Instrument calibration, chemometric

There are other advances in the RS technique, unrelated to sensitivity improvement, that need to take place in order for it to be more widely applied to environmental problems in aqueous systems. Many of these advances will require more sheer determination than innovation. These advances include the following improvements in methods for routine intensity and frequency calibration availability of inexpensive, accurate, and large Raman computerized spectral libraries, including spectra recorded in aqueous media development of chemometric techniques specifically tailored for RS improvements in methods for cross-instrument calibration. Currently, there are efforts underway to address most of these needs, and considerable progress is being made. Fortunately, there are lessons that can be... 

So, overall the chemometrics bridge between the lands of the overly simplistic and severely complex is well under construction one may find at least a single lane open by which to pass. So why another series Well, it is still our labor of love to deal with specific issues that plague ourselves and our colleagues involved in the practice of multivariate qualitative and quantitative spectroscopic calibration. Having collectively worked with hundreds of instrument users over 25 combined years of calibration problems, we are compelled, like bees loaded with pollen, to disseminate the problems, answers, and questions brought about by these experiences. Then what would a series named Chemometrics in Spectroscopy hope to cover which is of interest to the readers of Spectroscopy ... 

There are other mysteries in NIR (and other applications of chemometrics) that nonlinearity can also explain. For example, as indicated above, one is the difficulty of transferring calibration models between instruments, even of the same type. Where would our technological world be if a manufacturer of, say, rulers could not reliably transfer the calibration of the unit of length from one ruler to the next ... 

Sections on matrix algebra, analytic geometry, experimental design, instrument and system calibration, noise, derivatives and their use in data analysis, linearity and nonlinearity are described. Collaborative laboratory studies, using ANOVA, testing for systematic error, ranking tests for collaborative studies, and efficient comparison of two analytical methods are included. Discussion on topics such as the limitations in analytical accuracy and brief introductions to the statistics of spectral searches and the chemometrics of imaging spectroscopy are included. 

In NIR, a series of samples are scanned and then analyzed by a referee method. An equation is generated and used for future unknowns. This equation is used after the instrument is checked for compliance with initial performance criteria (at the time of the equation calibration). No standard is available for process or natural samples. The value(s) is gleaned from chemometric principles. This is defined as a prediction. 

The calibration problem in chromatography and spectroscopy has been resolved over the years with varying success by a wide variety of methods. Calibration graphs have been drawn by hand, by instruments, and by commonly used statistical methods. Each method can be quite accurate when properly used. However, only a few papers, for example ( 1,2,15,16,26 ), show the sophisticated use of a chemometric method that contains high precision regression with total assessment of error. 

Calibration Most process analyzers are designed to monitor concentration and/or composition. This requires a calibration of the analyzer with a set of prepared standards or from well-characterized reference materials. The simple approach must always be adopted first. For relatively simple systems the standard approach is to use a simple linear relationship between the instrument response and the analyte/ standard concentration [27]. In more complex chemical systems, it is necessary to adopt either a matrix approach to the calibration (still relying on the linearity of the Beer-Lambert law) using simple regression techniques, or to model the concentration and/or composition with one or more multivariate methods, an approach known as chemometrics [28-30]. 

An additional feature of chemometrics that is appealing to process analytical applications is the use of qualitative models to detect and characterize faults in the analyzer system (calibration, instrument, sampling interface, and sampling systems), sample chemistry, and process dynamics. Such faults can be used to trigger preventive maintenance, and to troubleshoot- thus supporting the long-term reliability of the analyzer system. Specihc examples of such fault detection are given in references [15-16]. 

Multiway methods For analyzer data where a single sample generates a second order array (ex. GC/MS, LC/UV, excitation/emission fluorescence), multiway chemometric modehng methods, such as PARAFAC (parallel factor analysis) [121,122], can be used to exploit the second order advantage to perform effective calibration transfer and instrument standardization. 

In more recent development, chemometric or multivariate calibration techniques have been applied into spectrophotometric methods. As reported by Palabiyik and Onur [24], principal component regression and partial least square were used to determine ezetimibe in combination with simvastatin. This method offers advanfages such as no chemical prefreafmenf prior to analysis as well as no need to observe graphical spectra and calculations as with the derivative method. In addition, the instrumentation used is also simpler. 

The American Society for Testing and Materials (ASTM) recently published an official document providing a guide to spectroscopists for the multivariate calibration of infrared spectrometers. The scope of the publication, entitled Standard Practices for Infrared Multivariate Quantitative Analysis includes a description of multivariate calibration methods for the determination of physical or chemical characteristics of materials. This document is the first official standard for the application of chemometric multivariate analysis to near-infrared and infrared instruments. 

The ultimate development in the field of sample preparation is to eliminate it completely, that is, to make a chemical measurement directly without any sample pretreatment. This has been achieved with the application of chemometric near-infrared methods to direct analysis of pharmaceutical tablets and other pharmaceutical solids (74-77). Chemometrics is the use of mathematical and statistical correlation techniques to process instrumental data. Using these techniques, relatively raw analytical data can be converted to specific quantitative information. These methods have been most often used to treat near-infrared (NIR) data, but they can be applied to any instrumental measurement. Multiple linear regression or principal-component analysis is applied to direct absorbance spectra or to the mathematical derivatives of the spectra to define a calibration curve. These methods are considered secondary methods and must be calibrated using data from a primary method such as HPLC, and the calibration material must be manufactured using an equivalent process to the subject test material. However, once the calibration is done, it does not need to be repeated before each analysis. 

This excitement about second-order sensor calibration has led to a search by chemometric researchers to find equivalent instrumentation that gives rise to second-order data. The definition of second-order instruments is slowly solidifying and currently a foundation has been established to classify which techniques are true second-order devices. This definition of second-order instruments is simply two sensor arrays which are independent of each other. However, in order for the arrays to be independent, one of the arrays must modulate the sample s analyte concentrations. The best known instrument... 

Compared with traditional methods, NIR and Vis/NIR are less expensive because of no demand of other materials such as chemical reagents except the electrical consumption. Many works are focused on the study of chemometrics. This is because an important challenge is to build robust calibration models, in fact it is important to apply chemometric methods able to select useful information from a great deal of spectral data. Moreover food researchers and analysts are looking for the sensitive wavelength in Vis/NIR region representing the characteristics of food products, with the aim of develop some simple and low-cost instruments (Cen He, 2007). 

Requirement for sophisticated and costly instrumentation can sometimes be replaced by quite inexpensive methods, the performance of which is enhanced with respect to data interpretation and calibration modeling by chemometrics. 

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