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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">abb</journal-id>
      <journal-title-group>
        <journal-title>Advances in Bioscience and Biotechnology</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2156-8502</issn>
      <issn pub-type="ppub">2156-8456</issn>
      <publisher>
        <publisher-name>Scientific Research Publishing</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.4236/abb.2026.179025</article-id>
      <article-id pub-id-type="publisher-id">abb-153722</article-id>
      <article-categories>
        <subj-group>
          <subject>Article</subject>
        </subj-group>
        <subj-group>
          <subject>Biomedical</subject>
          <subject>Life Sciences</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>A Prevalence Study and Prevention Strategies for Shigella and Salmonella Contamination in Fruit and Vegetables in Kindia (Guinea)</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Bah</surname>
            <given-names>Mariama</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0003-2750-6570</contrib-id>
          <name name-style="western">
            <surname>Kolié</surname>
            <given-names>Bonaventure</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0003-3868-0710</contrib-id>
          <name name-style="western">
            <surname>Assogba</surname>
            <given-names>Abado Sylvestre</given-names>
          </name>
          <xref ref-type="aff" rid="aff3">3</xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0001-8630-3798</contrib-id>
          <name name-style="western">
            <surname>Sina</surname>
            <given-names>Haziz</given-names>
          </name>
          <xref ref-type="aff" rid="aff3">3</xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0000-9980-7901</contrib-id>
          <name name-style="western">
            <surname>Aïgbe</surname>
            <given-names>Agossou Marcellin</given-names>
          </name>
          <xref ref-type="aff" rid="aff3">3</xref>
        </contrib>
        <contrib contrib-type="author">
          <name name-style="western">
            <surname>Kalivogui</surname>
            <given-names>Siba</given-names>
          </name>
          <xref ref-type="aff" rid="aff4">4</xref>
        </contrib>
        <contrib contrib-type="author">
          <name name-style="western">
            <surname>Bah</surname>
            <given-names>Boubacar Sidy Sily</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
      </contrib-group>
      <aff id="aff1"><label>1</label> Faculty of Science, Department of Biology, University of Kindia, Kindia, Republic of Guinea </aff>
      <aff id="aff2"><label>2</label> Laboratory for Applied Research in Natural Sciences (LARASCINA), Kindia, Republic of Guinea </aff>
      <aff id="aff3"><label>3</label> Research Unit on Omics and Biomarkers of Living Systems (UR-OBioV), Laboratory of Biology and Molecular Typing in Microbiology-EWE MEDJI Polytechnic Centre, Abomey-Calavi, Benin </aff>
      <aff id="aff4"><label>4</label> Guinea Institute for Research in Applied Biology (IRBAG), Kindia, Republic of Guinea </aff>
      <author-notes>
        <fn fn-type="conflict" id="fn-conflict">
          <p>The authors declare no conflicts of interest regarding the publication of this paper.</p>
        </fn>
      </author-notes>
      <pub-date pub-type="epub">
        <day>09</day>
        <month>09</month>
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="collection">
        <month>09</month>
        <year>2026</year>
      </pub-date>
      <volume>17</volume>
      <issue>09</issue>
      <fpage>419</fpage>
      <lpage>437</lpage>
      <history>
        <date date-type="received">
          <day>14</day>
          <month>07</month>
          <year>2026</year>
        </date>
        <date date-type="accepted">
          <day>06</day>
          <month>09</month>
          <year>2026</year>
        </date>
        <date date-type="published">
          <day>09</day>
          <month>09</month>
          <year>2026</year>
        </date>
      </history>
      <permissions>
        <copyright-statement>© 2026 by the authors and Scientific Research Publishing Inc.</copyright-statement>
        <copyright-year>2026</copyright-year>
        <license license-type="open-access">
          <license-p> This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link> ). </license-p>
        </license>
      </permissions>
      <self-uri content-type="doi" xlink:href="https://doi.org/10.4236/abb.2026.179025">https://doi.org/10.4236/abb.2026.179025</self-uri>
      <abstract>
        <p>Salmonella and Shigella are ubiquitous pathogens. Fresh fruits and vegetables provide a favorable environment for the growth of these microorganisms. The objective of this study was to contribute to the assessment of the level of Salmonella and Shigella contamination in fruits and vegetables in various localities of Kindia. The study was conducted from July 2021 to June 2022 and involved a sample of 1630 specimens, including 557 fruits and vegetables, 577 irrigation water samples, and 496 organic fertiliser samples. Bacteriological methods were used to isolate <italic>Salmonella spp</italic>. and <italic>Shigella spp</italic>. The results showed a contamination rate of 19.57% for fruits and vegetables, with contamination rates varying by fruit and vegetable variety. No Shigella was detected in the samples. Overall, three serotypes of Salmonella were isolated: <italic>Salmonella typhi</italic> (0.54%), <italic>Salmonella typhimurium</italic> (9.87%), and <italic>Salmonella enteritidis</italic> (9.16%). The contamination of fruits and vegetables, irrigation water, and organic fertilisers with Salmonella and Shigella may be associated with poor agricultural practices, contaminated irrigation water, or inadequately composted organic fertilisers. However, these potential sources of contamination were not statistically evaluated in the present study. Appropriate measures must be implemented to limit and prevent the contamination of fruits and vegetables, with the aim of preventing the proliferation of these bacteria and ensuring consumer health and safety.</p>
      </abstract>
      <kwd-group kwd-group-type="author-generated" xml:lang="en">
        <kwd>Contamination</kwd>
        <kwd>Salmonella</kwd>
        <kwd>Shigella</kwd>
        <kwd>Fruits and Vegetables</kwd>
        <kwd>Kindia</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec1">
      <title>1. Introduction</title>
      <p>Fruits and vegetables are essential components of a healthy diet because they provide vitamins, minerals, dietary fibre, and numerous bioactive compounds that contribute to the prevention of cardiovascular diseases, obesity, type 2 diabetes, and several types of cancer. Consequently, international health organisations continue to recommend the regular consumption of fresh fruits and vegetables as part of a balanced diet and a key strategy for improving population health [<xref ref-type="bibr" rid="B1">1</xref>]-[<xref ref-type="bibr" rid="B3">3</xref>].</p>
      <p>Despite their recognised nutritional value, fresh fruits and vegetables have increasingly been implicated in foodborne disease outbreaks worldwide. Because these products are frequently consumed raw or undergo minimal processing before consumption, they may serve as vehicles for pathogenic microorganisms. Contamination may occur at any stage of the food chain, including cultivation, harvesting, transportation, storage, marketing, and retail distribution. The principal sources of contamination include irrigation with microbiologically contaminated water, the use of inadequately composted organic fertilisers, contaminated soil, poor hygiene practices among food handlers, and cross-contamination during post-harvest handling [<xref ref-type="bibr" rid="B4">4</xref>]-[<xref ref-type="bibr" rid="B7">7</xref>]. Recent FAO and WHO reports emphasize that fresh produce remains one of the major food commodities associated with microbiological hazards and foodborne outbreaks, highlighting the need for improved food safety management throughout the production chain [<xref ref-type="bibr" rid="B4">4</xref>].</p>
      <p>Foodborne diseases continue to represent a major global public health challenge. According to the World Health Organisation, more than 200 diseases are transmitted through contaminated food, causing substantial morbidity, mortality, and economic losses worldwide. The burden of these diseases disproportionately affects low- and middle-income countries, where inadequate sanitation, limited access to safe water, and weak food safety systems increase the risk of exposure to enteric pathogens [<xref ref-type="bibr" rid="B8">8</xref>][<xref ref-type="bibr" rid="B9">9</xref>].</p>
      <p>Among bacterial foodborne pathogens, <italic>Salmonella</italic><italic>spp</italic>. and <italic>Shigella</italic><italic>spp</italic>. remain of particular public health importance. Non-typhoidal Salmonella is one of the leading causes of bacterial gastroenteritis worldwide and is responsible for millions of infections annually. Likewise, <italic>Shigella</italic><italic>spp</italic>. remains a major cause of bacillary dysentery, especially among children under five years of age in resource-limited settings. The emergence and dissemination of antimicrobial-resistant strains of both pathogens have further complicated disease management and increased their public health significance [<xref ref-type="bibr" rid="B10">10</xref>]-[<xref ref-type="bibr" rid="B12">12</xref>].</p>
      <p>In sub-Saharan Africa, rapid urbanization, expanding vegetable production, inadequate sanitation infrastructure, and the widespread use of untreated wastewater for irrigation contribute substantially to the microbiological contamination of fresh produce. In Guinea, diarrheal diseases remain among the leading causes of morbidity, particularly among children, yet information regarding the contamination of fruits and vegetables by <italic>Salmonella</italic><italic>spp</italic>. and <italic>Shigella</italic><italic>spp</italic>. is still scarce. </p>
      <sec id="sec1dot1">
        <title>1.1. General Objective</title>
        <p>To assess the occurrence of <italic>Salmonella</italic><italic>spp</italic>. and <italic>Shigella</italic><italic>spp</italic>. in fruits and vegetables produced and marketed in Kindia and to provide evidence supporting food safety and contamination prevention strategies. </p>
      </sec>
      <sec id="sec1dot2">
        <title>1.2. Specific Objective</title>
        <p>Identify and characterise the presence of Shigella and Salmonella in various types of fruits and vegetables collected from production and sales sites in Kindia;Describe the agricultural and post-harvest practices that may contribute to microbial contamination of fruits and vegetables;Propose prevention and control strategies adapted to the local context to reduce contamination risks and improve the food safety of fruits and vegetables in Kindia.</p>
      </sec>
    </sec>
    <sec id="sec2">
      <title>2. Materials and Methods</title>
      <sec id="sec2dot1">
        <title>2.1. Study Area (Kindia Prefecture)</title>
        <p>Our study was conducted in Kindia Prefecture (<xref ref-type="fig" rid="fig1">Figure 1</xref>), which covers an area of 9115 km<sup>2</sup>. It is bordered by:</p>
        <p>Coyah Prefecture to the west, Télimélé Prefecture to the northeast,Mamou Prefecture to the east,the Republic of Sierra Leone to the south, and Forécariah Prefecture to the southeast.</p>
        <fig id="fig1">
          <label>Figure 1</label>
          <graphic xlink:href="https://html.scirp.org/file/7302289-rId20.jpeg?20260909103631" />
        </fig>
        <p>Figure 1. Map of Kindia prefecture (Kindia Urban Municipality, 2015).</p>
        <p>Today, all ethnic groups and their languages are represented in Kindia; the main ones are: the Soussous, the Malinkés, the Peulhs, the Djakankés, the Bagas, the Mikiforés, the Kissis, the Tomas, the Guerizés, the Manos, the Bssaris, etc. The Kindia Prefecture has a population of 438,315 inhabitants, including 226,300 women; the population density is 52 inhabitants per km<sup>2</sup>, unevenly distributed among ten (10) decentralized local authorities and one (1) urban municipality comprising thirty-three (33) neighborhoods. The ten (10) rural communes are: Bangouyah, Damakaniya, Friguigbé, Kolenté, Mambia, Madian-Oulla, Molota, Samaya, Souguéta, and Linsa. The population growth rate is 34%. The main economic activities include trade, livestock farming, agriculture, various socio-professional sectors, and a well-developed artisanal sector, among others. </p>
      </sec>
      <sec id="sec2dot2">
        <title>2.2. Study Setting</title>
        <p>The study was conducted in Kindia Prefecture, Guinea, at selected vegetable production sites and markets. Bacteriological analyses of fresh fruits and vegetables, irrigation water, and organic fertilizers were performed at the Institut de Recherche en Biologie Appliquée de Guinée (IRBAG).</p>
      </sec>
      <sec id="sec2dot3">
        <title>2.3. Study Type and Duration</title>
        <p>This was a prospective descriptive study conducted at selected vegetable production sites and markets in Kindia Prefecture. Samples were collected prospectively across all selected study sites.</p>
      </sec>
      <sec id="sec2dot4">
        <title>2.4. Biological Materials</title>
        <p>The biological materials consisted of fresh tomatoes, cucumbers, chili peppers, leaf cabbage, eggplants, and okra collected from vegetable production areas and markets. Irrigation water and organic fertilizers used in vegetable production were also collected and analyzed.</p>
      </sec>
      <sec id="sec2dot5">
        <title>2.5. Sampling Strategy and Sample Collection</title>
        <p>Sampling was carried out across multiple vegetable production sites and markets in Kindia Prefecture, selected for their significance in local production and commercial activities.</p>
        <p>Fresh fruit and vegetable samples were collected directly from cultivated fields and market points of sale, while irrigation water and organic fertilizer samples were obtained from agricultural farms. All samples were placed in sterile containers, appropriately labeled, and transported to the IRBAG laboratory in coolers maintained at 4˚C - 8˚C. Microbiological analyses were initiated within 24 h of collection.</p>
      </sec>
      <sec id="sec2dot6">
        <title>2.6. Laboratory Equipment</title>
        <p>Bacteriological analyses were performed using standard microbiological laboratory equipment, including a bacteriological incubator, autoclave, centrifuge, homogenizer, analytical balance, pH meter, and conventional sterile laboratory glassware and consumables.</p>
      </sec>
      <sec id="sec2dot7">
        <title>2.7. Culture Media</title>
        <p>The culture media used for the detection and identification of <italic>Salmonella</italic><italic>spp</italic>. and <italic>Shigella</italic><italic>spp</italic>. included Hektoen enteric agar, bismuth sulfite agar, xylose lysine deoxycholate (XLD) agar, selenite F broth (SFB), Hajna-Kligler medium, and Salmonella-Shigella (SS) agar.</p>
        <p>The bacteriological examination consisted of sample preparation and pre-enrichment, selective enrichment, isolation on selective and differential agar media, presumptive identification of colonies, biochemical confirmation, and serotyping of confirmed Salmonella isolates.</p>
      </sec>
      <sec id="sec2dot8">
        <title>2.8. Sample Processing and Pre-Enrichment</title>
        <p>Solid samples were aseptically processed and homogenized prior to microbiological analysis. An appropriate portion of each sample was transferred into buffered peptone water for pre-enrichment. Following incubation, the enriched cultures were transferred to selective enrichment media before being inoculated onto selective and differential agar media.</p>
      </sec>
      <sec id="sec2dot9">
        <title>
          2.9. Selective Enrichment and Isolation of
          <italic>Salmonella</italic>
          <italic>spp</italic>
          .
        </title>
        <p>Selenite F broth was used for selective enrichment of <italic>Salmonella</italic><italic>spp</italic>. The medium contains selenite, which inhibits the growth of many competing microorganisms and facilitates the recovery of <italic>Salmonella</italic><italic>spp</italic>. After incubation, the enriched cultures were streaked onto selective and differential media, including Hektoen enteric agar, XLD agar, bismuth sulfite agar, and SS agar.</p>
        <p>Hektoen enteric agar was used for the selective and differential isolation of enteric pathogens. Its selective components inhibit many competing bacteria, while carbohydrate fermentation and hydrogen sulfide (H<sub>2</sub>S) production allow presumptive differentiation of Salmonella and Shigella colonies. Salmonella colonies may appear blue-green or green, frequently with black centers due to H<sub>2</sub>S production.</p>
        <p>Presumptive colonies were selected according to their characteristic morphology and subjected to biochemical identification.</p>
      </sec>
      <sec id="sec2dot10">
        <title>2.10. Biochemical Identification</title>
        <p>Presumptive Salmonella colonies obtained from the selective isolation media were purified and subjected to biochemical characterization. Hajna-Kligler medium was used as a differential biochemical medium to assess glucose and lactose fermentation, gas production, and H<sub>2</sub>S production.</p>
        <p>2.10.1. Hajna-Kligler Medium</p>
        <p>Hajna-Kligler medium is a differential medium used for the preliminary characterization of enteric bacteria based on carbohydrate fermentation, gas production, and H<sub>2</sub>S production. The reactions are interpreted according to the colour of the slant and butt, gas production, and the presence or absence of H<sub>2</sub>S.</p>
        <p>Glucose fermentation causes acidification of the butt, resulting in a yellow coloration. Bacteria capable of fermenting lactose produce acid in the slant, resulting in yellow coloration of the slant. Gas production is indicated by cracks, bubbles, or lifting of the agar. H<sub>2</sub>S production is detected by the formation of a black precipitate resulting from the reaction of H<sub>2</sub>S with iron-containing compounds in the medium.</p>
        <p>2.10.2. Inoculation and Incubation</p>
        <p>A characteristic colony from each selective isolation medium was inoculated into Hajna-Kligler medium. The slant surface was inoculated by streaking, followed by stabbing the butt with an inoculating loop. The tubes were incubated at 37˚C for 24 h under the conditions recommended for the medium.</p>
        <p>The biochemical profile was interpreted together with the results of the other biochemical tests used for the identification of presumptive Salmonella isolates.</p>
      </sec>
      <sec id="sec2dot11">
        <title>2.11. Salmonella Serotyping</title>
        <p>All Salmonella isolates confirmed by biochemical identification were subjected to serotyping according to the Kauffmann-White-Le Minor scheme, following the recommendations of the WHO Collaborating Centre for Reference and Research on Salmonella.</p>
        <p>Pure colonies obtained from selective media were subcultured on nutrient agar and incubated at 37˚C for 18 - 24 h. Serological identification was performed using the slide agglutination method with commercially available polyvalent and monovalent antisera (Bio-Rad) directed against somatic (O) and flagellar (H) antigens. Agglutination reactions were interpreted according to the manufacturer’s instructions.</p>
        <p>The O antigen was first determined using polyvalent O antisera and subsequently confirmed using the corresponding monovalent antisera. Flagellar phase 1 (H1) and phase 2 (H2) antigens were then identified using specific H antisera. The resulting antigenic formula was interpreted according to the Kauffmann-White-Le Minor classification to determine the corresponding Salmonella serovar.</p>
        <p>Quality control was ensured using reference Salmonella strains with known antigenic formulas and by including positive and negative controls during each serotyping session.</p>
      </sec>
      <sec id="sec2dot12">
        <title>2.12. Quality Control</title>
        <p>Quality control procedures were applied throughout the microbiological analyses. Reference strains and appropriate positive and negative controls were included during the biochemical and serological identification procedures to ensure the reliability of the analytical results.</p>
      </sec>
    </sec>
    <sec id="sec3">
      <title>
        3. Detection and Isolation of
        <italic>Shigella</italic>
        <italic>spp</italic>
        .
      </title>
      <p>The detection of <italic>Shigella</italic><italic>spp</italic>. in various matrices (fresh fruit and vegetables, irrigation water and organic fertilisers) was carried out in accordance with the recommendations of ISO 21567, with specific adaptations depending on the nature of each sample.</p>
      <sec id="sec3dot1">
        <title>3.1. Pre-Enrichment and Sample Preparation</title>
        <p><bold>Fresh</bold><bold>fruit</bold><bold>and</bold><bold>vegetables:</bold> A 25 g test portion taken from a composite sample was transferred aseptically into a sterile bag containing 225 mL of Shigella enrichment broth (Shigella Broth). Mechanical washing by vigorous manual agitation was carried out for 2 minutes to ensure that bacterial cells adhering to the plant surface were dislodged.<bold>Irrigation</bold><bold>water:</bold> A volume of 500 mL of water was vacuum-filtered through a sterile cellulose ester membrane with a pore size of 0.45 μm. The membrane was then aseptically immersed in 100 mL of Shigella enrichment broth.<bold>Organic</bold><bold>fertilisers</bold><bold>(compost/manure):</bold> A 25 g sample of the matrix was suspended at a ratio of 1:10 in 225 mL of Shigella enrichment broth, then homogenised using a laboratory mixer (Stomacher) for 60 s.</p>
      </sec>
      <sec id="sec3dot2">
        <title>3.2. Selective Enrichment</title>
        <p>To limit the proliferation of competitive Gram-positive flora, the Shigella enrichment broth was supplemented with sterile novobiocin sulphate at a final concentration of 3.0 mg/L. The suspensions were incubated under microaerophilic conditions at 41.5˚C ± 1˚C for 16 to 20 hours.</p>
      </sec>
      <sec id="sec3dot3">
        <title>3.3. Isolation on Selective Media</title>
        <p>From each enriched broth, a 10 μL loopful was taken and inoculated using the quadrant method onto two selective agar media:</p>
        <p>Xylose-Lysine-Deoxycholate (XLD) agar;Hektoen (HE) agar.</p>
        <p>The isolation plates were incubated at 37˚C ± 1˚C for 20 to 24 hours in an aerobic environment. After incubation, the presence of presumptive colonies characteristic of <italic>Shigella</italic><italic>spp</italic>. was assessed:</p>
        <p>On XLD agar: translucent to opaque colonies, pink to red in colour, without a black centre.On Hektoen agar: green to blue-green colonies, without a black centre.</p>
      </sec>
      <sec id="sec3dot4">
        <title>3.4. Biochemical and Serological Confirmation</title>
        <p>For each sample yielding a positive isolate, at least five suspect colonies per selective medium were streaked onto non-selective nutrient agar (TSA) and then incubated at 37˚C for 18 to 24 hours to obtain pure cultures.</p>
        <p><bold>Biochemical</bold><bold>profile:</bold> The pure strains were subjected to confirmatory biochemical tests. The characteristics used for <italic>Shigella</italic><italic>spp</italic>. included: the absence of lactose and sucrose fermentation; the absence of hydrogen sulphide (H<sub>2</sub>S) and gas production on Kligler-Hajna (or TSI) medium; the absence of urease activity; and immobility on Urea-Indole-Motility (UIM) medium.<bold>Serological</bold><bold>confirmation:</bold> The generic classification and serogrouping of biochemically compatible strains were validated by a slide agglutination test using specific polyvalent antisera directed against the somatic O antigens of the four Shigella subgroups (<italic>S.</italic><italic>dysenteriae</italic>, <italic>S.</italic><italic>flexneri</italic>, <italic>S.</italic><italic>boydii</italic>, and <italic>S.</italic><italic>sonnei</italic>).</p>
      </sec>
    </sec>
    <sec id="sec4">
      <title>4. Data Collection and Statistical Analysis</title>
      <p>Data were entered into Microsoft Excel 2019 and subsequently analyzed using IBM SPSS Statistics version 26.0. Qualitative variables were summarized using frequencies and percentages, while quantitative variables were summarized using means and standard deviations where appropriate.</p>
      <p>The prevalence of <italic>Salmonella</italic><italic>spp</italic>. and <italic>Shigella</italic><italic>spp</italic>. was calculated as the proportion of positive samples among the total number of samples tested. The corresponding 95% confidence intervals (95% CI) were calculated.</p>
      <p>Information on farming practices, irrigation water quality, and the use of organic fertilizers was collected for descriptive purposes. However, these variables were not available at the individual sample level. Therefore, no multivariable regression analysis was performed to assess their association with contamination status. Consequently, the present study should be considered a contamination survey rather than a formal risk-factor assessment.</p>
      <p>Seasonal variations in contamination were not specifically analyzed in the present study. Although sampling covered approximately one year, the results were not stratified by season. Therefore, no conclusions regarding seasonal differences in <italic>Salmonella</italic><italic>spp</italic>. or <italic>Shigella</italic><italic>spp</italic>. contamination were drawn.</p>
    </sec>
    <sec id="sec5">
      <title>5. Results</title>
      <p><bold>Table 1</bold> shows the breakdown of the 1630 samples analysed by source material. The samples came primarily from irrigation water (35.40%), followed by fresh fruits and vegetables (34.17%) and organic fertilisers (30.43%).</p>
      <p>Table 1. Number of samples by biological material.</p>
      <table-wrap id="tbl1">
        <label>Table 1</label>
        <table>
          <tbody>
            <tr>
              <td>
                <bold>N</bold>
                <bold>˚</bold>
              </td>
              <td>
                <bold>Biomaterials</bold>
              </td>
              <td>
                <bold>Number</bold>
                <bold>of</bold>
                <bold>samples</bold>
              </td>
              <td>
                <bold>Percentage</bold>
                <bold>(%)</bold>
              </td>
            </tr>
            <tr>
              <td>
                <bold>1</bold>
              </td>
              <td>Fruits and Vegetables</td>
              <td>557</td>
              <td>34.17</td>
            </tr>
            <tr>
              <td>
                <bold>2</bold>
              </td>
              <td>Water for irrigation</td>
              <td>577</td>
              <td>35.40</td>
            </tr>
            <tr>
              <td>
                <bold>3</bold>
              </td>
              <td>Organic Fertilisers</td>
              <td>496</td>
              <td>30.43</td>
            </tr>
            <tr>
              <td colspan="2">
                <bold>Total</bold>
              </td>
              <td>
                <bold>1630</bold>
              </td>
              <td>
                <bold>100</bold>
              </td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p>This distribution reflects a balanced methodological approach that takes into account not only products directly consumed by the population (fruits and vegetables) but also the main environmental sources of microbial contamination (irrigation water and fertilisers).</p>
      <p>The relatively high number of irrigation water samples can be explained by their critical role as a potential vector for pathogenic bacteria. Similarly, the emphasis placed on organic fertilisers reflects their frequent involvement in the contamination of vegetable crops.</p>
      <p>Thus, this sampling strategy provides a better understanding of the potential transmission chain of Salmonella and Shigella, from the agricultural environment to the food consumed, and enables the development of appropriate public health prevention measures.</p>
      <p>For Shigella, when no cases are observed, it is recommended to report an exact 95% binomial confidence interval. For 0/557 samples, the upper confidence limit is approximately 0.69%.</p>
      <p><bold>Table 2</bold> presents the prevalence of <italic>Salmonella spp</italic>. and <italic>Shigella spp</italic>. among the 557 fruit and vegetable samples analysed. <italic>Salmonella spp</italic>. contamination was detected in 109 samples, corresponding to a prevalence of 19.57% (95% CI [16.49 - 23.07]). In contrast, no <italic>Shigella spp</italic>. isolates were recovered from any of the samples analysed (0%; 95% CI [0.00 - 0.69]). These findings indicate that fruits and vegetables marketed in Kindia Prefecture constitute a potential reservoir of <italic>Salmonella spp</italic>. and highlight the need to strengthen preventive measures and microbiological surveillance throughout the production and marketing chain.</p>
      <p>Table 2. Prevalence of Salmonella and Shigella in fruits and vegetables.</p>
      <table-wrap id="tbl2">
        <label>Table 2</label>
        <table>
          <tbody>
            <tr>
              <td>
                <bold>N</bold>
                <bold>˚</bold>
              </td>
              <td>
                <bold>Sprouts</bold>
              </td>
              <td>
                <bold>Workforce</bold>
              </td>
              <td>
                <bold>Confirmed</bold>
                <bold>cases</bold>
              </td>
              <td>
                <bold>Percentage</bold>
                <bold>(%)</bold>
              </td>
              <td>
                <bold>95%</bold>
                <bold>CI</bold>
              </td>
            </tr>
            <tr>
              <td>
                <bold>1</bold>
              </td>
              <td>Salmonella</td>
              <td rowspan="2">557</td>
              <td>109</td>
              <td>19.57</td>
              <td>[16.49 - 23.07]</td>
            </tr>
            <tr>
              <td>
                <bold>2</bold>
              </td>
              <td>Shigella</td>
              <td>0</td>
              <td>0.00</td>
              <td>[0.00 - 0.69]</td>
            </tr>
            <tr>
              <td colspan="2">
                <bold>Total</bold>
              </td>
              <td>557</td>
              <td>109</td>
              <td>19.57</td>
              <td>[16.49 - 23.07]</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p><bold>Table 3</bold> presents the prevalence of <italic>Salmonella spp</italic>. according to the type of fruit or vegetable analysed. Contamination rates varied among the different produce types. The highest prevalences were observed in lettuce (37.50%) and cabbage (36.36%), followed by tomato (29.60%) and chili pepper (26.92%). Intermediate prevalences were recorded for onion leaves (21.43%) and cucumber (21.21%), whereas white eggplant (14.17%), okra (14.89%), and black eggplant (9.85%) showed lower contamination rates. No <italic>Shigella spp</italic>. isolates were recovered from any of the 557 fruit and vegetable samples analysed. These findings indicate that the risk of Salmonella contamination differs among plant species, which may be related to the morphological characteristics of the produce, cultivation practices, and handling conditions prior to marketing.</p>
      <p>Table 3. Breakdown of positive cases by type of fruit and vegetable.</p>
      <table-wrap id="tbl3">
        <label>Table 3</label>
        <table>
          <tbody>
            <tr>
              <td rowspan="2">
                <bold>N</bold>
                <bold>˚</bold>
              </td>
              <td rowspan="2">
                <bold>Common</bold>
                <bold>Names</bold>
                <bold>for</bold>
                <bold>Fruits</bold>
                <bold>and</bold>
                <bold>Vegetables</bold>
              </td>
              <td rowspan="2">
                <bold>Scientific</bold>
                <bold>Names</bold>
                <bold>of</bold>
                <bold>Fruits</bold>
                <bold>and</bold>
                <bold>Vegetables</bold>
              </td>
              <td rowspan="2">
                <bold>Number</bold>
                <bold>of</bold>
                <bold>cases</bold>
                <bold>analysed</bold>
              </td>
              <td colspan="2">
                <bold>Number</bold>
                <bold>of</bold>
                <bold>positive</bold>
                <bold>cases</bold>
              </td>
              <td colspan="2">
                <bold>Percentage</bold>
                <bold>(%)</bold>
              </td>
            </tr>
            <tr>
              <td>
                <bold>Salmonelles</bold>
              </td>
              <td>
                <bold>Shigelles</bold>
              </td>
              <td>
                <bold>Salmonelles</bold>
              </td>
              <td>
                <bold>Shigelles</bold>
              </td>
            </tr>
            <tr>
              <td>1</td>
              <td>
                <bold>Chili</bold>
                <bold>pepper</bold>
              </td>
              <td>
                <italic>Capsicum</italic>
                <italic>fructescens</italic>
              </td>
              <td>52</td>
              <td>14</td>
              <td>0</td>
              <td>26.92</td>
              <td>0.00</td>
            </tr>
            <tr>
              <td>2</td>
              <td>
                <bold>Tomato</bold>
              </td>
              <td>
                <italic>Lycopercicum</italic>
                <italic>esculentum</italic>
              </td>
              <td>125</td>
              <td>37</td>
              <td>0</td>
              <td>29.60</td>
              <td>0.00</td>
            </tr>
            <tr>
              <td>3</td>
              <td>
                <bold>White</bold>
                <bold>Eggplant</bold>
              </td>
              <td>
                <italic>Solanum</italic>
                <italic>aethiopicum</italic>
              </td>
              <td>127</td>
              <td>18</td>
              <td>0</td>
              <td>14.17</td>
              <td>0.00</td>
            </tr>
            <tr>
              <td>4</td>
              <td>
                <bold>Black</bold>
                <bold>Eggplant</bold>
              </td>
              <td>
                <italic>Solanum</italic>
                <italic>melongena</italic>
              </td>
              <td>132</td>
              <td>13</td>
              <td>0</td>
              <td>9.85</td>
              <td>0.00</td>
            </tr>
            <tr>
              <td>5</td>
              <td>
                <bold>Cabbage</bold>
              </td>
              <td>
                <italic>Brassica</italic>
                <italic>oleracea</italic>
              </td>
              <td>11</td>
              <td>4</td>
              <td>0</td>
              <td>36.36</td>
              <td>0.00</td>
            </tr>
            <tr>
              <td>6</td>
              <td>
                <bold>Okra</bold>
              </td>
              <td>
                <italic>Hibiscus</italic>
                <italic>esculenthus</italic>
              </td>
              <td>47</td>
              <td>7</td>
              <td>0</td>
              <td>14.89</td>
              <td>0.00</td>
            </tr>
            <tr>
              <td>7</td>
              <td>
                <bold>Green</bold>
                <bold>onions</bold>
              </td>
              <td>
                <italic>Alium</italic>
                <italic>sepa</italic>
              </td>
              <td>14</td>
              <td>3</td>
              <td>0</td>
              <td>21.43</td>
              <td>0.00</td>
            </tr>
            <tr>
              <td>8</td>
              <td>
                <bold>Lettuce</bold>
              </td>
              <td>
                <italic>Lactuca</italic>
                <italic>sativa</italic>
              </td>
              <td>16</td>
              <td>6</td>
              <td>0</td>
              <td>37.50</td>
              <td>0.00</td>
            </tr>
            <tr>
              <td>9</td>
              <td>
                <bold>Cucumber</bold>
              </td>
              <td>
                <italic>Cucurbita</italic>
                <italic>pepo</italic>
              </td>
              <td>33</td>
              <td>7</td>
              <td>0</td>
              <td>21.21</td>
              <td>0.00</td>
            </tr>
            <tr>
              <td colspan="3">
                <bold>Total</bold>
              </td>
              <td>
                <bold>557</bold>
              </td>
              <td>
                <bold>109</bold>
              </td>
              <td>
                <bold>0</bold>
              </td>
              <td>
                <bold>19.57</bold>
              </td>
              <td>
                <bold>0</bold>
                <bold>.</bold>
                <bold>00</bold>
              </td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p><bold>Table 4</bold> shows the geographical variation in <italic>Salmonella spp</italic>. contamination across the different neighbourhoods of the urban municipality of Kindia; no cases of <italic>Shigella spp</italic>. were identified.</p>
      <p>Table 4. Distribution of strains by origin or collection site in the neighborhoods of the urban municipality of Kindia.</p>
      <table-wrap id="tbl4">
        <label>Table 4</label>
        <table>
          <tbody>
            <tr>
              <td rowspan="2">
                <bold>N</bold>
                <bold>˚</bold>
              </td>
              <td rowspan="2">
                <bold>Sampling</bold>
                <bold>location</bold>
              </td>
              <td rowspan="2">
                <bold>Number</bold>
                <bold>of</bold>
                <bold>cases</bold>
              </td>
              <td colspan="2">
                <bold>Number</bold>
                <bold>of</bold>
                <bold>positive</bold>
                <bold>cases</bold>
              </td>
              <td colspan="2">
                <bold>Percentage</bold>
              </td>
            </tr>
            <tr>
              <td>
                <bold>Salmonella</bold>
              </td>
              <td>
                <bold>Shigella</bold>
              </td>
              <td>
                <bold>Salmonella</bold>
              </td>
              <td>
                <bold>Shigella</bold>
              </td>
            </tr>
            <tr>
              <td>
                <bold>1</bold>
              </td>
              <td>
                <bold>Pastoria</bold>
              </td>
              <td>16</td>
              <td>7</td>
              <td>0</td>
              <td>43.75</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>2</bold>
              </td>
              <td>
                <bold>Bamban</bold>
              </td>
              <td>16</td>
              <td>9</td>
              <td>0</td>
              <td>56.25</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>3</bold>
              </td>
              <td>
                <bold>Yogountamba</bold>
              </td>
              <td>16</td>
              <td>8</td>
              <td>0</td>
              <td>50.00</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>4</bold>
              </td>
              <td>
                <bold>Wondy</bold>
              </td>
              <td>16</td>
              <td>6</td>
              <td>0</td>
              <td>37.50</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>5</bold>
              </td>
              <td>
                <bold>Koliah</bold>
              </td>
              <td>11</td>
              <td>6</td>
              <td>0</td>
              <td>54.55</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>6</bold>
              </td>
              <td>
                <bold>Dadia</bold>
              </td>
              <td>17</td>
              <td>5</td>
              <td>0</td>
              <td>29.41</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>7</bold>
              </td>
              <td>
                <bold>Foulayah</bold>
              </td>
              <td>33</td>
              <td>7</td>
              <td>0</td>
              <td>21.21</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>8</bold>
              </td>
              <td>
                <bold>Banlieue</bold>
              </td>
              <td>34</td>
              <td>2</td>
              <td>0</td>
              <td>5.88</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>9</bold>
              </td>
              <td>
                <bold>Ferefou</bold>
              </td>
              <td>23</td>
              <td>4</td>
              <td>0</td>
              <td>17.39</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>10</bold>
              </td>
              <td>
                <bold>Tabouna</bold>
              </td>
              <td>27</td>
              <td>4</td>
              <td>0</td>
              <td>14.81</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>11</bold>
              </td>
              <td>
                <bold>Gare</bold>
              </td>
              <td>8</td>
              <td>1</td>
              <td>0</td>
              <td>12.50</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>12</bold>
              </td>
              <td>
                <bold>Mangoyah</bold>
              </td>
              <td>31</td>
              <td>6</td>
              <td>0</td>
              <td>19.35</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>13</bold>
              </td>
              <td>
                <bold>Comoyah</bold>
              </td>
              <td>11</td>
              <td>2</td>
              <td>0</td>
              <td>18.18</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>14</bold>
              </td>
              <td>
                <bold>Manquepas</bold>
              </td>
              <td>13</td>
              <td>3</td>
              <td>0</td>
              <td>23.08</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>15</bold>
              </td>
              <td>
                <bold>Cacia</bold>
              </td>
              <td>13</td>
              <td>2</td>
              <td>0</td>
              <td>15.38</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>16</bold>
              </td>
              <td>
                <bold>Abattoir</bold>
              </td>
              <td>14</td>
              <td>1</td>
              <td>0</td>
              <td>7.14</td>
              <td>0</td>
            </tr>
            <tr>
              <td>
                <bold>17</bold>
              </td>
              <td>
                <bold>Garanguelayah</bold>
              </td>
              <td>22</td>
              <td>3</td>
              <td>0</td>
              <td>13.64</td>
              <td>0</td>
            </tr>
            <tr>
              <td colspan="2">
                <bold>Total</bold>
              </td>
              <td>
                <bold>321</bold>
              </td>
              <td>
                <bold>76</bold>
              </td>
              <td>
                <bold>0</bold>
              </td>
              <td>
                <bold>23</bold>
                <bold>.</bold>
                <bold>68</bold>
              </td>
              <td>
                <bold>0</bold>
              </td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p>The most affected neighbourhoods are Bamban (56.25 percent), Kolia (54.55 percent), Yogountamba (50.00 percent), Pastoria (43.75 percent) and Wondy (37.50 percent), followed by Dadia (29.41 percent), Manqueras (23.08 percent), Foulaye (21.21 percent) and Mangoyah (19.35 percent). In contrast, areas such as the abattoir (7.14 percent) and Banlieu (5.88 percent) have the lowest contamination rates.</p>
      <p>This variation could be explained by:</p>
      <p>the variable quality of irrigation water, which is often exposed to different sources of pollution depending on the neighbourhood,agricultural and irrigation methods, which are to a greater or lesser extent exposed to animal faeces or contaminated organic fertilisers,post-harvest hygiene conditions and sales practices at local markets.</p>
      <p>Consequently, certain neighbourhoods (notably Bamban, Yogountamba and Pastoria) appear to be priority hotspots for contamination, requiring targeted corrective measures to reduce health risks.</p>
      <p><bold>Table 5</bold> shows the prevalence of <italic>Salmonella spp</italic>. and <italic>Shigella spp</italic>. in samples of irrigation water and organic fertilisers. Of the 577 irrigation water samples, 68 (11.79%; 95% CI [9.40 - 14.67]) tested positive for <italic>Salmonella spp</italic>., whilst only one sample (0.17%; 95% CI [0.03 - 0.98]) tested positive for <italic>Shigella spp</italic>. Of the 496 organic fertiliser samples, 59 (11.90%; 95% CI [9.33 - 15.04]) were contaminated with <italic>Salmonella spp</italic>., whilst no strains of <italic>Shigella spp</italic>. were detected. These results show that irrigation water and organic fertilisers constitute potential reservoirs of <italic>Salmonella spp</italic>., whilst the presence of <italic>Shigella spp</italic>. remained rare, with only a single isolate observed in irrigation water.</p>
      <p>Table 5. Prevalence of <italic>Salmonella</italic><italic>spp</italic>. and <italic>Shigella</italic><italic>spp</italic>. in irrigation water and organic fertilisers.</p>
      <table-wrap id="tbl5">
        <label>Table 5</label>
        <table>
          <tbody>
            <tr>
              <td>
                <bold>Sample</bold>
                <bold>source</bold>
              </td>
              <td>
                <bold>Bacterial</bold>
                <bold>genus</bold>
              </td>
              <td>
                <bold>Sample</bold>
                <bold>size</bold>
                <bold>(n)</bold>
              </td>
              <td>
                <bold>Positive</bold>
                <bold>samples</bold>
                <bold>(n)</bold>
              </td>
              <td>
                <bold>Prevalence</bold>
                <bold>(%)</bold>
              </td>
              <td>
                <bold>95%</bold>
                <bold>CI</bold>
              </td>
            </tr>
            <tr>
              <td rowspan="2">Irrigation water</td>
              <td>
                <italic>Salmonella</italic>
                <italic>spp</italic>
                .
              </td>
              <td rowspan="2">577</td>
              <td>68</td>
              <td>11.79</td>
              <td>[9.40 - 14.67]</td>
            </tr>
            <tr>
              <td>
                <italic>Shigella</italic>
                <italic>spp</italic>
                .
              </td>
              <td>1</td>
              <td>0.17</td>
              <td>[0.03 - 0.98]</td>
            </tr>
            <tr>
              <td rowspan="2">Organic fertilisers</td>
              <td>
                <italic>Salmonella</italic>
                <italic>spp</italic>
                .
              </td>
              <td rowspan="2">496</td>
              <td>59</td>
              <td>11.90</td>
              <td>[9.33 - 15.04]</td>
            </tr>
            <tr>
              <td>
                <italic>Shigella</italic>
                <italic>spp</italic>
                .
              </td>
              <td>0</td>
              <td>0.00</td>
              <td>[0.00 - 0.74]</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p>Exact binomial 95% confidence interval calculated for zero positive cases among 496 samples.</p>
      <p><bold>Table 6</bold> shows the distribution of positive cases of <italic>Salmonella spp</italic>. and <italic>Shigella spp</italic>. by sampling site in the rural communes of Kindia.</p>
      <p>Table 6. Presence of germs in fruits and vegetables by sampling location in the subprefectures.</p>
      <table-wrap id="tbl6">
        <label>Table 6</label>
        <table>
          <tbody>
            <tr>
              <td rowspan="2">
                <bold>N</bold>
                <bold>˚</bold>
              </td>
              <td rowspan="2">
                <bold>Sampling</bold>
                <bold>location</bold>
              </td>
              <td rowspan="2">
                <bold>Number</bold>
                <bold>of</bold>
                <bold>cases</bold>
                <bold>analysed</bold>
              </td>
              <td colspan="2">
                <bold>Number</bold>
                <bold>of</bold>
                <bold>positive</bold>
                <bold>cases</bold>
              </td>
              <td colspan="2">
                <bold>Percentage</bold>
              </td>
            </tr>
            <tr>
              <td>
                <bold>Salmonella</bold>
              </td>
              <td>
                <bold>Shigella</bold>
              </td>
              <td>
                <bold>Salmonella</bold>
              </td>
              <td>
                <bold>Shigella</bold>
              </td>
            </tr>
            <tr>
              <td>1</td>
              <td>Damakanya</td>
              <td>27</td>
              <td>6</td>
              <td>0</td>
              <td>22.22</td>
              <td>0</td>
            </tr>
            <tr>
              <td>2</td>
              <td>Friguiagbé</td>
              <td>28</td>
              <td>4</td>
              <td>0</td>
              <td>14.29</td>
              <td>0</td>
            </tr>
            <tr>
              <td>3</td>
              <td>Molota</td>
              <td>23</td>
              <td>2</td>
              <td>0</td>
              <td>8.70</td>
              <td>0</td>
            </tr>
            <tr>
              <td>4</td>
              <td>Madina Oula</td>
              <td>37</td>
              <td>7</td>
              <td>0</td>
              <td>18.92</td>
              <td>0</td>
            </tr>
            <tr>
              <td>5</td>
              <td>Kolenten</td>
              <td>44</td>
              <td>5</td>
              <td>0</td>
              <td>11.36</td>
              <td>0</td>
            </tr>
            <tr>
              <td>6</td>
              <td>Sougueta</td>
              <td>33</td>
              <td>3</td>
              <td>0</td>
              <td>9.09</td>
              <td>0</td>
            </tr>
            <tr>
              <td>7</td>
              <td>Mambia</td>
              <td>17</td>
              <td>3</td>
              <td>0</td>
              <td>17.65</td>
              <td>0</td>
            </tr>
            <tr>
              <td>8</td>
              <td>Linsan</td>
              <td>27</td>
              <td>3</td>
              <td>0</td>
              <td>11.11</td>
              <td>0</td>
            </tr>
            <tr>
              <td colspan="2">
                <bold>Total</bold>
              </td>
              <td>
                <bold>236</bold>
              </td>
              <td>
                <bold>33</bold>
              </td>
              <td>
                <bold>0</bold>
              </td>
              <td>
                <bold>13.98</bold>
              </td>
              <td>
                <bold>0</bold>
              </td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p>Out of a total of 236 samples, 33 positive cases of Salmonella were detected (13.98 percent), and no cases of Shigella were observed.The most contaminated sites were Damakanya (22.22 percent), Madina Oula (18.92 percent) and Mambia (17.65 percent), whilst Molota (8.70 percent) and Sougueta (9.09 percent) had the lowest rates.</p>
      <p>This distribution indicates that Salmonella contamination is uneven across the various rural localities, probably due to:</p>
      <p>the quality of the irrigation water used,agricultural practices and the application of organic fertilisers,hygiene conditions during harvesting and transport.</p>
      <p>These results suggest that certain rural areas (notably Damakanya, Madina Oula and Mambia) could be prioritised for targeted interventions aimed at reducing microbiological contamination of fruit and vegetables.</p>
      <p>Table 7. Distribution of Salmonella serotypes isolated from fruits and vegetables.</p>
      <table-wrap id="tbl7">
        <label>Table 7</label>
        <table>
          <tbody>
            <tr>
              <td>
                <bold>Salmonella</bold>
                <bold>serotype</bold>
              </td>
              <td>
                <bold>Samples</bold>
                <bold>analysed</bold>
                <bold>(n)</bold>
              </td>
              <td>
                <bold>Positive</bold>
                <bold>samples</bold>
                <bold>(n)</bold>
              </td>
              <td>
                <bold>Prevalence</bold>
                <bold>(%)</bold>
              </td>
              <td>
                <bold>95%</bold>
                <bold>CI</bold>
              </td>
            </tr>
            <tr>
              <td>
                <italic>S.</italic>
                <italic>t</italic>
                <italic>yphi</italic>
              </td>
              <td>557</td>
              <td>3</td>
              <td>0.54</td>
              <td>[0.11 - 1.56]</td>
            </tr>
            <tr>
              <td>
                <italic>S.</italic>
                <italic>t</italic>
                <italic>yphimurium</italic>
              </td>
              <td>557</td>
              <td>55</td>
              <td>9.87</td>
              <td>[7.53 - 12.66]</td>
            </tr>
            <tr>
              <td>
                <italic>S.</italic>
                <italic>e</italic>
                <italic>nteritidis</italic>
              </td>
              <td>557</td>
              <td>51</td>
              <td>9.16</td>
              <td>[6.89 - 11.86]</td>
            </tr>
            <tr>
              <td>
                <bold>Total</bold>
              </td>
              <td>
                <bold>557</bold>
              </td>
              <td>
                <bold>109</bold>
              </td>
              <td>
                <bold>19.57</bold>
              </td>
              <td>
                <bold>[16.35</bold>
                <bold>-</bold>
                <bold>23.11]</bold>
              </td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p>Serotyping of the 109 Salmonella isolates recovered from fruits and vegetables revealed the presence of three serotypes. <italic>Salmonella</italic><italic>t</italic><italic>yphimurium</italic> was the most frequently identified serotype, with 55 isolates, corresponding to a prevalence of 9.87% (95% CI [7.53 - 12.66]), followed by <italic>Salmonella</italic><italic>e</italic><italic>nteritidis</italic> with 51 isolates (9.16%; 95% CI [6.89 - 11.86]). In contrast, <italic>Salmonella</italic><italic>t</italic><italic>yphi</italic> was detected in only three samples, corresponding to a prevalence of 0.54% (95% CI [0.11 - 1.57]). Overall, 109 of the 557 samples analysed were positive for Salmonella, corresponding to an overall prevalence of 19.57% (95% CI [16.35 - 23.11]) (<bold>Table 7</bold>).</p>
      <p>The predominance of <italic>S.</italic><italic>t</italic><italic>yphimurium</italic> and <italic>S.</italic><italic>e</italic><italic>nteritidis</italic> is consistent with findings reported in the scientific literature, as these two serotypes are the leading causes of foodborne salmonellosis worldwide and are frequently associated with animal-derived products, animal feces, inadequately composted organic fertilisers, and contaminated irrigation water. Their presence in fruits and vegetables most likely reflects contamination occurring during primary production or post-harvest handling.</p>
      <p>Conversely, the low frequency of <italic>S.</italic><italic>t</italic><italic>yphi</italic> was expected because this serotype is strictly human-adapted, and its transmission is primarily associated with human fecal contamination, particularly through contaminated water or poor hygiene practices. Nevertheless, the detection of <italic>S.</italic><italic>t</italic><italic>yphi</italic>, even at a low frequency, remains a matter of concern, as it suggests contamination of human origin and highlights the need to strengthen hygiene measures, improve the microbiological quality of water used in agriculture, and reinforce microbiological surveillance throughout the production and marketing chain of fresh produce.</p>
    </sec>
    <sec id="sec6">
      <title>6. Discussion</title>
      <p>During the present study, a total of 1.630 samples were collected, comprising 557 (34.17%) fruits and vegetables, 577 (35.40%) irrigation water samples, and 496 (30.43%) organic fertiliser samples (manure, poultry droppings, compost, and cattle manure). Among the fruit and vegetable samples, <italic>Salmonella</italic><italic>spp</italic>. was detected in 109 of 557 samples, corresponding to an overall prevalence of 19.57% (95% CI [16.49 - 23.07]), whereas <italic>Shigella</italic><italic>spp</italic>. was not detected in any sample (0%; 95% CI [0.00 - 0.69]).</p>
      <p>The relatively high prevalence of Salmonella observed in the present study indicates that fresh fruits and vegetables marketed in Kindia constitute a potential source of foodborne salmonellosis. Fresh produce is recognised as one of the food commodities most frequently associated with foodborne outbreaks because it is commonly consumed raw and can become contaminated at any stage of the production chain, from cultivation to retail. Recent FAO/WHO expert reports have highlighted irrigation water, inadequately composted organic fertilisers, contaminated soil, poor hygiene during harvesting, and cross-contamination during transportation and marketing as the principal sources of microbiological contamination of fresh produce [<xref ref-type="bibr" rid="B4">4</xref>].</p>
      <p>The prevalence of Salmonella observed in our study (19.57%) was higher than those reported by Brockmann <italic>et</italic><italic>al.</italic> in Germany (12.5%) [<xref ref-type="bibr" rid="B13">13</xref>] and Van Doren <italic>et</italic><italic>al.</italic> in the United States (9.87%) [<xref ref-type="bibr" rid="B14">14</xref>]. These differences are probably related to variations in climatic conditions, agricultural practices, irrigation water quality, environmental sanitation, and food safety management systems [<xref ref-type="bibr" rid="B15">15</xref>][<xref ref-type="bibr" rid="B16">16</xref>]. In low-resource settings such as Guinea, untreated surface water, limited implementation of Good Agricultural Practices (GAP), inadequate composting of organic fertilisers, and insufficient post-harvest hygiene may substantially increase the risk of contamination of fresh produce [<xref ref-type="bibr" rid="B4">4</xref>][<xref ref-type="bibr" rid="B17">17</xref>][<xref ref-type="bibr" rid="B18">18</xref>].</p>
      <p>Our findings also differ from those of Cetinkaya <italic>et</italic><italic>al.</italic> [<xref ref-type="bibr" rid="B15">15</xref>], who reported only 0.24% Salmonella contamination and no Shigella isolates among food samples. This discrepancy may be explained by differences in food matrices, environmental conditions, sampling strategies, laboratory methodologies, and hygiene practices. The absence of <italic>Shigella</italic><italic>spp</italic>. in the present study is consistent with recent evidence indicating that this pathogen is less frequently isolated from fresh produce than Salmonella. Unlike Salmonella, Shigella has a limited ability to survive outside the human host and is generally associated with direct fecal contamination rather than prolonged environmental persistence [<xref ref-type="bibr" rid="B19">19</xref>][<xref ref-type="bibr" rid="B20">20</xref>].</p>
      <p>Marked differences in contamination rates were observed among the different fruit and vegetable species. Lettuce (37.50%) and cabbage (36.36%) exhibited the highest prevalence, followed by tomato (29.60%) and chili pepper (26.92%). Intermediate prevalences were found in onion leaves (21.43%) and cucumber (21.21%), whereas white eggplant (14.17%), okra (14.89%), and black eggplant (9.85%) showed lower contamination rates. These findings agree with recent systematic reviews demonstrating that leafy vegetables generally present the highest risk of bacterial contamination because their rough and folded leaf surfaces facilitate the attachment and persistence of pathogenic microorganisms, soil particles, irrigation water, and organic matter, making decontamination more difficult than for smooth-surfaced vegetables [<xref ref-type="bibr" rid="B18">18</xref>][<xref ref-type="bibr" rid="B19">19</xref>].</p>
      <p>Compared with the study by Toe [<xref ref-type="bibr" rid="B16">16</xref>], the prevalence observed in tomatoes was higher in the present study, whereas those recorded for lettuce and cucumbers were lower. Such variations may be explained by differences in geographical location, climatic conditions, agricultural practices, irrigation methods, fertiliser management, season of sampling, and post-harvest handling. Recent meta-analyses have shown that the occurrence of Salmonella in fresh produce varies considerably between countries and production systems, reflecting differences in environmental conditions and food safety practices [<xref ref-type="bibr" rid="B19">19</xref>].</p>
      <p>Overall, the present findings highlight the need to strengthen microbiological surveillance of fresh fruits and vegetables in Guinea and to promote the implementation of Good Agricultural Practices (GAP), Good Hygiene Practices (GHP), and Hazard Analysis and Critical Control Point (HACCP)-based food safety systems throughout the production and marketing chain. Improving irrigation water quality, ensuring adequate composting of organic fertilisers, and reinforcing hygiene during harvesting, transportation, and retail marketing would substantially reduce the risk of Salmonella contamination and improve consumer safety [<xref ref-type="bibr" rid="B17">17</xref>][<xref ref-type="bibr" rid="B18">18</xref>][<xref ref-type="bibr" rid="B20">20</xref>][<xref ref-type="bibr" rid="B21">21</xref>].</p>
      <p>The contamination of irrigation water (11.79 percent) and organic fertilisers (11.90 per cent) by <italic>Salmonella</italic><italic>spp</italic>. observed in this study confirms that these agricultural inputs may act as potential reservoirs of enteric bacteria and contribute to the contamination of fruit and vegetables. These findings are consistent with those of the [<xref ref-type="bibr" rid="B18">18</xref>], as well as with the work of [<xref ref-type="bibr" rid="B20">20</xref>] and [<xref ref-type="bibr" rid="B19">19</xref>], who identify contaminated irrigation water and inadequately composted organic fertilisers as major sources of microbiological contamination of market garden produce.</p>
      <p>Conversely, <italic>Shigella</italic><italic>spp</italic>. was detected in only one irrigation water sample (0.17 percent) and was absent from organic fertilisers. This low prevalence is consistent with the literature, which shows that Shigella struggles to survive in the environment and is primarily associated with faecal contamination of human origin [<xref ref-type="bibr" rid="B11">11</xref>][<xref ref-type="bibr" rid="B20">20</xref>].</p>
      <p>Serotyping of the isolates revealed the presence of three Salmonella serotypes, with a clear predominance of <italic>Salmonella</italic><italic>t</italic><italic>yphimurium</italic> (55 isolates; 9.87%), followed by <italic>Salmonella</italic><italic>e</italic><italic>nteritidis</italic> (51 isolates; 9.16%), whereas <italic>Salmonella</italic><italic>t</italic><italic>yphi</italic>was detected in only three samples (0.54%). This distribution indicates that non-typhoidal Salmonella (NTS) serotypes (<italic>S.</italic><italic>t</italic><italic>yphimurium</italic> and <italic>S.</italic><italic>e</italic><italic>nteritidis</italic>) accounted for nearly all Salmonella isolates recovered from fruits and vegetables marketed in Kindia Prefecture.</p>
      <p>The predominance of <italic>S.</italic><italic>t</italic><italic>yphimurium</italic> and <italic>S.</italic><italic>e</italic><italic>nteritidis</italic> is consistent with findings reported in recent studies. A systematic review and meta-analysis by Ma <italic>et</italic><italic>al.</italic> [<xref ref-type="bibr" rid="B18">18</xref>] identified <italic>S.</italic><italic>t</italic><italic>yphimurium</italic> and <italic>S.</italic><italic>e</italic><italic>nteritidis</italic> among the most frequently isolated serotypes from retail fresh fruits and vegetables worldwide. According to the authors, the predominance of these serotypes is associated with their broad animal reservoirs, remarkable adaptability to diverse environmental conditions, and their ability to persist on fresh produce throughout the production and distribution chain [<xref ref-type="bibr" rid="B17">17</xref>].</p>
      <p>The high frequency of these two serotypes in the present study may be attributed to crop contamination through animal feces, inadequately composted organic fertilisers, contaminated irrigation water, or cross-contamination during harvesting, transportation, and marketing. Thomas <italic>et</italic><italic>al.</italic> [<xref ref-type="bibr" rid="B19">19</xref>] reported that <italic>S.</italic><italic>t</italic><italic>yphimurium</italic> and <italic>S.</italic><italic>e</italic><italic>nteritidis</italic> exhibit a strong capacity to adhere to plant surfaces, form biofilms, and persist on fresh fruits and vegetables, thereby increasing their likelihood of surviving until consumption.</p>
      <p>Joint FAO and WHO reports have also emphasized that fresh produce can become contaminated throughout the production chain, primarily through the use of microbiologically contaminated irrigation water, inadequately composted organic fertilisers, contact with animals, and poor implementation of Good Agricultural Practices (GAP) and Good Hygiene Practices (GHP) [<xref ref-type="bibr" rid="B19">19</xref>]. These factors are likely to have contributed to the contamination observed in the present study.</p>
      <p>In contrast, the low prevalence of <italic>S.</italic><italic>t</italic><italic>yphi</italic> observed in this study is consistent with its unique epidemiology. Unlike non-typhoidal Salmonella, <italic>S.</italic><italic>t</italic><italic>yphi</italic> is a strictly human-adapted pathogen whose transmission is primarily associated with fecal contamination through contaminated water or infected food handlers. Its low occurrence suggests that contamination of fruits and vegetables in the study area is more likely to originate from environmental or animal sources than from direct human contamination. Nevertheless, the detection of <italic>S.</italic><italic>t</italic><italic>yphi</italic>, even at a low frequency, remains a public health concern because it indicates possible fecal contamination and underscores the need to improve the microbiological quality of irrigation water and hygiene practices during produce handling.</p>
      <p>Recent surveillance data from the Centres for Disease Control and Prevention (CDC) FoodNet network further confirm that <italic>S.</italic><italic>e</italic><italic>nteritidis</italic> and <italic>S.</italic><italic>t</italic><italic>yphimurium</italic> remain among the leading serotypes responsible for human salmonellosis worldwide [<xref ref-type="bibr" rid="B21">21</xref>]. Furthermore, the emergence and spread of multidrug-resistant strains of these serotypes have become a major public health concern because of their ability to circulate among animals, the environment, and humans within a One Health framework [<xref ref-type="bibr" rid="B22">22</xref>].</p>
      <p>Overall, the predominance of <italic>S.</italic><italic>t</italic><italic>yphimurium</italic> and <italic>S.</italic><italic>e</italic><italic>nteritidis</italic> observed in the present study highlights a significant risk of foodborne salmonellosis associated with the consumption of raw fruits and vegetables in Kindia Prefecture. These findings emphasize the need to strengthen Good Agricultural Practices (GAP), Good Hygiene Practices (GHP), the use of microbiologically safe irrigation water, proper composting of organic fertilisers, and routine microbiological surveillance of fresh produce in order to reduce the risk of contamination and protect consumer health [<xref ref-type="bibr" rid="B19">19</xref>][<xref ref-type="bibr" rid="B22">22</xref>].</p>
      <p>The risk factors discussed in this study were not subjected to a specific statistical analysis. They are proposed as explanatory hypotheses based on field observations and evidence from the scientific literature. Agricultural practices, the use of irrigation water of inadequate microbiological quality, the application of insufficiently composted organic fertilisers, as well as harvesting, transportation, and marketing conditions, are recognised as factors that may promote the contamination of fruit and vegetable products by enteric bacteria.</p>
      <p>These results confirm that fruits and vegetables, as well as agricultural inputs (irrigation water and manure), are significant vectors of contamination. They underscore the need to raise awareness among producers, promote good agricultural practices, and implement microbiological monitoring measures to reduce the risks associated with diarrheal diseases. </p>
    </sec>
    <sec id="sec7">
      <title>7. Conclusions</title>
      <p>The findings of this study demonstrate a substantial contamination of fruits and vegetables marketed in Kindia Prefecture by <italic>Salmonella</italic><italic>spp</italic>., with an overall prevalence of 19.57%, whereas no <italic>Shigella</italic><italic>spp</italic>. isolates were recovered from these products. Among the agricultural inputs, <italic>Salmonella</italic><italic>spp</italic>. was also detected in 11.79% of irrigation water samples and 11.90% of organic fertiliser samples. In contrast, Shigella spp. was detected in only one irrigation water sample (0.17%) and was not detected in any organic fertiliser sample.</p>
      <p>The predominance of <italic>Salmonella</italic><italic>t</italic><italic>yphimurium</italic> and <italic>Salmonella</italic><italic>e</italic><italic>nteritidis</italic> highlights the importance of this pathogen in the vegetable production environment of Kindia Prefecture and confirms that fresh produce may constitute a potential route for the transmission of foodborne salmonellosis.</p>
      <p>Although agricultural practices, the microbiological quality of irrigation water, and the use of organic fertilisers are recognised as potential sources of contamination, their association with contamination status was not evaluated by statistical analysis in the present study. Therefore, these factors should be considered as hypotheses based on field observations and current scientific evidence rather than as statistically demonstrated risk factors.</p>
      <p>These findings highlight the need to strengthen the microbiological surveillance of fresh produce and its production environment, improve the microbiological quality of irrigation water, ensure the proper composting of organic fertilisers, and promote the implementation of Good Agricultural Practices (GAP) and Good Hygiene Practices (GHP) throughout the production and marketing chain. Further studies incorporating multivariable statistical analyses will be necessary to identify the independent determinants of contamination and to better guide prevention strategies.</p>
    </sec>
    <sec id="sec8">
      <title>8. Recommendations</title>
      <p>Recommendations at the national level, as well as from stakeholders in the fruit and vegetable production sector and consumers, are necessary to reduce the risk of contamination and protect consumer health.</p>
      <sec id="sec8dot1">
        <title>8.1. To Government Authorities</title>
        <p>Inform and educate farmers about good agricultural practices for fruits and vegetables; Promote good hygiene and handling practices throughout the fruit and vegetable food chain through awareness campaigns;Establish a unit to monitor the quality of fruits and vegetables. </p>
      </sec>
      <sec id="sec8dot2">
        <title>8.2. To Stakeholders Involved in Fruit and Vegetable Production</title>
        <p>Inform stakeholders about compliance with hygiene regulations and best practices for cultivation and handling regarding the quality of fruits and vegetables intended for human consumption. </p>
        <p>Implementing vegetable disinfection measures and training stakeholders involved in handling these fruits and vegetables are essential to reducing the risk of contamination and protecting consumer health. To reduce health risks associated with the consumption of vegetables contaminated by irrigation water, it is necessary to implement safe agricultural practices and proper irrigation management.</p>
        <p>Allow at least one day to elapse between the last irrigation and harvest, or use irrigation techniques that minimize contact between water and the crops, such as drip irrigation. </p>
      </sec>
      <sec id="sec8dot3">
        <title>8.3. For Consumers</title>
        <p>Properly disinfect fruits and vegetables using chlorinated water or another disinfectant before consuming them at home. </p>
        <p>When purchasing fruits and vegetables for institutional dining, choose vendors operating in an environment that appears clean and where hygiene conditions seem acceptable.</p>
      </sec>
    </sec>
    <sec id="sec9">
      <title>Author Contributions</title>
      <p>Conceptualisation: Mariama Bah, Bonaventure Kolié, Siba Kalivogui, Boubacar Sidy Sily Bah; Data curation: Mariama Bah, Bonaventure Kolié, Haziz Sina, Agossou Marcellin Aïgbe, Boubacar Sidy Sily Bah; Formal analysis: Mariama Bah, Bonaventure Kolié; Investigation: Mariama Bah, Bonaventure Kolié, Abado Sylvestre Assogba, Haziz Sina, Agossou Marcellin Aïgbe, Siba Kalivogui, Boubacar Sidy Sily Bah; Methodology: Mariama Bah, Bonaventure Kolié, Haziz Sina, Agossou Marcellin Aïgbe, Boubacar Sidy Sily Bah; Project administration: Boubacar Sidy Sily Bah, Haziz Sina, Agossou Marcellin Aïgbe; Supervision: Boubacar Sidy Sily Bah, Siba Kalivogui; Validation: Mariama Bah, Bonaventure Kolié, Boubacar Sidy Sily Bah; Visualisation: Bonaventure Kolié, Mariama Bah; Editing—original project: Bonaventure Kolié, Mariama Bah, Boubacar Sidy Sily Bah; Writing—revision and editing: Bonaventure Kolié, Mariama Bah, Boubacar Sidy Sily Bah.</p>
    </sec>
  </body>
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