The low-cost multi-channel biosensor for the quick detection of different food pathogens

The low-cost multi-channel biosensor for the quick detection of different food pathogens | 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 The low-cost multi-channel biosensor for the quick detection of different food pathogens Mareeswaran Jeyaraman, Kun Jia, Evgeni Eltzov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6496632/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 This study introduces an innovative biosensor designed to utilize specific enzymatic activities of extracellular pathogens enzymes to enable rapid, sensitive, and specific pathogens detection. The biosensor employs a multi-layer construction that includes a measuring chamber, a waterproof stopping layer sensitive to enzymatic degradation, and a color development system. The key innovation lies in the stopping layer, which is composed of materials specifically selected for their susceptibility to degradation by pathogen-secreted enzymes. This design allows the biosensor to detect enzymatic activity indicative of pathogen presence, triggering a visible response when bacterial enzymes degrade the layer and permit fluid to activate the color development system. Results demonstrated that the biosensor could effectively identify significant pathogens, such as Bacillus and Staphylococcus species, with high sensitivity and specificity. Additionally, the biosensor responded differently to pathogen presence depending on the food matrix, illustrating the influence of food composition on sensor functionality. Pathogenic bacteria food poison biosensors point of care devices Figures Figure 1 Figure 2 Figure 3 1. Introduction Foodborne illnesses, a worldwide health problem, are mainly caused by harmful bacteria like Bacillus , Salmonella , and E. coli strains, which are big reasons for food poisoning around the world. Bacillus cereus , for example, a bacterium capable of surviving harsh conditions due to its spore-forming ability, is notorious for causing vomiting and diarrhea, primarily through the consumption of improperly stored or handled rice and pasta(Leong et al., 2023 ). Salmonella , on the other hand, is a genus encompassing a wide array of bacteria that lead to gastrointestinal distress, fever, and, in severe cases, life-threatening complications. Foods commonly associated with Salmonella include undercooked poultry, eggs, and even fruits and vegetables, highlighting the bacteria's versatility in contaminating various food types (Ehuwa et al., 2021 ). E. coli , particularly the O157:H7 strain, presents a grave public health concern due to its severe symptoms, including bloody diarrhea and acute renal failure, especially in children and seniors(Ameer et.al, 2023 ) The primary sources of E. coli outbreaks have been traced back to undercooked ground beef and contaminated fresh produce (Luna-Guevara et al., 2019 ; Rangel et al., 2005 ). The persistence and adaptability of these pathogens underscore the complexities of ensuring food safety, as they can contaminate a wide range of food products at any point from farm to fork. Conventional methods for detecting foodborne pathogens, such as culture-based techniques, PCR (Polymerase Chain Reaction), and ELISA (Enzyme-Linked Immunosorbent Assay), are foundational in ensuring food safety (Priyanka et al., 2016 ). These techniques offer high accuracy and reliability; culture methods can specifically identify bacterial strains, PCR excels in detecting minimal DNA traces, and ELISA effectively identifies specific proteins or antigens. Such precision and the standardization of these methods contribute to their widespread validation and trust within the scientific community. However, these conventional approaches come with significant disadvantages that can impede their effectiveness in rapid response scenarios. One major drawback is the time-consuming nature of these methods, particularly culture-based techniques, which require several days to yield results (Nnachi et al., 2022 ). This delay is problematic in food safety, where rapid action is crucial to prevent the spread of contamination. Additionally, both PCR and ELISA involve complex procedures and require sophisticated equipment and skilled personnel, limiting their application to well-equipped laboratories. This dependency not only increases the cost but also restricts their utility for on-site testing in critical locations such as farms, processing plants, or retail points where immediate decision-making is essential. The complexity and resource intensity of these methods highlights the need for more practical solutions in pathogen detection. As a result, there is a growing interest in developing innovative technologies such as portable biosensors. These new tools aim to address the limitations of conventional methods by enabling simple-to-operate, cost-effective, and on-site detection of pathogens (Ullah et al., 2024 ; Xu et al., 2021 ). By simplifying testing procedures and reducing reliance on extensive laboratory infrastructure, biosensors have the potential to revolutionize food safety protocols, offering timely insights into food quality and safety and significantly reducing the risk of foodborne illness outbreaks. Pathogenic bacteria secrete specific enzymes that aid in their survival and infection processes, such as nutrient acquisition and immune evasion. These enzymes, unique to each bacterial strain, can serve as precise biomarkers for biosensor construction. For instance, Staphylococcus aureus produces coagulase, while E. coli releases lysine decarboxylase and glucose oxidase, and Bacillus cereus generates extracellular enzymes like cereolysin (Wang et al., 2024 ). Leveraging these extracellular enzymes in biosensor designs allows for rapid, direct, and accurate pathogen detection. By targeting these specific enzyme activities, biosensors can improve both sensitivity and specificity, enhancing pathogen detection in food safety and clinical diagnostics. This approach turns bacterial survival mechanisms into a tool for their own detection, providing timely results crucial for effective pathogen detection. The central concept of the proposed biosensor design is the creation of a waterproof layer from two different material compositions (e.g., alginate/gelatin, cornstarch/gelatin, starch/gelatin, lactose/gelatin, glycine/gelatin) sensitive to bacterial enzyme activity. Structurally, the biosensor consists of several layers: at the top, a measuring chamber; beneath that, a waterproof stopping layer crafted from materials that react to external enzymes; and at the bottom, a layer of paper integrated with dry color and an absorption component. In samples devoid of bacteria, the liquid cannot penetrate the stopping layer; hence, no color change occurs, resulting in no visible signal. However, in samples containing bacteria, the pathogens secrete extracellular enzymes that degrade the stopping layer, creating micro-cracks. These cracks allow the sample fluid to seep through, rehydrate the dry color, and subsequently saturate the absorbent layer below, yielding a visible, colored response indicating a positive detection of bacterial activity. This design leverages the natural enzymatic actions of bacteria to facilitate direct and effective pathogen detection. After formulation and optimization steps, the biosensor has demonstrated high sensitivity and specificity to various pathogens in pure growth solutions and has proven effective in detecting bacterial presence across different food types, such as chicken soup, milk, and rice. The influence of food composition on sensor functionality was also evident, highlighting the device's adaptability. This represents the initial step towards developing a portable, easy-to-use, and cost-effective device that enables on-site detection of multiple pathogens in a single sample, streamlining the process for rapid and accurate food safety assessments. 2. Materials and methods 2.1. Chemicals Cornstarch (cas. 9005-25-8), Starch (cas. 65996-62-5), Lactose (cas. 10039-26-6) and Glycine (cas. 56-40-6) were purchased from Beith Dekel (BDL), Israel. Alginate (cas. 9005-38-3), Gelatin Type B (cas. 9000-70-8), and LB Broth Lennox (L3022) were procured from Merck group Sigma-Aldrich (St.Louis, Missouri, United States). The different membrane layers were fabricated and set up for the experiment, such as a polyester (i.e., separation layer) (cat. no. PT-R5) and absorbent pad (cat. no. AP-080) that were purchased from Advanced Microdevices Pvt. Ltd. (Ambala, India). To make the coloured layer, a polyester membrane (cat. no. PT-R5) was stained with blue ink (Encre Noire, Waterman, Paris). The customized 3D-printed set-up, sample holder and isolating layer were developed using Autodesk Fusion 360 software (version 15509.2.0.0) and printed using MakerBot 3D Printer Method X (Stratasys, Rehovot, Israel) with polyvinyl-alcohol (cat. 901031) and polylactic acid (cat.TRD3D0014) as supporting material to design a 3D-printed system for the entire bioassay test. 2.2. Food pathogenic bacteria This study involved six bacterial strains: E. coli (DH5α), Bacillus licheniformis , Bacillus cereus , Bacillus subtilis , Staphylococcus aureus , and Serratia marcescens , all sourced from the Department of Food Science at the Agriculture Research Organization, Volcani Institute, Israel. These strains were cultured in 25 mL of fresh LB medium and incubated overnight at 37ºC on a rotatory thermo-shaker MaxQ 4450 (Thermo Scientific, Marietta, OH, USA) at 150 rpm. Subsequently, 100 µL of these overnight cultures were diluted in 10 mL of fresh LB to reach the early log phase with a density of 10 6 CFU/mL, confirmed by measuring the optical density at 600 nm (OD600nm = 0.5) using an Ultrospec 2100 Pro spectrophotometer (Amersham Bioscience, Biochrom, Cambridge, England). 2.3. Preparation and formation of multi-substrate gelatin film layers for biosensor application Multi-substrates such as alginate, cornstarch, starch, lactose, and glycine were combined with gelatin to prepare the stopping layer at various concentrations. As these multi-substrates cannot polymerize independently, gelatin was employed as the supporting polymer material in the formation process. The film-making solution was adjusted to maintain a consistent proportion of gelatin throughout the polymerization process. Different combinations of multi-substrates with gelatin were prepared, including alginate 2% (w/v)/gelatin 2% (w/v), alginate 2.5% (w/v)/gelatin 2.5% (w/v), cornstarch 3% (w/v)/gelatin 2% (w/v), cornstarch 4% (w/v)/gelatin 2.5% (w/v), starch 2% (w/v)/gelatin 2% (w/v), starch 3% (w/v)/gelatin 2.5% (w/v), starch 3% (w/v)/gelatin 3% (w/v), lactose 4% (w/v)/gelatin 2.5% (w/v), lactose 6% (w/v)/gelatin 3% (w/v), glycine 4% (w/v)/gelatin 2.5% (w/v), and glycine 6% (w/v)/gelatin 3% (w/v). The multi-substrate film layers were further developed using the drop-casting method, employing different concentrations of these gelatin combinations. These included alginate 2.5% (w/v)/gelatin 2.5% (w/v), cornstarch 4% (w/v)/gelatin 2.5% (w/v), starch 3% (w/v)/gelatin 3% (w/v), lactose 6% (w/v)/gelatin 3% (w/v), and glycine 6% (w/v)/gelatin 3% (w/v). Accurate quantities of multi-substrates and gelatin were weighed and dissolved in deionized water to reach the desired concentrations. This mixture was then thoroughly mixed and heated on a hot plate at 200 rpm and temperatures of 80–90˚C for 20 to 25 minutes, ensuring the development of a homogeneous solution and enhancing the layer's structural integrity.. 2.4 Assessment of Multi-substrates film layers thicknesses and permeability The multisubstrate-based gelatin solutions were poured into disposable Thermo Scientific™ Remel plastic Petri dishes (cat no. R80150) in volumes of 3, 3.5, and 4 mL, forming films of three different thicknesses: 0.8, 1, and 1.2 mm. These Petri dishes were then refrigerated at 4ºC overnight to solidify the gelatin films. After film formation, a permeability test was conducted by applying 330µL of water and LB (Luria-Bertani medium) onto films of varying thicknesses. This test included different combinations such as Alginate 2.5% (w/v)/Gelatin 2.5% (w/v), Cornstarch 4% (w/v)/Gelatin 2.5% (w/v), Starch 3% (w/v)/Gelatin 3% (w/v), Lactose 6% (w/v)/Gelatin 3% (w/v), and Glycine 6% (w/v)/Gelatin 3% (w/v) to evaluate their permeability characteristics. 2.5 Effect of the physical properties on the films formation and stability 2.5.1. Texture analysis The bloom strength of multi-substrates-based gelatin films was measured using TA.XT plus C Texture Analyser (Stable Micro Systems, Godalming, UK). A standard 0.25 mm radius cylinder probe (P/O.25S) was selected to penetrate the films with 0.25 mm/s. The gel bloom strength is measured with maximum force to compress the multi-substrates-based gelatin film before its permanent distortion. The analysis data was collected in triplicate, and the results were presented as the mean value along with the corresponding standard deviation. 2.5.2 Moisture content The moisture loss from the film layers was assessed after an 8-hour drying period in a hot air oven set at 65ºC to determine their dry matter content. To calculate the percentage of moisture content, the initial wet weight of the films was subtracted from their dry weight after drying. This difference was then divided by the initial wet weight and multiplied by 100 to convert the result into a percentage form. All measurements were performed in triplicate, with results presented as the mean and corresponding standard deviation. 2.6. Establishing of multi-substrates system configureation The multi-substrate film layers were constructed using a layer stacking technique. The structure from top to bottom included: a multi-channel sample chamber (80mm×20mm), a multi-substrate-based gelatin layer (15mm×10mm), a multi-channel isolating layer (80mm×20mm), a multi-channel holder box (80mm×30mm), a color layer (10mm×5mm), and an absorption layer (20mm×10mm), arranged in sequence (Fig. 1 ). Each channel of the multiwell setup is encased within a different gelatin layer, each sensitive to distinct types of bacteria. The gelatin layers were positioned atop the 3D-printed multi-channel isolating module, with a multi-channel holder system directly beneath. Above the color layer, an absorption layer was placed, both of which were encapsulated within the multi-channel holder box (Fig. 1 ). Square-shaped multi-substrate film layers were excised from solidified Petri dishes (as described in section 2.3 ) using a thin scalpel and set on the multi-channel isolating layer. A multi-channel sample chamber was then placed on top of these layers, ready to receive the optimal sample volume. 2.7. Sampling procedure All experiments were conducted at a controlled room temperature of 25ºC using disposable sterile containers with lids. A sample volume of 330 µL, spiked with or without bacteria, was applied to the multi-substrate-based gelatin layers using a 3D-printed multi-channel sample holder. Each bacterial strain secretes specific enzymes that can break down the gelatin layers, causing them to liquefy and allowing the sample to migrate by capillary action to the colored layer below. This interaction with the dissolved ink in the colored pad results in a visible color change, detectable by the naked eye, indicating a positive detection of bacteria. In contrast, samples without bacteria show no color change due to the absence of enzyme activity, preventing the passage of liquid through the film layer. The response time, measured in hours, is defined by how quickly the sample traverses through the colored pad, mobilizing the dye toward the absorbent layer below and producing a visible colorimetric signal. 2.8. Assessment of multi-substrate setup specificity Six distinct bacterial strains namely E.coli (DH5α), Bacillus licheniformis, Bacillus cereus, Bacillus subtilis, Staphylococcus aureus , and Serratia marcescens were employed to evaluate the bioassay’s specificity with multi-substrates based gelatin film layers. Each strain was diluted in LB medium to a concentration of 10 6 CFU/mL for the bioassay. A volume of 330 µL of these bacterial suspensions was introduced into a 3D-printed biosensor setup. The diffusion of color through to the absorbent layer was then monitored to analyze the bioassay's response to each bacterial strain 2.9. Assessment of multi-substrate setup sensitivity Different bacterial strains were cultured in 20 mL of freshly prepared LB medium and incubated overnight at 37ºC using a rotary thermo-shaker. After incubation, the cell cultures were prepared for use in the bioassay. To assess the bioassay's sensitivity, various concentrations of pathogenic bacteria, including B. subtilis (9 and 1 CFU/mL), S. marcescens (12 and 1 CFU/mL), S. aureus (14 and 2 CFU/mL) B.licheniformis (10 and 1 CFU/mL) and S. aureus (14 and 2 CFU/mL) were adjusted to target levels (10 6 and 10 7 CFU/mL, respectively). These bacterial concentrations were then introduced into multi-substrate-based gelatin layers of different compositions (e.g., alginate 2.5% (w/v) + gelatin 2.5% (w/v), cornstarch 4% (w/v) + gelatin 2.5% (w/v), starch 3% (w/v) + gelatin 3% (w/v), lactose 6% (w/v) + gelatin 3% (w/v), and glycine 6% (w/v) + gelatin 3% (w/v)) in 3D setups. Due to the enzymatic activity of the bacteria, enzymes penetrated and liquefied the gelatin layers, resulting in positive test outcomes. 2.10. Analysis of food samples The validation of the bioassay was conducted using three different types of food: homemade chicken soup (Rehovot, Israel), boiled rice (Daawat Rice, Bombay, India), and 3% (w/v) pasteurized milk (Tnuva, Rehovot, Israel). The boiled rice was prepared by mixing with sterile distilled water in a 1:6 ratio to achieve a liquid consistency. These food samples were spiked with various concentrations of bacterial strains as described previously, and then tested using the 3D-printed biosensor setup system. Uninfected food samples served as controls to ensure the accuracy of the bioassay results. 2.11. Statistical analysis All collected data were analyzed statistically using GraphPad Prism (Software package, version 8, San Diego, USA). Both one-way and two-way analysis of variance (ANOVA) with Tukey’s post hoc test were employed to assess variations among the tested parameters. Error bars in the graphical representations were expressed as the standard error of independent replicates, and significance levels were marked accordingly (****p < 0.0001, ***p < 0.0005, **p = 0.008, *p < 0.05, and n.s. for not significant by ANOVA). 3. Results and discussion 3.1 Determination of Optimal Layers for Biosensor Development The development of point-of-care biosensors for detecting pathogens in food is crucial for ensuring public health safety by enabling rapid, on-site identification of contaminants, which significantly reduces the risk of widespread foodborne illnesses and enhances the effectiveness of response and mitigation strategies. In the previous study, a new point-of-care approach based on a colorimetric device for detecting Bacillus cereus in food specimens was introduced (Kaur et al., 2022 ). This study expands on the initial findings to explore a broader application in detecting multiple pathogens within a single sample. In the initial phase of the study, the focus was on determining the optimal concentrations of different materials that enable the development of stable layers essential for effective biosensor construction. Analysis of various material combinations, as detailed in Table 1 , demonstrates that alginate and gelatin concentrations needed to be increased from 2% (w/v) to 2.5% (w/v) each for the formation of the stable solid layer. Cornstarch and gelatin required an adjustment from 3% (w/v) cornstarch and 2% (w/v) gelatin to 4% (w/v) and 2.5% (w/v), respectively, to achieve stability. All tested mixtures stabilized only at higher tested concentrations, with some chemicals achieving solidification at 2.5% (w/v) gelatin, while others only solidified at concentrations of 3% (w/v) (Table 1 ). The observed differences in stabilization concentrations among the chemical mixtures can be attributed to the unique molecular interactions between the substrates and gelatin. Substrates that form stronger hydrogen bonds or ionic interactions with gelatin may achieve solidification at lower concentrations (El Sayed, 2023 ). Additionally, the intrinsic properties such as molecular weight, polarity, and solubility of each substrate influence the gelatin matrix’s stability, with substances that integrate more seamlessly into the gelatin structure requiring lower concentrations for stabilization (Olijve et al., 2001 ). Lastly, the viscosity and density of the mixtures also play an important role, as higher viscosity can enhance stability but may necessitate higher substrate concentrations to optimize the biosensor's performance. Table 1 List of Multi-substrates with supporting material Multi-substrates with supporting material Different concentration (W/V) Observation Results Alginate + Gelatin 2% + 2% Semi-solid Not stabled 2.5% + 2.5% Solidified Stabled condition Cornstarch + Gelatin 3% + 2% Semi-solid Not stabled 4% + 2.5% Solidified Stabled condition Starch + Gelatin 2% + 2% Semi-solid Not stabled 3% + 2.5% Colloidal form Not Stabled 3% + 3% Solidified Stabled condition Lactose + Gelatin 4% + 2.5% Colloidal form Not stabled 6% + 3% Solidified Stabled condition Glycine + Gelatin 4% + 2.5% Colloidal form Not stabled 6% + 3% Solidified Stabled condition 3.2 Determination of Physical properties on multi-substrates Then, the effect of the film thicknesses on the mechanical properties, specifically bloom strength and layer wetness, was evaluated (Table 2 ). The analysis showed two opposing trends in bloom strength related to changes in layer thickness. An increase in bloom strength was observed for mixtures of gelatin with alginate, cornstarch, and lactose, which corresponded with relatively stable or slightly increasing moisture levels, suggesting that these combinations maintain or enhance their structural integrity and cohesion as they thicken. Notably, the alginate and gelatin mixture showed the most significant increase in bloom strength and moisture retention, likely due to the synergistic interactions between the strong gel-forming capabilities of alginate and the thermal gelling properties of gelatin (Panouillé and Larreta-Garde, 2009 ). This effective cross-linking within the matrix results in a cohesive and structurally robust film as the thickness increases, making this mixture particularly useful in applications like 3D cultures and bioprinting, where gelation properties are critical for structuring and supporting biological materials (Łabowska et al., 2021 ). Conversely, a decrease in bloom strength was noted in mixtures containing starch and glycine as the layer thickness increased, accompanied by a decrease in moisture retention. This suggests that thicker layers in these mixtures might suffer from over-saturation of the gel matrix, leading to compromised cross-linking and reduced structural robustness. The decrease in moisture content with increased thickness could further exacerbate the weakening of the matrix, highlighting a complex interaction between mechanical strength and moisture dynamics within these layers. Glycine and gelatin mixture exhibited a highest decrease in bloom strength as the film thickness increased. This decrease was also accompanied by a relatively higher water content, suggesting that the glycine might be interfering with the gel matrix's ability to form strong, cohesive bonds, resulting in a less stable structure with more liquid inside. The drying experiments, conducted at temperatures ranging from 65°C to 70°C, aimed to determine the most effective method for moisture removal, essential for maintaining the integrity of the biosensor films. The findings indicated that oven drying might not consistently remove moisture, potentially leaving residual wetness within the layers. Specifically, the combination of glycine 6% (w/v) + gelatin 3% (w/v) exhibited lower moisture content at volumes of 3 and 3.5 mL compared to other combinations. Conversely, higher moisture retention was observed in alginate 2.5% (w/v) + gelatin 2.5% (w/v) at volumes of 3.5 and 4 mL (Table 2 ). These results underscore the complex interaction between drying methods and film composition on moisture dynamics within the layers, highlighting the necessity to optimize drying processes to enhance the structural stability and functional reliability of the biosensor films. This aspect of the research connects directly to the broader theme of film sensitivity to mechanical strength weaknesses as previously noted in the literatureThese findings underscore the importance of optimizing both the mechanical and moisture retention properties of biosensor films to ensure their functionality and reliability in proposed application (Fematt-flores et al., 2022 ; Grad et al., 2003 ). Table 2 The specific composition, volume on the film's bloom strength and drying content on the layer's physical properties Optimum concentrations (W/V) Plate volumes (ml) Film thicknesses (mm) Bloom Strength (g) Loss of Moisture (%) Alginate 2.5% + Gelatin 2.5% 3.0 0.8 ± 0.06 191.55 ± 26.16 94.89 ± 0.16 3.5 1.0 ± 0.07 409.09 ± 281.07 94.80 ± 0.09 4.0 1.2 ± 0.06 614.93 ± 50.21 95.03 ± 0.05 Cornstarch 4% + Gelatin 2.5% 3.0 0.8 ± 0.06 85.12 ± 44.42 92.65 ± 0.07 3.5 1.0 ± 0.07 126.76 ± 24.49 92.85 ± 0.13 4.0 1.2 ± 0.04 184.47 ± 15.33 93.07 ± 0.09 Starch 3% + Gelatin 3% 3.0 0.8 ± 0.05 169.29 ± 25.01 93.32 ± 0.08 3.5 1.0 ± 0.04 100.68 ± 38.16 93.51 ± 0.09 4.0 1.2 ± 0.08 107.86 ± 38.59 91.95 ± 0.41 Lactose 6% + Gelatin 3% 3.0 0.8 ± 0.06 125.03 ± 35.64 90.52 ± 0.07 3.5 1.0 ± 0.06 181.45 ± 27.98 90.14 ± 0.10 4.0 1.2 ± 0.05 218.12 ± 32.43 90.41 ± 0.09 Glycine 6% + Gelatin 3% 3.0 0.8 ± 0.05 111.59 ± 14.12 89.97 ± 0.15 3.5 1.0 ± 0.07 86.02 ± 12.06 89.39 ± 0.28 4.0 1.2 ± 0.06 78.73 ± 7.87 90.01 ± 0.17 3.3 Determination of Water/LB permeability test on multi-substrates The final formulation of the multi-substrate layer, along with different membrane thicknesses, was evaluated for its permeability to water/LB medium. Experiments confirmed that multi-substrate compositions—Alginate 2.5% (w/v) + Gelatin 2.5% (w/v), Cornstarch 4% (w/v) + Gelatin 2.5% (w/v), Starch 3% (w/v) + Gelatin 3% (w/v), Lactose 6% (w/v) + Gelatin 3% (w/v), and Glycine 6% (w/v) + Gelatin 3% (w/v)—maintained impermeability across various thicknesses (0.8, 1.0, and 1.2 mm) at all testing intervals (Table S1 ). In contrast, layers with lower substrate concentrations exhibited semi-solid states or insufficient polymerization (Table 1 ). At higher concentrations, the multi-substrate layers demonstrated robust polymerization, likely due to the formation of a three-dimensional triple helical structure, stabilized by hydrogen bonds and van der Waals forces, which effectively prevented fluid passage (Djabourov et al., 1988 ). 3.4 Determining the Specificity of Proposed Material Mixtures for Different Bacterial Strains The next phase of this study was focused on determining the specificity of various material mixtures to different bacterial strains, as shown in Fig. 2 and Table 3 . This figure and table compares the detection times across different bacterial solutions at a constant concentration of 1 CFU/mL(Fig. S1 ). This high sensitivity means that even a small amount of enzyme activity, enough to create a tiny crack in the biosensor's initially impermeable layer, can trigger a detectable response. This sensitivity is crucial because it indicates that if we will start even from a single cell's enzyme output, it will be enough to create a minor disruption in the protective layer to trigger a positive detection signal. The results indicate that specific material combinations exhibited shorter detection times for certain bacteria (Fig. S2), suggesting potential applications for targeted pathogen detection. For instance, the cornstarch/gelatin and alginate/gelatin layers showed the shortest detection times for S. marcescens and B.subtilis at 15.4 hours and 15.9 hours, respectively (Fig. 2 B, A), making it a potentially effective material for specifically detecting these strains. Similarly, lactose/gelatin and starch/gelatin layers demonstrated relatively lower detection times for B. licheniformis and S. aureus at 16.4 hours and 16.8 hours, respectively (Fig. 2 D, C). In Fig. 2 E, S. aureus demonstrated a quicker response in the setups constructed from a mixture of gelatin with glycine. The specificity of detection times observed in the study could be attributed to the interaction between the material layers of the biosensors and external enzymes produced by different bacterial strains. Each bacterial type secretes specific enzymes that can degrade or alter the structural integrity of the biosensor layers, which are initially designed to be waterproof, preventing sample permeation. For example, the alginate/gelatin mixture, which demonstrated the shortest detection time for B. subtilis , may be particularly susceptible to enzymes produced by this bacterium. B. subtilis is known to secrete proteases and other enzymes that can effectively break down gelatin's protein structure and potentially modify alginate's gel matrix, making the layer permeable enough for the bacterial sample to pass through and trigger a detection signal (Pant et al., 2015; Su et al., 2020 ). Similarly, the detection of S. marcescens and S. aureus by the cornstarch/gelatin and starch/gelatin mixtures might be facilitated by staphylococcal enzymes like lipases or nucleases, which can degrade the starch component, disrupting the layer's integrity and allow the bacterial sample to diffuse through the stopping layer and produce positive responses (Tam and Torres 2019 ). The quicker response of the lactose/gelatin mixture to B. licheniformis could be due to the enzymatic activity specific to this bacterium, which might include the production of enzymes capable of hydrolyzing lactose, thereby compromising the gelatin matrix's ability to maintain its waterproof barrier (Amin et al., 2023 ; Kamran et al., 2016 ). The possible reason for a quicker detection response of B. licheniformis and S. aureus in gelatin-glycine-based applications may be due to enhanced enzymatic interaction and optimal pH buffering by glycine, facilitating faster enzymatic degradation and sensor responsiveness (Silva-Salinas et al. 2021 ; STROMINGER and BIRGE 1965). These interactions highlight how the enzymatic activity specific to each bacterial type can directly impact the permeability and effectiveness of different material mixtures used in biosensor layers. By understanding these dynamics, it is possible to design biosensors with material compositions that are selectively permeable to certain bacterial strains based on their enzymatic profiles, thereby optimizing the specificity and speed of pathogen detection. Such specifity and response times was clearly demonstrated through this section. Furthermore, the specificity of the biosensor's responses was rigorously validated, showing that in negative control scenarios, no false positives were recorded across any of the tested material compositions. This absence of response in control setups reinforces the biosensor's reliability, ensuring that only the target bacterial enzymes capable of degrading the sensor layers will produce a signal. This level of sensitivity and specificity not only enhances the practical application of the biosensor in detecting low levels of pathogens but also confirms its robustness against potential false activations. Table 3 The specificity of different bacterial solutions same constant (1 CFU/mL) on Multi-substrates Multi-substrates Different bacterial solutions same constant concentration (1 CFU/mL) LB Water E.coli B.licheniformis B.cereus B.subtilis S. aureus S.marcescens Alginate 2.5% + Gelatin 2.5% 24.1h 24.7h 23.8h 23.4h 23.3h 15.9h 24.7h 23.5h Cornstarch 4% + Gelatin 2.5% 24.3h 24.3h 23.9h 23.7h 23.9h 23.8h 16.3h 15.4h Starch 3% + Gelatin 3% 23.7h 23.9h 23.2h 22.4h 21.9h 22.7h 16.8h 17.9h Lactose 6% + Gelatin 3% 23.9h 24h 23.6h 16.4h 21.2h 23h 20.2h 23.2h Glycine 6% + Gelatin 3% 24.5h 24.7h 24.2h 18.8h 22.1h 23.1h 17.2h 23.4h 3.5 Detection of Pathogens in Real Food Samples Using the Biosensor System Food safety testing is crucial but fraught with challenges, including the complexity of accurately detecting pathogens in multi-component food environments, which complicates testing procedures. The most common traditional methods, like culture-based techniques, specialized skills and equipment, often yield slow results (2–3 days) and risk false negatives or positives due to cross-contamination(Harinathan, 2024). The proposed system may offer a promising solution by enabling rapid, on-site detection that simplifies testing in diverse food matrices and reduces dependency on extensive laboratory infrastructure. So, the next step of this study was determination the capability of the biosensor device to detect pathogenic bacteria in two of the most common fundamental food types (e.g., milk and rice) and also in chicken soup. These foods are staples in diets worldwide, with milk serving as a primary source of calcium and protein and rice as a major carbohydrate. Both are crucial for global food security but are also susceptible to contamination by pathogenic bacteria, posing significant public health risks (Kumar et al., 2023 ; Ntuli et al., 2023 ). Milk can harbor bacteria such as Listeria and E. coli due to improper handling and processing, while rice is often at risk from B. cereus , which can survive cooking temperatures (Navaneethan and Effarizah, 2023 ; Williams et al., 2023 ). Chicken soup is a globally popular food, widely consumed not only for its comforting qualities but also because it serves as a base for many other dishes across various cuisines. Pathogens in chicken soup can pose significant health risks, as the product's rich nutrient content provides an ideal breeding ground for bacteria such as Salmonella if not properly prepared and stored (Akter et al., 2019 ). Testing for pathogens in these foods is challenging due to their complex compositions. Milk's high nutrient content and varied chemical makeup can interfere with the sensitivity of detection methods, while the starch in rice can obscure bacterial presence, complicating the isolation and identification of pathogens. Furthermore, the diversity of microorganisms that can be present means that any testing method must be sensitive and selective to identify specific pathogens without cross-reactivity effectively. Figure 3 illustrates the capability of the biosensor system to detect bacteria in food. For this demonstration, chicken soup, milk and ground rice samples were spiked with a concentration of 1 CFU/ml of various bacterial strains and then introduced to the biosensor. Similar to the results observed with pure bacterial solutions in growth media (Fig. 2 ), the biosensor demonstrated comparable response patterns when testing spiked samples of chicken soup, milk and ground rice (Fig. 3 ). Sensors constructed with an alginate/gelatin stopping layer showed consistent specificity to the presence of B. cereus across all tested samples, with similar response times observed for milk and soup, while in rice, the response was slightly faster (Fig. 3 A). The slightly faster response time observed in rice could be attributed to the physical and chemical properties of the rice matrix, which may facilitate quicker diffusion of bacterial enzymes to the sensor layer. Rice, being less dense and more granular compared to milk or soup, could allow for more rapid permeation of these enzymes, enhancing the sensor's ability to detect B. cereus more quickly. What is important is that the composition of all tested foods does not compromise the integrity of the sensor layers to produce false positive responses, as evidenced by results showing that uncontaminated food samples could not pass through the stopping layer, confirming the sensor's specificity and reliability in detecting true bacterial presence. In the setup with a cornstarch/gelatin layer, a faster response was observed with S. marcescens followed by Staphylococcus strains (Fig. 3 B), similar to results with pure bacterial cultures (Fig. 2 B). In this case, the response times for rice samples also were slightly quicker than for chicken soup and milk. In contrast to the pure cultures, positive responses also were observed with Bacillus strains. Such response times varied across the different tested foods and were generally longer compared to the Staphylococcus strains. Specifically, the setup exposed to rice samples responded similarly to all Bacillus -spiked samples. For chicken soup and milk, only samples spiked with B. cereus and B. subtilis demonstrated positive responses, indicating a clear effect of the chemical and physical properties of the food on biosensor interaction dynamics. Similar responses were observed in the starch/gelatin-based setups (Fig. 3 C), where, unlike in pure cultures that responded selectively only to Staphylococcus strains, the setups with food samples also detected Bacillus strains. In this case, such response were consistent across all tested food samples. Lactose/gelatin layer-based setups demonstrated consistent response patterns across all tested food types (Fig. 3 D). Similar to the pure cultures, a faster response was observed with the B. licheniformis strain and system also detected S. aureus presence in all food samples. For other Bacillus strains, detection times exceeded 22 hours in milk and soup, with no responses observed in rice samples. A similar response pattern was also observed in glycine/gelatin-based setups, where the faster response were observed with S. aureus and B. licheniformis . However, in these configurations, the biosensors detected S. aureus faster than B. licheniformis , indicating a specific sensitivity to the enzymes produced by S. aureus that may interact more effectively with the glycine/gelatin matrix. Table 4 provides a comprehensive summary of all response times, highlighting that a fixed checking time of 18 hours post-exposure is optimal for ensuring reliable sensor functionality. At this specified time, the setups yield specific results for the presence of particular microorganisms in food samples. Specifically, the alginate/gelatin layer is effective for detecting B. subtilis , while the lactose/gelatin layer is tailored for B. licheniformis . For other layer compositions, the cornstarch/gelatin and starch/gelatin layers successfully identify both S. aureus and S. marcescens , and the glycine/gelatin layer detects both B. licheniformis and S. aureus . Compared to pure cultures, where an 18-hour exposure time typically elicited specific responses to only one bacterial strain across all tested chemical compositions, food samples presented a different scenario. In these real food matrices, some setups responded to two different bacterial strains simultaneously within the same timeframe. This variation highlights the significant impact of food type on sensor functionality, indicating that not all material compositions are equally effective for measuring real samples. This observation underscores the necessity to tailor biosensor materials to the specific challenges presented by complex food environments, ensuring accurate pathogen detection. Table 4 The evaluation of multi-substrates biosensors for the detections of food pathogen bacterial strains same constant (1CFU/mL) Bacterial concentration 1 CFU/mL Food Samples Alginate 2.5%+ Gelatin 2.5% Corn starch 4%+ Gelatin 2.5% Starch 3%+ Gelatin 3% Lactose 6%+ Gelatin3% Glycine 6%+ Gelatin 3% LB 24.2h 24.0h 24.3h 23.9h 23.8h Water 24.0h 24.7h 25.2h 24.0h 24.2h Uninfected Milk 24.3h 24.0h 24.2h 24.7h 25.3h Uninfected Rice 24.2h 24.2h 24.4h 24.6h 24.3h Uninfected Chicken 23.8h 24.2h 23.7h 24.5h 24.1h E.coli Milk 23.8h 23.3h 24.2h 23.8h 23.8h Rice 24.7h 23.9h 24.0h 23.9h 24.0h Chicken 23.7h 23.2h 24.3h 23.4h 23.1h B.licheniformis Milk 23.3h 21.1h 20.9h 16:0h 18.0h Rice 23.2h 18.7h 22.5h 17:0h 17.0h Chicken 23.3h 21.7h 21.5h 14:5h 15.2h B.cereus Milk 22.2h 19.9h 20.5h 21.2h 21.5h Rice 22.3h 19.4h 21.4h 22.8h 20.9h Chicken 22.9h 21.1h 20.1h 22.3h 20.1h B.subtilis Milk 16.9h 22.7h 23.4h 22.2h 22.8h Rice 14.1h 19.2h 23.3h 22.8h 20.6h Chicken 15.9h 23.4h 23.7h 22.8h 20.8h S.aureus Milk 23.8h 16.8h 17:0h 20.3h 14.1h Rice 23.6h 14.2h 16.4h 21.5h 14.0h Chicken 23.3h 17.5h 18:2h 20.2h 12.6h S.marcescens Milk 23.7h 15.7h 18.1h 22.8h 23.7h Rice 23.8h 13.0h 19.0h 23.7h 22.3h Chicken 23.8h 16.2h 18.2h 23.2h 21.5h 4. Conclusion This study underscores the effectiveness of a newly developed biosensor for rapid detection of foodborne pathogens, focusing initially on optimizing the biosensor's layer compositions for enhanced sensitivity. These layers were carefully engineered to react to the activities of specific external enzymes produced by various pathogens, such as E. coli (DH5α), Bacillus licheniformis , Bacillus cereus , Bacillus subtilis , Staphylococcus aureus , and Serratia marcescens , demonstrating high specificity and sensitivity in controlled tests. Following the lab-based optimizations, the biosensor was applied to real food samples, including chicken soup, milk, and rice, where it successfully detected pathogens under varying food compositions. The biosensor's ability to perform consistently across different food matrices highlights its potential as a valuable tool for on-site pathogen detection, offering a quicker, more efficient alternative to traditional lab methods. This capability is particularly crucial for improving food safety measures and preventing foodborne illnesses. In summary, the development and application of this biosensor mark a significant step forward in food safety, combining scientific innovation with practical applications to enhance public health. Declarations Author Contributions : Mareeswaran Jeyaraman : Investigation, Validation, Data curation. Kun Jia : Writing - review & editing. Evgeni Eltzov : Writing - original draft, Conceptualization, Methodology, Resources, Supervision, Project administration, Funding acquisition, Writing - review & editing. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by the ICA Charitable Association (grant no. 430-2523). Conflicts of Interest : The authors declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article. Data Availability : The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Supplementary Data: The supplementary data on the full Table S1, Figure S1 and S2 are available in the supplementary data file. References Akter, M., Sultana, S., & Munshi, S. K. (2019). Microbiological Quality Assessment of Ready-To-Eat Fried Chicken and Chicken Soup Samples Sold in Dhaka Metropolis, Bangladesh Saurab Kishore Munshi *. Sumerianz Journal of Biotechnology , 2 (7), 2617–3123. Accessed 17 September 2024. Ameer, M. A., Wasey, A., & Salen, P. (2023). Escherichia coli (e Coli 0157 H7) - PubMed. ( n.d. ). https://pubmed.ncbi.nlm.nih.gov/29939622/. Accessed 17 September 2024. Amin, A. A., Olama, Z. A., & Ali, S. M. (2023). Characterization of an isolated lactase enzyme produced by Bacillus licheniformis ALSZ2 as a potential pharmaceutical supplement for lactose intolerance. 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A review on detection methods used for foodborne pathogens. The Indian Journal of Medical Research , 144 (3), 327. https://doi.org/10.4103/0971-5916.198677 Rangel, J. M., Sparling, P. H., Crowe, C., Griffin, P. M., & Swerdlow, D. L. (2005). Epidemiology of Escherichia coli O157:H7 Outbreaks, United States, 1982–2002. Emerging Infectious Diseases , 11 (4), 603. https://doi.org/10.3201/EID1104.040739 Silva-Salinas, A., Rodríguez-Delgado, M., Gómez-Treviño, J., López-Chuken, U., Olvera-Carranza, C., & Blanco-Gámez, E. A. (2021). Novel Thermotolerant Amylase from Bacillus licheniformis Strain LB04: Purification, Characterization and Agar-Agarose. Microorganisms , 9 (9). https://doi.org/10.3390/MICROORGANISMS9091857 STROMINGER, J. L., BIRGE, C. H (1965). Nucleotide Accumulation Induced in Staphylococcus aureus by Glycine. Journal of Bacteriology , 89 (4), 1124. https://doi.org/10.1128/JB.89.4.1124-1127.1965 Su, Y., Liu, C., Fang, H., & Zhang, D. (2020). Bacillus subtilis: a universal cell factory for industry, agriculture, biomaterials and medicine. Microbial Cell Factories 2020 19:1 , 19 (1), 1–12. https://doi.org/10.1186/S12934-020-01436-8 Tam, K., & Torres, V. J. (2019). Staphylococcus aureus Secreted Toxins and Extracellular Enzymes. Microbiology spectrum , 7 (2). https://doi.org/10.1128/MICROBIOLSPEC.GPP3-0039-2018 Ullah, N., Bruce-Tagoe, T. A., Asamoah, G. A., & Danquah, M. K. (2024). Multimodal Biosensing of Foodborne Pathogens. International Journal of Molecular Sciences. 2024, Vol. 25, Page 5959 , 25 (11), 5959. https://doi.org/10.3390/IJMS25115959 Wang, Y., Luo, J., Guan, X., Zhao, Y., & Sun, L. (2024). Bacillus cereus cereolysin O induces pyroptosis in an undecapeptide-dependent manner. Cell Death Discovery. 2024 10:1 , 10 (1), 1–9. https://doi.org/10.1038/s41420-024-01887-7 Williams, E. N., Van Doren, J. M., Leonard, C. L., & Datta, A. R. (2023). Prevalence of Listeria monocytogenes, Salmonella spp., Shiga toxin-producing Escherichia coli, and Campylobacter spp. in raw milk in the United States between 2000 and 2019: A systematic review and meta-analysis. Journal of food protection , 86 (2). https://doi.org/10.1016/J.JFP.2022.11.006 Xu, L., Bai, X., & Bhunia, A. K. (2021). Current State of Development of Biosensors and Their Application in Foodborne Pathogen Detection. Journal of food protection , 84 (7), 1213–1227. https://doi.org/10.4315/JFP-20-464 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.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. 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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-6496632","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448462876,"identity":"eed8af05-5365-44aa-a7e4-10395a0a316b","order_by":0,"name":"Mareeswaran Jeyaraman","email":"","orcid":"","institution":"Volcani Institute","correspondingAuthor":false,"prefix":"","firstName":"Mareeswaran","middleName":"","lastName":"Jeyaraman","suffix":""},{"id":448462877,"identity":"f8ad4f65-56ca-40bd-8c44-039cb69a469a","order_by":1,"name":"Kun Jia","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Jia","suffix":""},{"id":448462878,"identity":"a34fd57f-3d47-4611-ac2f-4a29aeebf43d","order_by":2,"name":"Evgeni Eltzov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie3RsWqDQBjA8e/4QJePuiop+goXhCy1zYN0EiFzRscrglkCWZMpr9C8QeBbJX0AJ8kLGNoh0A49JUubXJuxlPsPH4fe704QwGb7i7n9TE4L2Q2h+meOayCI3ZzoxRnBHwmfyNdXl4WHHreH8iVdIDav0ylE0SMr8ZYz3BhIUCAMV2WdrgonHiwlDDe7VOFtxcYPk4yQUVXHkgkGJEFs5kJhUE6MZKwJU7XrCL5rMv6VSERRUL4NNXG6W9I1CSUOZWIkPmMslnkWBoUzuiPpZ8+aMFQJmYg3e9q3rXwgzy32NX0k9+u52zTH3A+jmbpsvt8Kcqt/EwHQVfv7In22OF6/32az2f5/n24jSI1hxGllAAAAAElFTkSuQmCC","orcid":"","institution":"Volcani Institute","correspondingAuthor":true,"prefix":"","firstName":"Evgeni","middleName":"","lastName":"Eltzov","suffix":""}],"badges":[],"createdAt":"2025-04-21 13:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6496632/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6496632/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81572689,"identity":"7c2b56d3-e923-4317-bb36-e69feab2cb32","added_by":"auto","created_at":"2025-04-28 16:44:01","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204230,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the multi-layer biosensor construction\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6496632/v1/843a778c91028a5e26ed4342.jpeg"},{"id":81572688,"identity":"ecb398b8-1b64-49c8-84c7-6191365de1a3","added_by":"auto","created_at":"2025-04-28 16:44:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128249,"visible":true,"origin":"","legend":"\u003cp\u003eSpecificity of Detection for Various Bacterial Strains Across Different Biosensor Material Compositions: (A) Alginate 2.5% (w/v) + Gelatin 2.5% (w/v), (B) Cornstarch 4% (w/v) + Gelatin 2.5% (w/v), (C) Starch 3% (w/v) + Gelatin 3% (w/v), (D) Lactose 6% (w/v) + Gelatin 3% (w/v), and (E) Glycine 6% (w/v) + Gelatin 3% (w/v). Statistical significance denoted as (****p\u0026lt;0.0001, ns for non-significant) based on ANOVA results.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6496632/v1/3a5937c2fb211f5afbb07a40.png"},{"id":81572690,"identity":"18a79ca3-0efe-4a25-a308-8ed72cdcfca4","added_by":"auto","created_at":"2025-04-28 16:44:01","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":158934,"visible":true,"origin":"","legend":"\u003cp\u003eDetection Efficacy of Biosensors constructed from (A) Alginate 2.5% (w/v) + Gelatin 2.5% (w/v), (B) Cornstarch 4% (w/v) + Gelatin 2.5% (w/v), (C) Starch 3% (w/v) + Gelatin 3% (w/v), (D) Lactose 6% (w/v) + Gelatin 3% (w/v), and (E) Glycine 6% (w/v) + Gelatin 3% (w/v) Against Pathogens in Various Food Samples (e.g., Milk, Rice, and Chicken Soup), (****p\u0026lt;0.0001, *p\u0026lt;0.05, and n.s.- not significant by Two-Way ANOVA).\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6496632/v1/2e2b189e7f8fe3bc4ca77a77.jpeg"},{"id":84537491,"identity":"05cfe34b-175c-4287-afcd-8d9f10b332b0","added_by":"auto","created_at":"2025-06-13 07:31:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1903132,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6496632/v1/c7163a7e-2beb-4e4e-9ec4-32372621db70.pdf"},{"id":81572699,"identity":"36cef658-2f35-4365-b825-9568a582d118","added_by":"auto","created_at":"2025-04-28 16:44:01","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":500373,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6496632/v1/066eef863d4d0bb3ac7233d0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The low-cost multi-channel biosensor for the quick detection of different food pathogens","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFoodborne illnesses, a worldwide health problem, are mainly caused by harmful bacteria like \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e, and \u003cem\u003eE. coli\u003c/em\u003e strains, which are big reasons for food poisoning around the world. \u003cem\u003eBacillus cereus\u003c/em\u003e, for example, a bacterium capable of surviving harsh conditions due to its spore-forming ability, is notorious for causing vomiting and diarrhea, primarily through the consumption of improperly stored or handled rice and pasta(Leong et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eSalmonella\u003c/em\u003e, on the other hand, is a genus encompassing a wide array of bacteria that lead to gastrointestinal distress, fever, and, in severe cases, life-threatening complications. Foods commonly associated with \u003cem\u003eSalmonella\u003c/em\u003e include undercooked poultry, eggs, and even fruits and vegetables, highlighting the bacteria's versatility in contaminating various food types (Ehuwa et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eE. coli\u003c/em\u003e, particularly the O157:H7 strain, presents a grave public health concern due to its severe symptoms, including bloody diarrhea and acute renal failure, especially in children and seniors(Ameer et.al, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) The primary sources of \u003cem\u003eE. coli\u003c/em\u003e outbreaks have been traced back to undercooked ground beef and contaminated fresh produce (Luna-Guevara et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rangel et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The persistence and adaptability of these pathogens underscore the complexities of ensuring food safety, as they can contaminate a wide range of food products at any point from farm to fork.\u003c/p\u003e \u003cp\u003eConventional methods for detecting foodborne pathogens, such as culture-based techniques, PCR (Polymerase Chain Reaction), and ELISA (Enzyme-Linked Immunosorbent Assay), are foundational in ensuring food safety (Priyanka et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These techniques offer high accuracy and reliability; culture methods can specifically identify bacterial strains, PCR excels in detecting minimal DNA traces, and ELISA effectively identifies specific proteins or antigens. Such precision and the standardization of these methods contribute to their widespread validation and trust within the scientific community. However, these conventional approaches come with significant disadvantages that can impede their effectiveness in rapid response scenarios. One major drawback is the time-consuming nature of these methods, particularly culture-based techniques, which require several days to yield results (Nnachi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This delay is problematic in food safety, where rapid action is crucial to prevent the spread of contamination. Additionally, both PCR and ELISA involve complex procedures and require sophisticated equipment and skilled personnel, limiting their application to well-equipped laboratories. This dependency not only increases the cost but also restricts their utility for on-site testing in critical locations such as farms, processing plants, or retail points where immediate decision-making is essential. The complexity and resource intensity of these methods highlights the need for more practical solutions in pathogen detection. As a result, there is a growing interest in developing innovative technologies such as portable biosensors. These new tools aim to address the limitations of conventional methods by enabling simple-to-operate, cost-effective, and on-site detection of pathogens (Ullah et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By simplifying testing procedures and reducing reliance on extensive laboratory infrastructure, biosensors have the potential to revolutionize food safety protocols, offering timely insights into food quality and safety and significantly reducing the risk of foodborne illness outbreaks.\u003c/p\u003e \u003cp\u003ePathogenic bacteria secrete specific enzymes that aid in their survival and infection processes, such as nutrient acquisition and immune evasion. These enzymes, unique to each bacterial strain, can serve as precise biomarkers for biosensor construction. For instance, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e produces coagulase, while \u003cem\u003eE. coli\u003c/em\u003e releases lysine decarboxylase and glucose oxidase, and \u003cem\u003eBacillus cereus\u003c/em\u003e generates extracellular enzymes like cereolysin (Wang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Leveraging these extracellular enzymes in biosensor designs allows for rapid, direct, and accurate pathogen detection. By targeting these specific enzyme activities, biosensors can improve both sensitivity and specificity, enhancing pathogen detection in food safety and clinical diagnostics. This approach turns bacterial survival mechanisms into a tool for their own detection, providing timely results crucial for effective pathogen detection. The central concept of the proposed biosensor design is the creation of a waterproof layer from two different material compositions (e.g., alginate/gelatin, cornstarch/gelatin, starch/gelatin, lactose/gelatin, glycine/gelatin) sensitive to bacterial enzyme activity. Structurally, the biosensor consists of several layers: at the top, a measuring chamber; beneath that, a waterproof stopping layer crafted from materials that react to external enzymes; and at the bottom, a layer of paper integrated with dry color and an absorption component. In samples devoid of bacteria, the liquid cannot penetrate the stopping layer; hence, no color change occurs, resulting in no visible signal. However, in samples containing bacteria, the pathogens secrete extracellular enzymes that degrade the stopping layer, creating micro-cracks. These cracks allow the sample fluid to seep through, rehydrate the dry color, and subsequently saturate the absorbent layer below, yielding a visible, colored response indicating a positive detection of bacterial activity. This design leverages the natural enzymatic actions of bacteria to facilitate direct and effective pathogen detection. After formulation and optimization steps, the biosensor has demonstrated high sensitivity and specificity to various pathogens in pure growth solutions and has proven effective in detecting bacterial presence across different food types, such as chicken soup, milk, and rice. The influence of food composition on sensor functionality was also evident, highlighting the device's adaptability. This represents the initial step towards developing a portable, easy-to-use, and cost-effective device that enables on-site detection of multiple pathogens in a single sample, streamlining the process for rapid and accurate food safety assessments.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. \u003cem\u003eChemicals\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eCornstarch (cas. 9005-25-8), Starch (cas. 65996-62-5), Lactose (cas. 10039-26-6) and Glycine (cas. 56-40-6) were purchased from Beith Dekel (BDL), Israel. Alginate (cas. 9005-38-3), Gelatin Type B (cas. 9000-70-8), and LB Broth Lennox (L3022) were procured from Merck group Sigma-Aldrich (St.Louis, Missouri, United States). The different membrane layers were fabricated and set up for the experiment, such as a polyester (i.e., separation layer) (cat. no. PT-R5) and absorbent pad (cat. no. AP-080) that were purchased from Advanced Microdevices Pvt. Ltd. (Ambala, India). To make the coloured layer, a polyester membrane (cat. no. PT-R5) was stained with blue ink (Encre Noire, Waterman, Paris). The customized 3D-printed set-up, sample holder and isolating layer were developed using Autodesk Fusion 360 software (version 15509.2.0.0) and printed using MakerBot 3D Printer Method X (Stratasys, Rehovot, Israel) with polyvinyl-alcohol (cat. 901031) and polylactic acid (cat.TRD3D0014) as supporting material to design a 3D-printed system for the entire bioassay test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. \u003cem\u003eFood pathogenic bacteria\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThis study involved six bacterial strains: \u003cem\u003eE. coli\u003c/em\u003e (DH5α), \u003cem\u003eBacillus licheniformis\u003c/em\u003e, \u003cem\u003eBacillus cereus\u003c/em\u003e, \u003cem\u003eBacillus subtilis\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, and \u003cem\u003eSerratia marcescens\u003c/em\u003e, all sourced from the Department of Food Science at the Agriculture Research Organization, Volcani Institute, Israel. These strains were cultured in 25 mL of fresh LB medium and incubated overnight at 37\u0026ordm;C on a rotatory thermo-shaker MaxQ 4450 (Thermo Scientific, Marietta, OH, USA) at 150 rpm. Subsequently, 100 \u0026micro;L of these overnight cultures were diluted in 10 mL of fresh LB to reach the early log phase with a density of 10\u003csup\u003e6\u003c/sup\u003e CFU/mL, confirmed by measuring the optical density at 600 nm (OD600nm\u0026thinsp;=\u0026thinsp;0.5) using an Ultrospec 2100 Pro spectrophotometer (Amersham Bioscience, Biochrom, Cambridge, England).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. \u003cem\u003ePreparation and formation of multi-substrate gelatin film layers for biosensor application\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eMulti-substrates such as alginate, cornstarch, starch, lactose, and glycine were combined with gelatin to prepare the stopping layer at various concentrations. As these multi-substrates cannot polymerize independently, gelatin was employed as the supporting polymer material in the formation process. The film-making solution was adjusted to maintain a consistent proportion of gelatin throughout the polymerization process. Different combinations of multi-substrates with gelatin were prepared, including alginate 2% (w/v)/gelatin 2% (w/v), alginate 2.5% (w/v)/gelatin 2.5% (w/v), cornstarch 3% (w/v)/gelatin 2% (w/v), cornstarch 4% (w/v)/gelatin 2.5% (w/v), starch 2% (w/v)/gelatin 2% (w/v), starch 3% (w/v)/gelatin 2.5% (w/v), starch 3% (w/v)/gelatin 3% (w/v), lactose 4% (w/v)/gelatin 2.5% (w/v), lactose 6% (w/v)/gelatin 3% (w/v), glycine 4% (w/v)/gelatin 2.5% (w/v), and glycine 6% (w/v)/gelatin 3% (w/v). The multi-substrate film layers were further developed using the drop-casting method, employing different concentrations of these gelatin combinations. These included alginate 2.5% (w/v)/gelatin 2.5% (w/v), cornstarch 4% (w/v)/gelatin 2.5% (w/v), starch 3% (w/v)/gelatin 3% (w/v), lactose 6% (w/v)/gelatin 3% (w/v), and glycine 6% (w/v)/gelatin 3% (w/v). Accurate quantities of multi-substrates and gelatin were weighed and dissolved in deionized water to reach the desired concentrations. This mixture was then thoroughly mixed and heated on a hot plate at 200 rpm and temperatures of 80\u0026ndash;90˚C for 20 to 25 minutes, ensuring the development of a homogeneous solution and enhancing the layer's structural integrity..\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 \u003cem\u003eAssessment of Multi-substrates film layers thicknesses and permeability\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe multisubstrate-based gelatin solutions were poured into disposable Thermo Scientific\u0026trade; Remel plastic Petri dishes (cat no. R80150) in volumes of 3, 3.5, and 4 mL, forming films of three different thicknesses: 0.8, 1, and 1.2 mm. These Petri dishes were then refrigerated at 4\u0026ordm;C overnight to solidify the gelatin films. After film formation, a permeability test was conducted by applying 330\u0026micro;L of water and LB (Luria-Bertani medium) onto films of varying thicknesses. This test included different combinations such as Alginate 2.5% (w/v)/Gelatin 2.5% (w/v), Cornstarch 4% (w/v)/Gelatin 2.5% (w/v), Starch 3% (w/v)/Gelatin 3% (w/v), Lactose 6% (w/v)/Gelatin 3% (w/v), and Glycine 6% (w/v)/Gelatin 3% (w/v) to evaluate their permeability characteristics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 \u003cem\u003eEffect of the physical properties on the films formation and stability\u003c/em\u003e\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1. \u003cem\u003eTexture analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe bloom strength of multi-substrates-based gelatin films was measured using TA.XT plus C Texture Analyser (Stable Micro Systems, Godalming, UK). A standard 0.25 mm radius cylinder probe (P/O.25S) was selected to penetrate the films with 0.25 mm/s. The gel bloom strength is measured with maximum force to compress the multi-substrates-based gelatin film before its permanent distortion. The analysis data was collected in triplicate, and the results were presented as the mean value along with the corresponding standard deviation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 \u003cem\u003eMoisture content\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe moisture loss from the film layers was assessed after an 8-hour drying period in a hot air oven set at 65\u0026ordm;C to determine their dry matter content. To calculate the percentage of moisture content, the initial wet weight of the films was subtracted from their dry weight after drying. This difference was then divided by the initial wet weight and multiplied by 100 to convert the result into a percentage form. All measurements were performed in triplicate, with results presented as the mean and corresponding standard deviation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6. \u003cem\u003eEstablishing of multi-substrates system configureation\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe multi-substrate film layers were constructed using a layer stacking technique. The structure from top to bottom included: a multi-channel sample chamber (80mm\u0026times;20mm), a multi-substrate-based gelatin layer (15mm\u0026times;10mm), a multi-channel isolating layer (80mm\u0026times;20mm), a multi-channel holder box (80mm\u0026times;30mm), a color layer (10mm\u0026times;5mm), and an absorption layer (20mm\u0026times;10mm), arranged in sequence (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each channel of the multiwell setup is encased within a different gelatin layer, each sensitive to distinct types of bacteria. The gelatin layers were positioned atop the 3D-printed multi-channel isolating module, with a multi-channel holder system directly beneath. Above the color layer, an absorption layer was placed, both of which were encapsulated within the multi-channel holder box (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Square-shaped multi-substrate film layers were excised from solidified Petri dishes (as described in section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e) using a thin scalpel and set on the multi-channel isolating layer. A multi-channel sample chamber was then placed on top of these layers, ready to receive the optimal sample volume.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7. \u003cem\u003eSampling procedure\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eAll experiments were conducted at a controlled room temperature of 25\u0026ordm;C using disposable sterile containers with lids. A sample volume of 330 \u0026micro;L, spiked with or without bacteria, was applied to the multi-substrate-based gelatin layers using a 3D-printed multi-channel sample holder. Each bacterial strain secretes specific enzymes that can break down the gelatin layers, causing them to liquefy and allowing the sample to migrate by capillary action to the colored layer below. This interaction with the dissolved ink in the colored pad results in a visible color change, detectable by the naked eye, indicating a positive detection of bacteria. In contrast, samples without bacteria show no color change due to the absence of enzyme activity, preventing the passage of liquid through the film layer. The response time, measured in hours, is defined by how quickly the sample traverses through the colored pad, mobilizing the dye toward the absorbent layer below and producing a visible colorimetric signal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.8. \u003cem\u003eAssessment of multi-substrate setup specificity\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eSix distinct bacterial strains namely \u003cem\u003eE.coli\u003c/em\u003e (DH5α), \u003cem\u003eBacillus licheniformis, Bacillus cereus, Bacillus subtilis, Staphylococcus aureus\u003c/em\u003e, and \u003cem\u003eSerratia marcescens\u003c/em\u003e were employed to evaluate the bioassay\u0026rsquo;s specificity with multi-substrates based gelatin film layers. Each strain was diluted in LB medium to a concentration of 10\u003csup\u003e6\u003c/sup\u003e CFU/mL for the bioassay. A volume of 330 \u0026micro;L of these bacterial suspensions was introduced into a 3D-printed biosensor setup. The diffusion of color through to the absorbent layer was then monitored to analyze the bioassay's response to each bacterial strain\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.9. \u003cem\u003eAssessment of multi-substrate setup sensitivity\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eDifferent bacterial strains were cultured in 20 mL of freshly prepared LB medium and incubated overnight at 37\u0026ordm;C using a rotary thermo-shaker. After incubation, the cell cultures were prepared for use in the bioassay. To assess the bioassay's sensitivity, various concentrations of pathogenic bacteria, including \u003cem\u003eB. subtilis\u003c/em\u003e (9 and 1 CFU/mL), \u003cem\u003eS. marcescens\u003c/em\u003e (12 and 1 CFU/mL), \u003cem\u003eS. aureus\u003c/em\u003e (14 and 2 CFU/mL) \u003cem\u003eB.licheniformis\u003c/em\u003e (10 and 1 CFU/mL) and \u003cem\u003eS. aureus\u003c/em\u003e (14 and 2 CFU/mL) were adjusted to target levels (10\u003csup\u003e6\u003c/sup\u003e and 10\u003csup\u003e7\u003c/sup\u003e CFU/mL, respectively). These bacterial concentrations were then introduced into multi-substrate-based gelatin layers of different compositions (e.g., alginate 2.5% (w/v)\u0026thinsp;+\u0026thinsp;gelatin 2.5% (w/v), cornstarch 4% (w/v)\u0026thinsp;+\u0026thinsp;gelatin 2.5% (w/v), starch 3% (w/v)\u0026thinsp;+\u0026thinsp;gelatin 3% (w/v), lactose 6% (w/v)\u0026thinsp;+\u0026thinsp;gelatin 3% (w/v), and glycine 6% (w/v)\u0026thinsp;+\u0026thinsp;gelatin 3% (w/v)) in 3D setups. Due to the enzymatic activity of the bacteria, enzymes penetrated and liquefied the gelatin layers, resulting in positive test outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Analysis \u003cem\u003eof food samples\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe validation of the bioassay was conducted using three different types of food: homemade chicken soup (Rehovot, Israel), boiled rice (Daawat Rice, Bombay, India), and 3% (w/v) pasteurized milk (Tnuva, Rehovot, Israel). The boiled rice was prepared by mixing with sterile distilled water in a 1:6 ratio to achieve a liquid consistency. These food samples were spiked with various concentrations of bacterial strains as described previously, and then tested using the 3D-printed biosensor setup system. Uninfected food samples served as controls to ensure the accuracy of the bioassay results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.11. \u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eAll collected data were analyzed statistically using GraphPad Prism (Software package, version 8, San Diego, USA). Both one-way and two-way analysis of variance (ANOVA) with Tukey\u0026rsquo;s post hoc test were employed to assess variations among the tested parameters. Error bars in the graphical representations were expressed as the standard error of independent replicates, and significance levels were marked accordingly (****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.0005, **p\u0026thinsp;=\u0026thinsp;0.008, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and n.s. for not significant by ANOVA).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 \u003cem\u003eDetermination of Optimal Layers for Biosensor Development\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe development of point-of-care biosensors for detecting pathogens in food is crucial for ensuring public health safety by enabling rapid, on-site identification of contaminants, which significantly reduces the risk of widespread foodborne illnesses and enhances the effectiveness of response and mitigation strategies. In the previous study, a new point-of-care approach based on a colorimetric device for detecting \u003cem\u003eBacillus cereus\u003c/em\u003e in food specimens was introduced (Kaur et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study expands on the initial findings to explore a broader application in detecting multiple pathogens within a single sample. In the initial phase of the study, the focus was on determining the optimal concentrations of different materials that enable the development of stable layers essential for effective biosensor construction. Analysis of various material combinations, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, demonstrates that alginate and gelatin concentrations needed to be increased from 2% (w/v) to 2.5% (w/v) each for the formation of the stable solid layer. Cornstarch and gelatin required an adjustment from 3% (w/v) cornstarch and 2% (w/v) gelatin to 4% (w/v) and 2.5% (w/v), respectively, to achieve stability. All tested mixtures stabilized only at higher tested concentrations, with some chemicals achieving solidification at 2.5% (w/v) gelatin, while others only solidified at concentrations of 3% (w/v) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The observed differences in stabilization concentrations among the chemical mixtures can be attributed to the unique molecular interactions between the substrates and gelatin. Substrates that form stronger hydrogen bonds or ionic interactions with gelatin may achieve solidification at lower concentrations (El Sayed, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, the intrinsic properties such as molecular weight, polarity, and solubility of each substrate influence the gelatin matrix\u0026rsquo;s stability, with substances that integrate more seamlessly into the gelatin structure requiring lower concentrations for stabilization (Olijve et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Lastly, the viscosity and density of the mixtures also play an important role, as higher viscosity can enhance stability but may necessitate higher substrate concentrations to optimize the biosensor's performance.\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\u003eList of Multi-substrates with supporting material\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=\"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\u003eMulti-substrates with supporting material\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDifferent concentration\u003c/p\u003e \u003cp\u003e(W/V)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAlginate\u0026thinsp;+\u0026thinsp;Gelatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c2\"\u003e \u003cp\u003e2% + 2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSemi-solid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot stabled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c2\"\u003e \u003cp\u003e2.5% + 2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolidified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStabled condition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCornstarch\u0026thinsp;+\u0026thinsp;Gelatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c2\"\u003e \u003cp\u003e3% + 2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSemi-solid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot stabled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c2\"\u003e \u003cp\u003e4% + 2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolidified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStabled condition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStarch\u0026thinsp;+\u0026thinsp;Gelatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c2\"\u003e \u003cp\u003e2% + 2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSemi-solid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot stabled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c2\"\u003e \u003cp\u003e3% + 2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eColloidal form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot Stabled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c2\"\u003e \u003cp\u003e3% + 3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolidified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStabled condition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLactose\u0026thinsp;+\u0026thinsp;Gelatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c2\"\u003e \u003cp\u003e4% + 2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eColloidal form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot stabled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c2\"\u003e \u003cp\u003e6% + 3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolidified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStabled condition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGlycine\u0026thinsp;+\u0026thinsp;Gelatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c2\"\u003e \u003cp\u003e4% + 2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eColloidal form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot stabled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c2\"\u003e \u003cp\u003e6% + 3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolidified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStabled condition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Determination of Physical properties on multi-substrates\u003c/h2\u003e \u003cp\u003eThen, the effect of the film thicknesses on the mechanical properties, specifically bloom strength and layer wetness, was evaluated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The analysis showed two opposing trends in bloom strength related to changes in layer thickness. An increase in bloom strength was observed for mixtures of gelatin with alginate, cornstarch, and lactose, which corresponded with relatively stable or slightly increasing moisture levels, suggesting that these combinations maintain or enhance their structural integrity and cohesion as they thicken. Notably, the alginate and gelatin mixture showed the most significant increase in bloom strength and moisture retention, likely due to the synergistic interactions between the strong gel-forming capabilities of alginate and the thermal gelling properties of gelatin (Panouill\u0026eacute; and Larreta-Garde, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This effective cross-linking within the matrix results in a cohesive and structurally robust film as the thickness increases, making this mixture particularly useful in applications like 3D cultures and bioprinting, where gelation properties are critical for structuring and supporting biological materials (Łabowska et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, a decrease in bloom strength was noted in mixtures containing starch and glycine as the layer thickness increased, accompanied by a decrease in moisture retention. This suggests that thicker layers in these mixtures might suffer from over-saturation of the gel matrix, leading to compromised cross-linking and reduced structural robustness. The decrease in moisture content with increased thickness could further exacerbate the weakening of the matrix, highlighting a complex interaction between mechanical strength and moisture dynamics within these layers. Glycine and gelatin mixture exhibited a highest decrease in bloom strength as the film thickness increased. This decrease was also accompanied by a relatively higher water content, suggesting that the glycine might be interfering with the gel matrix's ability to form strong, cohesive bonds, resulting in a less stable structure with more liquid inside. The drying experiments, conducted at temperatures ranging from 65\u0026deg;C to 70\u0026deg;C, aimed to determine the most effective method for moisture removal, essential for maintaining the integrity of the biosensor films. The findings indicated that oven drying might not consistently remove moisture, potentially leaving residual wetness within the layers. Specifically, the combination of glycine 6% (w/v)\u0026thinsp;+\u0026thinsp;gelatin 3% (w/v) exhibited lower moisture content at volumes of 3 and 3.5 mL compared to other combinations. Conversely, higher moisture retention was observed in alginate 2.5% (w/v)\u0026thinsp;+\u0026thinsp;gelatin 2.5% (w/v) at volumes of 3.5 and 4 mL (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results underscore the complex interaction between drying methods and film composition on moisture dynamics within the layers, highlighting the necessity to optimize drying processes to enhance the structural stability and functional reliability of the biosensor films. This aspect of the research connects directly to the broader theme of film sensitivity to mechanical strength weaknesses as previously noted in the literatureThese findings underscore the importance of optimizing both the mechanical and moisture retention properties of biosensor films to ensure their functionality and reliability in proposed application (Fematt-flores et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Grad et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\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\u003eThe specific composition, volume on the film's bloom strength and drying content on the layer's physical properties\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimum concentrations (W/V)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlate volumes (ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFilm thicknesses (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBloom\u003c/p\u003e \u003cp\u003eStrength (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLoss of Moisture (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAlginate 2.5% + Gelatin 2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e191.55\u0026thinsp;\u0026plusmn;\u0026thinsp;26.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e94.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e409.09\u0026thinsp;\u0026plusmn;\u0026thinsp;281.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e94.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e614.93\u0026thinsp;\u0026plusmn;\u0026thinsp;50.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e95.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCornstarch 4% + Gelatin 2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e85.12\u0026thinsp;\u0026plusmn;\u0026thinsp;44.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e92.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e126.76\u0026thinsp;\u0026plusmn;\u0026thinsp;24.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e92.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e184.47\u0026thinsp;\u0026plusmn;\u0026thinsp;15.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e93.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStarch 3% + Gelatin 3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e169.29\u0026thinsp;\u0026plusmn;\u0026thinsp;25.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e93.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e100.68\u0026thinsp;\u0026plusmn;\u0026thinsp;38.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e93.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e107.86\u0026thinsp;\u0026plusmn;\u0026thinsp;38.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e91.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLactose 6% + Gelatin 3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e125.03\u0026thinsp;\u0026plusmn;\u0026thinsp;35.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e90.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e181.45\u0026thinsp;\u0026plusmn;\u0026thinsp;27.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e90.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e218.12\u0026thinsp;\u0026plusmn;\u0026thinsp;32.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e90.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGlycine 6% + Gelatin 3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e111.59\u0026thinsp;\u0026plusmn;\u0026thinsp;14.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e89.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e86.02\u0026thinsp;\u0026plusmn;\u0026thinsp;12.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e89.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e78.73\u0026thinsp;\u0026plusmn;\u0026thinsp;7.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e90.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \u003cem\u003eDetermination of Water/LB permeability test on multi-substrates\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe final formulation of the multi-substrate layer, along with different membrane thicknesses, was evaluated for its permeability to water/LB medium. Experiments confirmed that multi-substrate compositions\u0026mdash;Alginate 2.5% (w/v)\u0026thinsp;+\u0026thinsp;Gelatin 2.5% (w/v), Cornstarch 4% (w/v)\u0026thinsp;+\u0026thinsp;Gelatin 2.5% (w/v), Starch 3% (w/v)\u0026thinsp;+\u0026thinsp;Gelatin 3% (w/v), Lactose 6% (w/v)\u0026thinsp;+\u0026thinsp;Gelatin 3% (w/v), and Glycine 6% (w/v)\u0026thinsp;+\u0026thinsp;Gelatin 3% (w/v)\u0026mdash;maintained impermeability across various thicknesses (0.8, 1.0, and 1.2 mm) at all testing intervals (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In contrast, layers with lower substrate concentrations exhibited semi-solid states or insufficient polymerization (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At higher concentrations, the multi-substrate layers demonstrated robust polymerization, likely due to the formation of a three-dimensional triple helical structure, stabilized by hydrogen bonds and van der Waals forces, which effectively prevented fluid passage (Djabourov et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 \u003cem\u003eDetermining the Specificity of Proposed Material Mixtures for Different Bacterial Strains\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe next phase of this study was focused on determining the specificity of various material mixtures to different bacterial strains, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This figure and table compares the detection times across different bacterial solutions at a constant concentration of 1 CFU/mL(Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This high sensitivity means that even a small amount of enzyme activity, enough to create a tiny crack in the biosensor's initially impermeable layer, can trigger a detectable response. This sensitivity is crucial because it indicates that if we will start even from a single cell's enzyme output, it will be enough to create a minor disruption in the protective layer to trigger a positive detection signal. The results indicate that specific material combinations exhibited shorter detection times for certain bacteria (Fig. S2), suggesting potential applications for targeted pathogen detection. For instance, the cornstarch/gelatin and alginate/gelatin layers showed the shortest detection times for \u003cem\u003eS. marcescens\u003c/em\u003e and \u003cem\u003eB.subtilis\u003c/em\u003e at 15.4 hours and 15.9 hours, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, A), making it a potentially effective material for specifically detecting these strains. Similarly, lactose/gelatin and starch/gelatin layers demonstrated relatively lower detection times for \u003cem\u003eB. licheniformis\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e at 16.4 hours and 16.8 hours, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, C). In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, S. \u003cem\u003eaureus\u003c/em\u003e demonstrated a quicker response in the setups constructed from a mixture of gelatin with glycine. The specificity of detection times observed in the study could be attributed to the interaction between the material layers of the biosensors and external enzymes produced by different bacterial strains. Each bacterial type secretes specific enzymes that can degrade or alter the structural integrity of the biosensor layers, which are initially designed to be waterproof, preventing sample permeation. For example, the alginate/gelatin mixture, which demonstrated the shortest detection time for \u003cem\u003eB. subtilis\u003c/em\u003e, may be particularly susceptible to enzymes produced by this bacterium. \u003cem\u003eB. subtilis\u003c/em\u003e is known to secrete proteases and other enzymes that can effectively break down gelatin's protein structure and potentially modify alginate's gel matrix, making the layer permeable enough for the bacterial sample to pass through and trigger a detection signal (Pant et al., 2015; Su et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, the detection of \u003cem\u003eS. marcescens\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e by the cornstarch/gelatin and starch/gelatin mixtures might be facilitated by staphylococcal enzymes like lipases or nucleases, which can degrade the starch component, disrupting the layer's integrity and allow the bacterial sample to diffuse through the stopping layer and produce positive responses (Tam and Torres \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The quicker response of the lactose/gelatin mixture to \u003cem\u003eB. licheniformis\u003c/em\u003e could be due to the enzymatic activity specific to this bacterium, which might include the production of enzymes capable of hydrolyzing lactose, thereby compromising the gelatin matrix's ability to maintain its waterproof barrier (Amin et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kamran et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The possible reason for a quicker detection response of \u003cem\u003eB. licheniformis\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e in gelatin-glycine-based applications may be due to enhanced enzymatic interaction and optimal pH buffering by glycine, facilitating faster enzymatic degradation and sensor responsiveness (Silva-Salinas et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; STROMINGER and BIRGE 1965). These interactions highlight how the enzymatic activity specific to each bacterial type can directly impact the permeability and effectiveness of different material mixtures used in biosensor layers. By understanding these dynamics, it is possible to design biosensors with material compositions that are selectively permeable to certain bacterial strains based on their enzymatic profiles, thereby optimizing the specificity and speed of pathogen detection. Such specifity and response times was clearly demonstrated through this section. Furthermore, the specificity of the biosensor's responses was rigorously validated, showing that in negative control scenarios, no false positives were recorded across any of the tested material compositions. This absence of response in control setups reinforces the biosensor's reliability, ensuring that only the target bacterial enzymes capable of degrading the sensor layers will produce a signal. This level of sensitivity and specificity not only enhances the practical application of the biosensor in detecting low levels of pathogens but also confirms its robustness against potential false activations.\u003c/p\u003e \u003cp\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\u003eThe specificity of different bacterial solutions same constant (1 CFU/mL) on Multi-substrates\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMulti-substrates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003eDifferent bacterial solutions same constant concentration (1 CFU/mL)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eE.coli\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eB.licheniformis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eB.cereus\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eB.subtilis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eS.marcescens\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlginate 2.5% + Gelatin 2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.1h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.5h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCornstarch 4% + Gelatin 2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStarch 3% + Gelatin 3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactose 6% + Gelatin 3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.6h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine 6% + Gelatin 3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.5h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.1h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.1h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5 \u003cem\u003eDetection of Pathogens in Real Food Samples Using the Biosensor System\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eFood safety testing is crucial but fraught with challenges, including the complexity of accurately detecting pathogens in multi-component food environments, which complicates testing procedures. The most common traditional methods, like culture-based techniques, specialized skills and equipment, often yield slow results (2\u0026ndash;3 days) and risk false negatives or positives due to cross-contamination(Harinathan, 2024). The proposed system may offer a promising solution by enabling rapid, on-site detection that simplifies testing in diverse food matrices and reduces dependency on extensive laboratory infrastructure. So, the next step of this study was determination the capability of the biosensor device to detect pathogenic bacteria in two of the most common fundamental food types (e.g., milk and rice) and also in chicken soup. These foods are staples in diets worldwide, with milk serving as a primary source of calcium and protein and rice as a major carbohydrate. Both are crucial for global food security but are also susceptible to contamination by pathogenic bacteria, posing significant public health risks (Kumar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ntuli et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Milk can harbor bacteria such as \u003cem\u003eListeria\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e due to improper handling and processing, while rice is often at risk from \u003cem\u003eB. cereus\u003c/em\u003e, which can survive cooking temperatures (Navaneethan and Effarizah, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Williams et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Chicken soup is a globally popular food, widely consumed not only for its comforting qualities but also because it serves as a base for many other dishes across various cuisines. Pathogens in chicken soup can pose significant health risks, as the product's rich nutrient content provides an ideal breeding ground for bacteria such as \u003cem\u003eSalmonella\u003c/em\u003e if not properly prepared and stored (Akter et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Testing for pathogens in these foods is challenging due to their complex compositions. Milk's high nutrient content and varied chemical makeup can interfere with the sensitivity of detection methods, while the starch in rice can obscure bacterial presence, complicating the isolation and identification of pathogens. Furthermore, the diversity of microorganisms that can be present means that any testing method must be sensitive and selective to identify specific pathogens without cross-reactivity effectively.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the capability of the biosensor system to detect bacteria in food. For this demonstration, chicken soup, milk and ground rice samples were spiked with a concentration of 1 CFU/ml of various bacterial strains and then introduced to the biosensor. Similar to the results observed with pure bacterial solutions in growth media (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the biosensor demonstrated comparable response patterns when testing spiked samples of chicken soup, milk and ground rice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Sensors constructed with an alginate/gelatin stopping layer showed consistent specificity to the presence of \u003cem\u003eB. cereus\u003c/em\u003e across all tested samples, with similar response times observed for milk and soup, while in rice, the response was slightly faster (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The slightly faster response time observed in rice could be attributed to the physical and chemical properties of the rice matrix, which may facilitate quicker diffusion of bacterial enzymes to the sensor layer. Rice, being less dense and more granular compared to milk or soup, could allow for more rapid permeation of these enzymes, enhancing the sensor's ability to detect \u003cem\u003eB. cereus\u003c/em\u003e more quickly. What is important is that the composition of all tested foods does not compromise the integrity of the sensor layers to produce false positive responses, as evidenced by results showing that uncontaminated food samples could not pass through the stopping layer, confirming the sensor's specificity and reliability in detecting true bacterial presence. In the setup with a cornstarch/gelatin layer, a faster response was observed with \u003cem\u003eS. marcescens\u003c/em\u003e followed by \u003cem\u003eStaphylococcus\u003c/em\u003e strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), similar to results with pure bacterial cultures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In this case, the response times for rice samples also were slightly quicker than for chicken soup and milk. In contrast to the pure cultures, positive responses also were observed with \u003cem\u003eBacillus\u003c/em\u003e strains. Such response times varied across the different tested foods and were generally longer compared to the \u003cem\u003eStaphylococcus\u003c/em\u003e strains. Specifically, the setup exposed to rice samples responded similarly to all \u003cem\u003eBacillus\u003c/em\u003e-spiked samples. For chicken soup and milk, only samples spiked with \u003cem\u003eB. cereus\u003c/em\u003e and \u003cem\u003eB. subtilis\u003c/em\u003e demonstrated positive responses, indicating a clear effect of the chemical and physical properties of the food on biosensor interaction dynamics. Similar responses were observed in the starch/gelatin-based setups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), where, unlike in pure cultures that responded selectively only to \u003cem\u003eStaphylococcus\u003c/em\u003e strains, the setups with food samples also detected \u003cem\u003eBacillus\u003c/em\u003e strains. In this case, such response were consistent across all tested food samples. Lactose/gelatin layer-based setups demonstrated consistent response patterns across all tested food types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Similar to the pure cultures, a faster response was observed with the \u003cem\u003eB. licheniformis\u003c/em\u003e strain and system also detected \u003cem\u003eS. aureus\u003c/em\u003e presence in all food samples. For other \u003cem\u003eBacillus\u003c/em\u003e strains, detection times exceeded 22 hours in milk and soup, with no responses observed in rice samples. A similar response pattern was also observed in glycine/gelatin-based setups, where the faster response were observed with \u003cem\u003eS. aureus\u003c/em\u003e and \u003cem\u003eB. licheniformis\u003c/em\u003e. However, in these configurations, the biosensors detected \u003cem\u003eS. aureus\u003c/em\u003e faster than \u003cem\u003eB. licheniformis\u003c/em\u003e, indicating a specific sensitivity to the enzymes produced by \u003cem\u003eS. aureus\u003c/em\u003e that may interact more effectively with the glycine/gelatin matrix. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a comprehensive summary of all response times, highlighting that a fixed checking time of 18 hours post-exposure is optimal for ensuring reliable sensor functionality. At this specified time, the setups yield specific results for the presence of particular microorganisms in food samples. Specifically, the alginate/gelatin layer is effective for detecting \u003cem\u003eB. subtilis\u003c/em\u003e, while the lactose/gelatin layer is tailored for \u003cem\u003eB. licheniformis\u003c/em\u003e. For other layer compositions, the cornstarch/gelatin and starch/gelatin layers successfully identify both \u003cem\u003eS. aureus\u003c/em\u003e and \u003cem\u003eS. marcescens\u003c/em\u003e, and the glycine/gelatin layer detects both \u003cem\u003eB. licheniformis\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e. Compared to pure cultures, where an 18-hour exposure time typically elicited specific responses to only one bacterial strain across all tested chemical compositions, food samples presented a different scenario. In these real food matrices, some setups responded to two different bacterial strains simultaneously within the same timeframe. This variation highlights the significant impact of food type on sensor functionality, indicating that not all material compositions are equally effective for measuring real samples. This observation underscores the necessity to tailor biosensor materials to the specific challenges presented by complex food environments, ensuring accurate pathogen detection.\u003c/p\u003e \u003cp\u003e \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\u003eThe evaluation of multi-substrates biosensors for the detections of food pathogen bacterial strains same constant (1CFU/mL)\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacterial concentration\u003c/p\u003e \u003cp\u003e1 CFU/mL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood Samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlginate 2.5%+ Gelatin 2.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorn starch 4%+ Gelatin 2.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStarch 3%+ Gelatin 3%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLactose 6%+ Gelatin3%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGlycine 6%+ Gelatin 3%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.0h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.8h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.0h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.0h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.2h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUninfected Milk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.0h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.3h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUninfected Rice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.6h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.3h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUninfected Chicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.5h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.1h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eE.coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.8h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.0h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.0h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.1h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eB.licheniformis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.1h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16:0h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.0h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.5h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17:0h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.0h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.5h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14:5h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.2h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eB.cereus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.5h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.5h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.9h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.1h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.1h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.1h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eB.subtilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.8h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.1h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.6h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.8h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eS.aureus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17:0h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.1h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.6h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.5h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.0h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.3h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.5h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18:2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.6h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eS.marcescens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.1h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.7h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.0h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.3h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.5h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study underscores the effectiveness of a newly developed biosensor for rapid detection of foodborne pathogens, focusing initially on optimizing the biosensor's layer compositions for enhanced sensitivity. These layers were carefully engineered to react to the activities of specific external enzymes produced by various pathogens, such as \u003cem\u003eE. coli\u003c/em\u003e (DH5α), \u003cem\u003eBacillus licheniformis\u003c/em\u003e, \u003cem\u003eBacillus cereus\u003c/em\u003e, \u003cem\u003eBacillus subtilis\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, and \u003cem\u003eSerratia marcescens\u003c/em\u003e, demonstrating high specificity and sensitivity in controlled tests. Following the lab-based optimizations, the biosensor was applied to real food samples, including chicken soup, milk, and rice, where it successfully detected pathogens under varying food compositions. The biosensor's ability to perform consistently across different food matrices highlights its potential as a valuable tool for on-site pathogen detection, offering a quicker, more efficient alternative to traditional lab methods. This capability is particularly crucial for improving food safety measures and preventing foodborne illnesses. In summary, the development and application of this biosensor mark a significant step forward in food safety, combining scientific innovation with practical applications to enhance public health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMareeswaran Jeyaraman\u003c/strong\u003e: Investigation, Validation, Data curation.\u003cstrong\u003eKun Jia\u003c/strong\u003e: Writing - review \u0026amp; editing.\u003cstrong\u003e\u0026nbsp;Evgeni Eltzov\u003c/strong\u003e: Writing - original draft, Conceptualization, Methodology, Resources, Supervision, Project administration, Funding acquisition, Writing - review \u0026amp; editing. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research was supported by the ICA Charitable Association (grant no. 430-2523).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Data:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe supplementary data on the full Table S1, Figure S1 and S2 are available in the supplementary data file.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkter, M., Sultana, S., \u0026amp; Munshi, S. 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Current State of Development of Biosensors and Their Application in Foodborne Pathogen Detection. \u003cem\u003eJournal of food protection\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e(7), 1213\u0026ndash;1227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4315/JFP-20-464\u003c/span\u003e\u003cspan address=\"10.4315/JFP-20-464\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Pathogenic bacteria, food poison, biosensors, point of care devices","lastPublishedDoi":"10.21203/rs.3.rs-6496632/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6496632/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study introduces an innovative biosensor designed to utilize specific enzymatic activities of extracellular pathogens enzymes to enable rapid, sensitive, and specific pathogens detection. The biosensor employs a multi-layer construction that includes a measuring chamber, a waterproof stopping layer sensitive to enzymatic degradation, and a color development system. The key innovation lies in the stopping layer, which is composed of materials specifically selected for their susceptibility to degradation by pathogen-secreted enzymes. This design allows the biosensor to detect enzymatic activity indicative of pathogen presence, triggering a visible response when bacterial enzymes degrade the layer and permit fluid to activate the color development system. Results demonstrated that the biosensor could effectively identify significant pathogens, such as \u003cem\u003eBacillus\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e species, with high sensitivity and specificity. Additionally, the biosensor responded differently to pathogen presence depending on the food matrix, illustrating the influence of food composition on sensor functionality.\u003c/p\u003e","manuscriptTitle":"The low-cost multi-channel biosensor for the quick detection of different food pathogens","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 16:43:56","doi":"10.21203/rs.3.rs-6496632/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":"87afb394-37ec-4aed-9368-3dd897807aab","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-13T07:23:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-28 16:43:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6496632","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6496632","identity":"rs-6496632","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}