Classification trace analysis

Danzer K, De la Calle D, Thiel G, Reichenbacher M (1999) Classification of wine samples according to origin and grape varieties on the basis of inorganic and organic trace analysis. Am Lab 31 26... 

Modern technology has many possibilities at its disposal for solving problems in trace analysis. A uniform characterization and assessment of trace-analytical methods requires the clearest possible definitions For reporting a content G, mass relations — of which the simplest is percentage by mass — are unequivocal and independent of any additional information. The contents of the components to be determined can vary within wide limits. Very diverse proposals for the boundaries of these ranges are found. For practical purposes, the following classification has been established ... 

The standard Hilbert transform attributes [41] are given by the complex trace analysis, and produce the well-known reflection strength (amplitude of the envelope), instantaneous phase, instantaneous frequency, or apparent polarity. In this approach, the information obtained is time, amplitude, frequency or attenuation, and is used as an input into industry s standard grid-based classification. Additional information can be derived directly from the reflection amplitude or from summation of amplitude value within intervals. This is the case for composite amplitude, average absolute amplitude, root-mean-square amplitude, number of zero crossings, and of minima or maxima. 

By design, ANNs are inherently flexible (can map nonlinear relationships). They produce models well suited for classification of diverse bacteria. Examples of pattern analysis using ANNs for biochemical analysis by PyMS can be traced back to the early 1990s.4fM7 In order to better demonstrate the power of neural network analysis for pathogen ID, a brief background of artificial neural network principles is provided. In particular, backpropagation artificial neural network (backprop ANN) principles are discussed, since that is the most commonly used type of ANN. 

Cluster analysis Is used to determine the particle types that occur in an aerosol. These types are used to classify the particles in samples collected from various locations and sampling periods. The results of the sample classifications, together with meteorological data and bulk analytical data from methods such as instrunental neutron activation analysis (INAA). are used to study emission patterns and to screen samples for further study. The classification results are used in factor analysis to characterize spatial and temporal structure and to aid in source attribution. The classification results are also used in mass balance comparisons between ASEM and bulk chemical analyses. Such comparisons allow the combined use of the detailed characterizations of the individual-particle analyses and the trace-element capability of bulk analytical methods. 

R. J. H. Waddell, N. NicDaeid and D. Littlejohn, Classification of ecstasy tablets using trace metal analysis with the application of chemometric procedures and artificial neural networks algorithms. Analyst, 129(3), 2004, 235-240. 

Scale is from none of the element (X), to trace, minor, and major amounts. TIMS Sample column shows the samples chosen for TIMS analysis and the overall classification from least to most inclusions. 

Laboratory analysis of human samples for trace contaminants or their metabolites inevitably produces results that deviate quantitatively from the actual concentrations. Such deviations can, for example, complicate exposure classifications in epidemiologic studies, detection of time trends in exposure, and comparison of studies that use data produced with different analytic methods. Individual laboratories can use standard QA-QC methods to minimize and define the magnitude of the variations. However, federal agencies and statutes, such as CDC, the National Institute of Standards and Technology, and statutes such as CLIA, could play important roles in improving the overall quality of biomonitoring laboratory data and their utility in health-related applications. 

For analysis of minor components down to trace amounts or when continuous calibration curves are necessary, it is possible in certain cases to use specially adapted, mathematically simplified and hence speeded-up algorithms. Anyhow, here, even more than with classification tasks, the performance depends very much on the selection of a suitable algorithm and the careful adaptation towards the specific problem. In any case, such algorithms frequently are not overly stable against unforeseen spectral interferences. 

Examples illustrating the use of PCA for identification and classification are given in Chapter 9, including classification of American Indian obsidian artifacts by trace element analysis, identification of fuel spills by gas chromatography, identification of recyclable plastics by Raman spectroscopy, and classification of bees by gas chromatography of wax samples. 

Figure 8.1. Classification of English and Spanish white wines by discriminant analysis using 48 elements determined by FI-ICP-MS. Reprinted from Food Chem., 15, M. J. Baxter, H. M. Crews, M. J. Dennis, I. Goodall, D. Anderson, The determination of the authenticity of wine from its trace element composition, 1997, with permission from Elsevier [30].

It is possible to map the toxicity potential areas of trace elements by obtaining classification models based on national-scale data synthesis programs. To obtain reliable analytical information, "the main methods of structural analysis may be coupled two-by-two from the point of view of correlating various sources of information"1 (Figure 6.1). 

CLASSIFICATION OF ASHING METHODS FOR HUMAN BIOLOGICAL MATERIAL IN TRACE ELEMENT ANALYSIS... 

This chapter will examine the ways in which major element data axe used in geochemistry. The discussion will be restricted to the ten elements traditionally listed as oxides in a major element chemical analysis — Si, Ti, Al, Fe, Mn, Mg, Ca, Na, K and P. Geochemists make use of major element data in three principal ways — in rock classification, in the construction of variation diagrams and as a means of comparison with experimentally determined rock compositions, whose conditions of formation are known. Each of these uses will be discussed in a separate section of this chapter. In addition, major elements are used, often together with trace elements, in the identification of the original tectonic setting of igneous and some sedimentary rocks. This topic will be discussed in Chapter 5. 

Flint samples from four different mines and from prehistoric workshops were analyzed by neutron activation analysis to determine the contents of 14 trace elements. A successful separation of the four origins was possible by a Bayes classifier, by classification with centres of gravity, and by factor analysis C663. 

---