Prediction techniques identification

A number of excellent references are available for classical techniques, including those by Kowalski et al. [7, 8], Kramer [9], Brereton [10], compilations [11, 12], and series of periodic reviews [13]. There are two somewhat related problems in chemometrics that are relevant to spectral data. The first is to predict molecular identifications in mixtures from a spectrum and the second is to predict a class or label for a spectrum. While regression techniques work well for molecular identifications, problems of biomedical interest often involve stochastically varying compositions and spatial distributions of... 

NUS Corp., HAZOP Study Team Training Manual. Predictive Hazard Identification Techniques for Dow Corning Facilities, Gaithersburg, MD (July 1989). 

Ion exchange (qv see also Chromatography) is an important procedure for the separation and chemical identification of curium and higher elements. This technique is selective and rapid and has been the key to the discovery of the transcurium elements, in that the elution order and approximate peak position for the undiscovered elements were predicted with considerable confidence (9). Thus the first experimental observation of the chemical behavior of a new actinide element has often been its ion-exchange behavior—an observation coincident with its identification. Further exploration of the chemistry of the element often depended on the production of larger amounts by this method. Solvent extraction is another useful method for separating and purifying actinide elements. 

The identification of plant models has traditionally been done in the open-loop mode. The desire to minimize the production of the off-spec product during an open-loop identification test and to avoid the unstable open-loop dynamics of certain systems has increased the need to develop methodologies suitable for the system identification. Open-loop identification techniques are not directly applicable to closed-loop data due to correlation between process input (i.e., controller output) and unmeasured disturbances. Based on Prediction Error Method (PEM), several closed-loop identification methods have been presented Direct, Indirect, Joint Input-Output, and Two-Step Methods. 

Near-infrared spectroscopy is quickly becoming a preferred technique for the quantitative identification of an active component within a formulated tablet. In addition, the same spectroscopic measurement can be used to determine water content since the combination band of water displays a fairly large absorption band in the near-IR. In one such study [41] the concentration of ceftazidime pentahydrate and water content in physical mixtures has been determined. Due to the ease of sample preparation, near-IR spectra were collected on 20 samples, and subsequent calibration curves were constructed for active ingredient and water content. An interesting aspect of this study was the determination that the calibration samples must be representative of the production process. When calibration curves were constructed from laboratory samples only, significant prediction errors were noted. When, however, calibration curves were constructed from laboratory and production samples, realistic prediction values were determined ( 5%). 

Various hazard identification criteria can be defined if sufficient knowledge of the process and of the equipment is available such that a mathematical model of the overall process can be constructed. The model can then be used to detect a hazardous situation, such as a runaway, developing at an early stage. This technique has potentially the highest predictive power, but does require an extensive knowledge of the chemical process and of the equipment characteristics. However, fully sufficient models are rarely available and their development is time consuming. 

The prediction that LC-MS will become a powerful tool in the detection, identification and quantification of polar compounds such as surfactants in environmental analysis as well as in industrial blends and household formulations has proven to be true. This technique is increasingly applied in substance-specific determination of surfactants performed as routine methods. From this it becomes obvious that no other analytical approach at that time was able to provide as much information about surfactants in blends and environmental samples as that obtainable with MS and MS-MS coupled with liquid insertion interfaces. 

Capillary electrophoresis (CE) either coupled to MS or to laser-induced fluorescence (LIF) is less often used in metabolomics approaches. This method is faster than the others and needs a smaller sample size, thereby making it especially interesting for single cell analysis [215] The most sensitive mass spectrometers are the Orbitrap and Fourier transform ion cyclotron resonance (FT-ICR) MS [213]. These machines determine the mass-to-charge ratio of a metabolite so accurate that its empirical formula can be predicted, making them the techniques of choice for the identification of unknown peaks. 

Therefore, a flexible method to evaluate physical and chemical system parameters is still needed (2, 3). The model identification technique presented in this study allows flexibility in model formulation and inclusion of the available experimental measurements to identify the model. The parameter estimation scheme finds the optimal set of parameters by minimizing the sum of the differences between model predictions and experimental observations. Since some experimental data are more reliable than others, it is advantageous to assign higher weights to the dependable data. 

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