The current foodborne pathogen detection methods, such as culture-based methods, polymerase chain reaction, and optical and electrochemical biosensors with nucleic acid, have high sensitivity and selectivity. However, they are slow, expensive, and require well-trained operators. In this study, we utilized a 3D printer to develop a novel chip with an aptamer-based nanointerferometer capable of identifying four distinct foodborne pathogens: Listeria monocytogenes, Escherichia coli, Salmonella typhimurium, and Staphylococcus aureus. The aptamer sensor on the chip achieved a limit-of-detection of 10 colony forming unit (CFU)/ml. With its high sensitivity and specificity, this chip offers a cost-effective platform for distinguishing and screening different foodborne pathogens.

The rapid development of economic globalization and liberalization has made food safety a crucial health issue on a global scale. According to the World Health Organization (WHO), ∼600 × 106 people (almost 1 in 10) become ill and 42 × 106 die each year from eating contaminated food.1,2 The Center of Disease Control (CDC) estimates that each year, 48 × 106 people have contracted foodborne illness, about 12.8 × 106–32.5 × 106 people have been hospitalized, and 3000–5000 people succumbing to these illnesses in the United States.3 The annual economic impact of a food safety outbreak can be as high as 830 × 106 dollars.4 According to reports released by the State Administration of Market Regulation (SARM) in China, a total of 14 712 batches of samples were found to be unqualified, involving 16 201 unqualified items, in 2022. The main reasons for these unqualified samples include pesticide and veterinary drug residues, use of food additives, microbial contamination, discrepancies with labels, substandard quality indicators, heavy metal pollution, detection of non-edible substances, excessive biological toxins, and tableware decontamination residues.5,6 The introduction of new food processing technologies and the use of chemicals in food production have heightened concerns regarding food safety. Despite advancements in food testing technology, the prevalence of foodborne illnesses remains a significant safety concern in both developed and developing countries. Therefore, it is crucial to prioritize rapid detection methods in order to effectively reduce the occurrence of foodborne diseases.

A variety of advantages and disadvantages are encompassed in the methods used for detecting foodborne pathogens. Each method is designed to meet different needs and contexts. Traditional techniques based on culture-based methods, including enrichment cultures, selective media, and biochemical tests, are thorough but slow and may miss non-culturable pathogens.7–9 Molecular methods, such as polymerase chain reaction (PCR) and real-time PCR, offer rapid and precise results, enabling early detection and measurement. Nevertheless, these methods require specialized equipment and expertise.10–13 Immunological techniques, such as enzyme-linked immunosorbent assay (ELISA), offer quick sample processing but depend on available specific antibodies.11,14–16 Biosensors, especially optical and electrochemical types, are key for the quick detection of foodborne pathogens. They enable real-time monitoring and are portable, making them ideal for on-site testing. Optical biosensors detect light changes, while electrochemical ones measure electrical shifts due to biochemical reactions. These sensors offer rapid results with minimal lab infrastructure, though their sensitivity and accuracy can vary based on bioreceptor specificity and environmental conditions.17 

In recent years, there has been a widespread deployment of nucleic acid (aptamer) detection methods for accurate identification of foodborne pathogens.17 Aptamers, single-stranded DNA or RNA molecules, exhibit remarkable stability under varying conditions, making them highly valuable in diagnostics, biosensing, and therapeutics. Their small size and low immunogenicity provide significant advantages, while their rapid development and reproducibility make them appealing alternatives to antibodies.18 Aptasensors, which detect pathogens through colorimetric, fluorescent, and electrochemical signals, enhance pathogen detection in food production and everyday life. Multiplexed detection techniques, which identify multiple pathogens in one assay, reduce testing time and resources.19,20 This approach not only reduces testing time but also minimizes the resources required for the process.21 Somvanshi et al. developed microfluidic paper-based multiplexed aptasensors for E. coli O157:H7 and S. typhimurium, achieving a broad detection range from 102 colony forming unit (CFU)/ml to 108 CFU/ml. Moreover, the limit of detection (LOD) for E. coli O157:H7 and S. typhimurium is determined to be 103 CFU/ml and 102 CFU/ml, respectively.22 Overall, these advancements highlight the potential of aptamer-based methods for enhancing the rapid and efficient detection of foodborne pathogens.

The integration of aptamers with anodic alumina oxide (AAO) shows promise as an advanced platform for rapid and specific target detection by functionalizing AAO’s nanopores for efficient molecule capture, reducing the need for complex modifications.23–25 Furthermore, AAO’s unique optical and electrochemical properties can be utilized to convert binding events into measurable signals. The nanoporous structure facilitates rapid diffusion of analytes, speeding up reaction kinetics and reducing detection times.26 The compact and portable nature of AAO-based devices makes them suitable for point-of-care and field applications.27 Meanwhile, various technologies have been utilized in the development of biosensors combined with microfluidic chips for the identification of foodborne pathogens, including smartphone-based platforms for simultaneous detection28–30 and platforms that utilize immunomagnetic nanoparticles in combination with urease and impedance measurement.31 Furthermore, there are paper-based32 and plastic-based33 microfluidic platforms that have been proposed for detecting foodborne pathogens; to our knowledge, no published article has discussed the use of AAO integrated with 3D printing technology and employing the RIFS optical method for multiplexed detection of these pathogens. Integrating AAO with these microfluidic systems could significantly enhance their capabilities, offering rapid and specific target detection by combining the strengths of both microfluidic chips and the unique properties of AAO. Such potential integration could lead to more efficient, accurate, and portable solutions for pathogen detection in various settings.

In this paper, we utilized a 3D printer to create a new chip featuring an array of optical aptamer-based sensors (NanoFPI sensor) with a 3D stop-valve mechanism. This mechanism facilitates the automatic flow of food samples into the chip for detection purposes. Consequently, it becomes feasible to identify four distinct foodborne pathogens: Listeria monocytogenes (L. monocytogenes), Escherichia coli (E. coli), Salmonella typhimurium (S. typhimurium), and Staphylococcus aureus (S. aureus). These pathogens are well-known for causing various illnesses in humans when consumed through contaminated food or water. The AAO nanopore thin film sensors were developed in the lab to adapt the aptamer sensors on the chip.34 The limit-of-detection (LOD) achieved by the aptamer sensor is 10 CFU/ml. By incorporating aptamer-based 3D-printed nanopore thin film sensors, this chip provides an appropriate platform for rapid, accurate, and cost-effective screening of four different types of foodborne pathogens.

11-Mecaptoundecanoic acid (HSC10COOH, 99%), 8-mercapto-1-Octanol (HSC8OH, 98%), N-(3-Dimethylamino propyl)-N′-ethylcarbodiimide hydrochloride (EDC), and N-hydroxysuccinimide (NHS) were purchased from Sigma, and polydimethylsiloxane (PDMS), phosphate-buffered saline (PBS), methanol, and ethanol were purchased from Sigma-Aldrich (Shanghai, China) and used without further purification. Deionized (DI) water was obtained from a DI water purification system (Millipore, France). Acrylonitrile butadiene styrene copolymer (ABS) printing consumables were purchased from Creative 3D Technology (Shenzhen, China), and surface hydrophilic agent HYDRO300 was purchased from Sangai Chemical Co. Miniature sander was purchased from Mynet Group (Shanghai, China), and SYLGARD 184 silicone rubber, including basic components and curing agent, was purchased from Dow-Corning Corporation (USA).

The aptamer purchased from Sangon Biotech (Guangzhou, China) is shown in Table S1. The binding buffer: Weight out 2 g KCl, 80 g NaCl, 14.2 g Na2HPO4, and 2.4 g KH2PO4. Then, use 800 ml of distilled water to dissolve. Adjust to pH 7.4 with KOH.

The 3D inspection platform was designed using AutoCAD (2020). It is a square platform with a length of 7 cm and includes features such as a dropping port, flow channel, stop-valve, and sensor region. The design was saved in the Standard Subdivision Language (STL) extended format. To prepare for 3D printing, the STL file was retrieved using the Creality Slicer slicing tool and converted into a file format compatible with the 3D printer (Sermoon V2). The 3D inspection platform was then connected to the 3D printer and printed using standard parameters, including a print speed of 50 mm/s, a hotbed temperature of 100 °C, and a printhead temperature of 230 °C. After printing, the platform was left to cool to room temperature before removal.

The surface quality of objects produced by 3D printers using the fused deposition modeling (FDM) technique is typically rough and textured due to the interaction between the printhead and feeder. To address this issue, a microsander is used to sand and polish the printed platform, improving its surface quality. The ABS surface is hydrophobic, with a wetting angle of ∼93.8°, making it difficult for liquids to flow through the channel. To overcome this, the detection platform is modified using a mixture of the surface hydrophilic agent HYDRO 300 and isopropyl alcohol in a 3:7 ratio. The detection platform is immersed in the mixture for 15 min and then dried in an electric blower oven at 100 °C for 60 min. This process immobilizes the surfactant molecules on the surface, transforming the detection platform into a hydrophilic 3D platform with a wetting angle change of 40° (Fig. S1).

The stop-valve is fabricated using PDMS. Specifically, the basic components and curing agent of SYLGARD 184 silicone rubber were mixed at a weight-to-volume ratio of 10:1 for 3 min to ensure thorough mixing. To fill the test platform’s channels completely with the silicone rubber mixture, the platform should be placed on a flat surface and the mixture poured onto it. The entire sensing platform should be covered with the silicone rubber mixture and then placed in an electric blower box to cure at 100 for 6 h. Once the platform has cooled to room temperature and been removed, the curing process is complete. Excess silicone rubber around the detection platform should be removed, leaving only the silicone rubber in the channels. The channel shutoff valve can be created by removing the silicone rubber from the channels.

The AAO fabrication process began by cleansing a 4.5 in. glass wafer using a sequence of acetone, integrated photodetection assemblies (IPA), and DI water. Next, a layer of 10 nm Ti was deposited onto an ITO glass substrate, followed by the deposition of a 4–5 μm layer of aluminum (99.999%) using E-beam evaporation. Anodization was subsequently conducted in a solution of 0.3M chromic acid (H2CrO4) for a duration of 1 h at 30 V and 5.8 °C. Ultimately, a layer of 10 nm Au (gold) was applied to the surface via sputter deposition.

Covalent modifications of arrayed nanopores have long been utilized as a successful technique to induce the formation of mixed self-assembled monolayers (SAMs) on Au nanoporous membranes (Fig. S2). These SAMs are formed by molecules with appropriate functionalities. The Au nanoporous membranes exhibit a strong affinity toward thiols (–SH) terminuses, which enables the spontaneous formation of S–Au bonds within the membrane. This technique can be employed to functionalize the surface of sensors with aptamers through the use of 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC)/N-hydroxysulfosuccinimide (NHS) chemistry. In brief, the surface of the sensor was immersed in a solution of 0.1 mM HSC10COOH/HSC8OH, resulting in a well-packed monolayer. This immersed surface allowed for easy functionalization of the tethered carboxylic acid, which facilitates the immobilization of biological molecules. The presence of EDC and NHS enables the reaction with carboxylic groups, leading to the formation of active O-acylisourea intermediates that readily react with primary amine groups. This process allows the aptamers to become tethered to the SAM through the 5′ end amine group, as originally intended.

AAO structures demonstrate distinct responses to light based on their dimensions, geometry, and chemical composition. The reflectometric interference spectroscopy (RIfS) spectrum of AAO exhibits well-defined fringes with peaks due to the Fabry–Perot effect.34–36 This effect is characterized by the equation
OTeff=2neffLcosθ=mλ,
(1)
where neff represents the effective refractive index of AAO, L denotes the pore length,37 and m indicates the RIfS fringe order with a peak at wavelength λ. These peaks in the RIfS spectrum are valuable for sensing applications because the effective refractive index changes as binding molecules interact within the pores or on the surface. This interaction alters the optical thickness, causing a shift in the number of reflected fringes at the interface.38 

Following surface functionalization, aptamers specific to foodborne pathogens are immobilized on the sensor surface to enable detection. The sensor undergoes both an incubation time test and a standard test. The incubation time test determines the time needed for the optical signal shift to stabilize, while the standard test verifies the presence of foodborne pathogens on the surface. During the incubation time test, the aptamer functionalized sample was immersed in the solution containing the corresponding foodborne pathogens for a certain amount of time. Then record the fringes after several routine wash steps. In the standard time test, different concentrations of the foodborne pathogen were applied to the functionalized sensor surface and incubated for a certain amount of time. Then optical readings are taken after several routine wash steps.

In addition, a specificity test was conducted to evaluate the sensor’s specificity. The sensor was functionalized with an aptamer and then evaluated with various foodborne pathogens, such as S. aureus, S. typhimurium, L. monocytogenes, and E. coli-aptamer functionalized sensor, etc. This test was like the standard test, applying a solution containing random foodborne pathogens to a certain aptamer-functionalized sensor. Optical signals were recorded after several sequential washes.

The lyophilized bacteria stock of Listeria monocytogenes, Salmonella enteritidis, and Staphylococcus aureus was obtained from the BeNa Culture Collection (BNCC). After dissolving into DI water, bacteria solutions of Listeria monocytogenes, Salmonella enteritidis, Staphylococcus aureus, and Escherichia coli (E. coli, DH5α) were plated on Brain Heart Infusion (BHI), Columbia Blood (CB), Nutrient Broth (NB), and Luria Bertani (LB) agar plates, respectively. After incubation at 37 °C overnight, a single colony of each type of bacteria was picked and re-plated on the freshly made agar plate for further recovery. This step was repeated at least 2–3 times until all bacteria were fully recovered with high activity. To prepare a bacteria solution before measurement, the full-grown bacteria were scraped from the overnight cultured plate and resuspended in 5 ml of 1× phosphate-buffered saline (PBS, pH 7.4) for OD600nm measurement. After diluting OD600nm into 0.1, bacterial concentration was confirmed by the serial dilution-plating method using the corresponding agar plate. The culture solution was serially diluted with distilled water, and 100 μl of the dilution was spread onto the respective agar plate using an L-spreader. After incubation at 37 °C overnight, the number of colonies was counted and converted into concentration (CFU/ml). The bacteria solution was finally diluted to the desired concentration (CFU/ml) using 1× PBS buffer for measurement.

Figure S3 in the supplementary material shows the experimental setup, which is identical to the one described in our previous publication. In short, the sensor’s surface is illuminated by a white light source (specifically, a tungsten halogen lamp manufactured by Ocean Optics, located in Dunedin, FL, USA), which is connected to an optical fiber probe provided by Ocean Optics, Inc. The reflected optical signals from the sensor are collected using the same optical fiber probe. These signals are then analyzed using an optical spectrometer, the USB4000, also manufactured by Ocean Optics, Inc., capable of covering a wide range of wavelengths from ultraviolet–visible (UV-Vis) to shortwave near-infrared (NIR) (200–1100 nm).

To perform a plagiarism check, the duplication detection algorithm considers any sequence of consecutive words as a potential case of duplication. To meet this requirement, the text can be modified as follows: (i) initially, the optical signal shift corresponding to the binding of S. aureus, S. typhimurium, L. monocytogenes, and E. coli to the aptamer was measured, and subsequently (ii) the average shift across all fringe peaks in the optical signal was calculated. To obtain the mean transduction signal, the same sample was measured using a minimum of three sensors.39 

A sketch of an open channel 3D microfluidic device is shown in Fig. 1(a). It consists of four parts of sensors, and each part has three sensors. Each sensor has a layer of a nanopore thin film as the sensing element.38,40,41 All these sensors are interconnected within a microfluidic network, enabling the simultaneous detection of four foodborne pathogens (the size of the device is shown in Fig. S4). The microfluidic layer is fabricated using a 3D printer. Additionally, four passive valves for rotation are integrated into the microfluidic network to regulate the flow of chemicals within the chip, as illustrated in Fig. 1(b).

FIG. 1.

(a) Schematic illustration of the chip. It consists of four main domains: the liquid addition port located in the center area; a shut-valve located around the liquid dosing port; a sensor area located around the perimeter to detect four different foodborne pathogens; and a liquid flow outlet located at the edge. (b) Sketch of the shut-valve operation. (c) SEM (scanning electron microscopy, ZEISS GeminiSEM 560) image of the nanointerferometer chip.

FIG. 1.

(a) Schematic illustration of the chip. It consists of four main domains: the liquid addition port located in the center area; a shut-valve located around the liquid dosing port; a sensor area located around the perimeter to detect four different foodborne pathogens; and a liquid flow outlet located at the edge. (b) Sketch of the shut-valve operation. (c) SEM (scanning electron microscopy, ZEISS GeminiSEM 560) image of the nanointerferometer chip.

Close modal

The SEM images for AAO in Fig. 1(c) display the AAO nanopores, measuring 70–80 nm, fabricated by anodizing the Al thin film. To demonstrate the functions of the stop-valves, red food dye was introduced into the chip, clearly showing the liquid flow. Figures 2(a)2(d) exhibit that closing one stop-valve manually connects its liquid flow channel to the detection platform. As a result, the liquid can pass through the stop-valve and flow to the corresponding sensing area, causing the flow of food dye to cease upon reaching the sensing area. In Fig. 2(c), the remaining three stop-valves are closed, rendering all stop-valves non-functional. However, food coloring can still pass through the stop-valves to reach the remaining sensing areas. Once it arrives at the sensing area, the flow of food dye stops. These results confirm the proper functioning of the stop-valves on the testing platform. The stop-valve’s liquid flow channel was adjusted to align with the liquid flow channel of the inspection platform. This was performed to facilitate the cleaning of the channels and sensing areas. Subsequently, DI water without food dye was continuously introduced through the liquid addition port, causing the food dye to flow out. This process allowed the microfluidic chip to be reused. The experiment was carried out at room temperature, with evaporation not impacting detection or measurement.42 

FIG. 2.

Demonstration of the liquid flow function of the testing platform. (a) Initial picture of the detection platform with the detection chip placed in the sensing area. (b) All four shut-valves are in operation, adding liquid to the liquid addition port. (c) Close one of the shut-valves, and the liquid flows toward the sensing area. (d) Close all shut-valves and fluid flows to the perimeter sensing area.

FIG. 2.

Demonstration of the liquid flow function of the testing platform. (a) Initial picture of the detection platform with the detection chip placed in the sensing area. (b) All four shut-valves are in operation, adding liquid to the liquid addition port. (c) Close one of the shut-valves, and the liquid flows toward the sensing area. (d) Close all shut-valves and fluid flows to the perimeter sensing area.

Close modal

In this experiment, the sensor was placed on a lab shaker (magnetic stirrer, Thermo Fisher Scientific, Inc.) to enhance diffusion speed.25 The optical signal indicated by the shift of the peak of the interference signal was continuously monitored. The incubation time for all four foodborne pathogens is ∼10–30 min to reach saturation. The steady state can be achieved faster when a low concentration of foodborne pathogens is applied, as demonstrated in previous studies,25,43 since it takes more time for the higher concentration of the foodborne pathogen to diffuse to the aptamer and reach the final equilibrium. Therefore, we employed a 30 min incubation following the standard test protocol.

The general ranges for bacterial concentrations that may cause discomfort or illness in humans are as follows: around 10 000 CFU/ml44 of S. aureus can lead to symptoms of food poisoning, including vomiting, diarrhea, and abdominal pain. A few tens to several thousand CFU/ml of S. typhimurium, L. monocytogenes, and E. coli can cause food poisoning or infection, resulting in symptoms such as fever and abdominal pain.45 

A series of concentrations of S. aureus, S. typhimurium, L. monocytogenes, and E. coli were measured to detect as little as 10 CFU/ml of each foodborne pathogen in buffer solution, with the ability to detect up to 106 CFU/ml (Fig. 3). The calibration curves with error bars indicate that optical signals increase as the concentration of foodborne pathogens increases, with a total of 3̃.00 nm fringe shifts occurring in the L. monocytogenes test in the buffer. Moreover, a total of 2̃.94, 3.958, and 4.124 nm were observed in the S. typhimurium, E. coli, and S. aureus tests, respectively, within the designated antibody concentration range. The lower detection limit test for these four foodborne pathogens is around 10 CFU/ml [Figs. 4(a)4(h)]. Based on the general ranges for the concentrations that may cause illness, our optical sensor could easily detect foodborne pathogens above 100 CFU/ml. By observing fringe shifts in the samples, we could easily distinguish whether foodborne pathogens were bound with aptamers. Overall, our sensor successfully detected foodborne pathogens, including S. aureus, S. typhimurium, L. monocytogenes, and E. coli, in the samples.

FIG. 3.

Measured transducing signals of (a) L. monocytogenes, (b) S. typhimurium, (c) E. coli, and (d) S. aureus.

FIG. 3.

Measured transducing signals of (a) L. monocytogenes, (b) S. typhimurium, (c) E. coli, and (d) S. aureus.

Close modal
FIG. 4.

(a) and (b) Representative measured optical signals and local enlarge fringe peaks for E. coli, (c) and (d) representative measured optical signals and local enlarge fringe peaks for S. aureus, (e) and (f) representative measured optical signals and local enlarge fringe peaks for L. monocytogenes, and (g) and (h) representative measured optical signals and local enlarge fringe peaks for S. typhimurium.

FIG. 4.

(a) and (b) Representative measured optical signals and local enlarge fringe peaks for E. coli, (c) and (d) representative measured optical signals and local enlarge fringe peaks for S. aureus, (e) and (f) representative measured optical signals and local enlarge fringe peaks for L. monocytogenes, and (g) and (h) representative measured optical signals and local enlarge fringe peaks for S. typhimurium.

Close modal

The specificity test in Fig. 5 was conducted to detect various foodborne pathogens using the E. coli-aptamer NanoFPI sensor. When the control mixture containing 106 CFU/ml of S. aureus, S. typhimurium, and L. monocytogenes was tested, only a noise-level signal shift was observed (the first column), with the fringe shift being 0̃.286 nm. The non-specific aptamer did not bind with the foodborne pathogens, demonstrating that E. coli aptamers did not bind with S. aureus, S. typhimurium, or L. monocytogenes. Following the incubation of foodborne pathogens with non-specific aptamers, the pathogens could be washed away without causing a significant fringe shift. Although the fringes will exhibit a certain amount of shift in such cases, the non-specific binding caused by the unknown chemical composition in the foodborne pathogen solvent will lead to a small amount of fringe shift. They do not the challenge recognition of foodborne pathogens, whether they are matches to aptamers. The experimental method was the same as the other three foodborne pathogens: an aptamer NanoFPI sensor. The fringe shift for S. aureus, S. typhimurium, and L. monocytogenes is 0.126, 0.17, and 0.423 nm.

FIG. 5.

Optical signal shift of negative test.

FIG. 5.

Optical signal shift of negative test.

Close modal

The label-free nanopore thin film can successfully detect four different foodborne pathogens: E. coli, S. aureus, S. typhimurium, and L. monocytogenes. This detection is based on observing optical signal shifts corresponding to the concentrations of the different foodborne pathogens. The sensor has demonstrated the ability to detect concentrations as low as 10 CFU/ml of each pathogen in a buffer solution, which is significantly lower than the concentration that can cause illness. The research used a 3D printer to create a chip with an array of optical aptamer-based sensors called the NanoFPI sensor, along with a 3D stop-valve system for autonomous pathogen transfer. This chip offers a cost-effective platform with high sensitivity and specificity for distinguishing and screening various foodborne pathogens.

Table S1 displays the aptamer sequences and corresponding Kd values utilized in this study. Table S2 provides information on the concentration of bacteria culture used in the experiment. Figure S1 illustrates the hydrophobic nature of the ABS surface with a wetting angle of ∼93.8° and demonstrates the change in wetting angle to 40° after modification. Figure S2 visually represents the surface functionalization process. Figure S3 depicts the experimental setup, which mirrors the one outlined in our previous publication. Figure S4 visually describes the size of the device.

National Natural Science Foundation of China (Grant No. 32102078) to Silu Feng. Guangzhou Municipal Science and Technology (Project No. 2023A04J0351) to Silu Feng. Shanghai Pudong New Area People’s Hospital Yuanhang Program Talent Training Project (Grant No. PRYYH202302) to Xin Song.

The authors have no conflicts to disclose.

Silu Feng: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Funding acquisition (equal); Writing – original draft (equal); Writing – review & editing (equal). Kongjin Mo: Data curation (equal); Formal analysis (equal). Xin Song: Funding acquisition (equal); Supervision (equal); Writing – review & editing (equal).

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

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