Spectra classification

Vazquez C, Boeykens S, Bonadeo H (2002) Total reflection X-ray fluorescence polymer spectra classification by taxonomy statistic tools. Talanta 57 1113-1117... 

Another example is the noninvasive identification of species from NIR absorbance spectra. Classification is simply a matter of finding out whether new samples... 

K. Varmuza, P. Penchev, F. Stand, and W. Werther. Systematic structure elucidation of organic compounds by mass spectra classification./. Mol. Struct, 408/409 91-96,1997. 

K. Varmuza, W. Werther, F. Stand, A. Kerber, and R. Laue. Computer-assisted structure elucidation of organic compounds, based on mass spectra classification and exhaustive isomer generation. Software-Entwicklung in der Chemie, 10 303-314,1996. 

S. Sasic, Y. Katsumoto, H. Sato, Y. Ozaki. Apphcations of moving window Two-Dimensional correlation spectroscopy to analysis of phase transitions and spectra classification. Ana/ Chem 75 4010-4018, 2003. 

Sasic, S., Y. Katsumoto, and H. Sato, Applications of Moving Window Two-Dimensional Correlation Spectroscopy to Analysis of Phase Transitions and Spectra Classification. Anal. Chem., 2003. 75 4010 18. 

A priori it cannot be assumed that the relationships between spectral data and structural data are linear. A lot of effort therefore has been spent to improve spectra classification by the implementation of non-linear methods, especially by neural networks. Typical results show that neural networks are sometimes better than linear discrimination methods but not considerably better. Obviously the representation of spectra by appropriate features (which is a domain of chemistry) is more important than the applied classification methods. 

Figure 14 Application of spectra classification results in systematic structure elucidation. Given data for an unknown molecular structure are the molecular formula and the low resolution mass spectrum. MOLGEN is an isomer generator program, MSclass is a program for the automatic recognition of some substructures. The total number of isomers is 596. From the mass spectrum a substructure that has to be present (goodlist) and a substructure that has to be absent (badlist) was recognized. The final result consists of seven molecular candidate structures. [Reproduced from Ref. 103 with kind permission of Gesellschaft Deutscher Chemiker]...

Mass spectra of chemical compounds have a high information content. This article describes computer-assisted methods for extracting information about chemical structures from low-resolution mass spectra. Comparison of the measured spectrum with the spectra of a database (library search) is the most used approach for the identification of unknowns. Different similarity criteria of mass spectra as well as strategies for the evaluation of hitlists are discussed. Mass spectra interpretation based on characteristic peaks (key ions) is critically reported. The method of mass spectra classification (recognition of substructures) has interesting capabilities for a systematic structure elucidation. This article is restricted to electron impact mass spectra of organic compounds and focuses on methods rather than on currently available software products or databases. 

Today s performance of mass spectra classification by multivariate methods can be summarized as follows (1) only a rather small number of substructures can be recognized with a low error rate. (2) Predictions of the absence of a substructure are usually more accurate than predictions of its presence. (3) Erroneous classifications cannot be avoided completely therefore the intervention of a human expert and the parallel use of other spectra interpretation methods are advisable. (4) For small molecules a systematic and almost complete structure elucidation is sometimes possible by mass spectra classification and by application of the obtained structural restrictions in automatic isomer generation. 

Figure 9 Systematic structure elucidation using the molecular formula of the unknown, structural restrictions from automatic mass spectra classification and exhaustive isomer generation. Ethyl 2-(2-hydroxyphenyl)acetate is the unknown .

During the inspection of an unknown object its surface is scanned by the probe and ultrasonic spectra are acquired for many discrete points. Disbond detection is performed by the operator looking at some simple features of the acquired spectra, such as center frequency and amplitude of the highest peak in a pre-selected frequency range. This means that the operator has to perform spectrum classification based on primitive features extracted by the instrument. 

Diffey BL. A method for broad-spectrum classification of sunscreens. Int J Cosmet Sci 1994 16 47-52. 

Other methods consist of algorithms based on multivariate classification techniques or neural networks they are constructed for automatic recognition of structural properties from spectral data, or for simulation of spectra from structural properties [83]. Multivariate data analysis for spectrum interpretation is based on the characterization of spectra by a set of spectral features. A spectrum can be considered as a point in a multidimensional space with the coordinates defined by spectral features. Exploratory data analysis and cluster analysis are used to investigate the multidimensional space and to evaluate rules to distinguish structure classes. 

Neural networks have been applied to IR spectrum interpreting systems in many variations and applications. Anand [108] introduced a neural network approach to analyze the presence of amino acids in protein molecules with a reliability of nearly 90%. Robb and Munk [109] used a linear neural network model for interpreting IR spectra for routine analysis purposes, with a similar performance. Ehrentreich et al. [110] used a counterpropagation network based on a strategy of Novic and Zupan [111] to model the correlation of structures and IR spectra. Penchev and co-workers [112] compared three types of spectral features derived from IR peak tables for their ability to be used in automatic classification of IR spectra. 

One classification is of special importance there is a small minority of materials journals that can be described as broad-spectrum, compared with a much larger number which are specialised to a greater or lesser degree. Probably the first broad-... 

Applications of neural networks are becoming more diverse in chemistry [31-40]. Some typical applications include predicting chemical reactivity, acid strength in oxides, protein structure determination, quantitative structure property relationship (QSPR), fluid property relationships, classification of molecular spectra, group contribution, spectroscopy analysis, etc. The results reported in these areas are very encouraging and are demonstrative of the wide spectrum of applications and interest in this area. 

It should be noted that the decomposition shown in Eq. 3.7.2 is not necessarily a subdivision of separate sets of spins, as all spins in general are subject to both relaxation and diffusion. Rather, it is a classification of different components of the overall decay according to their time constant. In particular cases, the spectrum of amplitudes an represents the populations of a set of pore types, each encoded with a modulation determined by its internal gradient. However, in the case of stronger encoding, the initial magnetization distribution within a single pore type may contain multiple modes (j)n. In this case the interpretation could become more complex [49]. 

Chloramphenicol was the first orally active, broad-spectrum antibiotic to be used in the clinic, and remains the only antibiotic which is marketed in totally synthetic form. Its initial popularity was dampened, and its utilization plummeted when it was found that some patients developed an irreversible aplastic anemia from use of the drug. Of the hundreds of analogues synthesized, none are significantly more potent or certain to be safer than chloramphenicol itself. Two analogues have been given generic names and fall into this chemical classification. It was found early in the game that activity was retained with p-substituents, and that... 

Zhang et al.14 develop a neural network approach to bacterial classification using MALDI MS. The developed neural network is used to classify bacteria and to classify culturing time for each bacterium. To avoid the problem of overfitting a neural network to the large number of channels present in a raw MALDI spectrum, the authors first normalize and then reduce the dimensionality of the spectra by performing a wavelet transformation. 

This is the most common route, the reagent being a metal compound/solvent combination. Typical conditions call for the metal salt (e.g., acetate) in a buffer system (e.g., NaOAc/AcOH) and a co-solvent such as chloroform. Generally the reaction mixture is refluxed until the metal complex spectrum (see Section 9.22.5.6 and Table 4) is fully developed. Metal acetylacetonates and metal phenoxides have also been employed. The topic has been reviewed in detail by Buchler,51 who has also summarized the history and classification of metal complexes of this series, and the mechanisms of metalation.52... 

Now that we have a simple model for the continuum spectrum of the stars based around the Planck curve, the temperature and the luminosity, we can make some observations and classifications of the stars. There are some constellations that dominate the night sky in both the northern and southern hemispheres and even a casual look should inspire wonder. Star hopping in the night sky should lead to the simplest observation not all stars have the same colour. A high-quality photograph of the constellation of Orion (see page 2 of the colour plate section) shows stars... 

The spectral features observed by astronomers have led to the classification of stars into seven broad classes outlined in Table 4.1, together with their surface temperatures. The highest-temperature class, class O, contains may ionised atoms in the spectrum whereas the older stars in class M have a much lower temperature and many more elements present in the spectrum of the star. Observation of a large number of the stars has lead to extensive stellar catalogues, recently extended by the increased sensitivity of the Hubble Space Telescope. Making sense of this vast quantity of information is difficult but in the early 19th century two astronomers... 

The most frequently used classification system is that proposed by Vaughan Williams (Table 6-1). Type la drugs slow conduction velocity, prolong refractoriness, and decrease the automatic properties of sodium-dependent (normal and diseased) conduction tissue. Type la drugs are broad-spectrum antiarrhythmics, being effective for both supraventricular and ventricular arrhythmias. 

These results suggest that the taxon is overinclusive It includes 28% of low-risk participants—instead of the 10% predicted by Meehl s theory—and misses some cases that later become symptomatic. This might mean that the identified taxon is not isomorphic with specific genetic liability for schizophrenia and reflects a construct that is overlapping, but not identical to, the genetic risk factor. Another explanation is that the DSM criteria for schizophrenia and spectrum conditions may be too broad. Tyrka et al. (1995) proposed this hypothesis and estimated that at least two-thirds of the misses (symptomatic cases not assigned to the taxon) can be accounted for by errors in the taxon classification scheme, but the remaining misses are due to... 

Rubber-grade carbon blacks, 4 775 classification, 4 777 composition, 4 765t properties of, 4 778t spectrum of available products, 4 779 uses of, 4 793-796, 794t Rubber industry... 

Relevant examples of the use of classification techniques range from the simple to the complex. Schaper et al. (1985) developed and used a very simple classification of response methodology to identify those airborne chemicals which alter the normal respiratory response induced by C02. At the other end of the spectrum, Kowalski and Bender (1972) developed a more mathematically based system to classify chemical data (a methodology they termed pattern recognition). 

---