Which trace element and function is correctly matched

After proper wavelength and relative intensity calibration, and barring any substantial fluorescence or resonance effects, the spectra from the differing wavelength systems should nearly superimpose on one another.

In almost all cases our results have shown this to be the case. The reference data can readily serve as reference spectra for systems, as shown for the two FT-Raman systems shown here, as well as for dispersive handheld Raman systems [ 8 ].

This is seen for a typical example in Figure 4 and was the case for almost all measured species. The search algorithm and criteria for declaring a match are described below, but Figure 4 illustrates the results of this process for caprylic acid. The second hit, with a relatively high score, is the chemically and spectroscopically similar hexanoic acid, with tributyl phosphate detected as the third hit. For the explosives, the commercial grade materials such as those used for mining and demolition are sold by trade names and generally consist of a mixture of energetic materials.

The variety and relative percent concentrations of the materials used vary widely and depend upon required performance and cost. Many of the commercial products, while sold under different names, have some strong commonalities in the material components. For example, many mining materials contain various levels of ammonium nitrate and sodium nitrate along with other materials.

Due to the strong Raman activity of the moiety, both NH 4 NO 3 AN and NaNO 3 provide features that dominate the spectrum, while the smaller features help aide slightly in the identification of the specific formulation.

Thus, the explosive materials listed in Table 1 are segregated and categorized based upon their primary chemical components or compound groups. Subtle differences due to noise and baseline correction can lead to slight changes in relative peak intensity and even peak position. Due to the similar makeup of several of the explosives, such differences were likely the cause of the top hit of an analyte not being an exact match to that formulation but the top hit was for a formulation containing nearly identical chemical components to those in the analyte.

In fluorescence, the excitation light is absorbed and re-emitted via an electronic transition that does not involve a change in the spin quantum number; at room temperature the fluorescence bands typically manifest themselves as a broad and featureless background.

In some cases, this fluorescence background signal can be removed via baseline correction, but many researchers have shown that shifting to longer wavelength excitation is even more advantageous, as Lewis et al. Typically the method used in most sensors is to simply filter the analysis via baseline correction such as with a rolling circle filter or some other baseline correction algorithm.

While the phenomena are real optical phenomena, they are usually featureless and are simply removed as backgrounds via a software algorithm, leaving only the Raman peaks. Interestingly, there were also a few anomalous situations where there was significant spectral change not just a fluorescence background depending upon the excitation wavelength. One example, illustrated in Figure 5 , was for neat Er 2 O 3. The resulting fluorescence then dominates the emission spectrum.

The translanthanides and trans-actinides typically have multiple low-lying electronic states that give the species their colors. In their studies of Er 2 O 3 nanocolloids, Patel et al. The software has several different search algorithms, each with several possible variations to it. Therefore, the OPUS basic search algorithm was used. The OPUS basic search algorithm works by matching peaks without regard to their intensities. In particular, for any given spectrum, a peak table is constructed.

This peak table is used to create a signature spectrum consisting of Lorentz curves for every peak, each of which has a height of one. To compare a test case to a library compound, the algorithm looks up all of the test signature peaks in the library signature. Those test peaks that appear in the library signature are assigned a value of one, whereas those that are different or do not appear in the library signature are assigned a lesser value, with a minimum of zero.

A hit quality is then calculated as the average of these values times More simply, when all test case peaks are found in given library signature spectrum, the hit quality is When none of them are found, the hit quality is zero. For a given test case, this comparison process is repeated for every entry of the spectral library, and the three library compounds with the highest hit quality—the top three hits—are reported. OPUS allows for some parameter adjustment in its basic search algorithm.

While specific details about this parameter are unavailable, its value, an integer between 1 and 20, relates to the sensitivity with which OPUS identifies peaks, 20 dictating a much closer match.

Loosely interpreted, a higher search sensitivity means the peak locations must be more precise for the algorithm to give a higher hit score, to the point that Bruker did not recommend a setting of 15 or higher for this application.

The search sensitivity also affects the number of peaks created in the temporary peak table for the spectrum being searched. Thus, a higher search sensitivity implies a stronger match and implies more peaks may have been created and matched than a lower sensitivity value. OPUS suggests testing different values to determine which is best for a particular application. For our application, a search sensitivity of 7 was chosen because preliminary results showed this value tended to generate favorable search results in terms of good discrimination.

In terms of the explosives, using the chemical formulation grouping, all 20 explosives were correctly identified. It should also be noted that for many of these formulations the top 3 or 4 hits were for similar compounds and all had very similar high scores. Thus the explosive materials listed in Table 1 are segregated and categorized based upon primary chemical components or compound groups. We also note that, for the explosives, the first hit minus second hit refers to the first hit minus the first hit outside of its compound group.

Its second, third, fourth, and fifth hits are also with each hit belonging to the same compound group. Its first hit outside of the group is potassium nitrate with a hit score of Therefore the distance between first and second hits for AN-NaNO3 is , the difference of and While the explosives were all easily identified with the imported library, for the more general case of relying on an algorithm for identifications, it is important to first establish objective criteria for declaring a match.

For example, when the top hit score is near , there is strong evidence for a match, but not so when the top hit score is near Even in the case of a high score, if the top two hit scores are so close as to be virtually identical say and , then there is ambiguity in declaring one or the other a match. A concrete example of this is provided in Figure 6.

In the figure, hair gel had petroleum jelly as its top hit with hair gel as its second hit. This implies the search algorithm cannot, for certain, determine which spectrum is the correct match.

In this particular case, because the hair gel was determined to be made of almost exclusively petroleum jelly with a few trace additives namely a coloring agent the major peaks were therefore in the same positions. In such cases a more definitive criterion is needed to declare a match. The second method, referred to here as the dual criteria, is designed to take into account not only the top hit but also the difference between the first and second hits.

In this case, the top hit must be above a minimum threshold to be declared a match, and the distance between the first and second hit must be larger than a specified value. These dual criteria ensure that 1 the top hit closely matches the test spectrum, and 2 there are no other library spectra similar enough to the test spectrum to cause ambiguity. After initial experimentation, the dual criteria were set so that the minimum hit threshold was and the minimum required distance between the first and second hit was at least Those test spectra meeting both criteria were declared a match.

The -axis contains the top hit score and the -axis is the distance between the first two hit scores. The points inside the shaded region are the ones that are declared a match using the dual criteria. The markers indicate whether the corresponding spectrum was a 1 correct match true hit , 2 an incorrect match false hit , 3 an incorrect elimination false miss , or 4 a correct elimination mismatch excluded.

On the other hand, the dual criteria do not always find a match, and the number of mismatches is 7. This cross-section represented many different chemical classes e.

These results clearly show good discrimination and utility of reference data from a different NIR wavelength system. In the near-infrared there are now several dispersive and also Fourier transform spectrometers available. With but a few caveats our results have shown that the data can in fact be ported from one system to another.

This is especially true for algorithms that use only relative peak heights or peak positions for identification. For visible or UV systems, it is more important to consider those phenomena that can change relative peak heights of different vibration modes, resonance Raman, fluorescence, and so forth.

It is important to note that the reference data must be of equal or higher resolution compared with the target spectra and the spectrometers require rigorous wavelength calibration. Ideally wavelength-dependent intensity response should be accounted for as well. The authors thank sponsors for their support. We thank Drs. We also thank Dr. Trish McDaniel, Dr. Lou Wasserzug, and Dr. Peter Hotchkiss for their involvement and support of this research.

Johnson et al. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors. Read the winning articles.

Special Issues. Jarman, 1 Brenda M. Kunkel, 1 Jerome C. Birnbaum, 1 Alan G. Joly, 1 Eric G. Stephan, 1 Russell G. Tonkyn, 1 Robert G. Ewing, 1 and Glen C. Academic Editor: Augustus Way Fountain. Received 18 Feb Revised 13 Apr Accepted 17 Apr Published 05 Jul Abstract As Raman spectroscopy continues to evolve, questions arise as to the portability of Raman data: dispersive versus Fourier transform, wavelength calibration, intensity calibration, and in particular the frequency of the excitation laser.

Introduction There are many spectroscopic methods that have decreased the size and cost of the instruments and simultaneously increased their detection potential so as to become routine methods for chemical detection: infrared spectroscopy [ 1 — 3 ], visible-NIR reflectance [ 4 ], terahertz methods [ 5 , 6 ], and others.

Experimental and Calibration 2. Wavelength Calibration For a Fourier-transform Raman system, frequency calibration is not trivial as it requires an absolute calibration of both 1 the scattered light recording system and 2 the instantaneous e. Figure 1. Hg-line spectrum used for interferometer calibration. Several lines are used to calibrate the absolute wavelength response of the interferometer.

Figure 2. Calibration scheme demonstrating how Stokes and anti-Stokes lines are used to calibrate the laser frequency such that the Stokes lines are at the same shift with the opposite sign of the anti-Stokes lines.

The present data show Raman lines of potassium permanganate. The excitation laser line is nominally at Figure 3. These spectra are arbitrarily scaled 0 to 1 on the ordinate and have been vertically offset for clarity. Table 1. In several cases the materials are named by their primary chemical component s , followed by the common or commercial name, since the primary components define the spectrum.

Table 2. Chemical and material classes of the nonexplosive chemicals and materials that are amongst the species that were tested. For the pure chemicals, species with multiple functional groups were tabulated in each class. Figure 4. Scores for the top three hits are at right. Spectra are scaled 0 to 2 and have been vertically offset for clarity.

Figure 5. Results of the Er 2 O 3 spectra at the two different excitation wavelengths. The spectra have been vertically offset for clarity with -axis scaled 0—2. The results are discussed in the text. Figure 6. The distance between the top two hits is more than 60 with a distance of 77 and the top hit is above so it is declared a match.

These spectra are all scaled 0—2 on the -axis and have been vertically offset for clarity. Figure 7. The true hits occur when the top hit is the correct spectrum and is declared a match, the false hits occur when a top hit is an incorrect spectrum but is declared a match, the false misses occur when the top hit is the correct spectrum but is declared not a match, and the mismatch excluded occurs when the top hit is an incorrect spectrum and is declared not a match.

Search criteria No. Table 3. See text for details. The complete list of chemical species and breakdown by chemical category. Supplementary Table. References I. Burling, R. Yokelson, S. Akagi et al. Bernacki and M. Johnson, R. Sams, S. Burton, and T. Wendlandt and H. Johnson, N. Valentine, and S. Sheen, D. McMakin, and T. Yinon, Ed. View at: Google Scholar R. Chalmers and P. Griffiths, Eds. View at: Google Scholar J.

Weatherall, J. Barber, C. Brauer et al. View at: Google Scholar P. Vandenabeele, K. Castro, M. Hargreaves, L. Moens, J. Madariaga, and H. Butt, M. Nilsson, A. Jakobsson et al. Ehlerding, I. Johansson, S. Wallin, and H. View at: Google Scholar S. Wallin, A. Pettersson, H. Pettersson, I. Wallin, M. Nordberg, and H. Ray, A. Sedlacek, and M. View at: Google Scholar M.

Wu, M. Ray, K. Fung, M. Ruckman, D. Harder, and A. View at: Google Scholar B. Alternately, a very low-calorie diet may produce this deficiency. This includes people in weight-loss programs or with eating disorders. Older adults with poor appetites may also not get enough calories or nutrients in their diet. Restricted diets may also cause you to have a mineral deficiency.

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