Biosensors for Detecting Food Contaminants—An Overview

Abstract

Food safety is a pressing global concern due to the risks posed by contaminants such as pesticide residues, heavy metals, allergens, mycotoxins, and pathogenic microorganisms. While accurate, traditional detection methods like ELISA, HPLC, and mass spectrometry are often time-consuming and resource-intensive, highlighting the need for innovative alternatives. Biosensors based on biological recognition elements such as enzymes, antibodies, and aptamers, offer fast, sensitive, and cost-effective solutions. Using transduction mechanisms like electrochemical, optical, piezoelectric, and thermal systems, biosensors provide versatile tools for detecting contaminants. Advances in DNAzyme- and aptamer-based technologies enable the precise detection of heavy metals, while enzyme- and protein-based biosensors monitor metal-induced changes in biological activity. Innovations like microbial biosensors and DNA-modified electrodes enhance detection accuracy. Biosensors are also highly effective in identifying pesticide residues, allergens, mycotoxins, and pathogens through immunological, enzymatic, and nucleic acid-based techniques. The integration of nanomaterials and bioelectronics has significantly improved the sensitivity and performance of biosensors. By facilitating real-time, on-site monitoring, these devices address the limitations of conventional methods to ensure food quality and regulatory compliance. This review highlights the transformative role of biosensors and how biosensors are improved by emerging technologies in food contamination detection, emphasizing their potential to mitigate public health risks and enhance food safety throughout the supply chain.

1. Introduction

Food safety is an increasingly pressing global concern due to risks such as pesticide residues, food allergies, heavy metals, and pathogenic microorganisms. The extensive use of pesticides, combined with environmental pollution, has resulted in the persistent presence of contaminants in food sources [1]. In addition, food allergies have become a significant public health problem, particularly in developed countries [2]. Spoilage caused by pathogenic microorganisms is another critical food safety challenge [3].

Furthermore, heavy metals in the environment adversely affect plant growth, reduce crop quality, and accumulate in plants. This contamination affects human health through the food chain, as heavy metals have been associated with genetic damage and an increased risk of cancer. Ensuring food quality is critical because contaminants such as allergens, pathogenic microorganisms, heavy metals, and herbicides pose significant health risks [4]. With consumers becoming increasingly vigilant, the detection of contaminants in food has become essential [5]. Consequently, the identification of food contaminants has become a priority [6].

Traditional detection methods, including enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC), mass spectroscopy (MS), and gas chromatography (GC), are highly accurate but are often expensive, time-consuming, and require skilled operators. Therefore, there is an urgent need for faster, simpler, and highly sensitive detection techniques [7]. The development of sensitive and reliable on-site technologies for the detection and monitoring of food contaminants is essential to ensuring food safety and protection from harmful substances.

Biosensors are particularly well suited for monitoring contaminants. These devices, a subset of chemical sensors, use biological recognition elements for analyte detection. Recent research has focused on developing a variety of chemical and biological sensing devices, based on different operating principles, to identify hazardous substances in food. These advances represent a significant area of interest in the field of food safety [8].

Biosensors, which integrate biological components such as enzymes, DNA, RNA, antigens, living cells, or antibodies with electronic sensing elements like conductance, intensity, electromagnetic radiation phase, electric current, mass, viscosity, electric potential, temperature, and impedance [9,10,11], provide rapid and accurate results through their combined functionality. The incorporation of biosensors into food quality monitoring systems presents an innovative strategy to enhancing food safety and quality assurance [12].

Next-generation biosensor arrays incorporate artificial intelligence algorithms, enabling their specialization, selectivity, responsiveness, and consistency. With artificial intelligence support, these biosensors more accurately identify biological analytes, enhancing performance and reliability. Artificial intelligence is transforming food systems by providing tailored solutions through machine learning, natural language processing, computer vision, and reinforcement learning [13]. These advancements improve food safety through real-time detection and prevention of contamination. The application of machine learning has significantly increased the efficiency of various sensors used in food safety evaluation. By integrating machine learning with noninvasive biosensors, it is now possible to monitor food safety more efficiently, with a particular focus on the stability of bio-recognition molecules [14].

Machine learning enhances biosensors, transforming them into intelligent systems capable of predicting analytes using stable training models [15]. It improves biosensor specificity during data analysis and helps detect subtle patterns within sensor data. Additionally, machine learning enhances the sensor’s ability to monitor multiple analytes in complex food matrices, increasing the functionality and versatility of biosensors.

These devices allow rapid, accurate, and on-site detection of contaminants, revolutionizing the management of food safety risks throughout the supply chain [16,17,18]. The performance of a biosensor is evaluated based on several parameters, including sensitivity, selectivity, specificity, reproducibility, size, diagnostic speed, scalability for large-scale production, and cost effectiveness [19]. Various biosensors enhanced by machine learning have been employed to analyze a range of food contaminants [20].

This review aims to provide an overview of how biosensors are used in the food industry to monitor and control contaminants, as well as how biosensors have been enhanced by emerging technologies, ultimately enhancing the safety and quality of food products.

2. Biosensors for Detecting Food Contaminates

Biosensors can be categorized based on various criteria, with two common approaches being the type of transduction system employed, such as electrochemical, optical, piezoelectric, and thermal biosensors, and the type of biorecognition element (biocomponent or bioreceptor). The biocomponent or bioreceptor, such as isolated enzymes, whole cells, tissues, or aptamers, is essential in biosensors, enabling selective analyte detection and interaction. The energy released from this interaction is converted into a measurable electrical signal [21]. Common biological elements include enzymes and antibodies. Biosensors are further classified into biocatalytic sensors and affinity sensors based on their interaction with the analyte.

Biocatalytic sensors, or metabolism sensors, catalyze analyte conversion and measure the resulting changes, such as product formation or reaction inhibition [22]. In contrast, affinity sensors detect specific, irreversible binding between the analyte and the biological component, resulting in a measurable physicochemical change. Figure 1 illustrates the main biosensors used in the food industry to detect food contaminants.

Electrochemical biosensors, the first type of biosensor to be commercialized, are being studied extensively. These devices detect changes in electrical properties, such as current or potential, caused by chemical reactions between the bioreceptor and the analyte. These changes are converted into signals that correspond to the analyte concentration. Advantages of electrochemical biosensors include minimal sample preparation, high sensitivity with small volumes, and automation capabilities. However, challenges such as poor reproducibility and stability remain [23]. Electrochemical biosensors are further classified based on signal type as– potentiometric biosensors (measure potential differences using ion-selective electrodes, providing analyte concentration data), amperometric biosensors (measure current changes in the medium, offering high sensitivity and fast responses, though they are susceptive to interference from unwanted electroactive species), conductometric biosensors (detect conductivity changes due to biochemical reactions, operate at low voltages, and do not require reference electrodes), and ion-selective field-effect transistor biosensors (detect ion activity through potential changes at the gate electrode, offering direct ion detection) [24,25,26,27,28,29].

Optical biosensors detect changes in light properties resulting from interactions between the bioreceptor and the analyte, correlating these changes to analyte concentration. These devices measure changes in light intensity and offer advantages such as resistance to electromagnetic interference, compactness, simplicity, noninvasiveness, and suitability for in vivo use. Based on their optical configuration, optical biosensors are classified as intrinsic or extrinsic. Intrinsic configurations involve direct light passage through the sample, while extrinsic configurations use external pathways. Absorption-based biosensors measure analyte concentrations by detecting light absorption at specific wavelengths, utilizing single or multiple optical fibers [30]. Surface plasmon resonance biosensors detect refractive index changes, caused by analyte binding, at a metal–dielectric interface using surface plasmon propagation [31,32]. Fluorescence-based biosensors detect frequency changes in emitted radiation, often using fluorescent labels and fluorescence resonance energy transfer. Luminescence-based biosensors rely on light emitted from exothermic reactions, with bioluminescence occurring naturally in biological systems.

Piezoelectric biosensors integrate a biorecognition element with a piezoelectric material, typically quartz crystals, which are preferred for their availability, heat resistance, and stability in aqueous solutions [33]. These biosensors detect changes in mass, density, or viscosity on the surface of a piezoelectric crystal, based on affinity interaction recordings. Known for their simplicity and low cost, they are highly practical for real-world applications [33]. Piezoelectric biosensors work by generating an electrical potential when subjected to mechanical stress and by deforming elastically in response to an electric field.

Thermal biosensors, also known as calorimetric or thermometric biosensors, measure temperature changes resulting from bioreceptor–analyte interactions, which correlate with analyte concentration. These sensors use thermistors or thermopiles as transducers [24,34] and offer several advantages, including label-free detection, minimal recalibration, and resistance to sample interference [24]. Thermal biosensors are widely used for their ability to measure thermal changes proportional to molar enthalpy and product formation, and they are particularly valuable in biochemical reactions. Enzyme-based designs are often emphasized in research due to their exothermic reactions.

Immunosensors are designed to detect analyte–antibody interactions and are categorized into three main types: luminescent or colorimetric sensors, surface plasmon resonance sensors, and electrochemical sensors. Antibodies, or immunoglobulins, are Y-shaped proteins produced by B lymphocytes in response to foreign substances. Their specificity makes them ideal for biosensors, where they bind tightly to antigens, forming complexes. Antibodies used in biosensors can be monoclonal, polyclonal, or recombinant. Monoclonal antibodies target a single epitope, while polyclonal antibodies bind to multiple epitopes, offering stronger binding but higher cross-reactivity. Recombinant antibodies are genetically engineered. Key features for use in biosensors include high sensitivity and minimal cross-reactivity [35]. Effective antibody immobilization is essential for biosensor performance, with methods such as covalent binding, non-covalent techniques, and affinity coupling being employed. Factors like temperature, pH, and ionic strength also affect antibody activity and sensor accuracy [36].

Aptamers, typically single-stranded RNA or DNA molecules consisting of 2–60 nucleotides, bind specifically to targets such as organic molecules and cells [37]. Aptasensors, biosensors that use aptamers as biorecognition elements, were first introduced in 1996 [38] and have since found a variety of applications. Aptamers offer several advantages, including high stability and affinity, simplicity, cost effectiveness, and excellent reproducibility across different production batches.

Enzymes, biocatalysts that accelerate chemical reactions, are highly specific to certain substrates, making them ideal for use in sensors. Various enzymes, including cholinesterase, urease, glucose oxidase, and others, are widely used in enzymatic inhibition analysis, a well-established method [39].

Proteins such as phytochelatins and metallothioneins can also act as biorecognition elements when immobilized on a transducer surface [40].

Whole cell-based biosensors use living cells, such as microorganisms or plant cells, which can be natural or recombinant [41]. These biosensors are inexpensive, easy to cultivate, and resilient to changes in pH, temperature, or ionic strength. They can perform multistep reactions and regenerate by allowing the cells to regrow, often without the need for sample preparation. However, they have slower response time and are more susceptible to interference from contaminants.

Biosensors are indispensable tools for detecting and measuring contaminants in food, offering rapid, sensitive, and selective analysis. They are critical for identifying and monitoring a wide range of food contaminants, including heavy metals [10,42], pesticides [43], herbicides [44], allergens [45,46], mycotoxins [46,47,48,49], histamine [50], and other indicators of food quality, as shown in

Table 1. Biosensors for detecting food contaminant.

Target Food Contaminate	Biosensor	Reference
Heavy metals
Heavy metal—Hg2+; Ag+; Pb2+	Aptamers	[52,53,54,55];
Cadmium	Immunochromatography sensor	[56]
	Enzyme-linked immunosensor	[57,58,59]
Heavy metals	Conductometric biosensor	[60]
Pb2+; Cu2+	Enzyme biosensor	[61,62]
Heavy metals	DNAzymes	[59,63]
Heavy metals	Nucleic acid	[59,63]
Pesticides
Carbamate	Acetylcholinesterase biosensor	[64,65]
Organophosphorus	Non-enzymatic electrochemical sensors	[66]
Pesticide	Enzyme-based biosensor—acetylcholinesterase	[44]
Pesticide	Molecularly imprinted polymer-based biosensor	[43]
Allergens
Allergen	Antibody-based biosensor	[45]
Allergen	Nucleic acid-based biosensor	[46]
Egg ovalbumin	Electrochemical immunosensor	[67]
Egg ovalbumin	Surface plasmon resonance biosensor	[68]
Fungal toxins—Mycotoxins
Patulin	Immunochemical sensor	[69]
Aflatoxin B	Bio-electrochemical assay	[70]
Fusarium	Molecularly imprinted polymer-based biosensor	[48]
Ochratoxin A	Electrochemical immunosensor	[71,72,73]
	Immunosensor with fluorescence	[74]
Mycotoxins	Enzyme-based biosensor	[47]
Bacterial toxins
Botulinum neurotoxin (Clostridium botulinum toxin)	Meta-Nano-Channel (MNC) Field-Effect Transistor (FET) biosensor	[75]
Botulinum neurotoxin (Clostridium botulinum toxin)	Surface Acoustic Wave Immunosensor	[76]
Foodborne Pathogens–Bacteria
Campylobacter jejuni	Mechanical Biosensor QCM	[77,78]
Cronobacter sakazakii	Optical Biosensor Colorimetric	[79,80]
Cronobacter sakazakii	Optical Biosensor SPR Antibody	[81]
Cronobacter sakazakii	Electrochemical Biosensor Antibody	[82]
Escherichia coli O157:H7	Optical Biosensor Antibody	[83]
Escherichia coli	Optical Biosensor Antibody	[84]
Escherichia coli	Optical Biosensor Interferometric	[85]
Escherichia coli O157:H7	Electrochemical Biosensor Antibody	[78]
Escherichia coli	Electrochemical Chemiluminescence (ELC) Biosensors Aptamer-Based ECL Sensors	[86]
Listeria monocytogenes	Optical Biosensor Chemiluminescence	[87]
Listeria monocytogenes	Electrochemical Biosensor	[88]
Listeria monocytogenes	Electrochemical Biosensor Antibody	[89]
Listeria monocytogenes	Electrochemical Chemiluminescence (ELC) Biosensors Paper-Based Bipolar electrode ECL	[90]
Mycobacterium tuberculosis	Mechanical Biosensor Multi-Channel Series Piezoelectric Guartz Crystal (MSPQC)	[91,92]
Pseudomonas	Optical Biosensor Surface Plasmon Resonance (SPR)	[93,94]
Salmonella	Optical Biosensor Antibody	[95]
Salmonella enterica subsp. enterica Enteritidis	Optical Biosensor Antibody	[84]
Salmonella enterica subsp. enterica Typhimurium	Optical Biosensor Aptamer	[96]
Salmonella enterica subsp. enterica Typhimurium	Optical Biosensor localized Surface Plasmon Resonance (LSPR)	[97]
Salmonella enterica subsp. enterica Typhimurium	Electrochemical Impedimetric	[98,99]
Salmonella	Mechanical Biosensor Quartz Crystal Microbalance (QCM)	[100,101]
Staphylococcus aureus	Electrochemical Potentiometric	[102,103]
Staphylococcus aureus	Mechanical Biosensor QCM	[104,105]
Streptococcus agalactiae	Electrochemical Amperometric	[106,107]
Vibrio parahaemolyticus	Electrochemical Biosensor	[108]
Vibrio parahaemolyticus	Electrochemical Biosensor	[109]
Vibrio parahaemolyticus	Electrochemical Biosensor	[110]
Vibrio parahaemolyticus	Electrochemical Chemiluminescence (ELC) Biosensors ECL Immunosensor	[111]
Vibrio parahaemolyticus	SERS Biosensor	[112]
Vibrio vulnificus	Colorimetric Biosensor	[113]
	Foodborne Pathogens–Virus
Norovirus	Electrochemical biosensor	[114]
Histamine
Histamine	Molecularly imprinted polymer-based biosensor	[48]

. Consequently, biosensors are essential for ensuring the safety and quality of food throughout the supply chain. More recently, in Food Safety 4.0, intelligent biosensors played a key role in transforming traditional methods into data-driven solutions. Intelligent biosensors are advanced devices that combine biosensing technologies with digital systems. These sensors detect hazards like pathogens, contaminants, allergens, and quality issues early, enabling proactive risk management. Integrated throughout the food supply chain, these biosensors provide real-time data that empower stakeholders to make informed decisions, ensuring food safety and quality from production to consumption [51].

Regarding the limits of detection (LOD) of some heavy metals, the optical aptasensors for Pb²⁺ presented values from 0.07 to 100 nM, and the electrochemical aptasensor presented an LOD from 0.00000051 to 2.9 nM; for Hg⁺, the values varied from 0.026 to 10.5 nM, and from 0.0001 to 25 nM for optical and for electrochemical aptasensors, respectively [53]. The optimized surface plasmon resonance biosensor developed by [68] enabled the biosensor to achieve an LOD as low as 0.2 μg/mL for egg allergen detection in red wines. Zhou et al. [115], in a revision of the most recent progresses in photoelectrochemical biosensors and their applications for monitoring mycotoxins in food, presented the LOD values for AFB1 (from 0.00032 to 5.0 pg/mL), OTA (from 0.02 to 2.0 pg/mL), and for fumonisin B1 (4.7 pg/mL). The meta-nano-channel biosensor, developed by Ron et al. [75], employed for the specific and label-free sensing of Botulinum neurotoxin BoNT, presented a limit of detection in the fg/mL range 10–100 ng/mL, with good linearity and a tuneable sensitivity. Zaraee et al. [85] presented a rapid, label-free, and cost-effective optical biosensor for the detection of E. coli with an LOD of 2.2 CFU/mL. A limit of detection of 6 CFU/mL was shown by Bagheryan et al. [99] in a Diazonium-based impedimetric aptasensor for the rapid, label-free detection of Salmonella Typhimurium in food samples. A polydopamine-enhanced vertically ordered mesoporous silica film anti-fouling electrochemical aptasensor, developed by Jin et al. [109], for the purpose of indicator-free Vibrio parahaemolyticus discrimination, using a stable inherent Au signal, presented 10³ CFU/mL as a limit of detection. An LOD of 10 copies/μL of genomic DNA for Listeria monocytogenes and the possibility of distinguishing (high sensibility) Listeria monocytogenes from Salmonella, Escherichia coli O157:H7, and Staphylococcus aureus was achieved in a paper-based bipolar electrode electrochemiluminescence (pBPE-ECL) analysis system used for the sensitive detection of pathogenic bacteria. This system was developed by Liu et al. [90].

2.1. Heavy Metals

The most common metallic contaminants include chromium (Cr), cadmium (Cd), lead (Pb), arsenic (As), mercury (Hg), copper (Cu), and zinc (Zn) [116]. Heavy metals such as cadmium, lead, and mercury pose significant health risks when present in food products [117]. To protect public health, it is crucial to regulate heavy metals like lead, cadmium, and chromium in food sources [118]. Biosensors offer highly sensitive and rapid methods for detecting heavy metal contamination in food samples. For instance, a sensor has been developed to simultaneously detect lead and cadmium in fruits and vegetables [119]. These devices enable real-time monitoring with exceptional precision and selectivity, making them invaluable for ensuring food safety.

Various methods have been employed for the in situ detection of heavy metal ions, including amperometric sensors [120], electrochemical sensors [63], acoustic sensors [121], and inhibition-based biosensors [122]. Together, these techniques significantly enhance the capabilities of biosensors, allowing for the efficient and reliable monitoring of heavy metal contamination.

Biomaterials with biological activity and a specific affinity for heavy metals are widely used to modify electrodes used for detection [53]. Electrochemical sensors integrate sensitive biomaterials, such as nucleic acids, enzymes, antigens/antibodies, or whole cells, with an electrochemical transducer in order to convert biochemical signals into electronic signals [123,124,125]. Among these biomaterials, nucleic acids and enzymes are the most extensively studied for electrode modification in heavy metal detection [63,126].

Microbial biosensors provide a cost-effective and highly sensitive solution for the detection of heavy metal ions. For instance, microbial fluorescence-based biosensors [127,128] use reporter genes, activated in response to specific biochemical interactions between cellular reporters and inducer molecules. The integration of a chemostat-like microfluidic platform with microbial biosensors allows for molecular analytical detection on a chip [129]. Furthermore, optical DNA biosensors combined with evanescent wave analysis offer rapid, in situ detection of heavy metal ions [130]. Certain heavy metals bind to nucleic acid bases, forming metal ion-guided pairings like thymine (T)-Hg²⁺-T and cytosine (C)-Ag⁺-C [130]. This feature has garnered an interest in functional nucleic acids, including DNAzymes and aptamers, used for electrode modification in heavy metal detection [125].

DNAzymes are highly stable and specific molecules with a strong binding affinity, making them effective tools for heavy metal detection [59,131]. For instance, Tang et al. [132] developed a DNAzyme-based electrochemical sensor using rolling circle amplification to detect Pb²⁺ in water.

Aptamers, single-stranded DNA, RNA, or peptide sequences, exhibit a high affinity and specificity for target molecules. As cost-effective and easily produced alternatives to antibodies, aptamers are highly sensitive and specific. They have been effectively used to detect heavy metals such as lead (Pb), mercury (Hg), and cadmium (Cd) in food [125]. For example, Miao et al. [133] developed a DNA-modified Fe₃O₄@Au nanoparticle-based electrochemical sensor to detect Hg²⁺ and Ag⁺ in water, juice, and wine. Similarly, an aptamer-based electrochemical sensor was designed for the detection of arsenic (As³⁺) in water using the (GT)₂₁-ssDNA sequence for specific recognition [134]. Aptamer-based sensors for Pb²⁺ [135] and Cd²⁺ [136] further highlight their applicability in heavy metal detection.

Enzymes, as biocatalysts, accelerate chemical reactions and exhibit high specificity for substrates, making them ideal for use in sensors. Certain heavy metals interact strongly with enzymes, altering their activity. These changes in enzyme activity can be monitored indirectly by measuring the corresponding electrical signals [125]. Enzymatic biosensors have been developed for detecting specific heavy metals in food, such as a urease inhibition-based sensor for identifying Pb²⁺ and Hg²⁺ ions in water [137]. Enzyme-based biosensors detect heavy metals through the activation or inhibition of enzyme activity, often caused by interactions between metal ions and thiol groups in enzymes. Common enzymes used include glucose oxidase, urease, and alkaline phosphatase, although selectivity challenges exist, as some enzymes can interact with multiple metals.

Protein-based biosensors detect metal–protein complex formation without labeling, measuring changes in electrical capacitance or impedance. Capacitive protein-based biosensors are particularly sensitive to low heavy metal concentrations and outperform cell-based devices in detection capabilities.

This suite of biosensor technologies collectively represents a powerful toolkit for detecting and monitoring heavy metal contaminants in food, ensuring safety, and maintaining quality throughout the food supply chain.

2.2. Pesticides

Pesticides, also known as plant protection products, are used to enhance crop yields and protect crops from diseases and infestations [138]. They include herbicides, insecticides, fungicides, plant growth regulators, and repellents. Pesticides can be chemically classified into groups such as organochlorines, organophosphates, carbamates, pyrethrin, and pyrethroids [139]. Among these, organophosphates, organochlorines, and carbamates are the most problematic classes. Pesticides can accumulate in vegetables, fruits, and meat throughout the food chain [140], and their residues in food products pose significant health risks to consumers.

Biosensors have proven to be highly effective tools for detecting pesticide residues, thereby ensuring food safety and compliance with regulatory standards [141]. These devices facilitate the rapid, sensitive, and selective detection of pesticide and herbicide residues [142]. By enabling real-time monitoring, biosensors have demonstrated great success in detecting trace levels of pesticides, enhancing food safety protocols and protecting consumers from the health hazards associated with such contaminants [143,144,145].

Biomaterials, such as enzymes [146], antibodies [147], and aptamers [148], are employed to identify and measure pesticides at ultra-low concentrations. These biomaterials exhibit highly sensitive and consistent interactions with pesticide molecules. Biosensors that are specifically designed and optimized to detect particular pesticides, employ a variety of techniques, including the optical [149,150,151], electrochemical [152,153,154], calorimetric [155], and piezoelectric [156] methods, based on enzyme inhibition.

For example, electrochemical biosensors for pesticide detection utilize enzymes, whole cells, or antibody–antigen interactions (immunosensors) [157,158]. Immunosensors have proven to be highly effective at rapid monitoring in agricultural applications [159]. Sensing systems for herbicide detection include molecular imprinting fluorescent chemosensors [160] and chemiluminescence immunoassays [161].

Electrochemical sensors are categorized into enzymatic and non-enzymatic types [162]. Enzymatic sensors rely on enzyme-catalyzed reactions at the electrode surface, while non-enzymatic sensors depend on the direct electrochemical activity of the analyte on noble metal electrodes [163]. Enzymatic sensors generally offer higher selectivity than their non-enzymatic counterparts [164]. Numerous enzymatic sensors using acetylcholinesterase have been developed, leveraging the strong binding affinity between organophosphorus pesticides and the enzyme’s active sites [165,166].

Biosensors based on acetylcholinesterase inhibition are particularly effective for detecting organophosphate pesticides [167,168,169]. Organophosphates inhibit acetylcholinesterase by phosphorylating the serine residue at the enzyme’s active site, preventing the hydrolysis of acetylcholine [158].

Enzymatic biosensors are widely studied for their stability, sensitivity, and accuracy, making them particularly effective for detecting pesticides [170]. These biosensors utilize acetylcholinesterase to detect enzymatic inhibition caused by organophosphates and carbamates. The inhibition occurs when these compounds bind to the enzyme’s active site, blocking the hydrolysis of acetylcholine into choline and acetate [171]. Enzyme-based biosensors offer advantages such as high specificity, sensitivity, selectivity, availability, and versatility. They are classified into two types: direct and indirect. Direct biosensors measure analyte concentration or product formation during enzymatic reactions, while indirect biosensors detect enzyme inhibition caused by the interaction with the target analyte [167,172,173].

Although enzyme-based biosensors are highly specific, this specificity limits their ability to detect multiple analytes. Efforts are ongoing to address this limitation [174]. For instance, Borah et al. developed an amperometric biosensor based on the inhibition of the enzyme glutathione S-transferase [175]. Another approach involves integrating multiple enzymes, each sensitive to different pesticide types, into a single biosensing platform [157].

Electrochemical biosensors using whole cells have been proposed as an alternative to enzyme-based systems for pesticide detection [158]. Microbial cells are a cost-effective and stable option, eliminating the need for labor-intensive isolation and purification processes. Large quantities of cells can be easily cultivated [176,177]. Physiological changes in these cells, induced by exposure to toxicants (e.g., alterations in respiratory chain activity), are used to evaluate acute biotoxicity.

Using biosensors for pesticide detection provides efficient analysis, enhanced precision, low-concentration detection, continuous monitoring, and cost advantages over conventional methods, making them highly valuable for a variety of pesticide detection applications [178,179,180].

2.3. Allergens

Food allergies have become a significant food safety concern, with prevalence rates estimated at 1% to 3% in adults and 4% to 6% in children, primarily due to hidden allergens in processed foods [181]. These allergies result from the type I hypersensitivity reaction of the immune system to ingested allergens, posing life-threatening risk [182]. Clinical studies have documented 160 food allergens, with approximately 90% of allergic reactions attributed to eight major allergens: eggs, milk, shellfish, fish, peanuts, tree nuts, soybeans, and wheat [182].

Approximately 100 countries worldwide have legislation regarding the declaration of allergenic ingredients [183]. Since 1985, the Codex Alimentarius has included food allergens, with the General Standard for the Labelling of Prepackaged Foods mandating the declaration of eight ‘priority’ allergens: cereals containing gluten (such as wheat, rye, barley, oats, spelt, or their hybridized strains); crustaceans and their products; eggs and egg products; fish and fish products; peanuts, soybeans and their products; milk and milk products (including lactose); tree nuts and their products; and sulphites at concentrations of 10 mg/kg or more) [184].

In Europe, most countries adhere to European Union (EU) legislation, which mandates the declaration of 14 allergens. This list includes the Codex-8, with peanut and soya named separately, and adds celery, mustard, sesame, lupine, and mollusks [185]. In the United States, allergen declaration is mandated by the Food Allergen Labeling and Consumer Protection Act [186], which includes the Codex allergens but names only wheat among cereals, excluding other gluten-containing grains. The Food Allergy Safety, Treatment, Education, and Research Act amended the Food Allergen Labelling and Consumer Protection Act to add sesame to the ninth major food allergen, effective 1 January 2023 [187]. For tree nuts, the specific nut must be declared; for crustacea and fish, the species must be identified [188].

In Canada, priority allergens include the Codex-8, along with mollusks and mustard [189]. In Australia and New Zealand, a “contains statement” is mandatory for priority allergens. These include wheat, fish, crustaceans, mollusks, eggs, milk, lupine, peanuts, soy, sesame, almonds, Brazil nuts, cashews, hazelnut, macadamia nuts, pecans, pistachios, pine nuts, and walnuts, as well as barley, oats, and rye when they contain gluten. Sulphites must be declared if added at levels of 10 mg/kg or more [190].

Japan, the first country to regulate both intentional and unintentional allergen presence, categorizes allergens into those for mandatory disclosure (wheat, buckwheat, eggs, milk, peanut, shrimp, crab, and walnuts) and those for recommended disclosure (almonds, abalone, squid, salmon roe, oranges, cashews, kiwifruit, beef, sesame, salmon, mackerel, soybean, chicken, banana, pork, macadamia nuts, peach, yam, apple, and gelatin) [191]. South Korea has a similar approach, with a distinct list of mandatory allergens including eggs (confined to those from poultry), milk, buckwheat, peanuts, soybeans, wheat, mackerel, crab, shrimp, pork, peach, tomato, sulfurous acid (when present at 10 mg/kg or more), walnuts, chicken, beef, squid, clams (including oyster, abalone, and mussels), and pine nuts [192].

Food allergens originate from both animal and plant sources, with around 40% derived from organisms that produce five or more allergens. These allergens are often concentrated within a limited number of biochemically active protein families [193,194]. To safeguard individuals with food allergies, effective analytical methods capable of detecting trace amounts of allergenic ingredients in processed foods are essential. Biosensors provide the rapid and accurate detection of allergens, thereby enhancing food safety for sensitive populations [195].

Biosensors utilize recognition elements, such as antibodies or aptamers, to specifically target allergenic proteins from common sources like nuts and shellfish [196,197]. For instance, immunosensors leverage antibodies designed to detect specific allergenic proteins, enabling the quick and sensitive analysis of food samples [195]. DNA-based biosensors identify genetic sequences linked to allergenic components, offering a reliable approach for allergen detection [197].

Electrochemical biosensors have significantly advanced allergen detection due to their high sensitivity, selectivity, and user-friendliness [198]. Innovations in nanoscience and bioelectronics have further enhanced their performance by integrating biological receptors with nanomaterials such as metal nanoparticles, graphene, and quantum dots, which increase electrode surface activity and electron transfer efficiency [199,200].

Electrochemical immunosensors, which combine antibodies with electrochemical sensors, are widely employed for detecting food allergen [201,202]. These sensors detect allergenic proteins through antigen–antibody binding, generating electrical signals proportional to analyte concentration [203,204]. Their high selectivity arises from precise immunological interactions [195].

Nucleic acid-based electrochemical biosensors are prized for their compatibility with miniaturization and microfabrication, as well as their simplicity in detecting food allergens [205,206]. Despite the limited electrochemical activity of DNA probes and aptamers, innovative approaches to probe immobilization, signal amplification, and performance improvement are driving their development [167].

Sundhoro et al. [207] pioneered the use of molecularly imprinted polymers to detect the soybean allergen marker genistein in complex foods. The sensor demonstrated performance comparable to, or better than, portable allergen detection tools like lateral flow devices and ELISA, offering high selectivity, rapid detection, and cost effectiveness. However, its sensitivity still falls short of advanced methods like mass spectrometry and PCR.

Freitas et al. [208] designed an electrochemical dual immunosensor to simultaneously detect peanut allergens, Ara h 1, and Ara h 6, with detection limits as low as 0.05%. The sensor’s performance was validated through recovery studies and comparisons with ELISA, confirming its reliability and effectiveness in complex food matrices.

2.4. Mycotoxins

Mycotoxins are low-molecular-weight, heat-stable secondary metabolites produced by toxic molds belonging to the genera Aspergillus, Penicillium, Alternaria, and Fusarium. These toxins, found in the mycelium and spores of molds, include aflatoxins, ochratoxins, fumonisins, citrinin, patulin, zearalenone, trichothecenes, tremorgenic toxins, and ergot alkaloids. Mycotoxins pose significant risks to public health [209]. Their toxicity depends on factors such as species, mechanisms of action, metabolism, and the defense responses of organisms consuming contaminated food [210]. Due to these risks, most countries have established regulatory limits for mycotoxin levels in food, with thresholds varying by product type [211].

Ochratoxin A (OTA) has been identified in various crops, including cereals, grapes, coffee, and cocoa, as well as in derived food products, such as beer, wine, and vinegar. Biosensors for OTA offer rapid response times, cost-effective production, and reliable accuracy for on-site analysis [212]. OTA detection methods are broadly categorized into two approaches: (i) rapid screening tests providing qualitative results, and (ii) confirmatory tests offering precise quantitative measurements [213,214].

Portable biosensors, such as optical immunosensors, optical aptasensors, surface plasmon resonance biosensors, and photoelectrochemical biosensors, have been developed for detecting OTA in foods and beverages [215].

Optical methods for mycotoxin detection such as colorimetric, fluorescent, chemiluminescent, and surface plasmon resonance are valued for their simplicity, speed, reliability, and high sensitivity [216]. These biosensors combine a biological sensing element with an optical transducer to detect analytes binding to a bio-recognition element immobilized on a substrate [217]. This interaction generates an electronic signal proportional to the analyte concentration [218]. Commonly used biorecognition elements include enzymes, substrates, antibodies, and nucleic acids, with enzymatic systems often employed to convert analytes into measurable products [216].

Optical biosensors operate in two modes: label-free detection, where an analyte–transducer interaction generates a direct signal, and label-based detection, where labels produce colorimetric, fluorescent, or luminescent signals [219]. Optical biosensors for OTA detection represent a leading nanotechnological alternative to traditional methods, offering rapid, sensitive, and specific analysis with minimal noise, low detection limits, and multiplexing capabilities. Label-free biosensors require minimal sample volumes and are suitable for real-time, on-site monitoring [220]. These devices use transducers to convert biorecognition interactions into measurable optical signals, such as absorption, transmission, or polarization [221].

Photoelectrochemical biosensors detect OTA by converting the chemical energy of a semiconductor into electricity under light illumination, generating a photocurrent or photovoltage. These biosensors are cost-effective and high sensitivity, but their reliance on electrochemical processes and a light source limits portability [115].

Electrochemical immunosensors have been effectively employed to detect aflatoxin B1 in pistachios [222]. Immunological biosensors, which use antibodies specific to mycotoxins, and DNA-based biosensors, which target genetic sequences associated with mycotoxin-producing molds, show significant promise for detecting these contaminants [223].

Colorimetric and luminescent sensors convert visible or UV light into analytical signals [224]. For example, a colorimetric sensor for aflatoxin B1 detection employed a direct competitive ELISA principle, with a color change measured spectrometrically at 620 nm. This method achieved sensitivity as low as 0.2 ng/mL, outperforming microtiter plate ELISA [225].

Enzyme-based biosensors frequently utilize acetylcholinesterase due to its high susceptibility to mycotoxins, particularly aflatoxin B1, which inhibits its activity [226,227]. This inhibition is reversible, as the toxins bind non-covalently to the enzyme [228]. Among enzymatic inhibition methods, aflatoxins are among the most sensitive toxins [229]. Cholinesterase has been demonstrated to be effective for detecting aflatoxin B1 [230].

A portable biosensor for Aflatoxin detection using surface plasmon resonance technology has been developed. This sensor utilizes surface plasmon resonances in ~50 nm metallic films and surface functionalization for selectivity. Moon et al. [231] employed this device for in situ monitoring of aflatoxin B1 in grains. However, its high cost and lack of reusability necessitate further research to improve practicality.

Zearalenone, a nonsteroidal estrogenic mycotoxin produced by Fusarium fungi, poses significant risks in food [232]. Researchers have developed a label-free amperometric immunosensor using mesoporous carbon and trimetallic nanorattles for its detection. Panini et al. [233] created a microfluidic immunoassay with anti-Zearalenon antibodies.

Fumonisins, another class of mycotoxins from the Fusarium species, have been detected using competitive lateral-flow immunoassays. Mirasoli et al. [234] designed such an assay for total fumonisins in maize, integrating enzyme-catalyzed chemiluminescence detection and a portable charge-coupled device camera.

Lu and Gunasekaran [235] introduced an electrochemical immunosensor capable of simultaneously detecting two mycotoxins, fumonisin B1 and deoxynivalenol, in a single assay.

Deoxynivalenol, another Fusarium-derived mycotoxin [236], was detected using a biosensor by Romanazzo et al. [237]. This system employed an enzyme-linked immunomagnetic assay with immunomagnetic beads and magnetized screen-printed electrodes as transducers.

Patulin, a mycotoxin from the Penicillium expansum, Aspergillus, Penicillium, and Paecilomyces species, presents a significant health concern [238]. Detection methods include a competitive SPR-based immunoassay that utilizes laser-induced interactions to generate a detectable resonance shift. Funari et al. [239] developed a piezoelectric biosensor, immobilizing oriented antibodies on a quartz crystal’s gold surface using photonics-based techniques.

Many biosensors utilizing machine learning have been designed to detect mycotoxins, valued for their exceptional accuracy and precision [14].

2.5. Foodborne Pathogens

Foodborne pathogens are a major cause of food contamination during production, processing, and distribution. Consequently, the rapid and sensitive detection of pathogenic microorganisms is crucial to prevent food spoilage and foodborne illnesses. Numerous biosensor platforms have been developed to detect these pathogens [240,241,242]. The bacteria species most commonly responsible for outbreaks include Salmonella, Escherichia coli, Campylobacter spp., Vibrio cholerae, Listeria monocytogenes, and Shigella [243,244].

The primary function of a biosensor is to convert biochemical reactions into measurable electrical signals. Biosensors employing immunological, enzymatic, and molecular recognition elements are widely utilized to specifically identify genetic sequences, surface antigens, or metabolic by-products of pathogens [245]. DNA-based biosensors, which use nucleic acid probes, have demonstrated efficacy in detecting the genetic sequences of pathogens such as Escherichia coli and Salmonella [246,247,248,249].

Immunosensors, employing antibodies as recognition elements, are capable of identifying the surface antigens of bacteria like Salmonella, Campylobacter spp., Listeria monocytogenes, and Escherichia coli [250,251]. Biosensors have been specifically designed for the detection of pathogens including Salmonella [97,252,253,254,255,256], L. monocytogenes [251,257,258], E. coli [259,260,261,262], Campylobacter [263], C. perfringens [264], Staphylococcus aureus [257,265], and Toxoplasma gondi [266].

An electrochemical DNA biosensor for the selective identification of Salmonella enterica subsp. enterica serovar Typhi (S. Typhi) in real samples was proposed and fabricated by Bacchu et al. [267]. According to the authors, this biosensor showed excellent discrimination capability to some mismatched bases and to different bacterial cultures belonging to the same and distant genera. This DNA biosensor also presented a lower limit of detection and the capacity to be reused more than six to seven times.

Angelopoulou et al. [268] were able to simultaneous detect two bacteria, namely Salmonella enterica subsp. enterica serovar Typhimurium and Escherichia coli O157:H7, using, for the first time, a label-free optical immunosensor based on the arrays of Mach–Zehnder Interferometers monolithically integrated onto silicon chips.

Da Silva et al. [269], in a review, emphasize the importance of the application of electrochemical point-of-care devices for the monitoring of potentially harmful and/or toxic species that can be found in water resources, as well as waterborne pathogens (protozoa, bacteria, and viruses), allowing for faster on-site analysis. For the detection of the protozoa Giardia lamblia and Entamoeba histolytica, a metronidazole-probe sensor, based on an imprinted biocompatible nanofilm for the rapid and sensitive detection of anaerobic protozoan was used. The method was developed by Roy et al. [270]. For the detection of Cryptosporidium, a novel three-dimensional microTAS chip for the ultra-selective single-base mismatched Cryptosporidium DNA biosensor was used. The method was developed by Ilkhani et al. [271].

Concerning viruses that can be transmitted by food and water consumption, norovirus and hepatitis A virus are found to be the main cause of foodborne infections. Baek et al. [114] developed an electrochemical biosensor applied to detect human norovirus, prepared by standard procedure from an oyster. According to the authors, this biosensor can be used as a very sensitive and selective point-of-care bioanalytical platform for the detection of human norovirus in various food samples. The DNA sensor, developed by Manzano et al. [272], can be adapted to a portable format to be adopted as an easy-to-use and low-cost method for screening hepatitis A virus (HAV) in contaminated food and water.

The majority of these methods are based on immunosensors (antibody-based) or DNA-based sensors. Peptides have also been investigated as recognition biomolecules in the development of biosensors, offering high sensitivity, low-cost, and rapid response times. Some of these biosensors hold potential portable devices for on-site analyses, enhancing the detection of bacterial pathogens in food [51].

Nowadays, as a tool for helping improve risk management and ensure the highest standards of food safety, we can deal with intelligent biosensors, which offer attractive, smarter solutions, including real-time monitoring, predictive analytics, enhanced traceability, and consumer empowerment [273]. IoT-based intelligent biosensors for detecting Vibrio parahaemolyticus and smartphone-based intelligent biosensors for detecting ochratoxin A (OTA) in wine, instant coffee, and Salmonella enterica subsp. enterica Typhimurium are already available.

3. Strengths and Limitations of Biosensors for Detecting Food Contaminants

Biosensors offer significant advantages in detecting food contaminants, making them a valuable tool in ensuring food safety. A key strength of biosensors is their rapid detection capability, enabling real-time or near-real-time monitoring, which is crucial for timely intervention. Their high sensitivity and specificity, achieved through the use of biological recognition elements such as enzymes, antibodies, and aptamers, allow for the detection of contaminants at very low concentrations with remarkable accuracy [18].

Another notable advantage of biosensors is their cost effectiveness. Compared to traditional detection methods, biosensors are more affordable, lowering the overall cost of food safety monitoring. Additionally, many biosensors are designed to be portable and suitable for on-site application, eliminating the need for complex laboratory equipment or specialized expertise. Their versatility is highlighted by their ability to detect a wide array of contaminants, including heavy metals, pesticides, allergens, mycotoxins, and pathogens, making them suitable for diverse applications in food safety. The integration of nanomaterials further enhances the performance of biosensors, improving their sensitivity, stability, and overall efficacy [18,274].

Despite these strengths, biosensors also have limitations that need to be addressed to fully realize their potential. Sensitivity constraints can be an issue, especially when detecting low concentrations of certain contaminants. The complexity of food matrices can also interfere with the accuracy and reliability of biosensor readings, posing challenges for some applications. Furthermore, the stability and shelf life of biological components, such as enzymes and antibodies, can be limited, affecting their long-term usability [18,275].

Calibration and standardization are essential to ensure consistent and reliable results, due to the variability in biosensor performance. The process of obtaining regulatory approval and validation can be time-consuming, delaying the adoption of biosensors in the food industry. There are also integration challenges, as the integration of biosensors into existing food safety management systems requires overcoming compatibility issues and training personnel [18].

The application of machine learning to biosensors has grown significantly, but key challenges must be addressed to maximize its potential. A major hurdle is data availability, as large and diverse datasets from biosensors are expensive and difficult to obtain. Strategies for managing missing data are critical. The complexity of biological molecules also complicates data acquisition and analysis, requiring precise identification and segmentation. The quality and quantity of data used for training are critical, as they affect the algorithm’s performance. Ensuring the datasets can accurately identify target compounds is essential [14].

In summary, while biosensors have transformative potential for food safety monitoring due to their rapid, sensitive, and cost-effective nature, addressing their limitations is essential. Overcoming these challenges will be critical for the broader implementation and reliability of biosensors in detecting food contaminants, ultimately improving food safety and protecting public health.

4. Conclusions

Food safety is significantly threatened by contaminants such as heavy metals, pesticides, allergens, mycotoxins, and pathogenic microorganisms, all of which pose serious health risks. Heavy metals, including lead, mercury, and cadmium, are among the most hazardous contaminants. Detection methods include DNA-modified electrodes, enzymatic inhibition sensors, and aptamer-based systems. Biosensors for pesticide detection use various biomaterials, including enzymes, antibodies, and aptamers, to detect trace residues, with electrochemical biosensors, particularly enzymatic ones, being commonly used for detecting organophosphates and carbamates. Innovations include integrating multiple enzymes and using whole-cell biosensors. For allergen detection, biosensors utilizing antibodies, aptamers, and nucleic acids identify allergenic proteins. Nanotechnology-enhanced electrochemical sensors have improved both sensitivity and portability, although some systems still face sensitivity challenges. Mycotoxins, toxic compounds produced by molds, are detected using optical and electrochemical biosensors, such as immunosensors and aptasensors. For on-site analysis, advanced approaches, such as label-free biosensors, provide high sensitivity. The detection of foodborne pathogens has been revolutionized by immunosensors and DNA-based biosensors, allowing for the specific, efficient, and rapid identification of pathogens, thereby reducing the risks associated with foodborne illnesses. While traditional biosensors are valued for their simplicity, portability, and cost effectiveness, improvements in reproducibility and stability are necessary to meet the food industry’s demands. Enhancing the ability to trace and extract features from complex food matrices is essential for identifying contaminants and optimizing processes. Integrating machine learning enhances biosensor reliability and performance, addressing challenges like single-molecule detection and signal noise. Advanced machine learning techniques and improved computing hardware can increase sensitivity and pattern recognition. Developing portable biosensors, utilizing artificial intelligence, internet of things, and nanomaterials, will improve food safety monitoring. A multidisciplinary platform with high efficiency and portability is crucial for addressing global food safety and health issues. In summary, biosensors have the potential to transform multiple food safety applications, ensuring regulatory compliance and protecting public health with greater efficiency. Intelligent biosensors will be a powerful tool in improving risk management and ensuring the highest standards of food safety and quality both now and in the future.