Derek Guenther

Fiber optics have undoubtedly changed the world of spectroscopy and communications, existing as a foundation for countless platforms we encounter and work with each day. The use of multiple fibers in a bundle further expands these capabilities in several ways, some of which are obvious, and others that are more concealed and clever. Starting as a Central Intelligence Agency (CIA) encoding project under the guise of “gastroscope development,” fiber-optic bundles have evolved into many uses across industries. In this column, we explore this intriguing history and where it has led to, including the use of modern processing techniques that allow three-dimensional (3D) interpretations of traditionally two-dimensional (2D) readings.

When it comes to candy, would you rather have a single big chocolate bar, or a pack of M&M’s? Perhaps you may prefer a box of assorted chocolates, or you are really eccentric and prefer Skittles or some fruit-flavored sweet. Whatever your preference, any industry that exists long enough develops various form-factor and flavor options, and the world of fiber optics is no different. Over time, folks have moved away from the traditional chocolate bars and into more diverse offerings, and this article aims to touch on one such genre of fiber optics known as 

Those without much direct experience with bundles may wonder how much there is to say about putting fibers next to each other, and those who work closely with bundle systems may wonder how a single article could capture any meaningful and complete point on the topic. Indeed, it is an area with a deep and intriguing history that has led it down multiple paths serving countless applications. In this column, we take a glance at some key highlights of the past, and some of the coolest implementations in the present and near future.

But first, back to the chocolate—who doesn’t love a classic chocolate bar? It has everything you love in a single, homogeneous, bulk form factor, and it satisfies chocoholic cravings at the most basic level. Standard fiber is very similar, giving you the ability to route light in a single, homogeneous, bulk form factor in a way that satisfies many applications just fine. Also, folks have been working with glass for a while, or at least since 2500 BC in Egypt and Mesopotamia, with Egyptians already carving glass heads by 1500 BC (1). Although proper fiber optics would come much later, even glass blowers back in the time of Jesus must have seen their glass rods guide light when putting them into a glowing furnace and looking at the ends.

But two things had to happen before glass fiber could become a common reality: One was a demonstration by British physicist John Tyndall in 1854 showing that light could travel through a curved stream of water (2), and the other was the Industrial Revolution, which made glass commonplace for windows and bottles (1). With the concept of single-core glass fiber established in the 1800s, but with no spectrometer to connect to, these fibers existed largely as a novelty during the Victorian Era, providing elegant decorations and demonstrations. But the eggheads were still tinkering, as they do, and they could see the flickers of potential these new “light pipes” had to offer.

Fast forward to the 1900s, specifically Nazi Germany, and we find a fellow named Heinrich Lamm, who assembled an awkward and primitive bundle of glass fiber, and he used this to show how the ‘V’ image of a light bulb filament could be carried around a bend (1). Not terribly exciting today, but extremely revolutionary at the time! Unfortunately, Lamm’s Jewish background forced him to flee Germany and his work, which then sat idle for over a decade. A general timeline of some of these key events is shown in Figure 1, along with the gradual improvement of glass fiber transmission as the years went on.

FIGURE 1: Timeline of glass fiber quality development.

The end of World War II brought the beginning of the Cold War, and now information technologies were just as attractive as projectiles and explosives. Brian O’Brien had a clever idea for a fiber-bundle scrambler, which was immediately valuable to the CIA because the spy agency could use one half of the bundle to make ciphers that only the spy with the matching half could decode; the spy, in turn, could send messages back that only the spy masters could decode. Each pair of encoders would have a unique pattern, so loss of one scrambler would not break security for those using others. The CIA pushed this project forward under the guise of “gastroscopes,” which, indeed, had their own value for non-espionage purposes, but also had dramatically differ- ent requirements. Fibers in a gastroscope to be inserted down the esophagus need to be long, thin, and flexible, while fibers in a spy-device should be short, fat, and monolithic. Realizing this, a hungry MIT professor named Frederick Norton stole a clever idea from a Swiss roll cake. To be honest, there’s actually no proof of his relying on cake for the idea, but there’s also no proof against it, and the concept is quite similar. He would stack glass rods together, and then heat them to fuse and be twisted and stretched as desired; these could then be cut like a roll cake to create unique optical ciphers. Rather than cake, the technique he was more likely leaning on was the ancient craft of “millefiori” or “thousand flowers,” which is still used today in art and small objects like paperweights (1). Look up some examples, they are quite beautiful!

But as math usually ruins everyone’s day, it was determined that a mere 18-or-so intercepted images would be enough to crack the scrambling code, and the CIA eventually killed the cipher project to refocus (in honesty this time) on the gastroscopes they had said they were working on. The medical application evolved down its own path, but optical fiber bundles arguably had their first “real job” back in the 1960s reading the punchcards used in computers at the time. Early card readers used an array of 12 small and short-lived bulbs, with one to illuminate each row in a moving card. A fellow named Bob Potter at American Optical replaced this setup with a fiber bundle that collected light from one long-lived bulb and split the light into 12 smaller bundles, one for each hole. American Optical also developed 9–15-in bundles for NASA testing of the Saturn rocket boosters during the Apollo program, so these were finding some real-world usage (1).

We’ve gotten to the point in time where fiber bundles are a real thing, and people are figuring out they can be used to solve a wide range of problems. But let’s take a break from the history lesson, at least for the moment, and let’s talk about some of the key benefits fiber bundles offer us today in the world of spectroscopy and photonics:

“Mapped” bundles can be aligned to carry a coherent image.

Patterned bundles can sync with some type of complex receiver.

Randomized bundles do more than scramble images for the CIA, and can be useful to uniformly distribute multiple sources or perform some other type of photonic averaging or amplification.

Bundles allow you to mix and match various fiber types to create a diverse “box of chocolates.”

Assemblies may include single- or multi-mode fiber, different numerical apertures, diameters, claddings, tapering, and any other aspect you would want to optimize. For example, a bundle may be created with a 50:50 randomized mix of UV-visible (UV-vis) and visible-near-infrared (vis-NIR) grade fiber, creating an extended broadband range with the durability and flexibility benefits described below.

This is arguably the greatest benefit, as you can deploy bundle assemblies in very harsh and rugged environments with minimal risk of breakage or optical distortion. We will talk about this in greater detail since it can be such a blessing.

We’ve briefly touched on the concept of coherent bundles when looking at the history of Lamm’s work, and there is more to say about that shortly. But patterned bundles, which are meant more for data-based connectivity rather than image transmission, are a fulcrum in the world of communications. Like candy buttons where the right color is placed in the proper spot with its others, patterned bundles ensure signals are grouped as they should be. While Dorset, England is probably best known for its Jurassic Coast, its police force in 1975 was the first non-experimental entity to use a fiber-optic telecommunication link, and by the mid-1980s, Sprint had established the world’s first 100% fiber optic network (2). These fiber links require assemblies with consistent patterns and layouts, and the first telecommunications ribbon cables used 240 μm fiber in linear arrays of 6–12 held by mylar tape. A typical telephone line would have 12 ribbons of 12 fibers each, stacked on top of each other and surrounding a sheath embedded with strength supports in the center; a submarine version of this would essentially be the same thing with two layers of outer armor added (3). Today’s data servers use bespoke fiber-bundle patterns and connectors to relay the right information to the right place.

We all love and live for our communication technologies, but most readers are here for the real treat—that is, the theory behind it all. Thinking about the most basic multifiber assembly presents us with just two fibers side by side, and such multicore fibers can be split into two groups: Assemblies where fibers are designed for a controlled coupling of the guided modes between the cores, and assemblies where modes in the different cores are practically decoupled (4). Coupled assemblies may be similar to a KitKat bar, where the wafers are independent, yet connected, versus a Twix bar where each side is entirely decoupled; we will leave the mathematical proof behind the superiority of the left-side Twix for another article.

You can do some pretty cool things by arranging cores the right way. In a common twin-core fiber with two identical single-mode cores within the cladding, illuminating one core will equally excite two transversal modes in the fiber, one symmetrical and one asymmetrical. Energy is periodically transferred back and forth between cores, where the first core will be totally depleted of energy after some length, but this is now guided by the second core and will start to transfer back to the original core; this continues ad infinitum as light travels down the fiber. You can imagine how this would be useful in amplifiers and lasers, and the beat length, which is the fiber length necessary to complete one cycle of this, can be adjusted with core separation, geometry, refractive index, and wavelength (4).

Perhaps the greatest benefit of fiber bundles comes from their inherent resistance to harsh environments and rough handling. There are actually two areas to touch on here: mechanical durability and optical stability. Starting with the former, have you ever had a chocolate bar in a backpack or other tight space, only to take it out and find it broken into several pieces? Note this does not happen with a pack of M&M’s due to the small size of the individual components. Similarly, fiber assemblies using single, large fibers are prone to breakage from overbending or mechanical shock, whereas fiber bundles using many small-diameter fibers can undergo extreme bends and hits without compromising the internal fibers...or at least saving the vast majority of the signal. Theory states that fiber bending will cause some high-order modes to hit the core-cladding interface at less than the critical angle and become lossy, while low-order modes should be unaffected (5). More complete predictions, however, state all modes will lose power at a bend—the wavefront propagation in a fiber will be compelled to move faster at the outer periphery of the bend than at the center of the fiber, and since it would be necessary to exceed the speed of light in the material to accomplish this feat, some loss must occur. Because all modes are represented in the wavefront, all lose power at a bend (5). Therefore, it is difficult to achieve requirements of high bandwidth and low sensitivity to bending with single large fiber, but bundles using small core diameters solve this because the bend ratio to the fiber radius is larger for the same absolute bending radius (4). Keep this in mind because it will come up shortly when we loop back to gastroscopes. Figure 2 provides a sketch of this metaphor between the form factors of chocolate and optical fiber.

FIGURE 2: Mechanical and signal stability benefits of fiber bundles.

Techniques such as two-step cladding can further improve bandwidth and bend immunity, and graded-index profiles, where fibers have a continually diminishing refractive index from the fiber axis to the cladding, can increase bandwidth by two to three orders of magnitude, when done right (4). The benefit to that technique is how it keeps the beams in the fiber center, but that’s another topic for another day.

A popular configuration for fiber used in common optical oxygen sensor systems is randomized multimode bundles, and an old demonstration we often showed in prepandemic trade show days was literally tying the fiber between the probe and electronics into a knot and then hitting the knot with a large rubber mallet. The demonstration showed how stable the optical signal remained despite dramatic bending and physical impact. Such resilience makes bundles instantly attractive for handheld probes, vehicle systems, industrial sensors, and other applications with high vibration and movement. If you’ve ever had a fiber-based absorbance setup such as using a standard 1-cm cuvette, you may have noticed your broadband spectrum became “wobbly” or “wavy” any time you touched the connecting patch fiber or even bumped the table. This is due to the mode-mixing effect seen in typical single-core, multimode fiber. By moving to a bundle, you will see this effect becomes muted or even disappears entirely because all of the mode-mixing “noise” from the individual fibers is now averaged across potentially hundreds of fibers in the bundle. Handheld dip probes, for example, see great improvement in optical stability and repeatability when using bundles to transmit light, at least in the UV-vis range. Going back to the chocolate bar versus M&M analogy, fiber bundles let the chocolate melt in your mouth and not in your hand...or rather, they let the optical signal make it to its intended destination, rather than degrade along the way. So, in terms of both external durability and internal photonic quality, bundles are essentially “the M&Ms of fiber optics.”

Let’s loop back to Lamm’s work on coherent image transfer back in the 1930s, but this time with a modern twist. The most important use of coherent bundles is the endoscope, which saves thousands of lives each year by seeing problems that would otherwise remain undetected. A standard endoscope can have over 10,000 fibers, which creates a 1-mm spot capable of 70 μm resolution (6). This is great for seeing and transmitting 2D images, but a group at the ARC Centre of Excellence for Nanoscale BioPhotonics at RMIT University in Melbourne has developed a clever method to extract 3D information from such images.

As you can imagine, the pixelation of an image coming out of a bundle is a function of how many fiber cores make up that image, and in normal operation this image is somewhat homogenized or “downsampled” to remove that pixelation effect, basically adding a slight blur to make it less choppy (7). This ends up eliminating any modal information that may be contained in the cores. What Orth and associates (7) have shown is that the intracore intensity structure of the output light carries information about the angular distribution of the input light, which can then be post-processed to map the original depth of the object; this is demonstrated in Figure 3 with a single fluorescent point source as the input, and also showing a mockup depth map of multiple point sources at various distances from the input facet.

FIGURE 3: Imaging a point source with an optical fiber bundle. (a) Schematic of point source placed at distance 

 from the input fiber. (b) A schematic of the fiber bundle showing input and output. (c) Example: raw image of output facet. (d) Processed depth map of multiple point sources in space.

Several paragraphs earlier, we were talking about high- and low-order modes experiencing loss at a bend, but how the small diameters used in bundles helps to minimize this. The technique proposed by Orth and colleagues is possible because the higher-order modes, which are preferentially excited at larger angles of incidence, carry more energy near the core-cladding interface than the low-order modes, such that light is effectively pushed to the edge of each core with increasing ray angle. They then use a process called digital aperture filtering to remove peripheral light for each core, creating a synthetically constricted numerical aperture. Using light field moment imaging (LMI), they can extract missing azimuthal information to calculate the average ray direction at a given point in the image. Orth’s paper obviously goes into much greater detail, and is a recommended read, but the key takeaway is that this now adds depth mapping to a traditional 2D image. This group used a bundle of 30,000 fibers with 2 μm diameter, and was able to achieve better than 10 μm depth accuracy with samples up to 80 μm away from the fiberface.

What would Heinrich Lamm think of this modern evolution of his inspiring first steps? What an amazing technological leap in less than a century to be roughly translating a large, distorted, 2D “V” shape into then generating 3D depth maps with 10 μm accuracy! We’ve tracked these human-led developments in the world of fiber bundles, and it’s time to bring our discussion to a close, but it’s also worth noting a fiber-bundle design developed by the world’s most prevalent inventor: nature. Remember the millefiori we talked about earlier, with the fused glass rods cut like a Swiss roll cake? There is a mineral called ulexite found mainly in Boron, California, that’s created through evaporation of mineral-rich lakes, and this naturally creates the same type of structure seen in the millefiori. These are sold in toy and rock shops as “TV stones,” because they translate the image from one face coherently to the other. Try to find one next time you’re in a novelty or rock shop, and maybe it will inspire your kids to have the next brilliant idea in fiber optic design and write the next chapter of the story.

(2) “History of Fiber Optics,” Timbercon. 

https://www.timbercon.com/resources/blog/history-of-fiber-optics/ 

(3) Christian, N.; Passauer, L. “Fiber Optic Component Design, Fabrication, Testing, Operation, Reliability, and Maintainability,” Noyes Data Corporation, 1989.

An Introduction to Fiber Optics System Design

(7) Orth, A.; Ploschner, M.; Wilson, E. R.; Maksymov, I. S.; Gibson, B. C. Optical Fiber Bundles: Ultra-Slim Light Field Imaging Probes. 

 is a Senior Application Scientist at Ocean Insight. Direct correspondence to: 

Articles

A brief history of fiber-optic bundles helps us understand how this technology how it has evolved from use in telecommunications to enabling extraction of three-dimensional (3D) information from two-dimensional (2D) images.

Fiber-Optic Bundles Create a World of Pure Imagination

Spectroscopy offers a range of available techniques that can be differentiated by the use or omission of reference spectra. This differentiation means that techniques such as Raman or fluorescence typically look at raw intensity outputs, whereas techniques such as transmission or reflectance require some reference scan to calculate those relative outputs. Within the group of referenced spectral techniques, absorbance is easily one of the most common and offers much value because of the concentration dependence of Beer’s Law. However, this value is only properly captured when system components and samples are made to be repeatable, both for the reference scans and live acquisitions. This article discusses several useful techniques to establish this repeatability, including proper cuvette and probe handling, component setup, and sample considerations. By optimizing repeatability of the measurement system, the observed concentration values calculated from absorbance outputs are much more accurate and relevant to the sample being measured.


Think back to the first time you baked something, swung a golf club, or typed on a keyboard. How did you do? Were you as good the second time? Were you as good as you are today? Most likely, the answer is “No,” because it took some experience to build and understand the techniques needed to perform each activity effectively. Without proper technique, we do not see our efforts result in us reaching our true potential, but without experience, we never have the chance to build those techniques. Spectroscopy is no different, and each spectral method has its own tricks to pull more out of the measurement than would be possible otherwise.

Modern times give focus to more modern spectral techniques, and rightfully so. We now see a rapid democratization of Raman and time-resolved measurements that were once largely restricted by bulky instrument size and high cost, which is fantastic news because it gives more researchers opportunities to push boundaries and open new doors. However, there is also value in remembering traditional techniques and the methods that brought us to where we are today. Whether reflection, transmission, absorbance, fluorescence, or others, these traditional approaches are by no means “done” in their development and deployment; rather, we can always improve old techniques with new considerations.

Absorbance is one technique that stands a bit taller than the rest. The origin of the absorbance concept that we know today began in the early 1700s when French mathematician and scientist Pierre Bouguer expressed curiosity about the transmission of light through his wine when he was on vacation in Portugal. Bouguer’s curiosity resulted in him making some measurements to discover that the light attenuation was not linear as the distance (pathlength) increased, but rather followed an exponential trend. More than a century later, German physicist August Beer made a secondary discovery in this same area, noting that the same exponential trend was seen when the concentration of the attenuating species changed and the distance remained constant. Bringing these two observations together under a common relationship gives us the Beer-Lambert law we are all familiar with today:

equals the molar attenuation coef ficient; 

The discovery of the Beer-Lambert law was a big deal at the time and remains important today. Beer’s law allows us to quickly convert optical signals into meaningful concentration values for all sorts of systems. It lets us shine light on something and know how much light is there. The specific sample being measured determines what “mode” was used to interrogate the sample, which could include transmission, reflection, or in rarer cases, evanescent interactions.

Perhaps the most common mode of absorbance measurement is transmission, and within that, most commonly the transmission of liquids like Bouguer’s wine. Transmission is only feasible if the sample is transmissive in the spectral region of interest, though a simple visual check may not be sufficient. It is possible that a crystal-clear sample can be a massive UV-range filter, which would make UV absorbance regressions impossible in transmissive mode. Conversely, you could have something that looks solid and not optically transmissible but may give very usable transmission in the near-infrared (NIR) wavelength range. The more the sample is absorbed, the harder it will be to get meaningful and coherent photons through to the other side. Your two most important tools to help with this process include a stronger light source and a shorter pathlength. More photons at the input will give you more photons at the output, and the less sample the light has to interact with will also allow more light through.

Think back to Bouguer’s wine. Compared with a light white wine, a dark red wine would need a stronger light source, a shorter pathlength, or both, to get coherent measurements. However, at some point, the sample may be too thick or optically dense for transmission to work well. In that scenario, it may make more sense to look at the reflective mode (Figure 1). Samples like whole blood or plant extracts can give the same valuable absorbance information, but instead of passing light through the sample, we can bounce the light off the sample surface. A third approach using evanescent interactions could be considered a fusion of these two approaches because the evanescent waves interact “through” the sample but only at the primary surface interface. The specific system you’re working with will dictate what makes sense for data acquisition.

Figure 1: To derive meaningful spectral data from optically dense samples, such as dark wines, may require the use of reflectance techniques. The above is an image of wine in bottles during the packaging process.

When working in transmissive mode, you will likely be using some type of cuvette (or cuvet) and cuvette holder to manage your sample, and there are some key considerations with that setup to make the best measurements possible. When using fiber optics for connecting the various optical components, ensure the fiber connections are threaded tightly and do not have the ability to rotate. Also, it is important to ensure that the fibers are positioned so that they aren’t frequently moved or touched. Fiber movement can be a leading cause of odd absorbance behaviors, so always keep this thought in mind when you plan to work in transmissive mode.

The cuvette should be optically clean on the inside and outside. If being moved between measurements, the cuvette should be placed in the same orientation; cuvettes often have a marker near the top to assist with this step. The cuvette should sit snuggly within the holder, so that there is minimal-to-no play in its movement or resting position. There is often a set screw to tighten the fitting of the cuvette. It is important to be careful not to crack the cuvette when adjusting this, especially with expensive quartz cells. When using the same cuvette for multiple samples, it is also important that you ensure the cell has been sufficiently flushed with the new sample so that you do not see contamination from a previous sample.

Typically, it is more dif ficult to achieve repeatable absorbance scans in reflective mode than transmissive mode, which is largely because of the more consistent nature of the transmissive hardware we discussed above. Cuvettes are convenient because they snap right into place! Reflective probes and holders often require a greater level of physical sturdiness and placement considerations to get the same results, so taking the time to develop a proper opto-mechanical fixture can make all the difference in the world for this approach. Think about distance and angle, not just of the optical probe versus the stage but versus the sample. For example, if the sample is a powder that may have variable height, think about how the probe could be designed so that it sees a flat portion of the powder at the same distance each time. Sometimes, using a region of the spectrum known to have a good baseline for a sanity check can be valuable in ensuring the system “sees” the sample as it had before (Figure 2).

Figure 2: Absorbance of pH-responsive dye showing raw (red) and single-point baseline corrected (blue) spectra. Baseline correction of absorbance data can be applied to help mitigate the effects of measurement variability.

However, hardware is only half the battle in the world of modern science, where software plays just as (if not more) critical a role in determining the validity and value of our final result numbers. Although this article focuses on absorbance, a key first step of many traditional spectroscopy techniques is determining what sample condition creates the “brightest” scenario or the most photonic signal to the detector. Knowing the sample condition will allow you to complete the full range of acquisitions without saturating the detector and needing to adjust software parameters on the fly.

Set your integration time to approximately 80% saturation, and then adjust your averaging setting to establish the total scan time, which will be the integration time multiplied by the number of averages. Averaging can improve the stability of your output, but it also increases the measurement time. Boxcar, on the other hand, is a pixel-averaging setting that does not affect your total scan time and can be very helpful in cleaning up spectral outputs. The boxcar setting is the number of 2n+1 pixels that are averaged around the center pixel, such that a boxcar of 

= 0 will treat every pixel independently, whereas a boxcar of 1 will average each pixel with the pixel immediately to the left and to the right of it. Having a boxcar of at least 1 will let the spectrometer function as a coherent system rather than as an array of independent detectors. Many absorbance applications deal with broad peak activities and applying boxcar can significantly help stabilize these broad trends across many scans. However, be careful: the sharper the peak activities, the lower the boxcar setting that should be used.

Taking quality absorbance measurements goes beyond simply dropping liquid into a cuvette; there are multiple considerations around spectral method, hardware setup, and software parameters. With the proper planning on the front end of your tests, you will set yourself up for accurate and valuable results at the output. Whether with spectral measurements or other efforts, you will bring yourself closer to success by knowing the details of the techniques as much as knowing the basics of the technology.

Derek Guenther is a Senior Application Scientist at Ocean Insight. Direct corerspondence to: 

Spectroscopy offers a range of available techniques that can be differentiated by the use or omission of reference spectra. 

Valuable Techniques for Repeatable Absorbance Measurements

The detection of explosive residues is of crucial interest for security applications such as airport security screenings and terrorist threat assessments. Surface-enhanced Raman spectroscopy (SERS) is a powerful technique that offers high sensitivity and specificity, fast response time, and low limits of detection for various explosives, such as TNT, RDX, and PETN. These explosives can be detected down to picogram levels using a newly developed SERS substrate that consists of a sputtered gold–silver alloy film on a roughened glass surface. This enhancement is achieved with a benchtop integrated Raman instrument using a visible laser excitation at 638 nm, a 20-mW power level, and an acquisition time of only 10 s. Because of the spectral resolution of the Raman technique, the recorded signals allow for both identification of the explosive and quantification of the amount on the substrate. Principal component analysis (PCA) of the Raman spectra successfully showed distinctions among the different explosives. Future experiments will also test the specificity and sensitivity of these SERS substrates for chemical warfare agents, such as sarin or mustard gas.

	Safety applications including terrorist threats, improvised explosive devices (IEDs), and landmines emphasize the increasing need for quickly and accurately identifying bulk explosives and their residues. The method of explosives identification must allow for fast acquisition times, produce highly sensitive and reliable results, and at the same time be easy to use and portable for field measurements. Several types of methods have previously been used to detect explosives, including ion mobility and mass spectrometry (MS) (1). Both of these techniques offer a stable and sensitive platform; however, they are limited because of their cost and size as well as the requirement for electrospray ionization with MS (1). Significant research efforts have focused on Raman spectroscopy as a viable tool for explosives detection because it enables a unique spectral signature for the analyte of interest, there is no interference from water, it requires only minor sample preparation, and the instrumentation can be miniaturized for field portability (1,2). However, trace-level (sub-part-per-million) detection of explosives with normal Raman spectroscopy is difficult because of the low Raman scattering cross sections inherent to these compounds (3).

	Surface-enhanced Raman spectroscopy (SERS) is a powerful variant of normal Raman spectroscopy that enables trace-level detection of chemicals in solid, liquid, and gaseous samples (2–5). Increased Raman scattering occurs when molecules are adsorbed to metal surfaces and experience an enhanced electric field (5). The two mechanisms that are primarily responsible for the enhancement are an electromagnetic mechanism where there is an amplification of the electric field near the metal surface, and a chemical mechanism where a charge-transfer state is created between the metal and adsorbed molecules (4). The enhanced electric field generated near the metal surface (usually gold or silver) occurs when incident light induces a collective oscillation of electrons in the metal. This phenomenon is known as the 

 and is exhibited by rough metal surfaces and metallic nanoparticles. But SERS introduces a whole new set of challenges, where substrates need to be highly reproducible and achieve large enhancements.

	Trace-level detection of explosives using SERS has been previously investigated. Botti and colleagues (6) tested trinitrotoluene (TNT), pentaerythritol tetranitrate (PETN), and trimethylenetrinitramine (RDX) and potassium nitrate (KNO

) with a commercial SERS substrate known as Klarite (D3 Technologies Ltd.), which is based on a lithographically prepared, ordered structure of inverted pyramids coated with a thin layer of gold. They were able to detect these explosives down to tens of picograms and also were able to demonstrate the reproducibility of the results based on the highly ordered substrate pattern. A slightly different approach was carried out by Fierro-Mercado and colleagues (7) where they were able to detect TNT at 94 pg using inkjet-printed gold nanoparticles on filter paper. Another technique using gold nanoparticles was performed by Dasary and colleagues (8) in which the nanoparticles were functionalized with cysteine molecules to preferentially bind to TNT molecules to enhance the SERS signal. A Meisenheimer complex is formed when TNT interacts with the cysteine-bound nanoparticles, and they aggregate with other cysteine-bound nanoparticles to enable detection of TNT at a concentration of 2 pM.

	In this study, a novel SERS substrate is presented that is based on sputter deposition of a gold–silver alloy onto a roughened glass surface. These unique substrates enhance the Raman signals of explosives that are present in picogram quantities in neat solutions using a visible laser wavelength (638 nm) and a compact Raman instrument. The gold–silver film allows for adsorption of the explosives and their precursors, and it is sensitive enough to identify species such as PETN and RDX that are often difficult to detect with SERS because of their low Raman scattering cross sections (3). This new technology coupled with portable Raman instrumentation has the ability to detect trace levels of chemicals that play a core role in today’s defense and security efforts. Principal component analysis (PCA) of the data was carried out to support the validity of the current technique. Our results indicate that the SERS technique coupled with a sensitive substrate, compact Raman platform, and chemometrics provides a useful solution for trace-level explosives diagnostics.

	A 2-in. diameter silver sputtering target was purchased from ACI Alloys, Inc. Ammonium nitrate (NH

), ethylene glycol dinitrate (EGDN), HMX, gold foil, and roughened borosilicate glass slides were purchased from Sigma-Aldrich. Dilute solutions of TNT, RDX, and PETN were provided by the Defense Science and Technology Laboratory (DSTL) of the United Kingdom. The glass slides were diced to ~4 x 4 mm squares and cleaned before metal deposition by sonicating in separate baths of acetone and isopropyl alcohol. Thin films were then deposited on the precut and precleaned glass slides using a CRC sputter coater with a 300-W DC power supply using pressures ≥ 6 mTorr. A thin film of chromium (~5 nm) was first deposited, followed by a 200-nm film of gold–silver alloy.

	All solutions of the explosives or explosive precursors listed above were prepared with concentrations of 100 and 1000 µg/mL. Solutions of PETN, RDX, and TNT were prepared in acetone; NH

 were prepared in water; HMX and EGDN were prepared in acetonitrile. In a typical measurement, 10 µL of the prepared chemical solution was added to a single gold–silver substrate and allowed to dry in air completely.

	The substrates were interrogated with an integrated Raman system (IDRaman reader, Ocean Optics, Inc.) with 638-nm laser excitation. The laser power was typically 19-mW with a 10-s integration time and data were acquired using OceanView software (Ocean Optics, Inc.). The spot size of the focused laser was approximately 25 µm. A typical Raman measurement involved collecting the spectrum of the blank substrate first and subtracting the background peaks to obtain a flat baseline. Then, the spectrum was collected again after the analyte of interest was added, keeping the optics stable. This background subtraction method ensures that only Raman peaks from the analyte of interest appear in the spectrum. The amount of material detected was calculated based on the spot size of the laser, the area of the substrate, and the concentration of the analyte added. For example, 10 µL of 100 ppm analyte deposited on a 16-mm

 substrate using a 25-µm laser spot corresponds to ~30 pg of analyte detected in the laser beam area.

	Select SERS spectra were subjected to PCA after visual outliers were manually removed from the dataset. Manual baseline correction was also applied. The Unscrambler X v10.4 (Camo) was implemented to perform PCA on the spectra after a five-point moving average had been applied to smooth the data. A total of 47 spectra were pretreated and ultimately subjected to PCA using the singular value decomposition (SVD) method to visualize natural groupings within the data (9).

	A representative scanning electron microscope (SEM) image of the substrate is shown in Figure 1, which reveals the micro-roughened features of the metallic alloy surface. The SERS spectra of the explosives and explosive precursors analyzed in this study are displayed in Figure 2, and the results are summarized in Table I. The SERS technique coupled with the gold–silver alloy films enables detection of each of these analytes at mass quantities in the hundreds of picograms range. Some of the analytes (EGDN, NH

) experienced even greater sensitivities and were detected down to several tens of picograms (~20–50 pg). The difference in the limit of detection for these analytes is most likely because of the differing Raman cross sections inherent to these molecules at 638 nm and their differing adsorption properties to the gold–silver alloy film.

	Using the select SERS spectra, an average spectrum was derived for each of the 10 samples subjected to PCA. A graphical overlay of these averages that demonstrate the unique Raman shifts given by each explosive sample can be seen in Figure 3a. PCA was performed on the set of 47 select SERS spectra and results showed that PC1 (principal component), PC2, and PC3 explained 47.5%, 24.8%, and 9.66% of variability in the original dataset, respectively. The first five PCs explained 91.0% of the cumulative variance. Clusters of scores generated by PCA calculations via SVD can be visualized in Figure 3b. Here, a three-dimensional (3D) scatterplot shows the resultant PCA score groupings in relation to each explosive sample group. Some samples cluster close together while others exhibit a wider variability in 3D space. A group of five samples, including KClO

, RDX, and urea, can be seen as overlapping clusters in Figure 3b, while scores from samples of EGDN, HMX, NH

, PETN, and TNT are clearly separated from one another. It is likely that the samples with overlapping scores exhibit similar spectral characteristics that cause them to form clusters very close to each other.

	Figure 3c displays the graphical overlay of the average spectra from those samples whose PCA scores are overlapping onto one another. NaClO

 exhibits a distinct Raman peak at ~930 cm

 and urea exhibits a similarly shaped Raman peak at ~1005 cm

. Although these two peaks are shifted approximately 75 wavenumbers away from one another, they are similar in shape and general area on the 

-axis. These are some of the characteristics that would cause the scores from PCA to cluster close to each other. KMnO

, PETN, and RDX exhibit similar broad Raman shifts between ~600 cm

; these similarities likely contribute to overlapping score clusters.

	In general, PCA results show that some of the explosive samples are distinguished from others by visualizing score clusters in 3D space. The scores attributed to those five samples that cluster near each other do so because of similarities in the spectral profile. It is possible that using other types of classification techniques on SERS spectra collected from sputtered SERS substrates will be able to better characterize and identify explosive samples with a high rate of accuracy and precision. Works published by Botti and colleagues (10) and Hwang and colleagues (18) also support the use of PCA for the identification of explosives using SERS spectra. Botti and colleagues (10) analyzed explosive solutions (RDX, TNT, PETN, and EGDN) at a 100 ppm concentration and generated distinct score clusters in two-dimensional (2D) PCA space. Hwang and colleagues (18) report an average classification rate of 99.3% of the 14 explosive compounds used in their study; the authors note that PETN, AN, and HNIW were misclassified as HMX, AND, and RDX in 2% of cases.

	Surface-enhanced Raman spectra were reported for explosives and explosive precursors present at picogram levels using a gold–silver alloy film as the SERS substrate. The detection method involved drop-casting the solution containing the explosive onto the substrate and measuring with a benchtop integrated Raman system with 638-nm laser excitation. PCA results show that separation among explosive samples clustered in 3D space will support efforts to characterize and positively predict the identity of unknown explosives encountered in the field. Future experiments will include varying the ratio of gold to silver in the film, and exploring how different roughened features of the film contribute to the SERS response. The results are significant for safety applications requiring rapid identification of explosives.

	We would like to thank Dr. Laurent Chabanne and the Chemistry Imaging Facility at the University of Bristol in the UK for collecting SEM images of the SERS substrates. We would also like to thank Dr. Neil Shand from DSTL for providing solutions of TNT, RDX, and PETN.

		E. Finot, T. Brulé, P. Rai, A. Griffart, A. Bouhélier, and T. Thundat, 

		G. McNay, D. Eustace, W.E. Smith, K. Faulds, and D. Graham, 

		A.W. Fountain III, S.D. Christesen, R.P. Moon, J.A. Guicheteau, and E.D. Emmons, 

		P.H.B. Aoki, L.N. Furini, P. Alessio, A.E. Aliaga, and C.J.L. Constantino, 

		B. Sharma, M. Fernanda Cardinal, S.L. Kleinman, N.G. Greeneltch, R.R. Frontiera, M.G. Blaber, G.C. Schatz, and R.P. Van Duyne, 

		S. Botti, L. Cantarini, S. Almaviva, A. Puiu, and A. Rufoloni, 

		P.M. Fierro-Mercado and S.P. Hernández-Rivera, 

		S.S.R. Dasary, A.K. Singh, D. Senapati, H. Yu, and P.C. Ray, 

Introduction to Multivariate Statistical Analysis in Chemometrics

 (CRC Press Taylor & Francis Group, Boca Raton, Florida, 2009).

		S. Botti, S. Almaviva, L. Cantarini, A. Palucci, A. Puiu, and A. Rufoloni, 

		M.E. Farrell, E.L. Holthoff, and P.M. Pellegrino, 

		Y. Sun, Master of Science thesis, University of Iowa, 2013.

		T.J. Johnson, Y-F. Su, K.H. Jarman, B.M. Kunkel, J.C. Birnbaum, A.G. Joly, E.G. Stephan, R.G. Tonkyn, R.G. Ewing, and G.C. Dunham, 

		B. Zachhuber, G. Ramer, A. Hobro, E.T.H. Chrysostom, and B. Lendl, 

		D.D. Tuschel, A.V. Mikhonin, B.E. Lemoff, and S.A. Asher, 

		S.K. Sharma, A.K. Misra, and B. Sharma, 

		J. Hwang, N. Choi, A. Park, J-Q. Park, J.H. Chung, S. Baek, S.G. Cho, S-J. Baek, and J. Choo, 

 are with Ocean Optics, Inc., in Dunedin, Florida. 

 is Ocean Optics, Inc., in Winter Park, Florida. Direct correspondence to: 

This study explores the use of a novel SERS substrate that can enhance the Raman signals of explosives that are present in picogram quantities in neat solutions using a visible laser wavelength and a compact Raman instrument.

Trace-Level Detection of Explosives Using Sputtered SERS Substrates

Melamine is a dangerous substance when it is used to increase the apparent protein content of food. In this article, the use of novel gold nanoparticles for surface-enhanced Raman spectroscopy (SERS) measurements with raster orbital scanning sampling is described for the detection of melamine and other food contaminants. Although silver nanoparticles are often used for melamine SERS measurements, gold nanoparticles are more stable with less stringent requirements for storage and handling. Gold SERS substrates can also be used for the detection of other harmful food additives and adulterants, including antifungal dyes, antibiotics, and pesticides. 

Food safety is a global concern that has spawned rigorous guidelines and numerous regulatory agencies to protect consumers from both harm and fraud (1). Widespread illness and even death have resulted from the adulteration of food with compounds like melamine, which are added to ensure substandard foods meet dietary guidelines. Trace contaminants and residues from pesticides and antifungal agents used in the growth, processing, and storage of food products have been shown to be toxic to humans with impacts ranging from digestive problems to death. Counterfeit food products deceive consumers, risking their health while emptying their wallets. It is these ongoing deceptive and dangerous practices and the widespread illness and economic impact they have caused that have driven the establishment of strict food safety regulations and the need for careful food inspection.   

Consumers rely on government scrutiny and oversight for protection from dangerous food additives and contaminants. The use of melamine and its derivative, cyanuric acid, have invited scrutiny and inspired tighter regulation of fungicides, pesticides, and antibiotics used for crop protection. The antifungal agents crystal violet and malachite green are inexpensive and effective against fungal and parasite infections in fish, but are not approved for use in aquaculture because of their mutagenic impact on humans. The fungicide thiram is used to protect crops from deterioration both before and after harvest, but has high enough toxicity that it requires protective clothing for handling. The broad-spectrum antibiotic chloramphenicol is banned for food use and is tightly regulated to avoid toxicity and the potential for the creation of antibiotic-resistant bacteria. These compounds and many others have documented health and safety concerns requiring regulation and inspection of food products.   

With the potential for serious health and economic effects, there is a need for a rapid, low cost, and sensitive technique to detect and identify harmful food contaminants. Although there are a variety of methods and techniques now available for the identification and quantification of these potentially harmful compounds, these techniques (high performance liquid chromatography–mass spectrometry [HPLC–MS], gas chromatography–mass spectrometry [GC–MS], and ion-mobility spectrometry [IMS]) are often expensive, time- and labor-intensive measurements requiring significant sample preparation and highly trained personnel to administer. For a viable, commercial application, a method is needed that requires little to no sample preparation, is applicable to a range of food contaminants, and can provide fast, accurate identification and quantification. Surface-enhanced Raman spectroscopy (SERS) meets these requirements and is a good option for the detection of food contaminants and additives.   

Raman spectroscopy is a high-resolution, vibrational spectroscopic technique in which each molecule produces a unique spectral "fingerprint" that can be used to identify and differentiate it from other sample components. Recent advances in lasers, detectors, and optical filter technologies have lowered costs and enabled considerable downsizing of Raman instrumentation such that the technology is more accessible and field deployment is now feasible. As a detection technology, Raman spectroscopy (2) has gained increasing interest because the Raman band frequencies relate to chemical bonding in the compound to be identified. This chemical specificity gives detection by Raman spectroscopy a distinct advantage. Different compounds have unique Raman fingerprints that can be used to identify them.  

The major drawback of Raman spectroscopy is the low cross-section (2) of the spontaneous Raman scattering, which affects the detection of substances at trace level. This small scattering cross-section results in lower Raman signals and makes detection and identification difficult for compounds like food contaminants and additives that are present at low concentrations. In 1977, a technique to overcome this limitation was demonstrated when the compound of interest was placed on a roughened noble-metal substrate. The result was a significant enhancement in the magnitude of the Raman scattering signal (3). This process is known as 

The enhanced sensitivity achievable with SERS makes it a viable option for the detection of food contaminants often present at trace levels. SERS is a variation of conventional Raman spectroscopy whereby analytes are adsorbed onto a noble metal (typically gold or silver) covered surface before analysis. Through a combination of chemical and electromagnetic effects, the Raman signal intensity is significantly "enhanced" when measured from SERS substrates fabricated from these noble metals. In very specific cases, detection at the single-molecule level has even been demonstrated (6,7). The combination of signal enhancement and chemical specificity make SERS a good candidate for the sensitive and selective identification of compounds including harmful food contaminants and additives (8,9).  

Since the early days of SERS, gold (Au) and silver (Ag) have been widely used as SERS substrates because of their strong SERS activity (10). The use of these metals has advanced with the development of nanoscience and nanotechnology, which give researchers the ability to control the shape, size, and composition of silver and gold nanoparticles. As a result, silver and gold are the most widely used SERS substrates. Theoretical calculations indicate that the Raman enhancement of a single gold nanoparticle is about 10

  with enhancement for a single silver nanoparticle as high as 10

In this paper, we describe the novel combination of gold substrates and raster orbital scanning (ROS) for the detection of melamine and other harmful food additives and contaminants. Data illustrating the increased stability of the gold substrates relative to silver substrates are shown along with the enhancement in signal achieved with ROS sampling versus traditional Raman sampling. The broad applicability of the gold SERS substrates is also shown for several other food contaminants.   

Although melamine is typically detected using silver SERS substrates with 532-nm laser excitation, we used gold substrates with 785-nm laser excitation for our measurements. Although melamine binds well to silver, one of the limitations of the silver substrates is a shorter lifetime, requiring modified atmospheric packing of the substrates and special storage and handling conditions (12,13). Gold, on the other hand, ages much more slowly with better stability than silver (14,15). This increased stability and longer lifetime are why we used gold substrates even though melamine binds better to silver. Gold also has the added benefit of working with a broader range of harmful food additives and contaminants.   

When the more stable gold SERS substrates are combined with an ROS Raman sampling technique, the result is a rapid, sensitive method for detecting several regulated or banned food additives and contaminants. ROS combines the resolution and power achieved with a tightly focused laser spot with the sensitivity of sampling over a large sample area. By rastering the laser spot over a large sample area, there is a higher probability of measuring spectra from SERS hotspots (localized regions of Raman signal enhancement) dispersed on the substrate. This results in higher sensitivity than with traditional Raman sampling where the laser is focused on a single location on the substrate. This is especially important with SERS substrates, in which the density and coverage of nanoparticles on the substrate can vary from location to location. Not only does ROS overcome this challenge of working with SERS substrates, it also provides a lower average laser power at the substrate and reduces the possibility for substrate or sample damage during exposure to the tightly focused laser.  

Silver and gold nanoparticles were inkjet printed on cellulose substrates. For substrate aging studies, melamine was tested on the day of printing and up to one week after printing. Measurements were made by drop casting 12 µL of the compounds suspended in various solvents to the printed SERS substrates mounted on standard glass microscope slides.   

The substrates were interrogated with either an IDRaman reader integrated Raman system from Ocean Optics with 785-nm laser excitation and ROS sampling (IDRaman reader) or with a modular Raman system from Ocean Optics comprising a QE 

 spectrometer configured for 785-nm Raman measurements (QE 

 Raman), a 785-nm laser for excitation (LASER-785), and a Raman probe for detection (RIP-RPB-785). Typical acquisition parameters were three scans to average and a 1-s integration time. Data were acquired using OceanView software (Ocean Optics). 

In all the plots presented, the spectra were adjusted for an appropriate baseline to enable comparison of peak height values. A Raman-inactive area of the spectrum was determined for each analyte, and the average value across that region was subtracted from the rest of the spectrum. This achieves a baseline correction that accounts for the noise seen in the raw spectrum. The spectra reported are raw spectra that have been baseline corrected. 

The observed shifts in the assigned peaks for the analytes could result from a variety of factors. It is known that differences in solvents and, more importantly, physical features in SERS substrates will cause the observed peaks to change. These changes explain any variation in peak assignment when compared to reference literature.  

To demonstrate the impact of substrate aging on the SERS detection of melamine, silver and gold nanoparticles were inkjet printed on cellulose substrates. Melamine at 1 × 10

 M concentration in water was drop casted to the substrates and Raman spectra were measured on the day of printing and 24 h after printing.   

The Raman spectra measured with the modular Raman system using traditional Raman sampling for melamine on a silver substrate are shown in Figure 1. Note the characteristic peak for melamine near 700 cm

  in the spectrum measured just after the substrate was printed (day 0). Deterioration of the silver substrate is observed by the loss of this peak in the Raman spectrum measured 24 h after the substrate was printed (day 1). The silver substrate degraded significantly within 24 h of printing, resulting in an inability to detect melamine with this aged substrate.  

Figure 1: Aging of inkjet-printed silver SERS substrates 24 h after printing: Raman spectra for 1 Ã 10-2 M melamine in water measured using traditional Raman sampling.			

When a gold substrate is used to measure the same melamine solution, the improvement in stability is dramatic. As shown in Figure 2, the characteristic melamine peak is still observed one week after the gold substrate was printed. Additional studies (data not shown) have demonstrated even longer stability with measurements made with gold substrates more than 60 days after printing. This increased stability makes gold substrates easier to work with than silver substrates and more suitable for use in a commercial application.     

Figure 2: Improved substrate stability with inkjet-printed gold SERS substrates one week after printing: Raman spectra for 1 Ã 10-2 M melamine in water measured using traditional Raman sampling. 			

The improvement in the SERS measurements for melamine is even better when the excellent stability of the gold substrates is combined with the ROS sampling technique. When the relative intensity of the Raman peaks for melamine in Figure 2 is compared to the relative intensity of the melamine peaks in Figure 3, the signal measured with ROS sampling (Figure 3) is almost two times the relative intensity of the signal measured with traditional Raman sampling (Figure 2). This increase in Raman intensity measured using ROS sampling clearly demonstrates the dramatic improvement in sensitivity achieved with ROS when a larger area of the SERS substrate is sampled. By sampling a larger surface area, more Raman hotspots are measured leading to improved sensitivity and the possibility to detect even lower concentrations of melamine or other food contaminants and additives.  

Figure 3: Improvement in Raman signal with ROS sampling of inkjet-printed gold SERS substrates: Raman spectra for 1 Ã 10-2 M melamine in water measured using ROS sampling.			

Detection of Other Compounds of Food Safety Concern

As demonstrated in Figures 4–7, in addition to their stability, another advantage of gold substrates is that they can be used for the detection and discrimination of a wide range of potentially harmful food contaminants and additives. The Raman spectrum for the fungicide thiram is shown in Figure 4. The sample was prepared by drop casting a solution containing 1 × 10

  M thiram in acetone to the gold substrate. This toxic fungicide is used to prevent crop damage before and after crop harvest. The toxicity of this fungicide is high enough that it requires protective clothing for handling. As shown in Figure 4, gold SERS substrates and ROS sampling has sufficient sensitivity to detect relatively low levels of this toxic fungicide.  

Figure 4: The fungicide thiram on gold SERS: Raman spectrum for 1 Ã 10-3 M thiram in acetone measured using ROS sampling.			

Chloramphenicol is a broad-spectrum antibiotic used in aquaculture. It has been banned for food use and is tightly regulated to avoid toxicity and the potential for the creation of antibiotic-resistant bacteria. As shown in Figure 5, SERS with a gold substrate and ROS sampling enables detection of 1 × 10

  M concentrations in ethanol of this banned food contaminant.  

Figure 5: The antibiotic chloramphenicol on gold SERS: Raman spectrum for 1 Ã 10-3 M chloramphenicol in ethanol measured using ROS sampling.			

In Figures 6 and 7, Raman spectra for the fungicides crystal violet and malachite green are shown. These fungicides are also banned for use in aquaculture because of their toxicity. These antifungal agents are low cost and very effective against fungal and parasite infections in fish, but they are not approved for use in aquaculture because of their potential for mutagenic impact on humans. A comparison of these spectra demonstrates not only the sensitivity of the technique, but also the specificity of Raman analysis. Each fungicide has a distinct spectral fingerprint allowing for discrimination and identification. As shown in Figures 6 and 7, both fungicides are easily detected at 1 × 10

  M concentrations in ethanol with SERS using gold substrates and ROS sampling.  

Figure 6: The fungicide crystal violet on gold SERS: Raman spectrum for 1 Ã 10-4 M crystal violet in ethanol measured using ROS sampling.			

Food safety is a global concern. Outbreaks and even death have driven the search for improved technologies to detect very low levels of food contaminants. The tremendous signal enhancement associated with SERS, combined with ROS sampling of highly stable gold substrates, make SERS a good candidate for the detection of trace levels of food additives and contaminants. With recent advances in handheld Raman instrumentation including the availability of handheld devices with ROS sampling, SERS is a viable option for the fast, low cost, and sensitive detection of these dangerous compounds outside the laboratory setting. 

Figure 7: The fungicide malachite green on gold SERS: Raman spectrum for 1 Ã 10-4 M malachite green in ethanol measured using ROS sampling.			

Yvette Mattley, PhD, Maja Sourdaine, Derek Guenther, Cleo Harvey, and Adrian Guckian, PhD

, are with Ocean Optics in Dunedin, Florida. Direct correspondence to: 

Practical Food Safety: Contemporary Issues and Future Directions

, R. Bhat and V. Gomez-Lopez, Eds. (John Wiley and Sons, Hoboken, New Jersey, 2014), pp. 1–9.  

, D.J. Gardiner and P. R. Graves, Eds. (Springer-Verlag, Berlin, Germany 1989), pp. 1–12. 

 (4) M. Fleischmann, P.J. Hendra, and A.J. McQuillan, 

 (5) R.A. Alvarez-Puebla, D.S. Dos Santos Jr., and R.F. Aroca, 

 (8) A.P. Craig, A.S. Franca, and J. Irudayaraj, 

(10) J.A. Creighton, C.G. Blatchford, and M.G. Albrecht, 

Melamine is a dangerous substance when it is used to increase the apparent protein content of food. In this article, the use of novel gold nanoparticles for surface-enhanced Raman spectroscopy (SERS) measurements with raster orbital scanning sampling is described for the detection of melamine and other food contaminants.

Derek Guenther

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Trace-Level Detection of Explosives Using Sputtered SERS Substrates

Detection of Melamine and Other Harmful Food Contaminants Using Gold Nanoparticles, Surface-Enhanced Raman Scattering, and Raster Orbital Scanning