Gold-coated ZnO nanostructures on no-core optical fiber for ammonia detection in food spoilage applications

Abstract

A selective and cost-effective optical fiber sensor for detecting ammonia gas, a key byproduct of food spoilage, was developed using a no-core fiber (NCF) structure. The optical fiber was coated with a zinc oxide (ZnO) thin film decorated with gold nanoparticles (AuNPs) to enhance sensitivity and selectivity. Four sensor configurations were fabricated with varying AuNP concentrations (0.1 mM and 1 mM) and ZnO nanostructure morphology (nanoparticles and nanorods) and tested with ammonia (NH₃) gas concentrations ranging from 0.15 to 10 ppm, a typical range for food spoilage. The sensor functionalized with 0.1 mM AuNPs and ZnO nanoparticle exhibited the best performance, achieving a sensitivity of 0.093 nm/ppm, a limit of detection (LOD) of 0.96 ppm, a response time of 60 s, and a recovery time of 90 s. Mixed gas experiments demonstrated the sensor’s strong selectivity for ammonia, even in the presence of other volatile compounds such as isopropanol, ethanol, and acetone. Tests under varying temperature (21–27 °C) and humidity (45–75% RH) confirmed stable sensor response, with negligible effect on sensitivity. These results highlight the sensor’s potential as an effective and reliable tool for detecting food spoilage.

Introduction

Food spoilage is a significant global issue, resulting in economic losses and posing serious health risks due to the consumption of contaminated food. In many developing regions, inadequate storage and transportation infrastructure worsens the problem, increasing the risk of foodborne illnesses caused by microbial contamination and contributing to rising levels of malnutrition, which affected 9.1% of the global population in 2003¹. Additionally, the growing global demand for meat, with a 55% increase in meat production from 2000 to 2022², has further strain on supply chains. These proved to challenge the community sustainability growth. Therefore, the development of effective monitoring and detection systems for food spoilage is crucial to address this crisis.

Modern methods for detecting meat spoilage focus on identifying microbes or their metabolites, such as biogenic amines, total volatile basic nitrogen compounds (TVB-N), and hypoxanthine, alongside other adenosine triphosphate (ATP) decomposition products, which correlate with the degree of food freshness³. Recent advancements in polymerase chain reaction (PCR) techniques have enabled the detection and identification of Salmonella with high accuracy within 21 h—a significant improvement compared to previous standards⁴. However, this process remains unsuitable for real-time monitoring of food spoilage. Volatile organic compounds (VOC), such as ammonia, serve as early indicators of food spoilage, as TVB-N mainly consists of ammonia, with only small amounts of other nitrogen compounds³. Early detection of ammonia plays a crucial role in ensuring the safety and quality of food. For instance, an electronic nose (e-nose) sensor was reported to detect ammonia gas in canned fish spoilage, with a detection limit of 15 ppm⁵. However, the odor threshold of ammonia gas for humans is as low as 2.6 ppm⁶, there is a critical need to explore technologies capable of detecting ammonia at lower concentrations to ensure food safety and protect human health.

One of the most common types of gas sensors for food spoilage detection is the chemisresistive gas sensor. However, this type of sensor suffers from significant drawbacks, including a short lifespan, high operating temperatures, sensitivity to temperature variations, and baseline drift⁵. Optical sensors provide an alternative solution to overcome those limitations, such as fast signal processing, ease of operation, and immunity to electromagnetic interference, as they do not rely on electrodes⁶. Various optical fiber gas sensing structures have been developed, including fiber Bragg grating (FBGs), long-period gratings (LPGs), tilted gratings⁷ and multimode interference (MMI) devices such as single-mode-multimode-single-mode (SMS) structures⁸. SMS structures exhibit self-reimaging at specific periodic distances, providing an optimum sensor’s length to achieve maximum output⁹. While traditional SMS structures lack direct interaction between the sensing medium and the cladding layer, replacing the multimode fiber with a no-core fiber (NCF) enables the evanescent field to interact directly with the surrounding medium, significantly enhancing sensitivity and response^10,11.

To enhance gas sensor selectivity, metal oxide semiconductors (MOS), including CuO, WO₂, TiO₂, and SnO₂, have been widely studied^12,13. These materials exhibit changes in their electrical and optical properties when exposed to different gases, which are characteristic essentials for selective gas detection. Zinc Oxide (ZnO), an n-type semiconductor with a bandgap of 3.37 eV, is particularly suitable for ammonia detection due to its selective light response and morphology-dependent sensitivity^14–16. The morphology of ZnO, such as its hierarchical 3D structures, has been shown to significantly influence its selectivity and sensitivity toward ammonia gas, as reported by Zhu et al.¹⁶. Nanostructure metals such as platinum (Pt) and gold (Au) have gained extensive use in optical gas sensing devices by helping reduce the reaction barrier^17,18. Gold nanoparticles (AuNPs) are ideal for gas sensing due to their stability and ability to exploit Localized Surface Plasmon Resonance (LSPR), which enhances near-field electromagnetic waves and improves refractive index sensitivity^7,19,20.

In this work, a highly sensitive optical fiber sensor was developed using a no-core fiber (NCF) as the sensing region. Zinc oxide (ZnO), a metal oxide compound known for its high selectivity, is utilized with two distinct nanostructures: nanoparticles and nanorods. These ZnO nanostructures are then coated with gold nanoparticles to enhance sensitivity toward ammonia gas. The concentration of the gold colloidal nanoparticles is optimized to achieve maximum performance. The sensor is tested at room temperature with ammonia concentrations ranging from 0.15 to 10 ppm, offering a practical solution for detecting ammonia gas from food spoilage at low concentrations.

Sensor design and sensing mechanism

The optical fiber sensor is designed to have multimode fiber as the sensing region, connecting with single-mode fiber (SMF) at both ends as shown in Fig. 1a. At the sensing region, the no-core fiber (NCF) is coated with gold on ZnO nanostructures, either nanoparticles or nanorods, as shown in Fig. 1b. The NCF is proposed as the sensing region because it broadens the optical field guided within the fiber, thereby enhancing the evanescent field at the interface and improving light interaction with the analyte materials. When the light is coupled from the SMF into the NCF, multimode interference (MMI) occurs. The MMI behavior underlying the sensing mechanism was previously validated through beam propagation simulations²¹. The different propagation constants of multiple modal field profiles cause an interference pattern along the propagation direction. With MMI, there are specific distances where all the modes are in-phase, resulting in a high output signal that closely replicates the input signal. These distances, called re-imaging distance, represent the locations where maximum coupling efficiency is achieved, and can be calculated using Eq. (1). These re-imaging distances serve as the initial design parameter for the sensor’s length to achieve the maximum possible output.

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Fig. 1.

(a) Optical fiber sensor with Au/ZnO-coated no-core fiber (NCF) as sensing region. (b) ZnO nanorods and nanoparticles coated with gold on the fiber surface. (c) Coupling efficiency versus multimode length for optimizing the sensing length. (d) Sensing mechanism of Au/ZnO nanostructures under ammonia exposure.

In order to determine the optimum sensor length based on Eq. (1), the electric fields in the multimode fiber () and single mode fiber () need to be calculated. These electric fields depend on the fiber radius and the refractive indices of the media involved. The coupling efficiency is then calculated for various operating wavelengths ranging from 500 to 800 nm, varying sensing lengths (multimode lengths) between 0 and 10 cm. The resulting coupling efficiency curve identifies the distances at which maximum output signal is obtained, as illustrated in Fig. 1c. Based on this analysis, an optimized NCF length of 3.4 cm was selected to achieve maximum multimode interference coupling. This configuration ensures enhanced sensitivity at an operating wavelength of approximately 630 nm within the visible red region.

At the sensing region, the surface properties of ZnO influence its roughness and morphology, which in turn affect sensing performance²². Gold nanostructures further modify the ZnO surface by forming Au–Zn, Au–O, and Au–O–Zn bonds, enhancing electronic interactions²³. Under visible light excitation, the Au-decorated ZnO generates hot electrons that reduce the charge depletion layer thickness upon interaction with ammonia gas, as illustrated in Fig. 1d. Compared to the pristine ZnO structure, this additional charge transfer induced by NH₃ adsorption further modulates the local refractive index at the metal–semiconductor interface²⁴. This refractive index modulation alters the evanescent field distribution at the fiber interface, resulting in measurable shifts in the transmission spectrum that enhance the sensitivity of the sensor. Additionally, the presence of gold improves charge transfer and adsorption energy through significant changes in surface electronic structure, leading to improved selectivity compared to pristine ZnO nanostructures²⁵.

For sensor operation in ambient air, oxygen molecules are adsorbed onto the gold-coated ZnO surface, denoted as O₂(ads) in Eq. (2), where they react with electrons from the conduction band of ZnO, resulting in the formation of negatively charged oxygen species (O₂⁻, O⁻, O₂²⁻), as shown in Eq. (3). These species act as surface acceptor states, causing a depletion of electrons in the conduction band. In the presence of ammonia gas, the adsorbed oxygen anions are oxidized on the ZnO surface, initiating charge transfer that returns free electrons to the conduction band, as described in Eqs. (4)-(5)²⁶. This reduces the electron depletion layer and alters the refractive index at the Au/ZnO interface, further modulating the evanescent field in the optical fiber sensor. Additionally, the gold nanoparticles, with their high surface area, promote effective chemisorption of ammonia through interactions with nitrogen’s lone pair electrons. Consequently, ammonia adsorption induces a shift in the localized surface plasmon resonance (LSPR) peak, which is directly correlated with ammonia concentration and detected by the optical fiber sensor.

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Or

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The localized surface plasmon resonance (LSPR) describes the collective oscillation of free electrons at the surface of metal nanoparticles, induced by incident photons. This phenomenon is highly sensitive to changes in the refractive index of the surrounding environment, making it crucial for enhancing the sensitivity of the optical fiber sensor. In this work, LSPR occurs at the interface between the gold nanoparticles and the ZnO layer. Variations in the refractive index of the Au/ZnO interface, caused by ammonia adsorption, result in a shift of the LSPR absorption peak, which is detected through changes in the transmission spectrum of the optical fiber sensor.

Materials and methodology

Material and reagents

Zinc acetate dihydrate (Zn (CH₃COO)2·2H₂O) was obtained from KemAus, Australia. Tetrachloroauric (III) acid trihydrate (HAuCl₄·3H₂O), potassium hydroxide (KOH) pellets, and (3-Aminopropyl) triethoxysilane (APTES) were purchased from Merck, Sigma Aldrich, Germany. Ethanol (C₂H₅OH) with a purity of 99.9% was obtained from QReC, New Zealand. All the chemicals and reagents were used without further purification.

ZnO nanostructure preparation and deposition

The ZnO colloidal solution was synthesized using the wet chemical method following previous work by Swargiary et al.²⁷. A solution of 0.1 M Zinc precursor in ethanol was prepared by dissolving zinc acetate dihydrate (220 mg) in ethanol (10 mL) at 60 °C for 30 min. In addition, a solution of 0.1 M potassium hydroxide was prepared by slowly dissolving potassium hydroxide pellets (56 mg) in ethanol (10 mL) at room temperature. Next, a 0.1 M of ZnO colloidal solution was proceeded by drop-wise addition of a prepared 0.1 M KOH solution into a cloudy Zinc precursor solution at room temperature with constant stirring. The resulting clear colorless solution of ZnO nanoparticles was filtered through filter paper to remove undissolved Zinc precursor and impurities. To deposit ZnO nanoparticles on the surface of optical fiber, ZnO nanoparticles solution was sprayed by using an air brush positioned 15 cm above the optical fiber while maintaining the temperature at 250 °C on the hotplate for 15 s. The ZnO nanoparticles-coated optical fiber was sintered at 250 °C in an oven for 2 h. The ZnO nanorods was prepared by following the same procedure with 0.2 M of Zinc precursor in ethanol and 0.2 M of potassium hydroxide in ethanol.

Gold nanoparticle deposition

The gold nanoparticles were deposited on ZnO layer by UV photoreduction of gold ion precursor solution. The gold nanoparticle deposition process is shown in Fig. 2. A solution of 0.164 M APTES was prepared in ethanol by stirring at 60 °C for 1 h. The previously prepared ZnO-coated optical fiber was functionalized by submerging in the APTES solution at 60 °C for 1 h. After the treatment, the functionalized optical fiber was washed by ethanol to remove the excess amount of APTES solution. Separately, 0.1 mM, 1 mM and 10 mM of gold precursor solution was prepared by dissolving HAuCl4·3H2O with deionized water at room temperature. Then, the ZnO-coated optical fiber was dipped coating in the gold precursor solution while irradiated with UVA light source for 10 min each side. The optimal distance between the light source and the functionalized optical fiber was 3 cm to ensure a uniform UV irradiation. Following this, the optical fiber was annealed in an oven at 450 °C for 1 h to complete the gold nanoparticle deposition process.

Fig. 2.

Schematic of ZnO nanoparticle synthesis and spray coating process followed by UV photoreduction-based gold nanoparticle deposition on the no-core fiber (NCF).

Experimental setup and VOC gas preparation

To quantitatively validate the Au/ZnO-coated optical fiber sensor, optical measurements were conducted using various concentrations of VOC gases, including ammonia, isopropanol, acetone, and ethanol, to evaluate both sensitivity and selectivity. A schematic diagram and the actual experimental setup for testing the fabricated optical fiber sensor are shown in Fig. 3a,b. The setup includes a broadband tungsten halogen light source (Ocean Optics, HL-2000, USA) operating in the wavelength range of 200–2200 nm, connected to the fabricated optical fiber sensor placed inside the gas chamber. The output signal from the optical fiber sensor was detected by a compact spectrometer (Thorlabs, CCS200/M, USA), which captured spectral data within the range of 200–1000 nm. The gas chamber, constructed from acrylic, ensured no absorption or interaction with the analyte gases. The experiment was conducted under controlled environmental conditions, with a maintained temperature of 24 °C and relative humidity of 45%.

Fig. 3.

(a) Schematic of the optical measurement setup for ammonia sensing. (b) Actual experimental setup with a close-up of the sensing region.

The gas sensing experiments of the volatile compound gas were conducted by evaluating sensitivity, selectivity, and response time at various concentrations. Each gas concentration was prepared by measuring the amount of liquid analyte, calculated using the dilution equation C₁V₁ = C₂V₂. Here, C₁ represents the initial gas concentration in the Tedlar bag, which has a volume of 1 L, and V₁ is the injected volume of liquid analyte. C₂ represents the final gas concentration inside the chamber after the liquid analyte is heated up to 40 °C for 5 min to ensure homogeneity of the gas vapor in the Tedlar bag. V₂ is the volume of the chamber. Nitrogen gas was purged through the chamber before and after the experiment to eliminate interference from residual volatile compounds. The same procedure was repeated for all experiments.

Results and discussion

Characterization of ZnO and Au nanostructure on sensing area

The surface morphology and the elemental composition of the fabricated sensors were characterized using Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-Ray Spectroscopy (EDX), as shown in Fig. 4. The FESEM image in Fig. 4a, shows ZnO nanorods coated on the optical fiber surface, with an average length of approximately 2.5 μm and a diameter of about 600 nm. Gold nanoparticles were subsequently deposited on the ZnO nanostructures using the UV photoreduction technique, as shown in Fig. 4b. The uniform dispersion of the gold layer was confirmed by EDX mapping as shown in Fig. 4c.

Fig. 4.

(a) FESEM image of ZnO nanorods on the optical fiber surface. (b) FESEM image of gold nanoparticles deposited on ZnO nanostructures. (c) EDX elemental mapping of gold. (d) Absorption spectra comparing pristine ZnO and Au/ZnO-coated optical fibers showing an absorption peak of gold at approximately 540 nm.

The optical properties of the fabricated films were evaluated by UV-Vis spectroscopy. The absorbance spectra of pristine ZnO and Au/ZnO-coated optical fibers are shown in Fig. 4d. Au/ZnO-coated sample exhibits a significantly higher absorbance with a distinct peak at ~ 540 nm, corresponding to the localized surface plasmon resonance (LSPR) of the gold nanoparticles. This result confirms the effective formation of Au nanoparticles on the ZnO-coated optical fiber.

Ammonia gas sensing characteristics.

Transmittance spectra and sensitivity analysis

To systematically evaluate the effects of both ZnO morphology and gold nanoparticle concentration on performance for the sensor, four types of optical fiber sensors were fabricated: (i) 0.1 mM gold nanoparticles on a ZnO nanoparticle layer, (ii) 1 mM gold nanoparticles on a ZnO nanoparticle layer, (iii) 0.1 mM gold nanoparticles on a ZnO nanorod layer, and (iv) 1 mM gold nanoparticles on a ZnO nanorod layer. These fabricated sensors were tested under varying ammonia (NH₃) gas concentrations (0.15, 0.5, 1, 5, and 10 ppm), covering the typical range associated with food spoilage. Normalized transmission spectra and corresponding sensitivity were measured to investigate the effects of ZnO nanostructures and the concentration of gold nanoparticles on ammonia detection, as shown in Fig. 5. In this study, sensitivity is defined as the slope of the linear fit between the wavelength shift and ammonia concentration. This provides a clear measure of how effectively the sensor responds to changes in gas concentration, allowing straightforward comparison with other sensors.

Fig. 5.

Normalized transmission spectra and corresponding sensitivity curves for optical fiber sensors with gold nanoparticles on ZnO nanostructures. (a,b) 0.1 mM gold on ZnO nanoparticles layer. (c,d) 1 mM gold on ZnO nanoparticles layer. (e,f) 0.1 mM gold on ZnO nanorods layer. (g,h) 1 mM gold on ZnO nanorods layer. (i) Summary of sensitivity and regression for four sensor configurations.

The sensitivity of the sensor is determined by the change in resonance wavelength ( relative to the change in ammonia gas concentration ().

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For the gold-coated ZnO nanoparticle optical sensor, the normalized transmission spectra shift toward shorter wavelength (blueshift) with increasing ammonia gas concentrations for both 0.1 mM and 1 mM gold concentrations, as shown in Fig. 5a, c, respectively. The corresponding sensitivity curves in Fig. 5b, d demonstrate excellent linear relationships with ammonia concentration, with regression coefficients of R² = 0.99 for 0.1 mM and R² = 0.93 for 1 mM concentrations of gold. The calculated sensitivities were 0.093 nm/ppm for 0.1 mM and 0.042 nm/ppm for 1 mM. This confirms the reliable and consistent performance of the nanoparticle-based sensors for ammonia detection.

In comparison, the normalized transmission spectra for optical sensors with 0.1 mM and 1 mM gold nanoparticles deposited on ZnO nanorod layer are shown in Fig. 5e, g, respectively. The spectra also exhibit wavelength shifts with increasing concentration of ammonia. However, the regression coefficients are low, with R² = 0.72 for 0.1 mM and R² = 0.45 for 1 mM gold concentrations,, as shown in Fig. 5f, h. The corresponding sensitivity was 0.139 nm/ppm for 0.1 mM and 0.054 nm/ppm for 1 mM. This suggests non-linear behavior for the nanorod-based sensors, particularly at higher ammonia concentrations. A summary of the calculated sensitivities and regression coefficients for all four sensor configurations is presented in Fig. 5i.

Limit of detection (LOD) analysis

To further assess the detection capability of the fabricated sensors, the limit of detection (LOD) for each configuration was calculated based on the root mean square (RMS) noise method described in^28,29, as shown in Eq. (7).

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where is root mean square of the residuals from the baseline fitted optical fiber sensor response, representing the baseline noise level, and sensitivity is the slope of the calibration curve (nm/ppm). This method is used to provide a statistically reliable estimate of the smallest detectable analyte concentration.

A summary of sensitivities, regression coefficients, and LOD values for all configurations is presented in Fig. 5i. Among them, the ZnO nanoparticle sensor with 0.1 mM gold nanoparticles achieved the best overall performance, with a sensitivity of 0.093 nm/ppm, R² = 0.99, and the lowest LOD of 0.96 ppm. While nanorod-based sensors exhibited higher sensitivity values, their lower linearity limits reliability for low-concentration ammonia detection. Based on these results, the 0.1 mM Au/ZnO nanoparticle sensor was selected for further evaluation of response time, selectivity, and stability.

Response and recovery time analysis

The optical fiber sensor with 0.1 mM gold coated on the ZnO nanoparticle layer was evaluated for real-time measurement capability and repeatability through response testing, as shown in Fig. 6. The sensor was exposed to 5 ppm of ammonia gas for three consecutive cycles. In each cycle, the sensor was exposed to ammonia gas until the sensor response reached saturation, followed by purging with nitrogen (N₂) gas to remove any remaining residual gases. The sensor exhibited a response time of less than 60 s and a recovery time of less than 90 s, demonstrating consistent and repeatable performance.

Fig. 6.

Response and recovery time of the optical fiber sensor with 0.1 mM gold-coated ZnO nanoparticle layer exposed to 5 ppm ammonia gas.

Selectivity and mixed gas analysis

To evaluate the selectivity of the sensor, its response was tested against common interfering gases, including acetone, ethanol, and isopropanol, under the same test conditions as used for ammonia. These gases, which have low vapor pressures and are commonly present in urban environments, were chosen to assess the sensor’s ability to differentiate ammonia from other volatile compounds. The sensitivities of the fabricated sensor toward ammonia and the selected interference gases are compared in Fig. 7. The bar chart in Fig. 7a shows the sensor’s sensitivity (nm/ppm), while the inset presents the absolute change in wavelength shift as a function of concentration. The results demonstrate that the sensor exhibits the strongest response to ammonia (0.09 nm/ppm), followed by isopropanol (0.04 nm/ppm), ethanol (0.019 nm/ppm), and acetone (0.02 nm/ppm). These results clearly indicate good selectivity toward ammonia, as the sensitivity and wavelength shift are significantly larger than those of the other VOCs. High selectivity is crucial for ensuring that the sensor accurately identifies ammonia in complex environments containing multiple interfering gases. This enhanced response to ammonia can be attributed to the stronger chemisorption of ammonia molecules on the ZnO surface due to their interaction with oxygen vacancies and the lone pair electrons of nitrogen, which lead to more pronounced changes in the refractive index and electron density. In contrast, interfering gases interact primarily through weaker hydrogen bonding or physisorption, resulting in smaller changes at the sensing interface. Additionally, the presence of gold nanoparticles enhances the local electromagnetic field at the sensing surface, further amplifying the response to analytes with stronger electronic interactions, such as ammonia.

Fig. 7.

(a) Sensitivity of the Au/ZnO optical fiber sensor to ammonia and other VOCs. Inset shows absolute change wavelength shift vs. concentration. (b) Wavelength shift under pure ammonia and mixed gas conditions showing consistent sensitivity.

Mixed gas tests were also conducted to further evaluate the sensor’s practical performance under real-world conditions. For this experiment, ammonia at varying concentrations was mixed with a constant 1 ppm of acetone, ethanol, and isopropanol. Figure 7b shows that the sensor maintains similar sensitivity and linearity under mixed gas conditions, although the absolute position of the wavelength shift differs from that of pure ammonia due to the additional influence of adsorbed interfering gases, which slightly alter the local refractive index and baseline spectral position. Including both selectivity and mixed gas tests provides strong validation of the sensor’s robustness in complex environments where multiple VOCs may be present.

Environmental stability analysis

The effect of environmental factors, specifically temperature and humidity, on sensor performance was also investigated to evaluate the robustness of the sensor in real-world applications. The temperature was varied between 21 °C and 27 °C, while relative humidity was adjusted to 45%, 60%, and 75% RH, covering a realistic range encountered in food storage and transportation environments. In both cases, the sensor maintained a consistent sensitivity of approximately 0.09 nm/ppm with good regression values (R² > 0.88), indicating high linearity and reliability across different conditions.

Only slight shifts in the resonance wavelength were observed, ranging from 630 to 636 nm across the temperature conditions and from 633 to 640 nm across the humidity levels. These minor shifts are attributed to variations in the refractive index of the surrounding medium and changes in adsorption–desorption kinetics at the sensing surface. Despite these shifts, the calibration slope and overall sensor response remained stable, confirming the sensor’s reliable performance under typical ambient fluctuations.

Comparative sensor performance

To further evaluate the potential applicability of this work, the performance of the developed sensor was compared with those of previously reported optical fiber sensors for ammonia detection. A summary of the critical performance metrics of various optical fiber sensors for ammonia gas detection, as reported in previous studies, is shown in Table 1. The optical fiber, utilizing an NCF as the sensing region, demonstrated reliable performance, and the addition of gold nanoparticles on ZnO-coated fibers enhanced sensitivity. The low limit of detection achieved is comparable to similar sensors, making it suitable for early ammonia detection in food spoilage applications. In addition, temperature and humidity variation experiments were conducted to evaluate sensor stability under typical ambient conditions, reinforcing the sensor’s practical potential in real-world environments.

Table 1.

Comparison of ammonia gas sensing performance at room temperature for different optical fiber sensor structure.

Structure	Sensing material	Sensitivity (nm/ppm)	LOD (ppm)	Refs.
SMF-Capillary-NCF-SMF	ZnO	0.036	2.31	15
Microfiber interferometer	ZnO	0.00575	-	16
Microfiber interferometer	ZnO/GO	0.031	4	14
Hollow malposition structure	M-CQDs/MgxZnO	0.0089	1	30
No-core fiber	Au/ZnO	0.093	0.96	This work

Conclusion

In this work, a gold-coated ZnO optical fiber sensor, utilizing a no-core fiber (NCF) as the sensing region, was developed for ammonia gas detection, a key byproduct of food spoilage. Four different sensor configurations were fabricated by varying ZnO nanostructure morphology (nanoparticles and nanorods) and gold nanoparticle concentration, to systematically evaluate their effects on sensor performance. The optimized sensor, based on ZnO nanoparticles coated with 0.1 mM gold nanoparticles, exhibited high sensitivity and selectivity, attributed to enhanced light interaction in the evanescent region. It achieved a sensitivity of -0.093 nm/ppm, a response time of 60 s, a recovery time of 90 s, and a low detection limit (LOD) of 0.96 ppm. Selectivity tests confirmed that the sensor provides a significantly stronger response to ammonia compared to other VOCs such as acetone, ethanol, and isopropanol. Furthermore, mixed gas testing demonstrated that the sensor maintains its sensitivity and linearity even in complex environments containing multiple VOCs. Environmental condition tests further verified that typical variations in ambient temperature (21–27 °C) and relative humidity (45–75% RH) do not significantly affect sensor sensitivity or calibration slope, confirming robust performance. These results indicate that the developed optical fiber sensor offers excellent potential for ammonia detection and is suitable for practical applications in food spoilage monitoring across consumer and industrial settings.

Author contributions

S.T.: Writing – original draft, Validation, Methodology, Investigation, Formal analysis, Data curation. K.R. and G.V.: Investigation, Data curation. K.S.: Validation and Methodology. R.J., D.L and P.J.: Validation C.V. Supervision, Methodology, Conceptualization, Visualization, Writing – review & editing, Resources, Project administration, Funding acquisition.

Funding

This research is financially supported by Thailand Science research and Innovation Fund Chulalongkorn University (FOOD_ FF_68_336_2100_043), International School of Engineering (ISE) and Rachadapisek Sompote Fund for Biomedical Materials and Devices for Revolutionary Integrative Systems Engineering Research Unit (BMD-RISE), Chulalongkorn University.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to internal laboratory data policy and institutional restrictions but are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.