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Smartphone-based optical spectroscopic
platforms for biomedical applications: a review
[Invited]
I FTAK H USSAIN
AND
AUDREY K. B OWDEN *
Vanderbilt University, Vanderbilt Biophotonics Center, Department of Biomedical Engineering, 410 24th
Street South, Nashville, TN 37232, USA
* a.bowden@vanderbilt.edu
Abstract: Rapid advancements in smartphone technology have enabled the integration of many
optical detection techniques that leverage the embedded functional components and software
platform of these sophisticated devices. Over the past few years, several research groups have
developed high-resolution smartphone-based optical spectroscopic platforms and demonstrated
their usability in different biomedical applications. Such platforms provide unprecedented
opportunity to develop point-of-care diagnostics systems, especially for resource-constrained
environments. In this review, we discuss the development of smartphone systems for optical
spectroscopy and highlight current challenges and potential solutions to improve the scope for
their future adaptability.
© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
1.
Introduction
Ever since the demonstration of the first functional mobile phone in 1973 by Martin Cooper at
Motorola [1], mobile phones have become a critical mainstay of everyday life. According to the
International Telecommunication Union (ITU), there are more than 7.8 billion active cellular
subscriptions around the globe. The high penetration of mobile phones is largely due to their
affordability and user-oriented design. Mobile phones have great potential to connect people who
are isolated from the mainstream of economic and technological development due to political
and socio-economic challenges. As a result, their widespread availability and affordability in
developing and underdeveloped countries is prompting new initiatives by many governmental or
non-governmental organizations [2].
Early versions of mobile phones were primarily intended for voice communication and messaging applications. The rapid advancement in embedded technology, miniaturized electronics,
and fast computation has accelerated the evolution of mobile phone technology, ushering in the
modern-day smartphone. It is estimated that there were nearly 3.2 billion smartphone users
across the globe in 2019 [3]. The modern smartphone is not merely a communication device:
the enormous processing power, storage capacity and battery life of smartphones allows the integration of different consumer-oriented sensors (e.g., complementary metal-oxide semiconductor
(CMOS) cameras, light emitting diode (LED) flashlights, proximity and ambient light sensors
(ALS), accelerometers, global positioning system (GPS), wi-fi, graphical user interface (GUI))
with user-oriented software tools and smartphone applications (a.k.a., apps). Hence, the modern
smartphone is essentially a portable personal computer and sensing platform that lowers the
economic barriers to rapid development and deployment of scientific tools in traditional and
need-based communities [4–7].
Since the smartphone camera has become a primary selling point of these devices, continuous
efforts have been made to improve its quality over time. The modern camera phone first emerged
after the development of CMOS active pixel sensors in the early 1990s [8]. In 1999, Kyocera,
commercialized the first camera phone (VP-210) with a 0.11-megapixel (MP) front camera
#416753
Journal © 2021
https://doi.org/10.1364/BOE.416753
Received 10 Dec 2020; revised 25 Feb 2021; accepted 4 Mar 2021; published 10 Mar 2021
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[9]. After this, many companies realized the potential of the smartphone’s market penetration,
and the never-ending race to increase pixel count and pixel density began. In 2000, Samsung
released their first phone with a built-in 0.35-MP camera (Samsung SCH-V200). Following
the trend, Sprint released Audiovox PM8920 in the United States with a 1.3-MP camera in
2004. In 2005, Nokia introduced their 2-MP camera phone with Carl Zeiss optics, an LED flash,
and autofocus capabilities. In 2007, Samsung released the first 5-MP camera phone, and this
resolution remained as a high-end standard for many years [10].
Smartphone cameras with higher resolution again appeared in 2010. Since the footprint of the
smartphone is small, the key strategy used to increase the pixel count is to reduce the size of pixels.
Sony released a 12-MP autofocus camera with added facilities such as face detection, geotagging
and smart contrast in 2010. Afterwards, in mid-2013, Nokia announced their smartphone, Lumia
1020, with a 41-MP camera sensor with a 1.2-µm pixel size, embedded with an f/2.4 Carl-Zeiss
all-aspherical one-group lens [11]. Due to the high resolution of the camera, this smartphone
was used to demonstrate the detection of single DNA molecules [12]. The high-pixel-count
smartphone cameras further evolved as, in 2019, Samsung developed and commercialized the
64-MP and 108-MP cameras for smartphones [13]. These densities can be found in many new
smartphones available on the market today: for example, in the Galaxy S21 from Samsung, the
Mi 10I from Xiaomi, and the Edge+ from Motorola. The 108-MP cameras process images
through pixel binning, where four or nine pixels are combined to work as a single pixel. The
use of the larger pixel enables capturing more light, which results in a higher ISO rating and
lower noise. A higher ISO rating enables detection in low-light settings, which is essential for
applications such as fluorescence-based assays [14].
Owing to the low-cost, small foot-print, low-power requirement, and vast adaptability of the
smartphone – key factors for developing affordable and point-of-care disease diagnosis platforms
– different research-based and commercial biomedical devices have been demonstrated. For
example, microscopic imaging is an important tool for the early detection and diagnosis of
many significant diseases, such as malaria. Therefore, efforts have been made by many research
groups to develop smartphone-based microscopy platforms [15–17]. Some systems simply
integrate traditional optical components [18], while others use advanced digital holographic image
processing techniques [19]. Using a simple lens and LED configuration, Zhu et al. demonstrated
the usability of the smartphone as a cost-effective imaging tool for rapid blood analysis [20].
D’Ambrosio et al. demonstrated the first video microscopy platform in a smartphone for the
detection and quantification of blood-borne filarial parasites [21].
Outside of microscopy, other implementations of smartphone-based medical devices include
examples like the CellScope Oto, a commercially available smartphone-based otoscope to
diagnose pediatric ear infections [22]. Another prominent use of the smartphone includes
point-of-care urinalysis platforms. Using a smartphone camera and machine learning approaches,
several companies and research groups have already translated laboratory-based urinalysis
to home-based detection [23,24]. Some clinical applications do not rely on the smartphone
hardware but rather leverage specially designed apps to exploit its computational platform to
process data from external medical devices. KardiaMobile is a portable electrocardiogram
(EKG) monitor that can be connected to a smartphone through wi-fi, and the EKG chart and
other heart-related information can be analyzed in the corresponding phone application [25].
Butterfly iQ is a smartphone USB-powered commercial ultrasound imaging system that brings
affordable and portable ultrasound imaging anywhere [26]. The commercial adaptation of these
smartphone-based biomedical devices opens the door to enabling other lab-confined biomedical
devices to be used as point-of-care applications.
Optical spectroscopy is a key scientific strategy to detect the presence of different biochemical
analytes based on their unique interaction with light. It has been extensively used for non-invasive
disease diagnosis and to detect numerous disease-specific biomarkers in complex sample matrices
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[27]. Of central importance to most spectroscopic methods is a spectrometer – a device that
measures the constituent wavelength components of light that have been reflected from or
transmitted through a sample. Many commercial spectrometers utilize multi-pixel detectors
not altogether different from the camera sensor in modern smartphones. Spectrophotometry,
another branch of spectroscopy that quantifies the concentration of an analyte by measuring
its absorbance at a specific wavelength, utilizes a single photodetector. The embedded ALS,
which is a photodiode with a spectral detection range of 350 nm-1000 nm, is ideal for such
spectrophotometric applications [28]. The native flashlight in a smartphone can be used as a
light source for spectroscopic applications in the visible domain since it is a bright, white LED
with emission wavelengths ranging from 400 nm-700 nm [29]. Besides hardware, the existing
computational power and display capabilities of a modern smartphone are ideal for developing
apps for analysis, interpretation and transmission of spectral data.
Many research groups have introduced smartphone-based spectroscopic platforms for biomedical applications [30–39]. A review by McGonigle et al. discusses some instrumentational aspects
of smartphone-based spectroscopic systems based on their grating configurations [40], but there
is no focused review available on the recent development of systems based on spectroscopic
modalities (absorption, reflectance and fluorescence spectroscopy) that have been specifically
demonstrated for biomedical applications. The current review aims to provide an overview of
the current state-of-the-art in smartphone spectroscopic instrumentation and the development
of smartphone-based spectroscopic modalities for biomedical applications. We begin with an
overview of the embedded components of a standard smartphone that enable spectroscopy. Next,
we describe different modalities of smartphone spectroscopic platforms that have been reported
for biomedical applications. Finally, we discuss the advantages and disadvantages of the current
platforms and present potential opportunities for further exploration of this promising technology.
2.
Enabling embedded components used for developing smartphone-based spectroscopic platforms
Figure 1 provides a graphical overview of the components of a typical smartphone that may be
employed for various aspects of spectroscopic applications. Smartphone-based spectroscopic
platforms primarily aim to leverage the embedded camera as a spectral detector [41]. In addition
to the camera, the ambient light sensor (ALS), which detects the general level of light in the
environment, may be used as a detector in some spectroscopic applications [28], especially
those that require sensitivity to NIR light. The spectral signal detected by the camera or ALS
is typically processed within the phone by using a custom-developed phone application. To
demonstrate a truly self-contained platform, several research groups have deployed the embedded
flashlight as a light source [42]. Alternatively, the existing USB port of the phone can be utilized
to power external LEDs from the smartphone battery [43]. A detailed description of these
enabling functional components is provided below.
2.1.
Camera
The optical design of the embedded camera module may vary from phone to phone. For simplicity,
it can be considered as an assembly of a focusing lens, light filters and a CMOS sensor, as shown
in Fig. 2(a). The camera module is primarily designed and intended for consumer applications
such as photography; therefore, its response is limited to the visible region. Although the sensor
chip – typically fabricated from silicon – has sensitivity in the near infrared to nearly 900 nm, the
phones usually include an infrared (IR) filter to limit the response of the camera to the wavelength
range of 400 nm to 700 nm [44]. In addition, all current-generation smartphones are embedded
with a Bayer image sensor: a pixel-sized array with red, green, and blue filters arranged in a Bayer
pattern. The inset in Fig. 2(a) shows the schematic of the Bayer pattern and the corresponding
process of digital color image formation by the CMOS sensor of the smartphone. Each pixel
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Fig. 1. Typical position of the functional components of a smartphone used for developing
spectroscopic platforms.
records red, green, or blue light; therefore, the data from a single pixel of the image sensor
cannot fully specify a true color value on its own. A full-color image is obtained by using a
demosaicing algorithm, which interpolates a set of complete red, green, and blue values for each
super-pixel (comprising four pixels). These algorithms make use of the surrounding pixels of the
corresponding colors to estimate the values for a particular super-pixel. Each pixel contributes
a single 8-bit, grayscale intensity value (0 to 255 levels). Once reconstructed, images may be
displayed in color on the phone screen or analyzed to extract relevant information. Note that
while traditional benchtop spectroscopic systems use a 1-D photodetector array, which can detect
only one spectrum at a time, the embedded camera in a smartphone is two-dimensional and can,
therefore, be utilized to detect multiple spectra at the same time (e.g., for multiplexed detection
of biomarkers [45]).
2.2.
ALS
The ALS embedded in the front panel of the smartphone is meant to optimize the consumption of
battery power. The ALS controls the brightness of the display panel automatically in accordance
with the surrounding environment. Almost all branded smartphones contain an Avago APDS9930 or ams AG(TAOS) TMD2771 ambient light and proximity sensor chip [46,47]. This
sensor chip has two photodiode channels: CH0 is used for light sensing and CH1 is used for
proximity sensing. As shown in Fig. 2(b), the sensor chip includes on-chip integrating amplifiers,
analog-to-digital converters (ADCs), accumulators, clocks, buffers, comparators, a state machine
and an Inter-Integrated Circuit (I2C) interface. Upon detecting light on either photodiode
channel, the amplified photodiode currents are converted to 16-bit digital values by the ADC
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Fig. 2. General characteristics of the functional components of smartphone used for
developing spectroscopic platforms. (a) Optics and image detection process. (b) Functional
block diagram and wavelength response of the ALS. (c) Emission spectrum of the LED
flashlight. (d) Circuit diagram to use the phone battery as a source through USB.
unit. The converted digital values are then transferred to the CH0 and CH1 data registers of a
microprocessor for further processing. From the microprocessor, the data are sent to the central
smartphone processor through a fast, two-wire I2C serial bus. On Android phones, the ALS data
can be accessed by user-designed smartphone applications using the Android Sensor Manager
module. As shown in the posterior portion of Fig. 2(b), the responsivity for the CH0 photodiode
ranges from 350 nm to 1000 nm, while the CH1 photodiode has a responsivity range covering
450 nm to 1000 nm. The CH0 photodiode has a dynamic range of 0 Lux to 20000 Lux with a
resolution of 0.01 Lux. Due to its high dynamic range and resolution, the ALS can be an excellent
alternative to a laboratory-grade photodetector, which may find usability in many spectroscopic
applications.
2.3.
LED flashlight
The LED flashlight used in the smartphone is a bright white LED with emission wavelengths
ranging from 400 nm to 750 nm. Figure 2(c) shows the emission spectrum of the LED flash
embedded in a typical smartphone. The typical power level of the flashlight is 4.9W and the
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LED pulse width ranges between 20 ms and 200 ms [48]. When combined with external optical
filters, the embedded LED flash can be used as a light source for many sensing applications.
2.4.
Micro-USB port
The micro USB port of the smartphone is typically used to charge the smartphone battery;
however, the charging port can be used to interact with peripheral devices such as flash drives
through the USB On-The-Go (USB-OTG) protocol, a communication specification that provides
access and storage of data on the host device. The USB-OTG cable can also be used to power
external LEDs. Figure 2(d) shows the circuit diagram for connecting an external LED to the
micro USB port of the smartphone. The output current rating of the smartphone micro USB port
at 5 V is 500 mA. A resistor of 250 can be used to limit the current to illuminate an external
LED.
3.
Optical configurations of smartphone-based spectroscopic platforms and
their biomedical applications
The advancements in smartphones have enabled the development of inexpensive, portable, and
self-contained smartphone-based spectroscopic systems. These systems are largely based on
absorption, reflectance or fluorescence spectroscopy. Figure 3 provides an overview of the
instrumentation involved and process flow of a general smartphone-based spectroscopic system.
Initially, light from a source (halogen lamp, phone flashlight, or sunlight) interacts with the
sample based on the respective spectroscopic modality. The sample-modulated light (in reflection
or transmission) is then dispersed either using a transmission or reflection element (typically
a grating or prism) and enters the camera aperture, whereupon it is captured by the CMOS
camera sensor of the phone. The spectrum, in the form of an image, can be visualized on
the display unit of the smartphone. The spectrum is then digitally processed, which includes
converting to the necessary color space and performing pixel-to-wavelength conversion to obtain
the corresponding intensity vs wavelength curve. Generally, the analytes are detected and
estimated from a calibration equation, which is generated from a calibration curve. Some
biosensing applications detect a shift in wavelength, as shown in Fig. 3. Finally, the results are
saved in the phone memory or transmitted to a required location. If necessary, external optical
components (e.g., lens, pinhole, grating) may be enclosed in a custom-designed holder, which can
be fabricated by 3D printing and attached to the smartphone. The design of the holder primarily
depends on the position of the functional components in the smartphone. This requirement poses
significant challenges in developing a universal smartphone sensing system, since the position of
these functional components varies from phone to phone. The optical design and configurations
of these spectroscopic systems are optimized to facilitate integration with the smartphone.
In 2008, Wang et al. demonstrated the first application of smartphones for visible light
spectroscopy by attaching a transmission grating as a wavelength-selective element onto the
lens of the smartphone camera [49]. Smartphone-integrated spectroscopy systems have since
been utilized for vast biomedical applications. In what follows, we discuss the systems that have
been demonstrated and their applications in biomedical science and technology, segmented by
spectroscopic modality.
3.1. Smartphone spectroscopic systems developed based on absorption spectroscopy
All smartphone cameras contain an in-built lens unit to focus light from the object to its sensor;
therefore, the easiest way to develop a smartphone spectrometer is to place the dispersive element
directly in front of the phone camera to capture the wavelength spectrum. Smith et al. used this
configuration to demonstrate the first biomedical application of a smartphone spectrometer [18].
As shown in Fig. 4(a), a light beam interacts with the sample and then propagates through a
plastic holder having two slits of width 1 mm on both sides. The holder was fabricated in such a
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Fig. 3. Overview of possible configurations used in different smartphone-based spectroscopic systems.
way that it makes an angle of 45 degrees with a transmission grating of 1000 lines/mm, which
is essential to record the first-order diffracted wavelength spectrum by the phone camera. The
system was implemented on an iPhone 2G cellphone, which contains a 1600-pixel x 1200-pixel
CMOS sensor with a 2.2-µm pixel size. With this camera, they reported a spectral resolution of
5 nm over a 300-nm bandwidth. In order to demonstrate the potential biomedical applications
of the reported system, the transmission spectrum of 1 cm of human tissue was recorded by
inserting a finger in the path between a 60-W Tungsten bulb and the spectrometer slit. The color
bands in the middle of Fig. 4(a) show the spectrum captured by the phone from the tungsten bulb
and the finger, respectively, and the bottom figure represents the resulting transmission spectrum
generated after data processing.
Subsequently, Long et al. used a similar configuration to perform Enzyme-Linked Immunosorbent Assays (ELISA) at biologically relevant concentrations [50]. ELISA is one of the most
widely used biological assays for quantification of proteins and antibodies for diagnosis of
diseases ranging from cancer to HIV. The antibody-antigen interaction in an ELISA test yields
colorimetric changes to the liquid sample. The absorption of wavelengths generates a dark band
in the captured spectrum, as shown in Fig. 4(b), where the bottom portion demonstrates the
intensity plot vs. wavelength range for different dilution. The system was developed using an
iPhone 4 embedded with a 2592-pixel ⇥ 1936-pixel CMOS image sensor, which achieved a
spectral resolution of 0.334 nm/pixel with a 1200-lines/mm grating. A smartphone spectrometer
in a similar transmission configuration (Fig. 4(c)) was used by Dutta et al. to demonstrate its
usability for the detection of bioconjugation events using localized surface plasmon resonance
(LSPR) as the sensing scheme [51].
Reflection grating-based absorption spectrometers have also been demonstrated for various
biosensing applications. Wang et al. demonstrated a novel standalone smartphone sensing
platform that does not require any external light source, lens or filter [42]. In this work, the
flashlight of the smartphone was used as a light source, and a reflective compact disk (CD)
grating placed at a distance of 50 mm from the phone served as the dispersive element. As
shown in Fig. 5(a), light from the flashlight interacts with the sample solution after passing
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Fig. 4. Smartphone spectroscopic systems developed based on absorption spectroscopy with
a transmission grating. (a) Schematic of smartphone-based spectroscopic system developed
by Smith et al. and spectrum captured for a tungsten bulb and human finger, reproduced
from Ref. [18] with permission of plos.org. (b) Schematic of the system developed by Long
et al. and the spectrums captured by the system, reproduced from Ref. [50] with permission
from Optical Society of America. (c) Schematic and photograph of the system developed
by Dutta et al. and the plot of spectrum captured for different BSA protein concentrations,
reproduced from Ref. [51] with permission from Royal Society of Chemistry.
1981
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through a 1-mm pinhole; the modulated light gets dispersed and reflected by the CD grating,
which is then captured by the phone camera. This system was used to detect glucose utilizing
a well-known bienzymatic cascade assay. Figure 5(a) also shows the spectra captured by the
phone at different times. Since this system does not need an external light source or optics, it
reduces the overall complexity and showcases the potential of smartphone-based spectroscopic
systems to be self-contained, which is highly useful for field testing and home diagnostics.
Similarly, a reflection grating-based configuration was used by Ding et al. for the development of
a spectroscopic system for quantifying creatinine concentration with high spectral accuracy [52].
Fig. 5. Smartphone-based spectroscopic systems based on absorption spectroscopy with a
reflection grating. (a) Schematic of the system developed by Wang et al. using flashlight as
light source and the corresponding spectrum recorded for different glucose concentration,
reproduced from Ref. [42] with permission from Royal Society of Chemistry. (b) Spectroscopy system developed by Jian et al. and recorded monochrome spectrum for different
R6G concentrations, reproduced from Ref. [53] with permission from Elsevier.
One problem with using the flashlight as a light source is that its emission spectrum is not
distributed evenly in the visible wavelength range, as can be seen from Fig. 2(c). Due to this
reason, broadband sources such as halogen lamps are used to provide a more evenly distributed
spectrum in almost all smartphone-based spectroscopic systems. These sources are difficult
to integrate into a hand-held and portable smartphone spectrometer, however, due to their size
and need for an optical fiber cable for light transmission, a driver circuit, and external power.
To mitigate this issue, Jian et al. demonstrated the use of sunlight as the illumination source,
which has a more uniform spectrum in the visible wavelength range than both the smartphone
flashlight and halogen lamps [53]. The spectrometer was designed using a smartphone with a
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monochrome CMOS sensor (4224-pixels ⇥ 3192-pixels, 1.12-µm pixel size), which reduces the
color overlapping that occurs in traditional CMOS sensors with Bayer color filters. A resolution
of 0.276 nm/pixel was demonstrated with this system over a wavelength range of 380 nm to
760 nm. The stability of the developed platform was established using standard Rhodamine 6G
(R6G) diluted with deionized water. Figure 5(b) shows the monochrome spectrum captured
by the system and the intensity variation for different R6G concentrations. The sunlight-based
smartphone spectrometer was further used for detecting avian influenza virus (AIV) H7N9 and
porcine circovirus type 2 (PCV2) antibodies.
Many label-free biosensing methods such as surface plasmon resonance- (SPR), evanescent
wave- (EW), or photonic crystal-based (PC) biosensors are based on the intensity variation
or shift in wavelength of the transmitted light from the transducer due to the adsorption of
biomolecules [54–56]. The shift in wavelength is usually measured by a spectrometer. Gallegos et
al. demonstrated a label-free photonic crystal biosensor, where the smartphone camera was used
as the detector [57]. In this work, the PC structure was designed to behave like a high-efficiency
narrowband reflectance filter (565-nm central wavelength with 5-nm bandwidth), which allows
all wavelengths to transmit through the PC except the resonantly reflected wavelength, as shown
in Fig. 6(a). Upon adsorption of biomolecules on the PC surface, the effective refractive index
of the resonant mode increases, which results a shift in the resonantly reflected wavelength.
The magnitude of this shift is proportional to the optical density of the adsorbed molecule. A
smartphone-based spectroscopic system was developed to detect this shift in wavelength, and
its bio-detection capability was demonstrated by detecting immunoglobulin G (IgG) using an
immobilized layer of Protein A on the PC surface. The optical design is similar to that of a
smartphone-based absorption spectrometer, except the cuvette was replaced by the PC surface in
the optical path. The adjacent sub-figure in Fig. 6(a) shows the schematic and the fabricated
device. The dark band in the captured spectrum represents the wavelength band that is reflected
resonantly, and the plot shows the corresponding transmission spectrum generated by the system.
A similar configuration was used to develop an evanescent wave-coupled spectroscopic sensing
system [58]. Using a right-angled glass prism, the evanescent field generated due to total internal
reflection was allowed to interact with the external medium, which was attached to one face of
the prism. The smartphone-spectroscopic system was used to detect the shift in wavelength and
the corresponding analyte concentration.
Both the photonic crystal- and evanescent wave-based sensing systems reported above are
based on a free-space optical design. These systems require several external components to
guide the light from the external light source to the camera via the optical transducer, which
makes the overall footprint relatively bulky and costly. To reduce the overall size and cost of
such wavelength- or intensity modulation-based sensing system, Bremer et al. reported the
development of a fiber optic smartphone-based SPR sensing system. It has a very small footprint,
the required optical coupling and alignment are simple, and there is no need for external prisms
or lenses [59]. SPR sensors are based on the resonant excitation of surface plasmon waves (SPW)
or the electron density oscillations in a metal-dielectric interface caused by incident light having
a propagation constant equal to that of SPW. The associated transverse magnetic polarized waves
of the SPW are guided parallel to the metal-dielectric interface during resonance, which produces
a dip in the wavelength spectrum of the transmitted light. Since the propagation constant of the
SPW depends on the refractive index of the surrounding medium, SPR can be used for highly
sensitive biosensing applications. The SPR sensor was fabricated by coating 10 mm of the core
of an optical fiber with a thin silver layer. As shown in Fig. 6(b), the end faces of the optical fiber
were polished to 45 degrees in order to directly couple the light from the smartphone flashlight
via the SPR sensing region. The wavelength shift of the SPR due to the change in refractive index
of the sample was detected by dispersing the light into the camera using a grating. The adjacent
sub-figure in Fig. 6(b) shows the spectrum captured by the system and the shift in wavelength
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Fig. 6. Biosensing systems demonstrated with smartphone-based spectroscopic platforms.
(a) Schematic and process flow of the photonic crystal biosensor developed by Gallegos et al.,
reproduced from Ref. [57] with permission from Royal Society of Chemistry. (b) Schematic
of fiber optic SPR biosensor developed by Bremer et al. and the plot of shift in spectrum
due to RI, reproduced from Ref. [59] with permission from Optical Society of America.
due to variation of the refractive index. Since the SPR sensor is based on a fiber optic waveguide,
it is possible to integrate the whole system within the protective cover of the smartphone; thus,
the system can be implemented as a low-cost and disposable lab-on-a-chip system, as needed in
many biomedical applications.
3.2. Smartphone spectroscopic systems developed based on reflectance spectroscopy
Most smartphone-based spectroscopic systems have been designed to measure liquid samples,
but many biomedical applications require detection of analytes from solid samples such as tissue,
paper-based biosensors etc. Reflectance spectroscopy is generally used for such applications.
Hossain et al. demonstrated the first application of reflectance spectroscopy in a smartphone [30].
As shown in Fig. 7(a), a flexible endoscopic fiber bundle was used as a reflectance probe, which
was integrated into the phone spectrometer platform. Light from the in-built phone flashlight was
coupled to the endoscopic fiber with a custom-developed polymer light-guide and was delivered
to the sample using six fiber bundle rings. Reflected light was collected through the collection
fiber bundle as shown by the adjacent sub-figure. The collected light was then collimated using a
lens and subsequently diffracted by a reflective grating. The dispersed spectrum was captured by
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the phone camera and later processed with a custom-designed phone application to generate the
reflectance spectrum. With a Samsung Galaxy smartphone (13-MP camera), a spectral resolution
as high as 2.0 nm was obtained over a bandwidth of 250 nm with a slit of width 0.7 mm.
Many paper-based assays, such as urine dipsticks, are analyzed by visual comparison against
a standard color chart. Since this method depends on the user’s color perception and the
lighting conditions, it is difficult to differentiate color variation accurately. Moreover, the colored
compound produced in the test strip usually has a complex wavelength spectrum: that is, many
wavelength components are combined to produce the final visible color. Analysis of subtle color
differences can provide accurate information and can be detected by analyzing the scattered light
by means of reflectance spectroscopy. Woodburn et al. developed a smartphone-based reflectance
spectroscopic platform for the analysis of paper-based colorimetric assays [60,61]. Similar to the
work reported by Hossein et al., in this work, white light from the smartphone’s flashlight was
coupled to an optical fiber and made incident on test strips housed in a custom-designed cartridge.
The scattered light was then collected by another optical fiber. The cartridge was designed to
manually slide over the system, and the wavelength spectrum generated by a transmission grating
was recorded as a video file. The video was processed with a custom-designed application to
obtain the characteristic wavelength spectrum from the multi-analyte test strips. The developed
system can be used for analyzing different paper-based assays to obtain accurate and precise
results, more specifically for the class of assays where conventional phone-based colorimetric
detection or analysis of the red-green-blue pixel values of a camera image is not sufficient
to measure the complex scattered spectra. A similar strategy was used by Bayram et al. in
developing a portable reflectance spectrometer for colorimetric detection of Bisphenol-A, which
is a well-known endocrine disruptive agent [62].
Diffuse reflectance spectroscopy has been extensively used for many biomedical applications
where non-invasive investigation is required. In this spectroscopic method, incident light
penetrates deeply into the tissue, gets absorbed by chromophores and is scattered by cellular
and intercellular entities. The modulated light re-emerges to the surface carrying information
about chromophore concentrations and the scattering properties of the tissue. Diffuse reflectance
spectroscopy systems are often bulky and costly due to the need for a traditional spectrometer and
heavy computational requirements. An affordable, easy-to-use and portable diffuse reflectance
spectroscopy system could significantly improve accessibility to the technology, especially in low
resource settings. Hong et al. demonstrated a dual-modality smartphone-based microendoscope
system that integrates quantitative diffuse reflectance spectroscopy and high-resolution fluorescence imaging for quantification of physiological and morphological properties of epithelial
tissues [63]. Figure 7(b) shows the schematic diagram of the system, which consists of a Samsung
Galaxy S6 smartphone, a 3D-printed attachment for holding optical components, a fiber-optic
microendoscope and an app for data analysis. Light from a 20-mW white LED was delivered to
the tissue through two multimode optical fibers (200-µm core diameter), and the diffusely reflected
light was collected using a single detection fiber of the same core diameter. The collected light
was then propagated through a 100-µm slit and collimated by a collimating lens. A transmission
grating (1200 lines/mm) diffracted the collimated light and then imaged it by the phone camera.
The diffuse reflectance spectrum collected by the system was wirelessly transmitted to a server
through the developed app. The data processing module in the server automatically processed the
data and sent back the results to the app for display. A spectral resolution of 2 nm was obtained
over a spectral range of 395.5 nm to 693.3 nm. The feasibility of the system in characterizing the
properties of epithelial tissue was tested in a single human subject in vivo. Spectra were recorded
from oral mucosa, including labial mucosa tissue, gingival tissue and tongue dorsum tissue,
where the ↵ and bands of oxy-hemoglobin were clearly visible, as shown in the sub-figure of
Fig. 7(b). The differences in shape and intensity of the measured spectra from the oral tissues
represent their underlying differences in physiological and morphological characteristics.
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Fig. 7. Smartphone spectroscopic systems developed based on reflectance spectroscopy. (a)
Optical design and photograph of the smartphone reflectance spectroscopy system developed
by Hossain et al., reproduced from Ref. [30] with permission from Optical Society of
America. (b) Schematic of the diffused reflectance system developed by Hong et al. and
the plot of recorded spectrum with human mucosa tissue, reproduced from Ref. [63] with
permission from Nature publications. (c) Process flow and Schematic of G-Fresnel diffused
reflectance spectroscopy system developed by Edwards et al., reproduced from Ref. [33]
with permission from Nature publications.
1986
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Hemoglobin is an important biomarker for early diagnosis of several malignancies. Hypoxia
and angiogenesis are two crucial features for the growth of tumors; measurement of oxy- and
deoxy-hemoglobin non-invasively could potentially be used as an indicator for early detection
of different forms of cancer, such as oral cancer, cervical cancer and breast cancer. Thus, the
development of an affordable and portable systems for measurement of hemoglobin in local
tissue is an utmost need for point of care applications. Edwards et al. from the aforementioned
group developed a similar smartphone-based system for diffusive reflectance measurement
of hemoglobin in a tissue phantom [33]. The system was designed to operate over a broad
wavelength range: 400 nm to 1000 nm. Since the phone camera is limited to work only in the
visible range (400 nm-700 nm), an external camera, which works in the visible to near infra-red
range, was used to collect the spectra. As shown in Fig. 7(c), the spectroscopic system with the
external camera was connected to the phone using the micro-USB port of the smartphone for
operational control. An app was developed to communicate between the phone and the USB
camera to record the spectrum and compute the hemoglobin concentration. With the developed
system, a mean error of 9.2% was obtained in the measurement of hemoglobin concentration in
comparison to the results obtained with a commercial benchtop spectrometer. Considering the
affordability and portability of the presented device, the developed system has the potential to be
used as a point-of-care device for cancer screening in resource-limited settings.
3.3. Smartphone spectroscopic systems developed based on fluorescence spectroscopy
Fluorescence is an inherent property of certain molecules whereby they emit light at a higher
wavelength when irradiated by light falling within a certain excitation band. The measurement
of fluorescent intensity allows the determination of the presence of fluorophores and their
concentration. Fluorescent tags have been extensively used for many biological applications
including disease diagnosis, proteomics, drug discovery, and life science research. Although
fluorescence-based detection methods are highly sensitive and specific to the target molecule,
the instrumentation required is difficult to use outside of a standard laboratory. The availability
of a portable and low-cost system for the detection and analysis of fluorescence signals could
help to translate lab-confined methods to the point of care. Many papers have been written
about smartphone-based fluorescence systems that measure a single or few wavelengths to
capture fluorescence [64–66]. Very few works have been written that perform true fluorescence
spectroscopy, where they are capable of capturing the full fluorescence spectrum. This section
focuses on the latter.
Yu et al. demonstrated the first application of a smartphone spectroscopic system for read-out
of fluorescence-based biological assays [67]. The developed system was used to perform a
sensitive molecular beacon Foster resonance energy transfer (FRET) assay to detect specific
nucleic acid sequences from a liquid sample. FRET is a mechanism to observe changes in
the quenching efficiency between matched donor-acceptor pairs of molecules. This assay is
performed by adding the analyte to be detected to a solution containing a flurophore-tagged
probe molecule that specifically recognizes the target analyte, as shown in Fig. 8(a). FRET
is very effective for diagnostic applications because it is a single-step assay without need of
washing steps. A green laser pointer (power = 300 mW and wavelength = 532 nm) was used
to excite the fluorescent emitters placed in a transparent cuvette, and the light emitted by the
sample was collected through an optical fiber placed at an orthogonal angle in order to minimize
the light collected from the excitation laser. The output from the optical fiber was fed to a
smartphone-based spectrometer previously demonstrated by the same group [57]. The developed
system performed the assay with better sensitivity and specificity than a laboratory fluorometer
and detected miRNA sequences with a limit of detection of 10 pM.
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Fig. 8. Smartphone spectroscopic systems developed based on fluorescence spectroscopy.
(a) Schematic of the fluorescence spectroscopy system developed by Yu et al. and the
mechanism of FRET assay, reproduced from Ref. [67] with permission from American
Chemical Society. (b) Schematic of the system developed by Hossain et al. and the electronic
circuit diagram used to power the external LED with smartphone battery, reproduced from
Ref. [68] with permission from Optical Society of America.
1988
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To make the fluorescence detection more self-contained, Hossein et al. demonstrated the
use of a smartphone battery to power external light sources in a smartphone-based fluorescent
spectroscopic system [68]. In this work, a custom gold-coated polymer grating was used as a
dispersive element, which can be fabricated inexpensively using nano-imprinting as compared to
a commercial grating. The system was designed to detect the spectrum from two analytes: a
pH-sensitive amino-phthalimide fluorescent probe and a Zn2+-sensitive fluro-ionophore. As
shown in Fig. 8(b), the excitation LEDs were powered using the phone battery and placed at an
orthogonal angle to the sample cuvette; the emitted fluorescence spectrum from the sample was
dispersed by a reflection grating and imaged by the phone camera. The captured image was then
processed by a custom-developed Android application to generate the fluorescent intensity vs.
wavelength curve within the app interface. Ding et al. further demonstrated a smartphone-based
fiber optic fluorescent spectroscopic system for mHealth applications [52]. The developed system
was used to detect creatine and urinary glucose concentration.
4.
Discussion and potential roadmap for future strategies
We have described the major spectroscopic modalities that have been implemented using a
smartphone. Many of them were developed with the key goal of transforming lab-confined
healthcare applications into point-of-care assays to improve accessibility for people from all
economic and social backgrounds. The availability of these systems will significantly impact
the healthcare scenario in resource-constrained settings. According to the guidelines of the
World Health Organization, systems should closely follow the ASSURED (Affordable, Sensitive,
Specific, User-friendly, Rapid and robust, Equipment-free, and Deliverable to end-users) criteria
to ensure proper implementation in resource-constrained settings [69]. Some of these criteria are
inherently met by systems based on smartphones [70], but there is still some room to further
reduce the cost. A large contributor to the cost is the use of costly optical components, such as
commercial dispersive elements and lenses for collimation and focusing. For example, the cost
of components involved in fabricating the smartphone spectrometer used by Gallegos et al. for
biomolecular detection is $210 (excluding the smartphone), where more than half of the overall
cost is due to the diffraction grating ($82.78) and the lenses ($74) [57]. Similarly, the total cost
of the smartphone spectrometer system developed by Woodburn et al. for colorimetric analysis is
$550 (including the smartphone) using similar optical components [60]. Keep in mind, however,
that the reported costs for these devices is often higher than their cost once manufactured at
scale, and demonstrated smartphone spectrometers systems are still much cheaper than portable
commercial spectrometers (⇠ $2000). For in-field applications where a large number of samples
needs to be detected at the same time due to constraints such as low availability of skilled
personnel and consumables, high-throughput and multimodal detection are equally critical as
affordability and portability [71]. Unfortunately, most smartphone spectroscopic systems are
designed to perform single-analyte testing at a time.
4.1.
Strategies to improve affordability and compactness
To meet the need for affordability, alternative strategies of using a digital versatile disc (DVD),
compact disk (CD) (⇠$0.25) or custom-designed Fresnel lens as a grating element have been
proposed. CDs and DVDs comprise periodic metal-coated grating structures incorporated in a
polycarbonate substrate. These structures can be used as a reflection grating or a transmission
grating after removing the reflective metal layer. Wang et al. demonstrated a DVD grating-based
smartphone spectroscopic system to detect neurotoxins [72] using a similar configuration to
that demonstrated in section 3.1 (Fig. 9(a)). The DVD used in this work has a grating period
of 710 nm ± 19 nm. The usability of the system was investigated by comparing the absorbance
measured for Rhodamine B with a commercial microplate reader and when using a commercial
grating (1200 grooves/ mm, Thorlabs) in the same system; the authors confirmed that the DVD
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grating could achieve similar performance to that of the commercial grating. Similarly, Kong et
al. investigated the usability of a CD as a dispersive element in a smartphone-based spectroscopic
system and demonstrated its applicability for highly sensitive and cost-effective detection of
ascorbic acid [73]. Zhang et al. eliminated the use of a focusing lens as a spectrometer component
by using a custom-designed Fresnel lens, termed a G-Fresnel, for both focusing and dispersing
light [74]. The G-Fresnel element was fabricated by sandwiching the corresponding negative
PDMS molds of both a grating and a Fresnel lens. PDMS molds can be easily fabricated in an
affordable way through the surface-molding method. As shown in Fig. 9(b), since the G-Fresnel
element can both collimate and disperse the light, it significantly reduces the overall size of
the smartphone spectroscopic system. A spectral resolution of 1.6 nm was achieved at 595 nm,
which is more than sufficient for many biomedical applications. The usability of the system was
demonstrated by measuring protein concentrations in the well-known Bradford assay.
Fig. 9. Strategies to reduce cost and size. (a) Schematic of the smartphone spectroscopic
system developed using a DVD as a dispersive element and the SEM image of the DVD,
reproduced from Ref. [72] with permission from American Chemical Society. (b) Schematic
of the G-Fresnel element acting both as a dispersing and collimating unit and the corresponding spectroscopic system developed by Zhang et al., reproduced from Ref. [74] with
permission from Royal Society of Chemistry.
Besides linear gratings, other types of gratings such as the stacked, mutually rotated diffraction
grating from SpectroClick are commercially available [75]. These gratings are manufactured in
plastic films, which makes them very affordable ($1) for enabling the development of low-cost
spectroscopic devices [76]. One way of reducing both the cost and size of spectrometers is to use
pixel-level spectral filter arrays covering wavelength bands outside of those used by the traditional
RGB Bayer color filters. In this configuration, the wavelength response at every pixel can be
calculated using a suitable demosaicing algorithm [77]. This method is most commonly used in
snapshot spectral imaging systems [78]. Since the spectral filtering is performed in the detection
layer, the overall footprint of the system would be very small, making it suitable to integrate
into a smartphone as a standalone spectral sensor. For example, Bao and Bawendi developed
a quantum-dot spectrometer in which each pixel was covered by a filter comprising a unique,
heterogeneous mixture of quantum dots with varying responsivities [79].
Linear variable filters have also been demonstrated to enable low-cost hyperspectral imaging
systems [80], where the bandpass filter is directly placed above the image sensor to capture
the corresponding wavelength spectrum. A miniaturized version of such filters could be used
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to enable the capture of spectroscopic data by directly placing it on top of the smartphone
camera. In fact, such filters could even be used to enable responsivity outside of the traditional
spectral sensitivity of the camera if the filters themselves are capable of converting light from
one wavelength range to another. We demonstrated this general idea by using a miniature filter
composed of quantum dots to capture UV data using a smartphone [81]. Another potential way
of developing a standalone spectral sensing unit within a smartphone is to use interferometric
techniques for wavelength filtering. Custom-designed Mach-Zehnder crystal array interferometers
can be fabricated at the micro-scale using silicon photonics technology [82]. Similar to an FTIR
spectrometer, the smartphone camera could be used to capture the interferogram generated by an
interferometer array with known path differences, and the corresponding wavelength spectrum
can be generated from the interferogram using well-known Fourier transform techniques.
4.2.
Strategies to enable multiplexed operation
Owing to the 2D nature of the smartphone camera, different research groups have demonstrated
high-throughput and multichannel spectroscopic detection in a smartphone. Wang et al. demonstrated the first multichannel smartphone spectroscopic systems for high-throughput point-of-care
diagnostics [45]. As shown in Fig. 10(a), light from a backlight panel, which was used as a
light source, initially propagates through an aperture array with an aperture diameter of 6 mm to
separately illuminate eight individual micro-wells of a 96-well microplate. To reduce spectral
cross-talk, another aperture array of 4-mm diameter was placed above the micro-well array. A
PDMS micro-prism array integrated above the aperture array guides the transmitted light into the
field of view of the phone camera, whereupon it is diffracted by a grating, and the phone camera
captures the eight spectra individually, as shown in the adjacent figure. Two rails were used to
translate the system in order to scan every column of the 96-well microplate. The usability of the
system was validated by performing an immunoassay for human cancer biomarkers and measuring
protein concentrations. The same group later used 3D printing to reduce the overall cost and
clinically validated the system by detecting autoantibodies from human serum samples and
comparing the results with an FDA-approved instrument [83]. A similar strategy was used by Fan
et al. to develop a smartphone-based multi-spectral platform for detecting multiple biomarkers
with a microfluidic chip [84]. As shown in Fig. 10(b), a micro-hole array and a micro-lens array
were used to separately illuminate and record the spectrum from each channel of the microfluidic
chip. This system was used to measure the concentrations of protein solutions, sucrose solutions,
and serum specimens. Lo et al. demonstrated similar multichannel detection capability using a
lightweight plastic aspheric concave blazed grating. Biswas et al. further exploited the multi-order
characteristics of the diffraction pattern to develop a two-channel spectroscopic system [85]. All
these reported works confirmed that smartphone-based spectroscopic systems have the potential
to be used as a multi-testing platform when required.
We proffer that the complexity of the proposed multiplexed systems can be further reduced
by considering the use of parallel spectral acquisition. In a carefully designed experiment, this
can be achieved with line illumination and can eliminate the use of multiple light sources as
discussed above. Line illumination can be easily generated using a cylindrical lens. For example,
the spectra from multiple microfluidic channels could be acquired simultaneously by illuminating
the channels with line illumination of suitable length. Since the smartphone camera has a 2D
image sensor, the spectrum from all the points of the line illumination can be captured in parallel.
4.3.
Strategies to improve spectral resolution
Another key factor for any spectroscopic system is the spectral resolution, which inherently
depends on the optical design and components involved to develop it. The number of pixels
present in the phone camera sensor can play an important role in the overall spectral resolution
of the system. Table 1 provides a comparison of some of the demonstrated spectroscopic systems
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Fig. 10. Strategies for multiplexed operation. (a) the schematic of the multiplexed system
and the recorded spectrum developed by Wang et al., reproduced from Ref. [45] with
permission from Elsevier. (b) Microfluidics based multiplexed detection system developed
by Fan et al., reproduced from Ref. [84] with permission from MDPI.
in order of pixel count to showcase the impact of phone camera pixel resolution on the spectral
resolution. All of the systems in this table use the same dispersive element, a transmission
grating of 1200 lines/mm. It can be seen that the spectral resolution increases from 0.33 nm/pixel
for a 5-MP camera smartphone to 0.19 nm/pixel for a 20.7-MP camera smartphone. Although
the currently achieved spectral resolution is perfect for the demonstrated applications, other
biomedical applications based on different spectroscopic imaging techniques (e.g., hyperspectral
and multispectral) may require even higher spectral resolution, as the spectral resolution could
significantly impacts the overall imaging capability of the system [86]. In light of Moore’s law, we
anticipate that the quality of the CMOS sensor will improve over time with the integration of more
pixels, which can aid in increasing the spectral resolution of smartphone-based spectroscopic
systems [87]. Another way to increase the spectral resolution is to improve the integrated
optical design to cover more pixels for the target wavelength range. A higher pitch grating
can disperse the light more broadly, yielding higher resolution. If multiplexed operation is not
necessary, one may consider designing a 2D spectrometer, such as based on an echelle grating
configuration used for solar applications [88]. Besides hardware, computational algorithms such
as the high-throughput computational slit (HTCS) method can be implemented as post-processing
steps to enhance the spectral resolution [89], or one can implement other methods that combine
compressed sensing with non-linear dispersion, which have been shown to yield better spectral
resolution than could be anticipated using a traditional configuration [90].
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Table 1. Number of camera pixels vs. Spectral resolution
Paper Title
Smartphone
Brand
Number of pixels
Dispersive element
Spectral
Resolution
Label-free biodetection using a
smartphone [57]
iPhone 4G
5 MP (2592-pixels
⇥ 1936-pixels)
Transmission
grating (1200
lines/mm,
Thorlabs)
0.33 nm/pixel
Analysis of Paper-Based
Colorimetric Assays with a
Smartphone Spectrometer [60]
Samsung
Galaxy S3
8 MP (3264-pixels
⇥ 2448- pixels)
Transmission
grating (1200
lines/mm,
Thorlabs)
0.30 nm/pixel
Sunlight based handheld
smartphone spectrometer [53]
Nubia
13 MP
(4224-pixels ⇥
3192-pixels)
Transmission
grating (1200
lines/mm,
Thorlabs)
0.27 nm/pixel
A Dual-modality Smartphone
Microendoscope for Quantifying the
Physiological and Morphological
Properties of Epithelial Tissues [63]
Samsung
Galaxy S6
16 MP
(5312-pixels ⇥
2988-pixels)
Transmission
grating (1200
lines/mm,
Thorlabs)
0.20 nm/pixel
Smartphone biosensor system with
multi-testing unit based on localized
surface plasmon resonance
integrated with microfluidics chip
[82]
Meizu MX5
20.7 MP
(5248-pixels ⇥
3936-pixels)
Transmission
grating (1200
lines/mm,
Thorlabs)
0.19 nm/pixel
4.4.
Strategies to improve the detection range
Most of the systems discussed in the above sections were designed to work in the visible range
due to the limited spectral responsivity of the embedded camera sensor. As discussed in section
2, due to the presence of the infrared cut- off filter, the camera sensor is responsive only within
the visible wavelength range, 400 nm to 700 nm. Yet a vast number of biomedical applications
require spectral responsivity in the ultraviolet (UV) or infrared (IR) range. One way to use a
smartphone camera as a detector for such applications is to convert the light to visible range via
optical transduction using nanoparticle-based methods [81]. Alternatively, one might consider
using the embedded ALS as a photodetector [91] collect data in the near-infrared (NIR) spectral
range,. As shown by Fig. 2(b) of section 2, the ALS is responsive from the visible to NIR
wavelength range (CH0). Pereira et al. demonstrated an ultra-low-cost spectrophotometer (less
than $5) using the ALS and verified its usability with a protein assay [28]. As shown by Fig. 11(a),
the system simply consists of an LED-powered with a coin-cell battery and a 3D-printed cradle
to hold the cuvette. Light passing through the cuvette after its interaction with the analyte
sample was detected by the ALS and the developed application then quantifies the concentration.
As the ALS comprises a single photodetector, it can only measure photocurrent from a single
wavelength at a time, not a spectrum. Hence, one possible way to measure the spectrum for
different wavelengths is to use different LED sources to capture the absorption wavelengths of
interest. Hussain et al. demonstrated a compact ALS-based photometric platform that works
both in the visible and NIR spectral range [35]. To make the system self-contained, the LEDs
were powered by the smartphone battery using the USB-OTG protocol, as shown by Fig. 11(b).
The system was used to detect iron and phosphate ions in liquid samples by measuring their
absorbance at 510 nm and 880 nm, respectively. This work confirms the potential utility of
the ALS for developing a smartphone-based photometric platform beyond the visible spectral
range. Furthermore, ALS-based systems are very useful in developing affordable and portable
biosensing systems where detection can be done at a single wavelength.
The recent inclusion of face-recognition technology in smartphones may become a platform for
the development of IR spectroscopy. Figure 11(c) shows the different components embedded in
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Fig. 11. Smartphone spectrophotometric systems in NIR spectral range. (a) different
parts and photograph of ALS based spectrophotometric system developed by Pereira et al.,
reproduced from Ref. [28] with permission from plos.org. (b)Schematic of the photometric
system developed by Hussain et al. showing the location of embedded ALS chip in a
smartphone, reproduced from Ref. [35] with permission from IEEE. (c) Front panel of
Apple iPhone 11 smartphone.
the front panel of the Apple iPhone 11. The IR camera, flood illuminator, front camera, and dot
projector are together called a TrueDepth camera system and are used for face recognition [92].
Many other recent Android smartphones are equipped with similar face-recognition technology.
The dot projector illuminates the face with thousands of IR dots and the IR camera captures an
image of the face pattern. The IR image is then fed to a neural network to confirm its similarity
with the face pattern that was used during installation of the phone and is set to unlock the phone
if the pattern matches. Since iPhones use an encrypted platform, there is currently no publicly
available Application Programming Interface (API) to use the IR camera for functions other
than face recognition. In contrast, Android is an open-source platform, and different APIs and
applications are already available to use the IR camera to capture images [93]. The availability
of this technology will undoubtedly create new opportunities for developing IR imaging and
spectroscopic platforms for biomedical applications.
5.
Conclusions
Smartphone-based systems based on different spectroscopic modalities have been successfully
introduced and applied to a vast number of biomedical applications ranging from detection of
biomolecules (protein, nucleic acids etc.) to non-invasive detection of hemoglobin from human
tissue. This review summarized the development of different smartphone-based spectroscopic
systems by highlighting the current challenges and potential solutions in achieving affordability,
portability, higher accuracy and adaptability for point-of-care applications, which are important
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considerations for resource-constrained settings. The rapid growth of the smartphone market
paves the way for the integration of more sophisticated hardware and computational power to a
smartphone over the course of time.
In addition to the research systems described above, several portable spectrometers are available
commercially, such as the VS20-VIS from Horiba, the USB2000+ from Ocean Insight, and the
LI-180 from Licor. Their optics are distinct from components on the phone itself, but they are
able to communicate with smartphones (e.g., for data processing) using various communication
protocols (i.e., wi-fi, Bluetooth, or USB). Unfortunately, the overall cost of these spectrometers is
still high (⇠ $2000) for applications in low-resource settings. Many companies, however, have
already seized the opportunity to develop smartphone-integrated commercial spectrometers due
to their affordability. Changhong H2, a smartphone developed by Consumer Physics, Israel, has
an integrated spectrometer that can be directly used to assess the quality of medicine and food
[94]. GoSpectro is another commercially available optical attachment for the smartphone camera
designed to capture a spectrum using a phone application [95].
Besides developing the smartphone as a consumer-based product, companies like Samsung are
developing smartphones for military applications [96]. Use of smartphones on the battlefield will
open new avenues for rapid diagnostics testing such as monitoring wound infection, spectroscopic
detection of traumatic brain injury etc. The limiting factor for phone attachment-based systems is
the rapid evolution of phone designs, which change every two to three years for any phone brand. It
is envisioned that with the development of additive manufacturing techniques such as 3D printing
technology, different innovative and universal opto-mechanical designs can be created that are
suited for any phone brand. Another key limitation is the challenge of implementing high-end
machine learning (ML) or artificial intelligence (AI) algorithms in lower-end smartphones. For
these applications, data need to be processed on a remote server and then transferred back to
the smartphone for display. The ability to effectively perform this transfer depends on several
factors such as connectivity and bandwidth. The recent development of 5G technology enables
high-speed data transmission in an affordable way; therefore, it is envisioned that low-end,
5G-connected phones may be able to incorporate processing from sophisticated algorithms a
combination of using cloud-based processing and high-speed transfers. For now, widespread
adaptation of smartphone-based spectroscopic systems may seem unlikely, but we expect that
given the utility of spectroscopic analysis, this technology will eventually become ubiquitous,
similar to the adaptability of billions of smartphones currently blanketing the world.
Funding. Congressionally Directed Medical Research Programs (U0052609).
Acknowledgments. We wish to acknowledge Joseph D. Malone for his help and suggestions in preparing the
manuscript. This work was funded by the Dorothy J Wingfield Phillips Chancellor Faculty Fellowship and DOD CDMRP
U0052609.
Disclosures. The authors declare no conflicts of interest.
References
1. G. Goggin, Cell Phone Culture: Mobile Technology in Everyday Life, 1st ed. (Routledge, 2006).
2. M. T. Hutchings, A. Dev, M. Palaniappan, V. Srinivasan, N. Ramanathan, and J. Taylor, “MWASH: mobile
phone applications for the water, sanitation, and hygiene sector,” Nexleaf Analytics & the Pacific Institute (2012),
https://pacinst.org/wp-content/uploads/2012/05/mwash.pdf.
3. S. O’Dea, “Smartphone users worldwide 2016-2021,” (2020), https://www.statista.com/statistics/330695/number-ofsmartphone-users-worldwide/.
4. 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).
5. S. K. Vashist, P. B. Luppa, L. Y. Yeo, A. Ozcan, and J. H. T. Luong, “Emerging technologies for next-generation
point-of-care testing,” Trends Biotechnol. 33(11), 692–705 (2015).
6. 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).
7. K. Yang, H. Peretz-Soroka, Y. Liu, and F. Lin, “Novel developments in mobile sensing based on the integration of
microfluidic devices and smartphones,” Lab Chip 16(6), 943–958 (2016).
Review
Vol. 12, No. 4 / 1 April 2021 / Biomedical Optics Express
1996
8. E. R. Fossum and D. B. Hondongwa, “A review of the pinned photodiode for CCD and CMOS image sensors,” IEEE
J. Electron Devices Soc. 2(3), 33–43 (2014).
9. “History of videotelephony,” (2020), https://en.wikipedia.org/wiki/History_of_videotelephony#Japanese_videophones.
10. “A complete history of the camera phone,” https://www.digitaltrends.com/mobile/camera-phone-history/.
11. “Camera phone,” https://en.wikipedia.org/wiki/Camera_phone.
12. Q. Wei, W. Luo, S. Chiang, T. Kappel, C. Mejia, D. Tseng, R. Yan, L. Chan, E. Yan, H. Qi, F. Shabbir, H. Ozkan, and
S. Feng, “Imaging and sizing of single DNA molecules on a mobile phone,” 12, 12725–12733 (2014).
13. . “Samsung Takes Mobile Photography to the Next Level with Industry’s First 108Mp Image Sensor for Smartphones,”
(2019), https://news.samsung.com/global/samsung-takes-mobile-photography-to-the-next-level-with-industrys-first108mp-image-sensor-for-smartphones.
14. A. Roda, E. Michelini, M. Zangheri, M. Di Fusco, D. Calabria, and P. Simoni, “Smartphone-based biosensors: A
critical review and perspectives,” TrAC, Trends Anal. Chem. 79, 317–325 (2016).
15. D. N. Breslauer, R. N. Maamari, N. A. Switz, W. A. Lam, and D. A. Fletcher, “Mobile phone based clinical
microscopy for global health applications,” 4(7), 1–7 (2009).
16. C. W. Pirnstill and G. L. Coté, “Malaria diagnosis using a mobile phone polarized microscope,” Sci. Rep. 5(1), 13368
(2015).
17. R. Dendere, N. Myburg, and T. S. Douglas, “A review of cellphone microscopy for disease detection,” J. Microsc.
260(3), 248–259 (2015).
18. Z. J. Smith, K. Chu, A. R. Espenson, M. Rahimzadeh, A. Gryshuk, M. Molinaro, D. M. Dwyre, S. Lane, D.
Matthews, and S. Wachsmann-Hogiu, “Cell-phone-based platform for biomedical device development and education
applications,” PLoS One 6(3), e17150 (2011).
19. D. Tseng, O. Mudanyali, C. Oztoprak, S. O. Isikman, I. Sencan, O. Yaglidere, and A. Ozcan, “Lensfree microscopy
on a cellphone,” Lab Chip 10(14), 1787–1792 (2010).
20. H. Zhu, I. Sencan, J. Wong, S. Dimitrov, D. Tseng, K. Nagashima, and A. Ozcan, “Cost-effective and rapid blood
analysis on a cell-phone,” Lab Chip 13(7), 1282–1288 (2013).
21. M. V. D’Ambrosio, M. Bakalar, S. Bennuru, C. Reber, A. Skandarajah, L. Nilsson, N. Switz, J. Kamgno, S. Pion, M.
Boussinesq, T. B. Nutman, and D. A. Fletcher, “Point-of-care quantification of blood-borne filarial parasites with a
mobile phone microscope,” Sci. Transl. Med. 7(286), 286re4 (2015).
22. J. R. Richards, K. A. Gaylor, and A. J. Pilgrim, “Comparison of traditional otoscope to iPhone otoscope in the
pediatric ED,” American Journal of Emergency Medicine 33(8), 1089–1092 (2015).
23. G. T. Smith, L. Li, Y. Zhu, and A. K. Bowden, “Low-power, low-cost urinalysis system with integrated dipstick
evaluation and microscopic analysis,” Lab Chip 18(14), 2111–2123 (2018).
24. A. E. Burke, K. M. Thaler, M. Geva, and Y. Adiri, “Feasibility and acceptability of home use of a smartphone-based
urine testing application among women in prenatal care,” Am. J. Obstet. Gynecol. 221(5), 527–528 (2019).
25. “KardiaMobile,” (2021), https://www.alivecor.com/kardiamobile.
26. “Butterfly iQ,” (2021), https://www.butterflynetwork.com/.
27. H. Ahn, H. Song, D. M. Shin, K. Kim, and J. ryul Choi, “Emerging optical spectroscopy techniques for biomedical
applications—A brief review of recent progress,” Appl. Spectrosc. Rev. 53(2-4), 264–278 (2018).
28. V. R. Pereira and B. S. Hosker, “Low-cost (<Ä5), open-source, potential alternative to commercial spectrophotometers,”
PLoS Biol. 17(6), e3000321 (2019).
29. F. Kimme, P. Brick, S. Chatterjee, and T. Q. Khanh, “Optimized flash light-emitting diode spectra for mobile phone
cameras,” Appl. Opt. 52(36), 8779 (2013).
30. M. A. Hossain, J. Canning, K. Cook, and A. Jamalipour, “Optical fiber smartphone spectrometer,” Opt. Lett. 41(10),
2237 (2016).
31. H. Yu, Y. Tan, and B. T. Cunningham, “Smartphone fluorescence spectroscopy,” Anal. Chem. 86(17), 8805–8813
(2014).
32. J. Zhang, I. Khan, Q. Zhang, X. Liu, J. Dostalek, B. Liedberg, and Y. Wang, “Lipopolysaccharides detection on a
grating-coupled surface plasmon resonance smartphone biosensor,” Biosens. Bioelectron. 99(7), 312–317 (2018).
33. P. Edwards, C. Zhang, B. Zhang, X. Hong, and V. K. Nagarajan, “Smartphone based optical spectrometer for diffusive
reflectance spectroscopic measurement of hemoglobin,” Sci. Rep. 7(1), 12224 (2017).
34. M. A. Hossain, J. Canning, K. Cook, S. Ast, P. J. Rutledge, and A. Jamalipour, “Absorption and fluorescence
spectroscopy on a smartphone,” in Fifth Asia-Pacific Optical Sensors Conference (2015), 9655.
35. I. Hussain, A. J. Bora, D. Sarma, K. U. Ahamad, and P. Nath, “Design of a smartphone platform compact optical
system operational both in visible and near infrared spectral regime,” IEEE Sens. J. 18(12), 4933–4939 (2018).
36. I. Hussain, K. U. Ahamad, and P. Nath, “Low-cost, robust, and field portable smartphone platform photometric sensor
for fluoride level detection in drinking water,” Anal. Chem. 89(1), 767–775 (2017).
37. Y. Wan, B. T. Cunningham, J. M. Dallesasse, K. D. Long, E. Woodburn, B. Kesler, J. A. Carlson, P. Su, and S.
Al-Mulla, “Smartphone spectroscopy for mobile health diagnostics with laboratory-equivalent capabilities,” Proc.
SPIE 10657, 1 (2018).
38. J. Canning, “Smartphone spectrometers and other instrumentation,” SPIE Newsroom, 10.1117/2.1201512.006220
(2016).
39. R. Bogucki, M. Greggila, P. Mallory, J. Feng, K. Siman, B. Khakipoor, H. King, and A. W. Smith, “A 3D-printable
dual beam spectrophotometer with multiplatform smartphone adaptor,” J. Chem. Educ. 96(7), 1527–1531 (2019).
Review
Vol. 12, No. 4 / 1 April 2021 / Biomedical Optics Express
1997
40. A. J. S. McGonigle, T. C. Wilkes, T. D. Pering, J. R. Willmott, J. M. Cook, F. M. Mims, and A. V. Parisi, “Smartphone
spectrometers,” Sensors 18(1), 1–15 (2018).
41. W. Networks, Smartphone Instrumentations for Public Health Safety (n.d.).
42. Y. Wang, X. Liu, P. Chen, N. T. Tran, J. Zhang, W. S. Chia, S. Boujday, and B. Liedberg, “Smartphone spectrometer
for colorimetric biosensing,” Analyst 141(11), 3233–3238 (2016).
43. M. A. Hossain, J. Canning, Z. Yu, S. Ast, P. J. Rutledge, J. K. H. Wong, A. Jamalipour, and M. J. Crossley,
“Time-resolved and temperature tuneable measurements of fluorescent intensity using a smartphone fluorimeter,”
Analyst 142(11), 1953–1961 (2017).
44. K. D. Long, H. Yu, and B. T. Cunningham, “Smartphone spectroscopy: three unique modalities for point-of-care
testing,” in Next-Generation Spectroscopic Technologies VIII, 9482 (2015).
45. L. J. Wang, Y. C. Chang, R. Sun, and L. Li, “A multichannel smartphone optical biosensor for high-throughput
point-of-care diagnostics,” Biosens. Bioelectron. 87, 686–692 (2017).
46. “APDS-9930: Digital Ambient Light and Proximity Sensor,” (2021), https://www.broadcom.com/products/opticalsensors/integrated-ambient-light-proximity-sensors/apds-9930.
47. “ams:
Ambient
Light
Sensors
&
Proximity
Detection”
(2021),
https://www.mouser.com/datasheet/2/588/AMS_06282016_TMD2771-1214589.pdf.
48. “Driving LED lighting in mobile phones and PDAs,” https://www.eetimes.com/driving-led-lighting-in-mobilephones-and-pdas/.
49. S. X. Wang and X. J. Zhou, “Spectroscopic sensor on mobile phone,” U.S. Patent 7,420,663 (September 2.
50. K. D. Long, H. Yu, and B. T. Cunningham, “Smartphone instrument for portable enzyme- linked immunosorbent
assays,” Biomed. Opt. Express 5(11), 3792 (2014).
51. 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).
52. H. Ding, C. Chen, S. Qi, C. Han, and C. Yue, “Smartphone-based spectrometer with high spectral accuracy for
mHealth application,” Sens. Actuators, A 274, 94–100 (2018).
53. D. Jian, B. Wang, H. Huang, X. Meng, C. Liu, L. Xue, F. Liu, and S. Wang, “Sunlight based handheld smartphone
spectrometer,” Biosens. Bioelectron. 143, 111632 (2019).
54. B. A. Prabowo, A. Purwidyantri, and K. C. Liu, “Surface plasmon resonance optical sensor: A review on light source
technology,” Biosensors 8(3), 80 (2018).
55. H. Inan, M. Poyraz, F. Inci, M. A. Lifson, M. Baday, B. T. Cunningham, and U. Demirci, “Photonic crystals:
Emerging biosensors and their promise for point-of-care applications,” Chem. Soc. Rev. 46(2), 366–388 (2017).
56. C. R. Taitt, G. P. Anderson, and F. S. Ligler, “Evanescent wave fluorescence biosensors: Advances of the last decade,”
Biosens. Bioelectron. 76, 103–112 (2016).
57. D. Gallegos, K. D. Long, H. Yu, P. P. Clark, Y. Lin, S. George, P. Nath, and B. T. Cunningham, “Label-free
biodetection using a smartphone,” Lab Chip 13(11), 2124–2132 (2013).
58. S. Dutta, A. Choudhury, and P. Nath, “Evanescent wave coupled spectroscopic sensing using smartphone,” IEEE
Photonics Technol. Lett. 26(6), 568–570 (2014).
59. K. Bremer and B. Roth, “Fibre optic surface plasmon resonance sensor system designed for smartphones,” Opt.
Express 23(13), 17179 (2015).
60. E. V. Woodburn, K. D. Long, and B. T. Cunningham, “Analysis of paper-based colorimetric assays with a smartphone
spectrometer,” IEEE Sens. J. 19(2), 508–514 (2019).
61. K. D. Long, E. V. Woodburn, H. M. Le, U. K. Shah, S. S. Lumetta, and B. T. Cunningham, “Multimode smartphone
biosensing: The transmission, reflection, and intensity spectral (TRI)-analyzer,” Lab Chip 17(19), 3246–3257 (2017).
62. A. Bayram, N. Horzum, A. U. Metin, V. Kilic, and M. E. Solmaz, “Colorimetric bisphenol-a detection with a portable
smartphone-based spectrometer,” IEEE Sens. J. 18(14), 5948–5955 (2018).
63. X. Hong, T. Lu, L. Fruzyna, and B. Yu, “A dual-modality smartphone microendoscope for quantifying the
physiological and morphological properties of epithelial tissues,” Sci. Rep. 9(1), 15713 (2019).
64. Z. Zhao, L. Wei, M. Cao, and M. Lu, “A smartphone-based system for fluorescence polarization assays,” Biosens.
Bioelectron. 128, 91–96 (2019).
65. S. Umrao, S. Anusha, V. Jain, B. Chakraborty, and R. Roy, “Smartphone-based kanamycin sensing with ratiometric
FRET,” RSC Adv. 9(11), 6143–6151 (2019).
66. D. Bueno, R. Muñoz, and J. L. Marty, “Fluorescence analyzer based on smartphone camera and wireless for detection
of Ochratoxin A,” Sens. Actuators, B 232, 462–468 (2016).
67. H. Yu, Y. Tan, and B. T. Cunningham, “Smartphone fluorescence spectroscopy,” in Frontiers in Biological Detection:
From Nanosensors to Systems VII (2015), 9310.
68. M. Arafat Hossain, J. Canning, S. Ast, K. Cook, P. J. Rutledge, and A. Jamalipour, “Combined “dual” absorption and
fluorescence smartphone spectrometers,” Opt. Lett. 40(8), 1737–1740 (2015).
69. C. S. Kosack, A. L. Page, and P. R. Klatser, “A guide to aid the selection of diagnostic tests,” Bull. World Health
Organ. 95(9), 639–645 (2017).
70. S. Wang, M. A. Lifson, F. Inci, L. G. Liang, Y. F. Sheng, and U. Demirci, “Advances in addressing technical
challenges of point-of-care diagnostics in resource-limited settings,” Expert Rev. Mol. Diagn. 16(4), 449–459 (2016).
71. T. A. Wagner, C. A. Gravett, M. G. Gravett, and C. E. Rubens, “A global health opportunity: The potential of
multiplexed diagnostics in low-resource settings,” J. Glob. Health 1(2), 138–141 (2011).
Review
Vol. 12, No. 4 / 1 April 2021 / Biomedical Optics Express
1998
72. L. J. Wang, Y. C. Chang, X. Ge, A. T. Osmanson, D. Du, Y. Lin, and L. Li, “Smartphone optosensing platform using
a DVD grating to detect neurotoxins,” ACS Sens. 1(4), 366–373 (2016).
73. L. Kong, Y. Gan, T. Liang, L. Zhong, Y. Pan, D. Kirsanov, A. Legin, H. Wan, and P. Wang, “A novel smartphone-based
CD-spectrometer for high sensitive and cost-effective colorimetric detection of ascorbic acid,” Anal. Chim. Acta
1093, 150–159 (2020).
74. C. Zhang, G. Cheng, P. Edwards, M. Da Zhou, S. Zheng, and Z. Liu, “G-Fresnel smartphone spectrometer,” Lab
Chip 16(2), 246–250 (2016).
75. A. Scheeline and T. Anh Bùi, “Stacked, mutually rotated diffraction gratings as enablers of portable visible
spectrometry,” Appl. Spectrosc. 70(5), 766–777 (2016).
76. “Diffraction gratings in cardboard frames,” (2021), https://www.spectroclick.com/products/misc/diffraction-gratings/.
77. B. Geelen, N. Tack, and A. Lambrechts, “A compact snapshot multispectral imager with a monolithically integrated
per-pixel filter mosaic,” Adv. Fabr. Technol. Micro/Nano Opt. Photonics VII 8974(March 2014), 89740L (2014).
78. Q. Li, X. He, Y. Wang, H. Liu, D. Xu, and F. Guo, “Review of spectral imaging technology in biomedical engineering:
achievements and challenges,” J. Biomed. Opt. 18(10), 100901 (2013).
79. J. Bao and M. G. Bawendi, “A colloidal quantum dot spectrometer,” Nature 523(7558), 67–70 (2015).
80. S. Song, D. Gibson, S. Ahmadzadeh, H. O. Chu, B. Warden, R. Overend, F. Macfarlane, P. Murray, S. Marshall,
M. Aitkenhead, D. Bienkowski, and R. Allison, “Low-cost hyper-spectral imaging system using a linear variable
bandpass filter for agritech applications,” Appl. Opt. 59(5), A167 (2020).
81. S. A. Khan, G. T. Smith, F. Seo, and A. K. Ellerbee, “Label-free and non-contact optical biosensing of glucose with
quantum dots,” Biosens. Bioelectron. 64, 30–35 (2015).
82. D. M. Kita, B. Miranda, D. Favela, D. Bono, J. Michon, H. Lin, T. Gu, and J. Hu, “High-performance and scalable
on-chip digital Fourier transform spectroscopy,” Nat. Commun. 9(1), 4405 (2018).
83. L. J. Wang, N. Naudé, Y. C. Chang, A. Crivaro, M. Kamoun, P. Wang, and L. Li, “An ultra-low-cost smartphone
octochannel spectrometer for mobile health diagnostics,” J. Biophotonics 11(8), e201700382 (2018).
84. Z. Fan, Z. Geng, W. Fang, X. Lv, Y. Su, S. Wang, and H. Chen, “Smartphone biosensor system with multi-testing
unit based on localized surface plasmon resonance integrated with microfluidics chip,” Sensors 20(2), 446 (2020).
85. P. C. Biswas, S. Rani, M. A. Hossain, M. R. Islam, and J. Canning, “Multichannel smartphone spectrometer using
combined diffraction orders,” IEEE Sens. Lett. 4(9), 1–4 (2020).
86. Y. Garini, I. T. Young, and G. McNamara, “Spectral imaging: principles and applications,” Cytom. Part A 69A(8),
735–747 (2006).
87. I. Corp, “Moore ‘ s law: past, present,” (n.d.).
88. M. J. Porter, “Spectroscopy on small telescopes: the Echelle spectrograph,” Astrophys. Space Sci. 273(1/4), 217–224
(2000).
89. E. Kuang, F. Kazemzadeh, and A. Wong, “Enhanced smartphone spectroscopy via high-throughput computational
slit,” J. Comput. Vis. Imaging Syst. 2(1), https://doi.org/10.15353/vsnl.v2i1.97 (2016).
90. H. Podmore, A. Scott, and R. Lee, “On-Chip compressed sensing Fourier-transform visible spectrometer,” IEEE
Photonics J. 10(6), 1–10 (2018).
91. S. Dutta, “Point of care sensing and biosensing using ambient light sensor of smartphone: Critical review,” TrAC,
Trends Anal. Chem. 110, 393–400 (2019).
92. “About Face ID advanced technology,” (2021), https://support.apple.com/en-us/HT208108.
93. “Open Camera,” (2021), https://play.google.com/store/apps/details?id=net.sourceforge.opencamera&hl=en_US&gl=US.
94. “Changhong H2, World’s First Molecular Identification and Sensing Smartphone,” (2021),
https://www.consumerphysics.com/business/blog/changhong-h2-worlds-first-molecular-identification-sensingsmartphone-miniaturized-integrated-material-sensor-unveiled-ces-2/.
95. “GoSpectro handheld spectrometer,” (2021), https://www.goyalab.com/product/handheld-spectrometer-gospectro/.
96. “Galaxy S20 Tactical Edition,” (2021), https://www.samsung.com/us/business/solutions/industries/government/tacticaledition/.