.J.; Yang, H. Electrochemiluminescence biosensor for determination of organophosphorous pesticides based on bimetallic Pt-Au/multi-walled carbon nanotubes modified electrode. Talanta 2016, 158, 142–151. [Google Scholar]
  40. Itoh, T.; Shimomura, T.; Hayashi, A.; Yamaguchi, A.; Teramae, N.; Ono, M.; Tsunoda, T.; Mizukami, F.; Stucky, G.D.; Hanaoka, T.A. Electrochemical enzymatic biosensor with long-term stability using hybrid mesoporous membrane. Analyst 2014, 139, 4654–4660. [Google Scholar]
  41. Zou, B.; Chu, Y.; Xia, J. Monocrotophos detection with a bienzyme biosensor based on ionic-liquid-modified carbon nanotubes. Anal. Bioanal. Chem. 2019, 411, 2905–2914. [Google Scholar]
  42. Ji, C.; Tang, X.; Wen, R.; Xu, C.; Wei, J.; Han, B.; Wu, L. A Multienzyme Reaction-Mediated Electrochemical Biosensor for Sensitive Detection of Organophosphorus Pesticides. Biosensors 2024, 14, 62. [Google Scholar] [CrossRef] [PubMed]
  43. Zheng, Y.; Liu, Z.; Jing, Y.; Li, J.; Zhan, H. An acetylcholinesterase biosensor based on ionic liquid functionalized graphene–gelatin-modified electrode for sensitive detection of pesticides. Sens. Actuators B Chem. 2015, 210, 389–397. [Google Scholar] [CrossRef]
  44. Nesakumar, N.; Sethuraman, S.; Krishnan, U.M.; Rayappan, J.B.B. Electrochemical acetylcholinesterase biosensor based on ZnO nanocuboids modified platinum electrode for the detection of carbosulfan in rice. Biosens. Bioelectron. 2016, 77, 1070–1077. [Google Scholar] [PubMed]
  45. Magar, H.S.; Fayez, M.; Febbraio, F.; Hassan, R.Y.A. Esterase-2 mutant-based nanostructured amperometric biosensors for the selective determination of paraoxon (Neurotoxin). Anal. Biochem. 2025, 698, 115751. [Google Scholar] [CrossRef]
  46. Wang, D.; Wang, P.; Liu, D.; Zhou, Z. Fluorometric atrazine assay based on the use of nitrogen-doped graphene quantum dots and on inhibition of the activity of tyrosinase. Microchim. Acta 2019, 186, 527. [Google Scholar]
  47. Han, E.; Chao, P.; Xiao-Mei, C.; Jian-Rong, C. Electrochemical detection of atrazine pesticide residue based on the inhibition of tyrosinase. Food Sci. Technol. 2015, 40, 344–347. [Google Scholar]
  48. Song, S.H.; Gao, Z.F.; Guo, X.; Chen, G.H. Aptamer-Based Detection Methodology Studies in Food Safety. Food Anal. Methods 2019, 12, 966–990. [Google Scholar]
  49. Shin, M.C.; Min, K.A.; Cheong, H.; Moon, C.; Huang, Y.; He, H.; Yang, V.C. Preparation and Characterization of Gelonin-Melittin Fusion Biotoxin for Synergistically Enhanced Anti-Tumor Activity. Pharm. Res. 2016, 33, 2218–2228. [Google Scholar]
  50. Suarez-Ulloa, V.; Fernandez-Tajes, J.; Aguiar-Pulido, V.; Prego-Faraldo, M.V.; Eirin-Lopez, J.M. Unbiased high-throughput characterization of mussel transcriptomic responses to sublethal concentrations of the biotoxin okadaic acid. Peerj 2015, 3, e1429. [Google Scholar]
  51. You, F.; Wen, Z.; Yuan, R.; Qian, J.; Long, L.; Wang, K. Sensitive and stable detection of deoxynivalenol based on electrochemiluminescence aptasensor enhanced by 0D/2D homojunction effect in food analysis. Food Chem. 2023, 403, 134397. [Google Scholar] [CrossRef]
  52. Bin, Z.; Feng, L.; Yan, Y. Biomimetic metalloporphyrin oxidase modified carbon nanotubes for highly sensitive and stable quantification of anti-oxidants tert-butylhydroquinone in plant oil. Food Chem. 2022, 388, 132898. [Google Scholar] [CrossRef] [PubMed]
  53. Han, E.; Li, L.; Gao, T.; Pan, Y.; Cai, J. Nitrite determination in food using electrochemical sensor based on self-assembled MWCNTs/AuNPs/poly-melamine nanocomposite. Food Chem. 2024, 437, 137773. [Google Scholar]
  54. Zhang, X.; Fang, J.; Zou, L.; Zou, Y.; Lang, L.; Gao, F.; Hu, N.; Wang, P. A novel sensitive cell-based Love Wave biosensor for marine toxin detection. Biosens. Bioelectron. 2016, 77, 573–579. [Google Scholar] [CrossRef] [PubMed]
  55. Leonardo, S.; Campàs, M. Electrochemical enzyme sensor arrays for the detection of the biogenic amines histamine, putrescine and cadaverine using magnetic beads as immobilisation supports. Microchim. Acta 2016, 183, 1881–1890. [Google Scholar]
  56. Young, J.A.; Jiang, X.; Kirchhoff, J.R. Amperometric Detection of Histamine with a Pyrroloquinoline-Quinone Modified Electrode. Electroanalysis 2013, 25, 1589–1593. [Google Scholar] [CrossRef]
  57. Uludag, Y.; Esen, E.; Kokturk, G.; Ozer, H.; Muhammad, T.; Olcer, Z.; Basegmez, H.I.O.; Simsek, S.; Barut, S.; Gok, M.Y.; et al. Lab-on-a-chip based biosensor for the real-time detection of aflatoxin. Talanta 2016, 160, 381–388. [Google Scholar] [CrossRef]
  58. Chen, J.; Liu, D.; Li, S.; Yao, D. Development of an amperometric enzyme electrode biosensor for sterigmatocystin detection. Enzym. Microb. Technol. 2010, 47, 119–126. [Google Scholar]
  59. Catanante, G.L.; Espin, L.; Marty, J.L. Sensitive biosensor based on recombinant PP1α for microcystin detection. Biosens. Bioelectron. 2015, 67, 700–707. [Google Scholar] [CrossRef]
  60. Radulescu, M.C.; Bucur, M.P.; Bucur, B.; Radu, G.L. Biosensor based on inhibition of monoamine oxidases A and B for detection of β-carbolines. Talanta 2015, 137, 94–99. [Google Scholar] [CrossRef]
  61. Chen, Z.; Lian, X.; Zhou, M.; Zhang, X.; Wang, C. Quantitation of L-cystine in Food Supplements and Additives Using 1 H qNMR: Method Development and Application. Foods 2023, 12, 2421. [Google Scholar] [CrossRef]
  62. Zhou, C.; Li, C.; Cui, H.; Lin, L. Metabolomics insights into the potential of encapsulated essential oils as multifunctional food additives. Crit. Rev. Food Sci. Nutr. 2022, 64, 5143–5160. [Google Scholar] [CrossRef] [PubMed]
  63. Zhou, W.; Luo, J.; Zhao, L.; Zhao, M.; Ouyang, Z.; Yang, M. Influences of different storage environments and packaging materials on the quality of the traditional Chinese health food Herba Menthae Haplocalycis. Food Packag. Shelf Life 2017, 15, 52–61. [Google Scholar]
  64. Mu, R.; Hong, X.; Ni, Y.; Li, Y.; Zheng, Y. Recent trends and applications of cellulose nanocrystals in food industry. Trends Food Sci. Technol. 2019, 93, 136–144. [Google Scholar]
  65. Hu, D. Monte Carlo: A flexible and accurate technique for modeling light transport in food and agricultural products. Trends Food Sci. Technol. 2020, 102, 280–290. [Google Scholar]
  66. Sadeghi, S.; Fooladi, E.; Malekaneh, M. A New Amperometric Biosensor Based on Fe3O4/Polyaniline/Laccase/Chitosan Biocomposite-Modified Carbon Paste Electrode for Determination of Catechol in Tea Leaves. Appl. Biochem. Biotechnol. Part A Enzym. Eng. Biotechnol. 2015, 175, 1603–1616. [Google Scholar]
  67. Qu, J.; Lou, T.; Wang, Y.; Dong, Y.; Xing, H. Determination of Catechol by a Novel Laccase Biosensor Based on Zinc-Oxide Sol-Gel. Anal. Lett. 2015, 48, 1842–1853. [Google Scholar] [CrossRef]
  68. Wu, L.; Yin, W.; Tang, K.; Li, D.; Han, H. Enzymatic biosensor of horseradish peroxidase immobilized on Au-Pt nanotube/Au-graphene for the simultaneous determination of antioxidants. Anal. Chim. Acta 2016, 933, 89–96. [Google Scholar] [CrossRef]
  69. Taghdisi, S.M.; Danesh, N.M.; Ramezani, M.; Abnous, K. A novel M-shape electrochemical aptasensor for ultrasensitive detection of tetracyclines. Biosens. Bioelectron. 2016, 85, 509–514. [Google Scholar]
  70. Song, H.Y.; Kang, T.F.; Li, N.N.; Lu, L.P.; Cheng, S.Y. Highly sensitive voltammetric determination of kanamycin based on aptamer sensor for signal amplification. Anal. Methods 2016, 8, 3366–3372. [Google Scholar]
  71. Gan, H.; Liang, Y.; Tang, Y.; Huang, M.; Wan, Y. A practical electrochemical method for determining the inhibitory activity of New Delhi Metallo-β-lactamase-1 inhibitors. Microchem. J. 2023, 195, 109418. [Google Scholar]
  72. Dai, J.; Bai, M.; Li, C.; Cui, H.; Lin, L. Advances in the mechanism of different antibacterial strategies based on ultrasound technique for controlling bacterial contamination in food industry. Trends Food Sci. Technol. 2020, 105, 211–222. [Google Scholar]
  73. Qian, J.; Zhang, M.; Dai, C.; Huo, S.; Ma, H. Transcriptomic analysis of Listeria monocytogenes under pulsed magnetic field treatment. Food Res. Int. 2020, 133, 109195. [Google Scholar] [PubMed]
  74. Cui, H.; Chen, Y.; Aziz, T.; Al-Asmari, F.; Alwethaynani, M.S.; Shi, C.; Lin, L. Antibacterial mechanisms of diacetyl on Listeria monocytogenes and its application in Inner Mongolian cheese preservation via gelatin-based edible films. Food Control 2025, 168, 110920. [Google Scholar]
  75. Alenyorege, E.A.; Ma, H.; Ayim, I.; Aheto, J.H.; Hong, C.; Zhou, C. Reduction of Listeria innocua in fresh-cut Chinese cabbage by a combined washing treatment of sweeping frequency ultrasound and sodium hypochlorite. LWT 2019, 101, 410–418. [Google Scholar]
  76. Zhang, C.; Li, C.; Abdel-Samie, M.A.; Cui, H.; Lin, L. Unraveling the inhibitory mechanism of clove essential oil against Listeria monocytogenes biofilm and applying it to vegetable surfaces. LWT-Food Sci. Technol. 2020, 134, 110210. [Google Scholar]
  77. Chen, Q.; Lin, J.; Gan, C.; Wang, Y.; Wang, D.; Xiong, Y.; Lai, W.; Li, Y.; Wang, M. A sensitive impedance biosensor based on immunomagnetic separation and urease catalysis for rapid detection of Listeria monocytogenes using an immobilization-free interdigitated array microelectrode. Biosens. Bioelectron. 2015, 74, 504–511. [Google Scholar] [CrossRef]
  78. Wang, D.; Wang, Z.; Chen, J.; Kinchla, A.J.; Nugen, S.R. Rapid detection of Salmonella using a redox cycling-based electrochemical method. Food Control 2016, 62, 81–88. [Google Scholar]
  79. Ölcer, Z.; Esen, E.; Ersoy, A.; Budak, S.; Kaya, D.S.; Gök, M.Y.; Barut, S.; Üstek, D.; Uludag, Y. Microfluidics and nanoparticles based amperometric biosensor for the detection of cyanobacteria (Planktothrix agardhii NIVA-CYA 116) DNA. Biosens. Bioelectron. 2015, 70, 426–432. [Google Scholar]
  80. Cassotta, M.; Forbes-Hernández, T.Y.; Iglesias, R.C.; Ruiz, R.; Battino, M. Links between Nutrition, Infectious Diseases, and Microbiota: Emerging Technologies and Opportunities for Human-Focused Research. Nutrients 2020, 12, 1827. [Google Scholar] [CrossRef]
  81. Li, X.; Zhang, Z.H.; Zabed, H.M.; Yun, J.; Zhang, G.; Qi, X. An Insight into the Roles of Dietary Tryptophan and its Metabolites in Intestinal Inflammation and Inflammatory Bowel Disease. Mol. Nutr. Food Res. 2021, 65, 2000461. [Google Scholar]
  82. Suo, H.; Shishir, M.R.I.; Xiao, J.; Wang, M.; Chen, F.; Cheng, K.W. Red Wine High-Molecular-Weight Polyphenolic Complex: An Emerging Modulator of Human Metabolic Disease Risk and Gut Microbiota. J. Agric. Food Chem. 2021, 69, 10907–10919. [Google Scholar] [PubMed]
  83. Moradi, S.Z.; Jalili, F.; Farhadian, N.; Joshi, T.; Wang, M.; Zou, L.; Cao, H.; Farzaei, M.H.; Xiao, J. Polyphenols and neurodegenerative diseases: Focus on neuronal regeneration. Crit. Rev. Food Sci. Nutr. 2022, 62, 3421–3436. [Google Scholar] [PubMed]
  84. Liu, H.; Malhotra, R.; Peczuh, M.W.; Rusling, J.F. Electrochemical immunosensors for antibodies to peanut allergen ara h2 using gold nanoparticle-peptide films. Anal. Chem. 2010, 82, 5865–5871. [Google Scholar] [PubMed]
  85. Sun, X.; Jia, M.; Ji, J.; Guan, L.; Zhang, Y.; Tang, L.; Li, Z. Enzymatic amplification detection of peanut allergen Ara h1 using a stem-loop DNA biosensor modified with a chitosan-mutiwalled carbon nanotube nanocomposite and spongy gold film. Talanta 2015, 131, 521–527. [Google Scholar]
  86. Montiel, R.V.; Campuzano, S.; Conzuelo, F.; Torrente-Rodríguez, R.M.; Gamella, M.; Reviejo, A.J.; Pingarrón, J.M. Electrochemical magnetoimmunosensing platform for determination of the milk allergen β-lactoglobulin. Talanta 2015, 131, 156–162. [Google Scholar]
  87. Amaya-González, S.; de-los-Santos-álvarez, N.; Miranda-Ordieres, A.J.; Lobo-Castañón, M.J. Sensitive gluten determination in gluten-free foods by an electrochemical aptamer-based assay. Anal. Bioanal. Chem. 2015, 407, 6021. [Google Scholar]
  88. Cheng, C.; Yu, X.; Geng, F.; Wang, L.; Yang, J.; Huang, F.; Deng, Q. Review on the Regulation of Plant Polyphenols on the Stability of Polyunsaturated-Fatty-Acid-Enriched Emulsions: Partitioning Kinetic and Interfacial Engineering. J. Agric. Food Chem. 2022, 70, 3569–3584. [Google Scholar]
  89. Li, C.; Liu, D.; Huang, M.; Huang, W.; Li, Y.; Feng, J. Interfacial engineering strategy to improve the stabilizing effect of curcumin-loaded nanostructured lipid carriers. Food Hydrocoll. 2022, 127, 107552. [Google Scholar]
  90. Jin, J.; Ma, H.; Wang, W.; Luo, M.; Wang, B.; Qu, W.; He, R.; Owusu, J.; Li, Y. Effects and mechanism of ultrasound pretreatment on rapeseed protein enzymolysis. J. Sci. Food Agric. 2016, 96, 1159–1166. [Google Scholar]
  91. Wang, Y.; Li, Z.; Barnych, B.; Huo, J.; Wan, D.; Vasylieva, N.; Xu, J.; Li, P.; Liu, B.; Zhang, C.; et al. Investigation of the Small Size of Nanobodies for a Sensitive Fluorescence Polarization Immunoassay for Small Molecules: 3-Phenoxybenzoic Acid, an Exposure Biomarker of Pyrethroid Insecticides as a Model. J. Agric. Food Chem. 2019, 67, 11536–11541. [Google Scholar] [CrossRef]
  92. Yang, W.; Jia, W.; Ou, M.; Zhong, W.; Jiang, L.; Wang, X. Effect of Physical Properties of an Emulsion Pesticide on the Atomisation Process and the Spatial Distribution of Droplet Size. Agriculture 2022, 12, 949. [Google Scholar] [CrossRef]
  93. Zou, Y.; Shi, Y.; Wang, T.; Ji, S.; Zhang, X.; Shen, T.; Huang, X.; Xiao, J.; Farag, M.A.; Shi, J.; et al. Quantum dots as advanced nanomaterials for food quality and safety applications: A comprehensive review and future perspectives. Compr. Rev. Food Sci. Food Saf. 2024, 23, e13339. [Google Scholar] [CrossRef] [PubMed]
  94. Zhou, J.-W.; Zou, X.-M.; Song, S.-H.; Chen, G.-H. Quantum Dots Applied to Methodology on Detection of Pesticide and Veterinary Drug Residues. J. Agric. Food Chem. 2018, 66, 1307–1319. [Google Scholar] [CrossRef] [PubMed]
  95. Okeke, E.S.; Ezeorba, T.P.C.; Okoye, C.O.; Chen, Y.; Mao, G.; Feng, W.; Wu, X. Analytical detection methods for azo dyes: A focus on comparative limitations and prospects of bio-sensing and electrochemical nano-detection. J. Food Compos. Anal. 2022, 114, 104778. [Google Scholar]
  96. Liu, G.; Liu, Z.; Sun, Y.; Sun, M.; Duan, J.; Tian, Y.; Du, D.; Li, M. Cascade Amplifying Electrochemical Bioanalysis for Zearalenone Detection in Agricultural Products: Utilizing a Glucose–Fenton–HQ System on Bimetallic–ZIF@CNP Nanocomposites. Foods 2024, 13, 3192. [Google Scholar] [CrossRef]
  97. Meng, S.; Liu, D.; Li, Y.; Dong, N.; Chen, T.; You, T. Engineering the Signal Transduction between CdTe and CdSe Quantum Dots for in Situ Ratiometric Photoelectrochemical Immunoassay of Cry1Ab Protein. J. Agric. Food Chem. 2022, 70, 13583–13591. [Google Scholar] [CrossRef]
  98. Li, W.; Shi, Y.; Zhang, X.; Hu, X.; Huang, X.; Liang, N.; Shen, T.; Zou, X.; Shi, J. A DNA tetrahedral scaffolds-based electrochemical biosensor for simultaneous detection of AFB1 and OTA. Food Chem. 2024, 442, 138312. [Google Scholar] [CrossRef]
  99. Dong, X.; Huang, A.; He, L.; Cai, C.; You, T. Recent advances in foodborne pathogen detection using photoelectrochemical biosensors: From photoactive material to sensing strategy. Front. Sustain. Food Syst. 2024, 8, 1432555. [Google Scholar] [CrossRef]
  100. Gao, L.; Li, Z.; Wei, X.; Hao, M.; Song, W.; Zou, X.; Huang, X. A cell-based electrochemical biosensor for the detection of capsaicin. J. Food Meas. Charact. 2024, 18, 9341–9352. [Google Scholar] [CrossRef]
  101. Luo, L.; Liu, X.; Ma, S.; Li, L.; You, T. Quantification of zearalenone in mildewing Cereal crops using an innovative photoelectrochemical aptamer sensing strategy based on ZnO-NGQDs composites. Food Chem. 2020, 322, 126778. [Google Scholar] [CrossRef]
  102. Rzayev, J. Molecular Bottlebrushes: New Opportunities in Nanomaterials Fabrication. ACS Macro Lett. 2017, 1, 1146–1149. [Google Scholar]
  103. Ouyang, Q.; Zhang, M.; Yang, Y.; Din, Z.; Chen, Q. Mesoporous silica-modified upconversion biosensor coupled with real-time ion release properties for ultrasensitive detection of Staphylococcus aureus in meat. Food Control 2022, 145, 109444. [Google Scholar]
  104. Teng, H.; Zheng, Y.; Cao, H.; Huang, Q.; Chen, L. Enhancement of bioavailability and bioactivity of diet-derived flavonoids by application of nanotechnology: A review. Crit. Rev. Food Sci. Nutr. 2021, 63, 378–393. [Google Scholar] [CrossRef] [PubMed]
  105. Li, H.; Sheng, W.; Chen, H.Q. Recent advances in rare earth ion-doped upconversion nanomaterials: From design to their applications in food safety analysis. Compr. Rev. Food Sci. Food Saf. 2023, 22, 3732–3764. [Google Scholar] [CrossRef]
  106. Patra, J.K.; Shin, H.-S.; Paramithiotis, S. Editorial: Application of Nanotechnology in Food Science and Food Microbiology. Front. Microbiol. 2018, 9, 714. [Google Scholar]
  107. Zeng, K.; Wei, W.; Jiang, L.; Zhu, F.; Du, D. Use of Carbon Nanotubes as a Solid Support To Establish Quantitative (Centrifugation) and Qualitative (Filtration) Immunoassays To Detect Gentamicin Contamination in Commercial Milk. J. Agric. Food Chem. 2016, 64, 7874–7881. [Google Scholar]
  108. Li, H.; Liang, J.; Kong, F.; Ren, M.; Mohammed, A.E.A.Y.; Zhou, C. Preparation of lignin-containing cellulose nanofibers from walnut shell using deep eutectic solvent for nanotube conductive film. Ind. Crops Prod. 2024, 207 Pt 1, 117737. [Google Scholar] [CrossRef]
  109. Sharma, A.S.; Ali, S.; Sabarinathan, D.; Murugavelu, M.; Li, H.; Chen, Q. Recent progress on graphene quantum dots-based fluorescence sensors for food safety and quality assessment applications. Compr. Rev. Food Sci. Food Saf. 2021, 20, 5765–5801. [Google Scholar]
  110. Zhao, Y.; Ma, Y.; Zhou, R.; He, Y.; Wu, Y.; Yi, Y.; Zhu, G. Highly sensitive electrochemical detection of paraoxon ethyl in water and fruit samples based on defect-engineered graphene nanoribbons modified electrode. J. Food Meas. Charact. 2022, 16, 2596–2603. [Google Scholar]
  111. Li, W.; Hu, X.; Li, Q.; Shi, Y.; Zhai, X.; Xu, Y.; Li, Z.; Huang, X.; Wang, X.; Shi, J.; et al. Copper nanoclusters @ nitrogen-doped carbon quantum dots-based ratiometric fluorescence probe for lead (II) ions detection in porphyra—ScienceDirect. Food Chem. 2020, 320, 126623. [Google Scholar]
  112. Zeng, K.; Chen, B.; Li, Y.; Meng, H.; Wu, Q.; Yang, J.; Liang, H. Gold nanoparticle-carbon nanotube nanohybrids with peroxidase-like activity for the highly-sensitive immunoassay of kanamycin in milk. Int. J. Food Sci. Technol. 2022, 57, 6028–6037. [Google Scholar] [CrossRef]
  113. Luo, R.; Feng, Z.; Shen, G.; Xiu, Y.; Zhou, Y.; Niu, X.; Wang, H. Acetyicholinesterase Biosensor Based On Mesoporous Hollow Carbon Spheres/Core-Shell Magnetic Nanoparticles-Modified Electrode for the Detection of Organophosphorus Pesticides. Sensors 2018, 18, 4429. [Google Scholar] [CrossRef] [PubMed]
  114. Stankovic, V.; Manojlovic, D.; Roglic, G.M.; Tolstoguzov, D.S.; Zherebtsov, D.A.; Uchaev, D.A.; Avdin, V.V.; Stankovic, D.M. Synthesis and Application of Domestic Glassy Carbon TiO2 Nanocomposite for Electrocatalytic Triclosan Detection. Catalysts 2022, 12, 1571. [Google Scholar] [CrossRef]
  115. Kang, Y.; Ren, X.; Li, Y.; Yu, Z. Ni-Coated Diamond-like Carbon-Modified TiO2 Nanotube Composite Electrode for Electrocatalytic Glucose Oxidation. Molecules 2022, 27, 5815. [Google Scholar] [CrossRef]
  116. Daneshvar, L.; Rounaghi, G.H.; Es’ haghi, Z.; Chamsaz, M.; Tarahomi, S. Fabrication a new modified electrochemical sensor based on Au-Pd bimetallic nanoparticle decorated graphene for citalopram determination. Mater. Sci. Eng. C Mater. Biogical Appl. 2016, 69, 653–660. [Google Scholar] [CrossRef]
  117. Kaur, N.; Thakur, H.; Kumar, R.; Prabhakar, N. An electrochemical sensor modified with poly(3,4-ethylenedioxythiophene)-wrapped multi-walled carbon nanotubes for enzyme inhibition-based determination of organophosphates. Mikrochim. Acta Int. J. Phys. Chem. Methods Anal. 2016, 183, 2307–2315. [Google Scholar] [CrossRef]
  118. Du, D.; Wang, M.; Cai, J.; Qin, Y.; Zhang, A. One-step synthesis of multiwalled carbon nanotubes-gold nanocomposites for fabricating amperometric acetylcholinesterase biosensor. Sens. Actuators B Chem. 2010, 143, 524–529. [Google Scholar] [CrossRef]
  119. Wu, Q.; Zhong, Y.; Chen, R.; Ling, G.; Wang, X.; Shen, Y.; Hao, C. Cu-Ag-C@Ni3S4 with core shell structure and rose derived carbon electrode materials: An environmentally friendly supercapacitor with high energy and power density. Ind. Crops Prod. 2024, 222, 119676. [Google Scholar] [CrossRef]
  120. Murali, M.; Gowtham, H.G.; Singh, S.B.; Shilpa, N.; Aiyaz, M.; Alomary, M.N.; Alshamrani, M.; Salawi, A.; Almoshari, Y.; Ansari, M.A. Fate, bioaccumulation and toxicity of engineered nanomaterials in plants: Current challenges and future prospects. Sci. Total Environ. 2022, 811, 152249. [Google Scholar]
  121. Fang, Y.; Fan, L.; Bai, H.; Li, B.; Zhang, H.; Xin, F.; Ma, J.; Jiang, M. Bio-based molecules for biosynthesis of nano-metallic materials. Sheng Wu Gong Cheng Xue Bao = Chin. J. Biotechnol. 2021, 37, 541–560. [Google Scholar]
  122. Wang, B.; Zhang, Y.; Venkitasamy, C.; Wu, B.; Pan, Z.; Ma, H. Effect of pulsed light on activity and structural changes of horseradish peroxidase. Food Chem. 2017, 234, 20–25. [Google Scholar] [CrossRef] [PubMed]
  123. Zhu, X.; Zhao, Z.; Zhang, W. Determination of fat content in goat milk using annular photoelectric sensor system. Trans. Inst. Meas. Control 2018, 40, 102–110. [Google Scholar] [CrossRef]
  124. Lu, Y.; Zhang, B.; Tian, Y.; Guo, Q.; Yang, X.; Nie, G. An enhanced photoelectrochemical sensor for aflatoxin B1 detection based on organic-inorganic heterojunction nanomaterial: Poly(5-formylindole)/NiO. Mikrochim. Acta 2020, 187, 467. [Google Scholar] [PubMed]
  125. Chen, M.M.; Liu, Y.; Zhao, S.; Jiang, J.; Zhang, Q.; Li, P.; Tang, X. Carbon nanospheres bridging in perovskite quantum dots/BiOBr: An efficient heterojunction for high-performance photoelectrochemical sensing of deoxynivalenol. Carbon 2024, 221, 118919. [Google Scholar]
  126. Zhang, C.; Wang, Q.; Zhong, C. A simple photoelectrochemical aptasensor based on MoS2/rGO for aflatoxin B1 detection in grain crops. Anal. Methods 2024, 16, 1330–1340. [Google Scholar]
  127. Ding, S.; Lyu, Z.; Li, S.; Ruan, X.; Lin, Y. Molecularly imprinted polypyrrole nanotubes based electrochemical sensor for glyphosate detection. Biosens. Bioelectron. 2021, 191, 113434. [Google Scholar]
Foods 14 01254 g001
Figure 1. Illustration of the Cl/MWCNTs/AChE/GCE biosensor synthesis test chart (functionalized MWCNTs with diverse groups, alkyl chains, and ionic liquids) [11].
Figure 1. Illustration of the Cl/MWCNTs/AChE/GCE biosensor synthesis test chart (functionalized MWCNTs with diverse groups, alkyl chains, and ionic liquids) [11].
Foods 14 01254 g001
Foods 14 01254 g002
Figure 2. Illustration of the Cl/MWCNTs/HRP/AChE/GCE biosensor synthesis test chart (HRP for choline oxidation enhancement and sensing sensitivity improvement) [16].
Figure 2. Illustration of the Cl/MWCNTs/HRP/AChE/GCE biosensor synthesis test chart (HRP for choline oxidation enhancement and sensing sensitivity improvement) [16].
Foods 14 01254 g002
Foods 14 01254 g003
Figure 3. Illustration of the Cl/FePP-MWCNTs/AChE/GCE biosensor synthesis test chart (simulation of oxidase-FePP based on the excellent electrochemical properties of MWCNTs and ionic liquids) [41].
Figure 3. Illustration of the Cl/FePP-MWCNTs/AChE/GCE biosensor synthesis test chart (simulation of oxidase-FePP based on the excellent electrochemical properties of MWCNTs and ionic liquids) [41].
Foods 14 01254 g003
Foods 14 01254 g004
Figure 4. Schematic diagram of enzyme inhibition and the differential pulse voltammetry (DPV) detection of ETH [42].
Figure 4. Schematic diagram of enzyme inhibition and the differential pulse voltammetry (DPV) detection of ETH [42].
Foods 14 01254 g004
Foods 14 01254 g005
Figure 5. Illustration of Cl/MWCNTs and BF4/MWCNTs synthesis (functionalized MWCNTs with -SH, -(CH2)7CH3, and chloride ionic liquids as novel modifiers) [11].
Figure 5. Illustration of Cl/MWCNTs and BF4/MWCNTs synthesis (functionalized MWCNTs with -SH, -(CH2)7CH3, and chloride ionic liquids as novel modifiers) [11].
Foods 14 01254 g005
Table 1. Classification of enzyme electrodes based on enzyme-catalyzed reaction types.
Table 1. Classification of enzyme electrodes based on enzyme-catalyzed reaction types.
TypeDetection ObjectEnzyme Action MechanismMethodPerformance FeatureReference
Oxidoreductase electrodesIntracellular purinesCatalyze the purine oxidation reactionElectrochemical methodHigh sensitivity, high accuracy, wide application range, real-time monitoring[22]
Hydrolase electrodesOrganophosphorus pesticideCatalyze the hydrolysis of methyl parathionElectrochemical methodHigh selectivity, limited sensitivity, limited application range[24]
Isomerase electrodesSerum glucoseCatalyze the isomerization of glucoseElectrochemical methodHigh selectivity, good biocompatibility, limited sensitivity, complex preparation[25]
Transferase electrodesDopamineCatalyze the isomerization of glucoseElectrochemical methodHigh selectivity, high accuracy, limited application
range		[26]
Ligase electrodesOchratoxin ACatalytic phosphorylation of OTA aptamer connectionsElectrochemical methodHigh selectivity, high sensitivity, complex operation[27]
Synthase electrodesNon-esterified fatty acidCatalyze acyl-coA synthesis reactionElectrochemical methodHigh selectivity, high sensitivity, high preparation cost[23]
Table 2. Applications of different carbon nanomaterials in electrochemical sensing.
Table 2. Applications of different carbon nanomaterials in electrochemical sensing.
Material NameStructural PropertyThe Role of SensorsLimit of Detection (LOD)Reference
Carbon nanotubes (CNTs)Single/multilayer tubular structure, high aspect ratio, excellent electrical conductivityEnhanced electron transport efficiency, high specific surface area0.048 ng/mL[107]
Multi-walled carbon nanotubes (MWCNTs)Multilayer coaxial tubular structure with defect sites on the surface can be modified functionallyImprove the electrochemical activity of biomolecules, promote electron transfer in proteases, enhanced response to changes in surface conductivity11.2 pg/mL[112]
GrapheneSingle layer two-dimensional honeycomb structure, high conductivityEnhance electron transport rate, adsorb target molecules4.3 nM[110]
Mesoporous carbon spheresPorous spherical structure, pore size 2~50 nm, high specific surface areaProvide a large number of enzyme fixation sites, promote molecular diffusion0.0182 ppb [113]
Crystalline diamondThree-dimensional cubic crystal structure, ultra-high hardness and chemical inertnessAs electrode base material, provide stable interface0.07 μM[114]
Diamond-like carbonAmorphous carbon material with sp3 and sp2 hybrid bonds, high wear resistance, and chemical inertnessElectrode surface modification, reduce non-specific adsorption0.53 μM[115]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Qu, L.; Zhang, X.; Chu, Y.; Zhang, Y.; Lin, Z.; Kong, F.; Ni, X.; Zhao, Y.; Lu, Q.; Zou, B. Research Progress on Nanotechnology-Driven Enzyme Biosensors for Electrochemical Detection of Biological Pollution and Food Contaminants. Foods 2025, 14, 1254. https://doi.org/10.3390/foods14071254

AMA Style

Qu L, Zhang X, Chu Y, Zhang Y, Lin Z, Kong F, Ni X, Zhao Y, Lu Q, Zou B. Research Progress on Nanotechnology-Driven Enzyme Biosensors for Electrochemical Detection of Biological Pollution and Food Contaminants. Foods. 2025; 14(7):1254. https://doi.org/10.3390/foods14071254

Chicago/Turabian Style

Qu, Liang, Xue Zhang, Yanhong Chu, Yuyang Zhang, Zhiyuan Lin, Fanzhuo Kong, Xing Ni, Yani Zhao, Qiongya Lu, and Bin Zou. 2025. "Research Progress on Nanotechnology-Driven Enzyme Biosensors for Electrochemical Detection of Biological Pollution and Food Contaminants" Foods 14, no. 7: 1254. https://doi.org/10.3390/foods14071254

APA Style

Qu, L., Zhang, X., Chu, Y., Zhang, Y., Lin, Z., Kong, F., Ni, X., Zhao, Y., Lu, Q., & Zou, B. (2025). Research Progress on Nanotechnology-Driven Enzyme Biosensors for Electrochemical Detection of Biological Pollution and Food Contaminants. Foods, 14(7), 1254. https://doi.org/10.3390/foods14071254

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop