Abstract:
An automated screening device that performs standardized system suitability tests and evaluations and measures components of a submitted sample to assist in the quality control screening of raw materials, ingredients, pharmaceuticals, chemicals, polymers, food products, petroleum and many other materials. After determining the performance suitability of an NMR spectrometer, the system permits samples to be submitted for screening. An NMR spectrum of a sample is acquired and a qualitative analysis unit identifies at least one reference NMR spectrum corresponding a compound present in the sample and a quantitative analysis unit integrates relative signal intensity signals of the sample spectrum in regions of peak intensity in the one reference NMR spectrum and compares integration results to a number of atoms in each region in order to confirm identification of the compound.

Description:
BACKGROUND 
     Nuclear magnetic resonance (NMR) is a physical phenomenon involving quantum mechanical magnetic properties of atomic nuclei in the presence of an applied, external magnetic field. NMR phenomena can be observed with an NMR spectrometer and used to study molecular physics, crystalline and non-crystalline materials. In particular, nuclear spin phenomena can be used to generate a spectrum comprised of a pattern of lines representing the various spins and spin interactions. 
     In order to perform an NMR measurement, the instrument must be set up to perform a particular measurement. A sample to be measured must be prepared, inserted into the instrument and a measurement run. The resulting intensity signals must then be processed to generate a spectrum. Finally, the spectrum must be interpreted in order to determine the composition of the sample. 
     Conventional automation software can be used to control the NMR spectrometer to perform routine measurements and signal processing software is available to process the intensity signals in order to generate an NMR spectrum. However, in order to produce accurate results, the spectrometer must be checked and calibrated. Further, the interpretation of the NMR spectrum is complex process requiring training and experience and a thorough knowledge of the compounds that may be present in the sample. For example, the NMR spectrum may include intensity signals from one or more compounds of interest, the solvent used to dissolve the sample and impurities in the sample and solvent. Consequently, NMR spectrometers are generally maintained and operated by highly trained laboratory scientists known as NMR spectroscopists. 
     In many production facilities, it would be desirable to make NMR measurements routinely on samples, such as raw materials without having NMR spectroscopists on staff. Often these measurements are simple pass fail measurements. Nonetheless such measurements made on conventional NMR spectrometers still require the training and knowledge of an NMR spectroscopist to perform the measurement and interpret the result. 
     SUMMARY 
     In accordance with the principles of the invention, an automated screening device performs standardized NMR measurements on a sample to assist in the quality control screening of raw materials, ingredients and components used in a wide range of products, such as pharmaceuticals, chemicals, polymers, food products, petroleum and many other materials. After acquiring an NMR spectrum of the sample, a qualitative analysis unit identifies at least one reference NMR spectrum corresponding a compound present in the sample and a quantitative analysis unit integrates relative signal intensity signals of the sample spectrum in regions of peak intensity in the one reference NMR spectrum and compares integration results to a number of atoms in each region in order to confirm identification of the compound. The device performs the testing and detection of any compounds having NMR active nuclei using standard NMR techniques, but because it is highly automated, the device can be operated by non-NMR spectroscopists and the measurements performed on NMR spectrometers in good laboratory practice (GLP) environments. 
     In one embodiment, a qualitative and a quantitative analysis of a sample NMR spectrum are performed in order to initially identify and then confirm identification of various compounds expected in the sample, including a main component, adulterants, impurities and other compounds. A further quantitative analysis is then used to determine the relative amounts of each compound present. 
     In another embodiment, both the qualitative and the quantitative analysis use data that has been previously stored in databases. 
     In still another embodiment, the qualitative analysis is performed by comparing the sample NMR spectrum to a plurality of reference spectra stored in a spectral database. 
     In yet another embodiment, the quantitative analysis is performed by integrating signal intensities in selected regions of the sample spectrum and comparing the integration results to the number of atoms in each region. 
     In still another embodiment, in order to assure that the NMR spectrometer is performing to user-defined specifications, a series of system suitability tests are periodically run by the raw material screening software. 
     In yet another embodiment, reports designed for both nontechnical and advanced users are automatically generated to allow quick assessment of the results and to provide a permanent record of the material testing. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block schematic diagram of a raw material screening system constructed in accordance with the principles of the invention. 
         FIG. 2  is a detailed block schematic view of a compound identification and quantification system. 
         FIG. 3  is a flowchart showing the steps in an illustrative process for compiling a spectral database. 
         FIGS. 4A and 4B , when placed together, form a flowchart showing the steps in an illustrative program for performing a qualitative analysis by searching for compounds in the spectral database. 
         FIG. 5  is a flowchart showing the steps in an illustrative process for performing a quantitative examination of a raw material spectrum. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  illustrates the basic components of a material analysis system  100  constructed in accordance with the principles of the invention. The NMR spectrometer  102  is controlled by a conventional spectrometer automation program  104  which provides commands to the spectrometer  102  as indicated schematically by arrow  106  and receives information back as indicated schematically by arrow  108 . The spectrometer automation program handles a variety of tasks for setting up, running, and processing a routine NMR experiment on the NMR spectrometer  102 . These tasks include prompting a user for insertion of a sample  110  as indicated schematically by arrow  112 , prompting a user for data file name, solvent, and type of experiment from computer  114  as indicated schematically by arrow  116  and receiving the response as indicated by arrow  118 . Since the magnets used in the spectrometer are not perfect and are prone to drift, the automation software locks the spectrometer on the solvent by performing an NMR measurement on a predetermined reference atom (usually deuterium) in the solvent in which the sample  110  is dissolved. The resulting reference signal is used to shim the spectrometer magnet in order to make the magnetic field as uniform as possible. In one embodiment, the system uses the half width of the reference signal to determine if the spectrum is of a high enough quality to be passed on for spectral evaluation. The threshold cutoff for half height is user-selectable. A sample with a larger half width will be re-shimmed and re-acquired. Two consecutive sample failures result in automatic queuing of a System Suitability Test to check the calibration of the spectrometer as described in detail below. 
     Finally, the spectrometer automation software runs the experiment and acquires the intensity signal (called a free induction decay or FID) over time from the sample  110 . A spectrometer automation program which is suitable for use with the invention is sold under the name IconNMR® by the assignee of the present invention. 
     The FID signal acquired by the spectrometer  102  under control of the automation program  104  is provided to the spectrum generation program  120  as indicated schematically by arrow  122 . The spectrum generation program  120  processes the FID signal by Fourier transformation and does phasing to correct for phase shifts in the frequency components of the FID signal. In one embodiment, the program  120  integrates  1 H spectra, performs peak selection (peak-picking) and finally plots the spectrum as a table of values. A spectrum generation program suitable for use with the present invention is sold under the name TopSpin® by the assignee of the present invention. Alternatively,  13 C,  19 F or  31 P spectra can be integrated by program  120 . 
     As previously mentioned, the NMR spectrum consists of a plot of intensities versus chemical shift values. A chemical shift value is a resonance frequency at which an intensity peak occurs expressed with respect to the resonance frequency of an NMR reference standard (such as tetramethysilane (TMS) or trimethylsilyl propionate (TSP)) divided by the spectrometer frequency and is usually designated in parts per million (ppm) units. Therefore, identification of a particular compound from a NMR spectrum requires knowledge of the particular pattern of intensity peaks that corresponds to that compound. Since the NMR spectrum may include intensity signals from one or more compounds of interest, the solvent used to dissolve the sample and impurities in the sample and solvent, this identification is often difficult. 
     Accordingly, the NMR spectrum generated by the program  120 , typically as a table of intensity and chemical shift values, is provided to the compound identification and quantification program  124  as indicated schematically by arrow  126 . In accordance with the principles of the invention, program  124  evaluates the data with two parallel evaluations. One of these evaluations involves identifying the qualitative presence of one or more compounds by comparing the sample NMR spectrum to a plurality of reference NMR spectra contained in a spectral database base  128  as indicated schematically by arrow  130 . The other evaluation involves a quantitative evaluation of the integral proportions of the identified compounds and reporting of additional, unexplained intensity peaks using information in the compound knowledgebase  132  as schematically indicated by arrow  134 . 
     As schematically indicated by arrow  138 , compound identification and quantification program  124  generates reports  136  of the evaluation, including a basic report and a detailed report, which are stored along with data identifying the experiment. Both reports contain information on the instrument, the original spectrum and the date and time. The basic report typically contains a “pass” or “fail” result, while the detailed report contains extensive additional information regarding the results of the experiment. 
     The operation of the compound identification and quantification program  124  is disclosed in more detail in  FIG. 2 . Processed data  200  from the spectrum generation program  120  is passed to the compound identification and quantification program  202  for identification and quantification of individual compounds of the sample as indicated schematically by arrow  204 . Evaluation is performed by two analysis methods. The first analysis method is a qualitative analysis method  206  based on spectral matching of the spectrum  200  with reference spectra in spectral database  208  as indicated schematically by arrow  210  using information in knowledgebase  212  as indicated schematically by arrow  214 . When a compound in the spectrum  200  is identified by qualitative analysis method  206 , that information is forwarded to the quantitative analysis method  216  as indicated by arrow  220 . Quantitative analysis method  216  confirms the identification using information in knowledgebase  212  as indicated schematically by arrow  218 . In one embodiment, analysis methods  206  and  216  operate in parallel and positive identification from both of these methods gives confidence in the identification of a compound. Alternatively, analysis methods  206  and  216  may operate in tandem. 
     Both methods are controlled by parameters in a quantification or “quant” method  222  as indicated schematically by arrows  224  and  226  and as described in more detail below. The quant method  222  also sets the level of contaminants allowed in a passing sample and thresholds which result in a failing sample. 
     The inventive process uses a spectral database  208  and a knowledgebase  212 . The spectral database  208  can be a standard database containing common constituents. Each facility that screens materials different from those provided with a standard spectral database will need to establish a spectral database and a knowledgebase containing compounds screened in their facility. 
     A spectral database, such as database  208 , contains a plurality of spectrum entries. Each spectrum entry is created by examining a spectrum of a known pure reference material, assigning multiplicity and coupling patterns to peak intensity assignments and correlating them to the chemical structure of that material. Conventional software is available in order to assist with this process. For example, software which is suitable for creating and maintaining spectral databases is sold under the name AMIX® by the assignee of the present invention. Once the intensity peak assignments have been made, the AMIX software stores the information with the spectrum in the spectral database  208 . 
     There are three spectral notations required when assigning intensity peaks in order to properly import a spectrum into the spectral database, which notations can be automatically generated from the following data:
     1. One Dimensional (1D) proton spectrum data—the minimal amount of data required for proton only screening can be obtained from a 1D proton experiment. It is in this spectrum where all peak multiplicity and coupling assignments are made as described below. Peaks can also be annotated for importing atom count for quantification.   2. Two Dimensional Heteronuclear Single Quantum Coherence (2D-HSQC) spectrum data-screening for compounds containing carbon requires data from a conventional 2D-HSQC experiment. This experiment can be a standard HSQC experiment or multiplicity-edited HSQC experiment (Ed-HSQC).   3. Molecular structure file—this can be used for peak annotation, but is not absolutely necessary for annotation. The benefit when annotating in accordance with the molecular structure file is that concentration assignments reported by the quantification method will coincide with the numerical atom assignments from the structure file.   

     The process of creating entries in a spectral database, such as database  208 , is illustrated in the flowchart shown in  FIG. 3 . This process begins in step  300  and proceeds to step  302  where spectral data generated by an experiment on a pure reference compound that is to be entered into the spectral database is obtained using a spectrometer and spectrum generation program as discussed above. In step  304 , the data is first prepared for analysis by defining a noise level which sets a threshold intensity level above which peaks are retained and below which peaks are removed from the spectrum. In one embodiment, noise is defined by dividing the spectrum into sixteen equidistant regions, or a minimum of 512 points are used. The regions are then examined and the region best fulfilling the following criteria is then used to define noise: 
     The region mean and median are similar; 
     The region skewness is close to zero; 
     There are no real peaks in the region—no peaks above noise; 
     The region has the most Gaussian-like distribution; 
     Noise is then defined as the mean of this region plus a factor (F) times the standard deviation as follows:
 
Noise=Avg+( F *STD)
 
     Peak identification is then based on the factor F and a user-selected LOQ (Level of Quantification) and LOD (Level of Detection) for the appropriate spectral evaluation scheme. In a composite experiment, the user would define the LOQ for the  1 H spectrum and the LOD for the  13 C spectrum. For example, the user might set LOQ (STD=3.0) on the  1 H experiment and LOD (STD=10) on the  13 C experiment. 
     After noise has been defined, in step  306 , line shape analysis is performed on the spectrum to remove all peak data below the defined noise level. Peaks that are not associated with the reference compound (e.g. water and NMR reference compounds, such as TMS or TSP) are also removed. The resulting 1D or 2D-HSQC spectrum (as described above) is then plotted and displayed in a viewer. 
     Intensity peaks that will define the reference material are then selected or picked in step  308 . Tools for graphically assigning intensity peaks within a spectrum are found in the AMIX program and described fully in the AMIX Users Manual which is hereby incorporated by reference in its entirety. Using the AMIX program, peaks can be quickly picked with an “Auto Peak Pick” function or manually selected. The resulting picked peaks will be displayed on the plot with tick marks displayed above the peaks and the program stores the chemical shift numbers at which the peaks occur. 
     Next, in step  310 , the selected peaks are then annotated from a conventional molecular structure file. In order to do this, both the molecular structure and the spectrum are displayed side-by-side. One or more atoms from the molecular structure are selected and the signal peak in the spectrum generated by these atoms is selected causing the AMIX program to store the correspondence between the atoms and the signal peak. 
     Finally, in step  312 , peak multiplicity identification is performed by picking all peaks in a multiplet on the display and assigning the level of multiplicity (singlet, doublet, triplet, etc.). Once a multiplet has been indentified, the AMIX software measures the coupling constants. Once all of peaks have been picked, annotated and correctly defined by multiplicity the peak spectral data, any annotations and multiplicity information is imported into, and stored in, the spectral database. The process then ends in step  314 . This process is repeated for each reference compound likely to be encountered in the laboratory for which the spectral database is being constructed. 
     Quantification of each compound observed in a spectrum requires a list of specific chemical shifts and the atom count in a predetermined signal region surrounding each selected peak in the spectrum. The area under the peak is then determined by an integration process over the predetermined region. The presence of a compound will be confirmed when the all of the peak signal regions possess integrals and the integrals are in proportion to their respective atom counts. 
     The inventive screening process utilizes an NMR knowledgebase  214  to assist with the quantification process. The knowledgebase is a collection of definitions of spectral properties of all of the compounds that are to be screened. The knowledgebase includes the following information for each compound in the spectral database  208 : 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 Name 
                 the compound name used in the knowledgebase  
               
               
                   
                 (the same as the name used in the spectral database); 
               
               
                 Molecular weight 
                 used in the calculation of concentration; 
               
               
                 Compound regions 
                 the areas in the NMR spectrum of the compound  
               
               
                   
                 where a signal intensity peak is expected and the  
               
               
                   
                 number of atoms at the peak is defined for  1 H and  
               
               
                   
                   13 C spectra (and  19 F or  31 P spectra if the sample  
               
               
                   
                 spectra could be one of these); 
               
               
                 Quantification 
                 define the shape of the peak within each compound  
               
               
                   
                 region, including the expected coupling pattern, the  
               
               
                   
                 measured coupling of line splitting, and whether the  
               
               
                   
                 peak should be used for quantification. Defined 
               
               
                   
                 coupling patterns include singlet, doublet, triplet, 
               
               
                   
                 quartet, quintet, septet, doublet of doublets, doublet 
               
               
                   
                 of triplets and doublet of quartets; 
               
               
                 Multiplet  
                 defines tolerances on multiplet ratios (when to  
               
               
                 identification 
                 stop picking peaks relative to main peak in a  
               
               
                   
                 multiplet) and J-coupling ranges. 
               
               
                   
               
             
          
         
       
     
     The knowledgebase can be initially populated with reference samples by importing the spectral data and corresponding molecular file for each reference material from the spectral database described above. 
     A “quant” method or quantification method  222  defines how a spectrum of a particular raw material is evaluated by specifying which compounds are expected in the raw material sample, including the main component, adulterants and impurities, which signals are irrelevant (for example, the solvent signal), and how the final report should discriminate between a sample that passes or fails the adulterant threshold requirements. The quant method  222  incorporates the definitions in the knowledgebase  212  plus some additional settings for limits on adulterant levels and is specific to a raw material. Each quant method is defined by the following user-selectable parameters: 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 Method Name 
                 the name of the method; 
               
               
                 Compound list 
                 a list of compounds in the knowledgebase used to set  
               
               
                   
                 the compound definitions of known constituents. Each 
               
               
                   
                 compound in the list is assigned a compound type  
               
               
                   
                 selected from main component, additive, adulterant,  
               
               
                   
                 impurity, solvent or NMR reference signal representing  
               
               
                   
                 the likely reason for its presence in the sample; 
               
               
                 Report format 
                 specifies a ‘Pass/Fail’ or numerical results report and  
               
               
                   
                 the precision of a numerical report; 
               
               
                 Integrate by 
                 selects whether the method should integrate by peak  
               
               
                   
                 fitting or use a general region integration routine; 
               
               
                 Concentration 
                 defines the integration method that will be used; 
               
               
                 Minimum reported 
                 defines the level of integration relative to the main 
               
               
                 threshold 
                 component at which any signal not defined in the  
               
               
                   
                 Compound List is reported; 
               
               
                 Failure threshold 
                 defines the level of integration relative to the main 
               
               
                   
                 component at which any signal not defined in the  
               
               
                   
                 Compound List is reported and produces a FAIL  
               
               
                   
                 result in the final report; 
               
               
                 Noise factor 
                 number of standard deviations (STDs) above noise to  
               
               
                   
                 define real peaks for any signal. 
               
               
                 Spectrabase 
                 the spectral database which is used to detect matches  
               
               
                   
                 after quantification. It will determine if any compounds  
               
               
                   
                 in the database which are not in the Compound List are  
               
               
                   
                 sin the pectrum; 
               
               
                 Experiment type 
                 name of the experiment type which is defined in the  
               
               
                   
                 spectral database; 
               
               
                 Min match factor 
                 defines the minimum level of confidence from a match  
               
               
                   
                 at which the presence of a compound from spectral  
               
               
                   
                 database is reported. 
               
               
                 Max. shift 
                 plus and minus values creating a search region around a 
               
               
                   
                 spectrum peak used to determine spectral database 
               
               
                   
                 matching; 
               
               
                 Apply min.  
                 defines a minimum detectable level (used if screening  
               
               
                 concentration 
                 by a minimum detectable level is desired). 
               
               
                 Apply max.  
                 defines a maximum detectable level (used if screening  
               
               
                 concentration 
                 by a maximum detectable level is desired). 
               
               
                   
               
             
          
         
       
     
     The qualitative analysis  206  is performed by spectral matching. Spectral matching, in turn, is performed by projecting newly-acquired spectrum data  200  onto a previously-acquired spectrum contained in spectral database  208  of a known, pure reference sample and repeating this process with different reference samples until a spectral match is obtained. For proper performance, the conditions under which the spectra are acquired, such as solvent, pulse sequence, and temperature must be identical between the reference library/knowledgebase spectra and the newly acquired spectrum. As with the reference data in database  208 , the spectrum data  200  is first prepared for analysis by defining a noise level and performing lineshape analysis to remove peaks with amplitudes below the noise level and irrelevant peaks. 
     The spectral matching process is conducted in two parts. First, the process checks for the presence of compounds identified in compound list of the applicable quant method  222  by retrieving the spectra of these compounds from the spectral database  208 . After these compounds have been checked, a second matching process is conducted, possibly using another spectral database (as specified in the Spectrabase field of the quant method), to determine if compounds that are not on the quant method compound list exist in the sample. 
     The steps in each spectral matching process are illustrated in  FIGS. 4A and 4B . The process begins in step  400  and proceeds to step  402  where a determination is made whether any unchecked spectra exist either in the quant method compound list or in the spectral database  208  itself. If unchecked spectra exist, in step  404 , spectral data for the next reference spectrum in the compound list or in the database  208  is retrieved (as indicated by arrow  210 ) and compared to the sample spectrum data  200 . Additional information for the compound is also retrieved from knowledgebase  212  using the compound name as schematically indicated by arrow  214 . The criteria for a spectral match are that the sample spectrum  200  and the reference spectrum must both have intensity in a search region surrounding the chemical shift value stored in the spectral database. The search region is defined in the Max. shift information in the quant method and an exemplary search region is +/−0.02 ppm. In step  406 , a determination is made whether the intensities of the reference spectrum and the sample spectrum match. If not, the process returns to step  402  to determine whether additional unchecked reference spectra exist and to step  404  to select the next reference spectrum. 
     Alternatively, if in step  406 , it is determined that an intensity match exists between the sample spectrum and the selected reference spectrum, then the process proceeds to step  408  where the multiplicity of each peak in the sample spectrum is compared to the multiplicity information stored Multiplet identification field of the compound record retrieved from knowledgebase  212 . If the multiplicity does not match, the process returns to steps  402  and  404  to select another reference spectrum. 
     Alternatively, if the multiplicity matches, as determined in step  410 , the process proceeds, via off-page connectors  414  and  420 , to step  424  where a lineshape check is performed using the Quantification information in the knowledgebase record. If the lineshapes match as determined in step  426 , then the compound name is provided to the quantitative analysis  216  as indicated by arrow  220  in  FIG. 2  in step  428 . 
     If the lineshapes do not match as determined in step  426 , then the process returns, via off-page connectors  418  and  412  to steps  402  and  404  to select another reference spectrum. Operation continues in this manner until a match is determined in step  426  or no unchecked reference spectra exist, as determined in step  402 . In this latter case, the process proceeds, via off-page connectors  416  and  422  to step  430  where the process ends. 
     The quantitative analysis method is shown in  FIG. 5 . This method begins in step  500  and proceeds to step  502 . When a compound name is received from the qualitative analysis method  206  as shown in step  502 , the compound information is retrieved from the knowledgebase  212  as indicated by arrow  218 . In step  506 , an integration of the relative intensity of the sample spectrum  200  is performed for each compound region specified in the retrieved knowledgebase record using the integration by and concentration information specified by the quant method  222 . 
     In step  508 , the results of the integrations for each region are compared to the atom count for that region as specified by the compound region information in the retrieved knowledgebase record. If one atom is present in the region, then an integration value of one is expected. Similarly, an integration value of two correlates with two atoms, etc. If all of the integration values match the specified atom numbers as determined in step  510 , then compound identification is confirmed and a final integration is performed over all protons in the compound and the integration result is stored in step  512  under the compound name. The process then finishes in step  514 . 
     Alternatively, if in step  510  it is determined that no match is detected, then the process simply finishes in step  514 . 
     At the end of the entire process, integration values will have been stored for all compounds identified in the sample, including the main component, the adulterants, impurities and other compounds. After eliminating those values that are less than the minimum reported threshold specified in the quant method, ratios can then be formed of these values and compared to the failure threshold and concentration thresholds specified in the quant method in order to determine the result of the analysis. 
     In order to insure that the spectrometer  102  remains properly calibrated, system suitability tests may be performed either on a periodic basis or if the shim test fails as described above. System Suitability Tests consist of four experiments: lineshape,  1 H sensitivity,  13 C sensitivity and temperature. The results of each test must “pass”’ before the spectrometer is deemed to be operating properly and raw material samples can be analyzed. 
     The  1 H lineshape test, which is also referred to as the “humptest” automatically measures and determines the  1 H lineshape using a GLP  1 H lineshape standard sample of 1% chloroform in acetone. The width of the chloroform line at 0.55% height and 0.11% height is calculated with a double exponential fit along the left and right side of the signal. The resolution test is also performed and evaluates the width of the chloroform signal at half height. These values are compared with specifications set by the user. The test is passed if the results are better than the defined values. 
     The  1 H sensitivity test automatically measures and determines the  1 H sensitivity. While this test can be performed with almost any sample, the typical sample is 0.1% ethylbenzene in chloroform-d. The height of the biggest signal between user-specified signal limits is calculated. A noise window of width Noise delta in ppm is shifted in 25 steps along the spectrum between the specified noise limits. Each time, the noise value is determined and the signal-to-noise (S/N) ratio is calculated with respect to the height of the biggest signal within the signal limits. The best value must meet a user-defined specification. 
     The  13 C sensitivity test automatically measures and determines the  13 C sensitivity. While this test can also be performed with almost any sample, the typical is 10% ethylbenzene in cholorform-d. The height of the biggest signal between user-defined signal limits is calculated. A noise window of Noise delta ppm is shifted in 25 steps along the spectrum between the specified noise limits. Each time, the noise value is determined and the signal-to-noise ratio is calculated with respect to the height of the biggest signal. The best value must meet a user-defined specification. 
     The temperature test automatically measures and, if necessary, adjusts the temperature to a user-defined requested temperature. The experiment is designed to run after the first three suitability tests. In one embodiment, the requested temperature is set with a 99.8% methanol-d4 temperature calibration standard which has a linear range from 282° K. to 330° K. The test will attempt to adjust the temperature to the set point five times before failing. The final observed temperature after adjustment is recorded in status parameters. 
     The system is designed to adjust to shim changes over the lifetime of the spectrometer. This is done by updating and using a default shim file that is used for all samples including the system suitability test samples. Once a  1 H lineshape suitability test has been completed successfully as discussed above, the shim set is written to storage for a particular probe. If a default shim set does not exist for the probe, then the current shims are used. 
     While the invention has been shown and described with reference to a number of embodiments thereof, it will be recognized by those skilled in the art that various changes in form and detail may be made herein without departing from the spirit and scope of the invention as defined by the appended claims.