Rapid Isothermal detection and Quantification of Total Salmonella in Poultry Using the HyperKit Point-of-Use diagnostic test

Rapid Isothermal detection and Quantification of Total Salmonella in Poultry Using the HyperKit Point-of-Use diagnostic test | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Rapid Isothermal detection and Quantification of Total Salmonella in Poultry Using the HyperKit Point-of-Use diagnostic test Anitha Kumar, Rajeev Shrestha, Loic Deblais, Gireesh Rajashekara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7935441/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Food producers face mounting pressure to ensure both safety and efficiency as food systems expand in scale and complexity. However, existing diagnostic tools often force a trade-off between speed, accuracy, cost, and usability, leaving the industry with limited options for real-time, on-site pathogen detection. This study evaluated the performance of the HyperKit Total Salmonella (HK), a novel point-of-use rapid diagnostic tool for detecting and semi-quantifying Salmonella in artificially-spiked and commercial chicken primal samples. HK is composed of one-step sample processing tool and a room temperature fluorescent master mix. Targeted DNA was amplified at 65 o C for 60 min and fluorescence measure over time at 495 nm. HK successfully detected Salmonella in all spiked samples (1.0 to 7.5-log CFU/mL; n = 57) under 60 minutes. HK demonstrated high semi-quantitative accuracy (r² = 0.93; P < 0.001), particularly at concentrations ≥ 1.5 log CFU/mL (± 0.18-log precision), as well as strong repeatability (0.16-log; 95% CI: 0.11–0.21) and reproducibility across three operators and samples of multiple origins (Georgia, Illinois, Nebraska). Data obtained with HK were in close agreement with the reference microbiology method, but up to 300 times faster. Robustness studies confirmed reliable performance under varying sample preparation conditions. Importantly, the kit showed complete inclusivity for all tested Salmonella serotypes (n = 46) and strong exclusivity against non-target organisms (n = 37). In conclusion, this study demonstrated that HK is a rapid and accurate detection tool with a simple field workflow to support point-of-use applications in food processing environments, and thus, enhancing food safety monitoring and response in decentralized settings. Point-of-use poultry AOAC guidelines isothermal detection total Salmonella Figures Figure 1 Figure 2 Figure 3 Introduction Food industries face increasing challenges in ensuring both food safety and profitability due to the continuous expansion and intensification of food production across the farm-to-fork continuum (Mahony and van Sinderen 2022 ). According to the United States Center for Disease Control and Prevention (CDC), the incidence of foodborne illness has remained consistently high over the past several decades, with an estimated 48 million cases, 128,000 hospitalizations, and 3,000 deaths occurring each year in the U.S. alone (World Health Organization 2024 ; CDC 2025a ). Beyond public health consequences, foodborne pathogens impose substantial economic burdens on the food industry. Outbreaks often trigger costly product recalls, supply chain disruptions, and legal liabilities. The United States Department of Agriculture (USDA) estimates that foodborne illnesses cost the national economy over $ 15.6 billion annually in medical expenses, productivity losses, and economic fallout from recalls and litigation (United States Department of Agriculture, 2025). Reputational damage can be equally or more severe than financial losses. In 2024 alone, the U.S. experienced three multi-state foodborne outbreaks: Listeria monocytogenes linked to Boar's Head deli meats (CDC 2025b ), Escherichia coli associated with McDonald's onions (CDC 2025c ), and Salmonella contamination traced to cucumbers (CDC 2025d ). These incidents not only resulted in substantial financial losses but also severely undermined consumer trust, leading to long-term brand damage that is difficult to recover from (Hussain and Dawson 2013 ). The increasing frequency and severity of such incidents underscore the urgent need for improved pathogen detection methods that are rapid, reliable, and deployable at the point of need. A wide variety of diagnostic technologies are commercially available to detect pathogens in food products and the associated environment; however, most of them cannot provide the detection accuracy while keeping up with the speed and intensity of food industries (Law et al. 2015 ). Affordable point of use tests such as, lateral flow immunoassays (LFA, rapid test strips) (Chen et al. 2021 ), adenosine triphosphate (ATP) (Sun et al. 2022 ), microscopy (Ismail et al. 2024 ), microfluidic lab-on-a-chip (Lonchamps et al. 2022 ) are rapid, but not specific and not sensitive enough, leading to false results (cross-reactivity, detection of non-viable organisms, environmental conditions and sample matrix interference) (Shama and Malik 2013 ; Jayamohan et al. 2017 ; Maze et al. 2019 ). Spectroscopic methods (Hussain et al. 2022 ) offer rapid and high-resolution analysis; however, their application is limited by high equipment costs and reduced sensitivity in complex food samples. Culture-based methods (Fusco and Quero 2014 ) are cost-effective and provide sensitive and accurate results, but they are labor-intensive, require expensive infrastructures (e.g., BSL-2 facility, autoclaves, decontamination facilities, incubators, etc.), and trained operators. Critically, culturing results may take 4–5 days to obtain due to lengthy incubation steps, making them poorly aligned with the fast-paced decision timelines of modern food production, where actions must be taken within hours. On the other hand, molecular (real-time polymerase chain reaction (Du et al. 2021 ), sequencing (Sekse et al. 2017 ) and DNA microarrays (Panwar et al. 2023 )) and immunologic (enzyme-linked immunosorbent assay, ELISA (Li et al. 2024 )) methods provide sensitive and specific results within a short period of time, but they are expensive, required highly trained operators and unique working environment (e.g., clean room), and can yield false results (e.g., detection of non-viable organisms) (Notermans and Wernars 1991 ). Next generation detection technologies are consistently being evaluated (e.g., Biosensor-Based Techniques (Escobar et al. 2023 ), CRISPR-based diagnostics (Lu et al. 2024 ), and Next-Generation Sequencing (NGS) (Lewis et al. 2020 )) to rapidly detect low concentrations of pathogens; however, these methods may take more time being approved by governmental agencies. Furthermore, some of these detection methods must be conducted in specific environmental conditions (light, temperature, humidity, pH, pressure, airborne contaminants), reagents have limited shelf life and restricted storage conditions and cannot be used for the detection of pathogens directly on premises (Panwar et al. 2023 ). By consequence, the food industry is left with only two options; using rapid tests that are not accurate and reliable, or ship samples to expensive specialized laboratories to obtain delayed but accurate results (Bhowmik et al. 2024 ). In the latter, additional logistics burdens (e.g., cold storage, inventory cost, and reduced shelf-life) and false reported results may apply (e.g., sample leakage, improper handling during transport, or exposure to contaminated surfaces) (Osorio et al. 2017 ). Altogether, there is an urgent need for improved pathogen detection solutions that enable real-time, on-site testing while ensuring accurate and reliable results (Ye et al. 2025 ). The objectives of this study were to assess the sensitivity, specificity, robustness (repeatability and reproducibility) and quantitative precision of the HyperKit Total Salmonella test (HK). Given Salmonella is an adulterant in poultry industry, this study focused on the detection and quantification of Salmonella in artificially spiked poultry carcass rinse samples. This study is a proof of concept that HK is a simple point-of-use diagnostic test that provides rapid and accurate detection of pathogens to support the food industry’s needs. Materials and methods Bacterial strains and culture conditions . Bacterial strains used in this study were cultured under optimal growing conditions as recommended by the institutes from which the strains were obtained; US department of Agriculture National Center for Agricultural Utilization Research (USDA-NRRL, Peoria, Illinois, USA), National Institutes of Health Biological and Emerging Infections Research Resources Program (NIH BEI, Manassas, Virginia, USA) and American Type Culture Collection (ATCC, Manassas, Virginia, USA). Details about the bacterial strains used in this study are listed in Table 1 . A cocktail of three Salmonella serotypes ( Typhimurium, Enteritidis and Heidelberg; ratio 1:1:1) was used as a model for the validation of HyperKit Total Salmonella detection kit in chicken primal samples. The Salmonella inocula were prepared by 10-fold diluting an overnight culture grown in Buffered Peptone Water (BPW, pH 7.2; Neogen, Lansing, MI, USA) at 37 o C in cold fresh BPW until reaching the desired bacterial concentrations depending on the validation requirement. The Salmonella inocula were plated on selective media (Xylose-Lysine-Deoxycholate [XLD] agar and incubated at 37 o C for 24 hrs) to ensure accurate inoculum concentrations and kept at 4 o C until further use. Table 1 Inclusivity and exclusivity of HyperKit Total Salmonella Testing list Name Organisms reference ID (if available) HyperKit Total Salmonella specific results Exclusive list (n = 37) # Acidovorax citrulli Xu22-15 A Not Detected Acinetobacter baumannii ATCC 17978 C Not Detected Bacillus cereus B-4288 D Not Detected Bacillus subtilis NR-604 E Not Detected Campylobacter ureolyticus ATCC 33387 C Not Detected Citrobacter freundii B-41561 D Not Detected Clavibacter michiganensis michiganensis C290 A Not Detected Clostridium sordellii B-41209 D Not Detected Enterobacter cloacae B-413 D Not Detected Enterococcus faecalis ATCC 51188 C Not Detected Enterococcus faecium B-41204 D Not Detected Escherichia coli DH5alpha A Not Detected Escherichia coli O157 110200 A Not Detected Flavobacterium fuscum B-4264 D Not Detected Hafnia alvei B-41102 D Not Detected Klebsiella aerogenes B-407 D Not Detected Klebsiella oxytoca B-3567 D Not Detected Klebsiella pneumoniae subsp. pneumoniae B-423 D Not Detected Kleibsiella pneumoniae ATCC 13883 C Not Detected Lactobacillus casei ATCC 334 C Not Detected Lactobacillus gasseri B-4240 C Not Detected Lactobacillus paracasei subsp paracasei B-59137 D Not Detected Leuconostoc mesenteroides B-65335 D Not Detected Listeria monocytogenes NR-4098 E Not Detected Macrococcus caseolyticus B-14761 D Not Detected Megasphaera cerevisiae ATCC 43254 C Not Detected Neisseria meningitidis ATCC BAA-335 C Not Detected Pantoea agglomerans B-41484 D Not Detected Proteus mirabilis B-3404 D Not Detected Pseudomonas aeruginosa NR-51335 E Not Detected Saccharomyces cerevisiae NCYC 361 D Not Detected Shewanella baltica B-41146 D Not Detected Shigella flexneri NR-517 E Not Detected Staphylococcus aureus ATCC 29213 C Not Detected Staphylococcus pasteuri B-23827 D Not Detected Xanthomonas gardneri XCV761 A Not Detected Yersinia enterocolitica B-41479 D Not Detected Inclusive list (n = 46) $ Salmonella enterica subsp. diarizonae NR-516 E Detected Salmonella enterica subsp. enterica 4,[5],12:i:- NR-28790 E Detected Salmonella enterica subsp. enterica Aberdeen B Detected Salmonella enterica subsp. enterica Abortusovis (B) NR-13556 E Detected Salmonella enterica subsp. enterica Agona (B) B Detected Salmonella enterica subsp. enterica Albany A Detected Salmonella enterica subsp. enterica Anatum (E1) A Detected Salmonella enterica subsp. enterica Berta A Detected Salmonella enterica subsp. enterica Braenderup A Detected Salmonella enterica subsp. enterica Breda A Detected Salmonella enterica subsp. enterica Choleraesuis ATCC 10708 C Detected Salmonella enterica subsp. enterica Copenhagen B Detected Salmonella enterica subsp. enterica Dublin (D1) NR-22060 E Detected Salmonella enterica subsp. enterica Eastborne A Detected Salmonella enterica subsp. enterica Enteritidis (D1) A Detected Salmonella enterica subsp. enterica Gaminara ATCC 8324 C Detected Salmonella enterica subsp. enterica Give B Detected Salmonella enterica subsp. enterica Hadar (C2) NR-28799 E Detected Salmonella enterica subsp. enterica Heidelberg (B) A Detected Salmonella enterica subsp. enterica Infantis (C1) A Detected Salmonella enterica subsp. enterica Javiana (D1) A Detected Salmonella enterica subsp. enterica Kentucky (C3) NR-28795 E Detected Salmonella enterica subsp. enterica Kenya A Detected Salmonella enterica subsp. enterica Mbandaka (C1) B Detected Salmonella enterica subsp. enterica Montevideo (C1) B Detected Salmonella enterica subsp. enterica Muenchen (C2) A Detected Salmonella enterica subsp. enterica Muenster A Detected Salmonella enterica subsp. enterica Newport (C2) A Detected Salmonella enterica subsp. enterica Oranienburg (C1) B Detected Salmonella enterica subsp. enterica Orian A Detected Salmonella enterica subsp. enterica Paratyphi A NR-515 E Detected Salmonella enterica subsp. enterica Poona (G1) B Detected Salmonella enterica subsp. enterica Saintpaul (B) A Detected Salmonella enterica subsp. enterica Schwarzengrund (B) FSIS 11925832 B Detected Salmonella enterica subsp. enterica Senftenberg (E4) B Detected Salmonella enterica subsp. enterica Stanley (B) B Detected Salmonella enterica subsp. enterica Tennessee (C1) NR-20740 E Detected Salmonella enterica subsp. enterica Thompson (C1) NR-4319 E Detected Salmonella enterica subsp. enterica Typhi T2 (D1) NR-514 E Detected Salmonella enterica subsp. enterica Typhi T4 (D1) A Detected Salmonella enterica subsp. enterica Typhimurium CDC 6516-60 ATCC 14028 C Detected Salmonella enterica subsp. enterica Typhimurium NCTC 74 ATCC 13311 C Detected Salmonella enterica subsp. enterica Typhimurium LT2 A Detected Salmonella enterica subsp. enterica Umbilo B Detected Salmonella enterica subsp. enterica Virchow (C1) NR-28801 E Detected Salmonella enterica subsp. enterica Weltevreden (E1) NR-28798 E Detected A Dr. Gireesh Rajashekara, University of Illinois, Champaign, IL, USA; B Dr. John Gunn, Nationwide Children Hospital, Columbus, OH, USA; C American Type Culture Collection (ATCC), Manassas, Virginia, USA; D US department of Agriculture National Center for Agricultural Utilization Research (USDA-NRRL), Peoria, Illinois, USA; E National Institutes of Health Biological and Emerging Infections Research Resources Program (NIH BEI), Manassas, Virginia, USA; # Exclusivity was assessed by challenging the HyperKit with high concentrations (> 10⁶ CFU per reaction) of the designated organisms to confirm the absence of cross-reactivity and false-positive results; $ Inclusivity was determined by testing the HyperKit against a panel of targeted organisms (c.a., 1000 CFU per reaction), ensuring all strains were reliably detected without false negatives. A sample was considered positive if the relative fluorescence units (RFU) exceeded 1,000 (signal threshold for a positive result) within 60 minutes of incubation at 65°C. A result was considered negative if no increase in fluorescence above the threshold was observed within the same incubation period. Plating on XLD agar was used as the microbiological reference method to detect and quantify Salmonella in the spiked samples, and to conduct the head-to-head validation with the HyperKit Total Salmonella . For the inclusivity/exclusivity study, all strains were grown in an appropriate medium using pure stocks kept at -80 o C in 27% glycerol. All Enterobacteriaceae strains (e.g., Salmonella, Escherichia coli, Shigella) were cultured in Luria broth (LB) in aerobic conditions at 37 o C. Other aerobic bacteria with lower optimal growing temperature (e.g., Bacillus , Clavibacter , Listeria , Xanthomonas ) were cultured in Luria broth (LB) in aerobic conditions at 28-30 o C. Lactobacillus strains were cultured in De Man–Rogosa–Sharpe (MRS) medium at 37 o C with anaerobic conditions (BD GasPak anaerobic sachets; Franklin Lakes, NJ, USA). HyperKit Total Salmonella . The Hypercell detection kit (HyperKit) is composed of two items (HyperPen and HyperMix) enabling a rapid, sensitive, accurate, and on-premise detection of specific foodborne contaminants without specialized instrumentation (Fig. 1 ); a- HyperPen - a single-use, one-step sample prep tool allowing to processing a variety of field samples (e.g., environmental swabs, carcass rinses, meat, fresh produce, fermentation product, and other liquid matrices) under three minutes. The HyperPen utilizes a proprietary buffer (HCB4) with integrated purification chemistry and sequential distribution of filtration modules to capture and concentrate bacteria. Combined together, it allows a simple and rapid processing of complex matrices without the need for conventional lab-based extraction equipment or time-consuming procedures. b- HyperMix (lyophilized isothermal reagent tubes) – The amplification reagents are pre-aliquoted and lyophilized in high-profile optical PCR tubes, sealed in vacuum-packed aluminum pouches to prevent exposure to moisture, light, and UV. The DNA amplification of the targeted organisms was conducted using devices capable to incubate the inoculated HyperMix tubes at 65°C for 60 min and to monitor the fluorescence signal at 495 nm over time. Preparation of chicken primal rinse samples. The chicken primal rinse (CPR) samples were prepared according to the USDA Food Safety and Inspection Service (FSIS) compliance guideline (USDA 2013). CPR were used to mimic the sampling practices followed in the poultry industry and harvest to native chicken microbiome (background noise). One kilogram of chicken primals (breast meat and thigh) purchased at the local grocery shop (Champaign, IL, USA) were resuspended into cold BPW (pH 7.2; ratio 5 g of primal per ml of broth). After addition of the broth, the chicken primals were rinsed for one minute in a 90° arcing motion to ensure that the broth rinses the primals (Wages et al. 2022 ). The CPR was collected into sterile containers (e.g., 50 ml conical tubes) and stored at 4°C until spiking. CPR samples were confirmed free of Salmonella before spiking using both the reference microbiology method, which involved plating 1 ml of sample onto XLD agar (Nye et al. 2002 ) and HK. Spiking of chicken primal rinse samples. For spiking of the CPR samples, known concentrations of Salmonella were added to the samples (between 10 1 and 10 7 CFU/ml). Sixty-four CPR samples were tested, out of which 57 samples were spiked. An inoculum volume under 1% compared to the total sample volume was used to preserve the physico-chemical properties integrity of the samples (e.g., pH, concentration of salts, debris and other inhibitors). Samples were manually homogenized for 30 sec. Inoculated samples were allowed to equilibrate at 4°C for at least 12 hrs to allow for bacterial adaptation to the matrix. After the stabilization phase, similar microbiology counts between the original inocula and the spiked field samples demonstrated that no antimicrobial activities were observed and plating data can be accurately interpreted. Detection of Salmonella in chicken primal rinse samples. The experimental design was based on the Association of Official Agricultural Chemists (AOAC) Research Institute’s Performance Tested Methods (PTM) guidelines (Rosauer et al. 2022 ). A total of 64 CPR samples (57 spiked and seven blank samples) were tested in this study across five days. Final Salmonella concentrations between 10 1 - 10 7 CFU/ml were tested (n = 4–11 samples per spiking dose level; nine spiking levels tested). A head-to-head comparative study was conducted to assess the detection accuracy and sensitivity of HK compared to reference method (microbiology plating; XLD agar). Processing of chicken primal samples using HyperKit Total Salmonella . Samples were processed in this study according to the manufacturer’s recommendations. Briefly, an aliquot of chicken primal samples was collected using the Luer lock syringe (syringe 1 provided in the kit). The sample collection was conducted using the full volume of syringe 1 (3.35 ml ± 0.1 ml; maximum volume collected; BH supplies, Berwick, PA, USA ). The sample was injected inside the HyperPen by plugging syringe 1 in the top-hole A. Two “air pump” (push and pull the syringe plunger back and forth to gradually replacing the liquid inside the syringe with air) were conducted to ensure that the totality of the sample was loaded inside the HyperPen. After turning the controller knock 90 o to the left, HCB4 was injected inside the HyperPen by plugging the pre-loaded syringe (syringe 2; provided in the kit) in the side-hole B. Ten air pumps were conducted with syringe 2 to concentrate and purify the extract inside the HyperPen. On the tenth air pump, pulling the plunger enabled to extract the purified product (c.a., 50–100 µl) inside syringe 2. The recovered liquid was transferred into the 1.5 ml collection tube (provided in the kit). Fifty microliters of the purified product was transferred into the HyperMix tube before incubating it in the fluorescent incubator reader (Model LPCR-H800; Laboao, Zhengzhou, China) at 65 o C for 60 min. Additional studies demonstrated that HK is compatible with other fluorescent incubator readers such as CFX96 Touch Real-Time PCR System (Bio-Rad; Hercules, CA, USA) and Aladdin Analyzer (Hypercell; Ithaca, NY, USA; Supplemental Information 1 ). Robustness, repeatability and reproducibility testing . Robustness of the HK was evaluated to determine the reliability of the assay under intentional variations in operational conditions. Separate deviations from HK SOP includes: (1) reduce the number of “air pump” during the purification/concentration phase in the HyperPen (0, 2, 4, 6, 8, or 10 pumps); and (2) Improper transfer of the concentrated-purified product from the HyperPen into the HyperMix tube (final volume between 20 and 150 µl). Each variation was evaluated independently to identify bottle neck situation that could lead to generation of potential false results. HyperMix outcomes were defined based on the detection of a positive signal (≥ 1,000 RFU) within 60 minutes at 65°C. Results were considered robust if detection accuracy remained with no impact on time-to-result compared to the control group (proper procedure the HK SOP for the designated step). The reproducibility of the HK (semi-quantitative detection accuracy between aliquots collected from the same sample and processed by different operators at the same time) was evaluated by three independent users (Scientific staff members working in Dr Rajashekara’s lab. University of Illinois, Champaign, IL, USA), with varying expertise and levels of laboratory experience, following the HK processing procedure described above. The repeatability of the HK (semi-quantitative detection accuracy between aliquots collected from the same sample and independently processed by the same operator) was evaluated across the 11-spiking concentrations tested in this study (between 1.0-log and 7.5-log CFU.ml; n = 2–4 replicates per concentrations). Detection and quantification of Salmonella naturally contaminated commercial chicken primal rinse samples. A total of ten commercial CPR samples were obtained from a poultry processing facility in Georgia (Company G). The CPR samples were collected in accordance with the USDA FSIS compliance guidelines, directly from the processing line prior to the chilling step (cooling of chicken carcasses at 4°C to prevent microbial growth) (USDA 2013). Aliquots of the CPR samples (20–30 ml stored in 100 ml plastic bottles) were shipped the same day on ice and processed within 24 hours of collection for the detection of Salmonella using the HK assay (10 ml of CPR processed per reaction; as described above) and reference culturing methods. For culture confirmation, Aliquots of samples (0.1, 0.5, and 1 ml per plate) were plated on XLT4 agar and incubated at 42°C for 24 hours. An equal volume of 1X BPW (pH 7.2) was used as blank control, and 1X BPW (pH 7.2) spiked with 1000 CFU/ml was used as positive control. A second set of commercial samples (n = 11) were obtained from a poultry processing facility in Nebraska (Company N), using MicroTally® Mitt (San Jose, CA, USA). These samples were collected based on the manufacturer’s recommendation ( https://microtally.com/wp-content/uploads/2024/11/Validation-of-MT-Mitt-sampling-for-poultry.pdf ), directly from the processing line after to the chilling step. The used MicroTally® Mitt were shipped the same day on ice and processed within 24 hours of collection. MicroTally® Mitt were resuspended into cold 1X BPW (pH 7.2) and manually homogenized for at least 30 sec. The supernatant was used for the detection of Salmonella using the HK assay (10 ml processed per reaction; as described above) and reference culturing methods. For culture confirmation, Aliquots of samples (0.1, 0.5, and 1 ml per plate) were plated on XLT4 agar and incubated at 42°C for 24 hours. An equal volume of 1X BPW (pH 7.2) was used as blank control, and 1X BPW (pH 7.2) spiked with 1000 CFU/ml was used as positive control. The leftover of the resuspended MicroTally® Mitt were also enriched at 35 o C for 18 hrs. Then 1 ml of the enriched BPW was transferred into 9 ml of RV broth (Neogen, Lansing, MI, USA) and incubated at 42 o C for 24 hrs. The product of the second enrichment was used for the detection of Salmonella using the HK assay (0.1 ml processed per reaction; as described above) and reference culturing methods (as described above). Inclusivity and Exclusivity testing . The inclusivity and exclusivity of the HK were evaluated according to the AOAC Performance Tested Methods℠ (PTM) validation guidelines to assess detection specificity for the method. Briefly, inclusivity was determined by testing the HK against a panel of targeted organisms (c.a., 1000 CFU per reaction), ensuring all strains were reliably detected without false negatives. Exclusivity was assessed by challenging the kit with high concentrations (> 10⁶ CFU per reaction) of non-target organisms with distinct phylogenic differences to confirm the absence of cross-reactivity and false-positive results. The complete list of target and non-target organisms used in the validation is provided in Table 1 . The detection of the target organisms was based on a real-time fluorescence signal measured by the fluorescent incubator readers (e.g., Bio-Rad CFX96 Touch Real-Time PCR System). A sample was considered positive if the relative fluorescence units (RFU) exceeded 1,000 (signal threshold for a positive result) within 60 minutes of incubation at 65°C. A result was considered negative if no increase in fluorescence above the threshold was observed within the same incubation period. Each assay was complemented with a tube inoculated using only HCB4 (blank negative control) and another tube spiked with a standardized concentration of Salmonella genomic DNA (positive control; c.a., 1,000 genomic copies) extracted from a known bacterial strain detected by the designated kit. The quality and concentration of the extracted genomic DNA was confirmed using Nanodrop 2000C (Waltham, MA). Assays were considered valid if the positive control yielded a results within 10 min and the RFU was above 3,000. Two independent experiments were conducted. Statistical analysis. Bacterial data were log transformed. Fluorescent data (relative fluorescent unit; RFU) were recorded using the Laboao software (version H8801_V1004) or CFX Maestro Software (v2.3), and used to determine the detection speed (time to result in minute) required for the HK to detect a specific concentration of Salmonella in the spiked samples. A sample was considered positive if the fluorescent signal reached at least 1000 RFU within 60 minutes at 65°C incubation, after normalization of the data to remove the original background noise. Detection speed data were converted into Salmonella concentration data using the following algorithm (Log [ Salmonella concentration/ml] = 8 − 5 x TTR 4 − 0.0069 x TTR 3 + 0.216 x TTR 2 − 2.9027 x TTR + 15.82). Statistical analyses were performed using JMP 18 software (SAS Institute, Cary, NC, USA). Linear regression was used to assess the semi-quantitative accuracy of HK compared the reference microbiology method. Results HyperKit Total Salmonella detected low concentration of Salmonella in artificially spiked chicken rinse primal samples. All CPR samples spiked with a concentration of between 1.0-log and 7.5-log CFU/ml (n = 57) were detected positive in both the HK and the reference method. However, HK provided faster results (detection in less than one hour versus 24 hours for the reference method; Table 2 ). The detection speed of HK was invertedly proportional to the quantity of Salmonella present in the sample. Salmonella concentrations between 5.5-log and 7.5-log CFU/ml (n = 13/57) yielded positive results within 4.6 ± 0.5 min, Salmonella concentrations between 3.4-log and 5.3-log CFU/ml (n = 13/57) yielded positive results within 7 ± 0.7 min, Salmonella concentrations between 2.1-log and 2.5-log CFU/ml (n = 11/57) yielded positive results within 13.5 ± 2.6 min, and Salmonella concentrations between 1.0-log and 1.8-log CFU/ml (n = 13/57) yielded positive results within 18.4 ± 7.4 min (Fig. 2 ). No fluorescent signal was recorded from the blank samples (n = 7). A strong correlation was observed between the detection speed of the HK and the Salmonella concentration in the sample (r² = 0.93; P < 0.001). Table 2 Detection and quantification accuracy of Salmonella in chicken primal rinse samples using HK and reference method Chicken primal spiking group Sample size (total = 64) Reference method data after 24 hrs incubation HK data after 60 min reading Salmonella concentration difference between HyperKit and reference Salmonella prevalence Salmonella concentration (log CFU/ml) Salmonella prevalence Detection speed (time to result in min) Predicted Salmonella concentration (log CFU/ml) A 7 0% - 0% > 60 - - B 7 100% 1.08 (1.04–1.13) 100% 20.0 (13.0–27.0) 1.69 (1.66–1.72) -0.60 (-0.65–0.56) C 4 100% 1.36 (1.31–1.41) 100% 14.6 (13.4–25.9) 1.97 (1.39–2.55) -0.61 (-1.19–0.03) D 7 100% 1.53 (1.49–1.56) 100% 19.2 (10.6–27.8) 2.03 (1.47–2.58) -0.50 (-1.06-0.06) E 3 100% 1.79 (1.73–1.84) 100% 18.2 (11.8–24.5) 1.71 (1.55–1.86) 0.08 (-0.11-0.26) F 7 100% 2.15 (2.12–2.19) 100% 15.1 (13.5–16.6) 1.67 (1.64–1.69) 0.48 (0.43–0.54) G 4 100% 2.48 (2.43–2.52) 100% 10.9 (9.1–12.6) 2.09 (1.63–2.55) 0.39 (-0.11-0.89) H 6 100% 3.46 (3.42–3.50) 100% 7.6 (7.2-8.0) 3.49 (3.22–3.77) -0.03 (-0.29-0.22) I 6 100% 4.48 (4.44–4.52) 100% 6.5 (6.0–7.0) 4.34 (3.93–4.76) 0.13 (-0.28-0.55) J 5 100% 5.44 (5.40–5.48) 100% 5.2 (4.2–6.2) 5.72 (4.57–6.89) -0.28 (-1.37-0.8) K 4 100% 6.44 (6.39–6.48) 100% 4.5 (3.8–5.1) 6.55 (5.67–7.43) -0.11 (-0.90-0.66) L 4 100% 7.43 (7.38–7.48) 100% 4.4 (3.6–5.1) 6.73 (5.70–7.76) 0.7 (-0.23-1.63) Numbers in cells with parenthesis represent the mean and 95% confidence interval for the designated spiking groups The comparison in Salmonella counts obtained from both methods demonstrated that the predicted counts of HK were in accordance with the counts obtained in the reference microbiology method (0.6-log different between the two methods across the 57 spiked samples tested; Fig. 3 ). The semi-quantitative predictive accuracy of HK was higher at concentrations between 1.5-log and 7.5-log CFU/ml (0.2-log ± 0.1-log precision compared to the reference method; n = 39/57; Spiking groups E-L; Table 2 , Supplemental Information 2 ), while its predictive accuracy was reduced when spiking doses were under 1.5-log CFU/ml ( Salmonella population overestimated by 0.6-log ± 0.1-log compared to the reference method; n = 18/57; Spiking groups B-D; Table 2 , Supplemental Information 2 ). HyperKit Total Salmonella provided repeatable, reproducible and robust results. Across the 12-spiking concentrations tested in this study, HK displayed a repeatability of 0.16-log (95% CI: 0.11–0.21), while the reference method had a repeatability of 0.04-log (95% CI: 0.03–0.05; Supplemental Information 3 ). The repeatability of the two methods was independent to the Salmonella concentration in the CPR samples (data not shown). The reproducibility of HK was validated by three operators (Table 3 ). All operators detected Salmonella in the spiked samples with equivalent detection speed (TTR between 13.5 and 14.5 min) and predicted counts ( Salmonella counts between 1.65-log and 1.68-log CFU/ml; Table 3 ). Table 3 Reproducibility of HyperKit Total Salmonella by three operators Operator Salmonella concentration obtained with reference microbiology method (Log CFU/ml) HyperKit Total Salmonella detection data Time to results (min) Predicted Salmonella concentration (log CFU/ml) 1 2.20 13.5 1.68 2 2.21 14.5 1.65 3 2.21 13.5 1.68 The robustness studies demonstrated that reducing the number of “air pumps” from 10 to 6 during HK procedure will have no significant impact on the final volume extracted (average of recovery volume for "air pump” 6, 8, 10 = 68 ± 14 µl; Fig. 1 in Supplemental Information 4 ). On the other hand, the final volume extracted was increased if the number of “air pumps” was below this threshold (81 ± 18 µl with 4 air pumps; 101 ± 19 µl with 2 air pumps; and 499 ± 47 µl with no air pumps; P 0.05). However, significant reductions of detection speed (until not being detected) were recorded when final volumes were under 40 µl or exceeded 60 µl. HyperKit Total Salmonella detected Salmonella in the commercial chicken rinse primal samples. All CPR samples (#1–10) received by Company G were detected positive with the HK within 40 min and after 24 hrs with the reference method (Table 4 ). HK predicted Salmonella counts were in concordance with the Salmonella data obtained with the reference method. Overall, the Salmonella data fluctuated between 1.96-log and 3.47-log CFU/ml for HK and 1.91-log and 2.36-log CFU/ml for HK for the reference method across the 10 CPR samples tested. Only one outlier (sample #2) was obtained with the HK compared to the reference method (3.47-log CFU/ml versus 2.02-log CFU/ml, respectively). Excluding this outlier, the HK obtained Salmonella data within 0.24 ± 0.18-log CFU/ml compared to the Salmonella data obtained with reference method. Fifty percent (5/10) of the data obtained with HK underestimated Salmonella in the CPR samples compared to the reference method. Table 4 Detection and quantification of Salmonella in commercial chicken primal rinse samples using HK and reference method Sample ID Reference method (microbiology data after 24 hrs) HK data after 60 min Salmonella concentration difference between HyperKit and reference (log CFU/ml) Prevalence Salmonella counts (Log CFU/ml) Prevalence Predicted Salmonella counts (Log CFU/ml) 1 Detected 2.02 Detected 2.18 0.16 2 Detected 2.02 Detected 3.47 1.45 3 Detected 2.05 Detected 1.98 -0.07 4 Detected 2.16 Detected 1.96 -0.2 5 Detected 1.91 Detected 2.18 0.27 6 Detected 2.01 Detected 1.98 -0.03 7 Detected 2.36 Detected 1.96 -0.4 8 Detected 2.06 Detected 2.69 0.63 9 Detected 2.22 Detected 2.00 -0.22 10 Detected 2.00 Detected 2.18 0.18 For the MicroTally® Mitt samples received by Company N, three of the eleven samples (Sample #3, #4, and #9) were positive for Salmonella via direct plating with the reference method (Table 5 ). Based on the pre-enrichment plating data, sample #3, #4 and #9 had a suspected Salmonella concentration of 1 CFU/ml, 4 CFU/ml and 6 CFU/ml, respectively. Two of the three positive samples (Samples #4 and #6) were also positive with HK, with a predicted Salmonella concentration of 3 and 4 CFU/ml respectively. After enrichment, all MicroTally® Mitt samples were positive (n = 11/11) for Salmonella using both HK and reference method. All enriched samples were positive for Salmonella within 15 min of isothermal amplification. Table 5 Detection of Salmonella in commercial MicroTally® Mitt samples using HK and reference method Sample ID Salmonella prevalence pre-enrichment Salmonella prevalence post-enrichment (18 hrs at 42 o C) Reference method (microbiology data after 24 hrs) HK data after 60 min Reference method (microbiology data after 24 hrs) HK data after 15 min 1 Not detected Not detected Detected Detected 2 Not detected Not detected Detected Detected 3 Detected Not detected Detected Detected 4 Detected Detected Detected Detected 5 Not detected Not detected Detected Detected 6 Not detected Not detected Detected Detected 7 Not detected Not detected Detected Detected 8 Not detected Not detected Detected Detected 9 Detected Detected Detected Detected 10 Not detected Not detected Detected Detected 11 Not detected Not detected Detected Detected HyperKit Total Salmonella only detected Salmonella and none of the other prokaryotes and eukaryotes tested. All Salmonella serotypes (c.a., 1000 CFU/reaction) were rapidly detected (positive results within 10 min) with HK, while none of the strains belonging to the exclusive list (non- Salmonella organisms; n = 37) were detected by HK (no fluorescent signal detected within 60 min incubation at 65 o C; Table 1 ). Among the exclusive list, 14 Enterobacteriaceae genus closely related to Salmonella (e.g., Citrobacter , Enterobacter , Escherichia , Hafnia , Klebsiella , Pantoea , Proteus , Shewanella , Shigella , and Yersinia ), Gram + bacteria ( Staphylococcus ), and non-prokaryotic organisms ( Saccharomyces ) tested negative. Blank controls (DNA-free) were also negative. Furthermore, the detection of Salmonella spp. was not altered when HK were conducted using mixed samples containing approx. 10 3 Salmonella Typhimurium cells and > 10 6 Escherichia coli cells. Tests conducted with Salmonella alone yielded a TTR of 12 min while tests conducted with a mix Salmonella - E. coli yielded a TTR of 11 min. Discussion Microbial contaminations continue to challenge the integrity of food production systems, resulting in substantial economic losses and serious public health burden (Nganje et al. 2021 ). As food production systems become faster and more decentralized, the need for diagnostic tools that are equally rapid, accurate, and easy to use at the point of production is more critical than ever to consistently maintain high standards of food safety and quality (Jayan et al. 2020 ). The data presented in this study demonstrate that the HK offers robust detection performance and operational practicality, allowing food producers to make accurate and in real-time decisions to mitigate contamination and optimize production processes. This can lay the foundation for rapid detection of contaminants from farm to table, in particular for on-farm detection, flock monitoring, and testing in both live animals and processed food samples (Gong et al. 2016 ; Vinayaka et al. 2023 ). From a performance standpoint, HK achieved detection of Salmonella at concentrations below 10 CFU within 60 minutes, from sample collection to result, without the need for enrichment. The detection and quantification performance of the HK test was not affected by the nature of the samples (i.e., artificially spiked CPR versus commercial CPR naturally contaminated with Salmonella ). No false-positive and false-negative results were recorded under the tested conditions. These results are comparable in sensitivity and accuracy to reference methods such as microbiology (e.g., chromogenic plating) and molecular approaches (e.g., digital PCR, real-time PCR, and LAMP), which are widely accepted as gold standards in diagnostics (Brunelle et al. 2012 ; Rohde et al. 2017 ). The semi-quantitative predictive accuracy of HK aligns with the performance reported using other molecular technologies mentioned above (Li et al. 2013 ; Yang et al. 2016 ; Tirloni et al. 2017 ; Hwang et al. 2024 ; Velez et al. 2024 ); however, all these methods require complicated sample preparation procedures unlike HK. Furthermore, HK showed both inclusivity (successfully detecting all Salmonella serovars) and exclusivity (no cross-reactivity toward non-target or closely related organisms). These preliminary results are consistent with AOAC acceptance criteria for diagnostic assays (European Food Safety Authority 2016 ). For instance, equipment that is rapid, sensitive, and specific that utilizes minimal time for detection will play a key role in quality control in the food processing industry (Liu et al. 2017 ). In terms of operational practicality, HK offers substantial operational advantages compared to other diagnostic methods currently used (microbiology plating and PCR-based technique). It functions under isothermal conditions, utilizes lyophilized reagents that are stable at ambient temperature, and can be performed by non-specialists with minimal training, on premise, and without requiring specialized laboratory infrastructure. This study demonstrated that HK generated highly repeatable semi-quantitative data (r 2 = 0.93) and high reproducibility across users (n = 3; ±0.03-log CFU variation between users). Furthermore, HK is compatible with all tested fluorescent reader-incubators. It is also important to mention that current detection methods (e.g., molecular and microbiology) have a limited sample input size (< 1 ml), thereby, they rely on long enrichment period to enhance the targeted organisms load in the resuspended field samples. In this study, HK were conducted using a 3.35 ml syringe; However, larger sample input size (e.g., 200 ml) could be used to enhance the sensitivity of the test for the detection of key organisms present at low concentrations in large field samples. This extended processing capacity could allow earlier detection of pathogens such as E. coli O157 in beef primals and Listeria monocytogenes in ready-to-eat products by reducing enrichment time, enabling same-shift decision-making for food processors. Overall, HK integrates the analytical accuracy of molecular methods, with the simplicity and the ease of point-of-use tools, meeting key AOAC performance criteria for sensitivity, specificity, and operational robustness in food pathogen detection. Yet, the HyperPen has limited throughput sample processing capacity. It is a rapid (< 2 min) and simple (one-step) point-of-use tool that enable processing one sample at a time, which might not be optimal with large sample sizes (< 20 samples). To overcome this issue, an electro-mechanical device can be developed to automatize the processing of multiple samples at the same time. This approach will minimize the need for trained operators, reduce operators’ errors and enhance the accuracy of the results (Oh et al. 2016 ). Similarly, the HyperMix enables the detection of one pathogen per reaction. Recent studies demonstrated that multiplexing can be used with molecular techniques (qPCR and isothermal reactions) to facilitate the detection of multiple organisms within on reaction tube to answer the industry needs (Kasahara et al. 2014 ; Kline et al. 2022 ; Agel and Altın 2024 ; Jang et al. 2024 ). Preliminary studies demonstrated that HK is a robust detection test kit maintaining test accuracy even under suboptimal handling conditions (variations in the purification/concentration step or deviations in HyperMix inoculation); however, further validation studies must be conducted to identify bottle neck conditions that could affect the performance of HK (e.g., breaks between steps, accelerated stability study, short-term storage of extracted product at room temperature, 4 o C or -20 o C). To date, HK does not permit distinguishing between live and dead cells. However, this issue can be resolved by adding a PMAxx treatment (propidium monoazide) between the sample prep and isothermal incubation to prevent the detection of non-viable cells (Chen et al. 2011 ). Conclusion This study demonstrated that the technology developed by Hypercell Technologies could enable real-time microbial surveillance at critical control points across the food supply chain, using a simplified workflow that avoids complex sample preparation or extended incubation periods. This technology could be applied to food safety testing, environmental monitoring, supply chain quality control and emergency response conditions. Taken together, the results of this study demonstrated that the HyperKit Total Salmonella test Kit is a valuable addition to the suite of tools available for the detection of contaminants. Its combination of analytical sensitivity, operational simplicity, and field readiness makes it particularly well suited for rapid screening applications in food processing environments (Table 6 ). Future studies should focus on expanded validation on other pathogens of significance, comparison with existing AOAC-certified methods across a wider range of commodities and matrix types, and integration into routine food safety management systems. Table 6 Comparative Analysis of Foodborne Pathogen Detection Methods Detection method Cost Detection speed $ Sensitivity Specificity Point of use capability Throughput capacity Biohazard risks Processing complexity Expensive equipment Culturing Low Slow High Moderate Poor Low Yes Moderate No PCR High Fast High High Limited High No* High Yes DNA Microarray High Moderate High High Limited High No High Yes ELISA Moderate Moderate High Moderate Limited High No Moderate Yes LFA Low Very Fast Low Low Good High No Low No ATP Low Very Fast Low Low Good High No Low Yes Microscopy Moderate Fast Low Low Good Low No Moderate Yes Biosensor High Very Fast High High Limited High No Moderate Yes Spectroscopy High Very Fast Moderate Moderate Limited Moderate No High Yes Hypercell Low Very Fast High High Good Low No Low No $ Time to results including all required steps (i.e., sample preparation, incubation and results interpretation), slow: 24–72 hrs, moderate: 4–8 hrs, fast: 1–3 hrs, very fast: <60 min. The color of the cells highlights the strength and weaknesses of the designated method (green: good; orange: acceptable; red: bad). * Pre-enrichment is sometimes required before detection of the contaminants. Declarations Credit authorship contribution statement ASK, LD, and GR wrote the main manuscript text. ASK, LD, and RS were involved in the analysis of the data. ASK and LD were involved in the interpretation of the data. ASK, LD and RS were involved in the acquisition of the data. LD and GR were involved in the conception and design of the work. All authors reviewed the manuscript. Acknowledgement We thank Steven Smalley and Elizabeth Ducharme for their technical support. References Agel E, Altın KH (2024) Field-applicable simultaneous multiplex LAMP assay for screening HBV and HCV co-infection in a single tube. BMC Infect Dis 24:805 Bhowmik D, Oppenheimer PG, Rickard JJS, Jelinek R (2024) Resilient sustainable current and emerging technologies for foodborne pathogen detection. Sustainable Food Technology Brunelle S, LaBudde R, Nelson M, Wehling P (2012) Method Committee Guidelines for Validation of Microbiological Methods for Food and Environmental Surfaces. AOAC INTERNATIONAL CDC (2025a) Estimates: Burden of Foodborne Illness in the United States. In: Food Saf. https://www.cdc.gov/food-safety/php/data-research/foodborne-illness-burden/index.html. Accessed 1 July 2025 CDC (2025b) Investigation Update: Listeria Outbreak, Meats Sliced at Delis. In: List. Infect. List. https://www.cdc.gov/listeria/outbreaks/delimeats-7-24/investigation.html. Accessed 1 July 2025 CDC (2025c) E. coli Outbreak Linked to Onions Served at McDonald’s. In: E Coli Infect. https://www.cdc.gov/ecoli/outbreaks/e-coli-O157.html. Accessed 1 July 2025 CDC (2025d) Investigation Update: Salmonella Outbreak, Cucumbers - June 2024. In: Salmonella Infect. Salmonellosis. https://www.cdc.gov/salmonella/outbreaks/africana-06-24/investigation.html. Accessed 1 July 2025 Chen K, Ma B, Li J, et al (2021) A rapid and sensitive europium nanoparticle-based lateral flow immunoassay combined with recombinase polymerase amplification for simultaneous detection of three food-borne pathogens. Int J Environ Res Public Health 18:4574 Chen S, Wang F, Beaulieu JC, et al (2011) Rapid detection of viable salmonellae in produce by coupling propidium monoazide with loop-mediated isothermal amplification. Appl Environ Microbiol 77:4008–4016 Du M, Li J, Liu Q, et al (2021) Rapid detection of trace Salmonella in milk using an effective pretreatment combined with droplet digital polymerase chain reaction. Microbiol Res 251:126838 Escobar V, Scaramozzino N, Vidic J, et al (2023) Recent advances on peptide-based biosensors and electronic noses for foodborne pathogen detection. Biosensors 13:258 European Food Safety Authority (2016) European Food Safety Authority, & European Centre for Disease Prevention and Control. EFSA J 14:04634 Fusco V, Quero GM (2014) Culture‐dependent and culture‐independent nucleic‐acid‐based methods used in the microbial safety assessment of milk and dairy products. Compr Rev Food Sci Food Saf 13:493–537 Gong J, Zhuang L, Zhu C, et al (2016) Loop-mediated isothermal amplification of the sefA gene for rapid detection of Salmonella Enteritidis and Salmonella Gallinarum in chickens. Foodborne Pathog Dis 13:177–181 Hussain M, Zou J, Zhang H, et al (2022) Recent Progress in Spectroscopic Methods for the Detection of Foodborne Pathogenic Bacteria. Biosensors 12:869 Hussain MA, Dawson CO (2013) Economic impact of food safety outbreaks on food businesses. Foods 2:585–589 Hwang J, Lee JH, Kim YJ, et al (2024) Highly accurate measurement of the relative abundance of oral pathogenic bacteria using colony-forming unit-based qPCR. J Periodontal Implant Sci 54:444 Ismail BB, Wang W, Ayub KA, et al (2024) Advances in microscopy-based techniques applied to the antimicrobial resistance of foodborne pathogens. Trends Food Sci Technol 104674: Jang WS, Park S, Bae JH, et al (2024) Development of a multiplex Loop-Mediated Isothermal Amplification (LAMP) for the diagnosis of bacterial periprosthetic joint infection. Plos One 19:0302783 Jayamohan H, Romanov V, Li H, et al (2017) Chapter 11 - Advances in Microfluidics and Lab-on-a-Chip Technologies. In: Patrinos GP (ed) Molecular Diagnostics (Third Edition). Academic Press, pp 197–217 Jayan H, Pu H, Sun DW (2020) Recent development in rapid detection techniques for microorganism activities in food matrices using bio-recognition: A review. Trends Food Sci Technol 95:233–246 Kasahara K, Ishikawa H, Sato S, et al (2014) Development of multiplex loop-mediated isothermal amplification assays to detect medically important yeasts in dairy products. FEMS Microbiol Lett 357:208–216 Kline EC, Panpradist N, Hull IT, et al (2022) Multiplex target-redundant RT-LAMP for robust detection of SARS-CoV-2 using fluorescent universal displacement probes. Microbiol Spectr 10:01583–21 Law JWF, Ab Mutalib NS, Chan KG, Lee LH (2015) Rapid methods for the detection of foodborne bacterial pathogens: principles, applications, advantages and limitations. Front Microbiol 5:770 Lewis E, Hudson JA, Cook N, et al (2020) Next-generation sequencing as a screening tool for foodborne pathogens in fresh produce. J Microbiol Methods 171:105840 Li Q, Qin D, Zhu J, et al (2024) Development and validation of an ELISA kit for the detection of Staphylococcus aureus enterotoxin A. Food Control 166:110630 Li X, Ximenes E, Amalaradjou MAR, et al (2013) Rapid sample processing for detection of food-borne pathogens via cross-flow microfiltration. Appl Environ Microbiol 79:7048–7054 Liu HB, Zang YX, Du XJ, et al (2017) Development of an isothermal amplification-based assay for the rapid visual detection of Salmonella bacteria. J Dairy Sci 100:7016–7025 Lonchamps PL, He Y, Wang K, Lu X (2022) Detection of pathogens in foods using microfluidic “lab-on-chip”: A mini review. J Agric Food Res 10:100430 Lu Y, Yang H, Bai J, et al (2024) CRISPR-Cas based molecular diagnostics for foodborne pathogens. Crit Rev Food Sci Nutr 64:5269–5289 Mahony J, van Sinderen D (2022) Virome studies of food production systems: time for ‘farm to fork’ analyses. Curr Opin Biotechnol 73:22–27. https://doi.org/10.1016/j.copbio.2021.06.014 Maze MJ, Sharples KJ, Allan KJ, et al (2019) Diagnostic accuracy of leptospirosis whole-cell lateral flow assays: a systematic review and meta-analysis. Clin Microbiol Infect Off Publ Eur Soc Clin Microbiol Infect Dis 25:437–444. https://doi.org/10.1016/j.cmi.2018.11.014 Nganje WE, Burbidge LD, Denkyirah EK, Ndembe EM (2021) Predicting food-safety risk and determining cost-effective risk-reduction strategies. J Risk Financ Manag 14:408 Notermans S, Wernars K (1991) Immunological methods for detection of foodborne pathogens and their toxins. Int J Food Microbiol 12:91–102 Nye KJ, Fallon D, Frodsham D, et al (2002) An evaluation of the performance of XLD, DCA, MLCB, and ABC agars as direct plating media for the isolation of Salmonella enterica from faeces. J Clin Pathol 55:286–288 Oh SJ, Park BH, Choi G, et al (2016) Fully automated and colorimetric foodborne pathogen detection on an integrated centrifugal microfluidic device. Lab Chip 16:1917–1926 Osorio AE, Corradini MG, Dewi G (2017) In-store cold chain failures: food safety considerations. J Mark Channels 24:153–170 Panwar S, Duggirala KS, Yadav P, et al (2023) Advanced diagnostic methods for identification of bacterial foodborne pathogens: Contemporary and upcoming challenges. Crit Rev Biotechnol 43:982–1000 Rohde A, Hammerl JA, Boone I, et al (2017) Overview of validated alternative methods for the detection of foodborne bacterial pathogens. Trends Food Sci Technol 62:113–118 Rosauer ML, Lopez-Velasco G, Silbernagel KM, et al (2022) Validation of the 3MTM Molecular Detection Assay 2—Salmonella for detection of Salmonella in Dried Cannabis Flower and Dried Hemp Flower: AOAC Performance Tested Methods SM 091501. J AOAC Int 105:1684–1690 Sekse C, Holst-Jensen A, Dobrindt U, et al (2017) High throughput sequencing for detection of foodborne pathogens. Front Microbiol 8: Shama G, Malik DJ (2013) The uses and abuses of rapid bioluminescence-based ATP assays. Int J Hyg Environ Health 216:115–125. https://doi.org/10.1016/j.ijheh.2012.03.009 Sun Z, Guo J, Wan W, Wang C (2022) A system of rapidly detecting Escherichia coli in food based on a nanoprobe and improved ATP bioluminescence technology. Nanomaterials 12:2417 Tirloni E, Bernardi C, Drago S, et al (2017) Evaluation of a loop-mediated isothermal amplification method for the detection of Listeria monocytogenes in dairy food. Ital J Food Saf 6:6890 United Sates Department of Agriculture (2025) Cost Estimates of Foodborne Illnesses | Economic Research Service. https://www.ers.usda.gov/data-products/cost-estimates-of-foodborne-illnesses. Accessed 1 July 2025 USDA F (2013) Salmonella and Campylobacter verification program for raw meat and poultry products. FSIS Dir 10:2501 Velez FJ, Kandula N, Blech-Hermoni Y, et al (2024) Digital PCR assay for the specific detection and estimation of Salmonella contamination levels in poultry rinse. Curr Res Food Sci 9:100807 Vinayaka AC, Huynh VN, Quyen TL, et al (2023) Validation of point-of-care device for rapid detection of Salmonella enterica in meat products. Anal Chem 95:12656–12663 Wages JA, Dittoe DK, Feye KM, Ricke SC (2022) Consequences of implementing neutralizing buffered peptone water in commercial poultry processing on the microbiota of whole bird carcass rinses and the subsequent microbiological analyses. Front Microbiol 13:813461 World Health Organization (2024) Food safety. https://www.who.int/news-room/fact-sheets/detail/food-safety. Accessed 1 July 2025 Yang Q, Domesle KJ, Wang F, Ge B (2016) Rapid detection of Salmonella in food and feed by coupling loop-mediated isothermal amplification with bioluminescent assay in real-time. BMC Microbiol 16:1–10 Ye Y, Li L, Chen Y, et al (2025) Molecular methods for rapid detection and identification of foodborne pathogenic bacteria. World J Microbiol Biotechnol 41:1–17 Additional Declarations No competing interests reported. Supplementary Files HypercellJFPSupplementalInformation250703.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7935441","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":547668914,"identity":"329e3b37-2c0b-4750-b1e0-a3cfe5533789","order_by":0,"name":"Anitha Kumar","email":"","orcid":"","institution":"University of Illinois Urbana-Champaign","correspondingAuthor":false,"prefix":"","firstName":"Anitha","middleName":"","lastName":"Kumar","suffix":""},{"id":547668915,"identity":"d35933f9-257b-4f19-857d-705051aead3d","order_by":1,"name":"Rajeev Shrestha","email":"","orcid":"","institution":"University of Illinois Urbana-Champaign","correspondingAuthor":false,"prefix":"","firstName":"Rajeev","middleName":"","lastName":"Shrestha","suffix":""},{"id":547668916,"identity":"66ff4fb0-aa6b-44c5-85bf-ea99dbdcf539","order_by":2,"name":"Loic Deblais","email":"","orcid":"","institution":"Hypercell Technologies","correspondingAuthor":false,"prefix":"","firstName":"Loic","middleName":"","lastName":"Deblais","suffix":""},{"id":547668917,"identity":"0702b19c-7bf0-4bdf-93f7-c8c36b5ea5b2","order_by":3,"name":"Gireesh Rajashekara","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3PMQrCMBSA4ZQHzyXiWlHMFSIFQRQcvYYieA0jhTpJZ8FDCC6OkQe6eABBB7s4OTiJYgerrZMQHR3yD4GEfLyEMZvtH4PXqisFgKHuMMafW/cX4hV9R/1I0nR3phPy3hqJaOejwzneO/N1QqJFsywULHfcQKqU86qT4Ag1Skh30+dSY69hJD5iKa8IUxIQl4zXSl9I7h7HxD0/I0IVLkYiABEYkishI0xzNBIJCMVxQNJNH5b8hdCrT01TwpVzvsY0CENaRreg2RIjP9qeTFP0xxEYrr+mqC8XbDabzcYeEoNNA6Glj5EAAAAASUVORK5CYII=","orcid":"","institution":"University of Illinois Urbana-Champaign","correspondingAuthor":true,"prefix":"","firstName":"Gireesh","middleName":"","lastName":"Rajashekara","suffix":""}],"badges":[],"createdAt":"2025-10-23 22:53:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7935441/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7935441/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96407846,"identity":"67ca39bd-119c-42b2-a707-c837e1fc34a1","added_by":"auto","created_at":"2025-11-20 17:48:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1380549,"visible":true,"origin":"","legend":"","description":"","filename":"HypercellFAM251022.docx","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/eba7e4033b799b0ed22f51e5.docx"},{"id":96407843,"identity":"080bc9c9-3420-4d54-9b6c-3ade5da26432","added_by":"auto","created_at":"2025-11-20 17:48:33","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6244,"visible":true,"origin":"","legend":"","description":"","filename":"375fef9473ec4bfcae166fa437c5154d.json","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/4b48c2bab9f348af5852bef1.json"},{"id":96453991,"identity":"9ad34c03-6cc5-4373-bb28-e653b5f5cd00","added_by":"auto","created_at":"2025-11-21 10:02:14","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":184885,"visible":true,"origin":"","legend":"","description":"","filename":"HypercellJFPSupplementalInformation250703.docx","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/b8031c7634ec7e49bf454e16.docx"},{"id":96453974,"identity":"707a1bc9-f8f8-48ac-89c0-adf18aa2b7c8","added_by":"auto","created_at":"2025-11-21 10:02:10","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":180487,"visible":true,"origin":"","legend":"","description":"","filename":"375fef9473ec4bfcae166fa437c5154d1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/c10c121f49b593c5fe01bc67.xml"},{"id":96407842,"identity":"13ee3e87-1b8d-452a-8ea6-2b46b3a34f86","added_by":"auto","created_at":"2025-11-20 17:48:33","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":491944,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/58145b83e89375e603105132.jpeg"},{"id":96407847,"identity":"4a28f905-8418-453d-b97b-008ec892c2f1","added_by":"auto","created_at":"2025-11-20 17:48:33","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":240842,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/ca857b5cac121b69f20d432f.jpeg"},{"id":96453860,"identity":"21476a86-b70c-438f-aca4-c5536ef19c3c","added_by":"auto","created_at":"2025-11-21 10:01:57","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":254318,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/97c0ef71fc9e66207a32d456.jpeg"},{"id":96454862,"identity":"fb5185f7-bd55-42c6-bcf7-152f823e0971","added_by":"auto","created_at":"2025-11-21 10:03:13","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132431,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/cb00258b10bb6ab3804f6b5f.png"},{"id":96407851,"identity":"32aa4d5d-95e2-4e3c-a8a5-0a3ff9ca87d9","added_by":"auto","created_at":"2025-11-20 17:48:33","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102299,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/fc9ddf4fbceb70ae369232ff.png"},{"id":96454598,"identity":"e4b9757c-8a7e-4bc1-a0f8-718cac03b053","added_by":"auto","created_at":"2025-11-21 10:02:57","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62841,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/d90a6669cb3844701fc36bff.png"},{"id":96407853,"identity":"42a64331-93b3-47e5-8f00-bdc7a10eb199","added_by":"auto","created_at":"2025-11-20 17:48:33","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":178311,"visible":true,"origin":"","legend":"","description":"","filename":"375fef9473ec4bfcae166fa437c5154d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/84d4df7e84233e5a230a57be.xml"},{"id":96407854,"identity":"eab632eb-82ae-41c0-8574-c219aaaa0255","added_by":"auto","created_at":"2025-11-20 17:48:33","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187329,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/767eb999126f4e2f4a1c95fb.html"},{"id":96407840,"identity":"17b10ed8-b6cf-4f09-a4ef-f490622a1ea1","added_by":"auto","created_at":"2025-11-20 17:48:33","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":491944,"visible":true,"origin":"","legend":"\u003cp\u003ePhotography of HyperKit Total \u003cem\u003eSalmonella \u003c/em\u003eand Hypercell Aladdin Analyzer. A: Syringe 1 (sample collection graduated Luer syringe; 3.35 ml final volume). B: Example of pipette to transfer the extracted purified product of the HyperPen from the 1.5 ml collection tube (C) into the HyperMix (D; 0.2 ml PCR tube pre-filled with lyophilized isothermal reagents located inside the aluminum bag). E: HyperPen. E1: white cap protecting the top-hole A where syringe 1 will be plugged after sample collection. E2: red knob that must be rotated 90\u003csup\u003eo \u003c/sup\u003eon the left, until the red arrow is pointing at the red cap (E3). E3: red cap protecting the side hole B where syringe 2 (F; graduated Luer syringe prefilled with Hypercell buffer and closed with a Luer cap; 3.35 ml final volume) will be plugged to conduct the sample purification and concentration steps. E4: PLA 3D printed shell. G: HyperKit packaging (PET packaging container recovered with Tyvek sheet to preserve the sterility and integrity of the kit). H: Hypercell Aladdin Analyzer allowing to incubate the HyperMix at 65\u003csup\u003eo\u003c/sup\u003eC and conduct fluorescent reading at 495 nm every 30 sec. Hypercell Aladdin Analyzer must be plugged to a 120V power source plus USB cable or a computer via USB-C cable directly to the computer to be operated. Hypercell software is compatible with Window operating system. Black line: scale bar of 5 cm\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/58f66232b481f7003a623833.jpeg"},{"id":96407839,"identity":"2dae86b2-4e59-45ce-93b1-284c7b5152de","added_by":"auto","created_at":"2025-11-20 17:48:33","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":240842,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between \u003cem\u003eSalmonella\u003c/em\u003e concentration in chicken primal samples obtained using the reference method and detection speed using HyperKit Total \u003cem\u003eSalmonella\u003c/em\u003e. \u003cem\u003eSalmonella\u003c/em\u003e was detected using HyperKit Total \u003cem\u003eSalmonella\u003c/em\u003e based on the detection of fluorescent signal (495 nm) over time at 65\u003csup\u003eo\u003c/sup\u003eC. \u003cem\u003eSalmonella\u003c/em\u003e concentrations were obtained by counting \u003cem\u003eSalmonella\u003c/em\u003e typical colonies on Xylose-Lysine-Deoxycholate agar (XLD; reference method) after 24 hrs incubation at 37\u003csup\u003eo\u003c/sup\u003eC. Blue line indicates the cubic polynomial regression between the two data sets (r\u003csup\u003e2\u003c/sup\u003e=0.93). Blue dots indicate chicken primal samples (n=57). Blank samples (\u003cem\u003eSalmonella\u003c/em\u003e-free) were not plotted in this graph as no fluorescent signal or colonies were detected by both methods. Dark blue halo indicates the 95% confidence interval. Light blue halo indicates the confidence prediction.\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/58c2b8b57ba015b3a2536efc.jpeg"},{"id":96407841,"identity":"24310051-cd83-4db0-b97b-6686db96dad4","added_by":"auto","created_at":"2025-11-20 17:48:33","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":254318,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression of \u003cem\u003eSalmonella\u003c/em\u003e concentration in chicken primal samples based on the HyperKit Total \u003cem\u003eSalmonella\u003c/em\u003e and the reference method (r\u003csup\u003e2\u003c/sup\u003e=0.92; RMSE=0.59; P\u0026lt; 0.001). \u003cem\u003eSalmonella\u003c/em\u003e data provided by HyperKit Total \u003cem\u003eSalmonella \u003c/em\u003eare semi-quantitative predicted counts using Hypercell algorithm (Log [\u003cem\u003eSalmonella\u003c/em\u003e concentration/ml] = 8\u003csup\u003e-5 \u003c/sup\u003ex TTR\u003csup\u003e4\u003c/sup\u003e - 0.0069 x TTR\u003csup\u003e3\u003c/sup\u003e + 0.216 x TTR\u003csup\u003e2\u003c/sup\u003e - 2.9027 x TTR + 15.82). \u003cem\u003eSalmonella\u003c/em\u003e data provided by the reference method\u003cem\u003e \u003c/em\u003ewere obtained by counting \u003cem\u003eSalmonella\u003c/em\u003e typical colonies on Xylose-Lysine-Deoxycholate (XLD) agar after 24 hrs incubation at 37\u003csup\u003eo\u003c/sup\u003eC. Black dotted line indicates the linear regression between the two methods. Red dotted line indicates the predicted regression if both methods have similar quantitative accuracy. Black dots indicate chicken primal samples (n=57). Blank samples (\u003cem\u003eSalmonella\u003c/em\u003e-free) were not plotted in this graph as no fluorescent signal or colonies were detected by both methods. Blue halo indicates the 95% confidence interval.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/54a5846e9ce6fe6c37705de0.jpeg"},{"id":98775799,"identity":"380103e8-7265-4898-9f03-7565c375a5e5","added_by":"auto","created_at":"2025-12-22 12:21:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3079354,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/4d66adcf-f11f-4649-a094-27db2238ae61.pdf"},{"id":96454834,"identity":"d6e3341d-aa6f-4041-918a-40c201d6a383","added_by":"auto","created_at":"2025-11-21 10:03:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":184885,"visible":true,"origin":"","legend":"","description":"","filename":"HypercellJFPSupplementalInformation250703.docx","url":"https://assets-eu.researchsquare.com/files/rs-7935441/v1/66340debe209f152d1589878.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rapid Isothermal detection and Quantification of Total Salmonella in Poultry Using the HyperKit Point-of-Use diagnostic test","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFood industries face increasing challenges in ensuring both food safety and profitability due to the continuous expansion and intensification of food production across the farm-to-fork continuum (Mahony and van Sinderen \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to the United States Center for Disease Control and Prevention (CDC), the incidence of foodborne illness has remained consistently high over the past several decades, with an estimated 48\u0026nbsp;million cases, 128,000 hospitalizations, and 3,000 deaths occurring each year in the U.S. alone (World Health Organization \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; CDC \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). Beyond public health consequences, foodborne pathogens impose substantial economic burdens on the food industry. Outbreaks often trigger costly product recalls, supply chain disruptions, and legal liabilities. The United States Department of Agriculture (USDA) estimates that foodborne illnesses cost the national economy over \u003cspan\u003e$\u003c/span\u003e15.6\u0026nbsp;billion annually in medical expenses, productivity losses, and economic fallout from recalls and litigation (United States Department of Agriculture, 2025). Reputational damage can be equally or more severe than financial losses. In 2024 alone, the U.S. experienced three multi-state foodborne outbreaks: \u003cem\u003eListeria monocytogenes\u003c/em\u003e linked to Boar's Head deli meats (CDC \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e), \u003cem\u003eEscherichia coli\u003c/em\u003e associated with McDonald's onions (CDC \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025c\u003c/span\u003e), and \u003cem\u003eSalmonella\u003c/em\u003e contamination traced to cucumbers (CDC \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025d\u003c/span\u003e). These incidents not only resulted in substantial financial losses but also severely undermined consumer trust, leading to long-term brand damage that is difficult to recover from (Hussain and Dawson \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The increasing frequency and severity of such incidents underscore the urgent need for improved pathogen detection methods that are rapid, reliable, and deployable at the point of need.\u003c/p\u003e\u003cp\u003eA wide variety of diagnostic technologies are commercially available to detect pathogens in food products and the associated environment; however, most of them cannot provide the detection accuracy while keeping up with the speed and intensity of food industries (Law et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Affordable point of use tests such as, lateral flow immunoassays (LFA, rapid test strips) (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), adenosine triphosphate (ATP) (Sun et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), microscopy (Ismail et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), microfluidic lab-on-a-chip (Lonchamps et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) are rapid, but not specific and not sensitive enough, leading to false results (cross-reactivity, detection of non-viable organisms, environmental conditions and sample matrix interference) (Shama and Malik \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jayamohan et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Maze et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Spectroscopic methods (Hussain et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) offer rapid and high-resolution analysis; however, their application is limited by high equipment costs and reduced sensitivity in complex food samples. Culture-based methods (Fusco and Quero \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) are cost-effective and provide sensitive and accurate results, but they are labor-intensive, require expensive infrastructures (e.g., BSL-2 facility, autoclaves, decontamination facilities, incubators, etc.), and trained operators. Critically, culturing results may take 4\u0026ndash;5 days to obtain due to lengthy incubation steps, making them poorly aligned with the fast-paced decision timelines of modern food production, where actions must be taken within hours. On the other hand, molecular (real-time polymerase chain reaction (Du et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), sequencing (Sekse et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and DNA microarrays (Panwar et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)) and immunologic (enzyme-linked immunosorbent assay, ELISA (Li et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)) methods provide sensitive and specific results within a short period of time, but they are expensive, required highly trained operators and unique working environment (e.g., clean room), and can yield false results (e.g., detection of non-viable organisms) (Notermans and Wernars \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Next generation detection technologies are consistently being evaluated (e.g., Biosensor-Based Techniques (Escobar et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), CRISPR-based diagnostics (Lu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and Next-Generation Sequencing (NGS) (Lewis et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)) to rapidly detect low concentrations of pathogens; however, these methods may take more time being approved by governmental agencies. Furthermore, some of these detection methods must be conducted in specific environmental conditions (light, temperature, humidity, pH, pressure, airborne contaminants), reagents have limited shelf life and restricted storage conditions and cannot be used for the detection of pathogens directly on premises (Panwar et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By consequence, the food industry is left with only two options; using rapid tests that are not accurate and reliable, or ship samples to expensive specialized laboratories to obtain delayed but accurate results (Bhowmik et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the latter, additional logistics burdens (e.g., cold storage, inventory cost, and reduced shelf-life) and false reported results may apply (e.g., sample leakage, improper handling during transport, or exposure to contaminated surfaces) (Osorio et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Altogether, there is an urgent need for improved pathogen detection solutions that enable real-time, on-site testing while ensuring accurate and reliable results (Ye et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe objectives of this study were to assess the sensitivity, specificity, robustness (repeatability and reproducibility) and quantitative precision of the HyperKit Total \u003cem\u003eSalmonella\u003c/em\u003e test (HK). Given \u003cem\u003eSalmonella\u003c/em\u003e is an adulterant in poultry industry, this study focused on the detection and quantification of \u003cem\u003eSalmonella\u003c/em\u003e in artificially spiked poultry carcass rinse samples. This study is a proof of concept that HK is a simple point-of-use diagnostic test that provides rapid and accurate detection of pathogens to support the food industry\u0026rsquo;s needs.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eBacterial strains and culture conditions\u003c/b\u003e. Bacterial strains used in this study were cultured under optimal growing conditions as recommended by the institutes from which the strains were obtained; US department of Agriculture National Center for Agricultural Utilization Research (USDA-NRRL, Peoria, Illinois, USA), National Institutes of Health Biological and Emerging Infections Research Resources Program (NIH BEI, Manassas, Virginia, USA) and American Type Culture Collection (ATCC, Manassas, Virginia, USA). Details about the bacterial strains used in this study are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A cocktail of three \u003cem\u003eSalmonella\u003c/em\u003e serotypes ( Typhimurium, Enteritidis and Heidelberg; ratio 1:1:1) was used as a model for the validation of HyperKit Total \u003cem\u003eSalmonella\u003c/em\u003e detection kit in chicken primal samples. The \u003cem\u003eSalmonella\u003c/em\u003e inocula were prepared by 10-fold diluting an overnight culture grown in Buffered Peptone Water (BPW, pH 7.2; Neogen, Lansing, MI, USA) at 37\u003csup\u003eo\u003c/sup\u003eC in cold fresh BPW until reaching the desired bacterial concentrations depending on the validation requirement. The \u003cem\u003eSalmonella\u003c/em\u003e inocula were plated on selective media (Xylose-Lysine-Deoxycholate [XLD] agar and incubated at 37\u003csup\u003eo\u003c/sup\u003eC for 24 hrs) to ensure accurate inoculum concentrations and kept at 4\u003csup\u003eo\u003c/sup\u003eC until further use.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInclusivity and exclusivity of HyperKit Total \u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTesting list\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOrganisms reference ID (if available)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHyperKit Total \u003cem\u003eSalmonella\u003c/em\u003e specific results\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"36\" rowspan=\"37\"\u003e\u003cp\u003eExclusive list (n\u0026thinsp;=\u0026thinsp;37) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAcidovorax citrulli\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eXu22-15\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCC 17978 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBacillus cereus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-4288 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBacillus subtilis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-604 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCampylobacter ureolyticus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCC 33387 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCitrobacter freundii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-41561 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eClavibacter michiganensis michiganensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC290\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eClostridium sordellii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-41209 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEnterobacter cloacae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-413 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEnterococcus faecalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCC 51188 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEnterococcus faecium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-41204 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEscherichia coli DH5alpha\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEscherichia coli O157\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110200\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFlavobacterium fuscum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-4264 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHafnia alvei\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-41102 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKlebsiella aerogenes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-407 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKlebsiella oxytoca\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-3567 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e subsp. \u003cem\u003epneumoniae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-423 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKleibsiella pneumoniae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCC 13883 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLactobacillus casei\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCC 334 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLactobacillus gasseri\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-4240 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLactobacillus paracasei\u003c/em\u003e subsp \u003cem\u003eparacasei\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-59137 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLeuconostoc mesenteroides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-65335 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eListeria monocytogenes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-4098 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-14761 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMegasphaera cerevisiae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCC 43254 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eNeisseria meningitidis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCC BAA-335 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePantoea agglomerans\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-41484 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eProteus mirabilis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-3404 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-51335 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNCYC 361 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eShewanella baltica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-41146 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eShigella flexneri\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-517 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCC 29213 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus pasteuri\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-23827 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eXanthomonas gardneri\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eXCV761\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eYersinia enterocolitica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB-41479 \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot Detected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"45\" rowspan=\"46\"\u003e\u003cp\u003eInclusive list (n\u0026thinsp;=\u0026thinsp;46) \u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp.\u0026nbsp;diarizonae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-516 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e 4,[5],12:i:-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-28790 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Aberdeen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Abortusovis (B)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-13556 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Agona (B)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Albany\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Anatum (E1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Berta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Braenderup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Breda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Choleraesuis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCC 10708 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Copenhagen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Dublin (D1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-22060 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Eastborne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Enteritidis (D1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Gaminara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCC 8324 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Give\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Hadar (C2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-28799 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Heidelberg (B)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Infantis (C1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Javiana (D1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Kentucky (C3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-28795 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Kenya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Mbandaka (C1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Montevideo (C1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Muenchen (C2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Muenster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Newport (C2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Oranienburg (C1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Orian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Paratyphi A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-515 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Poona (G1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Saintpaul (B)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Schwarzengrund (B)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFSIS 11925832 \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Senftenberg (E4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Stanley (B)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Tennessee (C1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-20740 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Thompson (C1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-4319 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Typhi T2 (D1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-514 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Typhi T4 (D1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Typhimurium CDC 6516-60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCC 14028 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Typhimurium NCTC 74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCC 13311 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Typhimurium LT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Umbilo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Virchow (C1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-28801 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e Weltevreden (E1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR-28798 \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003eA\u003c/sup\u003e Dr. Gireesh Rajashekara, University of Illinois, Champaign, IL, USA; \u003csup\u003eB\u003c/sup\u003e Dr. John Gunn, Nationwide Children Hospital, Columbus, OH, USA; \u003csup\u003eC\u003c/sup\u003e American Type Culture Collection (ATCC), Manassas, Virginia, USA; \u003csup\u003eD\u003c/sup\u003e US department of Agriculture National Center for Agricultural Utilization Research (USDA-NRRL), Peoria, Illinois, USA; \u003csup\u003eE\u003c/sup\u003e National Institutes of Health Biological and Emerging Infections Research Resources Program (NIH BEI), Manassas, Virginia, USA; \u003csup\u003e#\u003c/sup\u003e Exclusivity was assessed by challenging the HyperKit with high concentrations (\u0026gt;\u0026thinsp;10⁶ CFU per reaction) of the designated organisms to confirm the absence of cross-reactivity and false-positive results; \u003csup\u003e$\u003c/sup\u003e Inclusivity was determined by testing the HyperKit against a panel of targeted organisms (c.a., 1000 CFU per reaction), ensuring all strains were reliably detected without false negatives. A sample was considered \u003cb\u003epositive\u003c/b\u003e if the relative fluorescence units (RFU) exceeded 1,000 (signal threshold for a positive result) within 60 minutes of incubation at 65\u0026deg;C. A result was considered \u003cb\u003enegative\u003c/b\u003e if no increase in fluorescence above the threshold was observed within the same incubation period.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePlating on XLD agar was used as the microbiological reference method to detect and quantify \u003cem\u003eSalmonella\u003c/em\u003e in the spiked samples, and to conduct the head-to-head validation with the HyperKit Total \u003cem\u003eSalmonella\u003c/em\u003e. For the inclusivity/exclusivity study, all strains were grown in an appropriate medium using pure stocks kept at -80\u003csup\u003eo\u003c/sup\u003eC in 27% glycerol. All \u003cem\u003eEnterobacteriaceae\u003c/em\u003e strains (e.g., \u003cem\u003eSalmonella, Escherichia coli, Shigella)\u003c/em\u003e were cultured in Luria broth (LB) in aerobic conditions at 37\u003csup\u003eo\u003c/sup\u003eC. Other aerobic bacteria with lower optimal growing temperature (e.g., \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eClavibacter\u003c/em\u003e, \u003cem\u003eListeria\u003c/em\u003e, \u003cem\u003eXanthomonas\u003c/em\u003e) were cultured in Luria broth (LB) in aerobic conditions at 28-30\u003csup\u003eo\u003c/sup\u003eC. \u003cem\u003eLactobacillus\u003c/em\u003e strains were cultured in De Man\u0026ndash;Rogosa\u0026ndash;Sharpe (MRS) medium at 37\u003csup\u003eo\u003c/sup\u003eC with anaerobic conditions (BD GasPak anaerobic sachets; Franklin Lakes, NJ, USA).\u003c/p\u003e\u003cp\u003e\u003cb\u003eHyperKit Total Salmonella\u003c/b\u003e. The Hypercell detection kit (HyperKit) is composed of two items (HyperPen and HyperMix) enabling a rapid, sensitive, accurate, and on-premise detection of specific foodborne contaminants without specialized instrumentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e);\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ea- HyperPen - a single-use, one-step sample prep tool allowing to processing a variety of field samples (e.g., environmental swabs, carcass rinses, meat, fresh produce, fermentation product, and other liquid matrices) under three minutes. The HyperPen utilizes a proprietary buffer (HCB4) with integrated purification chemistry and sequential distribution of filtration modules to capture and concentrate bacteria. Combined together, it allows a simple and rapid processing of complex matrices without the need for conventional lab-based extraction equipment or time-consuming procedures.\u003c/p\u003e\u003cp\u003eb- HyperMix (lyophilized isothermal reagent tubes) \u0026ndash; The amplification reagents are pre-aliquoted and lyophilized in high-profile optical PCR tubes, sealed in vacuum-packed aluminum pouches to prevent exposure to moisture, light, and UV.\u003c/p\u003e\u003cp\u003eThe DNA amplification of the targeted organisms was conducted using devices capable to incubate the inoculated HyperMix tubes at 65\u0026deg;C for 60 min and to monitor the fluorescence signal at 495 nm over time.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePreparation of chicken primal rinse samples.\u003c/b\u003e The chicken primal rinse (CPR) samples were prepared according to the USDA Food Safety and Inspection Service (FSIS) compliance guideline (USDA 2013). CPR were used to mimic the sampling practices followed in the poultry industry and harvest to native chicken microbiome (background noise). One kilogram of chicken primals (breast meat and thigh) purchased at the local grocery shop (Champaign, IL, USA) were resuspended into cold BPW (pH 7.2; ratio 5 g of primal per ml of broth). After addition of the broth, the chicken primals were rinsed for one minute in a 90\u0026deg; arcing motion to ensure that the broth rinses the primals (Wages et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The CPR was collected into sterile containers (e.g., 50 ml conical tubes) and stored at 4\u0026deg;C until spiking. CPR samples were confirmed free of \u003cem\u003eSalmonella\u003c/em\u003e before spiking using both the reference microbiology method, which involved plating 1 ml of sample onto XLD agar (Nye et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and HK.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpiking of chicken primal rinse samples.\u003c/b\u003e For spiking of the CPR samples, known concentrations of \u003cem\u003eSalmonella\u003c/em\u003e were added to the samples (between 10\u003csup\u003e1\u003c/sup\u003e and 10\u003csup\u003e7\u003c/sup\u003e CFU/ml). Sixty-four CPR samples were tested, out of which 57 samples were spiked. An inoculum volume under 1% compared to the total sample volume was used to preserve the physico-chemical properties integrity of the samples (e.g., pH, concentration of salts, debris and other inhibitors). Samples were manually homogenized for 30 sec. Inoculated samples were allowed to equilibrate at 4\u0026deg;C for at least 12 hrs to allow for bacterial adaptation to the matrix. After the stabilization phase, similar microbiology counts between the original inocula and the spiked field samples demonstrated that no antimicrobial activities were observed and plating data can be accurately interpreted.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDetection of\u003c/b\u003e\u003cb\u003eSalmonella\u003c/b\u003e\u003cb\u003ein chicken primal rinse samples.\u003c/b\u003e The experimental design was based on the Association of Official Agricultural Chemists (AOAC) Research Institute\u0026rsquo;s Performance Tested Methods (PTM) guidelines (Rosauer et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A total of 64 CPR samples (57 spiked and seven blank samples) were tested in this study across five days. Final \u003cem\u003eSalmonella\u003c/em\u003e concentrations between 10\u003csup\u003e1\u003c/sup\u003e- 10\u003csup\u003e7\u003c/sup\u003e CFU/ml were tested (n\u0026thinsp;=\u0026thinsp;4\u0026ndash;11 samples per spiking dose level; nine spiking levels tested). A head-to-head comparative study was conducted to assess the detection accuracy and sensitivity of HK compared to reference method (microbiology plating; XLD agar).\u003c/p\u003e\u003cp\u003e\u003cb\u003eProcessing of chicken primal samples using HyperKit Total\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e. Samples were processed in this study according to the manufacturer\u0026rsquo;s recommendations. Briefly, an aliquot of chicken primal samples was collected using the Luer lock syringe (syringe 1 provided in the kit). The sample collection was conducted using the full volume of syringe 1 (3.35 ml\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 ml; maximum volume collected; BH supplies, Berwick, PA, USA ). The sample was injected inside the HyperPen by plugging syringe 1 in the top-hole A. Two \u0026ldquo;air pump\u0026rdquo; (push and pull the syringe plunger back and forth to gradually replacing the liquid inside the syringe with air) were conducted to ensure that the totality of the sample was loaded inside the HyperPen. After turning the controller knock 90\u003csup\u003eo\u003c/sup\u003e to the left, HCB4 was injected inside the HyperPen by plugging the pre-loaded syringe (syringe 2; provided in the kit) in the side-hole B. Ten air pumps were conducted with syringe 2 to concentrate and purify the extract inside the HyperPen. On the tenth air pump, pulling the plunger enabled to extract the purified product (c.a., 50\u0026ndash;100 \u0026micro;l) inside syringe 2. The recovered liquid was transferred into the 1.5 ml collection tube (provided in the kit). Fifty microliters of the purified product was transferred into the HyperMix tube before incubating it in the fluorescent incubator reader (Model LPCR-H800; Laboao, Zhengzhou, China) at 65\u003csup\u003eo\u003c/sup\u003eC for 60 min. Additional studies demonstrated that HK is compatible with other fluorescent incubator readers such as CFX96 Touch Real-Time PCR System (Bio-Rad; Hercules, CA, USA) and Aladdin Analyzer (Hypercell; Ithaca, NY, USA; \u003cb\u003eSupplemental Information 1\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRobustness, repeatability and reproducibility testing\u003c/b\u003e. Robustness of the HK was evaluated to determine the reliability of the assay under intentional variations in operational conditions. Separate deviations from HK SOP includes: (1) reduce the number of \u0026ldquo;air pump\u0026rdquo; during the purification/concentration phase in the HyperPen (0, 2, 4, 6, 8, or 10 pumps); and (2) Improper transfer of the concentrated-purified product from the HyperPen into the HyperMix tube (final volume between 20 and 150 \u0026micro;l). Each variation was evaluated independently to identify bottle neck situation that could lead to generation of potential false results. HyperMix outcomes were defined based on the detection of a positive signal (\u0026ge;\u0026thinsp;1,000 RFU) within 60 minutes at 65\u0026deg;C. Results were considered robust if detection accuracy remained with no impact on time-to-result compared to the control group (proper procedure the HK SOP for the designated step).\u003c/p\u003e\u003cp\u003eThe reproducibility of the HK (semi-quantitative detection accuracy between aliquots collected from the same sample and processed by different operators at the same time) was evaluated by three independent users (Scientific staff members working in Dr Rajashekara\u0026rsquo;s lab. University of Illinois, Champaign, IL, USA), with varying expertise and levels of laboratory experience, following the HK processing procedure described above.\u003c/p\u003e\u003cp\u003eThe repeatability of the HK (semi-quantitative detection accuracy between aliquots collected from the same sample and independently processed by the same operator) was evaluated across the 11-spiking concentrations tested in this study (between 1.0-log and 7.5-log CFU.ml; n\u0026thinsp;=\u0026thinsp;2\u0026ndash;4 replicates per concentrations).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDetection and quantification of\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003enaturally contaminated commercial chicken primal rinse samples.\u003c/b\u003e A total of ten commercial CPR samples were obtained from a poultry processing facility in Georgia (Company G). The CPR samples were collected in accordance with the USDA FSIS compliance guidelines, directly from the processing line prior to the chilling step (cooling of chicken carcasses at 4\u0026deg;C to prevent microbial growth) (USDA 2013). Aliquots of the CPR samples (20\u0026ndash;30 ml stored in 100 ml plastic bottles) were shipped the same day on ice and processed within 24 hours of collection for the detection of \u003cem\u003eSalmonella\u003c/em\u003e using the HK assay (10 ml of CPR processed per reaction; as described above) and reference culturing methods. For culture confirmation, Aliquots of samples (0.1, 0.5, and 1 ml per plate) were plated on XLT4 agar and incubated at 42\u0026deg;C for 24 hours. An equal volume of 1X BPW (pH 7.2) was used as blank control, and 1X BPW (pH 7.2) spiked with 1000 CFU/ml was used as positive control.\u003c/p\u003e\u003cp\u003eA second set of commercial samples (n\u0026thinsp;=\u0026thinsp;11) were obtained from a poultry processing facility in Nebraska (Company N), using MicroTally\u0026reg; Mitt (San Jose, CA, USA). These samples were collected based on the manufacturer\u0026rsquo;s recommendation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://microtally.com/wp-content/uploads/2024/11/Validation-of-MT-Mitt-sampling-for-poultry.pdf\u003c/span\u003e\u003cspan address=\"https://microtally.com/wp-content/uploads/2024/11/Validation-of-MT-Mitt-sampling-for-poultry.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), directly from the processing line after to the chilling step. The used MicroTally\u0026reg; Mitt were shipped the same day on ice and processed within 24 hours of collection. MicroTally\u0026reg; Mitt were resuspended into cold 1X BPW (pH 7.2) and manually homogenized for at least 30 sec. The supernatant was used for the detection of \u003cem\u003eSalmonella\u003c/em\u003e using the HK assay (10 ml processed per reaction; as described above) and reference culturing methods. For culture confirmation, Aliquots of samples (0.1, 0.5, and 1 ml per plate) were plated on XLT4 agar and incubated at 42\u0026deg;C for 24 hours. An equal volume of 1X BPW (pH 7.2) was used as blank control, and 1X BPW (pH 7.2) spiked with 1000 CFU/ml was used as positive control. The leftover of the resuspended MicroTally\u0026reg; Mitt were also enriched at 35\u003csup\u003eo\u003c/sup\u003eC for 18 hrs. Then 1 ml of the enriched BPW was transferred into 9 ml of RV broth (Neogen, Lansing, MI, USA) and incubated at 42\u003csup\u003eo\u003c/sup\u003eC for 24 hrs. The product of the second enrichment was used for the detection of \u003cem\u003eSalmonella\u003c/em\u003e using the HK assay (0.1 ml processed per reaction; as described above) and reference culturing methods (as described above).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInclusivity and Exclusivity testing\u003c/b\u003e. The inclusivity and exclusivity of the HK were evaluated according to the AOAC Performance Tested Methods℠ (PTM) validation guidelines to assess detection specificity for the method. Briefly, inclusivity was determined by testing the HK against a panel of targeted organisms (c.a., 1000 CFU per reaction), ensuring all strains were reliably detected without false negatives. Exclusivity was assessed by challenging the kit with high concentrations (\u0026gt;\u0026thinsp;10⁶ CFU per reaction) of non-target organisms with distinct phylogenic differences to confirm the absence of cross-reactivity and false-positive results. The complete list of target and non-target organisms used in the validation is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The detection of the target organisms was based on a real-time fluorescence signal measured by the fluorescent incubator readers (e.g., Bio-Rad CFX96 Touch Real-Time PCR System). A sample was considered positive if the relative fluorescence units (RFU) exceeded 1,000 (signal threshold for a positive result) within 60 minutes of incubation at 65\u0026deg;C. A result was considered negative if no increase in fluorescence above the threshold was observed within the same incubation period. Each assay was complemented with a tube inoculated using only HCB4 (blank negative control) and another tube spiked with a standardized concentration of \u003cem\u003eSalmonella\u003c/em\u003e genomic DNA (positive control; c.a., 1,000 genomic copies) extracted from a known bacterial strain detected by the designated kit. The quality and concentration of the extracted genomic DNA was confirmed using Nanodrop 2000C (Waltham, MA). Assays were considered valid if the positive control yielded a results within 10 min and the RFU was above 3,000. Two independent experiments were conducted.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analysis.\u003c/b\u003e Bacterial data were log transformed. Fluorescent data (relative fluorescent unit; RFU) were recorded using the Laboao software (version H8801_V1004) or CFX Maestro Software (v2.3), and used to determine the detection speed (time to result in minute) required for the HK to detect a specific concentration of \u003cem\u003eSalmonella\u003c/em\u003e in the spiked samples. A sample was considered positive if the fluorescent signal reached at least 1000 RFU within 60 minutes at 65\u0026deg;C incubation, after normalization of the data to remove the original background noise. Detection speed data were converted into \u003cem\u003eSalmonella\u003c/em\u003e concentration data using the following algorithm (Log [\u003cem\u003eSalmonella\u003c/em\u003e concentration/ml]\u0026thinsp;=\u0026thinsp;8\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e x TTR\u003csup\u003e4\u003c/sup\u003e \u0026minus;\u0026thinsp;0.0069 x TTR\u003csup\u003e3\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.216 x TTR\u003csup\u003e2\u003c/sup\u003e \u0026minus;\u0026thinsp;2.9027 x TTR\u0026thinsp;+\u0026thinsp;15.82). Statistical analyses were performed using JMP 18 software (SAS Institute, Cary, NC, USA). Linear regression was used to assess the semi-quantitative accuracy of HK compared the reference microbiology method.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eHyperKit Total\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003edetected low concentration of\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003ein artificially spiked chicken rinse primal samples.\u003c/b\u003e All CPR samples spiked with a concentration of between 1.0-log and 7.5-log CFU/ml (n\u0026thinsp;=\u0026thinsp;57) were detected positive in both the HK and the reference method. However, HK provided faster results (detection in less than one hour versus 24 hours for the reference method; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The detection speed of HK was invertedly proportional to the quantity of \u003cem\u003eSalmonella\u003c/em\u003e present in the sample. \u003cem\u003eSalmonella\u003c/em\u003e concentrations between 5.5-log and 7.5-log CFU/ml (n\u0026thinsp;=\u0026thinsp;13/57) yielded positive results within 4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 min, \u003cem\u003eSalmonella\u003c/em\u003e concentrations between 3.4-log and 5.3-log CFU/ml (n\u0026thinsp;=\u0026thinsp;13/57) yielded positive results within 7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 min, \u003cem\u003eSalmonella\u003c/em\u003e concentrations between 2.1-log and 2.5-log CFU/ml (n\u0026thinsp;=\u0026thinsp;11/57) yielded positive results within 13.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 min, and \u003cem\u003eSalmonella\u003c/em\u003e concentrations between 1.0-log and 1.8-log CFU/ml (n\u0026thinsp;=\u0026thinsp;13/57) yielded positive results within 18.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4 min (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No fluorescent signal was recorded from the blank samples (n\u0026thinsp;=\u0026thinsp;7). A strong correlation was observed between the detection speed of the HK and the \u003cem\u003eSalmonella\u003c/em\u003e concentration in the sample (r\u0026sup2; = 0.93; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetection and quantification accuracy of \u003cem\u003eSalmonella\u003c/em\u003e in chicken primal rinse samples using HK and reference method\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChicken primal spiking group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSample size (total\u0026thinsp;=\u0026thinsp;64)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eReference method data\u003c/p\u003e\u003cp\u003eafter 24 hrs incubation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHK data after 60 min reading\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e concentration difference between HyperKit and reference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e prevalence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e concentration\u003c/p\u003e\u003cp\u003e(log CFU/ml)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e prevalence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDetection speed (time to result in min)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePredicted \u003cem\u003eSalmonella\u003c/em\u003e concentration\u003c/p\u003e\u003cp\u003e(log CFU/ml)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08 (1.04\u0026ndash;1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.0 (13.0\u0026ndash;27.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.69 (1.66\u0026ndash;1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.60 (-0.65\u0026ndash;0.56)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.36 (1.31\u0026ndash;1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.6 (13.4\u0026ndash;25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.97 (1.39\u0026ndash;2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.61 (-1.19\u0026ndash;0.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.53 (1.49\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.2 (10.6\u0026ndash;27.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.03 (1.47\u0026ndash;2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.50 (-1.06-0.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.79 (1.73\u0026ndash;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.2 (11.8\u0026ndash;24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.71 (1.55\u0026ndash;1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.08 (-0.11-0.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.15 (2.12\u0026ndash;2.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.1 (13.5\u0026ndash;16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.67 (1.64\u0026ndash;1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.48 (0.43\u0026ndash;0.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.48 (2.43\u0026ndash;2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.9 (9.1\u0026ndash;12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.09 (1.63\u0026ndash;2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.39 (-0.11-0.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.46 (3.42\u0026ndash;3.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.6 (7.2-8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.49 (3.22\u0026ndash;3.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.03 (-0.29-0.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.48 (4.44\u0026ndash;4.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.5 (6.0\u0026ndash;7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.34 (3.93\u0026ndash;4.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.13 (-0.28-0.55)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.44 (5.40\u0026ndash;5.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.2 (4.2\u0026ndash;6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.72 (4.57\u0026ndash;6.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.28 (-1.37-0.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.44 (6.39\u0026ndash;6.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.5 (3.8\u0026ndash;5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.55 (5.67\u0026ndash;7.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.11 (-0.90-0.66)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.43 (7.38\u0026ndash;7.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.4 (3.6\u0026ndash;5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.73 (5.70\u0026ndash;7.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.7 (-0.23-1.63)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNumbers in cells with parenthesis represent the mean and 95% confidence interval for the designated spiking groups\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe comparison in \u003cem\u003eSalmonella\u003c/em\u003e counts obtained from both methods demonstrated that the predicted counts of HK were in accordance with the counts obtained in the reference microbiology method (0.6-log different between the two methods across the 57 spiked samples tested; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The semi-quantitative predictive accuracy of HK was higher at concentrations between 1.5-log and 7.5-log CFU/ml (0.2-log\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1-log precision compared to the reference method; n\u0026thinsp;=\u0026thinsp;39/57; Spiking groups E-L; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eSupplemental Information 2\u003c/b\u003e), while its predictive accuracy was reduced when spiking doses were under 1.5-log CFU/ml (\u003cem\u003eSalmonella\u003c/em\u003e population overestimated by 0.6-log\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1-log compared to the reference method; n\u0026thinsp;=\u0026thinsp;18/57; Spiking groups B-D; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eSupplemental Information 2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHyperKit Total\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003eprovided repeatable, reproducible and robust results.\u003c/b\u003e Across the 12-spiking concentrations tested in this study, HK displayed a repeatability of 0.16-log (95% CI: 0.11\u0026ndash;0.21), while the reference method had a repeatability of 0.04-log (95% CI: 0.03\u0026ndash;0.05; \u003cb\u003eSupplemental Information 3\u003c/b\u003e). The repeatability of the two methods was independent to the \u003cem\u003eSalmonella\u003c/em\u003e concentration in the CPR samples (data not shown).\u003c/p\u003e\u003cp\u003eThe reproducibility of HK was validated by three operators (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All operators detected \u003cem\u003eSalmonella\u003c/em\u003e in the spiked samples with equivalent detection speed (TTR between 13.5 and 14.5 min) and predicted counts (\u003cem\u003eSalmonella\u003c/em\u003e counts between 1.65-log and 1.68-log CFU/ml; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReproducibility of HyperKit Total \u003cem\u003eSalmonella\u003c/em\u003e by three operators\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOperator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e concentration obtained with reference microbiology method (Log CFU/ml)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eHyperKit Total \u003cem\u003eSalmonella\u003c/em\u003e detection data\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTime to results (min)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePredicted \u003cem\u003eSalmonella\u003c/em\u003e concentration (log CFU/ml)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe robustness studies demonstrated that reducing the number of \u0026ldquo;air pumps\u0026rdquo; from 10 to 6 during HK procedure will have no significant impact on the final volume extracted (average of recovery volume for \"air pump\u0026rdquo; 6, 8, 10\u0026thinsp;=\u0026thinsp;68\u0026thinsp;\u0026plusmn;\u0026thinsp;14 \u0026micro;l; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ein Supplemental Information 4\u003c/b\u003e). On the other hand, the final volume extracted was increased if the number of \u0026ldquo;air pumps\u0026rdquo; was below this threshold (81\u0026thinsp;\u0026plusmn;\u0026thinsp;18 \u0026micro;l with 4 air pumps; 101\u0026thinsp;\u0026plusmn;\u0026thinsp;19 \u0026micro;l with 2 air pumps; and 499\u0026thinsp;\u0026plusmn;\u0026thinsp;47 \u0026micro;l with no air pumps; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Similarly, the detection of \u003cem\u003eSalmonella\u003c/em\u003e using HK was not significantly compromised if the final volume transferred into the PCR tube was between 40 and 60 \u0026micro;l (\u003cb\u003eSupplemental Information 4\u003c/b\u003e; P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, significant reductions of detection speed (until not being detected) were recorded when final volumes were under 40 \u0026micro;l or exceeded 60 \u0026micro;l.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHyperKit Total\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003edetected\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003ein the commercial chicken rinse primal samples.\u003c/b\u003e All CPR samples (#1\u0026ndash;10) received by Company G were detected positive with the HK within 40 min and after 24 hrs with the reference method (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). HK predicted \u003cem\u003eSalmonella\u003c/em\u003e counts were in concordance with the \u003cem\u003eSalmonella\u003c/em\u003e data obtained with the reference method. Overall, the \u003cem\u003eSalmonella\u003c/em\u003e data fluctuated between 1.96-log and 3.47-log CFU/ml for HK and 1.91-log and 2.36-log CFU/ml for HK for the reference method across the 10 CPR samples tested. Only one outlier (sample #2) was obtained with the HK compared to the reference method (3.47-log CFU/ml versus 2.02-log CFU/ml, respectively). Excluding this outlier, the HK obtained \u003cem\u003eSalmonella\u003c/em\u003e data within 0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18-log CFU/ml compared to the \u003cem\u003eSalmonella\u003c/em\u003e data obtained with reference method. Fifty percent (5/10) of the data obtained with HK underestimated \u003cem\u003eSalmonella\u003c/em\u003e in the CPR samples compared to the reference method.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetection and quantification of \u003cem\u003eSalmonella\u003c/em\u003e in commercial chicken primal rinse samples using HK and reference method\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSample ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eReference method\u003c/p\u003e\u003cp\u003e(microbiology data after 24 hrs)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eHK data after 60 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e concentration difference between HyperKit and reference (log CFU/ml)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrevalence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e counts (Log CFU/ml)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrevalence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePredicted \u003cem\u003eSalmonella\u003c/em\u003e counts (Log CFU/ml)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor the MicroTally\u0026reg; Mitt samples received by Company N, three of the eleven samples (Sample #3, #4, and #9) were positive for \u003cem\u003eSalmonella\u003c/em\u003e via direct plating with the reference method (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Based on the pre-enrichment plating data, sample #3, #4 and #9 had a suspected \u003cem\u003eSalmonella\u003c/em\u003e concentration of 1 CFU/ml, 4 CFU/ml and 6 CFU/ml, respectively. Two of the three positive samples (Samples #4 and #6) were also positive with HK, with a predicted \u003cem\u003eSalmonella\u003c/em\u003e concentration of 3 and 4 CFU/ml respectively. After enrichment, all MicroTally\u0026reg; Mitt samples were positive (n\u0026thinsp;=\u0026thinsp;11/11) for \u003cem\u003eSalmonella\u003c/em\u003e using both HK and reference method. All enriched samples were positive for \u003cem\u003eSalmonella\u003c/em\u003e within 15 min of isothermal amplification.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetection of \u003cem\u003eSalmonella\u003c/em\u003e in commercial MicroTally\u0026reg; Mitt samples using HK and reference method\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSample ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e prevalence pre-enrichment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e prevalence post-enrichment\u003c/p\u003e\u003cp\u003e(18 hrs at 42\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference method\u003c/p\u003e\u003cp\u003e(microbiology data after 24 hrs)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHK data after 60 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference method\u003c/p\u003e\u003cp\u003e(microbiology data after 24 hrs)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHK data after 15 min\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetected\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHyperKit Total\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003eonly detected\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003eand none of the other prokaryotes and eukaryotes tested.\u003c/b\u003e All \u003cem\u003eSalmonella\u003c/em\u003e serotypes (c.a., 1000 CFU/reaction) were rapidly detected (positive results within 10 min) with HK, while none of the strains belonging to the exclusive list (non-\u003cem\u003eSalmonella\u003c/em\u003e organisms; n\u0026thinsp;=\u0026thinsp;37) were detected by HK (no fluorescent signal detected within 60 min incubation at 65\u003csup\u003eo\u003c/sup\u003eC; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the exclusive list, 14 \u003cem\u003eEnterobacteriaceae\u003c/em\u003e genus closely related to \u003cem\u003eSalmonella\u003c/em\u003e (e.g., \u003cem\u003eCitrobacter\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003eHafnia\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003ePantoea\u003c/em\u003e, \u003cem\u003eProteus\u003c/em\u003e, \u003cem\u003eShewanella\u003c/em\u003e, \u003cem\u003eShigella\u003c/em\u003e, and \u003cem\u003eYersinia\u003c/em\u003e), Gram\u0026thinsp;+\u0026thinsp;bacteria (\u003cem\u003eStaphylococcus\u003c/em\u003e), and non-prokaryotic organisms (\u003cem\u003eSaccharomyces\u003c/em\u003e) tested negative. Blank controls (DNA-free) were also negative. Furthermore, the detection of \u003cem\u003eSalmonella\u003c/em\u003e spp. was not altered when HK were conducted using mixed samples containing approx. 10\u003csup\u003e3\u003c/sup\u003e \u003cem\u003eSalmonella\u003c/em\u003e Typhimurium cells and \u0026gt;\u0026thinsp;10\u003csup\u003e6\u003c/sup\u003e \u003cem\u003eEscherichia coli\u003c/em\u003e cells. Tests conducted with \u003cem\u003eSalmonella\u003c/em\u003e alone yielded a TTR of 12 min while tests conducted with a mix \u003cem\u003eSalmonella\u003c/em\u003e-\u003cem\u003eE. coli\u003c/em\u003e yielded a TTR of 11 min.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMicrobial contaminations continue to challenge the integrity of food production systems, resulting in substantial economic losses and serious public health burden (Nganje et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As food production systems become faster and more decentralized, the need for diagnostic tools that are equally rapid, accurate, and easy to use at the point of production is more critical than ever to consistently maintain high standards of food safety and quality (Jayan et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The data presented in this study demonstrate that the HK offers robust detection performance and operational practicality, allowing food producers to make accurate and in real-time decisions to mitigate contamination and optimize production processes. This can lay the foundation for rapid detection of contaminants from farm to table, in particular for on-farm detection, flock monitoring, and testing in both live animals and processed food samples (Gong et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vinayaka et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom a performance standpoint, HK achieved detection of \u003cem\u003eSalmonella\u003c/em\u003e at concentrations below 10 CFU within 60 minutes, from sample collection to result, without the need for enrichment. The detection and quantification performance of the HK test was not affected by the nature of the samples (i.e., artificially spiked CPR versus commercial CPR naturally contaminated with \u003cem\u003eSalmonella\u003c/em\u003e). No false-positive and false-negative results were recorded under the tested conditions. These results are comparable in sensitivity and accuracy to reference methods such as microbiology (e.g., chromogenic plating) and molecular approaches (e.g., digital PCR, real-time PCR, and LAMP), which are widely accepted as gold standards in diagnostics (Brunelle et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rohde et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The semi-quantitative predictive accuracy of HK aligns with the performance reported using other molecular technologies mentioned above (Li et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tirloni et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hwang et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Velez et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); however, all these methods require complicated sample preparation procedures unlike HK. Furthermore, HK showed both inclusivity (successfully detecting all \u003cem\u003eSalmonella\u003c/em\u003e serovars) and exclusivity (no cross-reactivity toward non-target or closely related organisms). These preliminary results are consistent with AOAC acceptance criteria for diagnostic assays (European Food Safety Authority \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For instance, equipment that is rapid, sensitive, and specific that utilizes minimal time for detection will play a key role in quality control in the food processing industry (Liu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn terms of operational practicality, HK offers substantial operational advantages compared to other diagnostic methods currently used (microbiology plating and PCR-based technique). It functions under isothermal conditions, utilizes lyophilized reagents that are stable at ambient temperature, and can be performed by non-specialists with minimal training, on premise, and without requiring specialized laboratory infrastructure. This study demonstrated that HK generated highly repeatable semi-quantitative data (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.93) and high reproducibility across users (n\u0026thinsp;=\u0026thinsp;3; \u0026plusmn;0.03-log CFU variation between users). Furthermore, HK is compatible with all tested fluorescent reader-incubators. It is also important to mention that current detection methods (e.g., molecular and microbiology) have a limited sample input size (\u0026lt;\u0026thinsp;1 ml), thereby, they rely on long enrichment period to enhance the targeted organisms load in the resuspended field samples. In this study, HK were conducted using a 3.35 ml syringe; However, larger sample input size (e.g., 200 ml) could be used to enhance the sensitivity of the test for the detection of key organisms present at low concentrations in large field samples. This extended processing capacity could allow earlier detection of pathogens such as \u003cem\u003eE. coli\u003c/em\u003e O157 in beef primals and \u003cem\u003eListeria monocytogenes\u003c/em\u003e in ready-to-eat products by reducing enrichment time, enabling same-shift decision-making for food processors.\u003c/p\u003e\u003cp\u003eOverall, HK integrates the analytical accuracy of molecular methods, with the simplicity and the ease of point-of-use tools, meeting key AOAC performance criteria for sensitivity, specificity, and operational robustness in food pathogen detection. Yet, the HyperPen has limited throughput sample processing capacity. It is a rapid (\u0026lt;\u0026thinsp;2 min) and simple (one-step) point-of-use tool that enable processing one sample at a time, which might not be optimal with large sample sizes (\u0026lt;\u0026thinsp;20 samples). To overcome this issue, an electro-mechanical device can be developed to automatize the processing of multiple samples at the same time. This approach will minimize the need for trained operators, reduce operators\u0026rsquo; errors and enhance the accuracy of the results (Oh et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, the HyperMix enables the detection of one pathogen per reaction. Recent studies demonstrated that multiplexing can be used with molecular techniques (qPCR and isothermal reactions) to facilitate the detection of multiple organisms within on reaction tube to answer the industry needs (Kasahara et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kline et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Agel and Altın \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Preliminary studies demonstrated that HK is a robust detection test kit maintaining test accuracy even under suboptimal handling conditions (variations in the purification/concentration step or deviations in HyperMix inoculation); however, further validation studies must be conducted to identify bottle neck conditions that could affect the performance of HK (e.g., breaks between steps, accelerated stability study, short-term storage of extracted product at room temperature, 4\u003csup\u003eo\u003c/sup\u003eC or -20\u003csup\u003eo\u003c/sup\u003eC). To date, HK does not permit distinguishing between live and dead cells. However, this issue can be resolved by adding a PMAxx treatment (propidium monoazide) between the sample prep and isothermal incubation to prevent the detection of non-viable cells (Chen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that the technology developed by Hypercell Technologies could enable real-time microbial surveillance at critical control points across the food supply chain, using a simplified workflow that avoids complex sample preparation or extended incubation periods. This technology could be applied to food safety testing, environmental monitoring, supply chain quality control and emergency response conditions. Taken together, the results of this study demonstrated that the HyperKit Total \u003cem\u003eSalmonella\u003c/em\u003e test Kit is a valuable addition to the suite of tools available for the detection of contaminants. Its combination of analytical sensitivity, operational simplicity, and field readiness makes it particularly well suited for rapid screening applications in food processing environments (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Future studies should focus on expanded validation on other pathogens of significance, comparison with existing AOAC-certified methods across a wider range of commodities and matrix types, and integration into routine food safety management systems.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative Analysis of Foodborne Pathogen Detection Methods\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDetection method\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCost\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDetection speed\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePoint of use capability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eThroughput capacity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBiohazard risks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eProcessing complexity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eExpensive equipment\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCulturing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSlow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLimited\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDNA Microarray\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLimited\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eELISA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLimited\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery Fast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery Fast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMicroscopy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiosensor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery Fast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLimited\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpectroscopy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery Fast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLimited\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypercell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery Fast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e$\u003c/sup\u003e Time to results including all required steps (i.e., sample preparation, incubation and results interpretation), slow: 24\u0026ndash;72 hrs, moderate: 4\u0026ndash;8 hrs, fast: 1\u0026ndash;3 hrs, very fast: \u0026lt;60 min. The color of the cells highlights the strength and weaknesses of the designated method (green: good; orange: acceptable; red: bad). * Pre-enrichment is sometimes required before detection of the contaminants.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eASK, LD, and GR \u0026nbsp;wrote the main manuscript text. ASK, LD, and RS were involved in the analysis of the data. ASK and LD were involved in the interpretation of the data. ASK, LD and RS were involved in the acquisition of the data. LD and GR were involved in the conception and design of the work. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Steven Smalley and Elizabeth Ducharme for their technical support.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgel E, Altın KH (2024) Field-applicable simultaneous multiplex LAMP assay for screening HBV and HCV co-infection in a single tube. BMC Infect Dis 24:805\u003c/li\u003e\n\u003cli\u003eBhowmik D, Oppenheimer PG, Rickard JJS, Jelinek R (2024) Resilient sustainable current and emerging technologies for foodborne pathogen detection. Sustainable Food Technology\u003c/li\u003e\n\u003cli\u003eBrunelle S, LaBudde R, Nelson M, Wehling P (2012) Method Committee Guidelines for Validation of Microbiological Methods for Food and Environmental Surfaces. AOAC INTERNATIONAL\u003c/li\u003e\n\u003cli\u003eCDC (2025a) Estimates: Burden of Foodborne Illness in the United States. In: Food Saf. https://www.cdc.gov/food-safety/php/data-research/foodborne-illness-burden/index.html. Accessed 1 July 2025\u003c/li\u003e\n\u003cli\u003eCDC (2025b) Investigation Update: Listeria Outbreak, Meats Sliced at Delis. In: List. Infect. List. https://www.cdc.gov/listeria/outbreaks/delimeats-7-24/investigation.html. Accessed 1 July 2025\u003c/li\u003e\n\u003cli\u003eCDC (2025c) E. coli Outbreak Linked to Onions Served at McDonald\u0026rsquo;s. In: E Coli Infect. https://www.cdc.gov/ecoli/outbreaks/e-coli-O157.html. Accessed 1 July 2025\u003c/li\u003e\n\u003cli\u003eCDC (2025d) Investigation Update: Salmonella Outbreak, Cucumbers - June 2024. In: Salmonella Infect. Salmonellosis. https://www.cdc.gov/salmonella/outbreaks/africana-06-24/investigation.html. Accessed 1 July 2025\u003c/li\u003e\n\u003cli\u003eChen K, Ma B, Li J, et al (2021) A rapid and sensitive europium nanoparticle-based lateral flow immunoassay combined with recombinase polymerase amplification for simultaneous detection of three food-borne pathogens. Int J Environ Res Public Health 18:4574\u003c/li\u003e\n\u003cli\u003eChen S, Wang F, Beaulieu JC, et al (2011) Rapid detection of viable salmonellae in produce by coupling propidium monoazide with loop-mediated isothermal amplification. Appl Environ Microbiol 77:4008\u0026ndash;4016\u003c/li\u003e\n\u003cli\u003eDu M, Li J, Liu Q, et al (2021) Rapid detection of trace Salmonella in milk using an effective pretreatment combined with droplet digital polymerase chain reaction. Microbiol Res 251:126838\u003c/li\u003e\n\u003cli\u003eEscobar V, Scaramozzino N, Vidic J, et al (2023) Recent advances on peptide-based biosensors and electronic noses for foodborne pathogen detection. Biosensors 13:258\u003c/li\u003e\n\u003cli\u003eEuropean Food Safety Authority (2016) European Food Safety Authority, \u0026amp; European Centre for Disease Prevention and Control. EFSA J 14:04634\u003c/li\u003e\n\u003cli\u003eFusco V, Quero GM (2014) Culture‐dependent and culture‐independent nucleic‐acid‐based methods used in the microbial safety assessment of milk and dairy products. Compr Rev Food Sci Food Saf 13:493\u0026ndash;537\u003c/li\u003e\n\u003cli\u003eGong J, Zhuang L, Zhu C, et al (2016) Loop-mediated isothermal amplification of the sefA gene for rapid detection of Salmonella Enteritidis and Salmonella Gallinarum in chickens. Foodborne Pathog Dis 13:177\u0026ndash;181\u003c/li\u003e\n\u003cli\u003eHussain M, Zou J, Zhang H, et al (2022) Recent Progress in Spectroscopic Methods for the Detection of Foodborne Pathogenic Bacteria. Biosensors 12:869\u003c/li\u003e\n\u003cli\u003eHussain MA, Dawson CO (2013) Economic impact of food safety outbreaks on food businesses. Foods 2:585\u0026ndash;589\u003c/li\u003e\n\u003cli\u003eHwang J, Lee JH, Kim YJ, et al (2024) Highly accurate measurement of the relative abundance of oral pathogenic bacteria using colony-forming unit-based qPCR. J Periodontal Implant Sci 54:444\u003c/li\u003e\n\u003cli\u003eIsmail BB, Wang W, Ayub KA, et al (2024) Advances in microscopy-based techniques applied to the antimicrobial resistance of foodborne pathogens. Trends Food Sci Technol 104674:\u003c/li\u003e\n\u003cli\u003eJang WS, Park S, Bae JH, et al (2024) Development of a multiplex Loop-Mediated Isothermal Amplification (LAMP) for the diagnosis of bacterial periprosthetic joint infection. Plos One 19:0302783\u003c/li\u003e\n\u003cli\u003eJayamohan H, Romanov V, Li H, et al (2017) Chapter 11 - Advances in Microfluidics and Lab-on-a-Chip Technologies. In: Patrinos GP (ed) Molecular Diagnostics (Third Edition). Academic Press, pp 197\u0026ndash;217\u003c/li\u003e\n\u003cli\u003eJayan H, Pu H, Sun DW (2020) Recent development in rapid detection techniques for microorganism activities in food matrices using bio-recognition: A review. Trends Food Sci Technol 95:233\u0026ndash;246\u003c/li\u003e\n\u003cli\u003eKasahara K, Ishikawa H, Sato S, et al (2014) Development of multiplex loop-mediated isothermal amplification assays to detect medically important yeasts in dairy products. FEMS Microbiol Lett 357:208\u0026ndash;216\u003c/li\u003e\n\u003cli\u003eKline EC, Panpradist N, Hull IT, et al (2022) Multiplex target-redundant RT-LAMP for robust detection of SARS-CoV-2 using fluorescent universal displacement probes. Microbiol Spectr 10:01583\u0026ndash;21\u003c/li\u003e\n\u003cli\u003eLaw JWF, Ab Mutalib NS, Chan KG, Lee LH (2015) Rapid methods for the detection of foodborne bacterial pathogens: principles, applications, advantages and limitations. Front Microbiol 5:770\u003c/li\u003e\n\u003cli\u003eLewis E, Hudson JA, Cook N, et al (2020) Next-generation sequencing as a screening tool for foodborne pathogens in fresh produce. J Microbiol Methods 171:105840\u003c/li\u003e\n\u003cli\u003eLi Q, Qin D, Zhu J, et al (2024) Development and validation of an ELISA kit for the detection of Staphylococcus aureus enterotoxin A. Food Control 166:110630\u003c/li\u003e\n\u003cli\u003eLi X, Ximenes E, Amalaradjou MAR, et al (2013) Rapid sample processing for detection of food-borne pathogens via cross-flow microfiltration. Appl Environ Microbiol 79:7048\u0026ndash;7054\u003c/li\u003e\n\u003cli\u003eLiu HB, Zang YX, Du XJ, et al (2017) Development of an isothermal amplification-based assay for the rapid visual detection of Salmonella bacteria. J Dairy Sci 100:7016\u0026ndash;7025\u003c/li\u003e\n\u003cli\u003eLonchamps PL, He Y, Wang K, Lu X (2022) Detection of pathogens in foods using microfluidic \u0026ldquo;lab-on-chip\u0026rdquo;: A mini review. J Agric Food Res 10:100430\u003c/li\u003e\n\u003cli\u003eLu Y, Yang H, Bai J, et al (2024) CRISPR-Cas based molecular diagnostics for foodborne pathogens. Crit Rev Food Sci Nutr 64:5269\u0026ndash;5289\u003c/li\u003e\n\u003cli\u003eMahony J, van Sinderen D (2022) Virome studies of food production systems: time for \u0026lsquo;farm to fork\u0026rsquo; analyses. Curr Opin Biotechnol 73:22\u0026ndash;27. https://doi.org/10.1016/j.copbio.2021.06.014\u003c/li\u003e\n\u003cli\u003eMaze MJ, Sharples KJ, Allan KJ, et al (2019) Diagnostic accuracy of leptospirosis whole-cell lateral flow assays: a systematic review and meta-analysis. Clin Microbiol Infect Off Publ Eur Soc Clin Microbiol Infect Dis 25:437\u0026ndash;444. https://doi.org/10.1016/j.cmi.2018.11.014\u003c/li\u003e\n\u003cli\u003eNganje WE, Burbidge LD, Denkyirah EK, Ndembe EM (2021) Predicting food-safety risk and determining cost-effective risk-reduction strategies. J Risk Financ Manag 14:408\u003c/li\u003e\n\u003cli\u003eNotermans S, Wernars K (1991) Immunological methods for detection of foodborne pathogens and their toxins. Int J Food Microbiol 12:91\u0026ndash;102\u003c/li\u003e\n\u003cli\u003eNye KJ, Fallon D, Frodsham D, et al (2002) An evaluation of the performance of XLD, DCA, MLCB, and ABC agars as direct plating media for the isolation of Salmonella enterica from faeces. J Clin Pathol 55:286\u0026ndash;288\u003c/li\u003e\n\u003cli\u003eOh SJ, Park BH, Choi G, et al (2016) Fully automated and colorimetric foodborne pathogen detection on an integrated centrifugal microfluidic device. Lab Chip 16:1917\u0026ndash;1926\u003c/li\u003e\n\u003cli\u003eOsorio AE, Corradini MG, Dewi G (2017) In-store cold chain failures: food safety considerations. J Mark Channels 24:153\u0026ndash;170\u003c/li\u003e\n\u003cli\u003ePanwar S, Duggirala KS, Yadav P, et al (2023) Advanced diagnostic methods for identification of bacterial foodborne pathogens: Contemporary and upcoming challenges. Crit Rev Biotechnol 43:982\u0026ndash;1000\u003c/li\u003e\n\u003cli\u003eRohde A, Hammerl JA, Boone I, et al (2017) Overview of validated alternative methods for the detection of foodborne bacterial pathogens. Trends Food Sci Technol 62:113\u0026ndash;118\u003c/li\u003e\n\u003cli\u003eRosauer ML, Lopez-Velasco G, Silbernagel KM, et al (2022) Validation of the 3MTM Molecular Detection Assay 2\u0026mdash;Salmonella for detection of Salmonella in Dried Cannabis Flower and Dried Hemp Flower: AOAC Performance Tested Methods SM 091501. J AOAC Int 105:1684\u0026ndash;1690\u003c/li\u003e\n\u003cli\u003eSekse C, Holst-Jensen A, Dobrindt U, et al (2017) High throughput sequencing for detection of foodborne pathogens. Front Microbiol 8:\u003c/li\u003e\n\u003cli\u003eShama G, Malik DJ (2013) The uses and abuses of rapid bioluminescence-based ATP assays. Int J Hyg Environ Health 216:115\u0026ndash;125. https://doi.org/10.1016/j.ijheh.2012.03.009\u003c/li\u003e\n\u003cli\u003eSun Z, Guo J, Wan W, Wang C (2022) A system of rapidly detecting Escherichia coli in food based on a nanoprobe and improved ATP bioluminescence technology. Nanomaterials 12:2417\u003c/li\u003e\n\u003cli\u003eTirloni E, Bernardi C, Drago S, et al (2017) Evaluation of a loop-mediated isothermal amplification method for the detection of Listeria monocytogenes in dairy food. Ital J Food Saf 6:6890\u003c/li\u003e\n\u003cli\u003eUnited Sates Department of Agriculture (2025) Cost Estimates of Foodborne Illnesses | Economic Research Service. https://www.ers.usda.gov/data-products/cost-estimates-of-foodborne-illnesses. Accessed 1 July 2025\u003c/li\u003e\n\u003cli\u003eUSDA F (2013) Salmonella and Campylobacter verification program for raw meat and poultry products. FSIS Dir 10:2501\u003c/li\u003e\n\u003cli\u003eVelez FJ, Kandula N, Blech-Hermoni Y, et al (2024) Digital PCR assay for the specific detection and estimation of Salmonella contamination levels in poultry rinse. Curr Res Food Sci 9:100807\u003c/li\u003e\n\u003cli\u003eVinayaka AC, Huynh VN, Quyen TL, et al (2023) Validation of point-of-care device for rapid detection of Salmonella enterica in meat products. Anal Chem 95:12656\u0026ndash;12663\u003c/li\u003e\n\u003cli\u003eWages JA, Dittoe DK, Feye KM, Ricke SC (2022) Consequences of implementing neutralizing buffered peptone water in commercial poultry processing on the microbiota of whole bird carcass rinses and the subsequent microbiological analyses. Front Microbiol 13:813461\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (2024) Food safety. https://www.who.int/news-room/fact-sheets/detail/food-safety. Accessed 1 July 2025\u003c/li\u003e\n\u003cli\u003eYang Q, Domesle KJ, Wang F, Ge B (2016) Rapid detection of Salmonella in food and feed by coupling loop-mediated isothermal amplification with bioluminescent assay in real-time. BMC Microbiol 16:1\u0026ndash;10\u003c/li\u003e\n\u003cli\u003eYe Y, Li L, Chen Y, et al (2025) Molecular methods for rapid detection and identification of foodborne pathogenic bacteria. World J Microbiol Biotechnol 41:1\u0026ndash;17\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Point-of-use, poultry, AOAC guidelines, isothermal detection, total Salmonella","lastPublishedDoi":"10.21203/rs.3.rs-7935441/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7935441/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFood producers face mounting pressure to ensure both safety and efficiency as food systems expand in scale and complexity. However, existing diagnostic tools often force a trade-off between speed, accuracy, cost, and usability, leaving the industry with limited options for real-time, on-site pathogen detection. This study evaluated the performance of the HyperKit Total \u003cem\u003eSalmonella\u003c/em\u003e (HK), a novel point-of-use rapid diagnostic tool for detecting and semi-quantifying \u003cem\u003eSalmonella\u003c/em\u003e in artificially-spiked and commercial chicken primal samples. HK is composed of one-step sample processing tool and a room temperature fluorescent master mix. Targeted DNA was amplified at 65\u003csup\u003eo\u003c/sup\u003eC for 60 min and fluorescence measure over time at 495 nm. HK successfully detected \u003cem\u003eSalmonella\u003c/em\u003e in all spiked samples (1.0 to 7.5-log CFU/mL; n\u0026thinsp;=\u0026thinsp;57) under 60 minutes. HK demonstrated high semi-quantitative accuracy (r\u0026sup2; = 0.93; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), particularly at concentrations\u0026thinsp;\u0026ge;\u0026thinsp;1.5 log CFU/mL (\u0026plusmn;\u0026thinsp;0.18-log precision), as well as strong repeatability (0.16-log; 95% CI: 0.11\u0026ndash;0.21) and reproducibility across three operators and samples of multiple origins (Georgia, Illinois, Nebraska). Data obtained with HK were in close agreement with the reference microbiology method, but up to 300 times faster. Robustness studies confirmed reliable performance under varying sample preparation conditions. Importantly, the kit showed complete inclusivity for all tested \u003cem\u003eSalmonella\u003c/em\u003e serotypes (n\u0026thinsp;=\u0026thinsp;46) and strong exclusivity against non-target organisms (n\u0026thinsp;=\u0026thinsp;37). In conclusion, this study demonstrated that HK is a rapid and accurate detection tool with a simple field workflow to support point-of-use applications in food processing environments, and thus, enhancing food safety monitoring and response in decentralized settings.\u003c/p\u003e","manuscriptTitle":"Rapid Isothermal detection and Quantification of Total Salmonella in Poultry Using the HyperKit Point-of-Use diagnostic test","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 17:48:28","doi":"10.21203/rs.3.rs-7935441/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"576225dd-9b9e-4ceb-abf3-4d7572555d60","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T18:08:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 17:48:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7935441","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7935441","identity":"rs-7935441","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}