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. 2022 Nov 9;16:100497. doi: 10.1016/j.fochx.2022.100497

The origin of mussels (Mytilus galloprovincialis): NIRS explanatory identification and the effect on consumers

Sharon Puleo a, Rossella Di Monaco a,b,⁎, Antonio Luca Langellotti b, Paolo Masi a,b
PMCID: PMC9743154  PMID: 36519095

Highlights

  • •

    The NIRS-based method allows to identify mussels from different geographic areas.

  • •

    The origin information influences the consumers’ willingness to buy mussels.

  • •

    Consumers' liking is driven by both origin information and mussels’ physical characteristics.

Keywords: Geographic origin identification, Traceability, Near-infrared spectroscopy, Consumers’ preferences, Consumers’ perception

Abstract

This study aimed to explore the possibility of using Near-infrared spectroscopy (NIRS) for the authentication of mussels from six different geographical areas (Campania (South-Italy), Marche (Central-Italy), Veneto (North-Italy), Greece, Ireland and Spain). Secondly, the influence of mussels origin information on liking and willingness to buy was investigated on 130 consumers (from Campania region) by conducting an online consumer test, under blind and informed conditions.

NIRS- based classification method clearly identified six clusters corresponding to mussels with different origins. Also, consumers resulted strongly influenced by the origin information, preferring the familiar and local mussels compared to those from other geographic regions.

Compared to methods based on chemical analysis, NIRS identification does not require reagents and is simpler, faster, more economical, and environmentally safer. Therefore, considering the importance of origin information perceived by consumers, NIRS could be a useful analytical method for mussels control to guarantee their authentication and traceability.

Introduction

Over the last five decades, world production of marine bivalves, particularly mussels (Mytilus galloprovincialis), has rapidly increased, reaching>17 million tonnes in 2020. (FAO, 2020). The increase in production has led to many food safety alerts mainly due to false labelling declarations (Galimberti, 2013). Consequently, to protect consumer interests and guarantee the food safety, authentication and traceability are critical (Li et al., 2016, Zhao et al., 2019). The EU Regulation, 2065/2001 regulates the traceability of sea products along the entire supply chain and the label information that must be provided to the consumer: the commercial name of the species, the origin and the production method (fished or farmed). Accordingly, traceability along the fish supply chain is a requirement shared by consumers, producers, and distributors

However, despite the evolution of the regulatory framework for seafood traceability, there are still several issues related to the authenticity and traceability of the sea-food area. According to the studies conducted by Charlebois (2014), and later by Costa Leal, Pimentel, Ricardo, Rosa, and Calado (2015) seafood traceability mainly suffer from a lack of information on routine audits of traceability procedures.

Secondly, those studies highlighted the still need for cheap and easy-to-use methods to address the seafood origin. Currently, the available methods used in traceability systems are geochemical (trace element finger-printing (TEF), Carson, 2013), biochemical (fatty acids composition, Kelly et al., 2012), and/or molecular (polymerase chain reaction (PCR) for DNA amplification (Galimberti, 2013) tools.

These techniques need to be performed by experts in the lab and cannot be applied at multiple trading locations throughout the supply chain. On the contrary, an ideal tool for tracing seafood products should be easy to perform, quick and cheap.

Near-infrared spectroscopy (NIRS) is perfectly in line with these characteristics. Indeed, as largely reported, NIRS has been used for food and beverage authentication showing to be particularly simple, cheap, versatile and also faster than many other techniques (Ottavian et al., 2012). In particular, the studies conducted by Novelli et al. (2009) and later by Ottavian et al. (2012) showed how NIRS successfully discriminated wild and farmed sea bass, with the same classification performance of the chemical and morphometric methods.

The geographical origin of marine bivalves is a cue of quality and needs to be guaranteed to protect both consumers and producers. However, to the best of our knowledge, the NIRS has not been applied to mussels.

Moreover, the origin of food products influences consumer choice in different ways. First, representing a cue for other quality properties such as sensory characteristics, the origin drives the expected quality (Verlegh & Van Ittersum, 2001). Secondly, the origin of a food product plays a pivotal role in determining consumer choices led by symbolic, affective or ethical reasons (Stefani, Romano, & Cavicchi, 2006). As a result, any reference to the origin of food conveys a sense of tradition and authenticity (Bell & Valentine, 1997).

The aim of this study was twofold. First, the possibility of using NIRS for the authentication of mussels from different geographical areas was explored. Secondly, the influence of mussels origin information on liking and willingness to buy was investigated on 130 consumers by conducting an online consumer test. Image analysis (length and colour parameters) was also conducted to physically characterise the mussels of different origins.

Materials and methods

Samples

Mussels were purchased from a local retailer which guaranteed the different provenance origins (detailed samples information are reported as supplementary data, in Table S1). Mussels from Campania (South-Italy), Marche (Central-Italy), Veneto (North-Italy), Greece, Ireland and Spain were collected for both the consumer test and NIRS analysis. Samples were eventually stored and refrigerated temperature (4 ± 1 °C) and used for analysis within 24 h of the collection time. The length (cm) of all the samples was measured with a millimetre calibre.

NIRS analysis

After cleaning and shelling, three aliquots of 15 individuals (approximately 15 g each) each were drained for 30 s on a filter paper and immediately placed in a 10 cm diameter Petri dish and scanned (10 times each) in reflectance mode at 4 cm-1 intervals from 4000 to 10000 cm-1 (NIRFLEX N-500, Buchi, Italy).

Image analysis

The image analysis was performed by a software-managed electronic eye (IRIS - Alpha M.O.S. Visual Analyzer VA400 – 500x, France). After the automatic calibration with the colour checker, samples were placed inside the capture chamber and pictures were taken. The software available in the instrument (Alphasoft, version 14.0) was used for the image (colour tones screening) and statistical analyses.

Consumer test

An online questionnaire was created using the Google Moduli platform and filled in by 130 consumers (females = 78; age average = 34 y.o.) from Campania region. The questionnaire was constituted of three sections, as presented in Table S2 of supplementary data. In the first section, a blind test was conducted, and consumers were asked to look at six pictures of fresh mussels with different origins (from Campania, Marche, Veneto, Greece, Ireland and Spain) identified with random three-digit codes. Willingness to buy (Yes/No answer) and expected liking (9-point Likert scale) were asked for each blind sample. In the second section, an informed test was conducted. Therefore, the procedure was the same (willingness to buy and global liking), but the samples were identified with their origin. In the last section, consumers were asked to indicate their gender, age, the consumption frequency of fresh mussels (responses were recorded on a 5-point category scale: never, 1 time/month, 2 time–3 times/month, 1 time/week, more) and the general liking for fresh mussels (9-point Likert scale: 1 = totally disliked; 9 = totally liked). Consumers who declared to not consume fresh mussels (“never” on the 5-point scale) or not like the fresh mussels were excluded from the dataset.

Participants signed written informed consent according to the principles of the Declaration of Helsinki and the ethical standards of the University of Naples Federico II. The privacy rights of human subjects were always observed and names and surnames were not collected. The extended content of the questionnaire is reported as supplementary data (S3).

Data analysis

Regarding the NIRS data, first, a comparison among all raw spectral data of mussels samples was performed by one-way analysis of variance (ANOVA). A mean spectrum, calculated by NIRFLEX N-500 software deriving from all spectra acquired for each sample of mussel, was used in a Principal Component Analysis (PCA) and the factor loadings of the first two components were extrapolated. Secondly, to reduce the light scattering and to remove the additional variation in base-line shift typically present in diffused reflectance spectra, mathematical pre-treatments were applied, as suggested by de la Roza-Delgado et al. (2007). The standard normal variate (SNV), first and second derivatives were calculated using Unscrambler X (version 10.4, CAMO software) to determine the best identification strategy. The models were evaluated considering the percentage of correctness extrapolated from the confusion matrix of the estimation sample and the cross-validation results. PCA was run on pre-treated data and the new scores were then analysed by Discriminant Factor Analysis (DFA) using XLSTAT (version 2016.02, Addinsoft).

Finally, the predictive power of PCoA axes for the prediction of mussels origin was assessed using a supervised logistic regression for a 2-class classification model. The construction, parameter tuning, and validation of the model were performed using the R caret package (https://CRAN.R-project.org/package=caret). The predictive model was used to determine the Campania or Spain origins based on the PCoA axes (from 1 to 7) as input data and the random forest as method for classification (the number of trees was set to 500 by default). Training input data was restricted to the samples with the status “Campania” (n = 6), or “Spain” (n = 2) representing 80 % of the cohort, while the test data was represented by “Campania” (n = 2, that is the 20 % of the cohort), randomly drawn. The global robustness of the model was evaluated through a Leave-One-Out Cross-Validation resampling method, while the performance was estimated on the test cohort with the area under the curve, sensitivity, and specificity.

To study the effect of origin on the willingness to buy, blind and informed data were analysed by Mann-Whitney (two-tailed) test. With the same aim, blind and informed liking data were analysed by t-test. XLSTAT (version 2016.02, Addinsoft) was used to analyse the consumer test data and the significance level was set at p < 0.05.

From the image analysis, 98 colour tones came out. Hierarchical clustering analysis was carried out to group the different tones in colour families, obtaining in this way 18 clusters (Alphasoft, version 14.0).

A Principal Component Analysis was run using the consumer data (willingness to buy and liking scores, in blind and informed conditions) as active variables. The 18 colour families and the length (cm) of the samples were used as supplementary variables.

Results and discussion

NIRS analysis

Fig. 1 shows the average spectra of the six examined mussels (Absorbance vS Wavelengths) and the distribution of the F-Fisher values extrapolated from the ANOVA model, in function of the explored wavelengths (nm). The F-Fisher distribution helps the reader to identify the wavelength ranges where the spectra are significantly different from each other. Below the dotted baseline (F = 2.261) the spectra are not significantly different (p > 0.05).

Fig. 1.

Fig. 1

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Average NIR spectra of mussels from six different origins (Inline graphic Campania; Inline graphic Greece; Inline graphic Veneto; Inline graphic Ireland; Inline graphic Spain; Inline graphic Marche). The dotted straight line (baseline) is the level of significance (p ≤ 0.05) for the Fisher value (F) obtained in the ANOVA. Below the dotted baseline, the F values do not show significant differences among spectra (p > 0.05).

Considering the untreated NIR spectra, the main statistical differences (p ≤ 0.05) between the samples were observed between 1000 and 2000 nm. As it can be seen from Fig. 1, the dominating absorption bands of mussels’ spectra were at 1933 nm 1 and 1454 nm, and at 1792 nm. According to the study conducted by Zhao, Chen, Huang, and Fang (2006), these bands could be mainly attributable to the 2nd and 1st overtone and the combination absorption band, of free and trapped water, respectively. This is in agreement with the high water content of the mussels (about 80 %). However, also polysaccharides could contribute to some of those wavenumbers, considering the absorption of alcoholic OH and C Created by potrace 1.16, written by Peter Selinger 2001-2019 O groups. Also, we expected the first overtone combination of CH, CH2 and CH3 groups between 1350 and 1400 nm; a gentle shoulder at about 1369 nm could be corresponding to those compounds. Similarly, fatty acids, protein, volatile and non-volatile acids and some aroma compounds could be responsible for absorption at 1193 nm which identifies the second overtone of CH linkages arising from CH2 and CH3 groups (Zhao et al., 2006).

Moreover, as can be observed in Fig. 2, the wavenumbers of 1160 nm and 1360 nm resulted in the highest contributions (loadings) on PC1. These signals correspond to the band at 1164 nm and 1369 nm in mussels’ spectra ascribable to methyl, methylene, and methoxy absorptions. This outcome suggests that the differences in alkyl chains mainly drive the discriminating property of mussels’ spectra on PC1. Considering the PC2, the most significant loadings were observed at 1454 nm and 1792 nm frequencies, corresponding to the OH signals of water and polysaccharides on mussels’ spectra.

Fig. 2.

Fig. 2

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Loadings line plots (-PC1 and --PC2) of raw NIR spectra.

Since mussels are filter-feeders whose diet consists mainly of phytoplankton (Alkanani, Parrish, Thompson, & McKenzie, 2007) to acquire proteins, lipids, carbohydrates, and other components, the chemical composition of mussels is primarily dependent on the aquatic environment quality (Orban et al., 2002). Moreover, the meat yield, chemical composition, and lipid profile of mussels are strongly influenced by water temperature and food availability. Consequently, different production sites, and especially different origins, with different conditions, promote changes in the growth and nutritional composition of mussels (Olivera et al., 2015). In particular, several studies demonstrated the influence of the different environmental and nutritional conditions on the composition of mussels (Astorga-España et al., 2007, Khan et al., 2006), especially including the free amino-acid profile, the fatty acid composition or the volatile fraction (McLean & Bulling, 2005; Fuentes, Fernández-Segovia, Escriche, & Serra, 2009). Also, the identified differences could be related to different aromatic profiles. Some aromatic compounds identified in mussels could be due to petroleum contamination since the uptake of aromatic hydrocarbons has previously been reported in the study of Ruiz et al. (2011). Accordingly, different areas may have different contamination levels as reported by Fuentes et al., 2009. Another main factor influencing the biochemical composition of mussels is the water temperature. Indeed, Ventrella et al. (2008) and later Fernández et al. (2015) concluded that the seasonal variations observed for the biochemical parameters studied (especially for glycogen, proteins and fatty acids) were closely linked to the water temperature. In particular, the highest accumulation of glycogen was observed during spring and summer, when the temperature is warmer, while a depletion was mainly observed during autumn and winter. Therefore, the differences noted in the present study could be also due to the differences in temperature of the different origin sites.

Furthermore, as explained above, mathematical pre-treatments were applied to spectra data to remove the additional variation in the base-line shift typical of the reflectance spectra. Table 1 shows the confusion matrix for the estimation sample and the cross-validation results, considering the standard normal variate (SNV), first and second derivatives pre-treatments.

Table 1.

Confusion matrix for the estimation sample and the cross-validation results of SNV, first derivate and second derivate pre-treatments.

 Estimation
 Cross-validation
Sample origin SNV Derivate1 Derivate2 SNV Derivate1 Derivate2
Campania 92.77 % 95.18 % 73.49 % 87.95 % 93.98 % 72.29 %
Marche 80.00 % 60.00 % 0.00 % 80.00 % 50.00 % 0.00 %
Greece 63.64 % 57.58 % 45.45 % 54.55 % 57.58 % 33.33 %
Ireland 100.00 % 90.00 % 40.00 % 90.00 % 76.67 % 36.67 %
Spain 85.00 % 85.00 % 65.00 % 85.00 % 85.00 % 65.00 %
Veneto 45.00 % 70.00 % 50.00 % 40.00 % 45.00 % 45.00 %
Total 82.65 % 83.63 % 56.63 % 75.22 % 77.04 % 53.06 %
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A confusion matrix was generated to summarize and quickly examine the % of well-reclassified observations, corresponding to the ratio of the number of observations well-reclassified over the total number of observations. Simultaneously, the resulting cross-validation was also examined to verify what would happen if a certain observation was excluded from the estimation sample. As can be observed in Table 1, SNV and first derivate pre-treatments produced the highest percentages of estimation (82.65 % and 83.63 %, respectively) and cross-validation (75.22 % and 77.04 %, respectively).

Fig. 3 shows the score plot from DFA (run on the first-derivate pretreated data) representing the observations on the factor axes. The total explained variance is equal to 91.30 %. This high value could suggest that the obtained discrimination is representative of the reality.

Fig. 3.

Fig. 3

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DFA results: centroids plot of mussels of six different origins.

As it can be observed, DFA clearly identified six clusters corresponding to mussels with different origins analysed by NIRS, well discriminated mostly on the first component (71.40 %).

Our results are in line with previous studies that used the NIRS for the authentication of seafood. Ottavian et al. (2012) successfully used NIRS for the authentication of wild European sea bass (Dicentrarchus labrax), obtaining the same results (in terms of performance) as the methods using chemical properties and morphometric traits. Also, Fasolato et al. (2012) successfully identified (81.1 % of accuracy) fresh and frozen-thawed swordfish (Xiphias gladius L) by using NIRS technology. In the present study, a classification model was built to evaluate the capacity of PCoA axes to predict mussels origin, reaching an overall predictive power with an area under the receiver operating characteristic curve of 0.9 (±0.1). However, this successful result needs to be interpreted with caution. Our sample size was quite little, and the few batches analysed were reproducible to each other.

The present study aimed to explore the potential of NIRS technology to identify mussels from different origins without explaining what drives the differences among the samples. However, some speculations about the possible influencing factors can be drowned. The NIR spectra contain information about the chemical composition and surface-specific physical properties of samples (Brunner, Eugster, Trenka, & Bergamin-Strotz, 1996). As largely reported, the mussel tissues and shells composition (including the inorganic element bioaccumulation) is influenced by the geographical origin, but weight, dimensions, and spatial-environmental distribution also play a significant role (Perošević et al., 2018). Forleo et al. (2021) efficiently discriminated mussels of different origins using the multi-elemental composition of mussel shells assessed by inductively-coupled plasma mass spectrometry. In turn, bioaccumulation of trace elements in mussels is influenced not only by several biological characteristics (such as sex, size, sexual maturity, reproduction stages, etc.) but also by environmental factors (including pH, temperature, salinity, dissolved oxygen, organic carbon, etc.) (Fattorini et al., 2008, Mubiana and Blust, 2007). Therefore, the levels of trace elements in mussels can be used to establish the geographical differences (Fattorini et al., 2008).

Considering all the influencing factors on the multi-elemental composition, Zhao et al. (2019) used the neodymium isotopic ratio (143Nd/144Nd ratios) to assess the geographical traceability of mussels collected in Japanese and Chinese coastal waters. They successfully discriminated the mussels in function of their origin, observing that the ƐNd distribution and variability perfectly followed the geological characteristics of the watersheds.

Both the multi-elemental composition and the radiogenic isotopic ratios of the heavy elements technique resulted in successful and reliable in guaranteeing the geographical traceability of mussels. However, these techniques require expert knowledge and lab work time resulting inefficient for on-line applications. On the contrary, NIRS-based identification methods do not need to be necessarily performed by experts, being simpler, faster and also more economical, and environmentally safer. Therefore, NIRS could be an easy and fast method to guarantee the mussels' quality and traceability, protecting the consumers' and producers' interests.

Consumer test

Consumers were asked to evaluate their willingness to buy six samples of mussels first in blind condition and then with the origin information. Fig. 4 shows the frequencies (%) of positive and negative answers given by 130 consumers from Campania region.

Fig. 4.

Fig. 4

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Frequencies (%) of positive (Inline graphic Yes) and negative (Inline graphic No) answers collected for blind and informed samples of six different mussels. *Significant differences (p ≤ 0.05).

As expected and according to the literature (Felzensztein et al., 2004, Di Monaco et al., 2005), going from blind to informed evaluation, the number of negative answers increased for all the evaluated samples, except for the Campania ones. The willingness to buy resulted significantly affected by origin information only for the samples from Campania, Ireland and Spain (p ≤ 0.05). In particular, by considering samples from Ireland, when they were evaluated under blind conditions, 63 % of consumers would buy the represented mussels; when consumers were provided with the origin information, the percentage of positive answers significantly decreases from 63 % to 42 %. Also, considering samples from Spain, 77 % of consumers would buy the represented mussels in blind conditions; when consumers were provided with the origin information, the percentage of positive answers significantly decreased to 65 %. On the contrary, positive answers significantly increased (from 54 to 73 %) for the mussels from Campania region.

Consumers were also asked to score the expected liking of the six samples, first under blind conditions and then with the origin information.

No significant differences in terms of expected liking were observed for all the evaluated samples, except for mussels from Campania region. In that case, the average expected liking score significantly increases when consumers are informed about the mussels’ origin (blind = 4.3 ± 0.2 vS info = 5.3 ± 0.2).

The present results clearly show that signalling of origin influences the willingness to buy and expressed liking, demonstrating that local products are preferred over the same products from elsewhere. These outcomes are in line with the literature which reports how liking is driven by expectations. Indeed, the influence of consumers’ origin is enhanced by the fact that belonging to a specific geographical area generates such positive feelings towards food from that same area (van der Lans et al., 2001).

As well known, food liking and choice are dynamic phenomena influenced by many interacting factors (with physiological, psychological, socio-cultural and economic meanings) related to both the consumers and food products (Hirschman, Carscadden, Fleischauer, Hasak, & Mitchell, 2004). Food origin plays a pivotal role in consumers’ decision, influencing the individual attitudes, the willingness to buy and finally the choice (Van Ittersum, Candel, & Meulenberg, 2003). In the study conducted by Dransfield et al. (2005), the involved subjects declared to prefer pork labelled as raised in their own country compared to imports. Similarly, in the Swedish study conducted by Ekelund, Fernqvist, and Tjärnemo (2007), consumers significantly preferred tomatoes labelled “native” compared to imports (Ekelund et al., 2007). Also, origin information showed similar effects in the studies conducted by Resano et al., 2007, Iaccarino et al., 2006, in which local cured ham and local salami, respectively, resulted significantly preferred compared to imports.

However, food preferences are also moved by other food characteristics, such as the aspect which may lead consumers to anticipate differences in product properties and the consumers’ purchase probability (Spence et al., 2010, Schifferstein et al., 2019). Food acceptability, intended as perceived sensory quality and preference, is affected by colour (Fernández-Vázquez, Stinco, Meléndez-Martínez, Heredia, & Vicario, 2011).

In the present study, consumer test and physical (image analysis and length) data were analyzed by Principal Component Analysis (PCA). Fig. 5 shows the resulting biplot obtained from PCA, including both observations (samples) and variables (active and supplementary), on the first two dimensions accounting for the 94.47 % of the variance.

Fig. 5.

Fig. 5

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PCA of consumer test and physical data of six different mussels. Inline graphic Observations (samples); Inline graphic Active variables (consumer data) Inline graphic Supplementary variables (physical data).

As it can be observed, the six different samples were characterised by different shadows of colours whose chromatic codes are reported on the graph. Secondly, mussels from Spain were mainly characterised by length and resulted in the most preferred samples when evaluated under blind conditions. On the other hand, if evaluated together with the origin information, mussels from Campania region resulted more associated with willingness to buy.

Therefore, colour features and size influence the liking and willingness to buy mussels, especially if evaluated in blind conditions. Indeed, liking and willingness to buy in blind conditions resulted positively correlated with mussels from Spain. It is worth noting that mussels from Spain are very common in the Campania region market, therefore the specific colour variety is reasonably familiar to consumers, leading to certainty about what qualities experiences to expect.

Strengths and limitations

The strength of this study refers to the practical characteristics of the NIR spectroscopy. This technique is indeed low-cost, non-destructive and not time-consuming. However, our findings should be interpreted under the limitations of this research. Indeed, the study was somewhat limited by the restricted time of analysis. Mussels were collected and analysed for a limited period of the year. Considering that this limit might impact the validity of NIRS prediction, these specific results should not be generalised to the yearly population. Nevertheless, the manuscript may offer to the readers a procedure that can be easily repeated and validated with a higher number of samples from different origins and seasonability.

Conclusions

The present study aimed to investigate the possibility of using NIRS for the authentication of mussels from different geographical areas and highlight the importance of the origin information felt by consumers living in Campania region (Italy).

NIR spectroscopy perfectly allows to identify mussels from different origins. This technique resulted in easy-to-perform, reliable and mostly, not time-consuming. NIRS could be used for on-line applications along the supply chain, which could help the monitoring and control systems.

Consumers resulted influenced by the origin information both in the willingness to buy and global liking. Therefore, the origin guarantee is a pivotal need for consumers and needs to be a just as pivotal objective for producers, retailers and, ultimately, authorities.

Institutional Review Board Statement.

The study was conducted in agreement with the guidelines of the Declaration of Helsinki and the Italian ethical requirements on research activities and personal data protection (D.L. 30.6.03n. 196). Informed consent was obtained from all subjects involved in the study.

Funding sources

This work is part of ISSPA Project, PO FEAMP 2014/2020, Campania Region, MIPAAF, EU, Mis. 5.68, CUP B69E19000700009.

CRediT authorship contribution statement

Sharon Puleo: Methodology, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Rossella Di Monaco: Conceptualization, Methodology, Writing – review & editing, Supervision. Antonio Luca Langellotti: Resources, Supervision, Project administration. Paolo Masi: Conceptualization, Methodology, Writing – review & editing, Funding acquisition.

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.

Acknowledgements

The authors are grateful to all of the volunteers for participating in this study.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.fochx.2022.100497.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary data 1
mmc1.docx (12.5KB, docx)
Supplementary data 2
mmc2.docx (12.7KB, docx)
Supplementary data 3
mmc3.docx (1.2MB, docx)

Data availability

The authors do not have permission to share data.

References

  1. Alkanani T., Parrish C.C., Thompson R.J., McKenzie C.H. Role of fatty acids in cultured mussels, Mytilus edulis, grown in Notre Dame Bay. Newfoundland. J. Exp. Mar. Biol. Ecol. 2007;348:33–45. doi: 10.1016/j.jembe.2007.02.017. [DOI] [Google Scholar]
  2. Astorga-España M.S., Rodríguez-Rodríguez E.M., Díaz-Romero C. Comparison of mineral and trace element concentrations in two molluscs from the Strait of Magellan (Chile) Journal of Food Composition and Analysis. 2007;20(3–4):273–279. doi: 10.1016/j.jfca.2006.06.007. [DOI] [Google Scholar]
  3. Brunner M., Eugster R., Trenka E., Bergamin-Strotz L. FT-NIR spectroscopy and wood identification. Wood Research and Technology. 1996;50(2) doi: 10.1515/hfsg.1996.50.2.130. [DOI] [Google Scholar]
  4. Carson H.S., et al. Temporal, spatial, and interspecific variation in geochemical signatures within gish otoliths, bivalve larval shells, and crustacean larvae. Marine Ecology Progress Series. 2013;473:133–148. doi: 10.3354/meps10078. [DOI] [Google Scholar]
  5. Charlebois S., et al. Comparison of global food traceability regulations and requirements. Comprehensive Reviews in Food Science and Food Safety. 2014;13:1104–1123. doi: 10.1111/1541-4337.12101. [DOI] [Google Scholar]
  6. Costa Leal M., Pimentel T., Ricardo F., Rosa R., Calado R. Seafood traceability: Current needs, available tools, and biotechnological challenges for origin certification. Trends in Biotechnology. 2015;33:331–336. doi: 10.1016/j.tibtech.2015.03.003. [DOI] [PubMed] [Google Scholar]
  7. de la Roza-Delgado B., Soldado A., Martinez-Fernández A., Vicente F., Garrido-Varo A., Pérez-Marín D. Application of near-infrared microscopy (NIRM) for the detection of meat and bone meals in animal feeds: A tool for food and feed safety. Food Chemistry. 2007;105:1164–1170. doi: 10.1016/j.foodchem.2007.02.041. [DOI] [Google Scholar]
  8. Di Monaco R., Di Marzo S., Cavella S., Masi P. Valorization of traditional foods: The case of Provolone del Monaco cheese. British Food Journal. 2005;107(2):98–110. doi: 10.1108/00070700510579171. [DOI] [Google Scholar]
  9. Dransfield E., Ngapo T.M., Nielsen N.A., Bredahl L., Sjödén P.O., Magnusson M., et al. Consumer choice and suggested price for pork as influenced by its appearance, taste and information concerning country of origin and organic pig production. Meat Science. 2005;69:61–70. doi: 10.1016/j.meatsci.2004.06.006. [DOI] [PubMed] [Google Scholar]
  10. Ekelund L., Fernqvist F., Tjärnemo H. Consumer preferences for domestic and organically labelled vegetables in Sweden. Food Economics – Acta Agriculturae Scandinavica C. 2007;4:229–236. doi: 10.1080/16507540701800665. [DOI] [Google Scholar]
  11. FAO (2020) The State of World Fisheris and Aquaculture: Sustainability in action, Food and Agriculture Organization of the United Nations.
  12. Fattorini D., Notti A., Di Mento R., Cicero A.M., Gabellini M., Russo A., et al. Seasonal, spatial and inter-annual variations of trace metals in mussels from the Adriatic sea: A regional gradient for arsenic and implications for monitoring the impact of off-shore activities. Chemosphere. 2008;72(10):1524–1533. doi: 10.1016/j.chemosphere.2008.04.071. [DOI] [PubMed] [Google Scholar]
  13. Felzensztein C., Hibbert S., Vong G. Is the country of origin the fifth element in the marketing mix of imported wine? A critical review of the literature. Journal of Food Products Marketing. 2004;10(4):73–84. doi: 10.1300/J038v10n04_05. [DOI] [Google Scholar]
  14. Fernández A., Grienke U., Soler-Vila A., Guihéneuf F., Stengel D.B., Tasdemir D. Seasonal and geographical variations in the biochemical composition of the blue mussel (Mytilus edulis L.) from Ireland. Food chemistry. 2015;177:43–52. doi: 10.1016/j.foodchem.2014.12.062. [DOI] [PubMed] [Google Scholar]
  15. Fernández-Vázquez R., Stinco C.M., Meléndez-Martínez A.J., Heredia F.J., Vicario I.M. Visual and instrumental evaluation of orange juice color: A consumers' preference study. Journal of Sensory Studies. 2011;26:436–444. doi: 10.1111/j.1745-459X.2011.00360.x. [DOI] [Google Scholar]
  16. Forleo T., Zappi A., Melucci D., Ciriaci M., Griffoni F., Bacchiocchi S., et al. Inorganic Elements in Mytilus galloprovincialis Shells: Geographic Traceability by Multivariate Analysis of ICP-MS Data. Molecules. 2021;26(9):2634. doi: 10.3390/molecules26092634. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fuentes A., Fernández-Segovia I., Escriche I., Serra J.A. Comparison of physico-chemical parameters and composition of mussels (Mytilus galloprovincialis Lmk.) from different Spanish origins. Food chemistry. 2009;112(2):295–302. doi: 10.1016/j.foodchem.2008.05.064. [DOI] [Google Scholar]
  18. Galimberti A., et al. DNA barcoding as a new tool for food traceability. Food Research International. 2013;50:55–63. doi: 10.1016/j.foodres.2012.09.036. [DOI] [Google Scholar]
  19. Hirschman C.E., Carscadden N., Fleischauer L., Hasak M., Mitchell M. In: Kahn B.E., Luce M.F., editors. vol. 31. Provo (UT); Association for Consumer Research: 2004. Exploring the architecture of contemporary American foodways; pp. 548–553. (Advances in consumer research). [Google Scholar]
  20. Iaccarino T., Di Monaco R., Mincione A., Cavella S., Masi P. Influence of information on origin and technology on the consumer response: The case of soppressata salami. Food Quality and Preference. 2006;17:76–84. doi: 10.1016/j.foodqual.2005.08.005. [DOI] [Google Scholar]
  21. Kelly J., Scheibling R. Fatty acids as dietary tracers in benthic food webs. Marine Ecology Progress Series. 2012;446:1–22. doi: 10.3354/meps09559. [DOI] [Google Scholar]
  22. Khan M.A., Parrish C.C., Shahidi F. Effects of environmental characteristics of aquaculture sites on the quality of cultivated Newfoundland blue mussels (Mytilus edulis) Journal of Agricultural and Food Chemistry. 2006;54(6):2236–2241. doi: 10.1021/jf051587. [DOI] [PubMed] [Google Scholar]
  23. Li L., Boyd C.E., Sun Z. Authentication of fishery and aquaculture products by multi-element and stable isotope analysis. Food Chemistry. 2016;19:1238–1244. doi: 10.1016/j.foodchem.2015.08.123. [DOI] [PubMed] [Google Scholar]
  24. Mubiana V.K., Blust R. Effects of temperature on scope for growth and accumulation of Cd Co, Cu and Pb by the marine bivalve Mytilus edulis. Marine Environmental Research. 2007;63(3):219–235. doi: 10.1016/j.marenvres.2006.08.005. [DOI] [PubMed] [Google Scholar]
  25. Novelli E., Balzan S., Tenti S., Mirisola M., Benozzo F., Santomauro S., et al. Authentication of wild and reared sea bass by infrared spectroscopy nirs (near infrared reflectance spectroscopy) Italian Journal of Food Safety. 2009;69–73 doi: 10.4081/ijfs.2009.1146. [DOI] [Google Scholar]
  26. Oliveira A.R., Sykes A.V., Hachero-Cruzado I., Azeiteiro U.M., Esteves E. A sensory and nutritional comparison of mussels (Mytilus sp.) produced in NW Iberia and in the Armona offshore production area (Algarve, Portugal) Food chemistry. 2015;168:520–528. doi: 10.1016/j.foodchem.2014.07.082. [DOI] [PubMed] [Google Scholar]
  27. Orban E., Di Lena G., Nevigato T., Casini I., Marzetti A., Caproni R. Seasonal changes in meat content, condition index and chemical composition of mussels (Mytilus galloprovincialis) cultured in two different Italian sites. Food Chemistry. 2002;77:57–65. doi: 10.1016/S0308-8146(01)00322-3. [DOI] [Google Scholar]
  28. Ottavian M., Facco P., Fasolato L., Novelli E., Mirisola M., Perini M., et al. Use of Near-Infrared Spectroscopy for Fast Fraud Detection in Seafood: Application to the Authentication of Wild European Sea Bass (Dicentrarchus labrax) Journal of agricultural and food chemistry. 2012;60:639–648. doi: 10.1021/jf203385e. [DOI] [PubMed] [Google Scholar]
  29. Perošević A., Pezo L., Joksimović D., Đurović D., Milašević I., Radomirović M., et al. The impacts of seawater physicochemical parameters and sediment metal contents on trace metal concentrations in mussels—a chemometric approach. Environmental Science and Pollution Research. 2018;25(28):28248–28263. doi: 10.1007/s11356-018-2855-8. [DOI] [PubMed] [Google Scholar]
  30. Commission Regulation (EU) N. 2065/2001 of 22 October 2001 which establishes the methods of application of Regulation (EU) N. 104/2000 of the Council regarding consumer information in the fishery and aquaculture products sector.
  31. Resano H., Sanjuán A.I., Albisu L.M. Consumers’ acceptability of cured ham in Spain and the influence of information. Food Quality and Preference. 2007;18:1064–1076. doi: 10.1016/j.foodqual.2007.05.002. [DOI] [Google Scholar]
  32. Ruiz Y., Suarez P., Alonso A., Longo E., Villaverde A., San Juan F. Environmental quality of mussel farms in the Vigo estuary: Pollution by PAHs, origin and effects on reproduction. Environmental pollution. 2011;159(1):250–265. doi: 10.1016/j.envpol.2010.08.031. [DOI] [PubMed] [Google Scholar]
  33. Schifferstein H.N., Wehrle T., Carbon C.C. Consumer expectations for vegetables with typical and atypical colors: The case of carrots. Food Quality and Preference. 2019;72:98–108. doi: 10.1016/j.foodqual.2018.10.002. [DOI] [Google Scholar]
  34. Spence C., Levitan C.A., Shankar M.U., Zampini M. Does food color influence taste and flavor perception in humans? Chemosensory Perception. 2010;3:68–84. doi: 10.1007/s12078-010-9067-z. [DOI] [Google Scholar]
  35. Stefani G., Romano D., Cavicchi A. Consumer expectations, liking and willingness to pay for specialty foods: Do sensory characteristics tell the whole story? Food Quality and Preference. 2006;17(1):53–62. doi: 10.1016/j.foodqual.2005.07.010. [DOI] [Google Scholar]
  36. Van Ittersum K., Candel M.J.J.M., Meulenberg M.T.G. The influence of the image of a product’s region of origin on product evaluation. Journal of Business Research. 2003;56:215–226. doi: 10.1016/S0148-2963(01)00223-5. [DOI] [Google Scholar]
  37. Ventrella V., Pirini M., Pagliarani A., Trombetti F., Manuzzi M.P., Borgatti A.R. Effect of temporal and geographical factors on fatty acid composition of M. galloprovincialis from the Adriatic sea. Comparative Biochemistry and Physiology – B Biochemistry and Molecular Biology. 2008;149(2):241–250. doi: 10.1016/j.cbpb.2007.09.012. [DOI] [PubMed] [Google Scholar]
  38. Verlegh P.W.J., Van Ittersum K. In: Food, people and society. Frewer L., Risvik E., Schifferstein H., editors. Springer; Berlin: 2001. The origin of spices: The impact of geographic product origin on consumer decision making; pp. 267–280. [DOI] [Google Scholar]
  39. Zhao J., Chen Q., Huang X., Fang C.H. Qualitative identification of tea categories by near infrared spectroscopy and support vector machine. Journal of Pharmaceutical and Biomedical Analysis. 2006;41(4):1198–1204. doi: 10.1016/j.jpba.2006.02.053. [DOI] [PubMed] [Google Scholar]
  40. Zhao L., Tanaka K., Tazoe H., Iizuka T., Kubota K., Murakami-Sugihara N., et al. Determination of the geographical origin of marine mussels (Mytilus spp.) using 143Nd/144Nd ratios. Marine environmental research. 2019;148:12–18. doi: 10.1016/j.marenvres.2019.05.002. [DOI] [PubMed] [Google Scholar]

Associated Data

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Supplementary Materials

Supplementary data 1
mmc1.docx (12.5KB, docx)
Supplementary data 2
mmc2.docx (12.7KB, docx)
Supplementary data 3
mmc3.docx (1.2MB, docx)

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