Pathogen infection has emerged as a significant threat to human survival and development worldwide [1]. Currently, prevalent pathogenic microflora including Staphylococcus aureus (S. aureus), Salmonella enterica (Salmonella), Escherichia coli (E. coli), and Listeria monocytogenes (Listeria) are commonly detected in contaminated food sources, leading to foodborne diseases [2,3]. The main means of contamination and transmission involves various aspects of human habitant environment, such as food, clothing, shelter, and transportation [4]. Hence, the detection of the foodborne pathogens present on contaminated surfaces is crucial. So far, valuable strategies for foodborne pathogen detection mainly involve traditional culture counting methods, molecular biology detection methods, and immunological detection methods, which are labor-intensive, time-consuming, and inconvenient for on-site detection [5]. Therefore, there is an urgent need for a simple yet rapid and reliable detection approach to enable early diagnosis and effective control of pathogen infection.

Surface-enhanced Raman spectroscopy (SERS) stands out in the field of microbial analysis due to its rich molecular vibrational fingerprint, fast analysis speed, and high sensitivity [5]. By simple mixing with the SERS enhancing colloid, several studies have realized the determination of a range of bacterial species, and some have even exhibited the possibility of differentiating the diverse pathogen species using multivariate discriminate analysis [6,7]. However, the elusory samples and the inconsistent spectra hinder its practical application [8]. Some studies have claimed in situ preparation of SERS substrates as a superior routine with greater reproducibility, owing to the close contact between the pathogens and the nanoparticles [9]. Especially, the function parameters (i.e., temperature, operating time) would be expected to have a profound effect on the spectra of microbes due to the associated differences in bacterial cell response but may be controlled by standardizing an easy-to-use experimental protocol [10].

Carbon dots (CDs) refer to novel quantum dot materials with unique physicochemical properties, such as programmable optical properties, excellent biocompatibility, low cost, easy synthesis, and functionalization. Now, they have been widely used in food safety, biomedicine, and environmental analysis [11,12]. Zhao et al. designed a CD-microsphere immunosensor for the ultrasensitive detection of E. coli in milk based on the fluorescence property of CDs [13]. In addition, the electrochemical characteristics of CDs were explored to monitor pH changes during bacterial growth [14]. Moreover, doping of electron donors and surface modification are effective ways to improve the electronic and optical properties of CDs and are suitable for diverse applications [15,16]. Zhou et al. reported europium-doped CDs as a colorimetric and fluorescent dual-mode biosensor for measurement of the anthrax biomarker, dipicolinic acid [17]. Core-shell metal-CD nanohybrids exhibit the intriguing localized surface plasmon resonance (LSPR) effect, rendering them highly suitable for SERS analysis [18,19]. The SERS performance of metal-CD nanohybrids is considered to be significantly superior to their individual components due to the sample enrichment and additional chemical enhancement provided by the CD shell [20]. Bhunia et al. also established a flexible SERS sensor by in situ fabrication of Ag-CD nanocomposites, utilizing CDs as an effective reducing agent, enabling rapid and accurate SERS fingerprint analysis of Pseudomonas aeruginosa [21]. However, it is worth noting that the current synthetic strategies for metal-CD SERS sensors heavily rely on high-temperature procedures due to the low reducibility of CDs, which not only pose environmental concerns but also present challenges for on-site analysis. Moreover, the synthesis of doped CDs and its application in SERS analysis have seldom been found.

Herein, we propose the utilization of molybdenum-doped gallic acid-derived carbon dots (MCDs) for the rapid synthesis of Au@MCDs core-shell nanocomposites at ambient temperature. The MCDs exhibit a remarkable reducing capacity attributed to the presence of phenolic hydroxyl residues on their surface, enabling swift reduction of Au ions to ${\mathrm{Au}}^0$ within a few seconds. Exploiting this capability, we establish an exceptionally straightforward approach for on-site fabrication of Au@MCDs in microbial-contaminated interfaces, thereby facilitating SERS detection of foodborne pathogens (Fig. 1). This approach enables the precise identification and differentiation of four model pathogens in interfaces with anisotropic morphologies of various daily items, leading to a significant reduction in detection time (within 5 min). This innovative strategy holds immense potential as a point-of-care testing (POCT) route for early diagnosis and effective control of pathogen infections.

MCDs were prepared using a solvent hydrothermal method. Briefly, 0.3 g gallic acid (GA) and 0.3 g ammonium molybdate were dissolved in 25 mL deionized water and stirred at room temperature for 30 min. The resulting mixture was then transferred to a poly(tetrafluoroethylene) (Teflon)-lined autoclave container and heated at 200°C for 8 h. After the reaction was completed, the product was centrifuged at 10,000 r/min for 15 min to remove large particles, followed by a purification process in a dialysis bag (1000 Da) for 10 h. Finally the product was collected for further usage.

To prepare Au@MCDs, ${\text{HAuCl}}_4$ solution (4 μL, 200 mM; 1 M = 1 mol/L) and MCDs solution (4 μL, 15 mg/mL) were sequentially added into 5 mL deionized water. The solution rapidly transformed to crimson within 3 s, indicating the successful instantaneous preparation of Au@MCDs. Subsequently, the obtained product was stored at 4°C for future utilization.

The SERS substrate was obtained via the spin-coating method [22]. Typically, Au@MCDs are added dropwise on the cleaned stainless steel and spun to form uniformly covered SERS substrate with Au@MCDs. The SERS signals were then captured after dropping the organic pollutants solution (4 μL, ${10}^{-5}\mathrm{M}$), including rhodamine 6G (R6G), crystal violet (CV), malachite green (MG), and methylene blue (MB), on the stainless steel and air-dried. MB (${10}^{-5}\mathrm{M}$) was utilized as the analyte for SERS sensitivity and reproducibility testing. SERS spectra were obtained using a confocal Raman microscope with a 785 nm excitation laser of 2.5 mW power. The static scan center was set at $1200{\mathrm{cm}}^{-1}$, accumulating twice with each accumulation time of 2 s. We calculated the enhanced factor (EF) values using the following equation: $$\mathrm{EF}=\frac{I_{\mathrm{SERS}}{C}_{\text{Raman}}}{I_{\text{Raman}}{C}_{\mathrm{SERS}}},$$(1)where $I_{\mathrm{SERS}}$, $I_{\mathrm{Raman}}$ and $C_{\mathrm{SERS}}$, $C_{\mathrm{Raman}}$ refer to the Raman intensity and concentration of probe molecules in the SERS and Raman spectra, respectively.

Three-dimensional finite-difference time-domain (3D FDTD) method was employed for optical analysis of the plasmonic nanostructures. The diameters of the Au core were set at 50 nm with a CD layer of 0, 1, 2, 4, 8 nm. The electromagnetic field distribution around Au NPs and Au@MCDs was simulated on a two-dimensional plane using the experimental dielectric function of Palik (0–2 μm). The excitation light source was set as a vertically incident full-field scattered light source ($-\mathit{Z}$ direction, 785 nm). Periodic boundary conditions were applied around the Au@MCD structures in the $x$-, $y$-, and $z$-axis directions. A power monitor was placed close to the samples to record the near-field enhancement results. The near-field enhancement was obtained from an integral volume average of $|E/E_0{|}^4$ where the maximum local electromagnetic field and incident amplitude of the light source were denoted as $E$ and $E_0$, respectively.

For S. aureus, Salmonella, E. coli, and Listeria, the strain was inoculated onto a lysogeny broth medium and incubated with continuous shaking (160 r/min) at 37°C for 14 h. The crude concentration of bacteria was determined by measuring the optical density at 600 nm (${\mathrm{OD}}_{600}$), while the precise bacterial concentration was obtained through plate counting methods.

The preparation of the sample was carried out in accordance with the previous work [23]. Briefly, a specific amount of bacterial pathogen was dissolved in phosphate buffer solution (PBS). 50 μL prepared bacterial solution (${10}^4\mathrm{CFU}/\mathrm{mL}$) was applied onto the clean items (cotton pad, towel, mask, stainless steel, and paper) and allowed to dry naturally. Then, MCDs and ${\mathrm{HAuCl}}_4$ solutions were sequentially added to the surfaces, and the synthesized Au@MCDs, formed within 3 s, served as the ultraclean substrate for collecting SERS spectra of foodborne bacteria. Following drying at 25°C, the signal was collected with a central wavelength of $1200{\mathrm{cm}}^{-1}$, the laser energy was set as 2.5 mW, and the exposure time was set as 3 s and accumulated three times. The infectious items without Au@MCDs were used as the control group.

Complex fingerprints of analytes can be distinguished by means of data dimensionality reduction [principal component analysis (PCA) or linear discriminant analysis (LDA)]. PCA is the conversion of data from potentially correlated variables to linearly uncorrelated variables by orthogonal transformation. The principal components are used to characterize the original data and thus achieve the purpose of data dimensionality reduction. The PCA algorithm calculates the covariance matrix based on the centroid operation on the sample data and constructs the projection matrix by taking the eigenvectors corresponding to the $n$ eigenvalues after eigendecomposition. The expression of the objective function in the $d\times n$-dimensional matrix space ${\mathbb{R}}^{d\times n}$ is $$\underset{b,\mathrm{rank}(Y)=k}{\min }{||X-b{\dot{1}}^T-Y||}_F^2,$$where the sample data matrix $X=[x_1,x_2,\dots ,x_n]\in {\mathbb{R}}^{d\times n}$, $Y$ is the low-rank approximation matrix, the mean of the sample data matrix is $b$, and the column vector is $\dot{1}$ [24]. Different from PCA, LDA is a dimensionality reduction method with supervised learning. After projecting the sample dataset into a low-dimensional space, the optimal transformation that satisfies both maximizing the between-class distance and minimizing the within-class distance can be found, which can be expressed as follows: $$\underset{W}{\max }\frac{\mathrm{t}\mathrm{r}(W^T{S}_{b}W)}{\mathrm{t}\mathrm{r}(W^T{S}_{w}W)},$$where $W$ is the projection matrix, and $S_b$ and $S_w$ are the between-class scatter matrix and the within-class scatter matrix, respectively [25]. Both statistical discrimination methods use the idea of reduced dimension and matrix feature decomposition, but there are differences in the supervision method, reduced dimension, and projection direction. In this paper, we all choose the first three features for analysis.

In this work, new crystalline MCDs with high reducibility were synthesized using metallic polyphenol networks (MPNs) via a hydrothermal method. Subsequently, Au@MCD core-shell nanocomposites were instantly prepared from the synthesized MCDs by the template effect under ambient conditions [Fig. 2(a)]. As shown in Fig. 2(b), Mo element doping induced the shift upward of Fermi energy level of CDs, showing the tunable bandgap of CDs through heteroatom doping [26]. The enhancement of Raman intensities was investigated using the MB molecule for different Au@MCDs at the same Au concentration. As depicted in Fig. 2(c), distinct characteristic peaks of MB are clearly observed across all conditions. Notably, the highest SERS activity is achieved when utilizing Au@MCDs synthesized with 4 μL MCDs as precursors. Transmission electron microscopy (TEM) was employed to study the prepared MCDs and optimal Au@MCDs. Well-dispersed MCDs are uniform with a diameter of about 4.7 nm, showing the lattice spacing of 0.21 nm corresponding to the (110) plane of graphite carbon [Figs. 2(d) and 2(e)] [27,28]. The TEM images of Au@MCDs clearly show the closely packed Au@MCDs with a diameter of about 47.6 nm, with the lattice spacing of 0.24 nm corresponding to the (111) plane of Au NPs [Figs. 2(f) and 2(g)] [29]. Additionally, an MCD shell of approximately 1 nm thickness is observed in Fig. 2(h), confirming the successful preparation of Au@MCD core-shell nanocomposite under the reduction of MCDs.

The UV-Vis absorption spectra of CDs and Au@MCDs in Fig. 3(a) exhibit distinct peaks at 265 nm and 544 nm, which can be attributed to the $\pi \text{-}{\pi }^{*}$ transition of Csp2 and gold spheres, respectively [30,31]. The bond species present in MCDs and Au@MCDs are confirmed by Fourier-transform infrared (FTIR) spectra [Fig. 3(b)]. The vibrational peaks at 3446, 2955, 1637, 1399, and $1246{\mathrm{cm}}^{-1}$ are attributed to O-H, C-H, N-H, C-N, and C-H bonds, respectively, indicating that MCDs retain multiple bonding groups from the precursors. And the weakened peak intensities of Au@MCDs prove the decrease of reducible functional groups (such as -OH and $\text{-}{\mathrm{NH}}_2$) after the reaction with ${\mathrm{HAuCl}}_4$ [32,33]. Furthermore, the surface chemical composition and electronic state of the Mo element of MCDs and Au@MCDs were determined by X-ray photoelectron spectroscopy (XPS). As depicted in Fig. 3(c), the XPS profile of MCDs confirms the presence of C, N, O, and Mo elements. Meanwhile, the new peak of the Au element occurs in Au@MCDs. With respect to MCDs, the binding energy values at 232.4, 234.9, 235.8, and 236.9 eV [Fig. 3(d)] are in accordance with ${\mathrm{Mo}}^{6+}$$3\mathrm{d}3/2$, ${\mathrm{Mo}}^{5+}$$3\mathrm{d}3/2$, ${\mathrm{Mo}}^{6+}$$3\mathrm{d}5/2$, and ${\mathrm{Mo}}^{5+}$$3\mathrm{d}5/2$, respectively [34]. And the XPS plot of Au@MCDs [Fig. 3(e)] also included four wave ranges centered at 232.2 eV (${\mathrm{Mo}}^{6+}$$3\mathrm{d}3/2$), 234.9 eV (${\mathrm{Mo}}^{5+}$$3\mathrm{d}3/2$), 235.8 eV (${\mathrm{Mo}}^{6+}$$3\mathrm{d}5/2$), and 236.9 eV (${\mathrm{Mo}}^{5+}$$3\mathrm{d}5/2$) [35]. By comparison, the peak areas of low valence states of Mo exhibit a reduction in size within Au@MCDs due to the oxidation of gold ions. Furthermore, binding energies of 84.0 and 87.7 eV are separately the characteristics of Au $4\mathrm{f}5/2$ and Au $4\mathrm{f}7/2$ [Fig. 3(f)], which matched with the signals of Au, providing further evidence of the formation of gold core in the composite structure [36].

Based on the plasmonic nanostructure, Au@MCDs and analytes were successively deposited onto a stainless steel substrate by the spin-casting method [Fig. 4(a)]. As shown in Fig. 4(b), the SERS enhancement effect of Au@MCDs is evident for four common organic pollutants: R6G, CV, MG, and MB, indicating the universality of nanocomposites for the SERS detection of varied electronic structures. The three-dimensional principal component analysis (3D PCA) score plot shows four separated clusters of SERS spectra originating from the four common organic samples, thus suggesting promising prospects for pollutant discrimination capabilities [Fig. 4(c)].

To acquire more in-depth messaging about the Raman performance of Au@MCDs, the SERS effect of different concentrations of MB dye was investigated [Fig. 5(a)]. As shown in Fig. 5(b), the SERS EF values for MB molecules increase with the decline of the molecular concentration, and the apex reaches ${10}^{-9}\mathrm{M}$. The maximal EFs of Raman bands at 1624, 1398, and $776{\mathrm{cm}}^{-1}$ in the SERS spectrum of MB are measured to be $3.97\times {10}^6$, $3.56\times {10}^6$, and $2.64\times {10}^6$, respectively. $1624{\mathrm{cm}}^{-1}$ in the range of ${10}^{-5}$ to ${10}^{-9}$ showed greater enhancement at each concentration, and this uneven fluctuation in intensity change is the result of selective enhancement of different vibrational modes. These findings unequivocally demonstrate the exceptional SERS enhancement capability exhibited by Au@MCDs. The SERS repeatability and uniformity of Au@MCDs were also investigated through testing dye samples on the stainless steel substrate fabricated with Au@MCDs at 20 randomly selected test sites. It could be seen from Fig. 5(c) that each average line obtained from three spectra of every point is displayed in the highly unified SERS spectral pattern. Going further, the relative standard deviations (RSDs) of multiple typical Raman peaks (1624, 1398, 776, 1183, 1303, 1503, and $1330{\mathrm{cm}}^{-1}$) are calculated as 5.44%, 6.71%, 8.07%, 7.25%, 7.69%, 8.35%, and 7.19%, respectively [Fig. 5(d)], and those are well within the acceptable limit (all less than 8.4%), suggesting the excellent repeatability of Au@MCD SERS substrate.

The batch-to-batch SERS reproducibility of the Au@MCD substrate was investigated across six different batches, with 10 random points collected for each substrate [37]. Figures 6(a) and 6(b) exhibit uniform color distribution in the pseudo-color map with an exceptional repeatability RSD of 6.01%, indicating excellent batch reproducibility of this composite nanostructure substrate. The structural stability and test stability of Au@MCDs were also investigated. As displayed in Figs. 6(c) and 6(d), no obvious changes are observed in the SERS spectra of MB induced by the Au@MCD nanohybrids during 7 weeks of observation and after 120 times continuous testing, illustrating the good stability of the core-shell nanocomposites.

The enhancement mechanism was explored by comparing SERS activity of Au@MCDs with 50 nm Au NPs as the control [Fig. 7(a)]. The zeta potential of Au@MCDs is found to be strongly negative as $-70.8\mathrm{mV}$, in contrast to the $-28.6\mathrm{mV}$ of spherical Au NPs [Fig. 7(b)]. The original Raman signals of dye probes are barely noticeable due to the huge fluorescence background under laser irradiation while the SERS signals of MB are enhanced by Au@MCDs and Au NPs owing to their fluorescence quenching effect [28,38]. In comparison with the SERS spectra induced by spherical Au NPs, the SERS signals of dye molecules excited by Au@MCDs exhibit significantly enhanced intensity. Physically, MCDs could absorb the target molecules via $\pi \text{-}\pi$ stacking or electrostatic attraction. Additionally, MCDs serving as a shell effectively shield Au NPs from the harsh chemical environment and interaction with analytes and metal NPs, showing high SERS sensitivity with excellent stability. However, it is important to note that an excessive increase in MCD thickness does not necessarily yield superior results.

Generally, the electromagnetic enhancement mechanism (EM) of SERS is closely related to the electromagnetic field distribution of the substrate. The finite difference time domain (FDTD) method is employed for calculating the electromagnetic field based on spatial and temporal derivatives of Maxwell’s equations [39]. Figure 7(c) illustrates a model diagram depicting this methodology. The electromagnetic field distribution of Au NPs with and without 1 nm thick MCD coating, with a diameter of 50 nm, is nearly identical, indicating minimal impact of the 1 nm carbon shell on the electromagnetic attenuation. However, with increasing thickness of the MCDs, the electromagnetic field distribution of Au@MCDs undergoes significant alterations, with the LSPR being shielded and the maximum field strength ($E_{\max }$) sharply decreasing. This indicates that thicker carbon coatings are unfavorable for the EM, consistent with previous research results [40].

Interestingly, properly coated MCDs can further enhance the Raman signal of the analyte compared to uncoated Au NPs. This suggests the existence of additional coupling effects in the Au@MCD system that positively contribute to Raman signal enhancement, in addition to the inherent LSPR of Au NPs. The intervention of non-metallic materials introduces a chemical enhancement mechanism (CM), which mainly includes the interaction, resonant excitation, and charge transfer (CT) between the substrate and the analyte [41,42]. The further significant enhancement of SERS signal of dye molecules on Au@MCDs can be attributed to the synergistic effect of CT from substrate to analyte induced by metal surface plasmon resonance (SPR) [43]. The combination of the CT process with the strong light absorption capability of the Au NP plasmon resonance and the charge separation characteristics at the metal-carbon shell interface to produce a direct charge transfer transition [Fig. 8(a)] [44]. According to the Fermi-Dirac distribution function, $$f(E)=\frac{1}{1+\exp \left(\frac{E-E_F}{kT}\right)},$$(2)where $T$ is the thermodynamic temperature, $k$ is the Boltzmann constant, and $E_F$ is the quasi-Fermi level [45,46]. From the UPS spectrum in Fig. 8(b), the Fermi level of MCDs is $-5.21\mathrm{eV}$, and it exhibits properties similar to $\mathrm{N}$-type semiconductors, where the $f(E)$ value is greater than 1/2, resulting in an increased probability of electron occupation in the conduction band and a higher number of electrons in higher energy quantum states [47]. The bandgap of MCDs can be evaluated based on the absorption spectrum, and the relationship is as follows: $$\alpha h\nu ={\alpha }_0(h\nu -E_g),$$(3)where $\alpha$ is the absorption constant related to the absorbance and the thickness of the colorimetric ware, while $h\nu$ and $E_g$ represent the photon energy and optical bandgap, respectively [48]. The estimated optical bandgap ($E_g$) value of MCDs based on these calculations is 5.26 eV, and the valence band energy ($E_{\mathrm{vb}}$) of MCDs was found to be $-9.19\mathrm{eV}$ by measuring the valence band (VB) spectrum [Figs. 8(c) and 8(d)].

Analogous to the plasmon-induced interfacial charge-transfer transition (PICTT) mechanism at the metal-to-semiconductor interface, in the Au@MCD system, Au acts as the light absorption unit, and the SPR induced by 785 nm excitation directly generates electrons in the conduction band (CB) of MCDs, as well as electron-hole pairs in the metal [49]. These electron-hole pairs then transfer to the lowest unoccupied molecular orbit (LUMO) of the molecule. We are also able to quantitatively analyze the contribution of charge transfer effects in the SERS signal intensity of dye molecules on Au@MCDs. The ${\rho }_{\mathrm{CT}}$ measure, which quantifies the relative contribution of charge transfer in the SERS system, is defined as follows: $${\rho }_{\mathrm{CT}}(k)=\frac{I^k(\mathrm{CT})-I^k(\mathrm{SPR})}{I^k(\mathrm{CT})-I^0(\mathrm{SPR})},$$(4)where $I^k(\mathrm{CT})$ is the measured intensity of a single molecular line in the Raman spectrum [50]. When ${\rho }_{\mathrm{CT}}$ tends towards 0 or 1, it is considered that no charge transfer contribution and charge transfer contribution play a dominant role, respectively [51]. Under 785 nm excitation, the contribution of CT in the Raman enhancement of Au@MCDs with different thicknesses of carbon shell encapsulation is shown in Fig. 8(e). When the thickness is 1 nm, the electromagnetic field intensity of Au@MCDs is barely affected, and ${\rho }_{\mathrm{CT}}$ is greater than 0.5, indicating that the SERS enhancement is a result of the synergistic effect between EM and CM.

Similar results were obtained in the analysis of SERS enhancement mechanisms for other molecules (CV, MG, and R6G) on Au@MCDs (Fig. 9). Furthermore, the SERS effect sharply weakened with the increase in MCDs thickness, which could be attributed to the thicker carbon shell reducing the photons incident upon the Au NPs, thereby weakening the PICTT efficiency [52].

In recent years, the emergence of food safety incidents caused by ubiquitous foodborne pathogenic microorganisms has become a grave global public health concern and garnered extensive attention [53]. Efficient on-site detection techniques are of great practical significance in ensuring food safety and maintaining consumer health [54,55]. Au@MCD SERS substrates of in situ instant preparation are utilized to achieve swift detection and fingerprint analysis of four common foodborne pathogens (Listeria, E. coli, S. aureus, and Salmonella) on various surfaces. To demonstrate its proof-of-principle, five common items encountered in daily life, including textile, paper, mask, cotton pad, and stainless steel, were subjected to testing with pathogens deliberately introduced onto their surfaces. Figures 10(a)–10(e) indicate that the remarkable SERS detection capability of instantaneously synthesized Au@MCDs for four types of pathogens present on the surfaces of the aforementioned items. Given the successful detection results of Au@MCD substrates for foodborne pathogens, we further extracted key features for recognizing SERS patterns of foodborne bacteria types through LDA analysis of bacterial Raman signals for classification of foodborne pathogens. The normalized SERS spectra exhibited significant similarities among the four bacteria, particularly between E. coli and Salmonella, both being gram-negative bacteria with nearly indistinguishable SERS spectra. However, by projecting the Raman spectra of Listeria, E. coli, S. aureus, and Salmonella onto a 3D LDA score space, distinct clusters were observed [Fig. 10(f)]. It can be concluded that Au@MCD SERS substrates in conjunction with LDA hold promise for discriminating foodborne pathogens effectively.

In summary, we have developed a straightforward strategy for on-site SERS identification of foodborne pathogens in various interfaces based on instant preparation of Au@MCDs for the first time. Au@MCD was in situ synthesized on the bacterial cell based on the high reducibility of MCDs under ambient conditions within 3 s. The instantly grown Au@MCDs as SERS substrates possess excellent reproducibility and sensitivity with a maximum EF of $3.97\times {10}^6$, attributed to the synergistic effect of electromagnetic field enhancement and charge transfer. The high performance SERS properties enable the effective detection and discrimination of multiple foodborne pathogens on various daily items, including textile, paper, mask, cotton pad, and stainless steel. The on-site “ready-to-use” SERS detection protocol shows the possibility of paving the path to future practical use of the SERS technology for early diagnosis and effective control of pathogen infections.