Harry Prest is with Agilent Technologies in Santa Clara, California.

Harry Prest

In the past, the signal-to-noise ratio of a chromatographic peak determined from a single measurement served as a convenient figure of merit used to compare the performance of two different mass spectrometry (MS) systems. The evolution in the design of MS instrumentation has resulted in very low noise systems that have made the comparison of performance based upon signal-to-noise increasingly difficult, and in some modes of operation, impossible. This is especially true when using ultralow-noise modes, such as high-resolution MS or tandem MS, where there often are no ions in the background and the noise is essentially zero. This occurs when analyzing clean standards used to establish the instrument specifications. Statistical methodology that is commonly used to establish method detection limits for trace analysis in complex matrices is a means of characterizing instrument performance that is rigorously valid for both high and low background noise conditions. Instrument manufacturers should begin to provide customers an alternative performance metric, in the form of instrument detection limits based on the relative standard deviation of replicate injections, to allow analysts a practical means of evaluating an MS system.

For decades, signal-to-noise ratio (S/N) has been a primary standard for comparing the performance of chromatography systems including gas chromatography–mass spectrometry (GC–MS) and liquid chromatography (LC)–MS. Specific methods of calculating S/N have been codified in the U. S., European, and Japanese Pharmacopeia (1–3) to produce a uniform means of estimating instrument detection limits (IDL) and method detection limits (MDL). The use of S/N as a measure of IDL and MDL has been useful, and is still in use, for optical-based detectors for LC, and flame-based detectors for GC. As MS instrument design has evolved, the ability to accurately compare performance based on S/N has become increasingly difficult. This is especially true for trace analysis by MS using ultralow-noise modes, such as high-resolution mass spectrometry (HRMS) or tandem MS (MS-MS). S/N is still a useful parameter, particularly for full scan (EI) MS, but the comparison of high-performance MS analyzers should be based upon a metric that is applicable to all types of MS instruments and all operating modes. Statistical means have long been used to establish IDL and MDL (4–7) and are suitable to all operating modes for mass spectrometers. 

Many sources of noise have been reduced by changes to the MS design such as low noise electronics, faster electronics allowing longer ion sampling (signal averaging), modified ion paths to reduce metastable helium (neutral noise), and signal processing (digital filtering). HRMS and MS-MS are also effective means to reduce chemical background noise, particularly in a complex sample matrix. For the signal, a longer list of improvements to the source, analyzer, and detector components have resulted in more ions produced per amount of sample. In combination, the improvements in S/N and the increase in sensitivity have resulted in significant and real lowering of the IDL and MDL.  

The measurement of the "signal" is generally accepted to be the height of the maximum of the chromatographic signal above the baseline (Figure 1). However, some of the changes in GC–MS S/N specifications have been artificial. The industry standard for GC–electron ionization (EI) MS has changed from methyl stearate, which fragments extensively to many lower intensity ions, to compounds that generate fewer, more intense ions, such as hexachlorobenzene (HCB) and octafluoronaphthalene (OFN). The change to OFN held a secondary benefit for noise: the 

 272 molecular ion is less susceptible to baseline noise from column bleed ions (an isotope of the monoisotopic peak of polysiloxane at 

 281 increased baseline noise for HCB at 

Figure 1: Analyte signal as a function of time for a chromatographic peak, demonstrating the time dependence in the amount of analyte present. 

Improvements in instrument design and changes to test compounds were accompanied by a number of different approaches to measuring the noise. In the days of strip chart recorders and rulers, a common standard for noise was to measure the peak-to-peak (minimum to maximum) value of baseline noise, away from the peak tails, for 60 s before the peak (Figure 1) or 30 s before and after the peak. As integrator and data systems replaced rulers, the baseline segments for estimation of noise were autoselected and noise was calculated as the standard deviation (STD) or root-mean-square (RMS) of the baseline over the selected time window.  

Automation of the calculation holds many conveniences for a busy laboratory, but the noise measurement criteria have become less controlled. In some cases, vendors have estimated noise based on a very narrow window (as short as 5 s) and location that can be many peak widths away from the peak used to calculate signal. These variable "hand-picked" noise windows make it easy for a vendor to claim a higher S/N by judiciously selecting the region of baseline where the noise is lowest. Generally, the location in the baseline where the noise is calculated is now automatically selected to be where the noise is a minimum. Figure 2 shows three different values of RMS noise calculated at different regions of the baseline. The value of the noise at positions a, b, and c is 54, 6, and 120, and the factor of 20 variation in S/N is exclusively because of where in the baseline the noise is measured. However, these S/N values will not correlate to the practical IDL for automated analyses that the laboratory performs. Therefore, the use of signal-to-noise as an estimate of the detection limit will clearly fail to produce usable values when there is low and highly variable ion noise and the choice of where to measure it is subjective.  

Figure 2: EI full-scan extracted ion chromatograms of m/z = 272 from 1 pg OFN. 

The situation becomes even more indeterminate when the background noise is zero, as shown in the MS-MS chromatogram in Figure 3. In this case, the noise is zero and S/N then becomes infinite. The only "noise" observed in Figure 3 is because of the electronic noise, which is several orders of magnitude lower than noise because of the presence of ions in the background. This situation can be made more severe by increasing the threshold for ion detection. Under these circumstances, it is possible to increase the ion detector gain, and hence the signal level, without increasing the background noise. The signal of the analyte increases, but the noise does not. This is obviously misleading since the signal increased, but there was no increase in the number of ions detected, and therefore, no change to the real detection limit. This allows S/N to be adjusted to any arbitrary value without changing the real detection limit.  

Figure 3: EI MS-MS extracted ion chromatogram of m/z 222.00 from 1 pg OFN exhibiting no chemical ion noise. 

Two decades ago, instrument specifications often described the detailed analytical conditions that affected the signal or noise value, such as chromatographic peak width, data rate, and time constant. Today, those parameters are missing or hard to find, even though a novice chromatographer realizes a narrow peak will have a larger peak height than a broad peak. In many cases, the GC conditions are selected to make the chromatographic peak extremely narrow and high, with the result that the peak is under-sampled (only one or two data points across a chromatographic peak). This may increase the calculated S/N, but the under-sampling degrades the precision and would not be acceptable for most quantitative methods. Once again, a false perception of performance has been provided. Accurate, meaningful comparison of instrument performance for practical, analytical use based on published specifications is increasingly difficult, if not impossible. Alternative means are required to determine the instrument performance and detection limit, which is generally applicable to all modes of MS operation. 

S/N is still useful and represents a good first estimate to guide other statistically based estimates of performance. Every analytical laboratory should understand and routinely use statistics to evaluate and validate their results. The analytical literature has numerous articles that utilize a more statistical approach for estimating an IDL or MDL. The U.S. EPA has dictated a statistical approach to MDL, and can be found in the recommended EPA "Guidelines Establishing Test Procedures for the Analysis of Pollutants" (6). The European Union also has supported this position. A commonly used standard in Europe is found in "The Official Journal of the European Communities;" Commission Decision of 12 August 2002; Implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results (8).  

Both of these methods are generally similar and require injecting multiple replicate standards to assess the uncertainty in the measuring system. A small number of identical samples having a concentration near the expected limit of detection (within 5–10) are measured, along with a comparable number of blanks. Because of the specificity of MS detection, the contribution from the blank is negligible and is often excluded after the significance of the contribution has been confirmed. The mean value 

and standard deviation (STD) of the set of measured analyte signals, 

 (that is, integrated areas of the baseline subtracted chromatographic peaks), is then determined. Even for a sensitivity test sample, the "sampling" process is actually a complex series of steps that involves drawing an aliquot of analyte solution into a syringe, injecting into a GC system, and detecting by MS. Each step in the sampling process can introduce variations in the final measured value of the chromatographic peak area, resulting in a sample-to-sample variation or "sampling noise." These variances will generally limit the practical IDL and MDL that can be achieved. 

The variance in the measured peak areas includes the analyte signal variations, the background noise, and the variance from injection to injection. Distinguishing the statistical significance of the mean value of the set of measured analyte signals from the combined system and sampling noise can then be established with a known confidence level. The IDL is then the smallest signal or amount of analyte that is statistically greater than zero within a specified probability of being correct. The IDL (or MDL) is related (9,10) to the standard deviation STD of the measured area responses of the replicate injections and a statistical confidence factor 

) (STD), where the STD and IDL are expressed in area counts. 

Alternately, many data systems report relative standard deviation (RSD = STD/Mean Value). In this case, the IDL can be determined in units of the amount of the standard (ng, pg, or fg) injected by

Figure 4: EI full-scan extracted ion chromatograms of m/z = 272 from 200 fg OFN, eight replicate injections, 3.3 Hz data rate. 

-distribution (11) is used to determine the confidence factor 

1 (number of measurements minus one) as the degrees of freedom, and 1–α is the probability that the measurement is greater than zero. The larger the number of measurements 

, and less the uncertainty in the estimate of the IDL or MDL. Unlike the statistical method of determining IDL and MDL, the use of signal-to-noise from a single sample measurement does not capture the "sampling noise" that causes multiple measurements of the same analyte to be somewhat different.

Figure 5: EI full-scan extracted ion chromatograms of m/z = 272 from 200 fg OFN, first entry in Table I, 3.3 Hz data rate. 

As an example, for the eight replicate injections in Figure 4 (

 = 7 degrees of freedom) and a 99% (1–α = 0.99) confidence level, the value of the test statistic from the 

 = 2.998. For the eight 200-fg samples, the mean value of the area is 810 counts, the standard deviation is 41.31 counts, and the value of the IDL is: IDL = (2.998)(41.31) = 123.85 counts. Since the calibration standard was 200 fg and had a measured mean value of 810 counts, the IDL in femtograms is: (123.85 counts)(200 fg)/(810 counts) = 30.6 fg. Alternately in terms of RSD, the IDL = (2.998)(5.1%)(200 fg)/100 = 30.6 fg. Thus, an amount of analyte greater or equal to 30.6 fg is detectable and distinguishable from the background with a 99% probability. In contrast, S/N measured on one single chromatogram (Figure 5) would give an IDL of 1.1 fg, assuming that IDL = 3 × rms noise (first entry in Table I). The high S/N is the result of a commonly used algorithm that calculates the noise using the lowest noise sections of the baseline adjacent to the peak where the noise is unusually low. The individual S/N and IDL values measured for each injection in Figure 4 are tabulated in Table I.

Table I: Comparison of S/N for eight injectionsThe IDL values range from 1.1 fg to 20.5 fg, and even the largest value is significantly smaller than the IDL determined by statistical means, which is more realistic.   

A secondary benefit to specifying detection limits in statistical terms is to use the RSD as an indirect measure of the relative number of ions on a chromatographic peak. It is known (12) that if a constant flux of ions impinge on a detector and the mean number of ions detected in a particular time interval averaged over many replicate measurements is 

, then the RSD in the number ions detected is  

Hence, decreasing the number of ions in a chromatographic peak will increase the area RSD. This effect can be seen by comparing the RSD in Figure 6 and Figure 4 where the analyte amounts are 1 pg and 200 fg, respectively. The increased RSD at the lower sample amount is, in large part, because of ion statistics. Five times lower number of ions means 

times increase in RSD. It follows that the 2.1% RSD at 1 pg will become 4.7% at 200 fg just from ion statistics. It is not unexpected that, at lower sample amounts, there also will be some additional variance from the chromatography. Thus, RSD measurements of the same amount of analyte on two different instruments can be used to indicate the relative difference in sensitivity near the detection limit, assuming that other contributions to the total variance of the signals are small. The more sensitive instrument will have the smaller RSD if all other factors are the same (that is, peak width and data rate). This avoids the uncertainty of inferring sensitivity from the measurement of peak areas where there is no baseline noise.  

Figure 6: EI full-scan extracted ion chromatograms of m/z = 272 from 1 pg OFN, eight replicate injections, 3.3 Hz data rate. 

For MS vendors, there is complexity and cost in providing customers this valuable information. If the GC is configured with an autosampler and a splitless inlet, it should be fairly simple to add 7–10 replicate injections. This might add a little time to the installation, but the use of an autosampler should keep this at a minimum. But how do you test a system that does not have an autosampler or the appropriate inlet? Manual injections will add operator-dependent imprecision to measuring the peak area. Reproducible injection using a manually operated injector device like the Merlin MicroShot Injector (Merlin Instrument Co., Half Moon Bay, California), are necessary to reduce the sampling noise. If the method uses headspace or gas analysis, it could be costly to test as a liquid injection and then re-configure for the final analysis. These costs must be managed appropriately, but factors like cost and complexity for some configurations should not prevent a transition to a better, statistically based standard such as %RSD for the majority of the systems that do not have these inlet limitations. 

From the customer's perspective, however, there is a significant benefit to having a system level test of performance that offers a realistic estimate of the IDL and the system precision near the limits of detection where it is the most critical. The purchase of a mass spectrometer is a significant capital expenditure, and often purchasing decisions are based on a customer's specific application need. Instrument manufacturers do not have the resources to demonstrate their product's performance for every application. It would be valuable to have a simple, but realistic, means of evaluating a particular MS instrument's performance in the form of the IDL and the system precision. These will form lower bounds to the MDL and method precision for specific applications.  

In the past, the S/N of a chromatographic peak determined from a single measurement has served as a convenient figure of merit used to compare the performance of two different MS systems. However, as we have seen, this parameter can no longer be universally applied and often fails to provide meaningful estimates of the IDL. A more practical means of comparing instrument performance is to use the multi-injection statistical methodology that is commonly used to establish MDLs for trace analysis in complex matrices. Using the mean value and RSD of replicate injections provides a way to estimate the statistical significance of differences between low level analyte responses and the combined uncertainties of the analyte and background measurement, and the uncertainties in the analyte sampling process. This is especially true for modern mass spectrometers for which the background noise is often nearly zero. The RSD method of characterizing instrument performance is rigorously and statistically valid for both high and low background noise conditions. Instrument manufacturers should start to provide customers with an alternative performance metric in the form of RSD-based instrument detection limits to allow them a practical means of evaluating an MS system for their intended application.    

(2) United States Pharmacopeia, XX Revision (USP Rockville, Maryland, 1988).

(3) Japanese Pharmacopoeia, 14th Edition. 

(4) ASTM: Section E 682–93, Annual Book of ASTM Standards, 

Handbook of Residue Analytical Methods for Agrochemicals 

(6) U.S. EPA – Title 40: Protection of Environment; Part 136 – Guidelines Establishing Test Procedures for the Analysis of Pollutants; Appendix B to Part 136 – Definition and Procedure for the Determination of the Method Detection Limit – Revision 1.11. 

(7) "Uncertainty Estimation and Figures of Merit for Multivariate Calibration," IUPAC Technical Report, 

(8) Official Journal of the European Communities; Commission Decision of 12 August 2002; Implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. 

Articles

Signal-to-noise of a chromatographic peak from a single measurement has been used determine the performance of two different MS systems, but this parameter can no longer be universally applied and often fails to provide meaningful estimates of the instrument detection limits (IDL).

Why Use Signal-To-Noise As a Measure of MS Performance When It Is Often Meaningless?

The 30-year history of advances in gas chromatography–mass spectrometry technology continues today. Recent improvements in hardware, electronics, and data analysis software have resulted in new levels of productivity and sensitivity that have broadened the potential applications for this laboratory mainstay.

For three decades, gas chromatography–mass spectrometry (GC–MS) technology has undergone a continuous evolution of hardware, electronics, and software improvements. Capillary column interfaces, positive and negative chemical ionization (CI), use of turbomolecular pumps, automation of CI reagent gases control, 50- to 100-fold improvement in levels of detection, improved scanning speeds, enhanced automated data, and target compound analysis are just a few examples of improvements that have made the analysis of volatile and semivolatile compounds by GC–MS a routine and easy-to-implement technique. Given the maturity of the technology, many have assumed that the evolution of GC–MS is approaching its limit. However, important new developments in GC–MS suggest otherwise. This article will discuss how the new techniques of synchronous selected ion monitoring and scan acquisition, trace ion detection, deconvolution reporting software, and new ion source materials are significantly enhancing the productivity and sensitivity of benchtop GC–MS.  

The first GC–MS systems that were commercialized over 30 years ago had one mode of operation for obtaining mass spectral data — full mass spectrum acquisition. Mass spectrometers that used a quadrupole mass filter ramped the rf and dc voltages applied to the quadrupole in a continuum to acquire a mass spectrum. The fastest scan rates of systems produced in the 1970s were approximately 650 amu/s, and the primary goal of GC–MS systems at this early stage of their development was the generation of qualitative information for compound identification. For several years, GC–MS was not considered a quantitative analytical system because of its slow scan rates and lack of software to analyze quantitative information. That changed when two new developments were introduced: improved electronics and quantitative data analysis software. Instruments were developed with the electronic capability to adjust the rf and dc voltages to filter specific ions selectively in a predetermined cycle rather than scanning the entire mass range. The technique was called selected ion monitoring (SIM) and provided the capability to perform quantitative analysis using GC–MS. Because the mass spectrometer was being used to monitor a fixed number of specific ions rather than scanning an entire mass spectrum, the limit of detection for a compound was improved significantly because the duty cycle of the mass spectrometer was greatly improved. Picograms of compounds could be detected using SIM, in contrast to the nanograms required for a fully scanned spectrum. SIM also produced a very accurate quantitative profile of a compound being detected. Almost simultaneously, computerized algorithms were introduced that made the automated analysis of the quantitative data from SIM analysis feasible. These two developments started the development of quantitative analysis via GC–MS. 

Generally, a GC–MS analysis was designed to be either qualitative (full scan) or quantitative (SIM only). GC–MS electronics did not have the bandwidth required to quickly change the rf and dc voltages needed to acquire both qualitative (scan) and quantitative (SIM) data synchronously during the same GC–MS analysis. However, GC–MS electronics have been improved significantly in the last few years. Current instruments can scan a spectrum at 10,000 amu/s (versus 650 amu/s in the 1970s) and switch between ions during an SIM analysis in 1 ms. These new capabilities allow synchronous collection of scan and SIM data (Figure 1) during an analysis without compromising the quality of data in either mode. No longer does a GC–MS method have to be developed to collect either qualitative or quantitative data, as both types of data can now be acquired during one analysis.  

A method that is designed primarily for qualitative analysis using full-scan spectra can now be combined with SIM acquisition to provide quantitative information on selected compounds via SIM, with significantly improved sensitivity. In an analogous manner, an experiment that is designed primarily for quantitative information via SIM can have full-scan data incorporated into the method. One benefit of this SIM–scan combination is that qualitative information can be collected during a SIM–scan analysis that helps in identifying coeluted compounds that could interfere with the quantitative SIM analysis. The coelution of matrix compounds from a complex sample that could interfere with a SIM analysis could be detected easily and confirmed via the scanned spectrum. 

One challenge of the SIM–scan acquisition mode that has been addressed is the generation of the optimal SIM 

 acquisition parameters. Software programs have been written that convert a full-scan acquisition file into the appropriate 

 and retention time groupings for a SIM analysis. Generating these parameters is done automatically and typically takes a few seconds to complete the conversion — a quantum leap compared to the manual methods of dividing up the chromatogram by "hand."  

The benefits of a SIM–scan method compared with a scan-only method are demonstrated in the analysis of volatile organic compound (VOC) target gases, USEPA Method 8260. Figure 2 shows the first 2 min of a reconstructed total ion chromatogram (RTIC) for a VOC sample obtained using a SIM–scan acquisition mode. The scan and SIM RTICs of the SIM–scan method look very similar, indicating no loss in signal. However, a closer examination of peak 5, vinyl chloride, Table I, shows that the SIM data has a 10-fold improvement in the root mean square (rms) signal-to-noise ratio (S/N) when compared with the full-scan data from the SIM–scan acquisition or from a scan-only method.  

Table I: SIMâscan and scan only sensitivity comparison. SIM data shows a 10-fold improvement in S/N			

The faster data-acquisition electronics in modern GC–MS make fast chromatographic analysis feasible with a quadrupole-based GC–MS system. Capillary columns with internal diameters of 100 μm can produce peaks with one-half height widths of 180 ms and should contain more than eight data points for acceptable quantitation. Figure 3 shows the peak profile that was obtained using a 3-ms dwell time/ion. A total of 15 data points were obtained for this peak while three other SIM ions were being acquired during the analysis.  

Another area of GC–MS technology that has been improved recently is ion-detection algorithms. The algorithms that analyze a mass spectrometer's raw data and generate mass and abundance pairs, perform the analysis in real time using algorithms that are coded in the mass spectrometer's firmware. Several groups have published algorithms to improve the S/N and chromatographic resolution in complex chromatograms, but most were run after a sample had been collected (1).  

Advances in microprocessor technology and software algorithms have now made the real-time analysis of complex matrices possible. One example is the Trace Ion Detection algorithm (Agilent Technologies, Palo Alto, California), which improves the analytical quality of the data by removing spectral noise and chemical artifacts in the mass spectral data. One of the primary benefits of this approach is improved analysis of compounds in complex matrices. The performance of the algorithm is illustrated in Figures 4 and 5.  

One of the major applications of GC–MS is target compound analysis. The detection and quantitation of known compounds in complex samples is a common application for many segments of GC–MS users. The analysis of foods, air, water, and drugs of abuse for target compounds is a routine analysis done thousands of time every day. Analytical laboratories, like other segments of industry, are constantly being challenged to improve their efficiency and reduce costs. One way to address this challenge is to automate the analysis of complex chromatograms through the use of software that performs unattended analysis in minutes compared with hours by human efforts. This can be achieved by combining the Automated Mass Spectral Deconvolution and Identification System (AMDIS) with databases that contain target-compound retention times. AMDIS was developed by NIST (Gaithersburg, Maryland) for the detection of chemicals that are in violation of the Chemical Weapons Convention (2). One of its primary goals is to identify target compounds in complex matrices. AMDIS deconvolves complex chromatograms with overlapping peaks into their individual spectra and performs a library search using a database of 147,000 compounds from the NIST library or a user-generated or commercially supplied database. The utility of AMDIS can be enhanced significantly by combining it with databases that include compound retention times. The AMDIS software does not perform any quantitative analysis of the data. However, by incorporating a database that contains qualifying ion ratios for three ions of a target compound and an internal standard calibration table, an estimate of compound amounts can be obtained. Combining the two reports produces a qualitative and quantitative analysis for the target compounds.  

The analysis of a spinach extract is shown in Figure 6. Typically, a human operation would take 45–60 min to analyze the extract and perform a library search for the major components. Using the combined AMDIS and a retention time and spectral library of 567 pesticides, the report shown in Figure 7 was produced in 1 min. Additional compound libraries are available for volatiles, semivolatiles, PCBs, and pesticides.  

Various stainless alloys have been the material of choice for ion-source manufacturing for many years. Although stainless steel alloys have many favorable properties for ion sources, such as resistance to oxidation and corrosion and being nonmagnetic, they also can react with many organic compounds at high temperatures and produce deleterious effects. This could be demonstrated by comparing an electron-capture detector chromatogram with an RTIC produced by an electron ionization source. The RTIC often would produce a very asymmetrical peak with excessive tailing, and the mass spectrum of the compound would contain peaks that were indicative of thermal reaction between the sample and stainless steel. Figure 8 illustrates the abundant 247 ion that originates from a reaction between the compound and the source. Use of new ion-source materials that are constructed from inorganic conductive materials containing nitrides and disulfides of metals has reduced the decomposition of reactive compounds significantly in the ion source. Figure 9 shows the virtual elimination of the 

 247 ion that was indicative of source interactions. An additional benefit of the new ion-source material is an increase in sensitivity for reactive compounds. Compounds containing phosphorus and sulfur moieties have shown 50–100% increases in response using the inert source material.  

Significant improvements in the hardware, software, and electronics used in modern GC–MS systems have led to improved scan and SIM acquisition parameters, quantitation, spectral fidelity, and sensitivity and have allowed higher productivity. As GC–MS systems have become easier to operate and capable of analyzing complex data sets faster, they are becoming an alternative to the use of selective GC detectors that have been used for many years to achieve selectivity and sensitivity.  

are with Agilent Technologies in Santa Clara, California. 

Harry Prest

Articles

Why Use Signal-To-Noise As a Measure of MS Performance When It Is Often Meaningless?

Recent Improvements in Benchtop GC–MS