Market Profile: Spectroscopic Process Water Analysis

Spectroscopic analytical techniques accounted for more than 10% of the process water analysis market in 2006. The two technologies that essentially make up this market segment are turbidimeters and UV photometers. Use of direct online spectroscopic methods is limited to a few relatively large application areas. 

Turbidimeters measure the haziness or cloudiness created by suspended solids in water. Modern turbidimeters consist primarily of nepholometers, which measure the reflected light at 90° from a visible light source. Process UV instruments that are used for water analysis consist primarily of single or dual wavelength photometric probes that can detect organic compounds. Discrete analyzers, which must perform some type of reaction prior to detection and can be designed to detect a range of species, are not included.  

Direct spectroscopic process water instrumentation market			

Online turbidity measurement is used heavily in both industrial effluent monitoring as well as water treatment facilities. Dirty water that is pumped into municipal water systems from industrial facilities often is required to meet specific turbidity requirements, especially in the U.S. Municipal water treatment plants also monitor incoming water for treatment, as well as outgoing water that already has been treated, to verify its quality. Direct UV photometric probes are used for analyzing nitrogen compounds, such as nitrates and nitrites, and are found primarily in water treatment facilities. 

The combined market for online direct spectroscopic water analysis methods was worth about $50 million in 2006 and is expected to see annual growth in the mid-single digits. The foregoing data were extracted from SDi's market analysis and perspectives report entitled 

The Water Testing & Analysis Market: Lab & Process — Wastewater to Ultrapure Water

. For more information, contact Stuart Press, Senior Consultant, Strategic Directions International, Inc., 6242 Westchester Parkway, Suite 100, Los Angeles, CA 90045, (310) 641-4982, fax: (310) 641-8851, 

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Spectroscopic analytical techniques accounted for more than 10% of the process water analysis market in 2006. The two technologies that essentially make up this market segment are turbidimeters and UV photometers. Use of direct online spectroscopic methods is limited to a few relatively large application areas.

Market Profile - Spectroscopic Process Water Analysis

Gas chromatography–mass spectrometry (GC–MS) is an example of a "complicated" analytical method that nevertheless can be explained in simple terms and in a short time. GC–MS is known to the mature general public in venues such as forensic and environmental analyses, and even to younger children through multiple shows on television (even cartoons touch on analytical instrumentation). GC–MS instruments themselves are small and not overly expensive, and the data produced, while sophisticated, still can be displayed and manipulated by a personal computer in a manner most folks can understand. The single topic of GC–MS and how it is used actually can fill a series of talks and demonstrations. But even within a single presentation, the basis of GC–MS can be highlighted. At the end of 20 minutes, the kids in my third-grade class understood the concepts of the chromatographic separation, the measurement of mass, and the principles of fragmentation as a structural fingerprint. The teacher, a bit more old-school perhaps, wondered why I, as a chemist, did not bring beakers and test tubes as part of my demonstration. I retreated quickly once the kids began chanting "GC–MS" as part of a catchy limerick about what to ask for at Christmas. The students in that class are just now graduating from high school, and I hope a few of them developed a spark for science.  

This column discusses some simple analogies to describe GC–MS to an elementary-school student audience. The analogies used can be made more sophisticated for an older group of school students, or for audiences of senior citizens. Remember that a detailed and technically correct explanation might seem more satisfying to you as a scientist, but your audience will remember simple, direct analogies and your skill in linking them to the points you are trying to explain. If you have similar experiences to share, I look forward to hearing from you. The contact information is in the biography at the end of the column. I divide the "GC–MS" explanation into four parts, the latter three of which are the mass spectrometer instrument itself. The section on the gas chromatograph can, with appropriate changes, be applied to most any chromatographic separation. Similarly, the MS analogies can be extended (with some modifications) to most mass spectrometers, and not just GC–MS instruments. I admit to some difficulty in putting together an elementary analogy for Fourier-transform MS (FT-MS); I hope to use a sound effect to demonstrate different frequencies of different ion masses trapped in the cell. 

The basis of a GC separation is a differential interaction of mixture components with the stationary phase of the column. Because there is a physical transport and a time involved, the differential interaction shows up as an elution time difference. The GC analogy is fairly easy to set up in a classroom setting. For elementary-school students, I use a cafeteria-line analogy. For older folks, I use a wedding receiving line to outline the situation. With the former, create a cafeteria food line in the classroom by rearranging chairs and recruiting three volunteers — call them Curly, Larry, and Moe. Each volunteer has a different behavior, which you explain clearly to the classroom, and which is rooted in jelly bean selection. Curly is hungry and stops at each station in the cafeteria line for only the time needed to grab some jelly beans — any jelly beans. Larry only likes red and pink and orange jelly beans, so he has to spend more time at each station to get only the jelly beans he likes. Moe is the picky eater and spends a long time at each stop trying to find the blue jelly beans, which are the only ones that he will eat. Start all three volunteers at the same time, encourage them to stay true to behavior, and the differential partitioning process becomes clear. The students are immediately able to determine the order of elution time as Curly, Larry, and then Moe. You can even repeat the "play" to show how retention times, given a constant set of behaviors and number of stations, remain the same, and even with more or less stations, in the same order. If the class is a bit more sophisticated, you can change the behaviors by "offering" different foods at the stops; this is the equivalent of a different stationary phase. You can then explain the reasons for subsequently different values and different orderings of retention times. The analogy to different food offerings in the cafeteria line (of more or less attractiveness to the students) is a rationale for chemical selectivity of different stationary phases.  

The students will remember the science because the analogy is simple, valid, and straightforward. They will be eager to participate because — well, because the analogy involves food (it's a bribe, I admit). My demonstration bag is packed with jelly beans of different sizes and colors. Different color jelly beans represent the food that elicits different behaviors of Curly, Larry, and Moe — who demonstrate different fondness for the various flavors. And once your volunteers have worked through your first aliquot of jelly beans, you have the attention of the class to segue into some elementary concepts of MS. With jelly beans, of course.  

Elementary-school students seem to have a basic idea of atomic structure, possibly from cartoons. ("Captain Proton" searched on Google provides links to both classic and modern science fiction escapades.) A web search also reveals many suggested elementary-school level demonstrations of atomic structure, using atomic particle surrogates of colored marshmallows, clay, or jelly beans. Jelly beans, in different colors and different sizes, will be used in each of the three different parts of the MS demonstration, and a polite guest lecturer leaves an ample supply in the classroom so that the students recall your demonstration for the next few weeks. NASA suggests using jelly beans and chocolates and raisins for the different atomic particles (1), and Matthew Besman describes an "edible atom lab" (2). Interestingly, the use of jelly beans to model atomic structure and reactivity extends through Grade 12, as described in state of Ohio standards (3).  

Explain to the students that, for purposes of simplicity, atoms will substitute for more complicated molecules that represent the compounds eluted from the gas chromatograph. They will grasp that molecules are collections of atoms, and atoms are collections of subatomic particles. Set up and explain an atomic-particle scheme, such as big black jelly beans for neutrons, big blue jelly beans for protons, and smaller white jelly beans for electrons. Choose your own easy-to-see colors, but remember that some students might be color-blind to red. (How many mass spectrometrists remember that Dalton was color-blind, did research in this field (4), and that an early term for color-blindness was Daltonism?) If you can prepare ahead of time, you can string white jelly beans along a nylon fishing line, and depending upon your dexterity, you might actually venture to show electrons in orbit around a jelly-beans-in-your-hand nucleus! Choose your student volunteers to assemble their own several-different-jelly-bean nuclei to match the number of electrons you pre-prepared on white jelly bean strings. The salient points are the presence of the protons and neutrons together in the nucleus, the balanced numbers of protons and electrons in the neutral atom, and the proclaimed lack of interaction of a "balanced" proton–electron atom with external forces such as a magnetic or an electric field. These main issues suffice to introduce the concept of atomic structure, and then ionization. If you have time (and the class has interest), the concept of isotopes can be introduced with the use of a different number of black jelly beans (neutrons). 

This simple demonstration works best for electron ionization of atoms; molecular ionization involves an ensemble of jelly bean atoms that is more difficult to assemble and explain. Protonation (such as occurs in chemical ionization) raises issues of chemical bonding that surpass most elementary student levels of understanding. 

The third point listed earlier — that an atom with a balanced number of protons and electrons is indifferent (to an elementary approximation) to external forces such as a magnetic or an electric field — is crucial to the understanding of ionization. Introduce ionization as a process through which we put a handle on the atom so that we can move it around at will, and perform a mass analysis with electric or magnetic fields. Students might recall common demonstrations of magnetic field interactions that rely on classic metallic or nonmetallic items, showing a presence or absence of the attractive force. But this effect is based upon the magnetic properties of the material, rather than the relevant fact in mass spectrometry that the ion carries a charge. It is best to avoid the analogy based upon a correct but somewhat irrelevant underlying premise; concentrate on the charge!  

How to achieve ionization in a jelly bean atom? Find a student group whose jelly bean atom has the same number of protons as the number of electrons on your chosen jelly bean string. Then, assemble a stable atom at the front of the class with you circling around the nuclear kids with your electrons on a string. Ionization is simple enough — blatantly eat an electron! A simple exercise then shows the students that there is an excess of protons now, and you can keep a tally on the board to show that the net charge on the atom is +1, corresponding to the charge on that extra unbalanced proton. If your compatriots want to eat the jelly bean nucleus, you can explain that such a reaction would fall into the realm of nuclear chemistry, and that is not what MS is all about. 

Now we demonstrate the effect of an external force. In this case, we are going to recruit the teacher to represent an electric force (the accelerating potential). Inform the students that the teacher only exerts a force on ions — the atoms with an unbalanced number of protons and electrons in this case. Describe the teacher as a repulsive force (students smile and tend to agree with that assessment). Thus, as the teacher approaches an ion, the charged student-atom will be repelled. If that single electron had not been consumed, and the atom remained neutral, the teacher would have no effect. In this single part of a simple overall exercise, the demonstration has shown atomic structure, carried out a process of ionization, and then shown the principle through which an ion is physically transported from the ion source into the mass analyzer of the mass spectrometer.  

Add some sophistication to the analogy. Now, instead of subatomic particles, the jelly beans will represent atoms of elements, and different colored jelly beans are now different elements. Explain that each element has a different mass and that the mass analyzer in the mass spectrometer is the measuring tool so that we can tell the difference. Gather three students at the front of the classroom, because these are your new partners for a demonstration of mass analysis via a time-of-flight mass analyzer. Repeat that molecules are made of atoms, and because each different combination of elements has a different mass, so each jelly bean molecule that you assemble in each student's hand will have a different mass. The volunteer students might be prompted to admit that the mass of the jelly beans in their hands does not feel that different, regardless of whether they have five, eight, or 15 jelly beans grasped there. So ask the students to count out the jelly beans; then ask them to imagine that each jelly bean weighs about 100 lb, some a little bit less and some a little bit more.  

Now a simple racetrack analogy serves to cement the concept of time-of-flight mass analysis for the students. Explain to each jelly bean–molecule student that they are about to run in a race with those several hundred pounds (some more, and some less) of jelly beans in their hands. Remind them, and get the remainder of the class to agree, that students with more mass will traverse your makeshift racecourse more slowly than the lighter-mass students. You adopt a position at the finish line, marking the finishing times of your jelly bean–molecule students at various times, and then explain the simple proportionality between mass and flight time. Then, you explain that just as the masses of real ions are orders of magnitude less than the jelly beans, or the students themselves, the flight times of the ions are orders of magnitudes faster than the race times you just measured in the classroom. But, with the right tools, we can measure these very short times and therefore measure the masses of the molecules as ions. When we know the masses of the molecules and how they fall apart (explain this by again examining the component parts of the jelly bean molecule), then we know something about what the molecule is! 

In this last part of the explanation, the concept of scales must be explained. The key concept here is the concept of amplification accomplished with an electron multiplier; the students will recognize the term "multiplier," and preview the topic. And, using jelly beans again, the analogy will remain with them long after your visit to the classroom. Explain that jelly beans represented mass in the previous demonstrations, and now the jelly beans are going to represent charge. "Mass and charge," you explain, "are what mass spectrometry is all about!" You will need student volunteers again, this time, students assembled in three rows of four students each. (Note that by this time, you will have had the chance to involve almost every student in the classroom in some part of these demonstrations.) I bring three paper bags to class, with one, two, or three jelly beans inside. I explain that the jelly bean charge is so small that if one were to examine the bags, you would find it difficult to decide about the number of jelly beans inside. What's needed is a multiplication so that the jelly beans are forced to reveal themselves. So for each student in each of the three lines, you explain that they are to take whatever number of jelly beans they find in the bag as it is handed to them, add matching jelly beans one-for-one, and then pass the bag on to the next person behind them in line. Here are the sequences: 

That last student in the third line has a lengthy counting task, and while that is accomplished, you explain the principles of multiplication in an electron multiplier, and how (holding up three bags now with obviously different numbers of jelly beans) multiplication makes it easier to detect small differences. 

Finally, at the end of the demonstrations, you ask if you should gather up all your jelly beans and go home. Perhaps the jelly beans should stay in the classroom if the students can remember the principles of separation based upon different behaviors of Curly, Larry, and Moe. Do they remember the parts of the atom, and that a charge results when electrons and proton jelly beans are unbalanced, and that the repulsive force finds you once you become unbalanced? Do they remember that different jelly bean molecules also can represent molecules of different masses, that heavier masses move slower in a race, and that we can use a race to reveal differences in mass? Do the students remember that multiplication can make small things and small differences easier to detect? If they do, leave the bags of jelly beans, thank the teacher and the students, and remember that twenty years from now, these students might be in attendance at the ASMS annual meeting, searching for jelly beans in the hospitality suites.  

 convinced students of a third-grade class to loudly chant "GC–MS" during an explanation of MS, and recalls that the teacher was not amused. He is therefore reminded that it is important to preview and gather the insights of the teacher on the class presentation. He looks forward to the collection, preparation, and publication of a collection of elementary school demonstrations in the field of MS. This column represents the views of the author and not the National Science Foundation. The author can be reached at 

www.nasaexplores.com/show_k4_teacher_st.php?id=04040593431

www.lessonplanspage.com/ScienceEdibleAtomLab-ForPeriodicTableAtomsGoodIdea46.htm

ims.ode.state.oh.us/ODE/IMS/Lessons/Content/CSC_LP_S03_BB_L12_I10_01.pdf

(4) J. Dalton, "Extraordinary Facts Relating to the Vision of Colours, with Observation," Mem. Literary Philos. Soc. Manchester 5, 28–45 (1798). 

Mass Spectrometry Forum

In this month's installment, columnist Ken Busch presents an elementary explanation of mass spectrometry that is appropriate for a young student audience, or for, perhaps just explaining to family what you actually do for a living.

Jelly Beans and Mass Spectrometry for K-4 Students

HORIBA Jobin Yvon's FluoroMax-4 benchtop spectrofluorometer reportedly features a sensitivity of at least 400,000 cps for the water-Raman peak at 397 nm and a signal-to-noise ratio of 3000:1 minimum. According to the company, the spectrofluorometer uses an ozone-free xenon arc lamp for broadband coverage from the UV to near-IR. Optional accessories include automatic polarizers, a stopped-flow device for kinetics, a microwellplate reader, and an autotitrator. 

PIKE Technologies' grazing angle option for its VeeMAX II ATR accessory is designed to aid in the analysis of monomolecular layers on silicon to obtain FT-IR spectra. The accessory reportedly provides high throughput to cut down on sampling time and facilitate detection of the monomolecular layers of silicon without utilizing a liquid nitrogen–cooled detector. Crystal options for the accessory include ZnSe, ZnS, and Si. 

Andor Technology's iKon-M CCD cameras reportedly offer rapid frame rates acquisition and fast focusing, direct USB 2.0 connectivity to PC, an integrated C-mount shutter, TE cooling to –100 °C, up to 95% QE, a low noise floor, and software-selectable readout speeds as high as 2.5 MHz. 

Bruker Daltonics announces its ultraflex III smartbeam MALDI-TOF-TOF mass spectrometer, optimized for MALDI molecular imaging. The product is designed to produce a laser focus size of 10–80 μm, with adjustable repetition rates from 1 Hz to 200 Hz. 

Bio-Rad Laboratories' KnowItAll Enterprise data management software is designed for centralizing, securing, and accessing IR, NMR, MS, Raman, UV, and chromatographic data. According to the company, the software enables data management and sharing across any enterprise behind the user's own firewall. 

LECO introduces the Pegasus HT TOFMS high-throughput time-of-flight mass spectrometer, which uses the company's ChromaTOF 3 software with a signal deconvolution feature that reportedly enables acquisition rates of up to 500 spectra/s and a fast processing speed. 

The Thermo Scientific LTQ XL linear ion-trap mass spectrometer is equipped with electron transfer dissociation (ETD), which reportedly provides advantages for protein and peptide characterization, posttranslational modification analysis, and top-down or middle-down sequencing of proteins and peptides. According to the company, the ETD feature allows researchers to conduct rapid, alternating collisionally induced dissociation and ETD scanning throughout the LC-MS analysis of complex samples. 

Melles Griot introduces single-frequency, low noise, diode-pumped solid-state lasers designed to provide options to traditional air-cooled krypton–argon ion lasers. The lasers produce up to 25mW at 561 nm and up to 15 mW at 473 nm. They also reportedly have a nearly circular (<1.1:1 aspect ratio), linearly polarized beam with an M

Enwave's EZRaman-M field portable Raman analyzer is designed for use in forensic and security applications, surface-enhanced Raman spectroscopy, and educational and research laboratories. The system features a 785 nm frequency stabilized laser with resolution of ~7 cm

A DVD available from Aabspec showcases their spectroscopy and microscopy products, including Raman, FTIR interfacing for GC, LC, GPC, SFC, TGA, evolved gas analysis, low to high temperature, ultra-high vacuum to high pressure, and dual sampling together with multiple optical modes including sample irradiation. 

The TrayCell cuvette from Hellma reportedly uses the surface tension of 4 μL of sample to form a repeatable optical path of 1 mm +/– 0.02 mm. According to the company, it is compatible with most spectrophotometers, operates at temperatures ranging from 5 °C to 50 °C, and has a wavelength range of 190–800 nm. Applications include biotechnology, drug discovery, and forensics. 

Featured products from the spectroscopy marketplace.

Product Resources

To understand audit trails within software, I'd like to take an unusual approach and start from a common starting point that should be understandable by all in the paper world: the laboratory notebook. Then this column looks at the translation to an electronic environment, any applicable regulations and guidance, and then addresses how audit trail functionality has been implemented in the software applications for spectrometers. Finally, we'll look at some of my views on how audit trails should be implemented.  

The Audit Trail in the Laboratory Notebook

What do we do now? Here's where we go back to paper and the humble laboratory notebook. The laboratory book is issued to an individual, each is uniquely numbered, and the pages are bound to make it secure. Furthermore, the pages are numbered sequentially to ensure the data integrity of the whole book: anyone can tell if pages have been removed simply by looking at the pagination. The book is also a linear record of laboratory activities; convention is that we start at the first page and write up our activities until we complete the book. This provides a time sequence of events as later work appears in the notebook after earlier activities.  

Work and observations written up in the notebook must be recorded in ink and not pencil and should be error free — but usually never is. Therefore, errors must be corrected manually by the notebook owner. This takes the form of the writer striking through the error but without obscuring the original entry; then the new entry is made close to the original one and the user then initials and dates the new entry. Typewriter correction fluid must never be used, nor the original entry scribbled through. In some quality systems, the reason for change is also added (for example, transcription error, wrong data entered, or an appropriate entry). This is a useful addition even if not mandated by a regulation because it might indicate that there is a problem with the process for which an individual might need some additional training. 

The bottom pages of the laboratory notebook are then signed by the spectroscopist to acknowledge that he or she is responsible for the content of the work and the conclusions reached. A supervisor will then check the work and if the calculations are correct and the conclusions reached are correct, the supervisor signs the bottom of the page to verify this. 

All of these mechanisms are designed to ensure the integrity of the work performed. 

Now comes the interesting bit — how does this linear paper-based approach translate when working in a dimensionless electronic environment? It all depends how the spectrometer software has been designed and implemented. 

First, there is the problem that software does not work in a linear way like the laboratory notebook, as data and files usually are created and maintained in either directories containing files or within tables of a database. Data are not stored in a linear manner similar to the notebook entries, and unless you want to get into detail, how are you going to demonstrate that a correct sequence of events has occurred? Typically, here is where the trusty laboratory notebook comes to the rescue. We use the spectrometer and the associated software as a very expensive typewriter and print out the data and stick this into the laboratory notebook. However, this is not helpful when the aim is to work electronically and reduce the amount of paper produced in an analysis. 

The audit trail within the software is the key when you are working in an electronic environment. In a way it can be considered as the equivalent of the binding and pagination of the laboratory notebook. The problem is that poor design of many audit trails currently available in spectrometry software will hold back the development of electronic working in many laboratories, as we shall see later.  

Before progressing further with this discussion, let us just check and see what the regulations say about audit trails. The main one to consider for our discussion is 21 CFR 11 (1), and §11.10(e) is specific for audit trails:  

Use of secure, computer-generated, time-stamped audit trails to independently record the date and time of operator entries and actions that create, modify, or delete electronic records. Record changes will not obscure previously recorded information. Such audit trail documentation will be retained for a period at least as long as that required for the subject electronic records and will be available for agency review and copying. 

In essence, the regulation requires a computerized system to have an independent monitor of user actions covering the life to death of data and records together with any changes that are made to them by individual users. The audit trail also must be secure and not alterable. This is the equivalent of the explicit and implicit security built into a laboratory notebook. 

In 2003, the FDA issued the guidance for industry on Part 11 Scope and Application (2), in which audit trails are one of the areas in which there is enforcement discretion when inspecting companies as follows: 

Persons must still comply with all applicable predicate rule requirements related to documentation of, for example, date (for example, § 58.130[e]), time, or sequencing of events, as well as any requirements for ensuring that changes to records do not obscure previous entries. 

Even if there are no predicate rule requirements to document (for example, date, time, or sequence of events in a particular instance), it can nonetheless be important to have audit trails or other physical, logical, or procedural security measures in place to ensure the trustworthiness and reliability of the records.  

We recommend that you base your decision to apply audit trails, or other appropriate measures, on the need to comply with predicate rule requirements, a justified and documented risk assessment, and a determination of the potential effect on product quality and safety and record integrity. We suggest that you apply appropriate controls based on such an assessment. Audit trails can be particularly appropriate when users are expected to create, modify, or delete regulated records during normal operation. 

While on the one hand appearing to let go of the requirement for a system to have an audit trail, on the other hand, the FDA still emphasized the need to have the information available either inside or outside of the software application as well as the need to comply with GMP or GLP. In my view, the enforcement discretion allowed around Part 11 by the Agency does not minimize a laboratory's need to ensure data integrity regardless of how it is achieved: by the software, by a handwritten log, or by a combination of the two approaches.  

Let's start from what audit trail options we currently have in spectrometry software. The first is fairly brief — nothing! This is not a useful option. 

Spectral Data File Contains the Audit Trail:

 One of the common implementations of an audit trail in spectrometer software is the incorporation of the functionality into the data file created during an analysis. This is fine for the data generation and the modification but is not compliant with Part 11 because when the file is deleted, there is no record since the file recording the deletion is no longer in existence (in other words, the file cannot record itself being deleted). This is a problem with software using directory structures versus a database. In a software application with a database, there should be a separate table for recording all audit trail entries associated with creation, modification, and deletion of data and records. 

One of the Part 11 requirements is that the audit trail should be secure. Therefore, when assessing some spectrometer software applications, it is amazing to find that some audit trail entries can be opened and edited using text editors. Obviously, this is a new definition of secure that I was not aware of. The audit trail needs to be viewable but not changeable. Some nonspectroscopic software will checksum or encrypt the audit trail entries so that they cannot be changed. 

 There is usually a single audit trail within a spectrometer system. This is fine, but the audit trail usually covers all the functions of the software such as user account management; user logins; creation, modification, and deletion of new methods; and creation, modification, and deletion of data. Everything is thrown in here — if you are looking at a specific item is there a search facility?  

 This is a feature that might be lacking in some software applications. There must be a function to search the information about a specific analysis within all the audit entries. This should be an essential feature for any audit trail. 

Once you have searched the audit trail and found entries associated with your query, it would be reassuring to know that the entries are easy to read and understandable. Ideally, the user's real name (Bob McDowall) should be entered into the audit trail rather than the user log on identity (FM8120BM) so that you can easily identify the individual who carried out an action .  

If the reason for change is needed, there should be an option of a drop-down list of predefined reasons for change. This would speed up data entry and avoid typographical errors and make it easy to review the entries. In addition, the original result and the change should be present explicitly in the audit trail rather than requiring a user to guess what happened. 

The aim here is for the audit trail to help reconstitute the analysis changes and be unambiguous.  

 Before we start, let's not confuse backup and recovery, which is short-term recovery from disaster, with archive and restore of data. This is the removal from the system of work that is completed along with the associated audit trail entries. You might be able to archive the data OK, but the problem is if you want to restore them, the data can go back into the system OK but often, the restored audit trail information cannot be inserted back into the existing audit trail. This can make it difficult to reinterpret the data. 

Of course we can. Perhaps the best way to look at ways to ensure the quality and integrity of spectrometry data is to go back to basics and consider the design of the system and the audit trail in particular. In my view, there should be more than one audit trail in a system. Here are some of my thoughts about this. 

 This would audit the whole system and include user account management (creation and modification of user types and user accounts), users logging on to the system, as well as security alerts. The creation and archiving of the work packages within the application would be covered by this audit trail as well as the copying of methods and reports between work packages. Set up of spectral libraries also would be covered by the system audit trail. 

Depending on where you work within an organization, your view of a work package will be different. In R&D, your view might be project- or program-based in comparison to a production view of batches. Regardless of the approach, each work package created in the system should have an individual audit trail. This would cover the setup and establishment of methods for analysis, plus the acquisition, analysis, calculation, and reporting of data. The advantage of this approach is that all the audit trail entries are associated intimately with the data they relate to. Searches of the audit trail will be more specific and quicker than from a single one. 

 We could go further if the design of the software is well thought through. Rather than trudge through a badly designed audit trail, perhaps we can have simple visual signs for a supervisor that manual changes have been made to data to help them focus their effort where it is best needed?  

I have looked at the way that an audit trail is implemented in many spectrometer software applications and made suggestions for improvements to aid electronic working. Unless there are effective audit trails to help scientists using spectrometer systems, we cannot work electronically and are stuck with paper as the archive medium. 

 is principal of McDowall Consulting and director of R.D. McDowall Limited, and "Questions of Quality" column editor for 

's sister magazine. Address correspondence to him at 73 Murray Avenue, Bromley, Kent, BR1 3DJ, UK. 

(1) 21 CFR 11, Electronic Records; Electronic Signatures Final Rule, 1997. 

(2) Guidance for Industry, Part 11 Scope and Application, FDA, Rockville, Maryland, 2003. 

Focus on Quality

Columnist Bob McDowall discusses the use of audit trails in the software applications used to control spectrometers that acquire, interpret, and report results from analyses. An effective audit trail is imperative to ensure the integrity of the data and the conclusions reached by the spectroscopist.

Validation of Spectrometry Software - Audit Trails for Spectrometer Software

Laser-induced breakdown spectroscopy (LIBS) offers an in-situ capability for instantaneous qualitative analysis of the elemental composition of materials. This technique differs from other forms of emission spectroscopy in that the sample is irradiated and evaluated in one step with little or no sample preparation: a laser pulse is focused on the sample to a sufficient energy density, volatizing a small amount of material into a microplasma or laser spark; light from the laser spark is spectrally resolved to identify the emitting species present in the plasma; compositional information about the target material is then deduced from the resulting spectrum. Since the phenomenon's discovery in 1962 (1), LIBS has found its way into such diverse applications as screening for toxic metals (2,3), industrial process control for analysis of ores in the mining industry and sorting of waste materials (4), inspection of art objects to detect fraudulent copies, and many others. A number of review articles (5,6) and, just recently, two books on LIBS have been published. The new book by Creamers and Radziemski (7) traces the historical development of LIBS, explains the underlying physical processes involved, describes the components of a LIBS apparatus and issues that arise in their assembly, and addresses various practical issues in the use of LIBS for qualitative and even quantitative analysis. The book concludes by summarizing current LIBS-related research and application activity and then projects the trajectory of the field into the near future. Those same authors treat LIBS history and fundamentals more succinctly in the first chapter of the new book edited by Miziolek, Palleschi, and Schechter (8), subsequent chapters of which feature a succession of prominent LIBS researchers discussing the nature of the LIBS plasma, experimental measurement and data analysis issues, and a wide variety of LIBS applications. 

Considered as a means to detect contaminants, LIBS is best suited for circumstances in which the sought-for contaminant contains one or more elements that are not proper (or desired) constituents of the test sample. The problem of detecting lead and other toxic metals in paint provides an excellent example. Lead-based paint, which once was preferred for use on building exteriors due to its durability, is now prohibited by environmental regulations in the U.S. and elsewhere because of the hazard it presents to anyone exposed to it, especially small children. Nevertheless, lead paint is still found on many older residences and other structures as well as in waste disposal sites holding their remains, and detection of old lead paint is obviously a necessary precursor to the remediation of this environmental hazard. Figure 1 shows a LIBS spectrum of an "unknown" paint that clearly contains lead. A qualitative analysis such as this can be performed instantaneously using LIBS. A more traditional laboratory analysis would be required to obtain a quantitative measure of the lead concentration in the paint; nevertheless, LIBS might eventually find application as a means to survey suspected sites for the presence of lead or other elemental contaminants. A thorough investigation into the potential of LIBS to detect environmental lead contamination was reported recently by Wainner and colleagues (2).  

Successfully detecting and discriminating biological materials using LIBS is a far more challenging problem because the same elements occur in all biological organisms; obviously, any success to be had will have to rely on analysis of the relative proportions of the biological elements present rather than their simple presence or absence. Indeed, the extensive LIBS literature includes a limited number of papers that specifically address biological analysis. They typically have a well-defined focus and scope and utilize a specific approach, with the intent to determine the feasibility of using LIBS for some form of biological analysis. The focus varies from biologically important inorganics (9–11), to pollens (12–15), molds (12,13), and proteins (13), to bacteria (12,13,15,16); sample types vary from solids (9,10), to aerosol particles (11,14,17,18), to dense aerosol clouds (12), to concentrations of organisms compressed into pellets (15), to thin films of biological material deposited onto porous silver (13). Some of these studies tried to distinguish between different types of biologicals (12,13,15), in one case even to the level of specific bacterial species (16). The present study is extending that line of inquiry to the problem of identifying biological contaminants on exterior, painted surfaces, and classifying them to the level of major categories like mold, pollen, and bacterial spores — with the latter constituting our primary objective. 

Hybl and colleagues (12) provide a thorough description of a study on the use of LIBS to analyze biological aerosols. They utilized both a broadband, multiparticle apparatus and a narrowband, single-particle apparatus to take LIBS spectra of an assortment of biological aerosols. Peak/base ratios were calculated for the atomic spectral responses, and principal components analysis (PCA) was used to discover distinguishing patterns. Their PCA results clearly distinguish between fungal spores, pollen, and the spore form of 

 (a nonpathogenic sporifying bacterium that is frequently used as a simulant for anthrax); other biologicals are distinguishable as well. But the authors caution that a mixture of compounds could provide a spectral signature similar to a biological warfare agent, which could then be interpreted as a (false) positive. 

Wormhoudt and colleagues (19) suggest that poor precision, difficulties in quantitation, and the inadequate detection limits that typically plague LIBS analysis might be less critical when testing biologicals because their detection may rely less on absolute quantification and more on a qualitative comparison to a known, finite set of "spectral fingerprints." Indeed, in all of the biological studies we reviewed, data analysis relied on some form of pattern recognition — attempting to identify distinguishing spectral patterns between different classes of organisms. The biological signatures or specific elemental lines for each organism were manipulated by several different techniques in attempts to identify indicative patterns. The elemental signatures of calcium, sodium, magnesium, manganese, silicon, potassium, phosphorus, iron, carbon, oxygen, and nitrogen, plus the transient recombinant molecular species cyanide and phosphate — all derivable from components of biological systems or their breakdown — were employed in the various biological studies. 

Of particular interest to us was a study by Samuels and colleagues (13), which utilized LIBS to measure pure preparations of bacterial spores, molds, pollens, and proteins deposited as thin films on porous silver substrates. Except for those attributable to the silver substrate, all of the lines in their spectra arise from the biological layer. They utilized PCA and demonstrated that the technique can distinguish between different specimen types, but suggested that a more robust modeling scheme would be needed to distinguish between individual species of bacteria. Of all the works we reviewed, this one bore the greatest similarity to our problem. 

More recently, DeLucia and colleagues (20) reported on a study in which LIBS was used to detect hazardous contaminants of several kinds, to include bacterial spores. Using data measured against the pure biological contaminants as well as the same contaminants in soil, they obtained excellent automated discrimination performance using an algorithm they devised that employed linear correlation analysis against a spectral library. 

The work presented here expands upon those previous studies by treating the case in which the sought-for biological contaminant exists as a thin layer over a complex substrate — paint — which will necessarily impart its own spectral signature as a background against which the signature of the contaminant layer must be processed to discern the presence of a target biological threat. This article presents the design of our apparatus and the nature of the first set of data acquired under our current effort to investigate whether biological materials on a surface painted "Navy gray" can be classified based upon LIBS spectra taken on the surface layer in situ, despite interference from the paint that is taken up in the process. Our initial intention was to collect data that could be analyzed to give an indication of whether any reasonable level of discrimination of biologicals is possible based upon the LIBS spectra of thin coatings over such a complex substrate as paint. So we chose to assemble our apparatus from components that were on hand in our laboratory for this first phase of our study in which basic feasibility needed to be established, even though the characteristics of our spectrometer and its associated collection optics limited the available spectral range to about a third of what we desired. Nevertheless, we enjoyed a level of classification performance well in excess of what we hoped to achieve, on the basis of which we have undertaken to upgrade the apparatus to support continued progress on this problem. 

Figure 2 illustrates the apparatus we designed and assembled based upon our literature review, the highlights of which are summarized above. We utilized our laboratory's New Wave Research (Fremont, California) ACL-1 Q-switched Nd:YAG laser as the excitation source because its characteristics fall within the scope of the lasers typically used for biological-related LIBS studies: it generates a 35-mJ pulse at the Nd:YAG fundamental wavelength (1064 nm), and it can be used to deliver a single pulse or to operate at a pulse repetition frequency of 1 Hz. Several optical components were included beyond the basic requirements for LIBS to facilitate careful control of the excitation of the laser spark and the collection of the resulting LIBS emission for spectral analysis. The beam was expanded through the microscope objective to fill the clear aperture of the collimating lens and was then reflected by the mirror, which could be adjusted to direct the collimated beam downward toward the sample at any desired angle of incidence. The sample was mounted with its top surface horizontal to accommodate the possibility that the contaminant is not adsorbed strongly onto the substrate — a precaution that turned out to be unnecessary. The laser-focusing lens between the mirror and the sample provided the means to vary the spot size, hence the energy density, of the incident pulse. We included the collection lens between the sample and the coupling lens to permit the region from which light is collected for analysis by the spectrometer to be limited virtually to a pinpoint. The desired adjustability was achieved by mounting this pair of lenses on a rotational 

 stage configured to permit extended motion along the axis of the fiber-optic cable, which in turn transmitted the emission collected from the laser spark to the spectrometer. The coupling lens and the fiber assembly (Ocean Optics [Dunedin, Florida] 74-ACR and P600-2-VIS-NIR, respectively) were intended for the visible and near-infrared regions of the spectrum, so attenuation in the UV region increased progressively starting about 380 nm and the assembly approached opacity near 300 nm.  

We performed a study that catalogued the principal relevant elemental emission lines and noted their separation in wavelength, based upon which we identified the spectral range of interest as ~200–1100 nm at a resolution of about 0.5 nm; this is comparable to several of the previous biologically oriented LIBS studies and includes the great majority of elemental emission lines we can reasonably expect to observe. For the experiment reported here, however, we had available only a single Ocean Optics HR 2000 spectrometer configured for ~200–650 nm. So in view of the aforementioned absorption characteristics of the collection optics, the spectral information on which our results are based is confined to the range ~300–650 nm. 

Ocean Optics OOIBase software running on a Microsoft Windows-based computer controlled the apparatus. The computer was interfaced to the spectrometer through its USB port. A data collection event was triggered by activating the laser controller, which fired the flashlamp and simultaneously signaled our Signal Recovery (AMETEK, Wokingham, UK) model 9650A digital delay generator. After the programmed delay, the delay generator triggered the spectrometer via its control port. The software automatically collected data sets from the spectrometer and stored them along with the values of all settings and a date-time stamp. 

A great advantage of LIBS — one of its characteristics that motivates this study — is the fact that the technique can be used to analyze samples in situ without the special preparation procedures that most analytical methods require. Even so, we have found that careful preparation of test samples strongly facilitates the effective use of the experimental control functionality that has been incorporated into the apparatus to obtain reproducible results. A sample substrate is fabricated by mounting a piece of pure sheet copper in phenolic resin, which is then polished to a ~1-μm finish using standard metallurgical methods. Subsequently, these substrates were coated with our "Navy gray" paint and then set aside in ambient air or a drying chamber for at least two weeks, until the paint had thoroughly hardened. This paint — a silicone alkyd anti-stain finish by International Marine Coatings, Inc. (Akzo Nobel, Arnhem, The Netherlands) called Interlac 1 — is used commonly on the weather surfaces of U.S. Navy ships. Before use, hardness was verified by pressing a hard metal probe onto the coatings, which were judged to be cured fully if no observable indentation resulted. 

Our means of applying the contaminant to the substrate is motivated by the way pollen is typically observed to adhere to an automobile windshield through the agency of morning dew: wind will not remove the pollen layer after the dew evaporates; one must rub or wash it off. We place a drop of water (to include some alcohol if required, which was the case for our mold specimens) on the substrate and then mix in a small quantity of the contaminant to form a thin slurry, which is then left to dry in the ambient air or in a drying chamber — typically for two days or until all of the liquid has evaporated and the remaining biological material adheres firmly to the substrate. Finished samples are stored in a desiccator to ensure that the coating does not absorb water from the ambient air. Figure 3 shows the key stages in their preparation. The density of the resulting contaminant layer varies over the substrate, but is everywhere sufficiently thin that each LIBS shot will ablate a portion of paint as well as the overlying contaminant. No attempt was made to obtain a layer of uniform thickness at this first stage of our project, because our technique is meant to respond to the relative intensities of emission from the sample's elemental constituents rather than absolute measures of intensity. In the future, we will be performing sensitivity studies for which precise quantitation of the contaminant layer will be required; here, however, our intention was to replicate, as closely as possible, the manner in which a biological contaminant would adhere to an exterior painted surface in the outdoor environment.  

In the early months of our study, we applied various contaminants directly to the unpainted polished-copper substrates, then LIBS spectra were taken with an earlier version of our apparatus that consisted of the laser, spectrometer, digital delay generator, and computer previously described, but with single-lens optics for laser focusing and data collection. A few examples are shown in Figure 4 to illustrate their elemental content and the resolution of our spectra. The copper lines obviously come from the substrate, while all others must be attributed to the contaminant. Excellent signal-to-background ratios clearly are apparent in these spectra, so it is not hard to convince oneself that a classification strategy based upon relative line intensities should have some chance of success.  

In an experimental run performed on February 6, 2006, 341 single-shot LIBS spectra were taken against 11 separatepainted substrates: one uncontaminated, the other 10 each coated with one of nine different biological materials. The sample was rotated slightly between shots to present a fresh target spot each time. For these data, which are listed in Table I, the gating delay was set to 183 μs — a figure we had arrived at previously by taking repeated measurements against a bare copper substrate to find the optimal signal-to-background ratio. This delay, far longer than one would typically find in the LIBS literature, includes the time interval between the triggering of the laser flash lamp and the subsequent opening of the Q-switch — which we cannot determine precisely from available documentation or data. The spectrometer's integration time was set to 3 ms (the minimum possible for that instrument), which far exceeds the duration of the laser spark. Each spectrum was processed by the operating software to correct for electronic noise and then stored for subsequent analysis. This entire set of spectra was processed using the analysis methodology described below, without screening for quality, content, or any other criterion.  

Table I: Specimens for LIBS analysis of biological contaminant coatings over paint			

We developed an automated software package in MATLAB (The Mathworks, Natick, Massachusetts) that first preprocesses the spectra, then builds a classification tool based upon a training set consisting of the major fraction of the spectra, and finally applies that classification tool to the remaining fraction — the test set. Each spectrum is treated as an ordered set of 2048 intensities. Except where indicated in the following discussion, the procedures described were implemented based upon the book by Beebe and colleagues (21). Each individual spectrum is preprocessed by first taking derivatives with respect to index using the running simple difference method and then integrating the result to correct for baseline drift; after that, autoscaling consisting of variance scaling and mean centering is applied (in that order) to remove the effect of weightings imposed by the scale of intensities. Then, using an automated procedure that randomly permutes the spectra into the mutually exclusive training and test sets, 70% of the spectra from each sample (rounding down if necessary to get an integer) are assigned to the former and the remainder to the latter; thus, the data set was partitioned into a 232-member training set and a 109-member test set. A PCA was performed on the preprocessed spectra in the training set using an algorithm developed originally for facial recognition applications (22). Subsequently, a linear discriminant analysis (LDA) was performed to sharpen the achievable classification performance. Although PCA is standard fare, LDA has been introduced only recently (23) as a procedure for chemometric applications and is not discussed in our primary chemometric reference (21). To perform LDA, one selects enough PCA coordinates to represent the spectral features that appear above the noise floor and then computes the linear combination that simultaneously minimizes the intraclass separations and maximizes the interclass separations. Through repeated trials against an earlier data set, we settled on using the first 11 PCA coordinates as the basis set for the LDA transformation, so that strategy is currently hard-coded into our analysis package. Hierarchical cluster analysis (HCA) was then applied against the training set, as represented in the 11-dimensional PCA or LDA space, to derive an objective classification tool, which was then used to classify the members of the test set in order to assess its performance against an independent set of observations.  

Figure 5 shows three examples from the February data — spectra of 

 mold over paint. The paint contributes so many lines to the background that the baseline is masked completely, and it might appear rather optimistic to expect to develop a classification tool capable of distinguishing different types of contaminant layers based on spectra such as these. Nevertheless, we were able to distinguish the bacterial spores from other biological and nonbiological contaminants with a high degree of confidence.  

A total of 100 instances of the procedure described earlier were performed, each time with a different random partitioning of the same 341 spectra to form the training and test sets. Figure 6 shows a typical training set plotted against the first three principal coordinates. In Figure 7, the complementary test set is plotted against those same three coordinates. The fundamental output of a PCA is a set of so-called loading vectors, each of which is (in our case) a 2048-element vector; one takes the inner product between a loading vector and a spectrum to obtain the value of that spectrum's principal component along the coordinate corresponding to that loading vector. Loading vectors such as these can be regarded as spectra in their own right; by examining them, one can recognize features attributable to the major biological elements in addition to the titanium — a nonbiological element that is a constituent of the paint. Thus, each point in these figures represents the "position" of a certain spectrum in the first three dimensions of the PCA space; however, the HCA is performed over the first 11 coordinates. The circles that appear around some of the symbols in Figure 7 denote spectra that were assigned erroneously by the HCA classifier; where a circle has the same color as the symbol, the spectrum was assigned to a different contaminant in the same class — 

Figure 8 clearly illustrates the enhanced separation that develops when the same training set of Figure 6 is plotted against the first three so-called canonical coordinates of the LDA transformation. Figure 9 shows that same test set of Figure 7 plotted in the LDA space of Figure 8, with circles representing erroneous HCA classifications as before.  

Table II summarizes the performance measures derived from the 100 different partitionings of the data set between the training and test sets, both when PCA alone is used prior to the HCA derivation and when the LDA is included. With respect to the summary performance of the classifier against the test sets, performance measures are provided according to the stringent criterion, whereby the specific contaminant specimen must be correctly assigned for the classification to be declared successful, and the class criterion, whereby success is declared if the classifier assigns the spectrum to the correct class of contaminant. A breakdown of classification performance by class is also included, in which — for each class — the success rate according to the class criterion is given, followed by the false positive and false negative rates. The false negative rate is the percentage of spectra belonging to the given class (pollen, for example) that failed to be classified as such; clearly, the success rate and the false negative rate must sum to 100%. The false positive rate is the percentage of spectra that were classified erroneously as belonging to the class.  

The primary significance of our results is the strong indication they provide that LIBS spectra of contaminant layers over a complex substrate contain the information on the basis of which their biological content can be discerned. For our purpose, what is most noteworthy is the fact that the 

 spores were classified with false-positive and false-negative performances of 1% and 3%, respectively. This far exceeded the modest indication of potential success that we were seeking as a justification to continue this line of applied research. The low false-positive rate is particularly gratifying.  

Table II: HCA classification performance against February 2006 Bio-LIBS data			

At this early stage, it would be well to acknowledge the preliminary nature of these results. While we do believe that our sample preparation methodology well imitates the manner in which contamination naturally occurs in the outdoor environment, there is a need to expand the number and variety of test specimens to adequately stress our developmental analysis methodology. Especially, we need to obtain several different species of bacterial spores and to obtain specimens of the same species that have been grown or prepared in different ways. We need to take data against contaminants formed from mixtures of bacterial spores with other biologicals in order to assess the robustness of our methodology and to develop experimental procedures to bring out the spore signature in the presence of interferents. The apparatus also should be improved to accommodate the greater part of the spectral region in which LIBS data can be measured. There is also the need to control for the 

-scale drift that inevitably afflicts raw spectrometric data; obviously, we will need to include a calibration procedure in our data acquisition methodology along with a preprocessing step that uses the calibration data to compensate for drift. And despite the success of our chemometric methodology, which attempts to classify each individual specimen uniquely, it would be well to explore how classification performance could be improved further by employing procedures designed to recognize bacterial spores (or some other single class of contaminant) only. Finally, the need to include automated logic for outlier detection has been identified. 

Thus, we see that LIBS spectra, measured against thin contaminant layers drawn from a limited set of representative specimens and applied over a representative exterior marine paint, contain the information on the basis of which the contaminants can be classified, with considerable confidence, into major categories such as bacterial spores, pollen, mold, and others. This summary result was taken as justification to proceed with this line of applied research. As this is being written, we are nearing the completion of a major upgrade of our apparatus, to include a more powerful and flexible laser that is capable of generating pulses at the Nd:YAG fourth harmonic (266 nm) having a higher pulse energy than we get at the fundamental from the laser described herein — which remains available to facilitate experiments involving dual-pulse excitation. We have moved to using Ocean Optics' new, more capable SpectraSuite software to control our apparatus. We have expanded the set of biological materials for our experiments to include spores of other bacterial species, spores of the same species that have undergone different preparation procedures, and a number of nonbacterial biologicals. Finally, we have initiated a thorough review of our existing chemometric processing scheme — which was designed to determine whether our spectra actually had the required information content — with the objective of developing a robust prototype analysis and classification tool oriented to the operational field-detection paradigm. 

We are thus poised to execute the program of experimentation we are planning, which is intended to systematically explore the potential of LIBS as a means of spectrally analyzing contaminants on painted surfaces, and other complex substrates, to indicate the presence of biologicals that may, themselves, pose a threat or indicate that a covert biological attack has occurred. We plan to report the results of these studies as they become available. 

The authors wish to thank the Joint Science and Technology Office for Chemical and Biological Defense (JSTO–CBD) of the Defense Threat Reduction Agency (DTRA) for supporting this work. The portion that was performed at The Pennsylvania State University was executed for the Naval Surface Warfare Center–Panama City under Delivery Order 0308 of Contract No. N00024-02-D-6604 with the Naval Sea Systems Command. 

 are with the Applied Research Laboratory, The Pennsylvania State University, State College, Pennsylvania. 

 is with the Naval Surface Warfare Center – Panama City, Florida. 

(2) R.T. Wainner, R.S. Harmon, A.W. Miziolek, K.L. McNesby, and P.D. French, 

(3) B.T. Fisher, H.J. Johnsen, S.G. Buckley, and D.W. Hahn, 

The potential utility of laser-induced breakdown spectroscopy (LIBS) as a means to detect biological contaminants on painted surfaces is investigated.

The Investigation of Laser-Induced Breakdown Spectroscopy for Detection of Biological Contaminants on Surfaces

The near-infrared (NIR) spectral region lies approximately from 780 nm to 2500 nm (4000 cm

) bridging the more well known and analytically used regions of the UV–vis (190–780 nm) and the infrared (4000–600 cm

). Until relatively recently, it has been called the "forgotten" region. However, with the arrival of Fourier transform (FT)–NIR instruments in the 1990s and the increasing awareness of the NIR's unique information content in the signal, which can provide both physical and chemical information, deployment of NIR spectrometry systems for both qualitative and quantitative purposes has become widespread, particularly in the pharmaceutical industry. This application in the NIR has led to new standards being generated by ASTM (1,2). 

This renewed interest has led to the need for traceable standards for the calibration and qualification of the wavelength scale of NIR spectrometers in the regulated environment. The purpose of this article is to review the current position with respect to wavelength standards availability and traceability. 

Because of the versatility in the sample presentation modes in the NIR, there is a need to ensure wavelength accuracy in transmittance, reflectance, and transflectance. For dispersive instruments, wavelength precision is also important because of the use of mechanical wavelength drives. However, for modern FT instruments, the precision is so good that only the accuracy of the wavelength scale is important. 

The wavelength scale accuracy is important because of the excellent signal-to-noise ratio (S/N) of the NIR, especially in the region above 1200 nm. This facet means that small changes in the spectral response function are spectroscopically significant; hence, transfer of spectral information between different NIR systems becomes problematic if the wavelength shifts, among others, are not controlled. Regulatory interest has led to the promulgation of monographs for the specification, calibration, and control of NIR spectrometers in both the U.S. (3) and European Pharmacopoeias (4). Unfortunately, these monographs have not been harmonized, and there are significant differences. 

In addition, some of the wavelength requirements are unsound scientifically. For example, USP General Chapter <1119> recommends the use of NIST SRM 2034, which is not certified by NIST for use in this region, and EP 2.2.40 gives a value of a water vapor line of 7299.86 cm

  without reference to its source and is demonstrably wrong (see section on water vapor for more details). One other source of wavelength calibration information is ASTM E275-01 (5) (currently under revision). This document recommends a third and different set of wavelength standards for the NIR. 

These three documents are summarized in Table I. In addition, other standards have been proposed for transmission mode, including trichloromethane (6), SRM 2036 (7), and polystyrene (8) for quality control.  

Table I: Comparison of the three major documents on wavelength calibration			

This article will review some of these standards from the perspective of traceability, "fitness for purpose," and practical ease of use. 

The use of atomic and molecular lines for the calibration of spectrometers is attractive scientifically because they often are linked to primary standards. However, the use of atomic line sources can present practical difficulties for end-users, especially for instruments designed to work in a routine environment. Instrument manufacturers often use these sources during instrument building and commissioning. 

One of the difficulties is finding traceable values for these lines. Table II lists the 19 most intense lines from neutral Hgz = 80 listed in the 

 87th Ed., 2006, and compares them with those listed in ASTM 275-01. The agreement is within the ASTM 275-01 rounding of the values, which are quoted in micrometers. Traceability support for these values is found in the NBS papers of Pyler (9,10) and Humphries (11). These tabulated values probably are sufficiently accurate for most dispersive spectrometers. For FT instruments, the values to full experimental precision probably would be required.  

Table II: Mercury line wavelengths in air for the spectral region 670 to 2325 nm			

The use of water vapor as a wavelength standard is very attractive, as it does not require the use of an artifact and is readily observable in single beam mode. The NIR spectrum of water is exceedingly complex. However, this has been explored extensively for the purposes of climate studies, and the HITRAN 2004 database (12) is the definitive source of values. The spectral region usually chosen for calibration is in the band set in the region of 7300 cm

  (1369 nm). Additional problems are that the wavenumber's conversion is dependent upon the refractive index of air and the spectra are heavily resolution-dependent. The wavenumber conversion is by the following formula:

  is needed to resolve the key band positions. For dispersive instruments, an SBW of 0.5 or better is required. The value cited in the EP of 7299.86 cm

  for water vapor is incorrect according to the HITRAN 2004 database.  

Table III: Water vapor calibration bands from the HITRAN 2004 database			

  (to nine significant figures) (1369.59 nm). The strongest bands in this region are shown in Table III, and a partial spectrum is shown in Figure 1 with the values given in nm.  

Gas Standards (NIST SRM 2517 and SRM 2519) 

SRM 2517 is a single-mode optical-fiber-coupled absorption cell containing acetylene (C

) gas at a pressure of 6.7 kPa (50 torr), for use in the 1510–1550 nm region. With a pathlength of 5 cm, the cell is packaged as a small instrument box, with two FC/PC fiber connectors for the input and output of a user-supplied light source. This SRM can be used for high-resolution applications, having more than 50 accurately measured absorption lines in this region, with an expanded uncertainty of 0.00006 nm (13,14). This is shown in Figure 2.  

SRM 2519 is a similar gas reference, but it uses hydrogen cyanide as the wavelength calibrant (15). A total of 51 lines from 1528–1563 nm are certified — 21 with an expanded uncertainty of 0.00006 nm and the rest with an expanded uncertainty of 0.0003 nm. The equivalent spectrum is shown in Figure 3.  

At this accuracy (with nanometer values quoted to nine significant figures) and these uncertainty levels, these gas standards essentially become primary standards for wavelength calibration, but the format and fiber-optic requirement tends to limit their areas of use to research applications and laser-based systems. 

After various discussions with interested parties in the fall of 1994, NIST started development of an NIR reference with the following design specification: 					

Wavelength coverage from at least 1000 nm to 2000 nm (10,000–5000 cm

Peak location precision of at least 0.02 nm (≈ 0.2 cm

Wavelength accuracy better than 0.1 nm (≈ 1 cm

This specification was designed to assist the development of chemometric methodology and to render the standard relatively insensitive to changes in resolution on a moderately high-resolution instrument (16).  

These requirements led to the certification of SRM 2035, a batch certified material, containing a mixture of the oxides of samarium, ytterbium, and holmium in a zirconia-stabilized borate matrix. 

It provides certified values (band locations and associated 95% uncertainty limits) for seven fairly symmetric and uniformly spaced bands from 976 nm to1946 nm (≈ 10,250–5139 cm

). The original batch of 100 filters was evaluated for homogeneity on a range of both dispersive and FT–NIR instruments, and subsequent batches (SRM 2035a) show band variation (compared to the original batch) of less than 1 cm

  (0.2 nm) for the seven certified bands. 

To achieve the precision specification mentioned earlier required a very robust peak assessment algorithm. After extensive testing, a center-of-gravity (COG) method was chosen. This method has been criticized for defining a band value significantly different from the visual peak maximum, especially for asymmetrical peaks (17).  

While the debate continues, NIST does provide on the SRM 2035 certification "information values" for the peak locations using a peak amplitude method. However, there are no uncertainty budgets associated with these values, as NIST considers these to be "a value that will be of interest and use to the user, but insufficient information is available to access the uncertainty associated with the value." While the temperature coefficient associated with the band assignment of 0.1 cm

  (< 0.02 nm/°C) will not make a significant contribution to laboratory usage of this reference, the effect in environments not at 25 °C must be taken into account (18). 

There are significant discrepancies from these informational values for FT–NIR instruments even though the water vapor calibration data are within ± 0.1 nm. For example, at 8 cm

  the following data, rounded to ± 0.1 nm, were observed (NIST Table IV data from reference 18 are given in brackets for the seven absorbance maxima in nanometers; 975.9 [975.8], 1077.3 [1075.7], 1151.9 [1151.7], 1224.1 [1222.5], 1368.9 [1366.8], 1471.8 [1469.4], and 1945.4 [1945.7]).  

Table IV: Comparison of dispersive wavelength maxima values with three FTâNIR instruments			

NIST SRM 2065 is essentially the same physical standard as SRM 2035 and is certified for the location of the seven absorbance bands (COG) in the spectral region from 10,300 cm

  resolution. SRM 2065 is also certified for the location of seven absorbance bands in the spectral region from 970 nm to 1946 nm and 13 additional transmittance peaks spanning the spectral region from 334 nm to 805 nm. 

In the late 1980s, a series of new rare earth-doped materials in a monocrystalline host material were developed (19). These doped yttrium aluminum garnet–type crystals have the property of having sharper and more numerous absorption peaks than the previous "didymium" type of glasses. The first one, M-27 (neodymium doped), had excellent lines within the UV-vis region but only had three lines in the NIR, at 869.2, 1486.2, and 1735.2 nm at a spectral bandwidth of 1 nm.  

A more recent material, M-42, has the same crystal structure but contains europium as the dopant and has the chemical composition Y

. From an artifact point of view, it is ideal in that it is stable and mechanically robust. However, the lines are sharp but concentrated into two groups. The resolution required is quite high: 0.5 nm SBW or 2 cm

  resolution or better. It is not currently known how these band maxima will behave with changes in SBW and resolution. 

This article compares three regulatory standards for near-infrared wavelength references and then reviews currently available materials. Reference types discussed include gas and vapor band spectra, solid artifacts in both transmittance and reflectance, and liquids in transmittance. Traceability issues are discussed in connection with calibration and qualification regulatory compliance requirements, particularly for the pharmaceutical and medical device industries.

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The Investigation of Laser-Induced Breakdown Spectroscopy for Detection of Biological Contaminants on Surfaces

Wavelength Standards for the Near-Infrared Spectral Region