Spectroscopy and analyzing spectroscopic data
Many analytical technologies use instrumentation to generate large amounts of data that are used in characterizing substances (samples of any kind), and can form the basis of qualitative and quantitative methods. Spectroscopy is branch of analysis that uses the interaction of energy with a sample to perform an analysis that can provide information on the chemical composition and conformation of the samples. Spectroscopy is based on the dispersion of light into its component wavelengths (i.e. energies). The data that is obtained from spectroscopy is called a spectrum. A spectrum is a plot of the intensity of energy detected versus the wavelength (or or frequency, etc.) of the energy and provides information on the structure of the sample under analysis. The spectrum is comprised of data across many wavelengths (frequencies, variables), and is best analyzed using multivariate analysis tools which provide a means to maximize the information from the spectral data.
In simplest terms, spectroscopy requires an energy source and a device for measuring the change in the energy source after it has interacted with the sample (a detector).
There are numerous instruments spanning the electromagnetic spectrum t that can be used for spectroscopic analysis and produce results. The different instrument s span the regions of the UV-Vis, infrared (FTIR and NIR), terahertz , NMR and X-ray. Mulitvaraite analysis is also applicable to Raman spectroscopy and many other types of multivariate data, including separations data (mass spectroscopy, chromatography, etc).
The infrared portion of the electromagnetic spectrum is divided into three regions; the near-, mid- and far- infrared, named for their relation to the visible spectrum. The mid-infrared, commonly referred to as FTIR, as the modern instrumentation is usually based on an interferometer and using the Fourier Transform (FT) spans the rage from 4000-400 cm-1 (2500–25,0000 nm) may be used to study the fundamental vibrations and associated rotational-vibrational structure. The higher energy near-IR, covering the range 14000-4000 cm-1 (2500–800 nm) measures overtone harmonic vibrations, and combinations of the fundamental vibrations from the mid-IR.
Mid-infrared spectroscopy (FTIR deals with the infrared region of the electromagnetic spectrum and is often called the fingerprint region, as with an IR spectrum, one can identify a sample´s identity or used in investigation of sample composition. It is used widely in many areas for problem solving and trouble shooting, including forensics, chemicals, food, agriculture, and any area where organic substances are used.
Near infrared spectroscopy has found wide use in applications that are best suited with rapid analyses that allow one to measure a sample in its natural state, as it does not require sample preparation. Because the NIR spectrum originates from combination and overtone vibrations, the spectra have broad and often overlapped features that require the use of chemometric methods for extraction of information from the data. NIR spectroscopy can also be used for applications where the instrument needs to be far removed from the sample, because it can be used in conjunction with fiber optics to carry light to and from the sample. IT has therefore found wide use in process analytical application in petroleum refining and blending, pharmaceutical processes including reactions, crystallization, blending, drying and tabletting, and in many other industries which benefit from real-time monitoring and control of their processes.
Typical applications of NIR include pharmaceutical, food and agrochemical quality control, such as for the determination of moisture, protein, active ingredients, additives and many other components in products.
Raman spectroscopy is a spectroscopic technique used in condensed matter physics and chemistry to study vibrational, rotational, and other low-frequency modes in a system.
It relies on inelastic scattering, or Raman scattering of monochromatic light, usually from a laser in the visible, near infrared, or near ultraviolet range. The laser light interacts with phonons or other excitations in the system, resulting in the energy of the laser photons being shifted up or down. The shift in energy gives information about the phonon modes in the system. Infrared spectroscopy yields similar, but complementary information.
A spectrometer is used in spectroscopy for producing a spectrum across the electromagnetic ragion of the spectrum being studies. Globally scientists across many disciplines, industries and research areas rely on multivariate with its powerful collection of processing routines to solve some of their most challenging data analysis problems. Scientists engaged in a wide variety of spectroscopic experiments and disciplines explore data processing, visualization and reporting packages for data from many types of spectroscopic instruments. Most tools provide advanced processing routines, data comparison and visualization features with ability to handle data from virtually any analytical instrument data station that have set the industry standard in scientific software. MVA applied to spectral data provides the necessary tools for analysis of data for qualitative applications, such as classification and identification (i.e. raw material identification, counterfeit detection, security screening). Quantitative models can be developed from spectral data, providing predictive models that can be used to predict values for composition, and concentration of various analytes from spectral data. Such models provide rapid means of obtaining results from analytical data that can readily be collected on samples in their natural state (in the field, lab, or process).
Multivariate data analysis
methods have become common tools in applying modern spectroscopic instruments to solve qualitative and quantitative analysis problems. Chemometric techniques such as PLS, PCR, PCA, MCR (multivariate curve resolution) and discriminant analysis have become standard approaches to quickly analysing complex samples from their spectral signatures. MCR can be applied to spectral data to extract the component spectra from mixtures or indeed any collection of spectra comprised of spectral contributions from various components in a system.
PCA for spectral data
MCR for spectral data
Explore your spectroscopic data
Whether it is electronic, vibrational or rotational transition spectroscopy. A complete multivariate data analysis
software for spectroscopic data processing, interpretation and database Management.
This comprehensive tool is compatible with data files from various spectroscopic instrument(s) / instrument applications including Bruker,Brimrose, Hitachi, PerkinElmer & Varian.
Also supports, a number of general-purpose data formats such as NSAS, Matlab, ASCII, JCAMP-DX, and Tracker
- Includes comprehensive MVA methods with fast & easy plotting
- Generates data models that can be used for on-line prediction and classification
- Generates data models for faster product and process optimization for spectroscopy and other applications
- Supports demands of FDA's 21 CFR Part 11