<figure><img src="image_1.png" /></figure>

HAL
open science

# Preventing illegal seafood trade using machine-learning assisted microbiome analysis

Luca Peruzza, Francesco Cicala, Massimo Milan, Giulia Dalla Rovere, Tomaso Patarnello, Luciano Boffo, Morgan Smits, Silvia Iori, Angelo de Bortoli, Federica Schiavon, et al.

<figure><img src="image_2.png" /></figure>

## To cite this version:

Luca Peruzza, Francesco Cicala, Massimo Milan, Giulia Dalla Rovere, Tomaso Patarnello, et al. Preventing illegal seafood trade using machine-learning assisted microbiome analysis. BMC Biology, 2024, 22 (1), pp.202. 10.1186/s12915-024-02005-w. hal-04711592

HAL Id: hal-04711592

https://hal.univ-brest.fr/hal-04711592v1

Submitted on 27 Sep 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_3.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
RESEARCH ARTICLE

Open Access

<figure><img src="image_4.png" /></figure>

Check for
updates

# Preventing illegal seafood trade using machine-learning assisted microbiome analysis

Luca Peruzza¹†, Francesco Cicala¹†, Massimo Milan¹*†, Giulia Dalla Rovere¹, Tomaso Patarnello¹, Luciano Boffo², Morgan Smits³, Silvia Iori¹, Angelo De Bortoli⁴, Federica Schiavon⁴, Aurelio Zentilin⁵, Piero Fariselli⁶, Barbara Cardazzo¹ and Luca Bargelloni¹

**Abstract**

**Background** Seafood is increasingly traded worldwide, but its supply chain is particularly prone to frauds. To increase consumer confidence, prevent illegal trade, and provide independent validation for eco-labelling, accurate tools for seafood traceability are needed. Here we show that the use of microbiome profiling (MP) coupled with machine learning (ML) allows precise tracing the origin of Manila clams harvested in areas separated by small geographic distances. The study was designed to represent a real-world scenario. Clams were collected in different seasons across the most important production area in Europe (lagoons along the northern Adriatic coast) to cover the known seasonal variation in microbiome composition for the species. DNA extracted from samples underwent the same depuration process as commercial products (i.e. at least 12 h in open flow systems).

**Results** Machine learning-based analysis of microbiome profiles was carried out using two completely independent sets of data (collected at the same locations but in different years), one for training the algorithm, and the other for testing its accuracy and assessing the temporal stability signal. Briefly, gills (GI) and digestive gland (DG) of clams were collected in summer and winter over two different years (i.e. from 2018 to 2020) in one banned area and four farming sites. 16S DNA metabarcoding was performed on clam tissues and the obtained amplicon sequence variants (ASVs) table was used as input for ML MP. The best-predicting performances were obtained using the combined information of GI and DG (consensus analysis), showing a Cohen K-score > 0.95 when the target was the classification of samples collected from the banned area and those harvested at farming sites. Classification of the four different farming areas showed slightly lower accuracy with a 0.76 score.

**Conclusions** We show here that MP coupled with ML is an effective tool to trace the origin of shellfish products. The tool is extremely robust against seasonal and inter-annual variability, as well as product depuration, and is ready for implementation in routine assessment to prevent the trade of illegally harvested or mislabeled shellfish.

**Keywords** Machine learning, Food traceability, Microbiota 16S, Manila clam, North Adriatic sea, Illegal unreported unregulated (IUU) fishing

<footnote>†Luca Peruzza and Francesco Cicala equally contributed to this work.</footnote>

<footnote>*Correspondence:
Massimo Milan
massimo.milan@unipd.it
Full list of author information is available at the end of the article</footnote>
## Background

Seafood is crucial for the human diet as it provides an excellent source of high-quality protein, essential omega-3 fatty acids, and various vitamins and minerals. Regular consumption of seafood is linked to numerous health benefits, including heart health, brain function, and overall well-being [1]. Marine bivalves are among the most traded seafood products worldwide [2]. Manila clam (*Ruditapes philippinarum*) is one of the most important mollusc species, with a global production of over four million metric tonnes. In Europe, Italy is the largest producer with ~96% of the European production (24,337 t) [3]. Native from South-east Asia (Indo-Pacific), *R. philippinarum* was imported in Europe in the second half of the 70s and it was introduced in Italy in 1983 [4]. Given its high adaptability, *R. philippinarum* is now commonly found in brackish waters along the northern Adriatic coast especially in the Venice lagoon and nearby areas such as the Po river delta and the lagoon of Marano [3–5].

Despite their high nutritional value, bivalve consumption might pose risks for human health. This is due to their filter-feeding strategy through which these molluscs may accumulate human pathogenic microorganisms as well as metals and/or chemicals present in the water [2, 6, 7]. The European Union (EU) has regulated bivalve harvesting (Regulation (EC) No 853/2004, No 854/2004, No 2073/2005 and No 1021/2008), classifying harvesting areas according to the levels of *Escherichia coli* in the intravalvular liquid per grams of bivalves (EC, 2004a; 2004b, 2005, 2008). Based on these regulations, all species of bivalve molluscs farmed in lagoons must be subjected to depuration processes in order to remove chemical compounds hazardous to humans. Seafood product labels must include the area of provenience (Regulation (EU) No 1379/2013; EU, 2013). Ecolabels have also recently been proposed to improve consumers' perception and market value of shellfish products [8]. Finally, as bivalves might accumulate significant amounts of chemical pollutants, highly polluted areas might be officially banned for mollusc harvesting. A well-known example is the area close to Porto Marghera in the Venice Lagoon, in Italy, where decades of discharging industrial wastes have led to high concentrations of several pollutants in the sediment (e.g. dioxins; PCBs; heavy metals). Despite the interdiction to harvest bivalves for human consumption in this area (Veneto regional regulations No 133/2018), however, illegal clam harvesting in Porto Marghera often occurs with major risks for consumers' health.

For all the above reasons it is clear that developing tools that allow verification of bivalve origin as stated in the product label is increasingly important. A broad array

of methodologies like fatty acids (FA) profiling, stable and unstable isotope and trace element fingerprinting, as well as several methods based on DNA analysis (DNA fingerprinting, microbial barcodes or profiles, among others), have been proposed as possible tools to trace the geographic origin and detect mislabelling [9–11]. These methods have variable performance depending on the geographic scale and mobility of the target species, and diverse feasibility in terms of time and cost of analysis. For instance, DNA analyses have been the most used tool for species identification (DNA barcoding) and the second most used to discriminate the geographic origin of bivalves (genetic traceability) [10]. This type of analysis is usually chosen for its relatively rapid and cost-effective results. However, the absence of barriers to the gene flow in species with highly vagile developmental stages may preclude genetic divergence between closely located populations (high connectivity), thus limiting the diagnostic power of genetic traceability [12]. FA profiles are among the most used and highly reliable approaches to discriminate the geographic origin of commercial bivalves as these species may modulate their FA profiles by several intrinsic (e.g. age, sex, reproductive cycle, and phylogeny) and extrinsic factors (e.g. diet, temperature, depth, and salinity) [13]. However, as previously mentioned for DNA analysis, recent studies have reported that natural seasonal and inter-annual FA variability may limit its accuracy when considering samples collected over multiple seasons/years [14].

A second major issue is that nearly all these methods have been validated using samples collected in different areas at a single time point (season, year). Even when a rigorous statistical analysis is implemented, site-of-origin discrimination accuracy is estimated based on a leave-one-out cross-validation. Such an approach is prone to overfitting, inflating the estimated accuracy. It also does not allow for the assessment of whether the discriminant function or the predictive model can generalise, i.e. correctly classifying never-seen-before samples. This is even more problematic when there are time dependencies among the data.

Microbiome profiling (MP) or 16S metabarcoding is an emerging alternative approach for unveiling geographical origin misrepresentations [15–17]. This method consists of the amplification and analysis of a fragment of the 16S rRNA gene from the microbial communities present in a given sample with the final aim to detect area-specific taxonomic composition profiles [18]. Recent studies have proven that although the microbial composition of molluscs is affected by environmental factors (e.g. temperature, salinity, chemical contamination), it is also characterized by a striking resilience to change; thus, making microbiota a suitable
candidate for traceability [19, 20]. In fact, previous
works employing this technique have reported its suc-
cessful use to trace the geographic origin of commer-
cially important wild [16, 21] and cultured species
[22]. However, as mentioned above, molluscs grown in
transition habitats such as lagoons and deltas need to
undergo a process of depuration before commerciali-
zation. Such a process might significantly change their
microbiota [15, 23] and ultimately impair our ability to
discriminate between molluscs collected at differing
locations.

The present study aimed at implementing MP in sea-
food traceability and addressing all the above-mentioned
issues. First, samples were collected and processed
(depurated) as is the case for commercial bivalve prod-
ucts. Second, the predictive model was based on two ML
algorithms that are less prone to overfitting (linear bag-
ging and random forests). The number of features used
was limited to those taxonomic units (amplicon sequence
variants, ASVs) showing significant abundance (> 5%),
to reduce noise and limit the “large p small n” problem.
Third, samples were collected across seasons to assess
the effects of seasonality on prediction accuracy. Fourth,
two comparable data sets were obtained from two dif-
ferent years, providing independent training/valida-
tion and test sets to correctly assess the generalization
ability of the trained model. Briefly, 16S metabarcoding
data for two tissues (gills (GI) and digestive glands (DG)
of Manila clam) were obtained for samples collected at
four different sites, representing the major clam farming
areas in the North Adriatic Sea, across different seasons
and years. A fifth site, the interdicted area of Porto Mar-
ghera was included to test the ability to classify illegally
commercialized products harvested in potentially con-
taminated areas. Overall, high accuracy of prediction
was observed, especially when identifying clams from the
interdicted area. The predictive model was also robust to
seasonal and inter-annual variation, showing significant
generalization ability.

Results

Data summary

A total of 1000 clams were collected during field expe-
ditions and pooled in 560 DNA-pool samples (280
per tissue). Raw DNA libraries were deposited at the
NCBI repository under the BioProject access number:
PRJNA1013079 [24]. After the quality-filter steps, a total
of 15,491,493 reads were retained and rarefied at 5463
reads per sample. Finally, after the exclusion of rare ASVs
(less than 5% of abundance), a number between 150 and
300 ASVs (depending on the comparison) were used to
train ML algorithms.

Predicting the origin of samples via ML

In the comparison of Porto Marghera (PM) vs Chioggia
(CL) (i.e. the polluted and farming sites within the Ven-
ice lagoon), the average precision in the prediction var-
ied between 0.85 and 0.95 (Additional File 1: Table S1),
with the GI tissue providing better results than the DG
(Fig. 1). The consensus model allowed to reach an over-
all AUC of 0.95 with all samples from PM being correctly
identified as PM while only 1 sample of CL was mis-
labelled (Fig. 1A). Binary classification was based on a
large set of features (predictors), as the analysis of feature
importance (Additional File 1: Table S1) showed that in
the case of PM vs CL the presence-absence of 40 different
ASVs for the GI and 50 ASV for the DG data explained
75% of classification importance. Similar evidence was
observed for other classification tests by comparing PM
vs other farming sites (Additional file 2: Fig. S1).

In the comparison of Porto Marghera (PM) vs Scardo-
vari (SC) (i.e. the polluted site within the Venice lagoon
and the farming site outside the Venice lagoon) ML was
able to correctly identify all animals with 100% accuracy
with no differences in performances among tissues con-
sidered (Fig. 1B).

The analysis between farmed areas was more challeng-
ing since accuracy ranged from 0.66 for GI to 0.83 for DG
(Fig. 1C). Once again, better results were obtained with
the consensus model that achieved an average accuracy
of 0.84. Using the combined GI+DG data sets for every
farming area, at least 8 out of 10 samples were correctly
classified (Fig. 1C). Not surprisingly, the mislabelled sam-
ples from Goro (GO) were assigned to SC which is the
closest site to GO in terms of geographic distance (Fig. 2).

Discussion

Traceability, the possibility to track each step along the
supply chain of any food product from its origin to the
final consumer, is essential to ensure product quality and
consumer safety. Traceability is particularly relevant for
shellfish species. These organisms are filter-feeders that
can bio-accumulate abiotic and biotic hazardous com-
pounds if grown in areas subjected to chemical and/or
biological contaminations, with potential risks for public
health [2, 12, 25]. In fact, episodes of molluscs harvested
from restricted areas and then sold in fish markets are
still frequent and it can be quite difficult and cost- and
time-consuming to detect such dangerous frauds. Thus,
the creation of diagnostic tools able to accurately and
easily discriminate the provenance of seafood, preventing
the introduction of unsafe products in the market [12, 25]
has long been sought for by health authorities. In addi-
tion, such tools could help producers and cooperatives
to guarantee quality and environmental certifications,
<figure><img src="image_5.png" /><figcaption>**Fig. 1** Confusion matrices showing the results of the ML predicted provenance ("Predicted label") versus the real provenance ("True label") for each of the tested samples by using gills (GI) only (left column), digestive gland (DG) only (middle column) or by combining GI and DG into a consensus prediction (right column). **A**, **B** Classification discriminating between the polluted site PM and the clean farming sites of CL (**A**) and SC (**B**). **C** Classification assessing the origin of samples among the four farming areas considered in the study. Colour scale is proportional to the number of samples that are assigned to a specific location. MA, Marano; PM, Porto Marghera; CL, Chioggia; SC, Scardovari; GO, Goro</figcaption></figure>

increasing consumer trust in specific brands and eco-
labels [12, 26–28].

In a previous study, we showed the potential for ML-
powered classification of MPs to discriminate clams from
Porto Marghera compared to animals collected from
farming sites located in the Venice lagoon [12]. However,
in that preliminary study temporal replication was not
extensive and sampling was limited to the Venice lagoon,
excluding other important clam farming areas along the
North Adriatic coast. In addition, a single tissue (digest-
tive gland) was analysed and, most importantly, clams did
not undergo the depuration process required by legisla-
tion, thus ignoring the potentially altering effects of this
treatment on the animal microbiota. It has in fact been
demonstrated that depuration may significantly influence
the bivalve microbial community [15]. In the present
work, we carried out an entirely new sampling campaign,

with extensive temporal replication and broad geo-
graphic representation. All samples underwent the stand-
ard depuration process, which constitutes a key step
forward. In fact, we have successfully demonstrated that,
despite the depuration process, a distinctive “signature”
is still detectable in the clam MP. As already mentioned,
existing evidence suggested that the composition of host-
associated microbiota was relatively stable and distinct
from the microbial communities present in the water and
the sediment [20]. This is likely explained by the inter-
actions between host and microbiota, which select and
maintain distinctive bacterial taxa associated to specific
organs and tissues [29].

The dynamics of host colonization and evolution of
host-microbiota associations, however, remain to be bet-
ter elucidated in bivalves, and such knowledge might
be highly relevant to more accurately understand the
<figure><img src="image_6.png" /><figcaption>**Fig. 2** Map showing the sampling areas: two from the Venice lagoon, two from the Po river delta and one from the Marano and Grado lagoon. MA, Marano; PM, Porto Marghera; CL, Chioggia; SC, Scardovari; GO, Goro</figcaption></figure>

potential and the limitations of MP analysis in trace-
ability. A first attempt in shading light on the dynamics
of clam biology and its associated microbiota has been
taken recently by Milan et al. [19] and by Cecchetto et al.
[30] by performing long-term monitoring campaigns
on clams farmed in four sites of the Venice lagoon. Our
works revealed that clams undergo biotic and abiotic
stressors in a site-specific way, with locations in close
proximity to the inlet of the lagoon experiencing more
frequent salinities and/or temperatures beyond the opti-
mum range of the species. Further, we showed how envi-
ronmental gradients, such as salinity and water residence
time, influenced the overall gene expression pattern of
clams, the beta diversity of microbial communities in the
host, and putatively translated into differences in clam's
growth, condition index (an index of general well-being
of the animal) and mortality. Interestingly, we found an
over-representation of the genera *Arcobacter* and *Vibrio*
(both described as opportunistic pathogens) at the end
of the summer in sites closer to the inlet and thus poten-
tially subject to a higher abiotic stress. Unfortunately, we
did not have multi-parametric probes in place when we

performed all sampling activities for the current work,
thus hampering the possibility to integrate environmen-
tal data in our ML-based traceability system.

Another key outcome of the present study is the highly
significant accuracy (> 95%) in discriminating clams col-
lected in the prohibited area (PM) from those harvested
in the nearby farming sites within the Venice lagoon
and even more accurately (100% recovery) when sam-
ples originated from a geographically close, but distinct
lagoon (SC). Similar results were obtained for pairwise
comparisons between PM and the two other farming
sites. We believe this is a significant proof-of-concept
that ML-empowered analysis of MPs could be used to
independently verify the origin of bivalves suspected of
being illegally collected in areas that are interdicted for
various reasons, as in the case of sites showing high levels
of chemical and/or microbiological contamination. The
specific environment present at contaminated sites might
shift the composition of microbial communities, making
MPs more easily identifiable. A second potential factor
that might boost classification accuracy is the fact that
clams in PM are generally undisturbed after settlement,
therefore early on during their life history, making them long-term residents in the same area and allowing them to be associated with a highly distinctive microbiome. This is not the case of clams collected in authorized farming sites as it is a common practice to use already metamorphosed juvenile individuals to seed on-growing sites. As mentioned, little is known about the dynamics of the establishment and maintenance of clam-associated microbiota, although the early life history phases seem to be crucial in determining microbiome composition in adult animals as observed in aquatic model species [31]. The frequent practice of seeding farming sites with juveniles from different areas or with spat produced by captive reproduction in bivalve hatcheries might explain the lower, though significant accuracy (k-score 0.76 compared to the expected 0 by random chance) in classifying the four most important Italian production areas. In fact, juveniles from natural nursery areas from the Po river delta (Scardovari and Goro; SC and GO) have been reported to be used to support farming sites in the Venice and Marano lagoons (CL and MA), where natural recruitment has been extremely limited in recent years. It should also be noted that multi-class discrimination cases are generally more problematic than binary classification, which might add to the problem of translocation of juvenile clams.

In the present study, ML-based classification provided excellent or very good accuracy. We believe that several factors contributed to such a positive outcome. First, presence-absence data were used, enforcing a conservative threshold (>5%) for considering a specific ASV as present. This approach reduces the potential noise linked to fluctuating abundance caused by technical and/or biological factors. Second, ASVs were used as features for classification, which means that discrimination is based on the presence of “unique” bacterial strains/species in a specific site. Third, reducing the number of features likely limited the risk of overfitting, which is generally high for ML-based classification methods. Fourth, a bootstrap aggregating (bagging) approach was implemented, which is expected to further reduce the problem of overfitting. Fifth, two different classification models were tested (linear bagging and random forests), and the one providing the best classification performance was used in the validation step. Last, and most important, as already mentioned, the set of samples used to train the classification algorithm was completely independent from the validation set. To the best of our knowledge, even for studies in which the classification tool (either using traditional statistical approaches or ML methods) was validated, the validation was performed as a leave-one-out cross-validation approach. This is known to greatly inflate the estimated accuracy and the generalization ability of the

model. Our work shows that MP analysis using properly selected and trained ML models is robust to seasonal variation and truly able to predict the origin in never-seen-before samples (collected in a different year). Indeed, reproducibility across temporal replicates is certainly one of the most relevant features for any traceability tool, but unfortunately is greatly overlooked.

Overall, the present study provides compelling evidence that traceability tools based on ML-enabled analysis of host-associated microbiota might be a powerful weapon in the war against illegal seafood trading. At the moment we have tested our ML method focusing on a relatively “short” geographic distances (even if we have included all major Manila clam farming sites of Europe in terms of production [32]) because it is more challenging to discriminate the provenance of samples on small geographic distances rather than on broader distances, as demonstrated by Mamede et al. [33]. However, the decreasing costs of DNA sequencing will also make it possible to create much larger training data sets, with broader geographic coverage and temporal replicates. This, in turn, should greatly improve classification accuracy and limit the “large p small n” problem, where p is the number of features/predictors used and n is the number of cases. The rapid and continuous decline of cost for DNA sequencing will make these tools increasingly affordable and the cost is already on par or even lower than for other methods such as FA profiling and trace elements analysis. Furthermore, DNA 16S rRNA metabarcoding data easily comply with the FAIR principles (Findability, Accessibility, Interoperability, and Reuse), as protocols and public data repositories are well established for this type of data, although further efforts are needed especially toward the standardization of DNA extraction methods and the harmonization of the 16S rRNA gene region to be used are needed. Based on our results and other available evidence, we believe that the relative stability of host-associated microbiota likely allows high traceability accuracy for sessile species (e.g. bivalves, macroalgae). For species that are highly mobile, such stability might trace the area where the animal/plant was located during the early life stages, but be less precise in tracking more recent movements.

Another potential limitation is the time required for data production and analysis. Currently, the limiting step is DNA sequencing, which is based on Illumina technology and requires a couple of weeks to obtain DNA sequence data. However, the advent of third-generation sequencing technologies is making it possible to greatly speed up the process and in the case of Nanopore 16S barcoding kit, also to make it easily portable if implemented on a MinION instrument. In addition, long-read sequencing provides full length sequence information
for the 16S rRNA gene, overcoming the previously mentioned problem of finding a consensus for the gene fragment to be sequenced and, at the same time, allowing for increased taxonomic resolution, with a higher likelihood to identify site-specific ASVs. We expect that in the near future, it will be possible to carry out rapid, in-the-field, 16S rRNA metabarcoding analyses. In fact, rapid DNA extraction protocols suitable for long-read sequencing are already becoming available [34], the array of technological solutions for portable, miniaturized PCR thermal cyclers is rapidly expanding [35], and the use of pre-trained ML-based algorithms for 16S data analysis using cloud computing has already been reported [36]. Furthermore, ML performance usually increases when the data size grows. Thus, we may expect significant improvement when more samples are collected and used to train the models. However, the successful implementation and improvement of the model will strongly depend on a close collaboration with regulatory bodies and industry stakeholders which will also favour its adoption and further extend its usage.

## Conclusions

In conclusion, the present study demonstrates that ML-enabled analysis of host-associated microbiota is already a key instrument complementing the toolbox for seafood traceability, showing the importance of addressing the most relevant steps to ensure classification accuracy. Looking ahead, we expect that highly portable and interoperable diagnostic tools based on the approach proposed here will become available, making prevention of illegal seafood trade rapid and affordable.

## Methods

### Samples collection, DNA extraction and sequencing

Commercially harvestable adults of Manila clam *Ruditapes philippinarum* were collected during four sampling campaigns conducted in July 2018 (S18), January 2019 (W19), July 2019 (S19) and January 2020 (W20) along the Venice lagoon and neighbouring areas (Fig. 2). During each sampling expedition, around 100 clams were collected from four farming areas (i.e. Marano Lagunare (MA); Chioggia (CL); Scardovari (SC); Goro (GO)) and one polluted site (i.e. Porto Marghera (PM)) by a mechanical rake and following official regulations for bivalve commercialisation. Landed animals were kept in a depuration centre for 16 h where they were kept in open flow-through systems that continuously receive natural sea water that is mechanically, biologically and chemically (i.e. UV) filtered. After this step, animals were brought to the laboratory where the entire gills (GI) and digestive gland (DG) were dissected from each clam using sterilized scalpels. Tissue samples were

immediately transferred to 1.5-ml microcentrifuge tubes containing molecular grade ethanol (90%). Samples were then refrigerated at 4 °C until further analysis. DNA was extracted from pooled tissues obtained by pooling 10 GI or DG tissue pieces of similar weight from clams collected from the same expedition and area. DNA was extracted and purified using a DNA Power Soil kit (QIA-GEN, Hilden, Germany) following the manufacturer's instructions with an additional step of Proteinase K to improve cell lysis. DNA integrity was verified using agarose gel electrophoresis (1%), while its quantity was estimated by NanoDrop ND1000. DNA aliquots were sent to BMR Genomics (Padua, Italy) where a fragment of the 16S rDNA spanning the V3-V4 regions was PCR-amplified and sequenced using MiSeq 2×300 pair-end (PE) sequencing technology.

### 16S rRNA libraries preparation

Raw reads were processed and analysed using Quantitative Insights into Microbial Ecology 2 v. 2019.1 (QIIME 2) [37]. Primer sequences were removed using cutadapt [38]. DADA2 [39] was used to filter low-quality sequences and to merge forward and reverse libraries to obtain high-quality representative sequences. The same program was also used to remove chimeric reads from the final sequence dataset. Representative sequences were aligned using MAFFT software [40] and classified using the Python library Scikit-Learn [41]. Taxa assignment was carried out using the v.142 SILVA database trained for V3-V4 regions. Finally, in order to standardize reads libraries to a common sampling depth, reads were rarefied by randomly subsampling by the minimum number of reads. These QIIME 2 outputs, including the abundance feature table and taxonomy, were used in the training and testing for machine learning (ML) based classification.

### Machine learning procedures

Using ML, two classification problems were tested: (i) the possibility of discriminating between clams harvested in authorized farming areas and those illegally collected in the interdicted polluted; (ii) the possibility of tracing the origin of clams grown in the four most important farming sites of the north Adriatic Sea, as each is these areas carries its own commercial brand (e.g. "Vongola verace di Scardovari"). In the first case, we trained and tested the ML-based tool in a two-class classification problem, i.e. to discriminate between samples from the interdicted area (PM) and the geographically closest farming site, Chioggia (CL) (Fig. 2). Likewise, we tested the comparison between PM and the most important farming area, Scardovari (SC). The second scenario consisted in a multiple class classification problem, where we tested the
ability of the ML algorithm to discriminate the prove-
nance of clams by including all farming sites together (i.e.
GO; CL; SC; MA).

Preparation of input data

For all case studies, input data were filtered to remove
ASVs with low counts, to reduce noise and to select a
smaller number of features (predictors). In total for each
case study, 8 input files were generated, one for each
combination of tissue (i.e. DG and GI) and sampling time
(i.e. S18, S19, W19, W20). More in detail, the ASVs table
was initially imported in R/v4.2.1 (R Core Team, 2014)
and filtered in order to keep only locations that were
involved in the relevant comparison. Then, for each tissue
and season (i.e. Summer or Winter) the ASVs table was
split between the first and second year of sampling and
was filtered to keep only ASVs with an abundance higher
than 5% in at least one of the samples, in both years.
Then the filtered ASV tables were converted to binary
matrices (i.e. presence/absence), and the taxonomy file
was updated to keep only ASVs that were present in the
binary matrices. The R code used to generate the input
files can be found on GitHub at the following link [42]:
https://github.com/GEMMA-BCA/Machine-Learning-
assisted-Microbiome-analysis.

Identifying clams from the interdicted area

Binary matrices from ASV tables were imported in Jupi-
ter notebook v6.4.12 (https://jupyter.org). The library
scikit-learn [41] was loaded in python/v3.9.13 and used
to train a random forest (RF) classifier with the function
"RandomForestClassifier". Initially, the RF model was
cross-validated (i.e. where the training set was split into
k-folds, k-1 folds were used for training and the resulting
model was validated on the remaining part of the data)
to define the best parameters (i.e. the number of estima-
tors "n_estimators", ranging from 12 to 18, and the max
depth of trees "max_depth", ranging from 1500 to 2000)
and the model with the highest Cohen kappa score was
chosen. Cross-validation was performed on second-
year data only (i.e. S19+W20 together) and a separate
RF model for each tissue was built and cross-validated
independently. After cross-validation, data from the sec-
ond year was fitted on the optimized RF model, and then
this was used to predict data on novel, never-seen-before
data from the first year (i.e. S18+W19) on each tissue
separately.

The performance of the model in predicting the prove-
nance of clams sampled in the first year was assessed sep-
arately for each tissue by using the Area Under the Curve
of a Receiver Operating Characteristic AUC-ROC with
the function "roc_auc_score" and by means of confusion
matrices. For each model the most important features

were obtained using the function “feature_importances”.
In addition, we assessed if a consensus model, derived by
combining the separate predictions from the two tissues,
would improve the performances over the single-tissue
models. For this purpose, we obtained the predicted
class probabilities of each input sample from both tissues
with the function “predict_proba”; then for each sample
these probabilities were summed, and the class having
the highest sum was deemed as the predicted class of
this consensus model. The performance of the consensus
model was evaluated by using AUC-ROC score and con-
fusion matrices, as previously described.

Tracing farming sites

For the multiple class problem, instead of a RF classifier, a
multinomial logistic regression was set up with the function
"LogisticRegression" of the python sklearn library and the
following options: "multi_class='multinomial', solver='lbfgs',
penalty='l2', class_weight='balanced', fit_intercept=False,
C=1". To achieve a better final prediction, this "base" model
was coupled with bootstrap aggregation by using the ensem-
ble meta-estimator bagging classifier (with the function
"BaggingClassifier"). Cross-validation was used to define the
best parameters of the bagging classifier (i.e. the number of
estimators "n_estimators", ranging from 500 to 1500, and the
maximum number of features to train each base estimator
"max_features", ranging from 100 to 300) and the model with
the highest Cohen kappa score was chosen. Cross-validation
was performed on second-year data only (i.e. S19+W20
together) and a separate model for each tissue was built
and cross-validated independently. After cross-validation,
data from the second year was fitted on the optimised bag-
ging classifier model, and then this was used to predict data
on novel, never-seen-before data from the first year (i.e.
S18+W19) on each tissue separately.

The performance of the model in predicting the prove-
nance of clams was assessed as described in the previous
section. Similarly, the assessment of the performances
of a consensus model derived by combining the sepa-
rate predictions from the two tissues was performed as
described above. Finally, for each model, the most rel-
evant features were extracted using a custom python
function and, for each farming site, the top 15 features
were plotted using the R package ComplexHeatmap/
v2.14.0 [43].

Abbreviations

<table>
   <tr>
    <td>
     MP
    </td>
    <td>
     Microbiome profiling
    </td>
   </tr>
   <tr>
    <td>
     ML
    </td>
    <td>
     Machine learning
    </td>
   </tr>
   <tr>
    <td>
     GI
    </td>
    <td>
     Gills
    </td>
   </tr>
   <tr>
    <td>
     DG
    </td>
    <td>
     Digestive gland
    </td>
   </tr>
   <tr>
    <td>
     ASVs
    </td>
    <td>
     Amplicon sequence variants
    </td>
   </tr>
   <tr>
    <td>
     PCBs
    </td>
    <td>
     Polychlorobiphenyls
    </td>
   </tr>
   <tr>
    <td>
     FA
    </td>
    <td>
     Fatty acids
    </td>
   </tr>
   <tr>
    <td>
     MP
    </td>
    <td>
     Microbiome profiling
    </td>
   </tr>
   <tr>
    <td>
     MA
    </td>
    <td>
     Marano
    </td>
   </tr>
  </table>
CL Chioggia

SC Scardovari

GO Goro

PM Porto Marghera

RF Random forest

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12915-024-02005-w.

Additional file 1: Table S1. Precision, recall, f1-score and support obtained in gills (GI) and digestive gland (DG) obtained in comparing Porto Marghera versus Chioggia (PM vs CL), Porto M'Arghera versus Scardovari (PM vs SC), farmin sites. Feature importance in gills and digestive are also reported.

Additional file 2: Figure S1. Confusion matrices showing the results of the ML predicted provenance ("Predicted label") versus the real provenance ("True label") for each of the tested samples by using gills (GI) only (left column), digestive gland (DG) only (middle column) or by combining GI and DG into a consensus prediction (right column). **A)** Classification discriminating between the polluted site PM and the clean farming sites of MA. **B)** Classification discriminating between the polluted site PM and the clean farming sites of GO.

### Acknowledgements

Authors acknowledge the funding of the Italian Ministry of Education, University and Research (MIUR) for the project "Centro di Eccellenza per la Salute degli Animali Acquatici ECCE AQUA". Authors are grateful to the following cooperatives/companies for their assistance in obtaining samples and/or assistance during the depuration process: Cam—Conservificio Allevatori Molluschi Srl, Blupesca Srl, cooperativa raccolta allevamento molluschi eduli C.R.A.M.E., Naturedulis, Finpesca SPA, Cooperativa Agricola ALMAR.

### Authors' contributions

LB2, MM, TP and PF conceived the project; LB1, AZ, ADB and FS coordinated sampling activities and clams depuration. GDR, BC, MS and SI performed animal sampling, DNA extraction and Qiime2 analyses; LP and PF performed machine learning analyses; FC and LB2 wrote the first draft of the manuscript with input from LP and MM; all authors revised and critically commented on the manuscript. All authors read and approved the final manuscript.

### Funding

Open access funding provided by Università degli Studi di Padova. POR FESR 2014–2020 – Bando DGR n. 1139 del 19/07/2017 – Domanda di sostegno ID 10062983.

European project FishEUTrust (grant agreement 101060712) awarded to TP.

### Availability of data and materials

Raw DNA libraries were deposited at the NCBI repository under the BioProject access number: PRJNA1013079 [24]. The R code used to generate the input files can be found on GitHub at the following link [42]: https://github.com/GEMMA-BCA/Machine-Learning-assisted-Microbiome-analysis.

### Declarations

#### Ethics approval and consent to participate

Not applicable.

#### Consent for publication

Not applicable.

#### Competing interests

The authors declare that they have no competing interests.

#### Author details

Chioggia, Chioggia, Italy. ³LEMAR, UMR 6539 CNRS/UBO/IRD/FIREMER, Institut Universitaire Européen de La Mer, Place Nicolas Copernic, Plouzané 29280, France. ⁴Experteam S.R.L, Via Della Libertà, 12, Marghera 30175, Italy. ⁵Almar Soc. Coop. Agricola Arl, Via G. Raddi, 2, Marano Lagunare 33050, Italy. ⁶Department of Medical Sciences, University of Torino, Via Santena 19, Turin 10126, Italy.

Received: 17 January 2024 Accepted: 3 September 2024

Published online: 11 September 2024

### References

1. Thomsen ST, Assunção R, Afonso C, Boué G, Cardoso C, Cubadda F, et al. Human health risk–benefit assessment of fish and other seafood: a scoping review. Crit Rev Food Sci Nutr. 2022;62:7479–502.

2. Mamede R, Ricardo F, Santos A, Díaz S, Santos SAO, Bispo R, et al. Revealing the illegal harvesting of Manila clams (*Ruditapes philippinarum*) using fatty acid profiles of the adductor muscle. Food Control. 2020;118:107368.

3. Martini A, Aguiari L, Capoccioni F, Martinoli M, Napolitano R, Pirlo G, et al. Is Manila clam farming environmentally sustainable? A Life Cycle Assessment (LCA) approach applied to an Italian Ruditapes philippinarum hatchery. Sustainability. 2023;15(4):3237.

4. Humphreys J, Harris MRC, Herbert RJH, Farrell P, Jensen A, Cragg SM. Introduction, dispersal and naturalization of the Manila clam Ruditapes philippinarum in British estuaries, 1980–2010. J Mar Biol Assoc UK. 2015;95:1163–72.

5. Turolla E, Castaldelli G, Fano EA, Tamburini E. Life cycle assessment (LCA) proves that Manila clam farming (Ruditapes philippinarum) is a fully sustainable aquaculture practice and a carbon sink. Sustainability. 2020;12(13):5252.

6. Bernardini I, Matozzo V, Valsecchi S, Peruzza L, Rovere GD, Polesello S, et al. The new PFAS C6O4 and its effects on marine invertebrates: First evidence of transcriptional and microbiota changes in the Manila clam Ruditapes philippinarum. Environ Int. 2020;2021(152):106484.

7. Masanja F, Yang K, Xu Y, He G, Liu X, Xu X, et al. Bivalves and microbes: a mini-review of their relationship and potential implications for human health in a rapidly warming ocean. Front Mar Sci. 2023;10:1–9.

8. Gray M, Barbour N, Campbell B, Robillard AJ, Todd-Rodriguez A, Xiao H, et al. Ecolabels can improve public perception and farm profits for shellfish aquaculture. Aquac Environ Interact. 2021;13:13–20.

9. Varrà MO, Zanardi E, Serra M, Conter M, Ianieri A, Ghidini S. Isotope fingerprinting as a backup for modern safety and traceability systems in the animal-derived food chain. Molecules. 2023;28(11):4300.

10. Santos A, Ricardo F, Domingues MRM, Patinha C, Calado R. Current trends in the traceability of geographic origin and detection of species-mislabeling in marine bivalves. Food Control. 2023;152:109840.

11. Araújo DF, Ponzevera E, Knoery J, Briant N, Bruzac S, Sireau T, et al. Can copper isotope composition in oysters improve marine biomonitoring and seafood traceability? J Sea Res. 2023;191:102334.

12. Milan M, Maroso F, DallaRovere G, Carraro L, Ferraresso S, Patarnello T, et al. Tracing seafood at high spatial resolution using NGS-generated data and machine learning: comparing microbiome versus SNPs. Food Chem. 2018;2019(286):413–20.

13. Zhukova NV. Fatty acids of marine mollusks: Impact of diet, bacterial symbiosis and biosynthetic potential. Biomolecules. 2019;9:1–25.

14. Grahl-Nielsen O, Jacobsen A, Christophersen G, Magnesen T. Fatty acid composition in adductor muscle of juvenile scallops (*Pecten maximus*) from five Norwegian populations reared in the same environment. Biochem Syst Ecol. 2010;38:478–88.

15. Liu X, Teixeira JS, Ner S, Ma KV, Petronella N, Banerjee S, et al. Exploring the potential of the microbiome as a marker of the geographic origin of fresh seafood. Front Microbiol. 2020;11:1–9.

16. Singh P, Williams D, Velez FJ, Nagpal R. Comparison of the gill microbiome of retail oysters from two geographical locations exhibited distinct microbial signatures: a pilot study for potential future applications for monitoring authenticity of their origins. Appl Microbiol. 2022;3:1–10.

17. Cohen FPA, Pimentel T, Valenti WC, Calado R. First insights on the bacterial fingerprints of live seahorse skin mucus and its relevance for traceability. Aquaculture. 2018;492:259–64.

18. Tatsadjieu NL, Maiworé J, Hadjia MB, Loiseau G, Montet D, Mbofung CMF. Study of the microbial diversity of *Oreochromis niloticus* of three lakes

<footnote n="1">¹Department of Comparative Biomedicine and Food Science, University of Padova, Viale Dell'Università 16, Legnaro 35020, Italy. ²La Vongola Verace Di</footnote>
of Cameroon by PCR-DGGE: application to the determination of the geographical origin. Food Control. 2010;21:673-8.

19. Milan M, Bernardini I, Bertolini C, DallaRovere G, Manuzzi A, Pastres R, et al. Multidisciplinary long-term survey of Manila clam grown in farming sites subjected to different environmental conditions. Sci Total Environ. 2022;2023(863):160796.

20. Iannello M, Mezzelani M, DallaRovere G, Smits M, Patarnello T, Ciofi C, et al. Long-lasting effects of chronic exposure to chemical pollution on the hologenome of the Manila clam. Evol Appl. 2021;14:2864-80.

21. Smith CJ, Danilowicz BS, Meijer WG. Bacteria associated with the mucus layer of Merlangius merlangus (whiting) as biological tags to determine harvest location. Can J Fish Aquat Sci. 2009;66:713-6.

22. Le Nguyen DD, Ngoc HH, Dijoux D, Loiseau G, Montet D. Determination of fish origin by using 16S rDNA fingerprinting of bacterial communities by PCR-DGGE: an application to Pangasius fish from Viet Nam. Food Control. 2008;19:454-60.

23. Zampieri A, Carraro L, Cardazzo B, Milan M, Babbucci M, Smits M, et al. Depuration processes affect the Vibrio community in the microbiota of the Manila clam. Ruditapes philippinarum Environ Microbiol. 2020;22:4456-72.

24. Peruzza L. The use of microbiota profile to trace the Manila clam provenance. NCBI Bioproject accession: PRJNA1013079. 2023. https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1013079.

25. Leal MC, Pimentel T, Ricardo F, Rosa R, Calado R. Seafood traceability: current needs, available tools, and biotechnological challenges for origin certification. Trends Biotechnol. 2015;33:331-6.

26. Giusti A, Tosi F, Tinacci L, Guardone L, Corti I, Arcangeli G, et al. Mussels (Mytilus spp.) products authentication: a case study on the Italian market confirms issues in species identification and arises concern on commercial names attribution. Food Control. 2020;118:107379.

27. Lawrence S, Elliott C, Huisman W, Dean M, van Ruth S. The 11 sins of seafood: assessing a decade of food fraud reports in the global supply chain. Compr Rev Food Sci Food Saf. 2022;21:3746-69.

28. Parrondo M, López S, Aparicio-Valencia A, Fueyo A, Quintanilla-García P, Arias A, et al. Almost never you get what you pay for: Widespread mislabeling of commercial "zamburiñas" in northern Spain. Food Control. 2021;120:2020.

29. Bernardini I, Quagliariello A, Peruzza L, Martino ME, DallaRovere G, Iori S, et al. Contaminants from dredged sediments alter the transcriptome of Manila clam and induce shifts in microbiota composition. BMC Biol. 2023;21:234.

30. Cecchetto M, Giubilato E, Bernardini I, Bettiol C, Asnicar D, Bertolini C, et al. A Weight of Evidence approach to support the assessment of the quality of Manila clam farming sites in a coastal lagoon. Mar Pollut Bull. 2023;197:115668.

31. Akbar S, Gu L, Sun Y, Zhang L, Lyu K, Huang Y, et al. Understanding host-microbiome-environment interactions: Insights from Daphnia as a model organism. Sci Total Environ. 2022;808:1-9.

32. Scientific, Technical and Economic Committee for Fisheries – Economic Report on the EU aquaculture (STECF-22-17). Nielsen R, Virtanen J, Guillen Garcia J, editors. Luxembourg: EUR 28359 EN, Publications Office of the European Union; 2023. JRC132648. https://doi.org/10.2760/51391. https://publications.jrc.ec.europa.eu/repository/handle/JRC132648.

33. Mamede R, Santos A, Díaz S, da Ferreira Silva E, Patinha C, Calado R, et al. Elemental fingerprints of bivalve shells (Ruditapes decussatus and R. philippinarum) as natural tags to confirm their geographic origin and expose fraudulent trade practices. Food Control. 2022;135:108785.

34. Graham KA, Gomez J, Primm TP, Houston R. Comparison of nine extraction methods for bacterial identification using the ONT MinION sequencer. Int J Legal Med. 2024;138(2):351-60. https://doi.org/10.1007/s00414-023-03092-0.

35. Lee NY. A review on microscale polymerase chain reaction based methods in molecular diagnosis, and future prospects for the fabrication of fully integrated portable biomedical devices. Microchim Acta. 2018;185:1-22.

36. Mann BC, Bezuidenhout JJ, Swanevelder ZH, Grobler AF. MinION 16S datasets of a commercially available microbial community enables the evaluation of DNA extractions and data analyses. Data Brief. 2021;36:107036.

37. Bolyen E, Rideout J, Dillon M, Bokulich N, Abnet C, Al-Ghalith G, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852-7.

38. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10-2.

39. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581-3.

40. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol. 2013;30:772-80.

41. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in python fabian. J Machine Learn Res. 2011;2011(12):2825-30.

42. Peruzza L, Fariselli P. The use of microbiota profile to trace the Manila clam provenance. Source code used for the analyses. 2023. GitHub: https://github.com/GEMMA-BCA/Machine-Learning-assisted-Microbiome-analysis.

43. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32:2847-9.

**Publisher's Note**

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.