The personal computer (PC) has made many advances in analytical instrumentation possible. The large volumes of data generated by instruments were once evaluated on paper, but have become much easier to deal with once desktop PCs were available to resea rchers.
Previously, specialized computer systems were developed to handle the job of translating analog data output into digital format and then into useful graphics and spreadsheets. However, these systems used their own programming code and were quite diffic ult to learn. As PC's became faster and more capable of handling larger volumes of data, they have taken over the job of instrument control and data acquisition. The software is often written in the familiar Windows based operating system with point and c lick functionality, making the job of learning specialized operating systems and command languages obsolete. Furthermore, many software packages now come with interactive, multimedia tutorials that aid in learning the software and the instrument. The novi ce instrument user can now focus more attention on learning the fundamentals of the instrument operation.
Bioinstrumentation lab will be using several instruments that use PC's to handle instrumental analysis. These instruments each have their own software systems developed by their manufacturer. These software packages utilize several operating systems. T he HPLC and the GC-MS use Windows 3.1 systems. The AAS uses an MS-DOS operating system that is similar to Windows 3.1. The NMR uses a UNIX-based Silicon Graphics workstation making it the most difficult system to learn. Students should note the differences in the operating systems when the instruments are demonstrated.
Although each system is quite different, they do share several primary characteristics. The parameters that effect instrument control, data acquisition, and data analysis are typically combined into what is referred to as a method. Methods can b e used repeatedly by setting up a sequence. A sequence entry tells the instrument to evaluate Sample X using Method Y.
Most instruments now have robotic autosamplers that will introduce a specified volume of a specified sample at a specified time. The autosamplers allow the instrument to run 24 hours a day, thus speeding up the research project. Autosamplers are co ntrolled by the commands entered into a sequence table (sometimes called a sample set). The sequence is written by the instrument users and consists of a table of sequence entries. A sequence entry typically tells the instrument to inject Vial X, evaluate it using Method Y, and store the data in File Z. the sequence is usually customized for a given series of samples, but they can be saved and used repeatedly to evaluate the same sample set.
Since there are infinite combinations of samples that analytical instruments must evaluate, they are designed to handle a large range of operating options. Instrument parameters such as temperature, flow rates, injection volumes, etc. must be modif ied to fit specific sample evaluating applications. This customization is typically performed through a lengthy trial and error process. Computers can store the operating parameters in an instrumental method. This method can be used repeatedly to e valuate samples, thus assuring reproducibility. Method development is the primary task of researchers, and it is often the most time-consuming aspect of any research project. Whenever a research project is published, special attention is usually paid to d etailing the method, so that further work can be simplified. (Biol 380 students should make careful notation of the methods used for their lab reports.)
Once an optimal method is developed, it is given a computer file name for storage and retrieval. The method can be used repeatedly. A series of samples can all be evaluated using a single method. This allows comparison between samples while eliminating many variables in the analytical techniques. A single sample can also be evaluated using different methods. This is very useful technique for testing different methods.
Methods also tell the instrument and the computer what data to collect and store. For example, an HPLC may have a UV/VIS detector that can collect data across a range of wavelengths. However, large ranges will produce enormous amounts of data. A resear cher may limit the range by telling the detector to only collect data over a smaller range of wavelengths. The commands for this specialized data acquisition are stored in the method.
Methods can also incorporate techniques for evaluating the data. This is most important when samples are being evaluated quantitatively. The method can distinguish between calibration standards and samples. The method can store a series of calibration curves that are then used to determine the quantities of specific compounds in the samples. methods can also use spectral libraries for identifying certain components in a sample. For example, a mass spectrum produced by a sample evaluated using a GC-MS c an be compared with similar spectra in a computerized library. When a statistical match is determined, the software can then report a confirmed identification of the compound that produced the spectrum.
Learning the software systems for instruments can be challenging at first. However, once the fundamental software functions are understood, that task is made easier. The software applications used on the instruments at SRIF are the same ones found in r esearch laboratories throughout industry, government and academia. Therefore, learning these applications here is tantamount to learning a marketable job skill. It is highly recommended that you spend some personal time taking the online tutorials that ty pically come with the software. It is time well spent, especially if you plan on a project or a career that incorporates instrumental analysis.
Continue to Quantitative Instrumental Analysis.
Last update: May 8, 1998
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