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1 Multivariate correction method Multivariate correction methods include: classical Zui small square method (CLS), anti-zui small square method (ILS), q-matrix, partial zui small square method (PLS) and basic component regression (PCR). The classical zui small square method and the anti-zui small square method are often referred to as K-matrices and P-matrices by infrared workers. CLS means that the calibration mode conforms to Beer's law, that is, the spectral absorbance is expressed as a linear function of the component concentration.
Several other methods are linear functions in which the concentration is expressed as a linear combination of spectral intensity or intensity. These calibration methods are not limited by the number of components in the analyzed spectrum to be consistent with the number of components in the calibration sample, and can be corrected using fewer or more linear components. Therefore, these methods have great flexibility. Experimental deviations may occur in Beer's law, which are often referred to as nonlinearities. When solving problems with the usual equations, ILS is limited by the number of spectral absorbances used, while CLS has the ability to perform full spectrum analysis. ILS uses the reverse form of Beer's law, ie the concentration is expressed as a linear function of spectral absorbance. Multi-point linear regression (MCR) is commonly referred to in the near-infrared literature as the ILS method because the CLS method is rarely used for near-infrared analysis.
PCR and PLS are two factor analysis methods. Factor analysis is the analysis of a collection of data that mathematically separates the multicomponent spectra. The PCR method, also known as target factor analysis, has the ability to perform full spectrum analysis and is also flexible. PCR and PLS have been widely used for quantitative analysis of infrared spectra. Although multivariate statistics have the ability to extract useful information from cluttered data, it is important to emphasize that they require high quality calibration samples. At the beginning of quantitative analysis, how to design an experiment is very important.
2 Experimental Application of quantitative infrared analysis For solutions and gases, the permeation method is common to Zui. However, potassium bromide tableting, paste, membrane and direct determination of solid raw materials are also useful. Attenuated total reflection (ATR), especially ATR with a circulating cell, is generally applicable to the determination of solutions, but selective absorption of solutes on ATR elements must be considered. The ATR method can also be used for the analysis of powder samples, textiles, rubber products and plastic products, but it must be ensured that the sample is in good contact with the ATR element and can be reproduced. Quantitative specular reflection measurements from a film on a reflective surface are also possible, but if the thickness of the film is the same as the wavelength of the infrared radiation, the nonlinear effects between the absorption band and the interference fringes become very important. The diffuse reflectance method begins with FT-IR quantitative analysis of solid samples, which is commonly used in near-infrared spectroscopy. Photoacoustic spectroscopy can be used for quantitative analysis of solids. Infrared emission spectroscopy has been used for hot gas analysis and solid phase analysis. With the development of infrared microscopy, quantitative FT-IR infrared microscopy has also become possible, but the effects of stray light must be considered because stray light can cause significant nonlinear phenomena.
Maris et al. used the ILS method to determine the concentration of gaseous light alkanes based on the CH stretching vibration zone. Kathryn et al. used the CLS method to quantitatively determine the ratio of cellulose to lignin on the surface of the cellulose filler. Charles et al. measured the morphological characteristics of PET using the PLS method. Mann et al. analyzed the anisaldehyde in o- and m-xylene by orthogonal design. Cahn et al. used the PCR method and the PLS method to quantitatively analyze the xylene isomers. Painter et al. used the CLS method to quantitatively determine minerals in coal. Haaland et al. used CLS, PLS and PCR to determine the content of OH and B2O3 in glass samples. Hans-Rene et al. successfully quantified the crystal form of silicon at low concentrations. Linn et al. used the PLS method to determine the amount of oxygen in the middle of the wafer. Fuller et al. successfully analyzed the six components in the detergent using PLS. Kisner et al. used the ILS method to analyze cholesterol and liquid plasma components. Koenig et al. determined the amount of each component in the polyphenylene oxide and polystyrene mixture by PCR based on the infrared spectrum of the film. Lee et al. used the PLS method to determine the hydroxyl number in polyester and compared the near-infrared and mid-infrared techniques. Garcia et al. measured the methanol and MTBE in the gas oil by the PLS method. Lin et al. determined the concentration of NaCl in the aqueous solution by PCR. In recent years, there have been many articles on the analysis and determination of sugar. For example, Dupuy et al. used PLS to determine glucose, fructose and sucrose in sugar powder. Dupuy et al. compared the determination of sugar and organic acid in juice by PCR and PLS. Disadvantages; Ward et al. used PLS method to determine the concentration of blood glucose in whole blood; Schindler et al. successfully measured glucose, fructose and sucrose in sugar solution by PLS method and sequential injection analysis technique; Mattu et al. determined the glucose in biological samples by PLS method. The content. Cadet et al. used the PCR method to statistically analyze the mid-infrared attenuated total reflection spectrum of sugarcane juice, and established a fast and accurate method for determination of sucrose content.
3 Sample Preparation and Design When preparing an analytical sample, it is important that the sample be mixed as much as possible and that the sample taken is representative. In order to eliminate the nonlinearity of the height and shape of the absorption band due to scattering, the size of the solid particles should be uniform or smaller than the wavelength of the infrared radiation used. The interference fringes produced by the parallel reflecting surfaces must be avoided or corrected. The sample or sample cell used may also cause quantitative errors. Errors can occur when any portion of the light leaks through the sample, such as pinholes, cracks, bubbles in the sample, uneven particle size, or infrared light passing through the edge of the sample. Sample tightness, particle size, temperature, humidity, and sample height are important for quantitative analysis of diffuse reflectance. These factors may also have a large impact on the photoacoustic spectroscopy. The temperature should be constant. This is especially true for samples with interactions between gas samples and component molecules. Because infrared light can cause significant temperature rises, the temperature balance of the sample in the optical path of the spectrometer can also be important. The pressure of the gas sample must be controlled. For gases and solutions with very low concentrations, more attention should be paid to the adsorption and reaction between the sample and the walls of the infrared cell.
The linearity of Beer's law is influenced by the interaction between molecules and is also affected by the factors mentioned above. The effects of these factors can usually be reduced by diluting the sample. Multiple reflections can result in non-linearity due to the non-constant sample reflectivity that causes the absorption band to be distorted. The dispersion of the refractive index that occurs in the strong absorption band also results in non-linearity of the result. This is especially important in the analysis of materials with high refractive indices, such as the analysis of Ge or Si. Scattering associated with particle size can also cause absorption band distortion. Impurity components with masked spectral features in the sample may also introduce large errors. Therefore, these components should also be included in the calibration sample or at least should be tested.
The success of quantitative analysis, the choice of calibration samples is important, especially when the number of components present in the sample is large. Calibration samples can usually be prepared independently, and validated experimental designs are used to design calibration samples, such as factor design and hybrid design. In general, in order to obtain more information about zui from a limited number of samples, the component concentrations are designed to be orthogonal. Such a design allows for interaction and non-linear effects in the sample. When the calibration samples cannot be prepared separately, it is necessary to randomly sample from a large number of samples. In this case, the accuracy generally obtained is relatively low. It is difficult to ensure that all sources of deviation and the entire range of deviations present in the unknown sample are also included in the calibration sample. If all independent sources of bias present in an unknown sample are not included in the calibration sample, then an unknown sample with more independent sources of bias will introduce a quantitative error. When designing a calibration sample, the target component should be the center of the design and the concentration range should be greater than the independent analysis concentration. These properties of the experimental design must be considered for improving the accuracy of the FT-IR analysis and preventing recalibration when the target concentration changes. As the concentration range increases, the complexity of the nonlinearity and mode also increases. For example, the use of pure components, which generally do not represent true mixed samples, typically contain a myriad of nonlinear sources in the sample mixture. Therefore, careful design of the calibration sample is an important part of FT-IR quantitative analysis.
4 Factors Affecting the Spectrometer Once the calibration sample is prepared, the problem is concentrated on the spectrometer. In order to ensure that the resolution-dependent deviation in Beer's law does not degrade the accuracy of the analysis results, the resolution of the spectrometer should be sufficiently high. Ramsy believes that the error due to the insufficient resolution of the spectrometer increases with increasing absorption intensity. In order to obtain high accuracy of zui, the spectrometer resolution value should be much smaller than the analyte absorption band width value. Ramsy believes that the Lorentzian band with an absorbance of 1.0 has a measurement error of 3% when the bandpass of the spectrometer is 1/5 of the absorption bandwidth. When the bandpass of the spectrometer has only 1/2 of the absorption bandwidth, there is a 24% measurement error. The nonlinearity of the spectrometer response is also often present and becomes more important as the absorbance increases.
Although the signal-to-noise ratio is important, the deviation from other aspects is far greater than the deviation caused by the detector noise. The deviation of the same sample in the spectrometer is greater than the deviation caused by the detector noise alone. Mainly caused by factors such as sample heterogeneity, thickness, surface, temperature difference, and reflection difference. These influencing factors can be avoided by reproducibly introducing the sample to the same location. For liquid samples, liquid cuvettes or liquid ATRs, gas samples using gas cells, it is not necessary to remove the cuvette from the spectrometer when changing samples. The effect of these factors can also be reduced by repeatedly measuring the spectrum and then taking the average spectrum.
5 Conclusion At present, more and more quantitative software is produced by FT-IR spectrometer manufacturers. Such as Specfit quant, PCR, QUANT and so on. However, none of the correction methods are good, they have their own advantages and disadvantages. The correction method selection depends on the type of information obtained. By a detailed understanding of the concepts of these methods, it is possible to determine the appropriate calibration method for a particular analysis. Satisfactory results can be obtained by understanding the various factors that may affect the accuracy and accuracy of the quantitative analysis.
Author: Xu Singapore dollars (Shanghai Institute for Drug Control 200233); Weimin (Shanghai Institute for Drug Control 200233)
Application of Multivariate Correction Method in Quantitative Analysis of FT-IR
Due to the birth of the Fourier infrared spectrometer, infrared quantitative analysis has been greatly promoted. From the Fourier infrared spectrometer, a digital infrared spectrum with high signal-to-noise ratio and wide linear range can be obtained, which can be used to compare infrared with modern statistical methods. The spectrum was analyzed and studied. The introduction of multivariate calibration methods has greatly improved the ability of infrared quantitative analysis and qualitative analysis. Problems that were previously thought to be unresolvable can now be solved by infrared spectroscopy. These statistical methods belong to the category of chemical statistics. Since FT-IR contains a large amount of spectral data, chemometrics for transforming spectral data into chemical information has become an area of ​​rapid development in recent years. Therefore, it is important for chemists to have a clear understanding of the various chemical statistical methods used for quantitative FT-IR analysis.