Adar, Fran. "Analytical Vibrational Spectroscopy — NIR, IR, and Raman," in Molecular Spectroscopy Workbench. October, p. 14. 

Adar, Fran. "Entering Raman's Realm," in Molecular Spectroscopy Workbench. March, p. 22. 

Adar, Fran. "Graphene: Why the Nobel Prize and Why Raman?" in Molecular Spectroscopy Workbench. February, p. 16. 

Ahmed, Selver; Wunder, Stephanie L.; and Nickolov, Zhorro S. Raman Spectroscopy of Supported Lipid Bilayer Nanoparticles. 

Raman Technology for Today's Spectroscopists

Almirall, Jose; and Miziolek, Andrzej. Review of the Third North American Symposium on Laser-Induced Breakdown Spectroscopy (NASLIBS) 2011 Conference. October, p. 48. 

Alonso, David E.; Binkley, Joe; and Siek, Kevin. Comprehensive Analysis of Persistent Organic Pollutants in Complex Matrices Using GC with High-Performance TOF-MS. 

Angel, S. Michael. See Gomer, Nathaniel R. 

Artaev, Viatcheslav. See Patrick, Jeffrey S. 

Asara, John M. Mass Spectrometry Advances Fossilomics. 

Assi, Sulaf; Watt, Robert; and Moffat, Tony. Comparison of Laboratory and Handheld Raman Instruments for the Identification of Counterfeit Medicines. 

Atkins, P.; Ernyei, L.; Driscoll, W.; Obenauf, R.; and Thomas, R. Analysis of Toxic Trace Metals in Pet Foods Using Cryogenic Grinding and Quantitation by ICP-MS, Part I. January, p. 46. 

Atkins, P.; Ernyei, L.; Driscoll, W.; Obenauf, R.; and Thomas, R. Analysis of Toxic Trace Metals in Pet Foods Using Cryogenic Grinding and Quantitation by ICP-MS, Part II. February, p. 42. 

Ball, David W. "Little Points of Light," in The Baseline. January, p. 20. 

Ball, David W. "Maxwell's Equations, Part I: History," in The Baseline. April, p. 16. 

Ball, David W. "Maxwell's Equations, Part II," in The Baseline. June, p. 14. 

Ball, David W. "Maxwell's Equations, Part III," in The Baseline. September, p. 18. 

Ball, David W. "Maxwell's Equations, Part IV," in The Baseline. December, p. 10.  

Balogh, Michael. The Nature and Utility of Mass Spectra. February, p. 60. 

Baquero, Esteban. See Stephenson, Serena. 

Beechem, Thomas E.; and Serrano, Justin R. Raman Thermometry of Microdevices: Choosing a Method to Minimize Error. November, p. 36. 

Begley, Benjamin; and Koleto, Michael. Single Multipoint Calibration Curve for Discovery Bioanalysis. 

Bloomfield, Matthew. See Matousek, Pavel. 

Brill, Laurence M. Responding to Data Analysis and Evaluation Challenges in Mass Spectrometry–Based Methods for High-Throughput Proteomics. 

Brouillette, Carl. See Donahue, Michael. 

Brueggemeyer, Thomas. See Lanzarotta, Adam. 

Burrows, Richard; Wilbur, Steve; and Clinkscales, Richard. "Analysis of Flue Gas Desulfurization Wastewaters by ICP-MS," in Atomic Perspectives. November, p. 30. 

Busch, Kenneth L. "Consequences of Finite Ion Lifetimes in Mass Spectrometry," in Mass Spectrometry Forum. September, p. 12. 

Busch, Kenneth L. "Detecting Ions in Mass Spectrometers with the Faraday Cup," in Mass Spectrometry Forum. November, p. 12. 

Busch, Kenneth L. "Hybrid Mass Spectrometers," in Mass Spectrometry Forum. March, p. 16. 

Busch, Kenneth L. "Mass Spectrometry for First Responders," in Mass Spectrometry Forum. July, p. 12. 

Busch, Kenneth L. "Underwater Mass Spectrometry," in Mass Spectrometry Forum. January, p. 30. 

Bush, Laura. The Dynamic World of X-ray Fluorescence. July, p. 40. 

Bush, Laura. ICP-MS for Forensic Applications. October, p. 57. 

Articles

Spectroscopy presents its annual index of authors and articles.

Editorial Index

The focus of this paper will be on the use of temporary online Fourier-transform infrared (FT-IR) spectroscopy in the chemical industry including two case-study applications involving fouling and product quality. These case studies will be followed by a discussion of the use of temporary online FT-IR analysis enabling process optimization.

 is the world's largest integrated chemical company utilizing a wide variety of unique chemistries in the production of both basic and specialty chemicals. The diversity of chemical processes and the interrelationships of production facilities in the integrated chemical complex provide significant opportunities for process optimization. Fouling, corrosion, plugging, or out-of-specification product occurrences in one manufacturing plant can have negative effects on the operation of one or more downstream facilities. Accurate and representative data for process characterization is a key factor in making informed decisions regarding the resolution of process problems. The information-rich nature of optical spectroscopy and, in particular, Fourier-transform infrared (FT-IR) spectroscopy is uniquely suited for rapid and continuous online process characterization.  

Sometimes grab samples of the process that are analyzed in the laboratory provide sufficient data for process characterization. However, the nature of a process stream frequently makes collecting representative grab samples difficult. Gas-phase samples, reactive samples, and samples under elevated (or depressed) temperature or pressure are a few examples of samples that are particularly difficult to collect and analyze by laboratory analysis. Streams where trace (for example, low parts per million) levels of water or carbon dioxide are the analytes of interest are also a particular challenge for grab sample laboratory analysis because concentration levels are easily influenced by atmospheric composition. Safety issues must also be considered when collecting samples of highly toxic or explosive materials such as phosgene, acrylates, ethylene oxide, hydrogen chloride, or isocyanates. Depending on the duration and extent of required toxic material analyses, properly designed continuous online analysis can help mitigate associated safety issues. Additionally, when the process event of interest is transient occurring over a short time-frame, occurring at unknown intervals, or if the process is not at steady-state, a continuous online analysis of a representative sample provides a richness of insight into the process that would not be achievable by the infrequent glimpses into the process that is attainable by grab sample analysis. Lastly, continuous analyses are an invaluable tool during new process development and process scale-up due to the ability to thoroughly characterize the process. 

Of the available analytical tools for temporary online process characterization, FT-IR is among the most powerful. FT-IR is a fast technique providing fundamental vibrational information for the components in the stream and allowing the capture and retention of information-rich spectra. The raw data obtained from FT-IR are often referred to as "traceable" because analysts can refer to primary and secondary standards corresponding to the spectral information from the process sample. Analysts may also examine the raw spectral data for interferences that have not been present previously or do not match the anticipated stream composition. A variety of chemometric modeling techniques (1) are available to obtain quantitative information, trending information, or simple qualitative stream composition analysis from the raw spectra depending on the purpose and requirements of the particular application. 

The insights and data obtained from continuous online FT-IR can be applied to optimize process control models, validate the consistency of production quality, or inform development of robust and reliable permanent process analyzer applications. The following case studies focus on process control optimization and product quality. 

Before discussing case studies it will be useful to highlight a few basic considerations for installing a FT-IR system in a process environment. Of course, spectrometer setup, operation, and data processing must be appropriately specified for a given application, but unless the sample is reliably and safely delivered to the spectrometer the analysis details are mute. Process analyzer installation and methods can be complex (2), but two major design considerations for temporary analysis are safety and sample system design. The first to consider is safety. Although some safety concerns associated with handling samples for laboratory analysis are mitigated by using a process analyzer and eliminating routine grab sampling, a distinct set of other safety related hazards must be addressed. Of primary concern is assuring area classifications are met to mitigate explosion or exposure risks. In the first case study, the stream was comprised mostly of ethylene and the area classification issue was of critical importance. Because the FT-IR system that was used was not classified for a Class 1 Division 2 area, operating procedures were developed designating attended operation with appropriate area lower explosion limit (LEL) monitoring and emergency shutdown plans should the LEL monitor indicate the presence of a flammable atmosphere. A picture of the field setup of the analyzer is shown in Figure 1. On days when there was a threat of rain, a canopy was placed over the whole system. The preferred option would have been to package the spectrometer in a z-purged analyzer enclosure. Cost and timing constraints prevented the preferred path of C1D2 packaging for the analyzer in the ethylene analysis case study. Figure 2 shows the analyzer packaged for the product quality case study where the analyzer was installed in the field and operating under nonattended operation 24/7. In the product quality case study, the location was a nonclassified area, but the stream composition was highly toxic and corrosive. The analyzer enclosure was exposed to the elements and vortex coolers were used to control the temperature inside the enclosure.  

Figure 1: Temporary setup of FT-IR analyzer for analysis of the ethylene stream in case study 1 for use with attended operation only. 

The second set of design considerations revolve around sample systems and sample handling panels. Having a continuous online analysis is not useful if the analyzer is not provided with a consistent and representative process sample for analysis. Sample systems are an integral part of the success or failure of a process analytical system (3). FT-IR and spectroscopic techniques in general demand much simpler sample systems than required by typical process gas chromatography systems, but fundamentals such as flow and pressure as well as avoiding condensation, particulates, and plugging are still essential. Key methods for liquid- and gas-phase sample systems for FT-IR implementation are different. Here, the focus is on gas-phase FT-IR as this technique has repeatedly been demonstrated to meet the temporary online stream characterization requirements in a variety of Dow processes. When it comes to liquid-phase analysis, attenuated total reflectance (ATR) FT-IR techniques are occasionally used, but probe fouling and lack of ability to monitor trace levels can be limiting and the use of a near-infrared or Raman system may be preferable. For gas-phase process streams, long-pathlength FT-IR analysis is a workhorse for stream characterization. The two systems that were used in the case studies discussed below are the MKS 2032 MultiGas FT-IR system (MKS Instruments, Andover, Massachusetts) and the Gasmet DX4000 FT-IR system (Gasmet Technologies Oy, Finland). Other FT-IR systems could also be utilized, but portability and 24/7 continuous data acquisition are key requirements.  

Figure 2: Temporary setup of FT-IR analyzer in case study 2 for continuous 24/7 data collection. 

The first case study is an application in a plant trying to solve plugging and fouling events that have been occurring regularly over multiple years. The stream was primarily ethylene with other C

 hydrocarbons and unknown low levels of acrylic acid monomer, water, and other small organics thought to be present in concentrations less than 1 mol%. It was known that the substance plugging the equipment at the facility was polyacrylic acid polymer and that minimizing the presence of acrylic acid and water in the stream would minimize the fouling. Several process control changes had been tried throughout the years, based purely on Aspen process models (Aspen Technology, Inc., Burlington, Massachusetts), but the plugging issues persisted. The unknown was how various process operating conditions in surrounding portions of the plant impacted the residual levels of acrylic acid and water in the region where fouling occurred. Attempts to validate the assumptions used for the process models using aluminum oxide moisture measurements failed because the aluminum oxide moisture analyses used were infrequent, slow to equilibrate, suffered from calibration drift because of stream components fouling the sensor, and were not reproducible. Additionally, the aluminum oxide sensor only provided data on the moisture component, not the acrylic acid concentration or any of the other hydrocarbons or organics that were suspected to be present. 

To achieve a fast response, quantitative results, low detection limits, and multicomponent analysis, a long-path FT-IR was installed at the process location with a sample line comprising 50 ft of ¼-in. diameter heat traced tubing to minimize condensation or changes in the sample between the process pipe and the analyzer. The process sample was returned to the plant's vacuum vent line. This process drop allowed for continuous flow at the rate of ~2–3 L/min and a constant pressure in the FT-IR sample cell. The sample cell pathlength was 5.11 m and 0.5 cm

  spectral resolution was used. Figure 3 shows an example spectrum of 100% ethylene as well as water and acrylic acid in the concentration range of interest. Figure 3 also provides a process spectrum clearly indicating that both water and acrylic acid are present in the process ethylene. A total of six components were monitored in the ethylene process stream, including the water and acrylic acid.  

Figure 3: Spectra of (a) 100% ethylene, (b) 500 ppm water, and (c) 932 ppm acrylic acid, and (d) a process spectrum at 150 Â°C and 1 atm with a 5.11-m pathlength at 0.5-cm-1 resolution and a 1-min collection time. 

A spectral data point was collected every minute to capture process fluctuations. A designed set of experiments was implemented that systematically changed the plant's operating conditions within acceptable ranges while levels of acrylic acid and water, among other components, were monitored. Figure 4 includes process trend comparisons between just two of the process condition experiments showing a better than threefold difference in water concentration while having negligible effects on acrylic acid levels. The fluctuations within each of the given trials also correlated with specific process parameters and provided insight into process stability. Other tested process conditions demonstrated decreases in residual acrylic acid.  

Figure 4: Acrylic acid and water trends during two different process trials showing significant impact on the water concentrations between the two sets of process conditions. 

The key information learned from continuous FT-IR data of acrylic acid and water, as well as the other components, in comparison with known process control parameters was identification of specific process parameters that enabled reduction in acrylic acid and water without adversely affecting quality or other process operations. The analytical results were used to optimize the process control model based on Aspen simulations. The new process model was implemented in the plant for control. Before implementation of the model, fouling occurred every 1–2 months causing 2–3 days of downtime per incident. Since implementation of the model more than 1 year ago there has not been a shutdown caused by the fouling problem. This case study is an excellent example of FT-IR spectroscopy being used for continuous online analysis to improve process operation by providing real-time data for process correlation. The plant's issue was solved without investment in installation and maintenance of a permanent FT-IR (or any other) process analyzer. 

A second case study, in which gas phase FT-IR was applied to characterize a process, was for the purpose of product quality optimization. In this case, the goal was product purity. The stream was very simple with a single component being greater than 99.99% pure with contaminants of interest for continuous analysis, carbon monoxide (CO) and carbon dioxide (CO

), being present at levels below 1 ppm under normal conditions. The major challenge with analyzing CO

 by grab sample and laboratory analysis is atmospheric contamination of the sample. By using a continuous online sample system that is free of leaks, a more representative sample is achieved. In this particular case study, the plant was interested in increasing the purity of its hydrochloric acid (HCl) product. The FT-IR data were used to validate process improvements with regard to minimizing the CO

Two different FT-IR systems were applied for the sub-part-per-million analysis of CO and CO

 in HCl because of changing equipment availability. Both used a 5-m pathlength multipass cell, a 1-min collection time, and a 60 °C cell temperature, and both operated with sample pressures slightly below atmospheric pressure. The primary difference between the two FT-IR systems was spectral resolution. One system was operated at 0.5-cm

. Both used modified classical least squares (CLS) algorithms for quantitative concentration determination. There were advantages and disadvantages of operating under the different resolutions (5), but both demonstrated capability for the analysis. 

The lower-resolution system required greater attention to the CLS model prediction because of spectral overlap between deuterium chloride (DCl) and CO. The ro-vibrational spectra of HCl is well known to have rotational structure from both the H

Cl isotopes. With the natural abundance of deuterium being 156 ppm, the spectrum of anhydrous HCl also contains rotational structure from the υ=1←0 band of D

  (7) and overlaps the fundamental CO stretch region. Figure 5 shows the overlap of DCl and CO at 0.5 cm

  resolution.  With the spectrometer system at 0.5 cm

  resolution, the best quantitative solution was to rely on CO transitions located in the DCl Q-branch gap. Specifically, the three CO transitions at 2094.8 (P(12)), 2090.6 (P(13)), and 2086.3 cm

  (P(14)) were used for the CO analysis on the 0.5 cm

  resolution spectrometer. A modified CLS method based off of peak areas was used for quantitative prediction with the 0.5-cm

  resolution, a CLS method was developed and validated assuming a constant deuterium concentration of 0.0156% of the HCl concentration and incorporating HCl concentrations into the model. This approach also allowed small pressure and temperature fluctuations to be accounted for within the model.  

Figure 5: Process spectra containing DCl and some CO2 compared to reference spectra of CO at both 8-cm-1 and 1-cm-1 resolution. 

Continuous at-line and online FT-IR implementation has proven to be effective for characterizing process streams that prove challenging from the perspective of traditional grab sampling and laboratory analysis. Care must be taken in sample handling and implementation to assure that a representative sample is delivered to the analyzer.  

The value of FT-IR spectroscopy for process characterization is the information-rich nature of the data obtained and how that information is applied to optimize or troubleshoot the process. In many cases, a temporary online setup that provides a few weeks to a couple of months of stream data is all that is required for improvements to be chosen, implemented, and validated. However, in other cases the information collected by the temporary online FT-IR spectrometer indicates the need for a permanent online analysis to be used for closed-loop control or routine monitoring. In these situations, the stream composition data from the FT-IR analysis are used to specify the most robust, reliable, and economical solution for a permanent online analysis. In a few cases this might be a FT-IR system, but more commonly the permanent solution is an alternative spectroscopic-based solution, such as a filter photometer or tunable diode laser system. Use of full-spectrum FT-IR stream composition information while specifying a permanent analyzer system increases the success rate of the new application development and installations of process analysis technologies.   

Serena Stephenson, Lamar Dewald, Esteban Baquero, Wendy Flory, Liane Mikolajczyk, 

are with The Dow Chemical Company. Direct correspondence to: 

(1) K.R. Beebe, R.J. Pell, and M.B. Seasholtz, 

 (John Wiley & Sons, Inc., New York, 1998), Chapter 5. 

 (John Wiley & Sons, Inc. New York, 1986), Chapter 21. 

Process Analyzer Sample-Conditioning System Technology

 (John Wiley & Sons, Inc, New York, 2002). 

(4) P. Jaakkola, J.D. Tate, M. Paakkunainen, and P. Saarinen, 

Molecular Spectra and Molecular Structure IV

 (Van Nostrand-Reinhold, New York, 1979). 

Case studies involving fouling and product quality illustrate the effective use of this method.

Temporary Online FT-IR Spectroscopy for Process Characterization in the Chemical Industry

The fourth part of this series continues our explanation of Maxwell's equations, the seminal classical explanation of electricity and magnetism (and, ultimately, light). For those of you new to the series, consider reading the last few appearances of this column to get caught up. Alternately, you can find past columns on our website: 

www.spectroscopyonline.com/The+Baseline+Column

 Words of warning: For my own reasons, the figures are being numbered sequentially through this series, which is why the first figure in this column is Figure 26. Also, we're going to get a bit mathematical. Unfortunately, that's par for the course. 

 is any object that produces a magnetic field. That's a rather circular definition (and saying such is a bit of a pun, when you understand Maxwell's equations), but it is a functional one: A magnet is most simply defined by how it functions. 

Technically speaking, all matter is affected by magnets. It's just that some objects are affected more than others and we tend to define 

 in terms of the more obvious behavior. An object is magnetic if it attracts certain metals such as iron, nickel, or cobalt and if it attracts and repels (depending on its orientation) other magnets. The earliest magnets occurred naturally and were called lodestones, a name that apparently comes from the Middle English "leading stone," suggesting an early recognition of the rock's ability to point in a certain direction when suspended freely. By the way, lodestone is simply a magnetic form of magnetite, an ore whose name comes from the Magnesia region of Greece, which is itself a part of Thessaly in central eastern Greece bordering the Aegean Sea. Magnetite's chemical formula is Fe

 mineral. Magnetite itself is not uncommon, although the permanently magnetized form is, and how it becomes permanently magnetized is still an open question. (The chemists among us also recognize Magnesia as giving its name to the element magnesium. Ironically, the magnetic properties of Mg are about 1/5000 that of Fe.) 

Magnets work by setting up a magnetic field. What actually is a magnetic field? To be honest, I'm not sure I can really say, but its effects can be measured all around the magnet. It turns out that these effects exert forces that have magnitude and direction: That is, the magnetic field is a vector field. These forces are most easily demonstrated by objects that either have a magnetic field themselves or have an electrical charge on them, as the exerted force accelerates (changes the magnitude and direction of the velocity of) the charge. The magnetic field of a magnet is represented as 

 and, again, is a vector field. (The symbol 

 is also used to represent a magnetic field, although in some circumstances there are some subtle differences between the definition of the 

Figure 26: Photographic representation of magnetic lines of force. Here, a magnetic stir bar was placed under a sheet of paper and fine iron filings were carefully sprinkled onto the paper. While the concept of lines of force is a useful one, magnetic fields are continuous and are not broken down into discrete lines like pictured here. (Photo by author, with assistance from Dr. Royce W. Beal, Mr. Randy G. Ramsden, and Dr. James Rohrbough of the US Air Force Academy Department of Chemistry). 

When Michael Faraday was investigating magnets starting in the early 1830s, he invented a description that was used to visualize magnets' actions: 

. There is some disagreement whether Faraday thought of these lines as caused by the emanation of discrete particles or not, but no matter. The lines of force are those things that are visualized when fine iron filings are sprinkled over a sheet of paper that is placed over a bar magnet, as shown in Figure 26. The filings show some distinct "lines" in which the iron pieces collect, although this is more of a physical effect than a representation of a magnetic field. There are several things that can be noted from the positions of the iron filings in Figure 26. First, the field seems to emanate from two points in the figure, where the iron filings are most concentrated. These points represent poles of the magnet. Second, the field lines exist not only between the poles, but arc above and below the poles in the plane of the figure. If this figure extended to infinity in any direction, you would still see evidence — albeit less and less as you proceed farther away from the magnet — of the magnetic field. Third, the strength of the field is indicated by the density of lines in any given space — the field is stronger near the poles and directly between the poles, and the field is weaker farther away from the poles. Finally, we note that the magnetic field is three-dimensional. Although most of the figure shows iron filings on a flat plane, around the two poles the iron filings are definitely out of the plane of the figure, pointing up. (The force of gravity is keeping the filings from piling too high, but the visual effect is obvious.) For the sake of convention, the lines are thought of as "coming out" of the north pole of a magnet and "going into" the south pole of the magnet, although in Figure 26 the poles are not labeled. 

Figure 27: Faraday used the concept of magnetic lines of force to describe attraction and repulsion. (a) When opposite poles of two magnets interact, the lines of force combine to force the two poles together, causing attraction. (b) When like poles of two magnets interact, the lines of force resist each other, causing repulsion. 

Faraday was able to use the concept of lines of force to explain attraction and repulsion by two different magnets. He argued that when the lines of force from opposite poles of two magnets interacted, they joined together in such a way as to try to force the poles together, accounting for the attraction of opposites (Figure 27a). However, if lines of force from similar poles of two magnets interacted, they interfered with each other in such a way as to repel (Figure 27b). Thus, the lines of force were useful constructs to describe the known behavior of magnets. 

Figure 28: Faraday used the lines of force concept to explain how objects behave in a magnetic field. (a) Most substances (like glass, water, or elemental bismuth) actually slightly repel a magnetic field; Faraday explained that they excluded the magnetic lines of force from themselves. (b) Some substances (like aluminum) are slightly attracted to a magnetic field; Faraday suggested that they include magnetic lines of force into themselves. (c) Some substances (like iron) are very strongly attracted to a magnetic field, including (according to Faraday) a large density of lines of force. Such materials can be turned into magnets themselves under the proper conditions. 

Faraday also could use the lines of force concept to explain why some materials were attracted by magnets (

 materials or in their extreme form called 

 materials). Figure 28 illustrates that materials attracted by a magnetic field concentrate the lines of force inside the material, while materials repelled by a magnetic field exclude the lines of force from the material. 

As useful as these descriptions were, Faraday was not a theorist. He was a very phenomenological scientist who mastered experiments, but had little mathematical training with which to model his results. Other scientists were able to do that, some of whom were based in Germany and France — but the important contributions came from other scientists in Faraday's own Great Britain.  

Two British scientists contributed to a better theoretical understanding of magnetism: William Thomson (also known as Lord Kelvin) and James Clerk Maxwell. However, it was Maxwell who did the more complete job. 

Maxwell was apparently impressed with the concept of Faraday's lines of force. In fact, the series of four papers in which he described what later became Maxwell's equations was titled "On Physical Lines of Force." Maxwell was a very geometry-oriented person; he felt that the behavior of the natural universe could, at the very least, be represented by a drawing or picture. 

Let's consider the lines of force pictured in Figure 26. Figure 29 shows one ideal line of force for a bar magnet in two dimensions. Remember that this is a thought experiment — a magnetic field is not composed of individual lines; rather, it is a continuous vector field. And within a vector field, the field lines have some direction as well as magnitude. By convention, the magnetic field vectors are thought of as emerging from the north pole of the magnet and entering the south pole of the magnet. This vector scheme allows us to apply the right-hand rule when describing the effects of the magnetic field on other objects, like charged particles and other magnetic phenomena. 

Figure 29: Hypothetical line of force about a magnet. Compare this to the photo in Figure 26. 

Consider any box around the line of force. In Figure 29, the box is shown by the dotted rectangle. What is the net change of the magnetic field through the box? By focusing on the single line of force drawn, we can conclude that the net change is zero: There is one line entering the box on its left side, and one line leaving the box on its right side. This is easily seen in Figure 29 for one line of force and in two dimensions, but now let's expand our mental picture to include all lines of force and all three dimensions. There will always be the same number of lines of force going into any arbitrary volume about the magnet as there are coming out. There is no net change in the magnetic field in any given volume. This concept holds no matter how strong the magnetic field and no matter what size the volume considered. 

How do we express this mathematically? Why, using vector calculus, of course. In the previous discussion of Maxwell's first law, we introduced the divergence of a vector function 

Note what the divergence really is; it is the change in the 

 dimension. However, we have already presented the argument from our lines of force illustration that the magnetic field coming in a volume equals the magnetic field going out of the volume, so that there is no net change. Thus, using 

Maxwell's second equation of electromagnetism

. It is sometimes called Gauss's law for magnetism. Because we can also write the divergence as the dot product of the del operator (∇) with the vector field, Maxwell's second equation becomes 	 

What does Maxwell's second equation mean? Because the divergence is an indicator of the presence of a source (a generator) or a sink (a destroyer) of a vector field, it implies that a magnetic field has no separate generator or destroyer points in any definable volume. Contrast this with an electric field. Electric fields are generated by two different particles, positively charged particles and negatively charged particles. By convention, electric fields begin at positive charges and end at negative charges. Because electric fields have explicit generators (positively charged particles) and destroyers (negatively charged particles), the divergence of an electric field is nonzero. Indeed, by Maxwell's first equation, the divergence of an electric field 

Figure 30: If you break a magnet, you don't get two separate magnetic poles ("monopoles," top), but instead you get two magnets, each having north and south poles (bottom). This is consistent with Maxwell's second law of electromagnetism. 

which is zero only if the charge density, ρ, is zero — and if the charge density is not zero, then the divergence of the electric field also is not zero. Furthermore, the divergence can be positive or negative depending on whether the charge density is a source or a sink. 

For magnetic fields, however, the divergence is exactly zero, which implies that there is no discrete source ("positive" magnetic particle) or sink ("negative" magnetic particle). One implication of that is that magnetic field sources are always dipoles; there is no such thing as a magnetic "monopole." This mirrors our experience when we break a magnet in half, as shown in Figure 30. We don't get two separated poles of the original magnet. Rather, we get two separate magnets, complete with north and south poles. 

In the next installment, we will continue our discussion of Maxwell's equations and see how 

 are related to each other. The first two equations deal with 

 separately; we will see, however, that they are anything but separate. 

 is normally a professor of chemistry at Cleveland State University in Ohio. For a while, though, things will not be normal: starting in July 2011 and for the commencing academic year, David will be serving as Distinguished Visiting Professor at the United States Air Force Academy in Colorado Springs, Colorado, where he will be teaching chemistry to Air Force cadets. He still, however, has two books on spectroscopy available through SPIE Press, and just recently published two new textbooks with Flat World Knowledge. Despite his relocation, he still can be contacted at 

. And finally, while at USAFA he will still be working on this series, destined to become another book at an SPIE Press web page near you. 

 (Greenwood Press, Westport, Connecticut, 2007). 

(3)  D. Halliday, R. Resnick, and J. Walker, 

. (John Wiley and Sons, New York, New York, 2001). 

 (Brooks-Cole Publishing Co, Pacific Grove, California, 1994). 

 (6) J.R. Reitz, F.J. Milford, and R.W. Christy, 

 (Addison-Wesley Publishing Company, Reading, Massachusetts, 1979). 

Div., Grad., Curl., and All That: An Informal Text on Vector Calculus 4th Ed

. (W.W. Norton and Company, New York, New York, 2005). 

The Baseline

A discussion of magnetism, leading into Maxwell's second equation

Maxwell's Equations, Part IV

 general chapter on analytical instrument qualification (

 <1058>) and the ISPE's Good Automated Manufacturing Practice (GAMP) Good Practice Guide on laboratory computerized systems are the two main sources of guidance for qualifying analytical instruments and validating computerized systems used in the laboratory. This column explains the discrepancies between the two documents as well as changes now being made to both in an attempt to enable an integrated approach to qualification and validation of laboratory instruments and systems.

There are many sources of advice on computerized system validation and analytical instrument qualification for the laboratory, including regulatory agencies, such as the United States Food and Drug Administration (FDA) (1,2); regulatory associations such as the Pharmaceutical Inspection Convention/Co-operation Scheme (PIC/S) (3,4); the Official Medicines Control Laboratories (OMCL) in Europe (5); and pharmacopeias such as the 

 (6). Information also can be obtained from scientific societies or associations such as the American Association of Pharmaceutical Scientists (AAPS) (7), the Parenteral Drug Association (PDA) (8), the Drug Information Association (DIA) (9), and the International Society of Pharmaceutical Engineering (ISPE) (10). All of these organizations have published guidance on instrument qualification or computer validation either for a general regulated audience or specifically for a regulated laboratory.  

There are two main sources, however, of regulatory guidance and advice for qualification of analytical instruments and validation of computerized systems used in the laboratory. The first is 

 general chapter <1058> on analytical instrument qualification (AIQ) (6), which was derived from an AAPS meeting on analytical instrument validation held in 2003. One decision that came from that conference was that the terminology being used at the time was incorrect, because the conference name itself should have referred to analytical instrument 

. The white paper published by AAPS in 2004 (7) was the major input to 

 <1058>, which became effective in 2008. 

The second source for guidance in a regulated laboratory comes from ISPE's 

Good Automated Manufacturing Practice (GAMP) Guide

, which is seen by many as a standard for computerized system validation. After the publication of version 4 of this guide in 2001 (11),  ISPE published several "good practice guides" (GPGs) on specific topics that were intended to take the principles of the version 4 guide and tailor them for a particular subject or focus area. The GAMP 

Good Practice Guide on the Validation of Laboratory Computerized Systems

 is one such guide that was published in 2005 (12). 

The major problem with analytical instruments that are used in a regulated laboratory is their great variety, complexity, and variations in intended use. Furthermore, the software associated with an instrument can vary from firmware in basic instruments to servers and workstations for multiuser networked data systems.  

Writing guidance for the qualification of this wide range of instrumentation and software is not easy, as qualification needs also depend on the intended use of the instrument or system. However, as we have both maintained for a number of years, only an integrated approach to instrument qualification and computer validation — focusing on the key elements that must be controlled in a single combined process — is efficient and effective (13–16). An integrated approach is not only beneficial from a regulatory and auditable context, but it also is cost effective for the business. This approach is in contrast to conducting an initial qualification of the instrument and a separate validation of the software, which is inefficient and may duplicate some work. Furthermore, because of organizational structures, instrument qualification may be carried out by the vendor and considerable time may elapse before the computer validation is conducted and the system is released into operational use. 

It would be highly advantageous if the regulations and guidance could all say similar things and avoid duplicate tasks. However, with the way the current versions of 

 <1058> (6) and the GAMP laboratory systems GPG (12) are written, this is not possible, as we will illustrate now. 

Critique of the GAMP GPG Laboratory Guide

In 2006, comments were made this column on the disconnection of the first edition of the GAMP GPG for validation of laboratory computerized systems from the rest of the regulated organization (13). The version of the GPG at that time had, from our perspective, the following issues:  

The guide had an unnecessarily complex classification of laboratory computerized systems (13) that did not match the software-based classification in the main GAMP guide (17,18).	

According to the GAMP GPG, everything required validation; there was no consideration of instrument qualification.  

There was no reference to the seminal instrument qualification papers, such as the discussion of modular and holistic qualification approaches by Furman and colleagues (19). 

To be fair, the GAMP GPG embraced a simplified life cycle model that was a great advance compared to the traditional V model shown in GAMP 4. In 2008, GAMP 5 was released (10) and was an improvement to the previous version of the guide (11). The new GAMP 5 was more risk-based and introduced several life cycle models depending on the software category. However, the major problem with this new version of the GAMP guide was the removal of category 2 (firmware) from the categorization of software (17,18). While understandable from a software system perspective, it is in direct conflict with 

 <1058>, in which group B instruments are firmware-based (6). 

Two earlier "Focus on Quality" columns commented on 

 <1058> (13,16). The classification of analytical equipment into one of three groups (A, B, and C) is a simple risk assessment, which is a good approach, but conflicts with the more complex GAMP laboratory GPG. Some of the other problems of <1058> are  

It makes the vendor, rather than the user, responsible for design qualification and defining the intended purpose for a specific laboratory. This is wrong. Only users can be responsible for defining their requirements and selecting the instrument or system that is appropriate to meet their scientific, regulatory, and business needs. The role of a vendor's specification is to sell instruments.  

The true role of the vendor is missing from the data quality triangle in <1058> (16). 

There are subcategories within group B (instruments) and group C (systems) that are not covered in the current version of <1058>. This can lead to noncompliance with 21 

 211.68(b) with regard to checking calculations embedded in some group B instruments (16).  

There is no control or guidance in group B instruments for users to program routines. 

There is poor software validation guidance for group C systems that could result in regulatory observations for a laboratory. 

General Principles of Software Validation 

(1) is inappropriate because the FDA guidance does not consider the software configuration that is common with laboratory computerized systems. 

 analytical general chapters will be undergoing revision between now and 2015, with updates being published in 

; this revision will be combined with efforts to harmonize with chapter with both the Japanese and European pharmacopeias. New general chapters will be published in pairs: The legal requirements will be in chapters numbered between <1> and <999> and the corresponding best practice will be in a general chapter numbered between <1000> and <1999>. 

AIQ and Computerized System Validation: Where Are We Now?

 <1058> and the GAMP GPG on laboratory computerized systems, if we ask the question, "Is integration possible?" the answer is a resounding 

. Specifically, there is no effective and efficient link between 

 <1058>, and the GAMP 5, or indeed the GAMP laboratory GPG. So, where do we go from here?  

The next steps will take place on two fronts: first, a stimulus to the revision process paper for the update of 

 <1058> (20); and second, the drafting of the second edition of the GAMP GPG on the validation of laboratory computerized systems (21), both of which are planned for publication in the first quarter of 2012.  

During the AAPS annual meeting in November 2010, we suggested to the USP that we write a stimulus to the revision process paper on <1058>. Our proposal was accepted and we began working on a draft of the paper, scheduled for publication in 

 in the January–February 2012 issue (20). The main aspects of our paper are described below.   

Software Is Important in Analytical Instrument Qualification

Two key points are necessary for effective and efficient AIQ. The first is defining the intended purpose of an item of laboratory equipment. The second is identifying the software used in that equipment. Typically, this software is either firmware inside an instrument or on a separate PC running a software application for controlling the instrument, as well as acquiring, interpreting, reporting, and storing the data. Neither of these software elements is adequately covered in the current version of <1058> (16). 

One of the first considerations for revising <1058> should be to close the gap in the approaches of GAMP 5 and <1058> to reach a unified approach to qualification and validation, which is shown in Figure 1. This figure shows our mapping of the current GAMP software categories against <1058> groups A, B, and C. Our contention is that there are subcategories within groups B and C that are not covered by the current version of <1058> but that should be there to ensure comprehensive regulatory guidance (16). It is important to realize that 

 <1058> is driven by an instrument or hardware approach (classification into Groups A, B, and C). In contrast, the GAMP approach is software driven. When developing laboratory guidance, we have to consider both sides of the equation: hardware and software.  

 Figure 1:  Mapping USP  and GAMP software categories.  

Dropping GAMP software category 2 requires category 3 to accommodate items ranging from simple analytical instruments with firmware to laboratory computerized systems with nonconfigurable commercial software. Potentially, we would require validating all group B instruments under GAMP rather than qualify them under <1058>. Because group A items do not contain software, there is no comparable mapping possible with GAMP 5, but we have included this group in Figure 1 for completeness. We also have added GAMP software category 5 under category 4 with it offset to the right in Figure 1 to show that with some category 4 systems it is possible to write user-defined macros.  

The basic risk assessment model in <1058> is the classification of all laboratory items into one of the groups (A, B, or C) based on a definition of intended use. This approach is generally sound, because apparatus (group A), instruments (B), and systems (C) are easily classified. However, there is a weakness in that the level of granularity offered is insufficient to classify the variety of instruments (B) and systems (C) used in combination with software in the laboratory today. Figure 1 illustrates this point by depicting three subtypes within group B instruments (that is, firmware, firmware with calculations, and firmware with the ability for users to define programs). 

Therefore, our basic proposal in the stimulus to the revision process paper (20) is to provide better means of  

unambiguously differentiating between apparatus (group A) and instruments (group B) based on functionality

 linking the software elements with the various types of instrument (group B) and systems (group C), because current instrumentation is more complex that the simplistic use of groups A, B, and C. This will identify subgroups within groups B and C.

identifying items used in a regulated laboratory that are not GXP relevant, to exclude them from the qualification and validation process. 

Figure 2:  Classification of laboratory items under the proposed  risk assessment.   

We see this risk assessment as essential for determining the proper business and regulatory extent of qualification and validation for a specific instrument or system with a defined intended use. It also is a necessary requirement for complying with US good manufacturing practice (GMP) regulations, specifically 21 

 21.68(b), which requires that calculations be checked if the instrument or system has calculations upon which the user relies (22). This requirement is not mentioned in the current version of <1058>.   

The risk assessment we propose is based on asking up to 16 closed questions (with only yes or no answers) that can classify an item in one of the four groups listed below and shown diagrammatically in Figure 2: 

 1. No GXP impact of the instrument or system 

2. Group A (apparatus) — no formal qualification impact, only observation 

Instrument only: qualification required (subcategory I)

Instrument with software:   qualification required and   calculations verified (sub-   category II)

Instrument with software:   qualification required plus   control of user-defined   programs (subcategory III) 

Focus on Quality

Here's what needs to be done to harmonize these two documents.

USP <1058> and the GAMP Guide on Laboratory Computerized Systems – Is Integration Possible?

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USP <1058> and the GAMP Guide on Laboratory Computerized Systems – Is Integration Possible?

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