Automation spectral analysis

The major bottleneck created by these high-throughput NMR techniques is in the analysis of the vast amount of data that is generated. A number of commercial packages are now available that use chemical shift/structure databases to aid in the interpretation of the spectra. However, fully automated spectral analysis systems are still under development. 

This chapter presented the foundations of automated projection spectroscopy (APSY) that uses the algorithm GAPRO for automated spectral analysis. We showed applications of APSY for high-dimensional heteronuclear correlation NMR experiments with proteins. Without human intervention after the initial setup of the experiments, complete peak lists for 4D to 7D NMR spectra, with a chemical shift precision of below 1 Hz, are typically obtained. 

Many versions of this basic approach exist, the most significant variation being whether matrix inversion is used to connect a library of radionuclides to observed peak intensities or whether a list of energies of interest is used to make key calculations. Thus, the two main types today are matrix inversion and list directed. Automated spectral analysis software is available from commercial and academic sources with a mix of national and international quality certifications, specialized capabilities, and user control. Programs of this type can handle thousands of automated analyses per day and run on most types of computers. 

Field, H.I., Fenyo, D., Beavis, R.C. (2002). RADARS, a bioinformatics solution that automates proteome mass spectral analysis, optimises protein identification, and archives data in a relational database. Proteomics 2, 36 17. 

H. I. Field, D. Fenyo, and R. C. Beavis. RADARS, a Bioinformatics Solution that Automates Proteome Mass Spectral Analysis, Optimises Protein Identification, and Archives Data in a Relational Database. Proteomics, 2, no. 1 (2002) 36-47. 

It has been recognized that many of the time-consuming interactive tasks carried out by an expert during the process of spectral analysis could be done more efficiently by automated computational systems [6]. Over the past few years, this potential has been realized to some degree. Today automated methods for NMR structure determination are playing a more and more prominent role and can be expected to largely supersede the conventional manual approaches to solving three-dimensional protein structures in solution. 

An integrated analysis of automated spectral mineral identifications and... 

In recent years, a new mathematical approach, based on the theory of non-linear dynamics has been used to analyze EEG signals through the sleep-wake cycle (Thakor Tong 2004). This kind of analysis has not been shown superior to the commonly used spectral analysis for pharmacological studies. This may rapidly change with the appearance of more powerful computers. In contrast to spectral analysis, which is basically not more than an automation of visual analysis, non-linear analysis could go well beyond the visual analysis of the EEG trace. 

Simple peak purity analysis is relatively accurate when the impurity is present at significant concentration levels but, as the level of impurity diminishes, its impact on the target analyte spectrum becomes subtler and may require more sophisticated techniques. For this, statistical software routines are available for automated spectral comparisons. In these cases, peak purity determination and analysis of spectral differences are achieved using vector analysis algorithms. The more similar the spectra are, the closer the value is to 0.0° the more spectrally different they are, the larger the value. All the spectral data points across the peak are analyzed the data are converted into vectors, compared, and graphically plotted so that the results can be visualized. These software routines provide both numerical results and graphical representations such as similarity and threshold curves. 

Software functions For an automated HPLC analysis, required software functions include instrument control, data acquisition, peak integration, peak purity checks, compound identification through spectral libraries, quantitation, file storage and retrieval, and a printout of methods and data. 

The massive surveys both ground based as well as from space missions provide large number of stellar spectra covering distant components of Galaxy. To understand the complex evolutionary history of our Galaxy, rapid and accurate methods of stellar classification are necessary. A short review of the automated procedures are presented here. The most commonly used automated spectral classification methods are based on (a) Minimum Distance Method (MDM) (b) Gaussian Probability Method (GPM) (c) Principal Component Analysis (PCA) and (d) Artificial Neural Network (ANN). We chose to describe only two of them to introduce the automated approach of classification. 

With the ever-increasing need to improve quality and productivity in the analytical pharmaceutical laboratory, automation has become a key component. Automation for vibrational spectroscopy has been fairly limited. Although most software packages for vibrational spectrometers allow for the construction of macro routines for the grouping of repetitive software tasks, there is only a small number of automation routines in which sample introduction and subsequent spectral acquisition/data interpretation are available. For the routine analysis of alkali halide pellets, a number of commercially available sample wheels are used in which the wheel contains a selected number of pellets in specific locations. The wheel is then indexed to a sample disk, the IR spectrum obtained and archived, and then the wheel indexed to the next sample. This system requires that the pellets be manually pressed and placed into the wheel before automated spectral acquisition. A similar system is also available for automated liquid analysis in which samples in individual vials are pumped onto an ATR crystal and subsequently analyzed. Between samples, a cleaning solution is passed over the ATR crystal to reduce cross-contamination. Automated diffuse reflectance has also been introduced in which a tray of DR sample cups is indexed into the IR sample beam and subsequently scanned. In each of these cases, manual preparation of the sample is necessary (23). In the field of Raman spectroscopy, automation is being developed in conjunction with fiber-optic probes and accompanying... 

FIGURE 15.3. Automated gamma-ray spectral analysis system. (By permission of Georgia Institute of Technology)... 

For gamma-ray spectral analysis, a set of bulk samples can be placed on a rotating tray that moves each sample in turn next to the massive shield that encases the detector (see Fig. 15.3). The door in the shield opens and a mechanical arm places the sample on top of the detector. After counting, the sample is lifted and returned to the tray. Alpha-particle spectral analysis generally uses no automation because the samples are counted for a long time. 

Pnor to automated sequencing or mass spectrometry, one may wish to concentrate the peptide in a somewhat smaller tube Transfer the sample to a 1500 or 500 uiL Eppendorf polypropylene tube and combine with a rinse of the 12 x 75 mm tube (rinse with 100 pL of 80% MeCN/0.01% TFA it is advisable to rinse the tube several times if practicable). Immediately remove the solvent by vacuum centrifugation. We advise using only the transparent (uncolored) Eppendorf tubes to handle peptide solutions. Do not use tubes of any kind with O rings or septum closures because they have been shown to contain compounds that can interfere with mass spectral analysis. If a microbore HPLC system is available,... 

Simple peak purity analysis is relatively accurate when the impurity is present at significant concentration levels but, as the level of impurity diminishes, its impact on the target analyte spectrum becomes subtler and may require more sophisticated techniques. For this, statistical software routines are available for automated spectral comparisons. In these cases, peak purity determination and analysis of... 

Multicomponent analysis is used to find the concentrations of various species in a mixture, in a sense it is automated spectral stripping although you never see successively substracted spectra. Standard spectra must be available. See ref. 9 for details, manufacturers usually supply software. 

Woodruff and co-workers introduced the expert system PAIRS [67], a program that is able to analyze IR spectra in the same manner as a spectroscopist would. Chalmers and co-workers [68] used an approach for automated interpretation of Fourier Transform Raman spectra of complex polymers. Andreev and Argirov developed the expert system EXPIRS [69] for the interpretation of IR spectra. EXPIRS provides a hierarchical organization of the characteristic groups that are recognized by peak detection in discrete ames. Penchev et al. [70] recently introduced a computer system that performs searches in spectral libraries and systematic analysis of mixture spectra. It is able to classify IR spectra with the aid of linear discriminant analysis, artificial neural networks, and the method of fe-nearest neighbors. 

---