Open Access
Add to BibSonomy
Abstract
The increasing demand for individualized health monitoring and diagnostics has prompted considerable research into the integration of portable optical fiber biosensors integrated with smartphones. By capitalizing on the benefits offered by optical fibers, these biosensors enable qualitative and quantitative biosensing across a wide range of applications. The integration of these sensors with smartphones, which possess advanced computational power and versatile sensing capabilities, addresses the increasing need for portable and rapid sensing solutions. This extensive evaluation thoroughly examines the domain of optical fiber biosensors in conjunction with smartphones, including hardware complexities, sensing approaches, and integration methods. Additionally, it explores a wide range of applications, including physiological and chemical biosensing. Furthermore, the review provides an analysis of the challenges that have been identified in this rapidly evolving area of research and concludes with relevant suggestions for the progression of the field.
© 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
With the growing concern for physical health and the need for personalized medicine, there is a growing demand for the development of portable, low-cost, and rapid-testing biosensing devices in clinical diagnostics, healthcare, and other application areas. Clinical diagnostic methods in laboratory techniques usually require specialized instruments and trained operators and are considered both expensive and time-consuming due to the long preparation time of reagents and clinical samples [1]. In healthcare, bulky laboratory instruments also make it difficult to achieve long-term monitoring of individual physiological information, which is not conducive to understanding accurate physiological conditions [2]. The United Nations Progress Report on Healthy Ageing states that concerted and accelerated action is needed to support healthy aging in low and middle-income countries, where 80% of the world’s older population will live by 2050 [3]. For these reasons, there is a need to develop a portable, fast, and sensitive method for personalized health monitoring and clinical diagnosis in resource-limited conditions or non-clinical settings. In recent years, researchers have been interested in biosensors capable of detecting biological responses or physiological information and converting them into measurable signals [4].
An optical fiber is a flexible, transparent, and cylindrical waveguide made of plastic or silica, with diameters slightly thicker than human hair [5]. Compared with electrical sensors, optical fiber sensors have the advantages of immunity to electromagnetic interference, chemical stability, and high multiplexing capability [6]. Optical fibers are widely used in biosensing due to their small size, flexibility, biocompatibility, chemical inertness, processability, and capability of functionalization [7,8]. The fine dimensions of the optical fiber make it suitable for integration with medical catheters and needles, forming a technology platform for in vivo sensing and point-of-care diagnostics [9]. In analytical chemistry and biomedical research, optical fibers can be used as biosensing elements for qualitative and quantitative detection of various analytes. With a variety of different optical fiber configurations, optical fibers can be used for various types of biosensing applications. Many geometrically modified and micro-structured optical fibers, including side-polished optical fibers [10,11], D-shaped optical fibers [12–16], U-bent optical fibers [17–20], tapered optical fibers [21–23], notched optical fibers [24,25], and microsphere-structured optical fibers [26,27] have been used to increase the evanescent field of the guided core or cladding modes. In turn, the interaction of the evanescent field with the surrounding environment enables the sensitivity of the biosensor. However, optical fiber biosensors usually require bulky light sources and demodulators or power meters to collect the optical signals and use computers to process and analyze the data, and thus optical fiber biosensors are mostly limited to laboratory research and difficult to extend to such real life situations as point-of-care and personal use [28].
According to statistics, in 2022, there are more than 3.2 billion smartphone users worldwide [29]. Smartphone hardware sensors include cameras, Light-Emitting Diode (LED) flashlights, Ambient Light Sensors (ALS), Wi-Fi, and more. In addition to the hardware, modern smartphones have powerful computing capabilities and display features that make them suitable for developing applications that fulfill different needs [30,31]. Based on the low cost, small size, low power consumption, and high adaptability of smartphones, portable optical fiber biosensors can be obtained by integrating smartphones with optical fibers. Many research groups have now introduced portable smartphone-based platforms for optical fiber biosensing applications, allowing real-time monitoring and instantaneous data analysis without complex laboratory setups. These platforms are widely used in developing luminescent sensors, spectrometers, leakage-based sensors, colorimetric sensors, and more. Cheng et al. built a smartphone-assisted mobile fluorescent biosensor by integrating an all-fiber optic system and a microfluidic system with a smartphone [32]. Ding et al. assembled a cavity on the rear camera of a smartphone, which mechanically arranged all optical components (optical fiber bundle, lens, diffraction grating, slit) to form a smartphone-based spectrometer [33]. Zhang et al. achieved monitoring of respiration and heart rate using a light leakage-based biosensor made of plastic optical fiber (POF) and polydimethylsiloxane (PDMS) materials integrated with a smartphone [34]. Existing features of smartphones can be seamlessly integrated with custom-designed brackets to facilitate the integration of ancillary components such as lenses, spatial filters, diffraction gratings, and additional laser sources to enhance the functionality of optical sensing systems. One such example is a recent work by Malone et al. [35], in which a smartphone-based optical coherence tomography system was demonstrated for the first time.
Several previous research reviews have focused on one aspect of optical fiber biosensors integrated with smartphones. For example, Li et al. presented a concise summary of current advancements in novel nanomaterial-based optical fiber biosensors [4]. Singh et al. reviewed smartphone-based surface plasmon resonances (SPR) sensors developed in recent years from the principle of SPR as an introductory point [36]. In addition, Roda et al. provided a critical review of papers on the use of smartphones as analytical devices and biosensors, of which sensors based on optical or fiber-optic principles are one aspect [37]. Hussain et al. [38] and Dutta et al. [39] provided reviews of smartphone-based optical biosensors, organized in terms of spectroscopic platforms as well as point of care, respectively. However, to the best of our knowledge, there is no review on the topic of portable optical fiber biosensors integrated with smartphones. This review has been developed to fill this gap and to report on the latest progress in this area. The review will introduce the recent progress of portable optical fiber biosensors integrated with smartphones in terms of technology (including hardware, optical fiber sensing technology, and integration methods), applications (including chemical and physiological biosensing), and challenges as shown in Fig. 1.
2. Hardware technology
A portable optical fiber biosensor integrated with a smartphone usually comprises four main hardware components (Fig. 2): optical transmitter, optical fiber, sensing structure, and photodetector. An optical transmitter sends photons transmitted to the sensing structure via an optical fiber. The sensing structure is designed with different characteristics for biosensing and the light signal continues to be transmitted through the optical fiber to the photodetector for signal acquisition. This part will discuss in detail the commonalities and differences between the existing studies in terms of the hardware used in each component.
Fig. 2. A typical hardware combination for a portable optical fiber biosensor integrated with a smartphone, reproduced from Ref. [34] with permission from IEEE (2024).
2.1 Optical transmitter
There are two main types of optical transmitters: LASER and LED light. LASER emission is typically achieved using fiber lasers, which are highly integrated with optical fibers. They are compact, small in size, have good beam quality, and are energy-efficient. LED light is another commonly used optical transmitter, and in the research, it is usually emitted by the flashlight of a smartphone. It has the advantages of being small, long-lasting, energy-efficient, and eco-friendly [40]. LED light is transmitted with much background noise compared to LASER light and is suitable for short-distance transmission in optical fiber sensing [41].
2.2 Optical fiber
There are two types of transmission optical fibers: silica optical fiber (SOF) and plastic optical fiber (POF), which are categorized based on their material. POF is made using polymers with a high refractive index [42], such as PMMA (polymethylmethacrylate), while SOF is made using silicate glass or silicon [6]. POFs are typically more flexible than SOFs [43], but the optical loss brought by POFs is greater than that of SOFs, which is not suitable for long-distance optical signal transmission [44]. Meanwhile, based on the number of transmission modes, optical fibers can be classified into single-mode optical fibers (SMF) and multimode optical fibers (MMF). The SMF has a narrow core diameter that only allows a single mode of light to pass through, whereas the MMF has a wider core that permits propagation of multiple modes, having different spatial distributions and propagation constants [45]. When using LEDs as light sources in optical fiber systems, it must be taken into account that only MMFs can be used for light transmission due to low spatial coherence of LED light, which can’t be efficiently coupled into typically used SMFs [46]. In contrast, SMF-based optical fiber systems require LASERs, superluminscent LEDs or supercontinuum sources. Therefore, the cost of a transmission system using MMF is typically lower than that of a SMF [47]. According to our comparison shown in Table 1, MMF systems are more commonly used in portable optical fiber biosensors integrated with smartphones.
Table 1. Selected hardware combinations for portable optical fiber sensors integrated with smartphones under different studies
2.3 Sensing structure
The design of the sensing structures plays a crucial role in hardware. In this part, the design of the sensing structures used in various studies has been categorized and interpreted according to transmissive and reflective approaches. The first category is to design the sensing structure based on the transmitted light signals. For example, Yetisen et al. obtained quantitative readouts by measuring the changes in the intensity of transmitted light through the hydrogel optical fiber [53]. The principle of transmission measurement is simple and can usually be realized by using straight optical fibers [54–59], U-bent optical fibers [48,60–64], etc. to modify the sensing area as shown in Fig. 3. The U-bent optical fiber probe ensures deeper penetration of the evanescent field in the investigated tissues or analytes in the bent region than a straight probe. When an optical fiber is macro-bent, the radius of curvature and refractive indices of the core and cladding vary depending on the numerical aperture of the optical fiber [65]. This change in light guiding conditions at the bending region of the fiber can cause a variation in output light intensity [66]. Figure 3(b) shows some common designs of transmissive optical fiber probe structures, such as triangular and crown-shaped notch designs based on the light-leakage principle and hydrogel-modified and quantum material-modified designs based on the SPR principle. To the best of our knowledge, only a few tapered, D-shaped and other modified shapes of optical fiber sensing probe designs are being used for integration with smartphones, which could be one of the directions of efforts to improve the sensing sensitivity and range afterwards. Different optical fiber probe shapes for specific applications aim at improving the performance [67].
Fig. 3. Transmissive light path design [(a) Different sensing region structures: (1) Spiral-shaped, (2) Straight, and (3) U-bent; (b) Different fiber probe structures: (1) Triangle notch, (2) Crown-shaped notch, (3) Hydrogels modification, and (4) Quantum material modification].
The second category is to design the sensing structure based on the reflective light signals. In this scheme, light is directed from the source to the detected object through a multimode optical fiber. When light reaches the tip of the optical fiber, a part of it is reflected from the tip and the investigated object outside the fiber and is further guided to the light detector through the 2${\times} $ 1 coupler or circulator, as illustrated in Fig. 4. Thus, the tip of an optical fiber is commonly used as a sensing structure for optical fiber sensors based on reflected light. An unmodified optical fiber tip is usually mechanically cut into the fiber tip using a sharp blade or a finer laser. It has been reported that such a device can be used to measure the refractive index of liquids [61]. For enabling a wider range of detection capabilities with the use of the reflection principle [4], the fiber tip can be functionalized. Reported functionalization schemes as shown in Fig. 4 include quantum dots (QDs) [50,68–74], metal film [75], metal nanoparticles [76–82], and hydrogel modifications [49,53,83].
Fig. 4. Reflective light path design [(a) Different fiber probe structures: (1) Quantum dot modified optical fiber tip, (2) Metal film modified optical fiber tip, (3) Metal nanoparticles modified optical fiber tip, and (4) Hydrogels modified optical fiber tip].
2.4 Photodetector
In most of traditional optical fiber sensing systems, optical power meters or dedicated demodulators were used as photodetectors to monitor fluctuations in light intensity or wavelength in optical fibers. However, numerous sensors integrated into smartphones can already do the job instead [38]. The Ambient Light Sensor is a photodetector embedded in a smartphone that adjusts the brightness of the phone's screen based on the intensity of the ambient light [48]. Equipped with light or color filters, ALS can convert ambient light into illuminance values with high accuracy [84]. Initially, researchers developed devices for chemical/biochemical applications using ALS in smartphones based on photonic crystals [85], evanescent fields [86], SPR [87,88], and other principles in open light paths. These reports used several external optical elements, which made the system bulky. However, the researchers’ implementation of smartphone sensors provided innovative concepts for future studies [36,55]. In portable optical fiber sensing integrated with smartphones, the use of the smartphone's ALS or camera instead of an external photodetector makes the device less bulky.
The Hardware section describes the types and roles of the four components. Several examples are used in the article to illustrate the hardware that worked successfully in the experiments, and more detailed hardware combinations from different studies can be found in Tab. 1. The reported hardware constitutes complete optical systems through different optical fiber sensing technologies with integration methods.
3. Optical fiber sensing technology
After the probe light is modulated by the sensing element, it is measured by the smartphone camera, and can be further demodulated for estimating the measured quantity. Currently, there are two main types of optical fiber sensing technology used in optical fiber biosensors integrated with smartphones: with intensity modulation and with spectral modulation. For smartphone-based sensing systems, intensity interrogation can be used with various types of the sensing structures, while spectral interrogation is typically used with SPR sensors. Implementing these two optical fiber sensing technologies requires different organization of the interrogation hardware. The processing flow for the collected data can be divided into three main elements (Fig. 5): pixel data acquisition, frame-by-frame conversion to grayscale, and extracting useful signals. It should be noted that due to the nonlinear amplitude characteristic of the camera, tailored to match the one of the human’s eye’s, some additional correction may be required in order to obtain the grayscale values proportional to the light intensity. SPR-based biosensors can be interrogated by monitoring intensity in different color channels, such as Red and Green as was done in [89], or even by measuring the overall light intensity, if the slope of the SPR spectrum covers most of the visible spectrum range. In both cases a wavelength to intensity calibration is required to measure the SPR central wavelength. A more complex, yet more informative approach is to measure the whole SPR spectrum using a smartphone-based spectrometer [55]. This part will provide an overview of the above-mentioned optical fiber sensing technologies.
3.1 Intensity interrogation
The smartphone continuously records video which allows us to monitor and process light intensity data. While recording video, the app can be used to adjust parameters such as ISO and shutter speed to control the sensitivity of the camera. ISO affects the camera sensor's sensitivity to light, but if it's set too high, it can cause noise that affects the accuracy of measurements [39]. To prevent this, we should adjust the shutter speed to control the amount of light coming into the camera, and ensure the intensity value in the video is high enough to collect noiseless data at the chosen ISO setting [90]. The parameters for video recording in some studies are organized in Tab. 2. To calculate the amount of light received, each frame of the video is processed by the function and converted to a grayscale value. This assigns a brightness value to each pixel, which is equivalent to its gray value. To determine the overall light intensity, the gray values of all pixels in each frame are added together using the following equation:
Table 2. Some camera parameters of the video recording process
3.2 Spectral interrogation
Surface plasmon resonance is one of the most promising optical sensing techniques and the first sensing application of the SPR technique was reported in 1983 [92]. The surface plasmon resonance effect (Fig. 6) generates a dip in the transmittance spectrum or a peak in the absorbance spectrum of the electromagnetic wave [36]. This dip or peak is shifted when there is a change in the refractive index of the surroundings. This shift is used as the basis for SPR-based sensing. The reflectivity of the multilayer structure can be determined by using the Fresnel matrix formulation as given by [87]
Fig. 6. Schematic diagram of the basic principle of SPR/LSPR [(a) Surface plasmon resonance, and (b) Localized surface plasmon resonance].
As can be seen from the eqs. above, the resonance occurs in case of a specific angle, at which the electromagnetic wave is incident on the waveguide-metal bound. This angle is called resonance angle (${\theta _R})$, that is the angle where the reflectivity value R is minimum. Accurate determination of the resonance condition is quite important for any SPR biosensing system.
Biosensors integrated with smartphones based on the SPR/LSPR principle are also interrogated using smartphone-based optical spectrometers, which often include an external widget to enable spectral measurements [38]. Several commercially available patented devices have emerged allowing to turn the smartphone into a spectrometer [93]. The implementation of the spectrometer requires wavelength calibration as well as intensity calibration of the spectra. Calibration of the wavelength (λ-axis) sets individual pixels’ positions on the photoreceptor chip as well as on the display to their corresponding wavelength value along the diffraction direction on the produced image. The calibration process takes into account the linear relationship between wavelength and pixel separation [94]. Some spectral calibration techniques use the relation of the spectral image hue with light wavelength [95,96]. Since the images generated by most smartphone cameras do not directly represent the actual response of the ASL detector, we had to calibrate the intensity when discussing the light intensity of different wavelengths of light. One method involved comparing the readings of a spectrometer in a smartphone with a spectrometer used in marine optics. This was done to adjust the filters and account for any other optical components that may impact the light [97]. Once these two calibrations were completed, we were able to obtain a portable, compact spectrometer. For example, Bremer et al. [55] & Liu et al. [89] developed a novel optical fiber SPR sensor system for smartphones using a straightforward silver coating technique. Dutta et al. proposed a smartphone-based LSPR sensing platform for bioconjugate detection and quantification [98].
The optical fiber sensing technologies mentioned above have different application scenarios, which we will describe in detail in the Applications section. Researchers have found that imaging is more difficult when the ambient conditions can change without any control. To solve this problem, external components such as light diffusers, dark boxes, white boxes, and disposable analysis boxes, as well as various integration methods have been proposed.
4. Integration technology
The previous parts discussed hardware and optical fiber sensing technologies for integrating optical fiber with a smartphone. But their integration into a complete optical system for better experimental results is also a technical challenge. This part reviews and comments on several aspects of existing integration technologies. The bracket physically integrates the optical fiber with the smartphone, the multiplexing path describes an optical multiplexing technology to increase the sensitivity or efficiency of the sensor, and the data processing describes how the optical signals captured in the recorded video have been processed in most studies.
4.1 Brackets
The bracket used for integrating optical fiber with smartphones is generally obtained through 3D printing and it needs to perfectly match the relevant dimensions of the smartphone. The simplest type of bracket is shown in Fig. 7(a). It is mainly because the designers used the smartphone's flash to act as an LED light source, which greatly simplifies the device. When this type of bracket is used, the optical fiber tends to be polymer optical fiber, as it is more flexible without protection [99]. The other type of bracket shown in Fig. 7(b) is more similar to a concealed box into which a smartphone can be tucked. As it has enough space inside, a small laser can be integrated into it [50]. This way the silica optical fiber or the plastic optical fiber can be connected to it and start working. Some additional optical elements such as gratings can also be integrated into this type of brackets, which can meet the need to transform a smartphone into a spectrometer. For example, Fig. 7(c) shows a bracket system integrating optical components such as gratings, lenses, and filters, which has been utilized in a study to achieve the integration of quantitative diffuse reflectance spectroscopy and high-resolution fluorescence imaging into a single smartphone platform [100].
Fig. 7. Typical bracket designs [(a) Connector type, reproduced from Ref. [34] with permission from IEEE (2024), (b) Concealed box type, reproduced from Ref. [50] with permission from Elsevier (2019), and (c) Bracket with integrated optical components and a physical view of it when connected to a smartphone and optical fiber, reproduced from Ref. [100]; Creative Commons BY 4.0; published by Nature Publishing Group (2019)].
4.2 Multiplex pathway
Optical fiber sensors have the advantage of being multiplexable, which is also useful when integrated with smartphones. When the system is operated according to spatial division multiplexing concept, each optical fiber delivers power to a different location on the camera matrix, which allows the images to be separated and processed separately [101]. The first of such techniques is the use of multiple pathways to measure different signals for multifunctional sensing [102,56]. Chen et al. utilized a centroid Voronoi tessellation-based approach for image segmentation and processing of multichannel data to improve the efficiency of the implementation [51]. The other is to utilize multiple pathways to obtain the same signal to improve single-function sensing performance. For example, the proposed sensor by Ilbeygi et al. consists of reference, measurement, and control optical fibers, and the use of control and reference optical fibers reduces some parasitic fluctuations, highly sensitive to the environment [59].
4.3 Data processing
In traditional optical fiber sensing systems, the experimental data usually needs to be transmitted back to a specific demodulator to be acquired, and then it was processed by a program to get the results. These cumbersome steps prevent a portion of the research from being translated into scientific and technological results that can truly serve society. Smartphones have a certain amount of arithmetic power of their own and are being constantly enhanced by researchers in the field of smart computing. In the research areas we reviewed, many researchers have implemented real-time demodulation of experimental results on smartphones through customized apps, although this still requires them to be connected to a web server [32,50,52,54,56,58,89]. The rapid development of smartphones leads us to believe that it is only a matter of time before the realization of offline processing of data entirely by smartphones is realized.
5. Applications for optical fiber biosensors integrated with smartphones
Portable optical fiber biosensors integrated with smartphones allow for simplified use of sensing technology with a wide range of applications. Among the possible areas of optical fiber sensing, this integrated sensing is more oriented towards real-time monitoring as well as portable monitoring applications. In other words, it is rare to see the application of a smartphone integrated with optical fiber sensing in the field of engineering, while it is widely used in chemical quantity detection, physiological monitoring, and so on.
5.1 Chemical biosensing
Chemical biosensors are miniaturized analytical devices that can deliver real-time and online information on the presence of specific compounds or ions in complex samples. A smartphone integrated with optical fibers showed good performance in chemical biosensing such as refractive index (RI), pH, H2S, Hg2+, glucose, etc. Table 3 lists the research in chemical biosensing in recent years using optical fiber biosensors integrated with smartphones. Due to the abundant nature of the chemicals, SPR techniques are widely used in these studies. This part will detail the results obtained from existing studies.
Table 3. Optical fiber biosensors integrated with smartphones for chemical biosensing
The refractive index is a simple chemical quantity that can be adjusted in many ways and is often used by researchers to validate the sensitivity of a proposed device (Fig. 8). Bremer et al. [55] & Liu et al. [89] developed an optical fiber SPR sensor system for smartphones using a straightforward silver coating technique to detect RI. They leveraged the inherent color selectivity of phone cameras to simultaneously monitor red and green color channels, reducing the likelihood of false detection and enhancing sensitivity. Liu et al. proposed an optical fiber SPR biosensor based on a smartphone platform to achieve highly sensitive sensing of refractive index (RI) changes [91]. The SPR sensing element was made of a light-conducting silica capillary whose cladding was stripped and coated with a gold film. The researchers also evaluated the performance of the device in terms of accurate and reproducible measurements by detecting different concentrations of antibodies bound to the functionalized sensing element.
Fig. 8. Application of chemical biosensors for refractive index measurement [(a) Design of optical fiber SPR sensing system developed with silver coating technology, reproduced from Ref. [55]; Creative Commons BY 4.0; published by Optica Publishing Group (2015): (1)Sensing system diagram, and (2) Measuring the refractive index of liquids using sensors; (b) Design of a three-optical fiber SPR sensing system developed with gold film coating technology, reproduced from Ref. [91]; Creative Commons BY 4.0; published by Nature Publishing Group (2015): (1) Schematic of the smartphone-based SPR sensor, (2)-(3) Schematic diagram of SPR sensor integrated with a smartphone, and (4) Measurement channel, control channel and reference channel images captured by the camera].
Dutta et al. reported their work on a compact and handheld smartphone-based pH sensor for monitoring ground and river water quality [103]. The sensor measures variations in the optical absorption band of pH-sensitive dye samples in different pH solutions. Sultangazin et al. present a smartphone-based low-cost chemical plastic optical fiber (POF) sensor for hydrogen sulfide (H2S) detection [54]. The sensor is functionalized to remote H2S detection through silver deposition on the POF outer surface. As illustrated in Fig. 9(c), To detect Hg2+ in remote and on-site mode, Liu et al. designed a smartphone-based optical fiber fluorescence sensor (SOFFS) [50], which is composed of a semiconductor laser for fluorescence signal excitation, a quantum dot-modified fiber probe for Hg2+ sensing, a smartphone with a filter for fluorescence signal collection, and a fiber coupler for connecting fiber probe, laser, and smartphone. A handheld dual-channel optical fiber fluorescence sensor based on a smartphone was likewise produced by them [104].
Fig. 9. Application of chemical biosensors for others [(a) Design for measuring glucose levels using optical fiber integrated with a smartphone, reproduced from Ref. [49] with permission from Elsevier (2019), (b) Design of PBA-based hydrogel sensor and embedded photonic nanostructures developed for continuous glucose monitoring, reproduced from Ref. [83]; Creative Commons BY 4.0; published by Elsevier (2023), and (c) Design of smartphone-based SOFFS for Hg2+ detection, reproduced from Ref. [50] with permission from Elsevier (2019)].
Continuous glucose monitoring enables tight blood glucose control in diabetics and ICU patients, making it a critical tool in the fight against diabetes, a leading cause of death worldwide. Elsherif et al. developed a method of measuring glucose levels using smartphone-integrated optical fiber [49]. Their functionalized fiber showed high sensitivity, rapid response, and high glucose selectivity as shown in Fig. 9(a). They also developed a biocompatible hydrogel fiber that can replace the silica optical fiber. Ahmed et al. developed low-cost, rapid, and reusable sensors for continuous glucose monitoring using Phenylboronic acid (PBA)-based hydrogel sensors with embedded photonic nanostructures, which is shown in Fig. 9(b) [83]. The optical power was measured using a smartphone, and the power changes were correlated with glucose concentrations. These sensors can be used in remote, continuous, and real-time glucose monitoring systems.
5.2 Physiological biosensing
Physiological sign monitoring plays a crucial role in the determination and prevention of many diseases. Currently, electronic sensors are the main solution for performing physiological monitoring, however electrical safety issues, electromagnetic interference problems, and narrow linear time response intervals are some of their major drawbacks. Several studies have been conducted in the past to propose and confirm the advantages of fiber optic sensors for physiological monitoring, but bulky interrogators have created obstacles for sensing systems to be put to use. Thankfully, with the development of smartphones, smartphone monitoring systems based on integrated optical fiber sensing seem to be able to solve the above problems well. As listed in Tab. 4, Research has already been done to achieve high-precision monitoring and analysis of physiological signals such as respiration, heartbeat, and pulse.
Table 4. Optical fiber biosensors integrated with smartphones for physiological biosensing
Heart rate (HR) and breathing rate (BR) are important in diagnosing health conditions and abnormalities like bradycardia, tachycardia, hypoxemia, and tachypnea. Aitkulov et al. proposed a sensor based on the integration of a smartphone with a plastic optical fiber (POF) [57]. The smartphone flashlight is used as the source and the camera is a photodetector, with a 3D-printed connector for light coupling. This sensor is a practical alternative to standard devices due to the widespread use of smartphones and the affordability of POF. Kuang et al. reported the integration of low-cost plastic optical fibers in smartphones for the measurement of respiratory rate and heart rate in human physiological monitoring as shown in Fig. 10(a) [24]. The system monitored heart rate and breathing rate during different postures, indicating that the POF sensor can evaluate dynamic posture. Aitkulov et al. designed and prototyped a smartphone multiplexed sensor for analyzing breathing rates using all-fiber technology [56]. This work implements the camera segmentation multiplexing concept in a three-fiber system and provides new ideas for subsequent system design.
Fig. 10. Physiological information (HR, RR, and gait) monitoring in optical fiber integrated with smartphone [(a) Designs of HR and RR sensors based on POF integration with smartphones, reproduced from Ref. [24] with permission from Elsevier (2022); (b) Designs of a portable optical fiber sensor for gait monitoring, reproduced from Ref. [51] with permission from IEEE (2023)].
Gait monitoring is crucial for monitoring physiological health and is linked to various chronic diseases. It involves detecting plantar pressure, step count, walking speed, and stride length. Kamizi et al. reported a portable optical fiber sensor that can monitor human gait in detail [105]. It uses a specially designed optical fiber sensor in insoles, and sensor interrogation is performed using a smartphone's CCD camera. The device provides information on gait parameters and ground reaction forces. Chen et al. showed a portable optical sensor based on a smartphone and plastic optical fiber (POF) for monitoring human gait as shown in Fig. 10(b) [51]. The sensor uses image segmentation to monitor multiple sensors. The sensor has a deviation of around 3.4% in the estimated steps compared to real values. Due to its affordability, non-invasiveness, and portability, this innovative sensor can find a wide range of applications such as motion feedback, geriatric health monitoring, etc.
Pulse wave signals can provide accurate and reliable estimates for arterial stiffness measurements and are therefore widely used to diagnose cardiovascular disease, diabetes, and other health problems. Compared to physiological features such as breathing, heartbeat, and gait, pulse signals are much weaker. Achieving high-quality acquisition of pulse signals using a smartphone with integrated fiber optic sensing is a challenge. Recently, Markvart et al. presented a pulse wave optical fiber sensor (Fig. 11) that can be easily accessed through a smartphone in their study [52]. The performance of the sensor was evaluated in terms of signal-to-noise ratio, repeatability of demodulated signal, and the capability of demodulated signals to extract information about direct and reflected waves. So far, smartphones with integrated optical fiber sensing have been experimentally proven to be feasible in most physiological signal monitoring. If the existing research results can be further integrated and developed, more portable and low-cost physiological monitoring devices will soon be realized.
Fig. 11. Physiological information (pulse signals) monitoring in optical fiber integrated with the smartphone, reproduced from Ref. [52]; Creative Commons BY 4.0; published by MDPI (2023) [(a-b) Schematic drawing and a photo of a smartphone-based pulse wave sensing system, including a smartphone, a fiber-optic beam splitter, and a transducer].
6. Discussion and conclusion
The majority of embedded systems use the smartphone camera as a detector system for colorimetry, reflectance, and luminescence. Any change in the light signal transmitted through the optical fiber can be coupled to the smartphone's optical sensor and the corresponding change in light intensity or wavelength can be recorded. Many laboratory studies have demonstrated the feasibility of integrating optical fibers with smartphones for portable sensing of physical and chemical quantities as well as physiological monitoring.
However, to meet the challenge of achieving reliable and accurate high-resolution real-time measurements in a variety of environments, the ideal portable optical fiber biosensors would need to address several technical aspects. First, in the realm of biosensing, the diverse designs of sensing regions have been demonstrated to exert a notable impact on the overall performance of optical fiber sensors. The nuanced intricacies of these designs play a pivotal role in determining the efficacy of biosensing applications. Simultaneously, the performance of the Ambient Light Sensor within smartphones, along with the effectiveness of the integration method, wields a considerable influence on the quality of the collected data. The intricate interplay between these factors underscores the multidimensional nature of the challenges faced in optimizing the performance of optical fiber sensors when seamlessly integrated with smartphones.
The differences in camera parameters among different smartphones may introduce variations during data processing. In-depth research and consideration of these parameters, including but not limited to focal length, aperture size, and sensor sensitivity, are crucial to understanding and mitigating potential discrepancies in measurement results across practical applications. Meanwhile, on-site calibration is pivotal to ensuring consistent and precise measurements across different devices. Due to variations in environmental conditions, lighting, and device characteristics, effective on-site calibration methods are necessary to guarantee the reliability of data from optical fiber sensors in diverse scenarios. This may involve the development of adaptive algorithms for real-time adjustment and correction of sensor outputs. High-resolution real-time measurements impose higher demands on data processing and transmission speeds. To ensure timeliness and accuracy, optimization of data processing algorithms and improvement of communication efficiency between the sensor and smartphone are essential. This might involve the adoption of advanced data compression and transmission protocols to meet the requirements of handling large volumes of data in real-time applications.
One of the current challenges, hindering widespread practical applications of smartphone-based optical fiber sensing systems is the broad range of smartphone back panel geometries. This leads to the need of designing adapters, responsible for correctly placing optical fibers in front of the smartphone camera and flash light, customized for each smartphone model. Therefore, the development of universal adapters, suitable for various smartphone models and still offering mechanical stability of optical fibers placement is of great importance. On the other hand, since not all smartphone models have the same camera and flash light properties, such universality may actually lead to a detrimental effect on the sensing system reproducibility. Therefore, this problem must be considered from both technical and metrological points of view. Nevertheless, smartphone-based optical fiber sensing systems can act as a technological platform for development of low-cost portable sensors, using the same physical principles, but developed in a unified manner, thus allowing their standardization, certification and following acceptance in practice as point-of-care biosensors, personal health monitoring devices and in various other fields.
The portability of optical fiber biosensors integrated with smartphones makes them ideal for on-site inspection and real-time monitoring. For this reason, its possible disadvantages in terms of sensitivity as well as signal-to-noise ratio are acceptable. By continuously improving the design of the sensing devices as well as the configuration of smartphones, they are expected to be continuously improved in subsequent studies. At the same time, applications that can be installed on a smartphone and enable data collection, processing, and analysis should be developed, which can provide more help for the portability of the device. There are many fields, including chemical biosensing, as well as physiological biosensing, that require on-site testing or long-term monitoring. We believe that optical fiber biosensors integrated with smartphones have a lot of room for development and real-world application scenarios in the future.
Funding
National Key Research and Development Program of China (2022YFE0140400); National Natural Science Foundation of China (62003046, 62111530238); Basic and Applied Basic Research Foundation of Guangdong Province (2021A1515011997); Special project in key field of Guangdong Provincial Department of Education (2021ZDZX1050); The Innovation Team Project of Guangdong Provincial Department of Education (2021KCXTD014).
Disclosures
The authors declare no conflicts of interest.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
1. M. Zhang, X. Cui, and N. Li, “Smartphone-based mobile biosensors for the point-of-care testing of human metabolites,” Mater. Today Bio 14, 100254 (2022). [CrossRef]
2. B. Zha, Z. Wang, L. Li, et al., “Wearable cardiorespiratory monitoring with stretchable elastomer optical fiber,” Biomed. Opt. Express 14(5), 2260 (2023). [CrossRef]
3. W. H. Organization, Progress Report on the United Nations Decade of Healthy Ageing, 2021-2023: Executive Summary (World Health Organization, 2023).
4. M. Li, R. Singh, Y. Wang, et al., “Advances in Novel Nanomaterial-Based Optical Fiber Biosensors—A Review,” Biosensors 12(10), 843 (2022). [CrossRef]
5. M. Elsherif, A. E. Salih, M. G. Muñoz, et al., “Optical Fiber Sensors: Working Principle, Applications, and Limitations,” Adv. Photonics Res. 3(11), 2100371 (2022). [CrossRef]
6. A. C. Peacock, U. J. Gibson, and J. Ballato, “Silicon optical fibres – past, present, and future,” Adv. Phys. X 1(1), 114–127 (2016). [CrossRef]
7. G. Kostovski, P. R. Stoddart, and A. Mitchell, “The Optical Fiber Tip: An Inherently Light-Coupled Microscopic Platform for Micro- and Nanotechnologies,” Adv. Mater. 26(23), 3798–3820 (2014). [CrossRef]
8. J. S. Paiva, P. A. S. Jorge, C. C. Rosa, et al., “Optical fiber tips for biological applications: From light confinement, biosensing to bioparticles manipulation,” Biochim. Biophys. Acta, Gen. Subj. 1862(5), 1209–1246 (2018). [CrossRef]
9. P. Vaiano, B. Carotenuto, M. Pisco, et al., “Lab on Fiber Technology for biological sensing applications,” Laser Photonics Rev. 10(6), 922–961 (2016). [CrossRef]
10. T. Woo-Hu, L. Yu-Cheng, T. Jiu-Kai, et al., “Multi-step structure of side-polished fiber sensor to enhance SPR effect,” Opt. Laser Technol. 42(3), 453–456 (2010). [CrossRef]
11. Y. Xu, M. Xiong, and H. Yan, “A portable optical fiber biosensor for the detection of zearalenone based on the localized surface plasmon resonance,” Sens. Actuators, B 336, 129752 (2021). [CrossRef]
12. A. A. Rifat, R. Ahmed, G. A. Mahdiraji, et al., “Highly Sensitive D-Shaped Photonic Crystal Fiber-Based Plasmonic Biosensor in Visible to Near-IR,” IEEE Sens. J. 17(9), 2776–2783 (2017). [CrossRef]
13. K. Tong, F. Wang, M. Wang, et al., “D-Shaped photonic crystal fiber biosensor based on silver-graphene,” Optik 168, 467–474 (2018). [CrossRef]
14. S. Liu, R. Ma, Y. Li, et al., “D-shaped surface plasmon resonance biosensor based on MoS2 in terahertz band,” Opt. Fiber Technol. 66, 102631 (2021). [CrossRef]
15. A. Kumar, P. Verma, and P. Jindal, “Surface plasmon resonance biosensor based on a D-shaped photonic crystal fiber using Ti3C2Tx MXene material,” Opt. Mater. 128, 112397 (2022). [CrossRef]
16. M. S. Soares, L. C. B. Silva, M. Vidal, et al., “Label-free plasmonic immunosensor for cortisol detection in a D-shaped optical fiber,” Biomed. Opt. Express 13(6), 3259–3274 (2022). [CrossRef]
17. A. D. S. Arcas, F. D. S. Dutra, R. C. S. B. Allil, et al., “Surface plasmon resonance and bending loss-based u-shaped plastic optical fiber biosensors,” Sensors 18(2), 648 (2018). [CrossRef]
18. H.-Y. Wen, C.-W. Huang, Y.-L. Li, et al., “A Lamping U-Shaped Fiber Biosensor Detector for MicroRNA,” Sensors 20(5), 1509 (2020). [CrossRef]
19. A. J. Y. Tan, S. M. Ng, P. R. Stoddart, et al., “Trends and applications of U-shaped fiber optic sensors: a review,” IEEE Sens. J. 21(1), 120–131 (2021). [CrossRef]
20. C. Teng, M. Li, Y. Cheng, et al., “Investigation of U-shape tapered plastic optical fibers based surface plasmon resonance sensor for RI sensing,” Optik 251, 168461 (2022). [CrossRef]
21. S. Kumar, B. K. Kaushik, R. Singh, et al., “LSPR-based cholesterol biosensor using a tapered optical fiber structure,” Biomed. Opt. Express 10(5), 2150–2160 (2019). [CrossRef]
22. G. Zhu, N. Agrawal, R. Singh, et al., “A novel periodically tapered structure-based gold nanoparticles and graphene oxide – Immobilized optical fiber sensor to detect ascorbic acid,” Opt. Laser Technol. 127, 106156 (2020). [CrossRef]
23. M. Li, R. Singh, M. S. Soares, et al., “Convex fiber-tapered seven core fiber-convex fiber (CTC) structure-based biosensor for creatinine detection in aquaculture,” Opt. Express 30(8), 13898–13914 (2022). [CrossRef]
24. R. Kuang, Y. Ye, Z. Chen, et al., “Low-cost plastic optical fiber integrated with smartphone for human physiological monitoring,” Opt. Fiber Technol. 71, 102947 (2022). [CrossRef]
25. C. Zhao, Y. Ye, Z. Wang, et al., “Notch POF integrated with smartphone for liquid level and refractive index monitoring,” Opt. Laser Technol. 167, 109751 (2023). [CrossRef]
26. S. Kumar, R. Singh, G. Zhu, et al., “Development of uric acid biosensor using gold nanoparticles and graphene oxide functionalized micro-ball fiber sensor probe,” IEEE Trans. Nanobioscience 19(2), 173–182 (2020). [CrossRef]
27. A. Bekmurzayeva, Z. Ashikbayeva, Z. Myrkhiyeva, et al., “Label-free fiber-optic spherical tip biosensor to enable picomolar-level detection of CD44 protein,” Sci. Rep. 11(1), 19583 (2021). [CrossRef]
28. C. Leitão, S. O. Pereira, C. Marques, et al., “Cost-Effective Fiber Optic Solutions for Biosensing,” Biosensors 12(8), 575 (2022). [CrossRef]
29. “Smartphone users by country 2022,” https://www.statista.com/statistics/748053/worldwide-top-countries-smartphone-users/. access 2023/10/20
30. K. E. McCracken and J.-Y. Yoon, “Recent approaches for optical smartphone sensing in resource-limited settings: a brief review,” Anal. Methods 8(36), 6591–6601 (2016). [CrossRef]
31. J. C. Contreras-Naranjo, Q. Wei, and A. Ozcan, “Mobile phone-based microscopy, sensing, and diagnostics,” IEEE J. Sel. Top. Quantum Electron. 22(3), 1–14 (2016). [CrossRef]
32. Y. Cheng, H. Wang, Y. Zhuo, et al., “Reusable smartphone-facilitated mobile fluorescence biosensor for rapid and sensitive on-site quantitative detection of trace pollutants,” Biosens. Bioelectron. 199, 113863 (2022). [CrossRef]
33. H. Ding, C. Chen, S. Qi, et al., “Smartphone-based spectrometer with high spectral accuracy for mHealth application,” Sens. Actuators, A 274, 94–100 (2018). [CrossRef]
34. H. Zhang, Z. Wang, C. Teng, et al., “Wearable cardiorespiratory sensor for real-time monitoring with smartphone integration,” IEEE Trans. Instrum. Meas. 73, 1–14 (2024). [CrossRef]
35. J. D. Malone, I. Hussain, and A. K. Bowden, “SmartOCT: smartphone-integrated optical coherence tomography,” Biomed. Opt. Express 14(7), 3138–3151 (2023). [CrossRef]
36. G. P. Singh and N. Sardana, “Smartphone-based surface plasmon resonance sensors: a review,” Plasmonics 17(5), 1869–1888 (2022). [CrossRef]
37. A. Roda, E. Michelini, M. Zangheri, et al., “Smartphone-based biosensors: A critical review and perspectives,” TrAC, Trends Anal. Chem. 79, 317–325 (2016). [CrossRef]
38. I. Hussain and A. K. Bowden, “Smartphone-based optical spectroscopic platforms for biomedical applications: a review [Invited],” Biomed. Opt. Express 12(4), 1974–1998 (2021). [CrossRef]
39. S. Dutta, “Point of care sensing and biosensing using ambient light sensor of smartphone: critical review,” TrAC, Trends Anal. Chem. 110, 393–400 (2019). [CrossRef]
40. S. Dutta and D. Paul, “A review on design and development of smartphone-integrated optical fiber sensors,” Fiber Integr. Opt. 42(5), 162–184 (2023). [CrossRef]
41. B. Gayral, “LEDs for lighting: Basic physics and prospects for energy savings,” Comptes Rendus Phys. 18(7-8), 453–461 (2017). [CrossRef]
42. J. Zubia and J. Arrue, “Plastic optical fibers: an introduction to their technological processes and applications,” Opt. Fiber Technol. 7(2), 101–140 (2001). [CrossRef]
43. Y. Koike and M. Asai, “The future of plastic optical fiber,” NPG Asia Mater. 1(1), 22–28 (2009). [CrossRef]
44. J. Ballato, H. Ebendorff-Heidepriem, J. Zhao, et al., “Glass and process development for the next generation of optical fibers: a review,” Fibers 5(1), 11 (2017). [CrossRef]
45. M. Brandt-Pearce and M. Noshad, “Chapter 17 - Optical transmission,” in Academic Press Library in Mobile and Wireless Communications, S. K. Wilson, eds. (Academic Press, 2016), pp. 661–687.
46. M. Salem and G. P. Agrawal, “Effects of coherence and polarization on the coupling of stochastic electromagnetic beams into optical fibers,” J. Opt. Soc. Am. A 26(11), 2452 (2009). [CrossRef]
47. M. Olivero, A. Vallan, R. Orta, et al., “Single-mode–multimode–single-mode optical fiber sensing structure with quasi-two-mode fibers,” IEEE Trans. Instrum. Meas. 67(5), 1223–1229 (2018). [CrossRef]
48. I. Hussain, M. Das, K. U. Ahamad, et al., “Water salinity detection using a smartphone,” Sens. Actuators, B 239, 1042–1050 (2017). [CrossRef]
49. M. Elsherif, M. U. Hassan, A. K. Yetisen, et al., “Hydrogel optical fibers for continuous glucose monitoring,” Biosens. Bioelectron. 137, 25–32 (2019). [CrossRef]
50. T. Liu, W. Wang, D. Jian, et al., “Quantitative remote and on-site Hg2 + detection using the handheld smartphone based optical fiber fluorescence sensor (SOFFS),” Sens. Actuators, B 301, 127168 (2019). [CrossRef]
51. J. Chen, C. Teng, R. Kuang, et al., “Plastic optical fiber integrated with smartphone for gait monitoring,” IEEE Sens. J. 23(16), 18207–18218 (2023). [CrossRef]
52. A. Markvart, A. Petrov, S. Tataurtshikov, et al., “Low-cost fiber-optic sensing system with smartphone interrogation for pulse wave monitoring,” Photonics 10(10), 1074 (2023). [CrossRef]
53. A. K. Yetisen, N. Jiang, A. Fallahi, et al., “Glucose-sensitive hydrogel optical fibers functionalized with phenylboronic acid,” Adv. Mater. 29(15), 1606380 (2017). [CrossRef]
54. A. Sultangazin, J. Kusmangaliyev, A. Aitkulov, et al., “Design of a smartphone plastic optical fiber chemical sensor for hydrogen sulfide detection,” IEEE Sens. J. 17(21), 6935–6940 (2017). [CrossRef]
55. K. Bremer and B. Roth, “Fibre optic surface plasmon resonance sensor system designed for smartphones,” Opt. Express 23(13), 17179–17184 (2015). [CrossRef]
56. A. Aitkulov and D. Tosi, “Design of an All-POF-fiber smartphone multichannel breathing sensor with camera-division multiplexing,” IEEE Sens. Lett. 3(5), 1–4 (2019). [CrossRef]
57. A. Aitkulov and D. Tosi, “Optical fiber sensor based on plastic optical fiber and smartphone for measurement of the breathing rate,” IEEE Sens. J. 19(9), 3282–3287 (2019). [CrossRef]
58. L. Lu, Z. Jiang, Y. Hu, et al., “A portable optical fiber SPR temperature sensor based on a smart-phone,” Opt. Express 27(18), 25420–25427 (2019). [CrossRef]
59. E. Ilbeygi, M. Jahanbakhshian, M. Arshadi Pirlar, et al., “Magnetic field sensor based on plasmonic fiber optics coupled with smartphone,” Opt. Laser Technol. 140, 107065 (2021). [CrossRef]
60. Y. Qian, Y. Zhao, Q. Wu, et al., “Review of salinity measurement technology based on optical fiber sensor,” Sens. Actuators, B 260, 86–105 (2018). [CrossRef]
61. E. Fujiwara, P. L. Machado, T. C. D. Oliveira, et al., “Using the smartphone as an ubiquitous platform for implementing optical fiber sensors,” in 2019 SBFoton International Optics and Photonics Conference (SBFoton IOPC) (2019), pp. 1–5.
62. O. S. Wolfbeis, “Fiber-optic chemical sensors and biosensors,” Anal. Chem. 80(12), 4269–4283 (2008). [CrossRef]
63. X. Wang and O. S. Wolfbeis, “Fiber-optic chemical sensors and biosensors (2015–2019),” Anal. Chem. 92(1), 397–430 (2020). [CrossRef]
64. M. Sanjay, N. K. Singh, L. Ngashangva, et al., “A smartphone-based fiber-optic aptasensor for label-free detection of Plasmodium falciparum glutamate dehydrogenase,” Anal. Methods 12(10), 1333–1341 (2020). [CrossRef]
65. “An overview of macrobending and microbending of optical fibers,” https://www.corning.com/media/worldwide/coc/documents/Fiber/white-paper/WP1212.pdf. access: 2023/12/20
66. J. W. Berthold, “Historical review of microbend fiber-optic sensors,” J. Lightwave Technol. 13(7), 1193–1199 (1995). [CrossRef]
67. R. He, C. Teng, S. Kumar, et al., “Polymer optical fiber liquid level sensor: a review,” IEEE Sens. J. 22(2), 1081–1091 (2022). [CrossRef]
68. S. Y. Lim, W. Shen, and Z. Gao, “Carbon quantum dots and their applications,” Chem. Soc. Rev. 44(1), 362–381 (2015). [CrossRef]
69. S. Yu, L. Ding, H. Lin, et al., “A novel optical fiber glucose biosensor based on carbon quantum dots-glucose oxidase/cellulose acetate complex sensitive film,” Biosens. Bioelectron. 146, 111760 (2019). [CrossRef]
70. Q. Jiang, Y. jing, Y. Ni, et al., “Potentiality of carbon quantum dots derived from chitin as a fluorescent sensor for detection of ClO−,” Microchem. J. 157, 105111 (2020). [CrossRef]
71. W. Wu, J. Huang, L. Ding, et al., “A real-time and highly sensitive fiber optic biosensor based on the carbon quantum dots for nitric oxide detection,” J. Photochem. Photobiol. Chem. 405, 112963 (2021). [CrossRef]
72. Y. Zhao, J. Chen, Z. Hu, et al., “All-solid-state SARS-CoV-2 protein biosensor employing colloidal quantum dots-modified electrode,” Biosens. Bioelectron. 202, 113974 (2022). [CrossRef]
73. Y. Zhang, L. Ding, H. Zhang, et al., “A new optical fiber biosensor for acetylcholine detection based on pH sensitive fluorescent carbon quantum dots,” Sens. Actuators, B 369, 132268 (2022). [CrossRef]
74. Q. Zhang, X. Zhang, F. Ma, et al., “Advances in quantum dot-based biosensors for DNA-modifying enzymes assay,” Coord. Chem. Rev. 469, 214674 (2022). [CrossRef]
75. T. Pan, W. Cao, and M. Wang, “TiO2 thin film temperature sensor monitored by smartphone,” Opt. Fiber Technol. 45, 359–362 (2018). [CrossRef]
76. S. Kumar, R. Singh, B. K. Kaushik, et al., “LSPR-based cholesterol biosensor using hollow core fiber structure,” IEEE Sens. J. 19(17), 7399–7406 (2019). [CrossRef]
77. N. Agrawal, C. Saha, C. Kumar, et al., “Development of uric acid sensor using copper oxide and silver nanoparticles immobilized SMSMS fiber structure-based probe,” IEEE Trans. Instrum. Meas. 69(11), 9097–9104 (2020). [CrossRef]
78. Z. Luo, Y. Xu, L. He, et al., “Development of a rapid and ultra-sensitive cytosensor: Ω-shaped fiber optic LSPR integrated with suitable AuNPs coverage,” Sens. Actuators, B 336, 129706 (2021). [CrossRef]
79. S. Kumar, Z. Guo, R. Singh, et al., “MoS 2 functionalized multicore fiber probes for selective detection of Shigella bacteria based on localized plasmon,” J. Lightwave Technol. 39(12), 4069–4081 (2021). [CrossRef]
80. L. Li, Y. Zhang, W. Zheng, et al., “Optical fiber SPR biosensor based on gold nanoparticle amplification for DNA hybridization detection,” Talanta 247, 123599 (2022). [CrossRef]
81. H.-M. Kim, H.-J. Kim, J.-H. Park, et al., “High-performance biosensor using a sandwich assay via antibody-conjugated gold nanoparticles and fiber-optic localized surface plasmon resonance,” Anal. Chim. Acta 1213, 339960 (2022). [CrossRef]
82. J. Zhang, T. Liang, H. Wang, et al., “Ultrasensitive glucose biosensor using micro-nano interface of tilted fiber grating coupled with biofunctionalized au nanoparticles,” IEEE Sens. J. 22(5), 4122–4134 (2022). [CrossRef]
83. I. Ahmed, M. Elsherif, M. Ali, et al., “Photonic hydrogel for continuous glucose monitoring using smartphone readout,” Mater. Des. 231, 112065 (2023). [CrossRef]
84. J. A. Sans, J. Gea-Pinal, M. H. Gimenez, et al., “Determining the efficiency of optical sources using a smartphone’s ambient light sensor,” Eur. J. Phys. 38(2), 025301 (2017). [CrossRef]
85. D. Gallegos, K. D. Long, H. Yu, et al., “Label-free biodetection using a smartphone,” Lab. Chip 13(11), 2124–2132 (2013). [CrossRef]
86. S. Dutta, A. Choudhury, and P. Nath, “Evanescent Wave Coupled Spectroscopic Sensing Using Smartphone,” IEEE Photonics Technol. Lett. 26(6), 568–570 (2014). [CrossRef]
87. C. A. de Souza Filho, A. M. N. Lima, and H. Neff, “Smartphone based, portable optical biosensor utilizing surface plasmon resonance,” in 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings (2014), pp. 890–895.
88. P. Preechaburana, M. C. Gonzalez, A. Suska, et al., “Surface plasmon resonance chemical sensing on cell phones,” Angew. Chem. Int. Ed. 51(46), 11585–11588 (2012). [CrossRef]
89. Q. Liu, Y. Liu, H. Yuan, et al., “A smartphone-based red–green dual color fiber optic surface plasmon resonance sensor,” IEEE Photonics Technol. Lett. 30(10), 927–930 (2018). [CrossRef]
90. Y. Ye, C. Zhao, Z. Wang, et al., “Portable multihole plastic optical fiber sensor for liquid-level and refractive index monitoring,” IEEE Sens. J. 23(3), 2161–2168 (2023). [CrossRef]
91. Y. Liu, Q. Liu, S. Chen, et al., “Surface plasmon resonance biosensor based on smart phone platforms,” Sci. Rep. 5(1), 12864 (2015). [CrossRef]
92. B. Liedberg, C. Nylander, and I. Lunström, “Surface plasmon resonance for gas detection and biosensing,” Sens. Actuators 4, 299–304 (1983). [CrossRef]
93. “Portable Spectrometer Device,” https://www.photonics.com/Products/Portable_Spectrometer_Device/pr64317. access 2023/10/20
94. A. Jamalipour and M. A. Hossain, “Smartphone optical fiber spectrometers,” in Smartphone Instrumentations for Public Health Safety, Wireless Networks (Springer International Publishing, 2019), pp. 67–87.
95. H. J. S. de Oliveira, P. L. de Almeida, B. A. Sampaio, et al., “A handheld smartphone-controlled spectrophotometer based on hue to wavelength conversion for molecular absorption and emission measurements,” Sens. Actuators, B 238, 1084–1091 (2017). [CrossRef]
96. A. Markvart, L. Liokumovich, I. Medvedev, et al., “Continuous hue-based self-calibration of a smartphone spectrometer applied to optical fiber Fabry-Perot sensor interrogation,” Sensors 20(21), 6304 (2020). [CrossRef]
97. T. Stephan, “Portable spectroscopy using a gospectro device with a smartphone,” J. Gemmol. 37(7), 683–685 (2021). [CrossRef]
98. S. Dutta, K. Saikia, and P. Nath, “Smartphone based LSPR sensing platform for bio-conjugation detection and quantification,” RSC Adv. 6(26), 21871–21880 (2016). [CrossRef]
99. L. A. Ngiejungbwen, H. Hamdaoui, and M.-Y. Chen, “Polymer optical fiber and fiber Bragg grating sensors for biomedical engineering Applications: A comprehensive review,” Opt. Laser Technol. 170, 110187 (2024). [CrossRef]
100. X. Hong, T. Lu, L. Fruzyna, et al., “A dual-modality smartphone microendoscope for quantifying the physiological and morphological properties of epithelial tissues,” Sci. Rep. 9(1), 15713 (2019). [CrossRef]
101. I. Gasulla, D. Barrera, J. Hervás, et al., “Spatial division multiplexed microwave signal processing by selective grating inscription in homogeneous multicore fibers,” Sci. Rep. 7(1), 41727 (2017). [CrossRef]
102. Q. Du, M. Gunzburger, L. Ju, et al., “Centroidal voronoi tessellation algorithms for image compression, segmentation, and multichannel restoration,” J. Math. Imaging Vis. 24(2), 177–194 (2006). [CrossRef]
103. S. Dutta, D. Sarma, and P. Nath, “Ground and river water quality monitoring using a smartphone-based pH sensor,” AIP Adv. 5(5), 057151 (2015). [CrossRef]
104. T. Liu, W. Wang, H. Ding, et al., “Development of a handheld dual-channel optical fiber fluorescence sensor based on a smartphone,” Appl. Opt. 59(3), 601 (2020). [CrossRef]
105. M. A. Kamizi, L. H. Negri, J. L. Fabris, et al., “A smartphone based fiber sensor for recognizing walking patterns,” IEEE Sens. J. 19(21), 9782–9789 (2019). [CrossRef]
