# Towards fast, sensitive, and selective viral detection methods in food/water: a systematic review

Samy de Oliveira, Elias Bou-Maroun, Perrine Mas, Alexis de Rougemont, Stéphane Guyot

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## To cite this version:

Samy de Oliveira, Elias Bou-Maroun, Perrine Mas, Alexis de Rougemont, Stéphane Guyot. Towards fast, sensitive, and selective viral detection methods in food/water: a systematic review. Trends in Food Science and Technology, 2025, 162, 10.1016/j.tifs.2025.105111. hal-05136370

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Submitted on 30 Jun 2025

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# Towards fast, sensitive, and selective viral detection methods in food/water: a systematic review

Samy De Oliveira<sup>a</sup>, Elias Bou-Maroun<sup>a, *</sup> <sup>□</sup>, Perrine Mas<sup>a</sup>, Alexis de Rougemont<sup>b</sup>, Stéphane Guyot<sup>a, **</sup> <sup>□</sup>

## ARTICLE INFO

**Keywords:**

Viral detection

Food safety

Water

Detection methods

Fast method

## ABSTRACT

**Background:** Viral detection in food and water is a major challenge for food safety and public health. Foodborne viruses such as noroviruses and hepatitis viruses are responsible for significant outbreaks worldwide. Reliable and rapid detection methods are essential for monitoring contamination and preventing infections.

**Scope and approach:** This systematic review evaluates the effectiveness of viral detection techniques, including genetic, immunoassay-based, optical, sequencing, and biosensor-based methods. The PRISMA 2020 statement was followed for article selection. The techniques were assessed based on trueness, precision, accuracy, and selectivity to compare their performance in food and water matrices.

**Key findings and conclusion:** Genetic methods, particularly RT-qPCR, remain the most reliable approach due to their sensitivity and standardized protocols. Biosensors show potential for rapid viral detection but require further validation for complex matrices. Next-generation sequencing (NGS) provides valuable insights but is costly and not widely adopted for routine analysis. Immunoassays offer an alternative but face limitations in food applications. Optical and emerging techniques such as molecularly imprinted polymers and aptamers present promising avenues for improving speed and selectivity. Overall, while RT-qPCR is the current gold standard, advances in biosensors and alternative techniques may enable faster, more selective, and cost-effective viral detection in the future. Further research is needed to optimize these technologies for real-world food safety applications.

# 1. Introduction

## 1.1. General context

Microorganisms in food encompass a wide range of entities, from those providing nutritional or sensory benefits to harmful or even lethal ones. Food's conducive nature makes it a target for various life forms, including foodborne pathogens that can cause human diseases. These microorganisms trigger numerous outbreaks annually, demanding extensive food safety measures. Without such precautions, public health consequences would be severe, leading to widespread infections, over-burdened healthcare systems, and significant fatalities.

The monitoring of foodborne pathogens allows for the identification of various organisms, including bacteria, viruses, protozoan or meta-zoan parasites, fungi, yeasts, and so forth. Bacteria are the primary

culprits due to their abundance, with the genera *Salmonella* and *Campylobacter* being well-known examples. Respectively, these two accounted for 2.3 and 13.6 % of human cases of illness for 2021–2022 in Europe (CDCP, 2017; EFSA/ECDC, 2023). While less prevalent than bacteria, viruses are nonetheless a significant concern. Among these pathogens, *Human norovirus (HuNoV)* and hepatitis viruses are of particular significance.

All viruses consist of at least two basic components: genetic material (DNA or RNA) enclosed in a protein coat, the capsid. Some also have an envelope, a double-layered lipid membrane derived from host cell membranes (nucleus, Golgi apparatus, endoplasmic reticulum, or cytoplasmic membranes) during budding. Extremely small, most foodborne viruses measure 20–40 nm (Koopmans et al., 2002; Zuckerman, 1996). As obligate parasites, they rely entirely on host cells for genome replication, protein synthesis, and assembly.

<footnote><sup>a</sup> UMR PAM, Institut Agro, Université Bourgogne Europe, INRAE, F-21000, Dijon, France</footnote>

<footnote><sup>b</sup> National Reference Centre for Viral Gastroenteritis, Laboratory of Virology, University Hospital of Dijon, Dijon, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author.</footnote>

<footnote>E-mail addresses: elias.bou-maroun@agrosupdijon.fr, elias.bou-maroun@institut-agro.fr (E. Bou-Maroun), stephane.guyot@agrosupdijon.fr (S. Guyot).</footnote>
Monitoring viruses presents significant challenges. Firstly, viruses are much smaller than bacteria, which typically measure a few micrometers. Secondly, unlike bacteria, which develop autonomously, viruses require specific conditions for replication, including the presence of permissive host cells that express appropriate receptors and intracellular machinery to support viral entry, genome replication, and assembly. Additionally, the high mutation rates of RNA viruses (Duffy, 2018) contribute to their vast genetic diversity, forming quasispecies (populations with extensive variability due to rapid mutations). This diversity complicates detection, as diagnostic tools may not recognize all variants within a quasispecies cluster. Therefore, detection methods must remain adaptable to account for viral genetic variability and maintain effectiveness (Peck & Lauring, 2018).

The Joint Expert Microbiological Risk Assessment (JEMRA) provided by both the World Health Organization (WHO), and the Food and Agriculture Organization of the United Nations (FAO) have identified a few foodborne viruses as particularly concerning (FAO/WHO, 2023). These include HuNoVs, hepatitis A (HAV) and hepatitis E (HEV) viruses which were respectively responsible for 15 %, 0.15 % and 0.02 % of cases in Europe (EFSA/ECDC, 2023). For HuNoVs and HAV, the concerned matrices were identified as shellfish, fresh and frozen produce, and prepared and ready-to-eat foods. It can be argued that water should be included on this list, given that the European Food Safety Authority (EFSA) has indicated that it is a vehicle for the HuNoVs. For HEV, the matrices targeted are pork and wild games (Amarasiri & Sano, 2019; Fanelli et al., 2022).

The minimal infectious dose (MID) is a critical parameter in assessing viral risk in food safety. Among foodborne viruses, human norovirus is considered the most infectious, with an MID estimated between 10 and 100 viral particles, based on human volunteer studies. In comparison, hepatitis A virus (HAV) requires a significantly higher infectious dose, typically ranging from $10^3$ to $10^6$ particles, although infections have been reported with doses as low as $10^2$ in some contexts (Teunis et al., 2020; Yezli & Otter, 2011). For hepatitis E virus (HEV), which is increasingly recognized as a foodborne pathogen, the MID is less well defined but is estimated between $10^4$ and $10^6$ viral particles, depending on genotype, matrix, and host factors (Capelli et al., 2020).

The goal of this review was to collect, compare and discuss current data obtained from various papers for the detection in food/water matrices of foodborne viruses such as the three mentioned earlier. Many methods are known to have some abilities in the identification, detection and/or quantification of these pathogens, but this work aims to rate these techniques based on sensitivity and selectivity when confronted with complex matrices. Selected parameters, targeted methods and the evaluation criteria will be presented in the next sections.

## 1.2. Defining the viral detection in food matrices and drinking water

Before discussing methodologies for detecting viruses in food and water, it is crucial to define relevant terminology to clarify the challenges of identifying such small particles.

A detection method is an analytical test designed to determine the presence or absence of a compound or element in a sample. While essential, it is only one step in a broader analytical process. A sampling strategy must be established beforehand, and results must be interpreted afterward. Both sample collection and interpretation are key to evaluating a detection method's performance.

The term “sampling” is defined as a procedure where a part of a substance, material, or product is taken for testing or calibration to provide a representative sample of the whole (ISO/IEC, 2017). Ensuring food quality requires obtaining representative samples before analysis. Viruses can aggregate (Pradhan et al., 2022), leading to uneven distribution in water and food matrices. Additionally, enveloped viruses are generally more prone to environmental degradation due to the fragility of their lipid envelope (Firquet et al., 2015), although their persistence may vary depending on specific conditions (French et al., 2023). This

makes proper sampling crucial for preserving a significant portion of these agents.

Detection methods use specific instruments and tools to generate a signal, targeting either the entire virus or specific components, such as protein patterns or genetic sequences, depending on the detection principle.

Finally, interpretation must be supported by appropriate statistical analysis. Biological samples exhibit variability, which rigorous statistical treatment helps mitigate. At this stage, the method's limit of detection (LoD), sensitivity, specificity, selectivity, precision, and accuracy can be assessed. Interpretation also depends on the limitations of previous steps; for instance, data may refer only to infectious viruses.

Particular attention is given to the vocabulary used in evaluating viral detection efficiency. This section clarifies common misunderstandings in analytical terminology and the methodology for assessing key analytical chemistry criteria. Fig. 1 outlines the overall procedure for determining evaluation parameters.

i) Sensitivity: In analytical settings, sensitivity refers to the smallest detectable concentration variation in a sample. It correlates with the slope of a linear regression in calibration. Though not identical, the limit of detection is related to sensitivity, representing the lowest concentration distinguishable from baseline noise. (Saah & Hoover, 1997).

ii) Selectivity/Specificity: in the same settings, the specificity of a method is defined as its ability to identify a specific target substance or organism and not similar ones (Saah & Hoover, 1997). In contrast, selectivity refers to the capacity of a method to identify a particular analyte in a complex mixture without interference from other components (Vessman et al., 2001). While the two terms are used interchangeably, the use of the term 'selectivity' is recommended according to the International Union of Pure and Applied Chemistry (IUPAC) guidelines.

iii) Accuracy: In the latest version of the ISO 5725-1 standard, this term is used to describe both the trueness and precision of a method. Trueness can be defined as "the closeness of agreement between the arithmetic mean of a large number of test results and the true or accepted reference value." In contrast, precision can be defined as "the closeness of agreement between test results obtained under stipulated conditions."

In consideration of these factors, quantitative analysis can be expressed in various units of measurement, depending on the target. The sensitivity of a methodology for detecting viruses should be expressed in terms of viral copies per unit of volume or a comparable metric. One of these units is the plaque-forming unit (PFU), commonly employed to determine the sensitivity of a method by counting infectious viruses (Baer & Kehn-Hall, 2014). Genome copies also allow for quantification under specific settings. The microbiological and virological context is addressed by ISO 15216-1, which provides a harmonized framework for the quantification of human norovirus (HuNoV) and hepatitis A virus (HAV) in food matrices using RT-qPCR. This standard also introduces key concepts such as recovery rate correction and internal process controls, which are critical when interpreting quantitative results in complex food matrices. For techniques that are employed to detect viral antigens or components, the sensitivity may also be expressed in units of mass or molar concentration. It should be noted that the LOQ is also described in ISO 11843 as a function of statistical parameters such as signal-to-noise ratio or the standard deviation over the slope of the calibration curve ($\sigma/S$).

The selectivity of a method is often challenging to assess in experiments. It is unfortunately rarely conceptualized in analytical chemistry textbooks, and it demands a significant amount of effort in practice to evaluate, particularly in complex matrices such as food. Some agencies, such as IUPAC, proposed the utilization of blank matrices derived from a minimum of six independent sources. However, most studies evaluating
<figure><img src="image_5.png" /><figcaption>Fig. 1. Global procedure for the determination of the evaluation parameters. 76 articles retained after applying PRISMA-based screening criteria on a total of 130 articles, as detailed in the Materials and Methods section and Fig. 2. Publication date range: 2015–2025.</figcaption></figure>

viral detection in complex matrices do not adhere to this recommendation.

In this study, selectivity will then be discussed aside, with each method receiving an appreciation grade for its selectivity on a scale of 1–5. One indicates that 20 % or less of articles using a specific method include a critical step of selectivity analysis. This includes the comparison of results with the appropriate blanks or other forms of controls to check the influence of other analytes present in the sample. The same rationale can be applied to determine the percentage range associated with each grade, which could be as follows: 20–40 %], [40–60 %], ] 60–80 %], [80–100 %] for 2, 3, 4 and 5, respectively. While this is a simplified version of more formal statistical models, this scoring approach allows for harmonized comparisons across studies with varying reporting practices, following principles used in quantitative data synthesis based on quantile intervals (Ozturk & Balakrishnan, 2020).

The accuracy of any method can be determined by employing stan-
dard evaluation techniques (such as those used to assess trueness with a
reference method), and by paying close attention to factors such as
repeatability and reproducibility (which are used to assess precision).
Another grade between 1 and 5 will be calculated for trueness and
precision individually, after which the mean between those will be
defined as the accuracy score.

1.3. Conventional viral detection methods

This section will provide a concise overview of the currently
employed detection techniques for foodborne viruses, outlining their
fundamental principles and the context in which they are applied. It will
present methods which are only conducted at the research level.

The most employed methods for the detection of food-borne viruses in the third millennium are genomic, facilitated by the extensive proliferation of polymerase chain reaction techniques (PCRs) (Bosch et al., 2018). The last Codex Alimentarius Committee acknowledges these methods, mainly due to the difficulty to culture enteric viruses (FAO/WHO, 2023). Genomic amplification necessitates the identification of specific sequences of nucleic acids and a method for extracting genomic material. The amplification process involves a series of cycles of heating and cooling, with 3 steps: the denaturation separating double DNA strand into single ones, the annealing with hybridization of specific primers and the elongation step in which the complementary strand is synthesized starting from the primer sequence. The final targeted

sequence is then replicated numerous times similarly upon the synthesis
of millions of copies, thereby facilitating its detection.

The quantification of numerous foodborne viruses, including HuNoVs and HAV, is conducted via real-time reverse transcription PCR (RT-qPCR). In addition to genome amplification, it employs specific reverse transcriptases to transcribe RNA into single-strand complementary DNA. Subsequently, while the amplification is being carried out, the quantity of amplified DNA is continuously monitored in real time using fluorescence detection at the end of each cycle. The fluorescence signal increases in proportion to the amount of amplified DNA, thereby enabling precise quantification of the initial RNA target so long as a calibration curve is validated. The method is highly sensitive and has been standardized (Cassedy et al., 2021; Watzinger et al., 2006; ISO, 2019).

Nevertheless, real-time RT-PCR is not without its own set of limitations. First, the technique employed for quantification of genome copies also includes the detection and enumeration of inactivated viral particles, making it impossible to assess their infectivity. Secondly, the method often requires several hours due to necessary preparation steps, particularly when applied to food matrices. Under optimal conditions, the reaction time may be reduced to less than 1 h. Finally, it requires proper installation, the costs of which may initially appear to be considerable, as it demands expertise and the significant use of reagents.

Next-Generation Sequencing (NGS) has transformed genomic analysis through its high-throughput sequencing capabilities. Millions of small reads of DNA are concurrently broken apart, amplified, and sequenced. The reconstruction of the original sequence is achieved by assembling the reads using bioinformatics methods. It encompasses a range of techniques that facilitate rapid and precise DNA sequencing, including Illumina, Ion Torrent, and PacBio. NGS offers insights into genetic variation, gene expression, and microbial diversity, with applications in genomics, transcriptomics, epigenomics, and metagenomics (Satam et al., 2023).

Furthermore, the use of immunoassay-based methods for the detec-
tion of viruses is also frequently addressed. For viral diagnostics or the
indiscriminate identification of all viral strains, they provide a well-
established and standardized alternative to RT-PCR. The method relies
on the use of antibodies, which can be monoclonal or polyclonal (based
on epitope specificity). Although it depends on the identification of
antigens produced by the host, its extensive use in determining sero-
prevalence is noteworthy. Nevertheless, the capacity of this technique to
identify viral antigens present in samples is of greater significance in the field of food viral detection (Cassedy et al., 2021).

Depending on the presentation of antibodies, antigens, and labels, immunoassays can take various forms (direct, indirect, competitive, sandwich, etc.). Once commercially available, antibodies are easily used in immunoassays due to their robust design and high sensitivity. However, these assays require time and can be challenging in food matrices due to viral cross-reactivity. The most widely used methods are enzyme immunoassay (EIA) and enzyme-linked immunosorbent assay (ELISA), particularly in medicine, where they have replaced radioactive labeling (Lequin, 2005). Yet, they are not always suitable for food matrices, as some compounds may interfere with antibody-antigen recognition. In food analysis, where both bacteria and viruses are common, bacterial binding to certain antibodies presents an additional challenge (Atkins et al., 2008).

Notably, Lateral Flow ImmunoAssay (LFIA) represents a diagnostic technique that allows for the rapid identification of target antigens in samples. The technique is well-suited for point-of-care testing of virus-associated antigens, as it employs immobilized antibodies on a strip to capture and visualize antigens, resulting in test lines that become apparent in a matter of minutes. The technique is highly resistant to sample matrix effects, although it does not achieve the sensitivity level of RT-PCR (Sajid et al., 2015).

NGS is a powerful tool for characterizing multiple viral genomes especially in clinical and environmental surveillance contexts. It can identify both known and unknown viruses by extracting and sequencing their nucleic acids, thereby facilitating investigations into epidemics. Its high-throughput nature and genomic resolution are particularly valuable for source attribution and understanding viral diversity. However, despite these advantages, NGS is rarely used as a standalone technique for detecting or quantifying viruses in complex food matrices. NGS can offer genomic characterization of foodborne viruses, which helps with risk assessment and source tracking (Ollivier et al., 2022) but turnaround times remain long, and the average cost of this technology makes it challenging for point-of-care testing (Barzon et al., 2013; Cassedy et al., 2021; Maree et al., 2018).

## 1.4. Innovative viral detection methods

Several challenges must be overcome to better prevent foodborne infections. While conventional viral detection methods offer standardized and sensitive solutions, faster and/or cheaper techniques are being investigated for food safety applications. Such methods will be briefly presented with their principle and their interest compared to conventional methods.

Recent efforts have focused on adapting molecular approaches to improve their usability in food testing environments. RT-LAMP is particularly attractive due to its rapid amplification under constant temperature, minimal equipment requirements, and robustness to sample inhibitors. It can also be combined with simple detection strategies such as turbidity, dyes, or lateral flow, facilitating its use directly on-site without complex preprocessing steps (Cassedy et al., 2021). Also, the real-time reverse transcription loop-mediated isothermal amplification bioluminescence assay (RT-LAMP-BART), which offers a simpler and more portable alternative to RT-qPCR. Although its amplification process is slower, it relies on isothermal amplification and bioluminescent detection via luciferin oxidation, enabling miniaturized, reagent-saving systems. The use of Bacillus stear thermophilus (Bst) DNA polymerase further enhances its tolerance to inhibitors (R. Wu et al., 2023).

Another notable development is digital droplet PCR (ddPCR), which partitions the sample into thousands of droplets and amplifies each one independently. Based on Poisson statistics, this method provides absolute quantification without calibration curves. Although not necessarily offering lower detection limits than RT-qPCR, ddPCR improves analytical precision and increases tolerance to inhibitory substances, which

can be particularly valuable in complex matrices (Kojabad et al., 2021).

Biosensor-based methods for the detection of pathogens have been catching the attention of researchers for several years. They consist of 3 different components that can take different forms. First, a bioreceptor (DNA, proteins, cells, antigens ...) that specifically interacts with the target analyte, then a transducer (electrochemical, optical, impedance ...) that characterizes the bioreceptor/analyte association, with a quantifiable signal measured by the last component, a detector. Two of the main advantages of biosensors over real-time RT-PCR are faster turnaround (typically minutes to 1 h) and long-term cost savings once developed (Kotsiri et al., 2022; Saylan et al., 2019).

Molecularly Imprinted Polymers are also emerging and act as highly specific biochemical sensors. They are formed using a targeted template, which forms the final MIPs, and complementary monomers, which are then polymerized around the template using crosslinking polymers. The choice of template and monomers, along with the specific solvent, allows the formation of a polymer with recognition sites whose conformation is complementary to molecules of the target analyte. MIPs can be associated with biosensor-based methods or aptamers to increase their selectivity and facilitate use in complex matrices (Ali & Omer, 2022; Cao et al., 2019).

Aptamers are short, single-stranded DNA or RNA molecules, selected from large random sequences for their ability to bind to specific target molecules with high affinity. They are used to replace traditional antibodies in targeting and recognizing specific analyte molecules. They are generated using Systematic, Evolution of Ligands through Exponential Enrichment (SELEX), through combinatorial libraries of random oligonucleotide sequences. As with Molecularly Imprinted Polymers (MIPs), their association with other methods can be considered to increase the selectivity in food matrices (Ali & Omer, 2022; Zou et al., 2019).

Surface-enhanced Raman spectroscopy (SERS) has emerged as a promising tool for the detection of viruses in food matrices (Neng et al. 2020; Pilot, 2018). SERS works by enhancing, by several orders of magnitude, the Raman scattering signal of molecules adsorbed on nanostructured metallic surfaces, typically silver or gold. This enhancement provides high sensitivity and selectivity for the detection of low concentrations of analytes, including viral particles.

One of the major advantages of SERS compared to traditional methods such as PCR is the rapid response time. It can deliver results in minutes, making it a valuable tool for monitoring food safety in real time. Additionally, the technique does not require extensive sample preparation or amplification steps, which reduces the risk of contamination and simplifies the detection process. This makes SERS a particularly attractive option for on-site testing in the food processing environment (Bernabé-Orts et al., 2022).

Hyperspectral Imaging (HSI) collects and processes data across the electromagnetic spectrum, providing detailed material composition and property analysis through spectral signatures (Lu et al., 2020). In virus detection, HSI differentiates viral particles by their spectral fingerprints, detecting subtle interactions with electromagnetic radiation over various wavelengths. This method shows promise for rapid, non-invasive virus detection, particularly in environmental monitoring and food safety. Its high sensitivity and selectivity enable early diagnosis and outbreak surveillance, facilitating swift intervention. HSI is still emerging, with spatial signatures potentially derived from infected plant structures or viral components (nucleic acids, protein coat, or capsid). Multivariate analysis then reduces data dimensionality (Gowen et al., 2007; Soni et al., 2022).

## 1.5. Sample preparation

Sample preparation is critical for detecting viruses in complex matrices like food and water, where targets are scarce and embedded in chemically diverse, inhibitor-rich environments. Detection success (via PCR, immunochromatography, NGS, biosensors, or spectroscopy) depends on efficient virus concentration, nucleic acid extraction, and
purification. Common methods like ultrafiltration and PEG precipitation help isolate targets but must also limit inhibitor co-extraction. Striking a balance between thorough purification (risking nucleic acid loss) and minimal processing (risking inhibition) is key to maintaining sensitivity and low LODs. Innovations like microfluidics and integrated platforms aim to streamline this step, but universally robust protocols are still lacking for routine use.

Poor sample preparation often leads to inhibitor co-purification, compromising sensitivity, especially for DNA polymerase-based methods. Inhibitors like humic acids, haemoglobin, and IgG can disrupt amplification (by interfering with polymerases, binding nucleic acids, or quenching fluorescence) causing quantification errors, high LODs, or false negatives (Sidstedt et al., 2020).

Digital PCR offers improved tolerance through partitioning and endpoint readouts but remains susceptible. Immunoassays also suffer from nonspecific binding and weak signals. In NGS, inhibitors reduce amplicon yield and bias early PCR steps (Cleveland et al., 2020; Elwick et al., 2018). Newer tools aren't exempt: biosensors may face non-specific adsorption (Lichtenberg et al., 2019), while SERS and hyperspectral imaging are vulnerable to matrix-induced signal interference (Cialla-May et al., 2024). Understanding these mechanisms is crucial for method optimization and data interpretation, particularly with low-copy samples. Countermeasures include inhibitor-tolerant polymerases, stable fluorescent chemistries, and internal amplification controls.

## 2. Materials & methods

This research followed the PRISMA 2020 statement. This statement, along with its checklist and flowchart, clarifies the research process and how to develop a systematic approach. It is a combination of a checklist and a flow diagram to follow for the writing process of systematic reviews.

### 2.1. Eligibility criteria

#### 2.1.1. Inclusion

This review is about the current state of the art of virus detection methods used in the food matrix or in water. Records were included if they specifically addressed detection methods for whole viruses or their antigens in water and complex matrices, excluding those for clinical or medical diagnostics. Here, "complex matrix" refers to any food or water sample whose physicochemical composition (high fat, protein, polysaccharide content, or particulates) may hinder virus extraction, concentration, or detection (Aguilera, 2019). Examples include shellfish, meats, dairy products, and wastewater.

Only peer-reviewed articles from 2015 to 2025 were included, except for RT-PCR studies, for which older publications were accepted due to its established status. Methods providing some detection results with at least 5 eligible articles found were selected. This number choice was decided after the idea of addressing scores from 1 to 5 to each technique.

#### 2.1.2. Exclusion

Articles were excluded if they provided only theoretical evidence or had incomplete and/or poorly defined protocols. Studies on non-water-borne and non-food-borne viruses were also omitted.

### 2.2. Search strategy

The two main priorities in the search for virus detection articles were targeted viruses and matrices. These priorities made sense for this review, which was designed to assess current improvements in detecting important viruses known to be harmful in water/food related environments. It also considerably limits the number of useable articles.

It is to be noted that the search strategy originally included only

drinking water matrices, but not enough data were collected. Therefore, some relevant articles on virus detection in laboratory-controlled water were included. Keywords were selected around a concept mapping from viruses and matrices, along with detection methods as more and more were included in this review. The use of different Boolean operators (e. g., "AND", "OR" ...) was also considered for refinement.

Keywords included: "virus detection methods", "food matrix", "foodborne virus", "waterborne virus", "Norovirus", "Hepatitis", "fruits", "oysters", "vegetables", "pig", "PCR", "immunochromatography", "optical detection methods", "electrochemical detection methods", "NGS" etc.

### 2.3. Selection process

Given the small number of articles selected prior to screening, no automation tools were used.

### 2.4. Screening results

Of the 130 articles collected, 37 were initially excluded. 10 reports were eligible for retrieval and 4 were not (i.e. 31 articles were removed because of misleading titles or insufficient focus on the target viruses/matrices).

Then, out of 95 records, 10 were not eligible because of incomplete/poorly defined protocols or results (unclear sample preparation, strange units ... etc). 13 records were excluded when publication date range was narrowed to ensure a more up-to-date evaluation. Finally, Fig. 2 summarizes each followed steps with a flow diagram of the PRISMA 2020 statement.

## 3. Results

The results of the analysis for the 76 selected studies in food and water were collected amongst the ten techniques presented earlier. Fig. 3 shows the number of studies for each technique based on the 2020 statement, with the majority of them being genomic-based or biosensors-based (44 articles accounting for 58 % of the papers).

In addition to the method, each article was considered based on which target viruses were detected and in which matrix. A record of each selected article is presented in Table 1, along with the corresponding limits of detection (LODs) and references. Limits were expressed using various units linked to the detection method.

HuNoV was the predominant target across a wide range of food matrices including shellfish, berries, leafy vegetables, meat and water sources. HAV and HEV were the next most frequently studied viruses in similar matrices.

Table 2 summarizes the lowest limits of detection (LOD) reported for HuNoV, HAV, and HEV across the selected studies, and compares them to their estimated minimal infectious doses (MID).

RT-qPCR consistently reached limits of detection (LODs) below 10 viral copies/g or mL for HuNoV and HAV in several studies, with the lowest values reported in shellfish and vegetable matrices. RT-ddPCR got at best similar results than RT-qPCR, with LODs typically below 200 copies/g, and in some cases down to ~10 viral particles/mL for HEV in water. For HAV, RT-LAMP assays achieved LODs under 10 PFU/µL, and electrochemical biosensors targeting HuNoV or HAV demonstrated LODs as low as 1.7 copies/mL or 10 fg/µL, respectively. Amongst the few MIP-coupled biosensor studies, the lowest LODs was below 10 pmol/L for HAV. Only RT-qPCR and RT-ddPCR registered LODs for the three foodborne viruses in complex matrix.

Selectivity, trueness, precision and accuracy of methods are evaluated independently. Each method received a score based on the proportion of articles that fulfill the corresponding criteria defined in section 1.2. The values for trueness, precision, accuracy and selectivity are shown in Table 3. Each technique scored a minimum of 3 in each category, showing their relevance for viral detection. NGS performed
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Completed PRISMA 2020 flow diagram of this review. Publication date range: 2015–2025. 31 records excluded (37 initially but 6 retrieved) for misleading titles or insufficient focus on the target viruses/matrices. 10 reports not eligible because of incomplete/poorly defined protocols or results. 13 reports excluded after narrowing the publication date range.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Number of articles selected per detection methods, including PCRs, immunoassays, new generation sequencing (NGS), biosensors, hyperspectral imaging and Raman spectroscopy.</figcaption></figure>

the best, as opposed to biosensors (and PCRs being close). Precision and selectivity scores were very high (4 for electrochemical biosensors, 5 for everything else). Trueness was the lowest score for every technique (NGS being at 5).

**4. Discussion**

As expected, all genomic methods performed well, from the classical RT-qPCR to its more recent counterparts such as ddPCR and isotherm
<caption>**Table 1**</caption>

Total record of each article. Targeted viruses and matrices as well as methods and their LODs are presented.

<table><thead><tr><th>Virus</th><th>Matrix</th><th>Method</th><th>Limit of detection</th><th>Reference</th></tr></thead><tbody><tr><td>HuNoV</td><td>Frozen strawberries</td><td>RT-qPCR</td><td>216 copies/25g</td><td>Bartsch et al. (2016)</td></tr><tr><td>HuNoV</td><td>Frozen strawberries</td><td>RT-ddPCR</td><td>185 cop/25g</td><td>Bartsch et al. (2018)</td></tr><tr><td>HuNoV</td><td>Frozen strawberries</td><td>NGS</td><td>N/A</td><td>Bartsch et al. (2018)</td></tr><tr><td>HuNoV GI</td><td>Oysters</td><td>RT-qPCR</td><td>32 cop/g</td><td>Persson et al. (2018)</td></tr><tr><td>HuNoV GII</td><td>Oysters</td><td>RT-qPCR</td><td>63 cop/g</td><td>Persson et al. (2018)</td></tr><tr><td>HAV</td><td>Food and clinical diverse samples</td><td>RT-ddPCR</td><td>111 cop/mL</td><td>Persson et al. (2021)</td></tr><tr><td>HuNoV GI &amp; GII/HAV/HEV</td><td>Multicomponent foodstuffs</td><td>RT-qPCR</td><td>102 cop/g</td><td>Hennechart-Collette et al. (2021)</td></tr><tr><td rowspan="3">Cucumber green mottle mosaic virus</td><td>Cucumber seeds</td><td>RT-qPCR</td><td>96 cop/g</td><td>Torre et al. (2020)</td></tr><tr><td>Melon seeds</td><td>RT-qPCR</td><td>740 cop/g</td><td>Torre et al. (2020)</td></tr><tr><td>Zucchini seeds</td><td>RT-qPCR</td><td>97 cop/g</td><td>Torre et al. (2020)</td></tr><tr><td rowspan="2">HuNoV GI &amp; GII/HAV</td><td>Shellfish</td><td>RT-qPCR</td><td>10 cop/g</td><td>Polo et al. (2015)</td></tr><tr><td>Pork raw sausages</td><td>RT-qPCR</td><td>2.93 * 10<sup>3</sup> cop/5g</td><td>Szabo et al. (2015)</td></tr><tr><td>HEV</td><td>Pork liver sausages</td><td>RT-qPCR</td><td>5.34 * 10<sup>4</sup> cop/2g</td><td>Szabo et al. (2015)</td></tr><tr><td>HEV</td><td>Bottled water</td><td>RT-ddPCR</td><td>1.4–7 cop/mL</td><td>Martin-Latil et al. (2012)</td></tr><tr><td>HEV</td><td>Tap water</td><td>RT-ddPCR</td><td>7 cop/mL</td><td>Pallerla et al. (2021)</td></tr><tr><td>HEV</td><td>Pork meat and liver</td><td>PCR</td><td>N/A</td><td>Pallerla et al. (2021)</td></tr><tr><td>HEV</td><td>Raw sausages</td><td>RT-qPCR</td><td>7.03 * 10<sup>3</sup> cop/g</td><td>Althof et al. (2019)</td></tr><tr><td>HEV</td><td>Liver sausages</td><td>RT-qPCR</td><td>3.2 * 10<sup>4</sup> cop/g</td><td>Althof et al. (2019)</td></tr><tr><td>HEV</td><td>Ready to eat meat</td><td>RT-qPCR</td><td>1.56 * 10<sup>2</sup> cop/g</td><td>Moor et al. (2018)</td></tr><tr><td>HEV</td><td>Meat, pork</td><td>RT-qPCR</td><td>1.56 * 10<sup>3</sup> cop/g</td><td>Moor et al. (2018)</td></tr><tr><td>HuNoV GI &amp; GII</td><td>Lettuce &amp; Grated carrots</td><td>RT-qPCR</td><td>10<sup>4</sup>–10<sup>6</sup> cop/g</td><td>Hennechart-Collette et al. (2019)</td></tr><tr><td>HAV</td><td>Mixutre of grated carrots/celery/ cabbage</td><td>RT-qPCR</td><td>10<sup>2</sup>–10<sup>3</sup> PFU/g</td><td>Hennechart-Collette et al. (2019)</td></tr><tr><td>HEV</td><td>Mussels</td><td>RT-qPCR</td><td>67 cop/g</td><td>Mesquita et al. (2016)</td></tr><tr><td>HAV</td><td>Digestive glands of bivalve shellfish</td><td>RT-qPCR</td><td>5.2 * 10<sup>6</sup> copies/µL</td><td>An et al. (2025)</td></tr><tr><td rowspan="2">8 vegetables- infecting viruses</td><td>Pepper, melon, tomato, lettuce seeds</td><td>RT-qPCR</td><td>N/A</td><td>Gumus and Paylan (2013)</td></tr><tr><td>Meat (beef, chicken, pork)</td><td>RT-qPCR nested PCR</td><td>N/A</td><td>Soares et al. (2022)</td></tr><tr><td rowspan="2">Adenovirus Rotavirus HEV</td><td>Frozen strawberries</td><td>RT-ddPCR</td><td>257 cop/25g</td><td>Bartsch et al. (2018)</td></tr><tr><td>Oysters</td><td>RT-ddPCR</td><td>251 cop/g</td><td>Persson et al. (2018)</td></tr><tr><td>HuNoV GI</td><td>Oysters</td><td>RT-ddPCR</td><td>200 cop/g</td><td>Persson et al. (2018)</td></tr><tr><td>HuNoV GII</td><td>Oysters</td><td>RT-qPCR magnetic nanoparticle</td><td>8.3 PFU/15g</td><td>Wu et al. (2022)</td></tr><tr><td>HAV</td><td>Green onions</td><td>RT-qPCR magnetic nanoparticle</td><td>83 PFU/50g</td><td>Wu et al. (2022)</td></tr><tr><td>HAV</td><td>Strawberries</td><td>RT-qPCR magnetic nanoparticle</td><td>8.3 PFU/5g</td><td>Wu et al. (2022)</td></tr><tr><td>HAV</td><td>Mussels</td><td>RT-qPCR magnetic nanoparticle</td><td>N/A</td><td>Bigoraj et al. (2020)</td></tr><tr><td>HEV gt3 ra</td><td>Rabbit liver/blood</td><td>RT-qPCR &amp; ELISA</td><td>8.3 PFU/15g</td><td>(R. Wu et al., 2023)</td></tr><tr><td>HAV</td><td>Green onions</td><td>RT-LAMP BART</td><td>83 PFU/50g</td><td>(R. Wu et al., 2023)</td></tr><tr><td>HAV</td><td>Strawberries</td><td>RT-LAMP BART</td><td>8.3 PFU/5g</td><td>(R. Wu et al., 2023)</td></tr><tr><td>HAV</td><td>Mussels</td><td>RT-LAMP BART</td><td>8.3 PFU/40 mL</td><td>(R. Wu et al., 2023)</td></tr><tr><td>HAV</td><td>Milk</td><td>RT-LAMP HNB, Cresol red and LFD</td><td>2.59 * 10<sup>6</sup> copies/µL</td><td>An et al. (2025)</td></tr><tr><td>Lettuce necrotic yellows virus</td><td>Lettuce</td><td>RT-LAMP</td><td>3.5 pg/mL</td><td>Zhang et al. (2020)</td></tr><tr><td>Cucumber mosaic virus</td><td>Lettuce</td><td>RT-LAMP</td><td>20 fg/mL</td><td>Zhang et al. (2020)</td></tr><tr><td>HEV</td><td>Pig meat, liver, blood</td><td>RT-qPCR</td><td>250-25000 cop/g</td><td>Geng et al. (2019)</td></tr><tr><td>HuNoV GI &amp; GII</td><td>Lettuce/cucumber/chicken</td><td>Nano-based immunoassay</td><td>10 PFU/mL</td><td>Alhadrami et al. (2020)</td></tr><tr><td>Henipavirus</td><td>Pigs</td><td>Indirect ELISA</td><td>N/A</td><td>Fischer et al. (2018)</td></tr><tr><td>Dengue virus</td><td>Water</td><td>Sandwich immunoassay (polyhydroxybutyrate fibers)</td><td>8 PFU/mL with the best coating</td><td>Hosseini et al. (2017)</td></tr><tr><td>Porcine circovirus type 2</td><td>Water</td><td>AuPt/SiO2 nanobased immunoassay</td><td>75 pg/mL</td><td>(L. Wu, Yin, et al., 2016)</td></tr><tr><td>Tree fruit viruses and viroids</td><td>Tree fruits</td><td>NGS</td><td>N/A</td><td>Rott et al. (2017)</td></tr><tr><td>HuNoV GI</td><td>Irrigation water</td><td>NGS</td><td>4.11 cop/L</td><td>Itarte et al. (2021)</td></tr><tr><td>HuNoV GI</td><td>Fresh produce</td><td>NGS</td><td>144 cop/25g</td><td>Itarte et al. (2021)</td></tr><tr><td>HuNoV GII</td><td>Irrigation water</td><td>NGS</td><td>29.60 cop/L</td><td>Itarte et al. (2021)</td></tr><tr><td>HuNoV GII</td><td>Fresh produce</td><td>NGS</td><td>1036 cop/25g</td><td>Itarte et al. (2021)</td></tr><tr><td>HEV</td><td>Irrigation water</td><td>NGS</td><td>28.57 cop/L</td><td>Itarte et al. (2021)</td></tr><tr><td>HEV</td><td>Fresh produce</td><td>NGS</td><td>1000 cop/25g</td><td>Itarte et al. (2021)</td></tr><tr><td>Human sapovirus</td><td>Raw sewage water</td><td>NGS</td><td>N/A</td><td>Song et al. (2021)</td></tr><tr><td>Papillomavirus adenovirus enterovirus</td><td>Urban sewage water</td><td>NGS</td><td>N/A</td><td>Martínez-Puchol et al. (2020)</td></tr><tr><td>Enterovirus</td><td>Sewage water</td><td>NGS</td><td>N/A</td><td>Tao et al. (2020)</td></tr><tr><td>HuNoV GI &amp; GII</td><td>Sewage water</td><td>NGS</td><td>N/A</td><td>Fumian et al. (2019)</td></tr><tr><td>Multiple human viruses</td><td>Sewage water</td><td>NGS</td><td>N/A</td><td>Fernandez-Cassi et al. (2018)</td></tr><tr><td>Human sapovirus</td><td>Raw sewage water</td><td>NGS</td><td>N/A</td><td>Mancini et al. (2019)</td></tr><tr><td>Multiple viruses</td><td>Sewage water</td><td>NGS</td><td>N/A</td><td>(Q. Yang et al., 2021)</td></tr><tr><td>Human and porcine adenovirus</td><td>Wastewater treatment plant</td><td>NGS</td><td>N/A</td><td>Itarte et al. (2024)</td></tr><tr><td>HAV</td><td>Water</td><td>Electrochemical DNA biosensor</td><td>6.94 fg/µL</td><td>Manzano et al. (2018)</td></tr><tr><td>Enterovirus 71</td><td>Water</td><td>Portable surface plasmon resonance biosensor</td><td>4.8 pg/mL</td><td>Prabowo et al. (2017)</td></tr><tr><td>HuNoV</td><td>Water</td><td>Electrochemical biosensor + specific binding peptides</td><td>1.7 cop/mL</td><td>Baek et al. (2019)</td></tr><tr><td>HuNoV</td><td>Oyster</td><td>Electrochemical biosensor with functionalized gold particles</td><td>6.21 cop/mL</td><td>Baek et al. (2020)</td></tr><tr><td>HuNoV</td><td>PBS &amp; Tap water</td><td>3D-architected electrochemical aptasensor</td><td>0.28 ng/mL</td><td>Jiang et al. (2022)</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Virus</th><th>Matrix</th><th>Method</th><th>Limit of detection</th><th>Reference</th></tr></thead><tbody><tr><td>HuNoV GII.4</td><td>PBS & Romaine lettuce rinse water</td><td>Electrochemical Impedance Spectroscopy</td><td>1.9 * 10<sup>5</sup> particles/mL</td><td>Kaur et al. (2024)</td></tr><tr><td>HAV</td><td>Water</td><td>MIP resonance light scattering</td><td>0.1 pmol/L</td><td>Luo et al. (2020)</td></tr><tr><td>HAV</td><td>Water</td><td>MIP resonance light scattering</td><td>8.6 pmol/L</td><td>(B. Yang et al., 2017)</td></tr><tr><td>VLP</td><td>Water</td><td>MIP amino silica nano particles</td><td>~ pmol/L</td><td>Sykora et al. (2015)</td></tr><tr><td>MS2 bacteriophage</td><td>Water</td><td>MIP combined with surface plasmon resonance</td><td>5 * 10<sup>6</sup> PFU/mL</td><td>Altintas et al. (2015)</td></tr><tr><td>Apple stem pitting virus</td><td>Leaf extract</td><td>MIP-GLaDiS</td><td>10 ng/mL</td><td>Bai and Spivak (2014)</td></tr><tr><td>Murine norovirus</td><td>Bilberries rasberries lettuce oysters</td><td>Electrochemical gold nanoparticle-based immunosensors</td><td>1 * 10<sup>-18</sup> TCID<sub>50</sub></td><td>Janicka et al. (2024)</td></tr><tr><td>Singapore grouper iridovirus</td><td>Epinephelus coioides</td><td>Aptamer-based ELASA</td><td>125 nmol/L</td><td>Li et al. (2016)</td></tr><tr><td>Influenza A & B</td><td>Microfluidic device environment</td><td>Aptamer</td><td>3.2 HAU</td><td>(C.-H. Wang et al., 2016)</td></tr><tr><td>Avian influenza virus subtype H5</td><td>Water, avian cloacal swab samples, human serum</td><td>Quantum dot-based lateral flow immunoassay</td><td>0.016 HAU</td><td>(F. Wu, Yin, et al., 2016)</td></tr><tr><td>subtype H9</td><td></td><td></td><td>0.25 HAU</td><td>(F. Wu, Yin, et al., 2016)</td></tr><tr><td>Grapevine vein-clearing virus</td><td>Grapevines</td><td>Hyperspectral imaging + deep learning</td><td>N/A</td><td>Nguyen et al. (2021)</td></tr><tr><td>Grapevine leafroll-associated virus 3</td><td>Grapevines</td><td>Hyperspectral imaging + regression analysis</td><td>N/A</td><td>MacDonald et al. (2016)</td></tr><tr><td>Potato virus Y</td><td>Potato plants</td><td>Hyperspectral imaging + deep learning</td><td>N/A</td><td>Polder et al. (2019)</td></tr><tr><td>Tomato spotted wilt virus</td><td>Sweet pepper</td><td>Hyperspectral imaging + generative adversarial nets</td><td>N/A</td><td>(D. Wang et al., 2019)</td></tr><tr><td>Mosaic virus</td><td>Sugar cane</td><td>Hyperspectral imaging + radiometric processing</td><td>N/A</td><td>Moriya et al. (2017)</td></tr></tbody></table>

cop: copies; PFU: plaque-forming units; HAU: hemagglutination units; HuNoV: Human norovirus; HAV: Hepatitis A Virus; HEV: Hepatitis E Virus; VLP: Virus-Like Particles*

<caption>**Table 2**</caption>

Lowest LODs of the three major foodborne viruses compared to their estimated MID.

<table><thead><tr><th>Virus</th><th>Estimated MID</th><th>Method</th><th>Lowest LODs</th><th>References</th></tr></thead><tbody><tr><td rowspan="5">HuNoV</td><td rowspan="5">10 - 100 viral particles</td><td>RT-qPCR</td><td>&lt;10 viral particles/mL or particles/g</td><td>(An et al., 2025; Polo et al., 2015)</td></tr><tr><td>RT-ddPCR</td><td>&lt;200 copies/g</td><td>Bartsch et al. (2018)</td></tr><tr><td>Immunoassay</td><td>10 viral particles/mL or particles/g</td><td>Alhadrami et al. (2020)</td></tr><tr><td>NGS</td><td>&lt;10 viral particles/L</td><td>Itarte et al. (2021)</td></tr><tr><td>Electrochemical biosensors</td><td>&lt;10 viral particles/mL</td><td>Baek et al. (2019)</td></tr><tr><td rowspan="5">HAV</td><td rowspan="5">10<sup>3</sup> - 10<sup>6</sup> viral particles</td><td>RT-qPCR</td><td>&lt;10 viral particles/mL</td><td>An et al. (2025)</td></tr><tr><td>RT-ddPCR</td><td>&lt;10<sup>3</sup> viral particles/mL or particles/g</td><td>Persson et al. (2021)</td></tr><tr><td>RT-LAMP</td><td>&lt;10 viral particles/µL</td><td>An et al. (2025)</td></tr><tr><td>Electrochemical biosensors</td><td>&lt;10 fg/µL</td><td>Manzano et al. (2018)</td></tr><tr><td>MIP-coupled biosensors</td><td>&lt;10 pmol/L</td><td>(Luo et al., 2020; B. Yang et al., 2017)</td></tr><tr><td rowspan="4">HEV</td><td rowspan="4">10<sup>4</sup> - 10<sup>6</sup> viral particles</td><td>RT-qPCR</td><td>&lt; 10<sup>3</sup> copies/g</td><td>(Hennechart-Collette et al., 2021; Mesquita et al., 2016)</td></tr><tr><td>RT-ddPCR</td><td>~10 viral particles/mL or particles/g</td><td>Martin-Latil et al. (2012)</td></tr><tr><td>NGS</td><td>&lt;100 viral particles/L</td><td>Itarte et al. (2021)</td></tr><tr><td></td><td></td><td></td></tr></tbody></table>

amplification techniques. Thanks to the universally adopted ISO methods for the detection of HuNoV or HAV, these techniques very often detect viruses below 10³ viral particles/mL or g. Though, the viral extraction seems to still be very limiting for food applications. It also

<caption>**Table 3**</caption>

Trueness, precision, accuracy and selectivity scores of each method amongst the analyzed articles. A grade of 1 in selectivity signifies that at least 20 % of the articles with this method are complying with its definition presented in section 1. Each subsequent grade corresponds to an increase of 20 % in that regard, for a maximum of 80–100 % with the grade of 5. The accuracy score is calculated as the mean of trueness and precision scores.

<table><thead><tr><th>Method</th><th>Trueness</th><th>Precision</th><th>Accuracy</th><th>Selectivity</th></tr></thead><tbody><tr><td>PCRs</td><td>3.5</td><td>5</td><td>3.5</td><td>5</td></tr><tr><td>Immunoassays</td><td>3</td><td>5</td><td>4</td><td>5</td></tr><tr><td>NGS</td><td>5</td><td>5</td><td>5</td><td>5</td></tr><tr><td>Electrochemical biosensors</td><td>3</td><td>4</td><td>3.5</td><td>4</td></tr><tr><td>MIP-coupled biosensors</td><td>3</td><td>3</td><td>3</td><td>5</td></tr></tbody></table>

doesn't offer the possibility of detecting viruses in real-time. Surprisingly, ddPCR and RT-LAMP are not overperforming RT-qPCR by much. At least, in best scenarios, LODs differ by up to one order of magnitude (Table 1).

In the context of food, immunoassay methods are too limited. While they are of significant value in clinical diagnostics, they are less effective in the case of complex food matrices, unless they are complemented by other methods. They also require prior extraction and are often used in parallel with RT-qPCR. For studies involving animal organs/tissues, they can be useful in detecting antibodies to viruses naturally present in these tissues, as is the case with HEV in pork meat. Nanoparticle functionalization can also help to reduce matrix effects on the analysis (Xiao et al., 2021). But overall, the immunoassay principle is not very attractive (Table 2).

Sequencing techniques face similar difficulties to immunoassays. It is difficult to find a study using NGS alone in a complex food matrix. They are often combined with RT-qPCR. Sequencing techniques are not typically used to detect and quantify a single virus. Due to its large-scale capability and associated costs, NGS is primarily used for metagenomic studies targeting multiple viruses in an environment. Table 1 underlines that most NGS studies for the detection of foodborne viral pathogens focus on wastewater (Itarte et al., 2024; Song et al., 2021; Q. Yang et al., 2021). While NGS is showing the best results of trueness and accuracy, the cost and the heavy sample preparation that it requires in complex matrices are of the main reasons behind its lack of attractivity.

Among emerging technologies, biosensor-based methods are increasingly prevalent (25 % of selected articles) due to their notable advantages over conventional PCR techniques, including portability,
rapid turnaround times, and reduced reagent requirements. While theoretically attractive, their practical application in food virology remains underexplored. Challenges include low validation against reference methods, limited matrix compatibility, and performance variability. Their modular design—comprising bioreceptors, transducers, and detectors—allows for flexible adaptation, and the use of molecularly imprinted polymers or aptamers has shown improvements in selectivity. Still, comparative studies against RT-qPCR are scarce. These biosensor approaches are rarely benchmarked against reference methods such as RT-qPCR, which contributes to their lower accuracy scores in our comparative evaluation.

SERS-based detection faces challenges, particularly regarding substrate uniformity and signal reproducibility. Sensitivity can also vary with the sample matrix and interfering substances. However, advances in nanotechnology and substrate design continue to enhance SERS reliability for virus detection, primarily in human serum, saliva, and expectoration.

Other optical methods, such as near-infrared spectroscopy (NIRS), may also emerge. Already used for detecting chemical residues and bacteria, NIRS could enable fast, cost-effective, solvent-free virus detection without extraction if further developed. However, establishing virus/media models would require advanced statistical methods like chemometrics, potentially slowing research. Notably, chemometrics is gaining traction with the rise of deep learning technologies (Mu et al., 2018; Teye et al., 2013).

Finally, HSI techniques are mainly used for the diagnosis of viruses that infect plants. They are less useful for food safety, as they primarily provide information on plant health in agriculture rather than preventing human viral infections. Nevertheless, their efficiency and usefulness are recognized as allowing rapid on-site diagnosis through statistical image processing, thus limiting economic and food losses.

The results of Table 2 indicated that multiple techniques reached at least occasionally the minimal infectious dose of HuNoV, HAV and HEV. The matrix is probably playing a crucial role in the observed variability. Consistency using standardized protocol and the use of reference methods is highly encouraged by the relatively low value of trueness measured.

Notably, amongst the three foodborne viruses, HEV has been targeted way less. Only 3 techniques allowed its detection: RT-qPCR, ddPCR and NGS. The absence of an official standardized protocol likely contributes to this limited attention. Establishing such a protocol could help promote the broader adoption of promising alternative approaches, such as biosensors, for HEV detection.

Finally, while most reviewed studies focused primarily on qualitative or semi-quantitative detection of foodborne viruses, a limited number explicitly addressed full quantification parameters. The limit of quantification (LOQ), although critical for interpreting low viral loads and comparing methods, was rarely reported. It highlights the need for standardized reporting of quantitative criteria, including LOQ, linear range, calibration models, and recovery rates, particularly when evaluating methods intended for risk assessment or regulatory purposes.

## 5. Conclusion

RT-qPCR continues to be trusted and to evolve, approaching the sensitivity and specificity required by global food safety agencies. However, neither RT-ddPCR nor RT-LAMP consistently improved detection limits in food, likely due to persistent challenges in sample preparation. Ultimately, PCR-based methods cannot assess viral infectivity, limiting their value for true risk assessment.

Biosensor-based methods show the most potential, particularly because of their real-time measurement speed. Such techniques are still gaining popularity, but studies are rarely carried within complex food matrices. They are for now limited to simple aqueous environments. Nevertheless, as biosensors became a candidate for point-of-care diagnostics, researchers should keep an eye on their use in food matrix.

Among the rest, the current situation requires more development. Either the method is new in the domain, like SERS which can also be coupled with CRISPR/Cas9, or the method is unadapted to a use in complex matrix for now, which is the case for NGS, immunoassay and hyperspectral imaging. It should be noted that these techniques still carry potential and a couple of efforts were implemented to strengthen/ compensate the advantages/disadvantages of them. Optical methods for instance are evolving with the enhancement of chemometric tools and these types of interdisciplinary collaborations could bring more answers.

Overall, the evaluation parameters scores within this study showed a certain care for selectivity and precision matters. For each method and parameter, at least 60% of all considered papers justified or established proper protocols. Studies were repeatable and reproducible, with a care over false positive/negative results. However, trueness is the least considered with a lack of reference methods to evaluate the results of a study. Microbiology and chemistry reference tools should be confronted more often to validate the results of a detection method, especially when searching for low detection limits.

The wide variety of detection methods available, despite some being currently better than others, should encourage innovations in viral detection to meet the growing demand for food safety. It is probably safe to assume that resolving the challenges for a fast, sensitive and selective detection technique will require either the enhancement of already powerful tools and/or the association of multiple principles to offer a complementary approach. Such strategies should be followed carefully, with similar attention to parameters such as repeatability and reproducibility, and more combined reference methods for validation purposes.

## Disclosure statement

The authors declare that they have no relevant or material financial interests that relate to the research described in this paper.

## Author contributions statement

Samy De Oliveira: Conceptualization, Methodology, Investigation, Writing - Original Draft, Visualization, Elias Bou-Maroun: Conceptualization, Methodology, Investigation, Writing - Review & Editing, Supervision, Project administration, Perrine Mas: Writing - Review & Editing, Alexis de Rougemont: Conceptualization, Investigation, Writing - Review & Editing, Supervision, Stéphane Guyot: Conceptualization, Methodology, Investigation, Writing - Review & Editing, Supervision, Project administration.

## Funding

This study has not received any funding

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

No data was used for the research described in the article.

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