Yvette Mattley

Raman spectroscopy is an excellent technique for the identification and characterization of fuels. With no requirement for sample preparation and the power to identify and quantify materials, Raman has many uses across a range of industries.

	Biodiesel is a nonpetroleum-based diesel fuel made from vegetable oil or animal fats. Biodiesel can be used in most diesel engines with little to no engine modification required, and can be blended with petroleum diesel fuel to provide a cleaner burning, lower emission diesel fuel.

	As biodiesel production increases, Raman spectroscopy will become a useful analytical tool during the refining process to assess incoming raw materials, monitor the production process, and confirm the quality of the final product.

	Today's Raman measurement options range from handheld and benchtop systems such as the Ocean Optics IDRaman series, to modular, "build your own" Raman systems including spectrometers, lasers, probes, and sample holders.

	The Raman measurements described here were made with a modular setup comprising the Maya2000 Pro-NIR spectrometer, a 785 nm Raman laser, a Raman-coupled probe for 785 nm Raman, and a sample holder. Acquisition parameters were a 500 ms integration time with no scans averaged and no boxcar smoothing. Samples of corn oil (sometimes used as a diesel alternative) and petroleum-based diesel were placed in small glass vials for analysis.

	The Raman spectra for corn oil and diesel are shown in Figure 1. While these spectra share some features due to the hydrocarbon content of the samples, there are several spectral differences observed in the fingerprint region from 500–2000 cm

. Even though both samples are suitable for use as fuel in diesel engines, they have distinct Raman spectra that distinguish the corn oil-based biodiesel fuel from the petroleum-based diesel fuel. Note that in addition to identifying the fuel type based on its Raman fingerprint, one could obtain more quantitative information from the spectra by applying the appropriate chemometrics models.


	Figure 1: Raman is an excellent method for distinguishing corn oil-based biodiesel fuels from petroleum-based diesel fuels.

	One notable difference for the spectrum of corn oil is the presence of stearate (a form of fatty acid found in animal and vegetable fats and oils) in the region from 1600–1800 cm

. While stearate artifacts and other spectral differences allow for easy discrimination of these fuels, samples with even more closely aligned spectral peaks can be distinguished. Indeed, by using a narrower slit in the spectrometer optical bench, you will achieve a higher Raman shift resolution over a narrower spectral range.

	With their unique hydrocarbon composition, fuels are well suited for identification and characterization using Raman analysis. The wealth of spectral features in the Raman spectra for fuels can be used in a range of applications including determination of critical fuel parameters, fuel classification, and detection of counterfeit fuels.

	With all the choices available, the modular approach to Raman measurements provides a nearly endless choice of setups with the ability to change or add components to meet your evolving measurement needs.


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Articles

Characterization of Diesel Fuel Using a Modular Raman System

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.

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

Although Raman spectroscopy is an excellent analytical tool, Raman signals are often weak and traditional slit spectrometers typically have poor optical throughput, limiting their effectiveness in low light level setups. Using a high throughput virtual slit design, the Apex 785 Raman spectrometer from Ocean Optics eliminates trade-offs between sensitivity and resolution.

Conventional spectrometers use a narrow entrance slit to achieve high resolution, but at the expense of throughput. The slit can reject as much as 75–95% of the light entering the optical bench. With high-throughput virtual slit (HTVS) technology licensed from Tornado Spectral Systems, Apex spectrometers overcome the compromise between throughput and spectral resolution.  

Apex uses a beam reformatting technology that eliminates the need for a physical slit to achieve high resolution. When combined with precision optical coatings, volume phase holographic (VPH) gratings, and solid state detector technology, spectrometer throughput as high as 95% is achievable. 

Also, Apex spectrometers use a large input aperture and specially configured optical components to compress, reformat, and then expand the light beam, which narrows the input aperture along the dispersion axis while preserving total flux. 

To maintain peak optical throughput, Apex uses a 1200 line/mm VPH grating blazed at 830 nm in an optical bench with an uncooled, NIR enhanced back-thinned CCD array detector. 

Raman measurements of paracetamol (acetaminophen), polystyrene, isopropanol, and toluene were made using the Apex spectrometer (APEX-RAMAN-785) in a setup with a 100 mW 785 nm laser diode, a Raman sample holder and a 785 nm Raman fiber optic probe. Integration time intervals ranged from 

195 ms for toluene to 750 ms for paracetamol. 

As shown in Figure 1, the Apex measures spectral data over a Raman shift range beyond 3800 cm

  with high spectral density, delivering Raman data with more spectral features for better library matching and sample identification. Also, the Apex spectrometer enables the acquisition of high quality Raman spectral data at very short integration times. 

Figure 1: This Raman spectrum for paracetamol was acquired at 750 ms integration time.			

Typical Raman measurements require seconds to achieve similar signal levels. This rapid measurement speed enables measurements in process flows and for samples that change over time. The low integration times required for the Apex also allow for the measurement of multiple samples in the time it takes to obtain one spectrum from a less sensitive spectrometer. This provides the opportunity for signal averaging to increase SNR, further improving measurement accuracy. 

A new spectrometer design based on HTVS optical slicer technology has been shown to provide a significant increase (a factor of 10–15×) in optical throughput without any detriment in optical resolution. In addition, the technology allows users to achieve high sensitivity without the need for long integration times, detector cooling, or high powered excitation sources; to use shorter measurement times for faster measurements; to acquire more accurate spectral signatures; and to use lower power excitation sources to minimize photodegradation. 

 (1) Tornado Spectral Systems contributed to this application note. 

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Although Raman spectroscopy is an excellent analytical tool, Raman signals are often weak and traditional slit spectrometers typically have poor optical throughput, limiting their effectiveness in low light level setups.

Eliminating the Trade-Off Between Resolution and Throughput in Raman Spectroscopy

As Raman spectroscopy becomes a more sophisticated and widely used research tool, it is also penetrating industry, where manufacturers are harnessing it for process monitoring and quality control. This tutorial examines how Raman is well-suited to tackle two of the challenges faced by industry: mixtures and isomers. We look at recent examples in the literature addressing real industry applications and illustrate the solutions using some simple Raman experiments. First, we demonstrate nondestructive measurement of the concentration of a specific component in a mixture (ethanol), even when the mixture is complex and its other components are unknown. Second, we show how Raman spectroscopy is well-suited to distinguish the different isomers of a compound, showing both alcohol and xylene isomer spectra as examples.

Raman spectroscopy has come a long way since the Raman effect was first observed in 1928. For decades it was an esoteric technique, one that was complicated to use because of the need for a powerful, coherent source and sensitive detectors. The last two decades have seen the field come to maturity, thanks in large part to lasers with improved stability, power, size, and cost, as well as more sensitive detectors. Better filtering technology enables steeper and deeper blocking of the laser and fluorescence background and chemometrics software extracts more information from the generated spectra. Furthermore, the market now offers "build your own" modular systems in which the components are designed to be easily interfaced, giving users greater flexibility to customize or alter their system as required.   

When turnkey operation is necessary, application-specific systems designed for industry address chemical identification, pharmaceutical quality control, process monitoring, and first responder applications with novice-friendly features like trigger operation and integrated display screens. Though not quite a household name, Raman spectroscopy has become a mature spectroscopic technique in its application to industry. 

Nonetheless, Raman spectroscopy still retains an air of mystery for inexperienced users. Just how does one coax these spectra with hundreds of peaks to reveal their secrets? This article looks at some of the complex problems being tackled by Raman spectroscopy in research and industry and illustrates the method of analysis via spectra from chemicals possessing much simpler Raman signatures. 

It's easy enough to grasp how Raman spectroscopy can be used to identify a specific chemical — a spectrum is generated where peaks correspond to specific vibrational and rotational modes within the molecule. Each chemical has its own unique fingerprint, which can be cross-referenced with a library of known Raman spectra for identification. When dealing with a mixture, however, peaks quickly begin to overlap and spectra become more convoluted.   

In some cases, the Raman spectra of the mixture's constituents are quite simple, and it is only the conditions of measurement that are complex. Raman spectroscopy has been evaluated for real-time monitoring of the quality of liquid oxygen in the delivery line during ground testing of rocket engines, in which high temperature and pressure limit the performance of commercially available sensors. Tiwari and colleagues (1) developed an all-optical cryogenic fluid sensor to measure the concentration of liquid nitrogen in liquid oxygen, working with the 2331 cm

  peak of liquid nitrogen and the 1556 cm

  peak of liquid oxygen. Their work compared performance using both standard 532-nm lasers and a more cost-effective 670-nm diode laser. They found the sensor to be very sensitive at low concentrations, capable of measuring liquid nitrogen percentages as low as 1% in a sample mixture for a 532-nm laser and ~20% for a 670-nm diode laser (limited by the need for better filtering of background light). 

At other times, the constituent to be measured is a relatively simple molecule, but the mixture is complex. Anand and colleagues (2) demonstrated that it is possible to measure the proportion of methanol by volume in gasoline mixtures using Raman spectroscopy. Methanol is favored as an alternative transport fuel because it is relatively inexpensive, is nonpetroleum based, burns cleanly, and can be stored as a liquid at room temperature. It can be used in a modified version of the standard combustion engine, with an 85:15 methanol–gasoline mix used most often. Anand and colleagues used a 785-nm excitation laser, observing that the peak height at 729 cm

  decreased linearly as the proportion of methanol by volume increased.   

To illustrate how a single component in a mixture can be quantified, we prepared standard solutions of ethanol (Aldrich Chemicals) in deionized water ranging from 0–100% in glass vials. Raman spectra were taken using a modular system from Ocean Optics, comprising a QE65000-Raman spectrometer (range 780–930 nm, optical resolution ~0.4 nm FWHM, and 90% quantum efficiency), SpectraSuite operating software, an InPhotonics 785-nm probe (RIP-Probe-785), and a 785-nm stabilized laser. All measurements were taken using the same acquisition parameters: an 8-s integration time, 1 scan to average, and 0 box car smoothing. We used the peak height at 880 cm

  to generate a second-order polynomial calibration curve against concentrations for the standard solutions (Figure 1).  

 Figure 1: Calibration curve for ethanol in water (inset shows 880 cm-1 peaks used to generate the curve).			

Also, a Raman spectrum was taken for some discount-store hand sanitizer using the same acquisition parameters, with the peak height at 880 cm

  used to determine the ethanol content from the calibration curve. Hand sanitizers contain up to a dozen inactive ingredients, such as carbomer, triethanolamine, propylene glycol, tocopherol acetate, dyes, and scents. The resulting spectrum is more complicated than ethanol in water, but the 880-cm

  peak remains very clear (Figure 2). Though most brand-name sanitizers contain 60% or more ethanol, as recommended by the FDA, the discount sanitizer was found to have only ~45% ethanol. 

Figure 2: Raman spectra of pure ethanol and an unknown hand sanitizer.			

Peak height is often sufficient for quantitative analysis, but using area under the peak can improve accuracy.  When there is significant interference between characteristic peaks, peak ratios are sometimes used for analysis, or partial least squares analysis is applied through commercially available chemometric software packages such as GRAMS AI from Thermo Scientific and Analyze IQ Lab from Analyze IQ Limited.   

Sometimes extensive data processing is needed to achieve the desired accuracy, as in the case of McGoverin and colleagues (3), who had some success in predicting the solid fat content and triacylglycerols of anhydrous milk fat, the most commercially important separated fat component of milk. The study used a 752-nm excitation laser with a spectrometer for detection. They used a combination of software to perform a number of corrections, including smoothing, correction for baseline, standard normal variate (to remove scatter), Norris derivative smoothing, and area normalization. They used additional analysis methods to determine which calibration treatments yielded the best quality prediction models, working with multiple bands within the spectra.  

Figure 3: 1-Butanol and 2-butanol isomers.			

Raman spectroscopy also has the ability to look beyond chemical structure in quantification. Fourier transform (FT)-Raman has been used to measure the degree of crystallinity of cellulose, which is important because it affects physical, mechanical, and chemical properties. Tensile strength, dimensional stability, and density all increase with crystallinity, but chemical reactivity and swelling decrease. Though most often measured by wide-angle X-ray scattering, researchers Agarwal and colleagues (4) found FT-Raman to be a more accurate method for determining the degree of crystallinity in cellulose. FT-Raman is preferred over conventional Raman in this case because of its ability to analyze samples that would typically fluoresce.   

Some of the mixtures under scrutiny in industry contain two or more isomers of a given chemical. Even though isomers share the same molecular formula, their properties can be very different depending on the type of isomer. Structural isomers differ in arrangement of their functional groups, such as degree of branching of hydrocarbons or the positions of functional groups on a chain or ring. Their chemical properties tend to be quite different. Stereoisomers share the same bond formation, but differ in how one or more atoms or functional groups are positioned geometrically in space; diastereomers possess one or more portions that appear "flipped" or rotated relative to the other, while enantiomers are full mirror images of one another. Stereoisomers share the same physical properties and behave very similarly in chemical reactions, unless reacting with other stereoisomers or enzymes, giving them very different biological effects.  

Figure 4: Structures of p-, o-, and m-xylene isomers.			

Separating isomers is a challenge that appears regularly in industry and can require highly specialized techniques. Chromatography, absorption, and crystallization all can be used, but become more difficult when the physical properties of the isomers are similar. Any improvement in efficiency or selectivity can mean significant cost savings, driving the development of new separation techniques.   

We find one such example in the gasoline industry, where benzene content has been decreased, increasing the need for catalytic isomerization to convert straight-chain hydrocarbons to branched hydrocarbons. Branched hydrocarbons burn more efficiently and have a higher octane number, but are difficult to separate from the residual linear hydrocarbons. Molecular sieving is typically used, but requires large amounts of zeolite. The use of novel sorbents such as silicalite to exploit subtle entropy effects may prove more efficient (5), but requires real-time monitoring.   

Another example comes from the textiles industry, where dinitrotoluene is a key component in making polyurethanes used in materials like insulation, soundproofing, linings for clothing and sleeping bags, cushions, and spandex. A mixture of the isomers 2,4- and 2,6-dinitrotoluene is used because of the difficulty of separation, despite the expectation that polyurethanes synthesized from pure 2,6-dinitrotoluene would have a higher glass transition temperature and greater stability (6). If it could be efficiently isolated, the reaction speed with a single isomer would also be more consistent. 

Raman spectroscopy is well-suited to serve as a diagnostic tool for these types of separation challenges, both for development and in-process. It is rapid, noninvasive, moves easily between solids and liquids, and deals well with mixtures. Its ability to instantly probe the vibrational and rotational modes of any molecule with a single incident wavelength makes it extremely versatile. To better understand how Raman would be used in these applications, let's look at the spectra of some simple compounds occurring as isomers: butanol and xylene.   

Butanol occurs as 1-butanol, a naturally occurring minor product of fermentation and an intermediate in the production of butyl acetate (a flavorant and industrial solvent), and as 2-butanol, produced on a large scale as a precursor to the industrial solvent methyl ethyl ketone (found in many domestic cleaning agents and paint removers). 2-Butanol is chiral, found as an equal mix of the two stereoisomers.   

Xylene occurs as three isomers: meta-, ortho-, and para-xylene, depending on where the methyl groups are attached to the benzene ring. Technical grade xylene is typically 45–55% 

-xylene, with the remainder consisting of ethyl benzene. Xylene is used as a solvent in many industries, from leather and printing to rubber and plastic. It is also used for cleaning steel, as paint thinner, and can be found in gasoline and airplane fuel. Knowledge of the relative proportion of the three isomers is important in the manufacturing of polymer compounds.  

Because of its potentially adverse health effects, xylene falls into the volatile organic compound (VOC) category. Regulatory agencies monitor the levels of xylene in drinking water, in the workplace and in the environment. Because only 

-xylene are man-made), a technique that can discriminate among the three isomers is useful.   

Samples of each chemical isomer were prepared in glass vials (Aldrich Chemicals) and placed in sequence into a closed container into which the laser probe was directed. Measurements were made using the same modular Raman system from Ocean Optics described previously, but using an 8-s integration time.   

The spectra acquired for butanol are shown in Figure 5. Some peaks are shared between the two isomers, including the typical CH

  (7). Alcohols have distinct skeletal vibrations that are caused by hydrogen bonding. The addition or deletion of groups causes changes in the geometry and distribution of electrons, altering the Raman spectra. This is seen in the range 700–1200 cm

  because of differences in the C-C and C-O stretching modes of the two isomers. The broadened peak at 775–785 cm

  for 2-butane is consistent with a wider range of vibrational frequencies calculated for the different conformers relative to the more consistent vibration at 842–844 cm

Figure 5: Raman spectra of 1- and 2-butanol.			

Spectra acquired for xylene are shown in Figure 6. Although all three isomers of the xylene share peaks that are caused by ring modes in the 1400–1600 cm

  range, with a unique C-H bending band for 

  (7). By using partial least-squares regression analysis, Cooper and colleagues (9) demonstrated that the concentration of an individual xylene isomer can be measured to within ±0.15% using a low-cost dispersive spectrometer. More recently, coherent anti-Stokes Raman scattering (CARS) has been used by Li and colleagues (10) to measure concentrations as low as 1% in binary mixtures of 

Figure 6: Raman spectra of p-, m-, and o-xylene.			

When the isomerism is more subtle, such as in stereoisomers, multiple peaks and peak ratios may be needed for identification, as demonstrated by Zheng and colleagues (11) in their studies of reversibly photoswitchable azobenzene. Photoswitchable molecules can act as molecular machines. Azobenzene is a popular choice for study because of its simple molecular structure and spectra. It alternates between the cis and trans isomers upon exposure to UV and blue light, respectively. Zheng and colleagues had the idea to combine the single-molecule sensitivity of surface enhanced Raman scattering (SERS) with a controlled nanoscale environment of self-assembled monolayers of alkanethiol molecules on gold. They matched the excitation wavelength to the resonant wavelength of the Au substrates, using a 632.8-nm He-Ne laser with a Raman system for detection. They then took SERS spectra of the tethered azobenzene while alternating exposure of the sample to UV (365 nm) and blue (450 nm) light to trigger photoisomerization of the azobenzene between the cis and trans forms of the molecule (Figure 7). 

Figure 7: Isomerization of azobenzene between cis and trans forms. Image adapted from http://en.wikipedia.org/wiki/File:Azobenzene_isomerization.png.			

As a benchmark, they compared their data to theoretical calculations of the isomers' Raman spectra, generated via B3LYP/6-31G* calculations (12) using a NWChem program package. Reversible cis

trans photoisomerization was observed by monitoring peak area ratios as a function of time as the sample was exposed to UV and then blue light in turn, showing good agreement with simulation. Their work has applicability to all-optical molecular devices. 

Raman spectroscopy is even finding its way into medical applications and biotechnology. Cysteamine exhibits rotational isomerism and is used in the treatment of radiation sickness as well as other disorders of cystine excretion. Goto and colleagues (13) used SERS to probe the molecule while exerting a magnetic pulling force to control the rotational isomerization equilibrium between the trans and gauche modes. They used a 514.5-nm laser for excitation in combination with a Raman microscope system. Relative peak intensities for the C-S stretching modes for the two isomers clearly showed that the exerted magnetic force was able to shift the molecule from the gauche  to the trans conformation by exerting a stretching force.   

Finally, our review would not be complete without an example of quantifying isomers using Raman. P. Novak and colleagues (14) used FT-Raman spectroscopy for in-situ monitoring during the synthesis of entacapone, a drug used in the treatment of Parkinson's disease. Entacapone exists as two isomers, E (trans-) and Z (cis-), with the drug form consisting of 99.5% E-isomer, the more pharmaceutically active of the two. They used the technique to compare reaction speed in different solvents, analyzing their data using principal component analysis.   

Raman spectroscopy has moved well beyond basic identification of compounds in its applicability to industry and now tackles more complex problems like quantification in mixtures and isomers with ease. The increasing availability of both turnkey and high quality modular systems allows even inexperienced users to harness this powerful technique.  

, is senior applications scientist with Ocean Optics in Dunedin, Florida. 

 is an applications laboratory specialist for Ocean Optics. 

  is principal at Mapleseed, LLC in Dunedin, Florida. Direct correspondence to 

 (1) V.S. Tiwari, R.R. Kalluru, F.Y. Yueh, J.P. Singh, W. St. Cyr, and S.K. Khijwania, 

 (2) K. Anand, V. Asundi, and R. Vasudeva, 

 (3) C.M. McGoverin, A.S.S. Clark, S.E. Holroyd, and K.C. Gordon, 

 (4) U.P. Agarwal, R.S. Reiner, and S.A. Ralph, 

Process for Separating Isomers of Dinitrotoluene, 

 Infrared Spectral Interpretation: A Systematic Approach

 (CRC Press, Boca Raton, Florida, 1999). 

 (8) H. Hageman, J. Mareda, C. Chiancone, and H. Bill, 

This tutorial demonstrates that Raman is well-suited to tackle two challenges faced by industry: complex mixtures and isomers.

Raman Spectroscopy as a Quantitative Tool for Industry

Raman spectroscopy is becoming a more commonly used technique for chemical analysis. Raman analysis is nondestructive, requires very limited sample preparation, and allows for the analysis of sample volumes in the microliter range. Also, Raman techniques can be used to acquire data through vial walls, pill pack windows, and bags — packaging forms that are frequently used in the biomedical and pharmaceutical industries. A major barrier to the acceptance of Raman in these industries is the cost of high-resolution Raman systems, often upward of $40,000.  

In recent years, low resolution Raman spectroscopy (LRRS) has gained increased attention and acceptance in the analytical laboratory as an affordable, convenient, and robust alternative to cumbersome wet chemical analysis or expensive FT-Raman and FT-IR spectrometers. LRRS systems relinquish resolution in favor of emphasizing basic identifying spectral features, or fingerprints, that often are distinct and clearly separated. High-resolution Raman systems perform very well, but might be unnecessary for most simple spectral identification applications. 

The introduction of the silicon charged-coupled device (CCD) detector was the first technological development to enable LRRS. The CCD sensor has replaced the more light-sensitive single-element detectors such as the photomultiplier tube and avalanche photodiode. Depending upon the coating applied to the CCD sensor, it can detect light in the 200-1200 nm range. Integrated into an optical bench, the CCD can allow for resolution as fine as 4 cm

. A back-thinned CCD detector, which is more light-sensitive than a standard CCD, has been integrated into Raman spectrometers allowing for  6 cm

 resolution, increased quantum efficiency, and high signal-to-noise ratios (S/N). Advances in these devices have improved wavelength stability and increased S/N, both crucial factors in building libraries and performing unambiguous spectral identification of raw materials. 

Another development that has contributed to the acceptance of LRRS in identification applications is the availability of low-cost laser diodes used as excitation sources. Unlike single-mode lasers that demand careful control of the laser line, diode lasers often have multiple modes with the instrument utilizing one of the available modes to excite the sample. While this advantage translates into a reduction of the price tag of the overall system, it introduces one drawback, the "mode-hopping" phenomenon, which can significantly degrade the repeatability of a spectral measurement.  

Portable, high-resolution spectrometers used in Raman applications are able to display the spectral shifts associated with mode-hopping occurrences. Spectral shifts of as much as 8-10 cm

 are sometimes observed among consecutive spectra, significantly reducing the transferability of a calibration model or a library from one instrument to another. A variety of techniques have been used to correct this problem, including fiber and volume Bragg gratings and distributed feedback lasers (DFBs). 

This article demonstrates a technique to obtain very high wavelength stability and reproducibility of a Raman spectrum using an inexpensive alternative to other single-mode laser options called grating-corrected laser stabilization. Proof-of-concept of this laser is first shown with a comparison in pixel drift between an off-the-shelf multimode laser and the grating-corrected laser. Finally, a specific Raman application is shown for pharmaceutical raw material fingerprinting and comparison with unknown samples.  

Two separate Raman spectrometers were used for this study. An Ocean Optics (Dunedin, FL) QE65000 Spectrometer (configured for the appropriate measurement range) was used for the first phase of the study. The QE65000 has a back-thinned detector that can be cooled to -10 °C with the onboard TE-cooler to reduce dark noise. The reduction of noise and dark signal allows integration times of up to 10 min for low-light level applications. 			

Two integrated R-3000HR instruments from Raman Systems, Inc. (Austin, TX) were used for the cross-comparison of pharmaceutical material libraries. The R-3000HR integrated system contains both a laser and an Ocean Optics HR2000 Spectrometer that can provide resolution of 6 cm

The off-the-shelf multimode laser is an undisclosed laser of relatively poor quality. The grating-corrected wavelength isolation laser, developed by Raman Systems, has a patent pending and is fully integrated into the Raman Systems R-3000HR system. Both lasers have approximately the same power output of 500 mW. 

The samples studied consisted of simple organics, and raw and active pharmaceutical ingredients. They were contained in standard, clear borosilicate scintillation vials with no additional preparation applied. Most samples were purchased from Sigma-Aldrich (Milwaukee, WI) or were obtained from a major pharmaceutical company. The Raman probes used with the Raman R-3000HR were standard lab probes provided by Raman Systems. The probes used with the QE65000 were standard probes provided by InPhotonics (Norwood, MA). Data was acquired using the prepackaged spectrometer system software with subsequent library analysis performed using Thermo Galactic's (Salem, NH)  Spectral ID software. 

The grating- corrected wavelength isolation approach should ensure reproducibility of Raman spectra on the 

-axis, thereby facilitating easy transfer of calibration models independent of spectral acquisition systems. The set of experiments described below show the results from measurements made with a grating-corrected multimode laser. We compare these results with the observed variation in the wavelength (8-10 cm

) of a noncorrected multimode 785-nm diode laser. 			

Spectra for mixed xylenes, which are very rich in spectral features, were measured using the back-thinned spectrometer with the multimode laser over a 12-h period. Spectral data was acquired every  10 min to establish the 

-axis drift. The results are shown in Figure 1.   

 Figure 1. Spectra of mixed xylenes collected over a 12-h period, one spectrum every 10 min, total of 72 spectra. Spectra collected using a back-thinned, TE-cooled CCD-array detector and a low-performance multimode laser.			

This same experiment was performed on the integrated Raman system containing the grating-corrected laser on 

-xylene over 12 h with spectral acquisition every 12 min. The results for the integrated system are shown in Figure 2.    

Figure 2. m-Xylene spectra collected over a 12-h period, one spectrum every 12 min, total of 60 spectra. The reproducibility of the peaks is better than a single pixel of the CCD. The inset plot is a closer look at the strongest peak of the spectrum 1000 cm-1 peak of m-xylene.			

As demonstrated in the pixel shift comparison between the two figures, the relatively inexpensive solution to correcting poor-quality multimode lasers provides exceptional pixel stability. This stability is required to make Raman instrument-independent libraries for fingerprinting pharmaceutical samples. Also, because grating-corrected lasers are a less-expensive alternative to single-mode lasers, LRRS are attractive for the pharmaceutical QA/QC laboratories. 

An internal study was performed to illustrate the importance of reproducibility of spectra along the 

 axis. Here a raw pharmaceutical material identification application within the pharmaceutical industry was performed. The point to note is not so much the unambiguous identification of the unknown, but the transfer of the calibrated library from one instrument to another, facilitated by the reproducibility of the selected laser wavelengths. 

In this study, 12 pharmaceutical raw materials were obtained, five of which were clear liquids and seven of which were white powders. The study was conducted on the two higher-resolution instruments with both integrated systems containing the new grating-corrected lasers.  

Spectra for all of the materials were recorded on the first Raman instrument (with the grating-corrected laser), and a library then was created, based upon the fingerprint peaks identified by the acquisition software. After the complete library was acquired, spectra of two "unknown" samples, one clear liquid and one white powder, were then recorded on the other instrument (also containing the grating-corrected laser) and tested with the library. 

Identification was unambiguous. The unknowns were identified easily with the application of the grating-corrected isolation laser with the maximum off-shift of peaks from one instrument to the other of 2 cm

. Figure 3 shows the overlay of library sample 5 and unknown 1 (collected on the different instruments).  

Figure 3. Overlay of Library Spectrum 5 and Unknown 1 collected on different Raman instruments.			

Table I lists the relevant peaks identified in the library software. Sample 5 was recorded on the first Raman instrument and compared with the peaks identified in the sample unknown 1, recorded on the second Raman instrument.  

The same comparison was made with unknown 2. The library immediately detected that unknown 2 was sample 7 in the library (sample will remain unnamed due to proprietary issues). Table II lists the peaks identified for sample 7 recorded on the first instrument and then compared with the peaks identified in the unknown 2, recorded on the second instrument.  			

Cross-correlation of libraries in different instruments definitely is possible when using the grating-corrected laser. The largest shift is 2.24 cm

, which still is much smaller than the standard multimode laser shift of up to 8-10 cm

When comparing with library spectra and the subsequent transfer of such models across instruments, one of the most important requirements for unambiguous identification of peaks is the reproducibility of the spectral features. This study has demonstrated that by using a grating-corrected isolation of the excitation wavelength, reproducibility within an average of  1 cm

 can be obtained. Such grating-corrected, low-cost multimode diode lasers help make Raman a preferred method for quick and non-destructive testing. This methodology has proved extremely reproducible and provides definitive pharmaceutical material identification.

is director of business development for Raman Systems (Austin, TX). 

is marketing communications manager, for Ocean Optics (Dunedin, FL). E-mail: 

The authors present a novel technique for obtaining very high stability and reproducibility of a Raman spectrum, using grating corrected laser stabilization. An externally stabilized laser with a grating spectrometer provides exceptional quantum efficiency in the entire dynamic range. These components then are used to build a library of pharmaceutical raw materials and tested on samples of unknown material.

Yvette Mattley

Articles

Characterization of Diesel Fuel Using a Modular Raman System

Characterization of Diesel Fuel Using a Modular Raman System

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

Eliminating the Trade-Off Between Resolution and Throughput in Raman Spectroscopy

Raman Spectroscopy as a Quantitative Tool for Industry

Grating Corrected Laser Stabilization: A Case Study in Pharmaceutical Raw Material Identification