Sensors and Biosensors as Viable Alternatives in the Determination of Contaminants in Corn: A Review (2021–2025)

Abstract

Corn is one of the most produced cereals in the world and exerts a significant economic impact on a billion-dollar market. It is utilized globally as a food source for humans and livestock and as a source of carbohydrates, fiber, vitamins, minerals, and antioxidants, and also for fuel production and industrial products. However, their production is adversely affected by chemical contamination, primarily by mycotoxins, pesticides, and trace elements. Sensors and biosensors have become reliable alternatives to traditional spectroscopic and chromatographic methods for detecting these substances to enhance processes from harvesting to consumption. Here, we thoroughly evaluated studies on sensors and biosensors as alternatives to the growing demand for the determination of these contaminants as point-of-care devices in the past five years. This review reports innovative systems, using cutting-edge technology in expanded interdisciplinary research, supported by computational simulations to elucidate the interaction/reaction prior to experimentation, exploring the latest developments in nanostructures to create devices with excellent analytical performance. Many systems meet the demands of multiple and simultaneous determinations with fast results, in loco analyses with portable devices connected to personal smartphones, and simple operations to assist farmers, producers, and consumers in monitoring product quality throughout each stage of corn production.

1. Introduction

Corn (Zea mays L.) is one of the most important cereals produced in the world [1], known for its nutritional properties, with large use in human and animal food, consumed in natural or derived products from grain industrialization [2]. Corn is primarily composed of carbohydrates, an important source of energy, protein, fiber, and lipids, as well as vitamins (A, B, E, and K), minerals, phenolic acids, flavonoids, plant steroids, and other phytochemicals, with scientific evidence of health benefits that lower the risk of developing chronic diseases [3]. Moreover, studies have shown that corn possesses anticancer properties [4]. Even corn waste has been studied for its medicinal properties, such as reduction of blood lipids, lowering blood pressure and blood sugar, regulation of dyslipidemia, and antioxidant and hypoglycemic effects [5].

Compared to other important grains, corn stands out with the highest total antioxidant activity among rice, wheat, and oats, mainly because of its phytochemical content [3]. Unlike other grains, corn and sorghum have a C4 photosynthetic metabolism, which is more efficient in carbon fixation to produce carbohydrates stored in leaves and stalks. Alongside an improvement in tolerance to abiotic and biotic stresses, corn is one of the most widely grown crops in the world, the major staple food in many countries, and the main grain for animal feed in temperate climates [6].

In addition, corn-related research is under development, seeking improvements and applications in many fields, such as ethanol production, oil processing, thickener production, and dyes [7]. Corn production is a billion-dollar market, which is extremely important for the world economy. The USA was the largest corn producer in the 2023/2024 harvest, over 289.67 million metric tons (mmt), which corresponds to 32% of total global production, followed by China (288.84 mmt) and Brazil (122 mmt) [8].

Since the discovery of the most efficient synthesis of ammonia (a major material in the production of nitrogen-based fertilizers) by the Haber-Bosch process, food production has intensified, leading to a vertiginous population growth in what Vaclav Smil called a “detonator of the population explosion” in his millennium essay published in Nature in 1999 [9]. This has increased the demand for novel technologies to supply products of higher quality and widespread agricultural practices in undeveloped countries with high-yield crop varieties during a period known as the Green Revolution [10].

The concern of avoiding and controlling plague infestations and diseases in plantations has become more important, and transgenic technology has become a reality [11]. However, these advances and solutions have resulted in other problems with the increased use of chemicals, such as pesticides, leading to the contamination of water, soil, and food. Harmful microorganisms and trace elements in crops have also intensified, leading regulatory agencies to tighten inspections and implement rigorous legislation [12,13].

Food quality control is important not only for identifying and quantifying contaminants, but also for determining the source and exact stage of production at which contamination occurs. This facilitates the specific reorganization of the process without shutting down all production lines, thus decreasing financial losses and ensuring better product quality. Some contaminations are easily identified directly during the plant growth stages by visible physical alteration of its parts, such as the bacterial citrus canker [14], characterized by colored necrotic lesions on the leaves of citrus fruit crops, or Asian soybean rust [15], a fungal disease that affects soybean crops, characterized by small tan-colored lesions on the leaf veins that evolve into rust pustules. However, these and other physical changes become visible after a prolonged period following contamination without any useful quantitative information.

Microbiological assays, such as cell culture, polymerase chain reaction (PCR), and enzyme-linked immunosorbent assay (ELISA) [16,17,18], and analytical analyses, such as gas chromatography (GC), high-performance liquid chromatography (HPLC), mass spectrometry (MS), and atomic absorption spectroscopy (AAS) [19,20,21], are the traditional methods for the detection of most contaminants in food, providing reliable and sensitive results. However, they rely on costly equipment that requires constant maintenance, skilled labor, and the use of large amounts of chemicals in time-consuming experiments, usually conducted at a facility away from the plantation, which is onerous or inaccessible to farmers [22,23].

In the past few decades, emerging technologies have been applied to detect analytes in complex samples, such as foods, to overcome these drawbacks and offer practical alternatives to traditional methods. Since the introduction of the first biosensor by Clark and Lyons in 1962 [24] for glucose determination using the glucose oxidase (GOD) enzyme, much research has been conducted with different biorecognition elements, transducers, and assembly strategies, with efficient results in the detection of diverse analytes. Alongside sensors, biosensors are advantageous systems because they present results that are better than those of traditional methods, require small sampling, generate little waste, and can be designed as cost-effective point-of-care devices applicable in loco during every step of food production, from plant growth to the product in the consumer’s hand [25,26,27].

Considering the numerous design possibilities that these devices can be assembled and the perspectives of direct and practical applications, in this review, we comprehensively evaluated sensors and biosensors developed in the past five years to determine the main contaminants in several types of corn and corn products, describing the latest discoveries in the field, the application of novel technologies, analyzing the synergy with different areas of knowledge, and discussing, within each section, the future perspective in the quality control of one of the most important cereals produced worldwide.

2. Sensors and Biosensors in the Food Industry

Sensors are devices responsible for converting physical or chemical stimuli into quantifiable signals [28,29] and provide information regarding the system in use. Biosensors work on the same premises as sensors but with a biomolecule-modified transducer responsible for converting a signal obtained from a biochemical reaction or interaction in the presence of an analyte into a measurable response. Enzymes, DNA-based materials, antibodies, whole cells (bacteria, fungi, yeasts, etc.), and viruses are examples of biomolecules used in the development of biosensors [30]. These devices can be categorized based on the nature of the interaction between the transducer and analyte. These categories include optical, electrochemical, piezoelectric, and thermometric systems using optical fibers, electrodes, quartz crystals, and thermistors as physical transducers [29,31].

These systems are widely applied in industry for monitoring, security, and process control; in health areas for diagnosis; in laboratories for physicochemical analysis; and even at home for personal use [32]. The levels of oxygen and carbon dioxide, pH and temperature changes, and humidity are some important examples of well-known sensors applied in many fields [33], including the food industry. They present cost-effective alternatives for the determination of specific molecules/biomolecules, which are typically performed using standard techniques. These systems present a premise to offer the same or better sensitivity than traditional approaches with high reliability and specificity, in addition to fast responses in miniaturized and user-friendly devices. In the food industry, sensors and biosensors are pivotal in quality control for the determination of chemical and/or biological contaminants, such as pathogens, allergens, pesticides, mycotoxins, adulterants, trace elements, and harmful or precursors of harmful compounds to human and animal health [31,34].

The literature contains numerous examples of sensors, biosensors, and studies performed on novel devices to detect unexplored analytes, introduce novel systems to meet the growing demand in the market, or improve the parameters of old versions of systems, taking advantage of the advent of novel technologies [35,36,37,38,39]. Because of the higher demand for food control, to prevent contamination, and to improve the quality along the production chain until consumption, compliance with legislation, novel devices, and procedures for analyte detection, including corn and its derivatives, is needed for analyte detection.

Challenges and Perspectives

Sensors and biosensors combine practicality in analysis, high sensitivity, reliability, cost-effectiveness, portability, and use of very small amounts of chemicals and samples. However, these systems have drawbacks that impose challenges for the development of applicable devices. The main drawback of sensors is their lack of specificity, especially in complex samples, where interferents might present the same response expected from the target analyte [40]. This is particularly difficult when a specific analyte is part of a class of compounds with very similar structures or different compounds with the same functional groups that interact or react with no virtual response differences, such as organophosphate pesticides (OP). This pesticide class consists of compounds that have been reported to cause several health problems [41,42]. A traditional approach to detecting OPs is based on acetylcholinesterase (AChE) inhibition upon contact with a pesticide by monitoring the decrease in the signal of the enzymatic product. However, this enzyme is not specifically inhibited by a single OP and can be affected by other pesticides of the carbamate class, some nerve agents, and even other toxins [43,44]. The issue of specificity is more evident in sensors than in biosensors because of the lack of a biological component that interacts with the analyte in the sample.

Driven by these and other challenges, researchers have been developing methodologies and systems that are gradually being used in the application of devices for more specific, accurate, and cost-effective determination of desired analytes, compared with the use of costly enzymes or electrode modifications with multiple steps, such as using aptamers. These single-stranded oligonucleotides were formed for the first time by Tuerk and Gold in 1990 [45] through a novel method called systematic evolution of ligands by exponential enrichment (SELEX), but were only named aptamers a short time after [46], from the Latin “aptus,” meaning to fit. The objective is to find sequences that can recognize and bind to an analyte in a specific and strong way among a vast oligonucleotide library. As an alternative to well-known immunosensors and enzymatic biosensors, aptasensors have received considerable attention, with many different strategies used to develop practical and useful devices.

Immobilization of biomolecules over the transducer surface can be challenging because of the complexity of the biological structure regarding stability in non-biological environments and the effects of pH, temperature, and orientation in a solid template without the loss of its activity toward the analyte. In the search for alternatives to transducer modification with substances that can mimic the biological activity of typical biomolecules, molecularly imprinted polymers (MIPs) have shown promising results. MIPs are synthetic structures typically built to entrap a known target molecule that can be easily eluted, leaving a template consisting of target-specific sites. Thus, when a sample is added to a modified transducer, the target molecule binds specifically to the template, generating a measurable signal [47,48,49].

With the advent of nanotechnology and the development of new structures and strategies, such as MIPs, aptamers, and magnetic assays, aided by computational models and artificial intelligence [50,51], researchers are more likely to pinpoint a better solution based on the specificity of each analyte and analyze the operational viability more assertively, thus presenting more reliable, versatile, original, and innovative systems. In the next section, sensors and biosensors developed toward this end are addressed, with fine examples of these systems applied for determining contaminants in corn.

3. Sensors and Biosensors for the Determination of Contaminants on Corn

The European Commission is an independent executive branch of the European Union that, alongside the European Food Safety Authority (EFSA), is responsible for regulating food by establishing standards for food and food supplements. According to the Commission Regulation (EU) 2023/915 of 25 April 2023, the maximum accepted levels (µg kg⁻¹) for mycotoxins in corn (mostly unprocessed grains) are 10.0 for aflatoxins (AFB), 5.0 for ochratoxin A (OTA), 1750.0 for deoxynivalenol (DON), 350.0 for zearalenone (ZEN), 4000.0 for fumonisins (FUM), and 100.0 for T-2 and HT-2 toxins. The same regulation also presents the maximum levels for lead (0.2 mg kg⁻¹), cadmium (0.1 mg kg⁻¹), and inorganic tin applied to canned food (200 mg kg⁻¹).

The maximum residue limit (MRL) for corn was obtained from the European Commission pesticide database available online [52]. Most pesticides found in this review present an MRL of 0.01 mg kg⁻¹, such as mesotrione, trichlorfon, lindane, propiconazole, imidacloprid, carbofuran, clothianidin, bromoxynil octanoate, simazine, chlorpyrifos, and carbendazim. Glyphosate presents the highest MRL (1.0 mg kg⁻¹). The concentrations of the other pesticides ranged between the two values.

The determination of harmful contaminants in corn, such as mycotoxins and pesticides, is mainly based on highly precise and reliable chromatographic and spectroscopic techniques [53,54]. Atomic absorption spectrometry (AAS) and inductively coupled plasma-mass spectrometry (ICP-MS) are often employed for metals such as lead, cadmium, and mercury because of their high sensitivity and response to several metals [55,56,57,58] that comply with European Commission requirements [59].

3.1. Mycotoxins

In 2019, Munkvold et al. [60] published an interesting book chapter specifically dedicated to a thorough yet not comprehensive survey of papers on mycotoxins in corn and dried distillers’ grains and solubles. The data presented by the authors are alarming, with many different mycotoxins detected in samples worldwide, with special attention paid to AFB, ZEN, DON, T-2, FUM, and OTA, which were found in more than 11,237 corn samples collected from 75 countries (January 2014 to June 2017). These secondary metabolites of fungi are toxic to humans and animals, resulting in economic losses on a billion-dollar scale, from crop management to consumer health. Sensors and biosensors are viable alternatives for identifying and quantifying these species, which can partially decrease costs and help producers control their production before contamination spreads.

It is worth mentioning that every study presented in

Table 1. Sensors and biosensors for the determination of mycotoxins in corn have been reported in the literature in the past five years.

Type	Transducer	Toxin *	Sample	Linear Range (pg mL−1) **	LOD (pg mL−1) **	Ref.
Sensor	Optical	AFB1 DON ZEN	Maize	-	-	[63]
Sensor	Optical	FB1 OTA ZEN OA PAT	Corn	2.0 × 104–9.0 × 104	-	[64]
Sensor	Electrochemical	OTA	Corn grain	1.61 × 105–4.04 × 106	1.21 × 104	[65]
Sensor	Electrochemical	ZEN	Maize powder	1.59 × 105–2.86 × 106	2.31 × 104	[66]
Sensor	Photoelectrochemical	AFB1	Maize	10.0–1.0 × 106	6.8	[67]
Sensor	Electrochemical	ZEN OTA	Corn flour	4 × 104–1.02 × 107 1.6 × 105–4.1 × 107	1.4 × 104 4.5 × 104	[68]
Sensor	Optical	ZEN	Corn	1.0–30.0 μg Kg−1	0.5 μg Kg−1	[69]
Sensor	Electrochemical	OTA	Corn	1.61 × 104–4.04 × 106	1.45 × 103	[70]
Sensor	Photoelectrochemical	OTA	Corn flour	2.0 × 104–2.5 × 108 and 2.0 × 10−3–3.0 × 105 (PEC)	8,33 × 103 and 8.0 × 10−4 (PEC)	[71]
Sensor	Optical	ZEN OTA	Corn flour	50.0–1.0 × 105 100–1.0 × 104	4.40 × 102 98.0	[72]
Sensor	Electrochemical	ZEN	Spiked maize extract	0.1–1.0 × 105	3.4 × 10−2	[73]
Sensor	Optical	AFB1	Corn	1.0 × 103–2.0 × 104	3.8 × 102	[74]
Sensor	Optical	AFB1	Corn flour	5.0 × 102–1.0 × 105	1.3 × 102	[75]
Sensor	Optical	AFB1	Corn oil	10.0–2.0 × 104	4.0	[76]
Biosensor	Electrochemiluminescence	ZEN	Maize	1.0 × 10−3–2 × 102 µg kg−1	9.75 × 10−5 µg kg−1	[77]
Biosensor	Electrochemical	ZEN	Corn	1.0–1.0 × 106	0.02	[78]
Biosensor	Electrochemical	AFB1	Corn	10.0–2.0 × 104	0.94	[79]
Biosensor	Electrochemical Optical	AFB1	Maize	31.23–7.81 × 103 (impedimetric) 31.23–1.56 × 105 (SPR)	31.23 (impedimetric) 124.91 (SPR)	[80]
Biosensor	Electrochemical	AFB1	Corn oil	10.0–1.0 × 105	1.82	[81]
Biosensor	Electrochemical Optical	OTA	Corn	0.10–140.0 0.10–160.0	0.024 0.051	[82]
Biosensor	Electrochemical	ZEN	Corn	0.01–1 × 104	7 × 10−3 (EIS) 3.5 × 10−3 (DPV)	[83]
Biosensor	Electrochemical	OTA	Corn	1.0–5.0 × 103	0.1	[84]
Biosensor	Electrochemical	DON	Maize flour	10.0–1.0 × 105	6.9	[85]
Biosensor	Electrochemical	OTA	Maize	0.1–5 × 104	0.081	[86]
Biosensor	Electrochemical	AFB1	Corn	100.0–1 × 105	38.8	[87]
Biosensor	Electrochemical	AFB1	Corn flour	0.1–1 × 104	0.061	[88]
Biosensor	Electrochemical	OTA	Corn	5–5 × 104	2.35	[89]
Biosensor	Chemoluminescence	DON	Corn and cornmeal	1.0–1.32 × 105	0.166	[90]
Biosensor	Optical	AFB1	Corn	0.2 × 103–6 × 103	90.0	[91]
Biosensor	Optical	AFB1	Corn	1.0 × 103–1.0 × 105	2.5 × 103	[92]
Biosensor	Optical	AFB1	Corn	50–5.0 × 105	85	[93]
Biosensor	Optical	OTA AFB1	Corn flour	1.0–5.0 × 105 1.0–1.0 × 106	0.33	[94]
Biosensor	Optical	ZEN	Corn	2.5 × 103–1.0 × 105	980.0	[95]
Biosensor	Electrochemical	AFB1	Spiked corn	1.0–1.0 × 107	0.54	[96]
Biosensor	Optical	OTA	Corn	4.04 × 10−5–0.40	8.48 × 10−6	[97]
Biosensor	Optical	ZEN	Corn flour	10.0–1.0 × 105	5.0	[98]
Biosensor	Electrochemical Optical	OTA	Corn	0.01–2.0 × 105	0.025	[99]
Biosensor	Electrochemical	AFB1 FB1	Ground corn	2.0–20.0	0.46 0.34	[100]
Biosensor	Electrochemical	AFB1	Corn	1 × 10−4–0.5	1.9 × 10−4	[101]
Biosensor	Electrochemical	AFB1	Corn	1 × 10−4–0.2	1.0 × 10−5	[102]
Biosensor	Electrochemical	AFB1 ZEN OTA	Corn	1.0 × 103–1.0 × 105	41.2 27.6 30.0	[103]
Biosensor	Mechanical (pressure)	T-2	Corn	0.02–6.0 × 106	5.6 × 10−3	[104]
Biosensor	Optical	AFB1	Corn flour	1.0 × 103–5.0 × 105	38.0	[105]
Biosensor	Optical	FB1	Corn	32.0–5.0 × 105	12.1	[106]
Biosensor	Optical	FB1	Corn flour	10.0–2.0 × 106	2.7	[107]
Biosensor	Magnetic Relaxation Switching	AFB1	Corn	10.0–1.0 × 104	6.0	[108]
Biosensor	Photoelectrochemical	OTA	Corn	1.0–2.0 × 103	0.33	[109]
Biosensor	Electrochemical Optical	AFB1	Corn	1.0–3.0 × 104	0.6 0.8	[110]
Biosensor	Electrochemical	DON	Maize flour	5.0 × 103–3.0 × 104	3.2 × 103	[111]
Biosensor	Optical	FB1	Corn flour	1.80 × 104–3.61 × 105	1.21 × 104	[112]
Biosensor	Thermometric Optical	OTA	Corn	5.0–5 × 104	50.0	[113]
Biosensor	Electrochemical	OTA	Corn	1.0–1.0 × 105	0.135	[114]
Biosensor	Photoelectrochemical	AFB1	Moldy corn	0.01–100.0	1.32 × 10−3	[115]
Biosensor	Electrochemical	OTA AFB1	Corn flour	10.0–1 × 105	4.3 5.2	[116]
Biosensor	Electrochemiluminescence	AFB1	Corn	1.0–1.0 × 105	0.79	[117]
Biosensor	Thermometric Optical	DON	Moldy corn	0–1.78 × 106	2.87 × 103 (colorimetric) 4.65 × 103 (temperature)	[118]
Biosensor	Optical	T-2 ZEN	Corn flour	1.0–1.0 × 104 10.0–1 × 105	0.1 1.2	[119]
Biosensor	Electrochemical	DON AFB1	Corn extract	3.0 × 102–3.0 × 106	0.172 7.74	[120]
Biosensor	Electrochemical	AFB1	Corn	1.0–1.0 × 105	3.9	[121]
Biosensor	Photoelectrochemical	FB1	Corn paste	1.0–1.0 × 105	0.13	[122]
Biosensor	Photoelectrochemical	DON	Corn	~50.0–1.0 × 106	34.3	[123]
Biosensor	Optical	OTA	Corn	30.0–3 × 103 (UV) 10.0–1.0 × 104 (fluorescence)	23.5 (UV) 992.1 (fluorescence)	[124]
Biosensor	Optical	AFB1	Corn oil	1.0 × 103–4.0 × 105	5.7 × 10−2	[125]
Biosensor	Electrochemical	AFB1	Corn	10.0–1.0 × 103	1.9	[126]
Biosensor	Electrochemical	ZEN	Corn	2.5 × 10−2–1.6 × 104	2.5 × 10−2	[127]
Biosensor	Electrochemical	AFB1	Corn	10.0–3.0 × 104 (ACV) 3.0–3.0 × 104 (EIS)	6.2 (ACV) and 0.8 (EIS)	[128]
Biosensor	Optical	ZEN OTA	Corn oil	4.88 nM–5.0 µM (simultaneously)	0.12 nM (simultaneously)	[129]
Biosensor	Electrochemiluminescence	AFB1	Corn	1.0 × 10−3–1.0 × 104	5.8 × 10−4	[130]
Biosensor	Optical	ZEN	Corn	1.0–2.0 × 105	0.8	[131]
Biosensor	Optical	DON	Corn flour	1.0 × 10−3–5.0 × 105	6.4 × 10−2	[132]
Biosensor	Optical	ZEN	Corn	10.0–1.0 × 105	4.0	[133]
Biosensor	Optical	AFB1	Corn	2.0 × 104–4.0 × 105	1.22 × 104	[134]
Biosensor	Electrochemical	OTA	Corn	50.0–2.0 × 105	0.2	[135]
Biosensor	Electrochemical	AFB1 OTA	Corn	10.0–3.0 × 103 30.0–1.0 × 104	4.3 1.33	[136]
Biosensor	Optical	AFB1	Corn	1.0–2.0 × 105	12.0	[137]
Biosensor	Electrochemical	AFB1	Corn flour	1.0–5.0 × 104	0.416	[138]
Biosensor	Chemiluminescence	OTA	Corn	1.0 × 102–1.0 × 105	2.8 × 102	[139]
Biosensor	Electrochemical	ZEN	Corn flour	0.01–1.0 × 104	5.0 × 10−3 (MB/Ag+) 2.86 × 10−3 (MB) 1.07 × 10−5 (Ag+)	[140]
Biosensor	Photoelectrochemical	AFB1	Corn	5.0–1.0 × 104	19.6 × 10−3	[141]
Biosensor	Optical	OTA	Powder corn	20.0–2.0 × 103	8.0	[142]
Biosensor	Electrochemiluminescence	DON	Corn	0.01–500 µg kg−1	9 × 10−3 µg kg−1	[143]
Biosensor	Optical	AFB1	Corn flour	50.0–5.0 × 105	23.0	[144]
Biosensor	Electrochemical	MYA	Corn silage	7.98 × 105–2.28 × 107	6.0 × 103	[145]
Biosensor	Optical	OTA AFB1	Corn	10.0–1.0 × 105 50.0–1.0 × 105	5.0 10.0	[146]
Biosensor	Electrochemical	AFB1	Corn	1.0 × 10−5–0.1	1.0 × 10−5	[147]
Biosensor	Electrochemical	CIT	Corn meal	10.0–1 × 107	7.67 (DPV) 1.57 (SWV)	[148]
Biosensor	Photoelectrochemical	FB1	Corn	0.1–10.0	0.0723	[149]
Biosensor	Electrochemical	OTA	Cornmeal	0.1–1.0 × 104	1.12 × 10−3	[150]
Biosensor	Optical	T-2	Corn flour	1.0 × 102–1.0 × 104	87.0	[151]
Biosensor	Optical	AFB1	Corn	0–3.33 × 103	80.0	[152]
Biosensor	Optical	AFB1	Corn	0–5.0 × 104	4.56 × 103	[153]
Biosensor	Optical	OTA	Corn flour	5.0 × 102–1.0 × 106	3.08 × 102	[154]
Biosensor	Electrochemical	DON	Corn flour	10.0–1.0 × 104	2.0	[155]
Biosensor	Optical	AFB1	Spiked corn	50.0–2.5 × 104	9.0	[156]
Biosensor	Electrochemical	ZEN	Corn oil and corn flour	1.0 × 10−2–1.0 × 103	3.64 × 10−3	[157]
Biosensor	Optical	AFB1	Corn	1.0–1.0 × 106	0.19	[158]
Biosensor	Optical	T-2	Corn	1.0 × 102–5.0 × 106	57.84	[159]
Biosensor	Electrochemical	ZEN	Corn	0.01–1.0 × 104	6.27 × 10−3	[160]
Biosensor	Optical	DON	Corn	1.0 × 102–3.0 × 105	13.67	[161]
Biosensor	Optical	ZEN	Corn flour	0.1–100.0	0.1	[162]
Biosensor	Optical	ZEN	Corn flour	0.32–320.0	0.32	[163]
Biosensor	Electrochemical	T-2	Corn	5.0 × 10−4–5.0 × 103	7.6 × 10−5	[164]
Biosensor	Electrochemical	ZEN	Spiked corn	0.1–1.0 × 106	4.57 × 10−3	[165]
Biosensor	Electrochemical	ZEN	Cornmeal	1.0 × 10−2–1.0 × 104	1.64 × 10−3	[166]
Biosensor	Optical	DON	Cornmeal	10.0–1.0 × 105	9.0	[167]
Biosensor	Electrochemiluminescence	AFB1	Corn	1.0–1.0 × 106	0.46	[168]
Biosensor	Electrochemical Optical	OTA	Corn	1.0 × 10−3–2.5 × 105	2.2 × 10−4	[169]
Biosensor	Electrochemical	AFB1	Corn extract	57.0–1157.0	24.0	[170]
Biosensor	Optical	ZEN	Corn	1.0 × 103–2.5 × 105	7.0 × 102	[171]
Biosensor	Electrochemical	ZEN	Corn flour	1.0–1.0 × 104	0.389	[172]
Biosensor	Electrochemical	OTA AFB1	Corn flour	1.0–5.0 × 105	0.564 0.229	[173]
Biosensor	Optical	FB1	Spiked corn	5.33 × 102–6.81 × 103	79.0	[174]
Biosensor	Optical	AFB1	Maize	10.0–100.0 5.0–1.0 × 104 5.0–2.0 × 104	0.85 (colorimetric) 0.79 (SERS) 1.65 (fluorescence)	[175]
Biosensor	Optical	OTA	Corn	0–6.46 × 104	1.61 × 104	[176]
Biosensor	Electrochemical	AFB1	Corn flour	5.0 × 10−4–5.0 × 10−3	0.43 × 10−3	[177]
Biosensor	Optical	ZEN	Corn flour	3.18 × 10−4–31.8	7.34 × 10−4	[178]
Biosensor	Electrochemical	OTA	Corn flour	0.5–5.0 × 104	38.0 × 10−3	[179]
Biosensor	Electrochemiluminescence	ZEN	Corn flour	0.5–1.0 × 105	0.37	[180]
Biosensor	Electrochemical	DON	Corn	1.0 × 10−3–1.0 × 103	0.14 × 10−3	[181]
Biosensor	Electrochemical	AFB1	Corn	5.0–5.0 × 105	1.0	[182]
Biosensor	Electrochemical	AFB1	Corn	3.12 × 103–1.46 × 107	8.74 × 10−2	[183]
Biosensor	Optical	AFB1	Corn flour	0–3.3 × 102 and 3.33 × 101–3.75 × 105	10.0	[184]

* Aflatoxin B1 (AFB1), Fumonisin B1 (FB1), okadaic acid (OA), patulin (PAT), citrinin (CIT), and mycophenolic acid (MYA). ** Otherwise stated.

applied their developed system to corn or corn derivative samples, alongside (or not) other types of food. Two terms (corn and maize) are present in the literature, which can generate confusion [61]. Apart from the historical and cultural debate in the Western world, these terms are used interchangeably [62]. Here, the term ‘corn’ is applied unless otherwise stated (e.g., in the tables), as it is the term used by most authors in this review.

In light of the importance of mycotoxin determination and mitigation in food, particularly corn, the large contributions of sensors and biosensors found in this review in the past five years have become clear. AFB, OTA, and ZEN are the most representative mycotoxins, with a little more than 80% of the total compounds analyzed in the literature (Figure 1).

3.1.1. Interdisciplinary Research and Simultaneous Determinations

Although SELEX is a powerful method that ensures specific analyte-aptamer interactions, more information is required for biosensor applications and has been the subject of analysis by some researchers to acquire knowledge, thus improving the assembly of the device toward a mycotoxin. Pengfei Ma et al. [133] developed a fluorescence polarization aptasensor for ZEN determination. To improve the system, the original 80-base ZEN aptamer was truncated to 19 bases and named as Z19. Isothermal titration calorimetry (ITC) experiments showed that the truncated Z19 obtained from one of the three stem-loops of the original aptamer resulted in an affinity improvement. Circular dichroism (CD) showed an increase in the melting temperature upon interaction of the aptamer with ZEN molecules, proving the increased stability of the system. Through molecular dynamics, simulations were performed to better understand Z19-ZEN interactions in terms of conformational movements, flexibility of the aptamer, stability of the biological system, binding free energy (with van der Waals energy being the main driving energy for the interaction), and vital bases participating in binding, which generated a 3D Z19-ZEN structure that can provide a structural basis for a better aptasensor. Computational studies have been frequently used to better understand the interactions between a biomolecule and an analyte or transducer, such as the biosensor developed by Hasret Subak et al. [111], which uses a molecular docking approach to model the aptamer-deoxynivalenol interaction.

This synergy, resulting from the combination of areas of knowledge and different techniques, improves the research quality, as mentioned in computational simulations and self-engineering systems [63,64,103,111,127,145]. Maozhen Qu et al. [63] worked with a different and interesting system based on volatile organic compounds (VOC), which interacts or adsorbs over arrays containing artificial olfactory sensors (AOS) modified with porphyrin and dyes. This generates color changes upon AOS contact with different VOCs consisting of the evaluated mycotoxins, thus mimicking the olfactory system of animals (Figure 2). The authors used function optimization algorithms (ISCA-MobileNetV3) combined with machine learning models and deep convolutional neural networks (DCNNs) to obtain multivariable data on porphyrins, dyes, and their interactions with mycotoxins. AFB1 and DON were simultaneously evaluated in 208 corn samples obtained from various cities in Heilongjiang Province, China, in 2022. Each array system was analyzed by image acquisition using a USB camera/scanner at different angles, and the images were expanded using a data augmentation algorithm such as Fancy PCA to eliminate color deviation. The authors concluded that it is difficult for an optimization algorithm to meet the needs of actual optimization, probably because of the many different chemical materials in the AOS composition, and that the system is affected by the corn-producing area and sensor manufacturing environment. However, the work presents potential data, useful to develop an applicable cost-effective sensor (40 USD camera and 0.5–1.5 USD for each AOS sensor) with an online evaluation around 1 s. Moreover, multiple and simultaneous detections are possible, as the system was developed for the detection of DON and AFB1.

Some authors have explored the redox mechanism, such as for AFB1 [177], for the direct detection of OTA [65,70] and ZEN [66], and both OTA and ZEN [68]. Huang et al. [65] proposed an oxidation mechanism for OTA applied in an electrochemical sensor, and Yifang Zeng et al. [66] proposed an oxidation mechanism for ZEN to develop a similar device. Veenuttranon et al. [68] improved these two systems by developing an electrochemical sensor for the simultaneous determination of OTA and ZEN. These sensors are important examples of how a simple system can accurately detect more than one analyte, which is a growing demand in science, especially in food areas, because of the multi-contamination of crops. The effective detection of a single mycotoxin is of utmost interest, but a multi-analyte determination device covers the current need for a broad application that ensures better production and human and animal welfare. Therefore, researchers have been working on the simultaneous determination of mycotoxins [68,72,94,103,116,119,129,136,146,173]. Yun Gao et al. [94] used a self-powered optical aptasensor for the simultaneous determination of OTA and AFB1. The TiO₂ nanorod/fluorine-doped tin oxide (FTO) photoanode was modified with an OTA aptamer, whereas the CuSCN nanorod/FTO photocathode was modified with an AFB1 aptamer. The limit of detection (LOD) was at the picogram level for both electrodes. The region-separation-type all-solid-state biosensor operates using only these two electrodes without an external power supply, which brings about an interesting approach toward miniaturization and offers a useful self-powered system with multiple and simultaneous determinations.

Some studies have developed dual-mode mycotoxin determination methods based on different markers, or even the same marker with multiple signal possibilities. Yaxing Liu et al. [86] used a methylene blue (MeB)-hairpin DNA probe 1 alongside an aptamer-tDNA in solution and a ferrocene (Fc)-hairpin DNA probe 1 over a glassy carbon electrode (GCE) surface. While this study developed an aptasensor over an electrode surface, Chengxi Zhu et al. [87] performed an assay in solution using a glassy carbon electrode (GCE to detect Fc and MeB. MeB interacts with free DNA in the presence of AFB1, which in turn interacts with the aptamer, decreasing the availability of MeB in solution, thus decreasing its electrochemical signal. The Fc signal remained constant over time, and its response was used as the internal reference signal. Ref. [86] presented a lower LOD than [87], probably because of the simultaneous determination of two signals, rather than just one, using Fc as the reference signal. Other interesting systems were also evaluated using the electrochemical responses of Fc and MeB [88], MeB and Ag ions [140], Fc and thionine through electrochemical impedance spectroscopy (EIS) and alternating current voltammetry (ACV), optical responses of Ag-doped core-shell nanohybrids through ultraviolet (UV), and fluorescence responses [184] of thioflavin T (ThT) and trans-2-[4′-(dimethylamino)styryl]-3-ethyl-1,3-benzothiazole (DMASEBT) through fluorescence, optical, and electrochemical responses [110] through fluorescence and differential pulse voltammetry (DPV) using a zirconium-metal organic framework (MOF), and three-signal [175] coulorimetric, surface-enhanced Raman spectroscopy (SERS), and fluorescence.

In addition to the SPCE, different electrodes have been applied in sensor/biosensor development, mainly gold [138] and GCE [116], n-type semiconductor indium-doped tin oxide (ITO) [80], p-type semiconductor FTO [144], optical fiber [178], and even paper-based electrodes [72,99,100,135], showing versatility and creativity. Xiaobo Zhang et al. [99] used a folding origami device from Whatman grade 1 chromatography paper using the wax printer. The working electrode (WE), carbon counter electrode (CE), and Ag/AgCl reference electrode (RE) were screen printed on the hydrophilic pattern (layers III and V) of the paper. While the authors used a paper-based system for dual-mode OTA determination, Yue He et al. [100] also used paper (Whatman Qualitative filter paper 5) to develop a cellulose-paper-based biosensor for a sensitive simultaneous determination of AFB1 and FB1, and Xianfeng Lin et al. [72] used Whatman No. 1 filter paper attached to A4 paper for a dual-color portable aptasensor for simultaneous determination of ZEN and OTA.

3.1.2. Non-Traditional and Innovative Systems

Apart from the traditional immunosensors and aptasensors that permeated most of the papers, some non-traditional systems have been applied for mycotoxin determination, such as the yeast biosensor developed by Han Yang et al. [90]. In their study, the authors used yeast surface display (YSD) DON to screen specific antigen-binding fragment (Fab) antibodies that could screen four types of DON-selective yeasts. Anti-DON Fab@YSD, DON-BSA@Biotin, and DON standard solution were added to a 96-well chemiluminescent microtiter plate alongside streptavidin-alkaline phosphatase (SA-ALP). An ALP luminescence substrate was added, and the relative light unit (RLU) was measured using a chemiluminescence immunoassay (CLIA). This is an interesting example of a chemiluminescent yeast-based biosensor with a remarkable LOD of 0.166 pg mL⁻¹ and an analysis of corn samples with excellent recoveries.

Optical and electrochemical transducers are the primary choices for mycotoxin determination. Nevertheless, every chemical or biochemical reaction is either endothermic or exothermic, even on the smallest scale. Temperature changes between the mycotoxin’s interaction/reaction over a thermistor as a transducer are also a viable option for mycotoxin detection. In this context, Jiang Tang et al. [113] used the knowledge that the classic 3,3′,5,5′-tetramethylbenzidine (TMB)-hydrogen peroxide reaction is accompanied by a strong near-infrared (NIR) laser-driven photothermal effect for the detection of OTA [185,186]. The results for the corn samples were consistent with those of a commercial enzyme-linked immunoassay kit and brought about another interesting and underexplored transducer option for light. In this review, only this paper and another [118] that used the same G-quadruplex (G4)-hemin principle applied thermometric principles to mycotoxin determination.

Xinyu Sun et al. [183] prepared a biosensor using rat liver microsomes (RLM) for the determination of AFB1. This approach is based on the knowledge that CYP450 enzymes present in animal liver can metabolize AFB1 into metabolites. Electrons are transferred to the CYP450 enzyme present in the RLM over an electrode surface for electrocatalysis to convert AFB1 into metabolites (Figure 3). The system was built over a GCE with Nafion and Au@Mxene to create a biocompatible environment for RLM. In the presence of AFB1 in an oxygen-saturated PBS solution, the amperometric signal increased, indicating electrocatalysis of AFB1. The increased concentration of AFB1 generated e linear relationship from 0.01 μM to 50 μM with a LOD of 2.8 nM. The authors stated that this study was the first to use Au@MXene-adsorbed liver microsomes for the detection of AFB1. This study is an example of a remarkable device using different biomolecule knowledge applied to detect an important mycotoxin and provides a perspective for further endeavors in this underexplored type of biosensor, such as an immunosensor based on a modified bacteriophage developed by Hao Fang et al. [161] to mimic the DON antigen for recognizing anti-DON monoclonal antibodies, the use of molecular biology knowledge by applying exonucleases [85,154,156,158], and the gene editing method, CRISPR [160,175].

A field-effect transistor (FET) can be described as a structure containing a semiconducting current path whose conductivity is modulated by the application of a transverse electric field [187]. When the sensing principle of FET changes from immobilization and interaction with biomolecules, FET-biosensors, also known as BIO-FETs, are developed. Based on this type of biosensor, Zhand et al. [147] developed an interesting n-type vertical organic electrochemical transistor (vOECT) for the determination of AFB1 in corn samples. The GCE or Au electrode was modified with chitosan-graphene nanosheet (CS-GN) nanocomposites, followed by covalent binding to AFB1 antibodies. The vOECT was built on a silicon/silicon dioxide substrate by using Cr/Au electrodes. Poly(benzimidazobenzophenanthroline) (BBL) in a methanesulfonic acid solution was spin-coated over the electrode, followed by thermal deposition of a gold layer (Au top drain electrode). Finally, the vertically prepared system was protected by the SU-8 photoresist patterns prepared using UV light. A 1–20 µL PBS solution droplet was used to connect the prepared vOECT to an Ag/AgCl pellet electrode (gate electrode). The modified GCE and vOECT were connected in series for AFB1 determination. The analyte in the corn sample interacted with the specific antibody on the GCE, changing its impedance in a ferro/ferricyanide solution. This also resulted in a potential decrease in the reaction cell, leading to an effective gate voltage increase/drain-source current decrease because of the ultrahigh gm of vOECT. The results obtained from this ultrasensitive device have an outstanding LOD of 0.01 fg mL⁻¹, along with selectivity, stability, and excellent recoveries in corn samples with low RSD. The authors stated that this was the first time that this kind of system was used for AFB1 detection, which, in turn, expanded the list of techniques to be explored for ultrasensitive determination of mycotoxins, among other contaminants.

Different, creative, innovative, and unusual approaches have been developed by some authors, proving that science is evolving in the correct direction. Zahra Khoshbin et al. [97] used liquid crystals (LC) over laboratory microscope slides to create an ultrasensitive OTA aptasensor. Unlike conventional solids or liquids, this state of matter presents optical activity that can be useful for biosensor development. The presence of different substances may alter the homeotropic direction of LC, which leads to changes in the optical birefringence properties of LCs and displays a color change through exposure to polarized light [188,189]. Therefore, the presence of the OTA aptamer in association with a surfactant, with and without the OTA analyte, drastically changed the system color, as detected by a polarized microscope. The result is the lowest LOD among all the other mycotoxins evaluated in this review (8.48 × 10⁻⁶ pg mL⁻¹) and positive responses in several samples, including corn.

Lateral flow assays (LFA) are paper-based platforms for the qualitative and quantitative determination of analytes, where the sample is placed on a test device and the results are displayed on a minute scale [190]. The hCG assay for pregnancy testing is probably the most traditional example of a commercial LFA that uses an immunochromatographic strip [191]. Based on this concept, Vijitvarasam et al. [153] developed a lateral-flow aptasensor for the determination of AFB1. The intent was to determine the analyte in a pregnancy-like test through the naked eye by color change on the strip using polystyrene dye particles. Quantitative analyses were performed using images captured using a digital camera through image programming. This work is of utmost importance because the authors not only studied and applied the system, but this user-friendly, instrument-free, cost-effective lateral flow aptasensor has already been designed for commercialization as a ready-to-use point-of-care system.

The catalysis of hydrogen peroxide by metal nanoparticles was applied by Xue et al. [104]. They developed an interesting approach for the determination of T-2 mycotoxins using an aptasensor (Figure 4). The T-2 aptamer was trapped in a DNA hydrogel composed of polyethyleneimine (PEI) embedded with platinum nanoparticles (PtNP). In the presence of the analyte, the hydrogel broke, and the nanoparticles were released into the solution. The supernatant was transferred to a small vial containing hydrogen peroxide and sealed with a perforated cap connected to a capillary tube containing red pigment solution. The released PtNP then catalyses the hydrogen peroxide decomposition reaction into oxygen gas, increasing the vial’s inner pressure, thus raising the red liquid column to a different height. Using a simple ruler, the authors measured the differences in responses to different T-2 concentrations in millimeters, and an excellent LOD was achieved at the nanogram level. The authors innovated in the detection of this aptasensor apart from usual electrochemical, optical, or piezoelectric techniques, in an out-of-the-box system using expanded knowledge and creativity. Based on the current library of mycotoxin aptamer availability, this innovative and easy-to-operate/read system can soon become a point-of-care test for multiple targets owing to its simplicity, speed, cost-effectiveness, and high sensitivity.

Immunosensors, DNA-based biosensors, and enzymatic biosensors are the primary choices for biosensor development. However, only a few strategies using enzymes have been reported for mycotoxin determination. However, Yu Ge et al. [145] used an enzyme-like structure in their work. The developed system is intricate, and it is the only one in this review that focuses on the detection of mycophenolic acid (MYA). MYA, a secondary metabolite of Penicillium roqueforti, is a potent immunosuppressant. Briefly, the authors modified an SPCE with a violet phosphorus-doped hierarchically porous carbon microsphere (VP-PCM), which is a novel structure with bioenzyme oxidase/peroxidase-like activities, based on a machine learning model using the Random Forest algorithm. Catalytic oxidation of the phenolic portion of MYA by the bioenzyme was explored and confirmed in solutions containing hydrogen peroxide and TMB under oxygen-limited conditions. Square wave voltammetry (SWV) was applied, and a calibration curve was obtained with different MYA concentrations, generating a LOD of 18.7 nM in a 30-s run experiment. The disposable SPCE was attached to a handheld portable intelligent sensor (EmStat Blue) electrochemical workstation, with Bluetooth data transmitted to a smartphone. This study introduces a novel structure applied to biosensor development related to machine learning to provide useful information regarding the interaction/reaction of a molecule. Moreover, the authors do not provide a perspective but the reality of a fast, reliable, and cost-effective miniaturized sensor using portable commercial instruments integrated with electronic devices for data reading and interpretation. The advantages required for a point-of-care system are directly applied to suppress the demand in health areas.

3.2. Pesticides

Paradoxically, the use of chemicals in crops has been a topic of debate worldwide over the past few decades. Although pesticides are essential agricultural inputs to comply with the growing global food demand, their chemical composition is harmful to human health. Therefore, the question raised is how to safely feed the entire global population with essential chemicals that are responsible for most people’s lives.

Pesticides cover a spectrum of products, including herbicides, fungicides, insecticides, rodenticides, and plant growth regulators. Their function is to protect crops against various unwanted organisms that can decimate the entire plantation and cause catastrophic damage to the economy. On the other hand, because of the high toxicity of chemicals or indiscriminate use, the use of pesticides is related to the contamination of soil, water, and living organisms, which has a negative impact on ecosystem health [192], especially in humans, such as cancer [193], diabetes [194], and infertility [195].

Michael Eddleston [196] recently published a paper stating that around 150,000 people die from pesticide poisoning, plus millions are hospitalized annually. Special attention was paid to organophosphorus pesticides; insecticides of the class of carbamate, neonicotinoid, organochloride, and N-phenylpyrazole; herbicides of the class of bipyridyl and glyphosate; and fumigant aluminum phosphide. These data are alarming and have been the subject of analysis by many researchers regarding toxicity and human safety despite the legislation on MRL, especially because more pesticides are recurrently being registered, such as florylpicoxamid, a broad-spectrum fungicide, announced by the United States Environmental Protection Agency in early 2025 [197].

However, it is interesting to observe a smaller

Table 2. Sensors and biosensors for the determination of pesticides in corn reported in the literature in the past five years.

Type	Transducer	Toxin	Sample	Linear Range (µM) *	LOD (µM) *	Ref.
Sensor	Electrochemical	Mesotrione	Corn food products	0.5–70.0	0.46	[198]
Sensor	Electrochemical	Neonicotinoid	Corn	0.16–2.86 2.86–220.86	2.0 × 10−3	[199]
Sensor	Electrochemical	Glyphosate	Corn	0–1.8 × 103	11.0	[200]
Sensor	Optical	Lindane	Corn flakes and maize flour	1.0 ppb–100.0 ppm	10.0 ppb, each	[201]
		Pretilachlor
		Propiconazole
Sensor	Electrochemical	Imidacloprid	Corn	0.5–60.0	0.026	[202]
		Thiamethoxam		1.0–60.0	0.062
		Dinotefuran		0.5–60.0	0.010
Sensor	Optical	Diquat	Corn	0–5 × 10−2	6.0 × 10−5	[203]
Sensor	Electrochemical	Fenitrothion	Corn	1.0 × 10−3–8.5 × 102	0.038	[204]
Sensor	Optical	Dibutyl Phosphate (DBP)	Corn	1.9–25.3	1.39	[205]
		Diphenyl Phosphate (DPP)			1.17
		Diethyl Chlorophosphate (DCP)			1.29
Sensor	Optical	Glyphosate	Corn	1.18–5.91	0.473	[206]
Sensor	Electrochemical	Mesotrione	Corn	0.1–261.0	4.5 × 10−2	[207]
Sensor	Optical	Imidacloprid	Corn	1.0 × 10−8–1.0 × 10−3	1.0 × 10−9	[208]
Sensor	Optical	Bromoxynil octanoate	Corn	0.1–0.8 (equivalent concentration)	2.1 × 10−2	[209]
Sensor	Optical/Electrochemical	Glyphosate	Corn	1.0 × 10−2–1.0 (SERS) 1.0 × 10−1–1.0 (DPV)	1.9 × 10−3 (SERS) 1.73 × 10−2 (DPV)	[210]
Sensor	Optical	Chlorpyrifos	Corn	0–120.0	1.89 × 10−2	[211]
Sensor	Electrochemical	Glyphosate	Corn	1.0−40.0 (EIS) 5.0 × 10−4–7.4 × 10−4 (CV) 2.0 × 10−1−2.34 (SWV)	6.4 × 10−1 (EIS) 4.0 × 10−4 (CV) 1.5 × 10−1 (SWV)	[212]
Sensor	Optical	Hydrazine	Corn	0–100.0	4.5 × 10−3	[213]
Sensor	Electrochemical	Carbendazim	Corn flour	2.5 × 10−4–1.0	8.0 × 10−5	[214]
Sensor	Optical	Glyphosate	Corn	29.57–591.47	2.07	[215]
Sensor	Optical	Methyl parathion	Corn	7.60 × 10−5–7.60 × 10−2	2.1. 10−5	[216]
Biosensor	Optical	Dithiocarbamates	Corn	0.6–6.0 × 102 µg kg−1	0.2 µg kg−1	[217]
Biosensor	Optical	Atrazine	Corn juice	1.85 × 10−4–0.464	7.7 × 10−3	[218]
Biosensor	Electrochemical	Atrazine	Corn	1.85 × 10−7–1.85 × 10−2	2.27 × 10−8	[219]
Biosensor	Electrochemical	Trichlorfon	Corn	1.94 × 10−5–3.88 × 10−5 5.82 × 10−4–2.72 × 10−3	1.16 × 10−5	[220]
Biosensor	Electrochemical	Carbaryl	Corn flour	0.8–32.3 µg kg−1	0.08 µg kg−1	[221]
Biosensor	Optical	Atrazine	Maize	2.31 × 10−4–2.31 × 10−1 (colorimetric) 4.63 × 10−5–2.31 × 10−1 (fluorescence)	3.43 × 10−5 (colorimetric) 1.16 × 10−3 (fluorescence)	[222]
Biosensor	Electrochemical	Carbofuran	Maize oil	1.8.10−2–1.8	9.0 × 10−4	[223]
Biosensor	Electrochemical	Glyphosate	Spiked corn residues	10.0–260.0	3.03	[224]
Biosensor	Cantilever	Atrazine	Corn crops	4.96 × 10−6–4.63 × 10−1 4.96 × 10−6–4.96 × 10−1	6.49 × 10−6 3.87 × 10−5	[225]
		Simazine

* Otherwise stated.

content (sensors and biosensors for pesticide determination in corn samples) compared with the large mycotoxins listed in Table 1, even with the proven toxicity and mortality caused by pesticide poisoning. Corn is contaminated with mycotoxins from fungal infections, which are a specific group of eukaryotic organisms to which fungicides are applied. In addition to the aforementioned pesticides, fungicides of the morpholine class are an understudied class of chemicals for sensors and biosensors. To date, no data have been found in the literature regarding sensors or biosensors developed to detect fenpropimorph, an important morpholine fungicide used in cereal crops. Therefore, it is not only important to highlight the pivotal systems performed in the past years for pesticide determination in corn but also to encourage researchers to contribute to this line of work in an expanded list of compounds to improve agricultural practices for this cereal, enabling better access to a high-quality product for consumers.

In 2022, the pesticide used worldwide was approximately 3.7 mmt [192], of which 1.94 mmt was represented by herbicides [226]. The widespread application of these chemicals has been attributed to their broad use in conventional agriculture, home gardens, horticulture, and landscaping. Glyphosate (GLY) is a broad-spectrum systemic herbicide responsible for over 72% of all global pesticide consumption [227]. In this review, six examples of sensors and biosensors [200,206,210,212,215,224] related to glyphosate determination in corn samples are presented. Bai et al. [215] developed a MOF-based sensor built from two organic ligands and enoxacin (ENX) embedded Eu³⁺ (ENX@EuMOF). The authors suggested that GLY absorbs over the MOF structure, changing its fluorescence properties, decreasing fluorescence at 613 nm, and increasing fluorescence at 520 nm. The authors also explored visual color changes by developing a skin-attachable ENX@EuMOF polyacrylamide-based fluorescent gel that was tested on the surfaces of eggplants, corn, sunflower seeds, and soybeans. Upon contact with GLY on the sample surface or on a small sample cut from the plant to avoid experimental contamination, the color changed from red to green under ultraviolet light irradiation.

While europium resulted in the best results in this study, terbium (TbIII) was the better choice found by Sun et al. [203], who also developed an MOF-based sensor for diquat (DQ) determination on apples, potatoes, and corn (Figure 5). The results using a paper-based kit containing a DQ colorimetric card were successfully applied to samples under a 365 nm UV light. Both studies presented simple and direct pesticide determination with responses obtained in a short period through portable devices using cost-effective strips that can be mass-produced for on-site determination of pesticide residues in samples without pretreatment and even during pre-harvest intervals. This is a very interesting approach, as it is a non-destructive method for obtaining information from different samples from different sections of long fields of corn.

In the past few years, nanoparticle-based sensors have attracted considerable attention, proving that they are an alternative replacement for conventional and labor-detection methods by increasing the selectivity and sensitivity of the system [228]. Metallic nanoparticle-modified electrodes have been the focus of electroanalysis because of their high surface area, catalysis, mass transport, and biocompatibility [229]. Dorozhko et al. [221] employed, for the first time, copper nanoparticles (CuNP) as active electrochemical labels in the direct competitive immunosensor for the determination of carbaryl insecticide residues. In solution, a carbaryl hapten containing a carboxyl group (Car 6C) was conjugated with bovine serum albumin (BSA) and CuNPs to form a Hap-Car-BSA@CuNP conjugate. Monoclonal anti-carbaryl antibodies were immobilized on an activated thiolated-GCE through cross-linking using glutaraldehyde (method 1) and covalent bonding using EDC and NHS (method 2). Regardless of the electrode activation method, the conjugate was incubated with the immunosensor for specific hapten-antibody interactions. The presence of the carbaryl pesticide displaced the conjugate away from the electrode by competition, which decreased the presence of CuNPs over the electrode surface, thus decreasing the electrochemical signal from its oxidation. This work is important, apart from the usual enzymatic biosensors for pesticide determination, which present low selectivity owing to the detection possibilities of several different compounds within the same specific class. By using antibodies and haptens, the authors achieved high selectivity because of the well-known high antibody-antigen affinity, alongside a good linear range from 0.8 to 32.3 μg kg⁻¹ and a LOD of 0.08 μg kg⁻¹, which points to a different path for pesticide determination, that can also be expanded as observed for mycotoxins with the use of aptamers and MIPs.

Zhou et al. [214] also used CuNPs deposited over reduced graphene oxide and reduced graphene nanoribbons-modified GCE for the determination of carbendazim (CBD). In this study, a different strategy was applied, using an MIP-based MOF for CBD interactions. MIP was immobilized on the modified electrode, and electrochemical analysis was performed based on the decrease in the copper probe reduction signal in the presence of CBD. Corn flour was analyzed as a food sample using the standard addition method, with excellent recovery rates (approximately 100%) and low RSD (between 2.44 and 3.07%), comparable to those of the HPLC technique. This study demonstrated a beneficial synergistic effect by combining a well-known MIP system with MOF structures into a viable and sensitive device to identify an important pesticide for a corn product.

Atrazine is the second most studied pesticide after glyphosate [218,219,222,225]. An interesting example of the determination of this harmful endocrine-disrupting chemical was identified in an intricate Confinement-Enhanced Microalgal Biosensing (C-EMBs) system by Liu et al. [218] based on biological and physical signal changes in microalgae. In this study, Chlamydomonas reinhardtii green algae were trapped in a hydrogel and added to replaceable microfluidic chips connected to an optical system consisting of a USB module, CMOS camera, and emission filter to record the increased fluorescence image emitted by microalgae upon contact with atrazine, stimulated using a blue LED (Figure 6). The fluorescence intensity was analyzed using a customized imaging algorithm to quantitatively determine the pesticides. This is a perfect example of a portable, ready-to-use, lab-on-a-chip system for the detection of important hazardous compounds in food, such as corn and sugarcane juice samples, using a unicellular aquatic living organism as a whole-cell biosensor, a viable system apart from traditional enzymatic biosensors, immunosensors, and DNA-based biosensors that are accessible for in situ application by farmers and consumers.

3.3. Other Contaminants

Trace elements are environmental pollutants known for their toxicity, persistence, and bioaccumulation in soft tissues when not metabolized in organisms [230,231]. Corn crops absorb and accumulate metals from contaminated soil, leading to high health risks to humans and ecosystems [231]. In this review, every ten sensors for the determination of trace elements are presented, some of which were evaluated simultaneously (

Table 3. Sensors and biosensors for the determination of other contaminants in corn reported in the literature in the past five years, apart from mycotoxins and pesticides.

Type	Transducer	Contaminant	Sample	Linear Range (pM)	LOD (pM)	Ref.
Sensor	Optical	Pb2+	Corn oil	4.83 × 104–4.83 × 105	2.41 × 104	[236]
		Hg2+		4.98 × 104–4.98 × 105	3.49 × 104
Sensor	Optical	Pb2+	Corn oil	9.65 × 104–2.41 × 103	1.45 × 103	[237]
		Hg2+		9.97 × 104–2.49 × 106	3.0 × 103
Sensor	Electrochemical	Cd2+	Corn	0.02–60	0.02	[238]
		Pb2+		0.032–60	0.032
		Cu2+		0.018–60	0.018
		Hg2+		0.041–60	0.041
Sensor	Electrochemical	Pb2+	Corn	0.1–2.0 × 102	0.042	[239]
		Cu2+			0.01
		Hg2+			0.031
Sensor	Electrochemical	Zn2+	Corn	4.59 × 104–1.38 × 107	7.34 × 103	[240]
		Cd2+		2.67 × 104–8.0 × 106	4.0 × 103
		Pb2+		1.45 × 104–4.34 × 106	1.40 × 103
Sensor	Electrochemical	Bisphenol A	Canned corn and corn	5.0 × 106–7.3 × 107	3.8 × 105	[241]
Sensor	Optical	Pb2+	Corn oil	~5 × 103 1.0–5.0 × 108	≤5.0 × 103	[242]
		Hg2+
Sensor	Electrochemical	Pb2+	Corn	50.0–2.0 × 104	9.50 × 102	[243]
		Cd2+		50.0–1.5 × 104	6.90 × 102
		Hg2+		10.0–1.5 × 104	3.30 × 102
Sensor	Electrochemical	Pb2+	Maize	4.83 × 103–6.27 × 106	3.38 × 102	[244]
		Cu2+		1.57 × 104–2.05 × 107	2.04 × 103
		Hg2+		5.0 × 103–6.48 × 106	1.05 × 103
Sensor	Electrochemical	Cd2+	Corn	8.9 × 104–8.9 × 105	3.65 × 103	[245]
		Pb2+		2.41 × 104–4.83 × 105	1.45 × 103
		Simultaneous		Cd2+ (8.9 × 104–8.9 × 105) and Pb2+ (4.83 × 104–4.83 × 105)	Cd2+ (4.27 × 103) and Pb2+ (1.45 × 103)
Biosensor	Electrochemical	Butolinum neurotoxin A	Packed corn	1.0–1.0 × 102	1.0	[246]

). Two different contaminants, BPA and botulinum neurotoxin A, were also identified and discussed in this review. Clostridium botulinum is a gram-positive spore-forming bacterium responsible for producing botulinum, the causative agent of botulinum infection. It is believed to be one of the most potent toxins in nature and can be found in various types of foods [232]. If untreated, botulism leads to death caused by respiratory muscle paralysis owing to the action of proteinases that cleave neuronal vesicle-associated proteins responsible for acetylcholine release into the neuromuscular junction [233]. Bisphenol A (BPA) is a synthetic compound widely used in disposable materials for the fabrication of polycarbonate plastics and epoxy resins and is often used for food containment [234]. It is classified as an endocrine-disrupting chemical that can act as a xenoestrogen and is harmful to humans [235].

Pb, Cd, and Hg were the main trace elements detected in the corn samples, followed by three studies related to the detection of Cu, and one study related to the detection of Zn. An example of a simple sensor using electrode modification with nanostructures was developed by Chen et al. [245] for the simultaneous determination of Cd and Pb ions in food samples. The SPCE was modified using amino-functionalized multilayer titanium carbide (NH₂-Ti₃C₂T_x) prepared via grafting with APTES. As observed for MOFs and MXenes, this type of structure has a high adsorption capacity with a large specific surface area, functional groups that can form coordination effects with metal ions, and excellent chemical stability and electrical conductivity, all of which are desirable features for enhancing sensor performance. Electrochemical measurements were performed using differential pulse anodic stripping voltammetry (DPASV). The sensor exhibited satisfactory electrochemical performance for the determination of trace element pollutants, with a low LOD.

Yadav et al. [246] explored the existence of specific aptamers obtained from the SELEX discovery tool to develop an electrochemical aptasensor toward Botulinum neurotoxin A (BoT/A) determination in six different types of food, including packed corn. The authors present one of the simplest and most efficient examples of sensor assemblies compared to others in this review (Figure 7). The amino-functionalized BoT/A aptamer was covalently bonded with activated carboxylic groups from rGO-modified commercial screen-printed gold electrodes (SPGE) through a nucleophilic acyl substitution reaction. The aptamer interacted specifically with neurotoxins during sample incubation, hampering access from the classic redox ferricyanide/ferrocyanide to the electrode surface, thus decreasing the electrochemical signal of the probe. This study used inexpensive commercial screen-printed electrodes that required minimal sampling, resulting in fast responses (6 s) with a picomolar LOD. However, despite its good analytical performance, the developed device still presents insulation problems, as observed in milk samples and samples containing Escherichia coli bacterial strains. As stated by the authors, it is not ready for on-site determination of neurotoxins. Nevertheless, this is the only example of a sensor/biosensor devoted to studying an important toxin in corn samples in the past five years in a simple manner that can be used by other researchers to apply different strategies to improve the system so that it can be applied as a point-of-care device.

Kaassamani et al. [241] used boron-doped diamond (BDD) electrodes modified with iron oxide nanoparticles (Fe₃O₄ NPs) coated with an MIP to form an electrochemical sensor toward BPA determination. The electrochemical performance was evaluated by DPV, and the results were validated by high-performance liquid chromatography (HPLC). It is very interesting to find a study that proves that the source of contamination in food, such as canned corn or other corn derivatives, is not only restrained in its origin, but also during storage and transportation.

Shi et al. [243] presented an interesting approach for the development of an electrochemical sensor for the simultaneous determination of three specific trace elements in food samples. As observed for pesticide determination, the authors also applied a combination of structures to improve the analytical sensitivity of the designed sensor by creating a nanocomposite composed of MXene and MOF incubated in aminopropyltriethoxysilane (APTES) solution that presents specific binding sites for Cd²⁺, Pb²⁺, and Hg²⁺. This composite was drop-coated onto the GCE, and the three metals were simultaneously detected by DPV, with a small peak width between them at each distinct potential. The optimized sensor was applied to pretreated samples of corn, fish, whole milk, and rice, all with excellent recovery rates and low RSD, and no significant changes in the electrochemical profiles were observed in the presence of interferents (Na⁺, K⁺, Ca²⁺, Fe³⁺, Ni²⁺, Mn⁷⁺, Mg²⁺, Al³⁺, Cl⁻, and SO₄²⁻). The results were validated using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) at a nanomolar scale, which is lower than almost every other system mentioned by the authors at the time in the literature.

These systems are assembled to comply with the growing demand for reliable simultaneous determination of more than one analyte in a single assay, especially in cases where there is no certainty of the presence of a single or multiple trace element contaminants. The same premise can be applied to multiple determinations of different contaminant classes. Based on what the authors presented in the most diverse types of work in this review, it is very likely that a novel sensor or biosensor will be developed to simultaneously detect pesticides, mycotoxins, and heavy metals in a single experiment in the near future.

4. Conclusions

Contamination in the corn production chain affects the health of consumers, damages raw materials for industrial products, and results in financial loss. Corn is susceptible to fungal infections, which generate harmful mycotoxins, and is affected by pesticide residues in the fight against pests and diseases, along with other contaminants such as trace elements. Using BPA-free packaging, improving storage conditions, and thoroughly rinsing the food are important mitigation strategies; however, data obtained from the analytical determination of such compounds are pivotal to ensure safety, especially for human and livestock consumption.

With advances in technology, sensors and biosensors have become increasingly attractive to producers. These devices are cost-effective and portable and can generate fast, reliable, and easy-to-read signals in loco. Proof-of-concept systems are slowly becoming state-of-the-art point-of-care devices with miniaturized systems that can be coupled with smartphones. Researchers have repeatedly applied designed nanostructures with the aid of computational simulations to improve the interaction/reactions with molecules/biomolecules for analyte determination, such as mycotoxins, pesticides, and trace elements.

The determination of multiple analytes in a one-pot experiment using a single transducer is increasing to cover a broader field of contaminants, along with a diversity of assembly approaches, biomolecules other than the traditional use of enzymes, aptamers, antibodies, and techniques other than the conventional electrochemical or optical systems. The application of corn or corn derivatives resulted in excellent analytical performance and validation. Overall, the studies performed and under development by these researchers point to a bright and applicable future for ready-to-use sensors and biosensors directly where they are needed, from producers to consumers, with reliability and assurance of better product quality.