The Developments on Lateral Flow Immunochromatographic Assay for Food Safety in Recent 10 Years: A Review

Abstract

Food safety inspections are an essential aspect of food safety monitoring. Rapid, accurate, and low-cost food analysis can considerably increase the efficiency of food safety inspections. The lateral flow immunochromatographic assay (LFIA) technique has recently grown in popularity due to its ease of use and high efficiency. It is currently commonly utilized in food inspection. In this review, we briefly introduce the principle and classification of LFIA, critically discuss the recent application status of LFIA in food contaminantion detection, and finally propose that artificial intelligence and information technology will further advance the development of LFIA in the field of food safety monitoring.

1. Introduction

Food safety accidents have been more common in recent years [1,2], resulting in massive economic losses as well as threats to human health. According to statistics from the Centers for Disease Control and Prevention (CDC), about 3000 people die each year in the United States of America (USA) due to food safety problems. China has 4.2 times the population of the USA, and food poisoning affects an estimated 200,000 to 400,000 people every year. To ensure food safety, many countries have established regulatory agencies, food safety regulations, and various analytical procedures that constitute the food safety monitoring system.

Food safety incidents should be addressed at every stage of the supply chain, from the farm (production) to the fork (final consumer) [3,4], because food contamination could occur at any stage (Figure 1). A typical example is mad cow disease, a zoonotic disease that can prove lethal in humans and occurs throughout the food harvest stage. Another example involved “Kinder” brand chocolates, which were linked to Salmonella sp. poisoning due to improper processing, which occurred in 11 countries in 2022. During the transportation and sales stages, food can also be contaminated by food-borne bacterial pathogens (Listeria monocytogenes, Staphylococcus aureus, Escherichia coli, and Salmonella sp.), as has been reported [5]. In addition, biological toxins, environmental pollutants, illegal additives, pathogens, plasticizers, viruses, and botulinum toxin [6,7,8] could also be present during harvesting, processing, storage, distribution, and other stages.

Detecting such food contaminants in time is critical to food safety, which would nip the problem in the bud. Many detection and analysis technologies have been developed to evaluate quality and safety, such as high-performance liquid chromatography (HPLC), gas chromatography (GC), enzyme-linked immunosorbent assay (ELISA), and polymerase chain reaction (PCR) [9]. However, all of these methods require sophisticated instruments and a stringent testing environment, and samples are usually transported to certain standardized laboratories for centralized testing, meaning that the experimental outcomes will take 2–3 days to obtain. Delayed reports make it impossible to track food quality in real time. Therefore, portable, rapid, sensitive, and widely adaptable detection technologies are more suitable for monitoring food safety and providing an early warning system.

Lateral flow immunochromatographic assay (LFIA) technology has recently gained popularity in the field of food inspection due to its low cost, rapid operation, simplicity, and ease of operation. In particular, with the dramatic increase in the use of immunoassays and nanotechnology, LFIA products have diversified into new fields. With improvements in the LFIA detection system, its detection sensitivity has been continuously improved, and it has become a hot spot for detection method development. A brief search on ScienceDirect with the keywords “Lateral flow strip” and “Food” found 14,534 research reports. Among them, 7236 articles had been published in the past 10 years, accounting for 49.8% of the total, and this has become a growing trend (Figure 2). This shows that LFIA detection technology is garnering more and more attention in the field of food safety analysis. In this review, we will introduce the principle and provide a brief classification of the LFIA, as well as emphatically summarize the current application status and highlight recent food safety regulatory advancements.

2. Principles and Classification of LFIA

The LFIA was originally designed as a point-of-care assay for the detection of human chorionic gonadotropin in urine. Because of its ease of operation, high specificity, and low requirements regarding the detection environment, LFIA has steadily spread beyond the biological field to environmental monitoring and food safety testing.

The core of LFIA detection technology is the specific recognition of antigens and antibodies. The principle is that a liquid sample (or an extract thereof) containing the analyte of interest moves across distinct sections of a polymeric test paper without the need for external forces (capillary action). As a result, the performance of the antibody is the core influencing factor of the detection method. As shown in Figure 3a, antibodies are usually generated by immunizing mice with specific immunogens. The antibody with a specific recognition capacity is then used to establish a test strip detection method after being labeled with an observable recognition material.

A typical test strip usually consists of four parts: sample pad, absorbent pad, nitrocellulose (NC) membrane with test (T) zone and control (C) zones, and conjugate pad with the labeled antibody. These components are overlapped and applied flat onto a backing plate. When the test liquid flows from the sample pad to the absorbent pad, the formation of a binding complex between the labeled antibody and analyte will appear on the NC membrane, causing a signal change of the T and C zones; the result will be displayed within 10 min. The diversity of core detection substances leads to a variety of detection types. LFIA can be classified based on the detection principle, product style, or labeling material.

2.1. Classification by Principle

Based on the difference in molecular weight of the target, an LFIA can be divided into competitive immunochromatography or double-antibody sandwich immunochromatography methods (Figure 3b,c) [7,10].

2.1.1. Competitive Immunochromatography

Competitive immunochromatography is effective for detecting small-molecule compounds (such as heavy metals or dioxins) that cannot directly activate immunity in an organism. When the small-molecule target is combined with the labeled antibody, changes in steric hindrance make it difficult to recombine with the coated antigen at the detection line. Therefore, the labeled antibody competes with the coated antibody on the detection line to bind to the small-molecule antigen target [11]. A special competition detection test strip can also be prepared by converting the labeled protein from an antibody to a receptor (Figure 3b). Receptors are target proteins for many drugs, so this type of test strip can only be used to detect target drugs with clear mechanisms of action.

2.1.2. Double-Antibody Sandwich Immunochromatography

Double-antibody sandwich immunochromatography is suitable for detecting macromolecular antigens (such as harpin and pathogenic microorganisms), which include many antigenic determinants and can immediately activate organism immunity and antibody production. During detection, the capture antibody is pre-immobilized on the detection area of the NC membrane. When one end of the immunochromatographic test strip is immersed in the sample solution (urine or serum), the sample liquid flows along the test strip by capillary action. Upward chromatography, under the action of the mobile phase, first binds to the labeled antibody at the binding pad, and when encountering the detection area immobilized with the capture antibody, the biomarker contained in the sample specifically binds to the capture antibody to form a double antibody. The “sandwich structure” of the sandwich forms an immunological complex consisting of nanoparticle–antibody probe-target biomarker-capture antibody [12].

The difference between competitive immunochromatography and double-antibody sandwich immunochromatography lies in the relationship between the color change of the T line and the content of the target material. In the former method, the higher the content of the target material, the lighter the color of the T line, while in the latter, the opposite is the case.

2.2. Classification by Antibody-Labeling Material

The above two classification methods rely on an antibody, which needs to be properly designed and highly purified. It is important to secure a stable supply of antibodies with demonstrated affinity and specificity. This classification depends on the type of signal material. The marker is a key factor in the color shift of the test strip. At present, the marking materials employed in colored markers, fluorescent markers, and others are based on the characteristics of nanoparticles (as shown in Figure 4).

2.2.1. Colored Markers’ LFIA

Colloidal gold is the most common labeling material among colored marker nanoparticles. The wine-red nanoparticles have good stability and dispersibility, and they are obtained via a simple synthesis method. After the colloidal gold is combined with the antibody, the test strip will show prominent wine-red bands. By comparing the color, the presence of the target substance can be distinguished with the naked eye. As a result, it is the most widely used label in commercial LFIAs today since it is intensely colored and visible without the need for a development process [13]. When a compound containing selenium is used as the synthetic basis material, orange-yellow nanoparticles are produced. However, in recent years, nano selenium has been less commonly used in lateral chromatography. Currently, only the human immunodeficiency virus nano-selenium rapid detection test strips developed by Abbott are put into use on the market.

Another typical label is latex, which can be labeled with a variety of detection reagents, and if the synthetic material of microspheres is magnetic material such as ferric oxide or silicon dioxide, magnetic microspheres will be obtained. Moreover, black carbon dot nanoparticles and white nitrocellulose membrane form a stronger color contrast, so they are also used in LFIA [14]. However, carbon dot is prone to contamination during the production process and has limitations such as a long labeling time; therefore, it has not been widely used.

2.2.2. Fluorescent Markers’ LFIA

Fluorescent microspheres include colorful and fluorescent dyes, as well as magnetic or paramagnetic components [12]. The surface or interior of the fluorescent microspheres contains fluorescent substances, which will make the test strips appear as obvious stripes under ultraviolet light after being combined with the antibody. These colored or magnetic labels can be identified and detected by measuring their absorption under various light sources. Fluorescent microspheres have gained a lot of interest as a new marker because of their unique advantages.

Quantum dots, which are special semiconductor nanoparticles composed of group II–VI or group III-V elements, exhibit unique optical properties [15]. Its fluorescence lifetime and stability are superior to those of common fluorescent materials such as rhodamine. After certain chemical modifications, it can be easily coupled with proteins and nucleic acids. However, because of the limitations of the synthesis method and route, there are few market applications.

2.2.3. Other Markers’ LFIA

In addition to colored and fluorescent markers, liposomes and emerging nanozymes are also used as markers in LFIA. Liposomes are amphipathic membrane microspheres composed of phospholipid molecules containing an aqueous phase space that can encapsulate and immobilize labeled probes, such as enzymes, dyes, fluorescent substances, etc. [16]. However, they are currently not widely used in lateral chromatography experiments due to the complex preparation process and high cost.

Nanozymes were invented and synthesized by Yan’s research group [17]. They exhibit nanoscale effects and enzymatic activity and are widely used in nanoscience and biology. However, there are currently just a few test strip products on the market.

As electronic technology has advanced, such as smartphones, AI image recognition, and model training, it has made significant progress. Large-scale light- emitting and reading instruments have also been gradually integrated into smartphones. The LFIA has gradually become simpler and more convenient to use.

2.3. Classification by Product Form

The original LFIA technology is a specific method used as a clinical means to detect a certain disease. Therefore, most test strips can only detect one substance and have a single test line. With developments in LFIA technique, application fields have increasingly expanded, and multiplexed LFIA (m-LFIA) has gradually become a research hotspot. m-LFIA detects many targets simultaneously in a single test, and its principle depends on the target substance. The strip allows for the employment of both the competition and sandwich methods. It is suitable for high-throughput analysis. As shown in Figure 5, there are three types of m-LFIA products: broad-spectrum antibody-based LFIA, multi-T line-based LFIA, and multi-channel-based LFIA.

The broad-spectrum antibody-based LFIA, such as traditional test strips, has only one T line. However, the antibodies used can detect multiple target substances at the same time through selective screening. Chen et al. used selective screening to acquire a monoclonal antibody capable of concurrently detecting 27 sulfa compounds, and then they created a generic LFIA for sulfa medications [13]. Li et al. greatly improved the detection range of LFIA by converting the antibody into a receptor, opening up a new research direction [19]. The multi-T line-based LFIA expands the number of T lines, and each T line immobilizes a different target antigen, allowing for the rapid detection of a variety of different substances while providing more intuitive findings. Lei et al. developed a broad-spectrum antibody on the T line to identify 83 high-residue species in five categories: β-lactams, sulfonamides, tetracyclines, quinolones, and amide alcohols in farmed fish [15]. This is an accomplishment never previously accomplished in the field of test strip testing. The multi-channel-based LFIA is capable of distinguishing and detecting multiple similar substances. Zhao et al. established a 10-channel upconversion fluorescence-based lateral flow (TC-UPT-LF) assay for the rapid and simultaneous detection of 10 prevalent foodborne pathogens. Ten different single-target UPT-LF strips were developed and optimally integrated into one TC-UPT-LF dish [21].

3. Application of LFIA in Food Safety

3.1. Pathogens

The broadest definition of a pathogen refers to anything that can cause disease. It is now also known as an infectious agent, which refers to a general term for organisms and non-organisms capable of causing disease. Foodborne pathogens mainly refer to viruses and bacteria (and their metabolites) that cause disease in food. Pathogens are highly contagious, contaminating food and spreading across organisms, resulting in a wide range of food safety incidents. These foodborne pathogens have been responsible for several well-known food safety issues throughout human history.

3.1.1. Food-Borne Viruses

Food-borne viruses include the avian influenza virus, prions, liver virus, rotavirus, foot-and-mouth disease virus, adenovirus, and norovirus. The detection of these viruses has always been the primary task of food safety testing. So far, the detection methods for food-borne viruses have included cell lysis detection, virus gene detection, and virus protein immune detection, among which the most widely used is genetic detection technology. However, this method has high instrument requirements and cannot adapt to the complexity of on-site food detection in a changing environment. Therefore, researchers turned their attention to LFIA detection technology. So far, a variety of test strip detection methods for foodborne viruses have been established, among which the test strip detection method for avian influenza is the most studied (

Table 1. The review of test strip-based immunoassay analysis methods for food-borne pathogens.

Types of Pathogen	Analyte	Signal Label	Sample	LOD	Reference
Foodborne virus
Avian influenza virus	H5, H7, and H9	Gold nanoparticles	Chicken serum	Qualitative	[22]
Foot and mouth disease	type O, A, Asia 1, and C SAT 1, 2, 3	Gold nanoparticles	Clinical specimens	Qualitative	[31]
Norovirus	G1 and G2	Fluorescent microspheres	Clinical samples	Qualitative	[32]
Rotavirus and adenovirus	Rotavirus (Wa strain)	Fluorescent microspheres and colored microspheres	Clinical samples	Qualitative	[33]
	Adenovirus (type 40)
Adenovirus	4 fowl adenovirus	Gold nanoparticles	Chicken tissues	106 TCID50/mL	[13]
Food-borne pathogens
S. typhimurium	S. typhimurium	Agglutinin-functionalized magnetic quantum dot Nanoprobe	Chicken	30.0 CFU/mL	[34]
L. monocytogenes	L. monocytogenes	Aptamer-gated silica nanoparticles	Chicken	105 CFU/mL	[35]
E. coli	E. coli O157	Gold nanoparticles	Milk	103 CFU/mL	[36]
S. aureus	S. aureus	Carbon-dots	Milk and potable water	102 CFU/mL	[37]
V. parahaemolyticus	V. parahaemolyticus	Gold nanoparticles	Medium	106 CFU/mL	[38]
C. jejuni	C. jejuni	Wheat germ agglutinin-modified magnetic SERS nanotags	Vegetable juice, and river water	102 CFU/mL	[39]

). Avian influenza is a type A influenza virus that mostly occurs in birds but also infects mammals and humans. The virus mutates very quickly. It was originally a low-pathogenic avian influenza virus strain (H5N2, H7N7, H9N2), but it can become a highly pathogenic strain (H5N1) after 6–9 months of rapid mutation among poultry. Peng et al. investigated whether a host was infected with avian influenza by identifying the content of antiviral antibodies in chicken serum [22]. In the same year, Manzoor and others developed antibodies against a single subtype of virus and successfully established a test strip detection technique for the H7 subtype virus [23]. After that, researchers began developing specific detection test strips. To date, test strip detection technology has been effectively established for the three subtypes of avian influenza viruses [24,25,26]. The development process of the foot-and-mouth disease viral test strip detection method is similar to that of the avian influenza virus. Intially, researchers established a test strip detection method to detect viruses of four common serotypes (A, O, C, and Asia-1) using a universal antibody against the virus. With advances in technology, four virus-specific detection methods have also been developed [27,28]. A large-scale epidemic occurred in South Africa in 2019, with SAT1, 2, and 3 being the three most important serotypes. Subsequently, a test-strip detection method for the three South African viruses was established by researchers [29,30]. To date, specific test-strip detection methods have been established for all seven main serotypes of foot-and-mouth disease virus [31]. In addition to these two viruses, test strip detection methods for norovirus [32], rotavirus [33], and adenovirus [13,33] have also been initially established.

3.1.2. Food-Borne Pathogens

Food-borne pathogens are also a major cause of food poisoning. Salmonella sp., L. monocytogens, E. coli, and S. aureus are the most common pathogenic bacteria responsible for the greatest number of food safety incidents. There are many serotypes of Salmonella sp., so the test strip detection method is mostly broad-spectrum [40]. By optimizing the signal label of the test strip, a highly-sensitive and specific test strip has been developed, and the detection limit can reach 30.0 CFU/mL [34]. Initially, Listeria sp. test strip detection did not deviate from the field of nucleic acid detection [32]. With advancements in nanomaterial technology, modified Fe₃O₄ became the first option. Finally, the detection limit was decreased to 10.0 cells/mL by modifying iron nanoparticles with malt agglutinin as a modification material [33]. At the same time, technologies to identify pathogenic E. coli and S. aureus in food have been developed. Among these, two extremely dangerous bacteria are E. coli O157 and S. aureus [33,37,38]. Many researchers have developed particular detection test strips for these two pathogenic bacteria that may be applied to a variety of meals, including meat, vegetables, and milk.

With continuous improvements in test strip detection technology, detection methods for other common pathogenic bacteria have also been established, such as V. parahaemolyticus [38] and Campylobacter jejuni [39]. Today, test strip detection technology is widely used in detecting food pathogens.

3.2. Biotoxins

Biotoxins are divided into microbial toxins, plant toxins, animal toxins, and marine biotoxins according to the biological classification of toxins. Microbial toxins are divided into bacterial toxins and mycotoxins, both of which are exotoxins produced by the metabolism of microorganisms that contaminate food. The majority of pathogenic bacteria create protein toxins with immunological activity, which belong to the category that can be detected by the test strip method. Toxoid antibodies are obtained by immunization with pure toxins as the main immunogen (Table 2). S. aureus produces a variety of enterotoxins (SEs), the most challenging of which is SEB; hence, research into the test paper detection technique focuses on detecting the SEB host [41,42,43,44]. However, some forms of enterotoxins are more hazardous than SEB. For this reason, some researchers have established a general-purpose enterotoxin detection method to meet different detection requirements [41,42]. Botulinum toxin, a secondary metabolite of Clostridium botulinum, is extremely lethal. Many people have died from consuming botulinum toxin-containing folate. At present, the test-strip method uses quantum dots as the signal substance for detection [43], the limit of detection (LOD) in the real sample can reach 2.5 ng/mL [44], and the strip allows simultaneous measurement of multiple types of botulinum toxin [45].

Mycotoxins, such as bacterial toxins, are produced during the metabolism of fungi that have contaminated food. Unlike bacterial toxins, mycotoxins are mostly small molecular molecules. Aflatoxin and its metabolites, as well as zearalenone, deoxynivalenol, and ochratoxin A, which are produced by Aspergillus flavus, Fusarium, Aspergillus, and Penicillium, are the most common fungal contaminants in grain. These toxins are highly carcinogenic. Initially, the test strip detection method for these toxins used colloidal gold as the signal label [46,47,48,49,50,51], and it was applicable to food sample testing but not for other food testing. Consequently, the signal label was changed from colloidal gold to fluorescent microspheres, which effectively improved the detection of fungi [52,53,54], as well as the detection accuracy and breadth of toxin detection of the test strips. In addition, all positive samples, when the test strip detection method was established, were quantitatively tested using multi-instrument methods such as liquid mass spectrometry. Now these test strips have been successfully commercialized and are widely used for agricultural product safety testing.

Microbial toxins in food are produced by the metabolism of foreign, contaminated microorganisms. This process can be controlled and eliminated by improving the storage environment. However, some toxins exist in the food itself, among which ricin, abrin, and tetrodotoxin are three representative deadly toxins. The construction process of detection methods for these three toxins is similar to that of bacterial toxins. Both use pure toxins as the immunogens to prepare sensitive antibodies, and then colloidal gold as the signal molecules to establish detection test strips [55,56]. When the test strip fails to meet the detection requirements, the detection sensitivity can be improved by changing the signal molecule [57,58,59,60]. Test strips for the three toxins are currently accessible online. In addition, some other test strip detection methods for animal and plant toxins that have been responsible for food safety accidents have also been established, such as aconitine [61] and okadaic acid [56]. The test results of these test strips are consistent with the instrument test results.

Table 2. The review of test strip-based immunoassay analysis methods for food-borne biotoxin.

Types of Biotoxin	Analyte	Signal Label	Sample	LOD	Reference
Bacteriotoxin	SEB	Gold nanoparticles	Milk/honey	0.3 ng/mL/0.5 ng/mL	[62]
	SEA, B, C, D, and E	Gold nanoparticles	Milk	For SEA, B and C were 2.5 ng/mL; for SED it was 1.0 ng/mL; and for SDE it was 5.0 ng/mL	[41]
	SEG, H, and I	Lanthanide-ion-based nanoparticle	Buffer solution	For SEG it was 20.0 pg/mL; for SDH, it was 30.0 pg/mL, for SEI, it was 10.0 pg/mL	[42]
	BoNT A, B, and E	Magnetic nanolabels	Milk, apple and orange juices	For BoNT/A, it was 0.2 ng/mL, for BoNT/A, it was 0.1 ng/mL, and for BoNT/E 0.4 ng/mL	[45]
Fungaltoxin	AFB1 and its metabolites	Gold nanoparticles	Corn	For B1, it was 2.0 ng/mL	[46]
		Gold nanoparticles	Tea	For B1, it was 50.0 pg/mL	[47]
		Fluorescent microspheres	Milk	For M1, it was 18.0 pg/mL	[54]
	ZEN and its metabolites	Gold nanoparticles	Rice and corn	10.0 ng/mL	[50]
		Gold nanoparticles	Corn flour	For ZEN, α-ZAL, and β-ZAL was 50.0 ng/mL, for α-ZOL, β-ZOL, and ZEA was 75.0 ng/mL	[51]
	ricin	SiO2@Au nanoparticles	Origin, apple juice and milk	0.1 ng/mL	[58]
Marine biotoxins	Aconitine	Gold nanoparticles	Real sample	20.0 ng/mL	[55]
	Tetrodotoxin	Fluorescence	Puffer fish	0.8 ng/mL	[60]
	Okadaic acid	Gold nanoparticles	Clams	15.0 ng/mL	[56]

Note: SEA, B, C, D, E, H and I is Staphylococcal enterotoxins A, B, C, D, E, H and I, respectively; BoNT A, B, and E is botulinum neurotoxin A, B and E respectively; AFB1 is aflatoxin B1; ZEN is zearalenone, α-ZAL and β-ZAL is α-zearalanol and β-zearalanol, α-ZOL and β-ZOL is α-zearalenol and β-zearalenol, ZEA is zearalanone, respectively.

Table 2. The review of test strip-based immunoassay analysis methods for food-borne biotoxin.

Types of Biotoxin	Analyte	Signal Label	Sample	LOD	Reference
Bacteriotoxin	SEB	Gold nanoparticles	Milk/honey	0.3 ng/mL/0.5 ng/mL	[62]
	SEA, B, C, D, and E	Gold nanoparticles	Milk	For SEA, B and C were 2.5 ng/mL; for SED it was 1.0 ng/mL; and for SDE it was 5.0 ng/mL	[41]
	SEG, H, and I	Lanthanide-ion-based nanoparticle	Buffer solution	For SEG it was 20.0 pg/mL; for SDH, it was 30.0 pg/mL, for SEI, it was 10.0 pg/mL	[42]
	BoNT A, B, and E	Magnetic nanolabels	Milk, apple and orange juices	For BoNT/A, it was 0.2 ng/mL, for BoNT/A, it was 0.1 ng/mL, and for BoNT/E 0.4 ng/mL	[45]
Fungaltoxin	AFB1 and its metabolites	Gold nanoparticles	Corn	For B1, it was 2.0 ng/mL	[46]
		Gold nanoparticles	Tea	For B1, it was 50.0 pg/mL	[47]
		Fluorescent microspheres	Milk	For M1, it was 18.0 pg/mL	[54]
	ZEN and its metabolites	Gold nanoparticles	Rice and corn	10.0 ng/mL	[50]
		Gold nanoparticles	Corn flour	For ZEN, α-ZAL, and β-ZAL was 50.0 ng/mL, for α-ZOL, β-ZOL, and ZEA was 75.0 ng/mL	[51]
	ricin	SiO2@Au nanoparticles	Origin, apple juice and milk	0.1 ng/mL	[58]
Marine biotoxins	Aconitine	Gold nanoparticles	Real sample	20.0 ng/mL	[55]
	Tetrodotoxin	Fluorescence	Puffer fish	0.8 ng/mL	[60]
	Okadaic acid	Gold nanoparticles	Clams	15.0 ng/mL	[56]

Note: SEA, B, C, D, E, H and I is Staphylococcal enterotoxins A, B, C, D, E, H and I, respectively; BoNT A, B, and E is botulinum neurotoxin A, B and E respectively; AFB1 is aflatoxin B1; ZEN is zearalenone, α-ZAL and β-ZAL is α-zearalanol and β-zearalanol, α-ZOL and β-ZOL is α-zearalenol and β-zearalenol, ZEA is zearalanone, respectively.

3.3. Allergens

There is an enormous variety of foods, and the ingredients are countless. In addition to the toxic substances listed above, there are numerous non-toxic substances that can trigger allergic responses in humans. These substances are known as allergens in food. Peanut allergy is the most common allergen among food-induced allergic events. As shown in

Table 3. The review of test strip-based immunoassay analysis methods for food-borne allergens.

Source of Allergen	Analyte	Signal Label	Sample	LOD	Reference
Shellfish	Shrimp tropomyosin	Quantum-dot	Food matrix, including fish, soybean paste, fine dried noodles, and spice blends	0.5 μg/mL	[65]
		Gold nanoparticles	Fish balls	0.5 ng/mL	[66]
Milk	Casein	Silver nanoparticles	Biscuit	Qualitative	[67]
Eggs	Ovalbumin	Silver nanoparticles	Biscuit	Qualitative	[67]
Nut	Peanut Ara h1	Gold nanoparticles	Peanut	0.3 ng/mL	[63]
	Peanut Ara h2	Gold nanoparticles	Peanut	1.0 ng/mL	[64]
	Hazelnut allergenic proteins	Silver nanoparticles	Biscuit	Qualitative	[67]
Herbaceous plant	Cesame allergen	Fluorescence	Bread, ham, and biscuits	For biscuits, it was 320.0 ng/mL, for bread and ham, it was 640.0 ng/mL	[12]

, the primary peanut allergens are Ara h1 and Ara h2, which can cause facial edema, oral ulcers, and skin wheals when ingested by mistake. In severe cases, acute laryngeal edema can occur, leading to asphyxia. The test-strip detection technology for these two proteins was fully established as early as 2015 [63,64], and it has been commercially marketed. Crustacean seafood, such as peanuts, is also a common allergen. These seafoods contain shrimp tropomyosin, which is the main protein that causes allergic responses. A test strip detection method for this protein has been successfully constructed and can be applied to a variety of detection matrices [65,66]. Compared with adults, children’s physical development is incomplete, and they are more susceptible to allergic responses caused by external stimuli. Ovalbumin and casein, derived from eggs and milk, are the main allergens that trigger allergy responses in children. At present, test strip detection methods for these two proteins are in their early stages, with only qualitative detection possible [67]. The detection methods of some other nut and herbal allergens have also been studied by researchers, such as hazelnut allergenic proteins [67] and sesame allergens [12]. This shows that the test strip detection method has begun to be gradually applied in this field and has great potential for development.

3.4. Illegal Additives

In addition to toxic substances naturally present in food, toxic substances are occasionally artificially added by some unscrupulous businesses in order to increase profits, but these can also induce food poisoning or raise the risk of human disease. These substances are collectively referred to as illegal food additives. The types and addition standards of these substances vary according to national policies. The classification and statistics of illegal food additives mentioned in this article are based on the “Inedible Substances and Food Additives That May Be Added Illegally in Food” issued by China in 2020. List (First Batch)” shall prevail. The illegal substances appearing on the list mainly fall into three categories: food additives, pesticides, and antibiotics.

Food additives are indispensable ingredients in the modern food industry since they effectively improve food quality and prolong the shelf life of food. Synthetic pigments, such as Sudan red and malachite green, improve the color brightness of food. As shown in

Table 4. The review of test strip-based immunoassay analysis methods for illegal addition in food.

The Type of Illegal Addition	Analyte	Signal Label	Sample	LOD	Reference
Pigment	Sudan red I	Gold nanoparticles	Tomato sauce and chili powder	10.0 ng/mL	[68]
	Malachite green	Gold nanoparticles	Fish	0.4 ng/mL	[69]
		Gold nanoparticles	Aquaculture water, fish, and shrimp	47.0 ng/mL in aquaculture water, 82.8 ng/mL in fish, and 152.4 ng/mL in shrimp	[70]
Nitrogen-containing compound	Melamine	Colloidal selenium	Milk, milk powder, and animal feed	150.0 ng/mL in milk, 1000.0 ng/mL in milk powder, and 800.0 ng/mL in animal feed	[75]
		Gold nanoparticles	Milk	1.4 ng/mL	[76]
		Gold nanoparticles	Milk	10.0 ng/mL	[77]
		Silver nanoparticles	Milk and animal feed	0.8 ng/mL in milk and 0.9 ng/mL in animal feed	[78]
β-receptor agonists	Clenbuterol, ractopamine, and salbuterol	Fluorescent	Animal urine and tissue	For Clenbuterol and ractopamine, were 0.1 ng/mL, salbuterol was 0.1 ng/mL	[74]
	Clenbuterol	Prussian blue nanoparticles	Pork, pork kidneys and bacon	1.0 ng/mL	[71]
	Clenbuterol	Gold nanoparticles	Milk, pork tenderloin, and swine liver	2.0 ng/mL	[11]
	Clenbuterol	Gold nanoparticles	Pork, chicken, and sausage	0.5 ng/mL	[6]
	Clenbuterol	Nanozymes	Pork and chicken	0.2 ng/mL	[72]
	Clenbuterol	Photothermal	Chicken and skim milk	0.3 ng/mL	[73]

, at present, colloidal gold test strips are the main method for detecting these two substances, with LODs of 10.0 ng/mL and 0.5 ng/mL, respectively [68,69,70]. Clenbuterol is used to suppress fat production in animals and increase meat yield, but excessive usage can damage the human body. Currently, clenbuterol is considered the most toxic drug in the clenbuterol class. The colloidal gold test strip detection method uses different sensitive monoclonal antibodies to reduce its LOD from 2.0 ng/mL to 0.5 ng/mL [6,11,71], and by changing the signal molecule, the LOD was eventually reduced to 0.2 ng/mL [72,73]. A fluorescent test strip detection method was also established for the multi-residual detection of this type of drug [74]. In addition to these additives used to change the properties of food, there are also illegal additives used to pass off material ingredients. Melamine is the most well-known, and it is commonly used in milk to pass off as a protein. Because most of the samples tested are milk, all metal nanoparticles are the first choice for the signal molecules in the test strip detection method. By changing from selenium [75] to gold [76,77], and finally to silver [78], the LOD of the final test strip detection method was reduced by 1.0 ng/mL. It is fully applicable to the detection of melamine in food.

3.5. Pesticide Residues

In the food industry, the supply of raw materials is typically the least profitable part, and increasing the production rate has become the most effective method of profit. The emergence of pesticides has greatly increased the output of fruits and vegetables, leading to the large-scale abuse of these two types of drugs. Organophosphorus pesticides, in particular, have always been at the top of the global pesticide sales list because of their low prices and good effects. However, most organophosphorus pesticides are highly toxic and have been banned in many countries. As shown in

Table 5. A review of test strip-based immunoassay analysis methods for residues of pesticides in food.

The Type of the Pesticide	Analyte	Signal Label	Sample	LOD	Reference
Organophosphorus	Methyl parathion	Luminol-reduced gold nanoparticles	Gardenia and folium moris	0.2 ng/mL	[82]
	Parathion, Parathion-methyl, and Fenitrothion	Up-converting nanoparticles	Fruit, vegetables, and water	3.4 to 12.5 ng/mL	[79]
	Organophosphorus	Fluorescence	Baby cabbage and rape	0.5 ng/mL	[80]
	Methyl parathion and triazophos	Gold nanoparticles	Pears, apples, cucumbers, and lettuces	For methyl parathion, it was 2.2 ng/mL and for triazophos, it was 4.2 ng/mL	[81]

, the test strip detection method has been widely applied to the detection of pesticide residues. Now it can detect a variety of organophosphorus pesticides, and can perform the mixed rapid detection of various types [79,80,81]. Furthermore, these test results are consistent with the instrumental test results, although the stability is slightly lower.

3.6. Veterinary Drug Residues

Veterinary drug residues mostly refer to the residues of antibiotics found in animal meat and by-products. The most prevalent antibiotics are quinolones, tetracyclines, sulfonamides, and β-lactams (

Table 6. A review of test strip-based immunoassay analysis methods for residues of antibiotics in food.

The Type of the Antibiotics	Analyte	Signal Label	Sample	LOD	Reference
Quinolones	Enrofloxacin and ofloxacin	Gold nanoparticles	Chicken muscle, and pork	10.0 ng/mL	[83]
	18 quinolones	Latex beads	Milk	2.0 ng/mL	[86]
	General Construction	Fluorescence	Milk	27.6 ng/mL	[87]
	Enrofloxacin	Dyed polymer microspheres and quantum dots	Animal tissue and milk	5.0 ng/mL in animal tissue, 10.0 ng/mL in milk	[84]
	6 tetracyclines	Latex beads	Milk	1.6 ng/mL	[86]
	Tetracycline	4-aminothiophenol modified gold nanostars	Buffer solution	40.0 pg/mL	[85]
	Tetracyclines	Quantum dot microsphere	Honey	0.4–4.0 ng/mL	[88]
Sulfonamides	27 sulfonamides	Gold nanoparticles	Honey and pork liver	<10.0 ng/mL	[18]
	27 sulfonamides	Gold nanoparticles	Honey	<10.0 ng/mL	[89]
	12 sulfonamide	Latex beads	Milk	0.3 ng/mL	[86]
	Sulfonamides	Quantum dot microsphere	Honey	0.4 ng/mL	[88]
	General Construction	Fluorescence	Milk	46.7 ng/mL	[87]
β-lactams	33 β-lactams	Gold nanoparticles	Milk	Qualitative	[19]

). The original test strip detection method detects one or more representative drugs in each class of antibiotics [83,84,85]. However, with the increase in the availability of new drugs, this method has gradually been eliminated. Therefore, some researchers exploited the mother nucleus structure of each class of antibiotics as an immunogen to prepare antibodies and established a test-strip detection method that could identify all drugs in a particular class of antibiotics [18,86,87,88,89]. However, this method has several limitations, and individual LODs for drugs with large structural variances cannot fulfill the practical requirements compared with instrument detection methods. Therefore, some researchers have proposed that by using antibiotic-binding receptor proteins as competitive binding substances to create test strips, the accuracy and breadth of detection can be effectively improved [19]. This is the future research direction for antibiotic test strip detection.

4. Summary and Outlook

LFIA technology has been widely used to detect hazardous substances in food, including pathogens, biotoxins, allergens, illegal additives, and so on, due to its advantages of high specificity, sensitivity, and rapid detection, as well as the fact that qualitative and semi-quantitative detection of harmful substances in food can be accomplished without the use of professional operators and expensive instruments. However, the LFIA also has obvious drawbacks. First, it cannot be applied to all food hazards, especially those with simple structures. For example, no detection method for arsenic has yet been established. Second, the establishment process of this method involves the preparation and synthesis of a specific antibody, nanoparticles, nitrocellulose membranes, and so on. All these factors may have an unknown impact on the test results. To address these drawbacks, several researchers have optimized and improved the detection method from multiple perspectives, including but not limited to changing the labeling material, optimizing the relative parameters, and combining computer algorithms. These technical enhancements have significantly improved the sensitivity and stability of test strip detection. Third, although some researchers have established a multi-substance detection platform that can quickly detect a wide range of harmful substances in various foods, it still remains low-throughput and cannot perform quantitative analysis on a par with classical instrumental methods. Thus, achieving accurate quantification and high throughput are still the future development targets of LFIA.

With the development of artificial intelligence recognition and internet technologies, the reliable quantification of m-LFIA will be realized. Combined with internet technology, real-time detection, display, and sharing of data will greatly improve the pertinence and effectiveness of government regulation. In brief, LFIA technology is set to play a greater role in food safety monitoring.