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frequently asked questions

What is signature analysis?

Signature analysis is a method of comparative analysis using the spectrum as a pattern, or "fingerprint" to characterize the composition of materials. In order to compare, or "identify" materials, the spectra are visually and mathematically fit in order to assess closeness of composition. This approach differs from traditional EDS which relies on the results of quantitative analysis for comparisons. Signature analysis is the basis of many other spectroscopies (FTIR, RAMAN, MS, for example).

When is signature analysis most useful?

Signature analysis is most useful where quant fails (frequent with small, non-flat, heterogeneous samples), but even more importantly where material "identification" is required!

What is SLICE?

Spectrum Library Identification and Classification Explorer (SLICE) is a software application with signature analysis as it's core. It provides many utilities to enhance the EDS as an analytical resource, and is intended to work with existing EDS systems.

With what EDS systems can SLICE be used?

SLICE is compatible with all modern EDS systems. In order to import spectra into SLICE, these systems must be able to export spectra in the standard EMSA format. In most installation, users will perform sample characterization with their system EDS, then export the spectrum to SLICE for further analysis and archive. SLICE is generally installed on the computer supplied with the EDS system in order to provide for convenient operation

How accurate is SLICE?

The best answer to this question is; "It depends". Because there many ways of using SLICE, and because of the many different sources of data, accuracy would have to be determined for each specific set of analytical circumstances. As a general rule, however, the "best" results will be obtained when the spectra to be compared to the database archive are acquired under similar analytical conditions, and when the database of similar materials is sufficiently populated.

How accurate is SLICE for quantitative analysis?

As mentioned above, SLICE was designed with emphasis on spectral comparisons. If an analyst requires quantitative analysis, the routines provided by his EDS system will often be more reliable than those included with SLICE. SLICE provides Quant only for purposes of rough estimation of weight percent, and to aid iin compsitional queries, but cannot know the detector characteristics necessary for best quant. SLICE was designed to enhance, not replace, existing EDS system functionality

How will my spectra compare to those in the SLICE database?

Most spectra in the database were acquired with a SiLi detector interfaced to an SEM, with standardized acquisition parameters. Although all detectors will exhibit some response differences, the most significant differences will be from those that are most different (Ge crystal, SDD, XRF). SLICE was designed to minimize the effect of these differences

How can I know "how different" my detector is?

The easiest way would be to select a material that already has a spectrum in the database, analyze it, and compare the spectra. It is likely that you will see some difference in the light elements (at least C, O). These elements are not generally as diagnostically useful for comparisons, and can be "de-selected" for analysis purposes.

How can I obtain more information regarding SLICE?

Either contact xk personnel through the contact resource in this website, or use the SLICE Forum (bulletin board) to contact the SLICE user community

What is Fuzzy Logic?

Fuzzy logic measures the degree to which something occurs or that some condition exists. Fuzzy logic can control or describe a system by applying 'common sense' rules that refer to indefinite quantities, when there is no specific mathematical algorithm that can be applied. In principle, Fuzzy logic can be used to model any continuous system and may be more useful and accurate than the standard bivalent mathematical ones. It is built on the principle that the world is gray, not black and white. In standard set theory, an object either does or does not belong to a set. Sets that are Fuzzy are not so constrained, and it is possible for an item to be only partially within a set. If-then rules govern Fuzzy sets, and are generally applied by experts, but may also be learned through an adaptive process. A full description of Fuzzy logic is beyond the scope of this page, and so we have included a brief bibliography, and some useful web sites.

Some Articles on Fuzzy Logic

Kosko, B. & Isaka, S. 'Fuzzy Logic', Scientific American, July, 1993, pp. 76-81.
Walsh, B. 'Fuzzy Logic: The American Market', AI Expert, Nov., 1993, pp. 35-39.
Viot, G. 'Fuzzy Logic: Concepts to Constructs', AI Expert, Nov., 1993, pp. 26-33.
I. Bondarenko, P. Van Espen, B. Treiger, R. Van Grieken, & F. Adams 'Classification of Coal Mine Dust Particles Through Fuzzy Clustering of Their Energy-Dispersive Electron Microprobe X-Ray Data', Microbeam Analysis, Jan./Feb. 1994, pp.33-37

How is Fuzzy Logic used in FLAME?

FLAME applies membership functions to various processing steps, such as the identification of elements, deconvolution, etc., rather than applying rigid constraints. The membership functions are constantly adapted as FLAME is being used for analysis. For example instead of saying an element is either present or not present, a membership probability is calculated, based on many parameters, with the result that the element may be classified as most probably present, or as unlikely. Deconvolution proceeds using whatever method is necessary, depending on the degree and complexity of overlap. Quantitation is dependent on analytical conditions. As FLAME is used, it learns from its input, and modifies its AI database as necessary, to compensate for the many inadequacies in the x-ray energy tables, absorption coefficients, and other physical parameters.

Where else is Fuzzy Logic used?

Fuzzy Logic controllers are used in most modern cameras and camcorders to relate image data to various lens settings for auto-focus and image clarity, and to nullify image jitter that may be caused by a shaking hand. Fuzzy controllers are used in automobiles to control fuel injection, braking, and transmission systems. Many new home appliances such as vacuum cleaners and washing machines use Fuzzy Logic to improve their overall performance. Perhaps the most famous Fuzzy application is the Sendai subway train in Japan. It has outperformed both human operators and conventional controllers, in providing safe, efficient, and comfortable performance. In all cases where Fuzzy controllers are being used, they have greatly improved performance over their human counterpart.