warning diagnostics for inductively coupled plasma-mass spectrometry

Ž .Spectrochimica Acta Part B 55 2000 311]326

Warning diagnostics for inductively coupledplasma-mass spectrometry

Hai Yinga, Jennifer Murphy a, John W. Trompa, J.-M. Mermetb,Eric D. Salina,U

aDepartment of Chemistry, McGill Uni ersity, 801 Sherbrooke St. W, Montreal, Quebec, H3A 2K6, Canadab ( )Laboratoire des Sciences, Analytiques CNRS 5619 , Uni ersite Claude Bernard-Lyon I, 69622, Villeurbanne Cedex, France

Received 24 September 1999; accepted 23 December 1999

Abstract

The performance of an inductively coupled plasma mass spectrometer is substantially impacted by changes in radiofrequency power, nebulizer gas flow, and sample liquid flow. A simple diagnostic procedure has been developed toautomatically ensure that the quality of results is not compromised by system degradation. The diagnosis procedurewas studied in two different stages of instrument operation: the normal operating stage and the warm-up stage. Fornormal operating stage monitoring, a decision tree was derived with the C4.5 induction engine applied to Ar-basedsignals allowing instrument performance to be classified as either normal or abnormal. Abnormal signals werefurther classified as to their probable cause. Signals present in both deionized water and 0.5% nitric acid spectra aresufficient for diagnosis with an accuracy of 96.0% and 94.0%, respectively, allowing the use of ‘blank’ solutions tomonitor instrument performance. For the warm-up stage diagnosis, a standard solution containing Li, In, Ba and Bi,

Ž .was used. Stepwise regression was applied as the key factor filter while partial least square PLS was adapted to bethe classifier. In addition to the modeling test, the importance of the intensities and intensity ratios of differentmasses was studied. By eliminating the standard element signals from the model, the possibility of using waterrnitricacid as test solutions was also studied. The overall prediction accuracy for the warm-up stage is approximately 92%using the standard solution or either the water or nitric acid type blank solutions. Q 2000 Elsevier Science B.V. Allrights reserved.

Keywords: ICP-MS; Diagnosis; Pattern recognition; Elemental analysis

U Corresponding author. Fax: q1-514-398-3797.Ž .E-mail address: [email protected] E.D. Salin

0584-8547r00r$ - see front matter Q 2000 Elsevier Science B.V. All rights reserved.Ž .PII: S 0 5 8 4 - 8 5 4 7 0 0 0 0 1 4 4 - 0

( )H. Ying et al. r Spectrochimica Acta Part B: Atomic Spectroscopy 55 2000 311]326312

1. Introduction

The autonomous instrument project is an efforttowards the development of ‘intelligent’ analyticalinstruments, and has been ongoing in our lab for

w xthe last decade 1]3 . The general thought pat-tern for the instrument is outlined in a previous

w xpaper 2 . This chart illustrates the thought pat-tern for a specific analysis, but does not mentionthe need to monitor the instrument itself. Ideally,this would be done constantly, but minimally mustbe done when the instrument starts up. So, animportant part of the project is an instrumentdiagnosis warning procedure, which notifies usersof subtle failures such as nebulizer blockage,abnormal gas pressure, loss of coupling efficiency,etc. This procedure will ensure that the instru-ment operates in a stable manner, permittinggood analytical accuracy and precision. Given thehigh throughput of modern instruments, theprocedure should be automatic, and should allowless experienced operators to evaluate the analyt-ical performance of instruments through simpleexperiments. Some previous work has been donefor inductively coupled plasma-atomic emission

Ž .spectrometry ICP-AES based on pre-identifiedw xfactors 4]6 . Mermet proposed a diagnosisŽ .procedure QUID that involved monitoring seven

w xatomicrionic lines of Mg, Ba, Zn and Ar 4 . Acomputer program, QUID Expert, has also beencreated that uses information obtained from spec-tral lines to recognize potential problems and

w xtheir sources, and alert the operator 5 . Our labhas extended this idea to a ‘blank-based diagno-

w xsis’ for ICP-AES using only Ar and H lines 6 .The final accuracy of that system was approxi-mately 90%.

This study explores the adaptation of the ICP-AES diagnosis concept to ICP-MS. The mostimportant external operating parameters for ICP-MS are nebulizer gas flow rate, nebulizer liquidflow rate, and RF power. Several other factorshave been found to be important, for example

w xsampling depth 7 . In this study, the samplingdepth has been optimized by the manufacturer ata recommended value. Other problems, such asnebulizer clogging, unsuitable sized tubing, or ashift in torch position, may result in changes in

these parameters or affect performance as thoughthese parameters had been altered. In turn, alter-ations in performance modify the precision, accu-racy, stability, limits of detection, and even inter-ferences in ICP-MS. Furthermore, the highthroughput and unattended operation capabilitiesof modern instruments require that diagnosticprocedures be both convenient and effective.

Development of the basic ICP-MS warningdiagnosis procedure involves three aspects:

1. collection of the full mass spectrum in orderto provide sufficient spectral information;

2. examination of instrument performanceunder various operating conditions to supplysufficient data to simulate problems; and

3. application of pattern recognition methods tocreate a model for diagnosis during normaluse.

The goal of the procedure is to develop aŽ .system capable of: 1 identifying when the instru-

Ž .ment is not performing optimally; 2 recognizingŽ .the source of the problem; and 3 alerting the

operator.

2. Algorithms and method development process

2.1. Diagnosis process

The general diagnosis development process canbe divided into two parts as illustrated in Fig. 1:methodology development as outlined above andnormal use.

During methodology development, patternrecognition methods are used to build a predic-tion model using full spectra obtained under vari-ous operating conditions. The experiments give usdifferent mass intensities and the related settingsfor each operating parameter. These settings canbe represented as different ‘classes’, e.g. Gas-High, Power-Low, etc., instead of the actualnumeric values. This both simplifies and general-izes the procedure. The experimental data aremade up of training and test data sets, which willbe described in more detail in the experimentalsection. The raw matrix was composed of mea-

( )H. Ying et al. r Spectrochimica Acta Part B: Atomic Spectroscopy 55 2000 311]326 313

Fig. 1. Overview of the diagnosis process.

sured intensities while the target matrix consistsof the classifications.

For normal use, the prediction model is used toidentify the problems for ICP-MS. One need onlyrun the test solution once and put the spectraldata obtained into the model. The calculation ofinference process result will show if and how theoperating conditions have changed from normal.

2.2. Algorithms

Some research has been done on the applica-tion of chemometric methods in ICP-MS, to solve

w xdifferent problems 8]10 . Based on the previousw xwork 6,11,12 , we chose an AI-based pattern

recognition technique, C4.5 inductive learning,and two common, powerful regression methods,

Ž .partial least squares PLS and stepwise linearregression, as classification tools. They are allsupervised learning methods, which means ‘solvedproblems’ are needed in the training process.

2.2.1. Inducti e learningw xInductive learning 13 is an approach com-

monly used for producing rules for expert sys-tems. The C4.5 algorithm is an induction engine

w xdeveloped by Quinlan 14 . It converts input datainto a decision tree, which can be easily convertedto ‘IF]THEN’ type rules. The tree can be used toclassify a case by starting from the root of the

Žtree and continuing down the branches testing.points until a terminating leaf is encountered. It

is a powerful pattern recognizer perhaps under-

appreciated for its ability to work very well incertain types of numeric problems. In order togenerate a decision tree, C4.5 applies informationtheory to the training samples with an informa-tion maximization process, which makes the finalclassification process computationally trivial andconsequently fast. Detailed C4.5 algorithms can

w xbe found elsewhere 11,14 .

2.2.2. Stepwise regressionUsing the data from the full mass spectra,

models were constructed with the most importantspectral information. The selection of the vari-

Ž .ables spectral features that provide the mostinformation is a critical process. Variable selec-tion methods have been developed which identify

Ž .good although not necessarily the best variables,with considerably less computing than is required

w xfor all possible regressions 15 . These methodsare referred to as stepwise regression methodsand are not commonly used in ICP spectroscopy.The variables are identified sequentially by addi-

Ž . Žtion forward selection or deletion backward.elimination , depending on the method. Forward

stepwise selection, used in the current work, as aninfluential variable filter, starts by choosing theindependent variable that accounts for the largestamount of variation in the dependent variable.This independent variable has the highest simplecorrelation with the target matrix. At each succes-sive step, the variable in the subset of variablesnot already in the model that causes the largestdecrease in the residual sum of squares is added


to the subset. This will be the variable that hasthe highest correlation with the residuals fromthe current model. The procedure terminateseither when a criterion is reached, e.g. a partialF-statistic at a particular step does not exceed apreset value F , or when the desired number ofincandidate regressors have been added to themodel. Related procedures can be found else-

w xwhere 15 .

2.2.3. Partial least squaresPartial least-squares path modeling with latent

Ž .variables PLS is a popular chemometrics methodwhich has been used to attack some specific prob-

w xlems in ICP-MS 8,9 . PLS performs simultaneousand interdependent principal component analysisŽ .PCA in both the independent variable matrix Xand dependent variable matrix Y. In our study,PLS is used to construct a model of instrumentalparameters depending on spectra. Therefore, Xcontains the spectral data while Y includes thedifferent levels of operating parameters. PCA isan orthogonal decomposition process for matricesand is used to extract systematic variations in asingle data set. The general goal of this mathe-matical manipulation of a data matrix is to repre-sent the variation present in many variables usinga small number of ‘factors’ named principal com-

Ž .ponents PCs . These PCs account for the maxi-mum amount of variation possible in the datamatrix defined directions. An important differ-ence of the PLS algorithm is that the informationin the Y matrix is applied directly as a guide foroptimum decomposition of the X matrix, andPLS subsequently performs regression of Y. Thismeans that the factors of PLS, unlike the PCs ofPCA, are computed based on the relationshipbetween X and Y. There are two approaches thatcan be used to develop a calibration model, i.e.PLS1 and PLS2. With PLS2, a single model isconstructed using all the variables in Y simulta-neously. We adopted PLS1, in which separatemodels are developed for each dependent vari-able, the most popular and thoroughly studiedmethod. In practice, PLS1 is not usually imple-mented using the two decomposition processes

discussed above, but instead by using the non-Ž .linear iterative partial least-squares NIPALS al-

gorithm. A detailed description can be found inw xthe literature 16,17 .

PLS has many strengths, including excellentmodel-diagnostic capabilities, the possibility ofovercoming significant noise in a system, and thecapability of self-prediction-diagnostics. Rankdetermination in the raw data space is consideredto be one of its big weaknesses. It is not straight-forward to determine how many and what raw

w xfactors to use in the model 17 .

3. Instruments and experiments

The present work can be divided into two partsaccording to the different operating stages of theinstrument, the normal working stage and thewarm-up stage. Normally, an ICP-MS needs towarm up to reach steady signal levels, usually forapproximately 15]30 min. It will save time to beable to monitor the instrument during warm-up,however, it is clear that the signal would be muchmore stable in the normal operating stage thanthat in the warm-up stage.

A PE-SCIEX Elan 6000 ICP-MS with a cross-flow nebulizer and a Scott-type spray chamberwas used for this study. The operating conditionsand experimental parameters are given in Table1.

Three operating parameters were varied in theexperiments in order to obtain a series of data fortraining and testing. Because it is rare for morethan one problem to occur simultaneously, theparameters were examined one at a time whilethe other two were kept normal. Seven settingswere selected for each operating parameter. One

Ž . Ž .is normal setting 4 , three are low settings 1]3 ,Ž .and three are high settings 5]7 , as listed in

Table 2. Several different low and several differ-ent high settings were added for each parameterin the warm-up stages in order to test our meth-ods more realistically. The normal settings weredetermined through an initial ‘daily optimizationprocess’ of the Sciex Elan 6000 ICP-MS. Sets of


Table 1ICP-MS operating conditions and experimental parameters

Instrument PE-SCIEX Elan 6000 ICP-MSNebulizer Cross-flow with Scott-type spray chamber

ICP RF power Varied between 0.7 and 1.3 kWSample liquid uptake Varied between 0.7 and 1.3 mlrminNebulizer gas flow rate Varied between 0.6 and 1.2 lrminMass spectrometer

Detector Dual

Ion lens voltage 6.25]6.75 VAuto lens Off

Peak scan parametersŽ .Mass range 2]15, 17]39, 42]250 amu

Replicates 3Dwell time 100 msScan mode Scanning

Ž .Resolution 0.1 amuSweeps per reading 1Reading per replicate 1

Data information

Stage of instrument Test solutions Training data set Test data set

Normal 1. Water 41 samples 50 samplesa a2. 0.5% nitric acid 7 settingsreach 7 settingsreach

Warm-up Li, In, Ba and Bi 180 samples 124 samplesa a10 ppb 7 settingsreach 11]13 settingsreach

a Number of settings for different operating parameters.

15 runs were performed for each of the threeoperating parameters to make up the trainingdata set using the order of settings 4r1r5r7r6r2r3r4r5r1r6r3r2r7r4. Note that setting4, normal, appears at both ends of the acquisitionprocess and in the center as a test for drift. Threereplicate spectra were collected for each run. Oneminute was allowed for the machine to stabilizebetween each acquisition.

w xIn preliminary work 12 , it was found thatblank solutions provided enough information fordiagnosis without using standards. Therefore, purewater and a more realistic blank, 0.5% nitric acidwere applied in the diagnosis of the normal work-ing stage. Meanwhile, a standard solution was

used as a test solution in the warm-up stage. Thestandard solution containing four elements, Li,In, Ba and Bi at 10 ppb, was chosen in order tocover the entire mass range. In total, 41 and 50experiments made up the raw training and testdata sets, respectively, for the normal workingstage, and 180 and 124 for the warm-up stage.The data were collected on many different days,corresponding to different startups of the ICP-MS.The standard daily optimization procedure wasapplied for the normal working stage while it wasnot for the warm-up stage.

All programs were written in Matlab except theC4.5 software which is written in the C pro-gramming language. A Visual Basic subroutine


Table 2Operating conditions for data sets

Levels Normal working stage Warming-up stage

Gas Liquid Power Gas Liquid Powery1 y1 y1 y1Ž . Ž . Ž . Ž . Ž . Ž .l min ml min kW l min ml min kW

1 Low 0.65 0.7 0.7 0.6 0.7 0.72 Low 0.7 0.8 0.8 0.7 0.8 0.83 Low 0.75 0.9 0.9 0.8 0.9 0.94 Normal 0.8 1.0 1.0 0.9 1.0 1.05 High 0.85 1.1 1.1 1.0 1.1 1.16 High 0.9 1.2 1.2 1.1 1.2 1.27 High 0.95 1.3 1.3 1.2 1.3 1.3

More ] ] ] 0.55 0.65 0.65settings 0.65 0.85 0.85for test 0.75 1.15 1.15

1.05 1.35 1.351.151.25

was also compiled to read and convert the Elan6000 report files to the desired format for pro-cessing.

4. Results and discussions

The target of this work is to find a convenientmethod to monitor instrument malfunctions toensure analytical quality. Various types of instru-ment malfunctions can be simulated by altering

Žinstrument settings e.g. clogged nebulizer by

.reduced nebulizer gas flow . These malfunctionsmay then lead to altered instrument response andsubsequent changes in measured analyte signallevels. Table 3 shows the percentage changes ofanalyte signals when the parameters were alteredroughly 10% from normal conditions. The abso-lute value of the signal changes for Li, In, Ba andBi varied from less than 1% to over 100%. Thisconfirms that minor changes in operating condi-tions can significantly alter analyte signals, andthat serious errors would result if the situation isnot diagnosed.

Table 3y1Ž .Percentage changes of analyte signals with operating parameters changed "10% from normal settings of gas flow 0.9 l min ,

y1 aŽ . Ž .liquid flow 1 ml min and power 1 kW

y1 y1Ž . Ž . Ž .Gas l min Liquid ml min Power kW

0.8 1.0 0.9 1.1 0.9 1.1

Normal working stageLi y4.9 y64.5 y1.1 y3.7 y54.0 104.9In y24.8 y58.5 y3.8 5.0 y26.8 11.2Ba y22.2 y50.1 y7.0 4.3 y22.1 10.0Bi y22.5 y3.5 y9.2 5.5 3.4 y11.0

Warm-up stageLi 41.4 y3.6 0.9 y0.7 y1.7 9.0In 23.2 y67.6 y5.4 y1.9 y32.6 30.2Ba 20.9 y64.3 y3.7 0.9 y25.2 24.1Bi y16.7 y36.4 9.0 14.3 3.0 14.3

a Normal working stage data is the average of two runs. The warm-up stage data is the average of six runs.


4.1. Diagnosis in the normal working stage

Argon-based species are a convenient startingpoint for ICP-MS diagnosis. A set of 10 variableswas generated using the 36Ar, 36Ar1H, 40Ar16 O,and 40Ar40Ar signals and their ratios: ArrArH,ArrArO, ArrArAr, ArHrArO, ArHrArAr andArOrArAr. The percent change from normal wasinvestigated for each of the variables to de-termine which were the most successful at pre-dicting instrument malfunction.

For the training data set, nine of the 45 runsrepresented normal conditions. Because it wasnecessary to account for slight natural variationsin the normal signal over time, the nine normalruns were averaged to find a standard ‘normal’signal. Subsequently, the percent change fromnormal for the 10 variables was calculated for alloperating conditions. It was found that ratioswere better indicators of instrument malfunctionthan raw signals, because they were more con-stant at standard conditions while respondingmore strongly to variations in the parameters.

Shown in Figs. 2 and 3 are the percent changesfrom normal at each setting for the six ratiovariables using both water and 0.5% nitric acidblank solutions. As the figures illustrate, theresponse of the six ratios to variations in gas flow,liquid flow, and power appear very similar for thetwo blank solutions. The presence of acid in asample solution is known to impact the results ofmany spectrometric elemental analysis techniquesw x18 . This can result from physical propertychanges leading to variations in aspiration rates,drop size distribution, and characteristics of thesolution transported to the plasma. Additionally,in mass spectrometry, other polyatomic interfer-ences may occur due to overlaps at certain mass

wpeaks of interest for example: both ArH andŽ . xH O H O have significant peaks at 37 . How-3 2

ever, as shown in Figs. 2 and 3, the presence of0.5% nitric acid produced only slight variations inthe ratios measured.

To generate the decision tree, each run wasŽlabeled according to the operating parameter e.g.

. Ž .gas flow and the setting e.g. high . Liquid flowhad a minimal effect on the signals recorded foreach variable. In fact, on liquid settings 3 and 5,

Fig. 2. The effect of variation of three operating parametersŽ .obtained during normal working stage for deionized water: a

Ž . Ž .nebulizer gas flow; b liquid flow; and c power. The settingsfor each parameter is listed in Table 2 and range from one

Ž . Ž .extreme 1 to the other 7 . Note that the percent change iscalculated relative to the average value of setting 4 which is‘normal’.

the average signals were not statistically distin-guishable from normal at the 99% confidencelevel. These conditions could not be accuratelyclassified as instrument malfunction, so they were


Fig. 3. The effect of variation of three operating parametersŽ .obtained during normal working stage for 0.5% nitric acid: a

Ž . Ž .nebulizer gas flow; b liquid flow; and c power. The settingsare as described in the caption to Fig. 2.

discarded for the purposes of generating a deci-sion tree.

Therefore, there were six runs each for Gas-Low, Gas-High, Power-Low and Power-High, fourruns each for Liquid-Low and Liquid-High, andnine runs at Normal. For each run, the percentchange from normal for each of the six ratios was

fed into the C4.5 induction engine as the trainingset. The decision trees generated for deionizedwater and nitric acid are displayed as Figs. 4 and5, respectively.

Although the behavior of the ratios appearsremarkably similar, the use of the C4.5 inductionengine reveals that different decision trees areoptimal for each type of blank solution to achievethe highest degree of success. Because the induc-tion engine methodically searches out the best setof rules to attain optimal classification, seeminglyslight differences can result in considerably dif-ferent decision trees for each kind of blank.

To determine the true success rate of a deci-sion tree, a test set was collected comprised of arandom sequence of 50 runs composed of allseven different classifications, e.g. Gas-Low,Power-High, etc. After collection and calculation,they were appropriately labeled and tested by thedecision tree. The results are tabulated in Tables4]6. It seems reasonable that most of the predic-tion selection failures were decisions between thelow power and high gas cases, which result insimilar spectral changes. It is interesting to notethat greater than 85% accuracy is achieved whenusing the decision tree for one type of solutionŽ .e.g. nitric acid blank on data for the other typeŽ .e.g. water as shown in Table 6.

4.2. Diagnosis in the warm-up stage

After the success of the instrument diagnosis inthe normal working stage, the next step was tosee whether it could be carried out during thewarm-up period. A more general approach wasfollowed to determine if there were more impor-tant factors than the previously identified Ar-

Ž .related species. All significant variables masseswere used rather than Ar-based species only.Besides the C4.5 algorithm, several other chemo-metric methods were applied. Fig. 6 shows thewhole flow chart of the process with multivariatecalibration methods. There are three differentdata sets and two modeling methods, which resultin six routines for comparison.

Several different data sets were generated asinput into the classification process. The first wasthe raw data set. The second data set consisted of


Ž .Fig. 4. Decision tree for deionized water obtained during the normal working stage .

all possible ratios of the raw data, and the thirdincluded all the raw data and all the ratios. Thelast two data sets will be referred to as increaseddimensionality data sets since they contain manymore variables, and hence dimensions than theoriginal data set. The reason for using this tech-nique will be described in the following section.There are two potential processing procedures for

creating the classification model. The first methodŽinvolves only the use of PLS paths b, d and f in

.Fig. 6 , while the other method involves applyingstepwise regression first to select key factors for

Ž .PLS paths a, c and e in Fig. 6 . Although PLS isan excellent regression method based on the la-tent variables, it cannot determine directly the

Ž .most important preliminary variable s . The two

Ž .Fig. 5. Decision tree for 0.5% nitric acid obtained during the normal working stage .


Table 4Test results for deionized water

Setting Classified as

Normal Low High Low High Low Highgas gas liquid liquid power power

Normal 17Low gas 7High gas 6Low liquid 4High liquid 3Low power 1 4High power 7

methods were combined not only to obtain pre-diction models, but also to identify key factors forthe classification.

4.2.1. Results of increased dimensionality methodsOne might intuitively expect that some signal

ratios would be more stable than raw signalsduring the warm-up stage. To illustrate this, wecan compare a signal to a signal ratio while vary-ing the parameters of interest. The 80Ar signal is2commonly used for signal correction and instru-

w xment diagnosis in ICP-MS 19 . It was found withstepwise regression that mass 19 over mass 32 isthe most important factor for predicting the ef-fect of gas flow changes. Fig. 7 demonstrates theeffect of changes on these two variables. As isseen from the much smaller standard deviationand more monotonic behavior, the intensity ratiowas more useful in the warm-up stage. Further-more, since PLS and stepwise regression are lin-ear regression methods, they may yield more ac-curate predictions with the introduction of non-

Table 5Test results for 0.5% nitric acid

Setting Classified as

Normal Low High Low High Low Highgas gas liquid liquid power power

Normal 17 1Low gas 7High gas 5 1Low liquid 4High liquid 3Low power 1 4High power 7

Table 6Test results for all decision trees

Training set Testing set SuccessŽ .rate %

Deionized water Deionized water 960.5% nitric acid 0.5% nitric acid 94Deionized water 0.5% nitric acid 880.5% nitric acid Deionized water 86Combined sets Combined sets 91

linear factors, such as intensity ratios. Beforeprocessing the data obtained with the four ele-ment standard solution, three data sets were builtin order to evaluate the practical performance ofthe intensity ratios. First, elimination of back-

Žground points points whose signal was below a.specified threshold yielded the 30 most promi-

nent masses, comprising the raw data set. Anincreased set of 870 was then generated com-posed of all the possible mass pairs. The total setis a combination of the above two, and contains30q870s900 variables.

4.2.2. Multi ariate calibration resultsPLS contributes not only a final linear regres-

sion model, but also a series of score vectors thatgives meaningful readable information. Their‘score plots’ or ‘discrimination plots’ show therelationships between the sample variables in PLSraw space. This is very useful in looking for un-usual or inconsistent patterns that can indicatepotential problems in the final models.

Fig. 8 shows comparisons of nebulizer gas score

Fig. 6. Flow chart of the model development process withmultivariate calibration methods during the warm-up stage.


Ž .Fig. 7. Comparison of signals during the warm up stage: aŽ .Ar only; and b ratio of mass 19 over mass 32.2

plots demonstrating how PLS worked with dif-ferent data sets and alternative modeling meth-

Žods i.e. the various datarprocessing routes.through Fig. 6 . On examination of the plots, the

reader should be aware that an ‘X’ is an experi-mental point in which the nebulizer gas flow isnormal. Other parameters such as liquid flow maynot be normal. There could then be three com-

Ž .plete sets nebulizer gas, liquid flow, and powerof the type of figures that we present for neb-ulizer gas in Fig. 8. PLS makes a model for eachof the parameters and these are combined tomake one complete model for which results arereported in the following paragraphs. In Fig. 8,the x- and y-axes are the first two score vectors ofPLS. The points indicate different experiments.For the raw data set shown in Fig. 8a and only

Ž .PLS processing path b in Fig. 6 , there are twoobvious overlap areas where the normal setting

Ž .points ‘X’ are mixed with points of low or high

Ž .settings circled . Fig. 8b illustrates what happenswith only PLS processing and the ratio-only data

Ž .set path f in Fig. 6 . Note that it has now gonefrom two areas to one area of overlap. The same

Žresult was obtained for the total data set path d.in Fig. 6 indicating the superiority of using ratios.

In Fig. 8c, the power of stepwise regression fol-Ž .lowed by PLS on raw data path a in Fig. 6 can

be appreciated. The data were reduced to onearea of overlap. In Fig. 8d, we see the full powerof using all the data, raw plus ratios, with stepwise

Ž .regression followed by PLS path c in Fig. 6 . Nowall of the normal points are isolated from theabnormal. It is important to note that if only the

Ž .ratio data is used path e in Fig. 6 , one obtainsexactly the same result, proving that only ratiosare important in the diagnosis. This indicates thatPLS combined with stepwise regression and in-creased dimensionality is superior. A set of simi-lar PLS score plots which are not shown herewere also generated for the other two operatingparameters, the liquid flow rate and the RF power.

ŽTable 7 lists the 15 key factors intensities only.or intensity ratios for nebulizer gas flow rate by

their importance as selected by stepwise regres-sion. All the mass species were determined byusing an ICP-MS database } MS InterView soft-

w xware 20 . Not surprisingly, except for the fourstandards, only water-, Ar-, C- and N-relatedspecies are listed for the raw data set. Meanwhile,for data sets including the intensity ratios, i.e.ratios and intensity data sets, only two standards,Ba and In, remained, which indicates the possibil-ity of monitoring the instrument without the stan-dard test solution. Table 8 lists the five mostimportant factors for liquid flow rate and powerand the three data sets. It should be noticed thatthose factors depend on the operating parame-ters, which imply that the variation of eachparameter impacts the instrument differently.

Table 9 is a comparison of PLS test results withdifferent data sets and alternative modelingmethods. Generally, the results for the ratio-onlydata set using the stepwise regression method arethe best. The average accuracy is 92% out of 124test samples. Consistent with the results for thenormal working stage test, the most difficulty wasexperienced with identifying liquid flow change


Fig. 8.

since it had the least effect on the spectra pro-duced.

4.2.3. Results of C4.5 algorithmFor comparison, the C4.5 method was used for

developing a decision tree to be used in thewarm-up stage. Instead of using the percentchanges from normal for six Ar-based species asinput to the C4.5 algorithm, the raw signals of allvariables were used. This is because the varianceof signals in the warm-up stage is not affectedsolely by the changes of operating parameters,but also by the unstable status of the instrument.The results, ;80% accurate predictions, werenot as good as those obtained for normal workingstage operation, as shown in Table 10. The deci-sion tree is much bigger while the test error rateis higher. Intensity ratios seemed to yield noimprovement. Most of the misclassified sampleswere obtained under normal settings. From thosePLS score plots, it was obvious that those samplepoints fluctuated considerably due to instrumentinstability during data collecting. The C4.5 algo-rithm maximizes the information by splitting datainto sub-groups, a process which is more sensitiveto noise. The PLS results were better due to itsreduced sensitivity to noise comparing to C4.5.

4.2.4. Diagnosis with ‘blank’ data setIn order to test the possibility of using water or

0.5% nitric acid as a monitoring solution, a raw‘blank’ data set was created by elimination of thesignals from the four standards from the raw dataset of the standard solution, leaving 26 variablesŽ .mass intensities . The procedure discussed abovewas then followed and 650 and 676 variables wereincluded in the new ratio-only and total data sets,respectively. The PLS score plots again indicatethe benefits of using intensity ratios and stepwise

Fig. 8. Comparison of PLS score plots for nebulizer gasobtained with different data sets and alternative modeling

Ž .methods using the standard solution. a Raw data set, withoutŽ .stepwise regression; b ratio-only and total data sets, withoutŽ .stepwise regression; c raw data set, with stepwise regression;

Ž .and d ratio-only and total data sets, with stepwise regression.


Table 7The first 15 key factors for nebulizer gas flow rate selected by stepwise regression methods

Raw data set Ratio-only and total data sets

Mass Species Mass ratios Species

w Ž . x w x w x37 H O H O , ArH 19r32 H O, OH, H O r O , NOH, NO3 2 3 2 2w x w x w x209 Bi 32r33 O , NOH, NO r HO , NOH, O , NO2 2 2w x w x w x12 C 138r19 Ba r H O, OH, H O3 2w x w x w x30 NO, CO, N H, N 76r30 Ar r NO, CO, N H, N2 2 2 2 2w x w x w x56 ArO, ArNH 80r115 Ar r In2w x w x w x138 Ba 34r18 H O , O , HO , NOH r H O, NH , O, OH2 2 2 2 2 4w x w x w x80 Ar 12r76 C r Ar2 2w x w x w x115 In 32r18 O , NOH, NO r H O, NH , O, OH2 2 4w x w x w x15 N 30r138 NO, CO, N H, N r Ba2 2w x w x w x19 H O, OH, H O 30r34 NO, CO, N H, N r H O , O , HO , NOH3 2 2 2 2 2 2 2w x w x w Ž . x34 H O , O , HO , NOH 30r37 NO, CO, N H, N r H O H O , ArH2 2 2 2 2 2 3 2w x w x w x7 Li 80r32 Ar r O , NOH, NO2 2w x w x w x44 N O, CO 80r19 Ar r H O, OH, H O2 2 2 3 2w x w x w x32 O , NOH, NO 36r76 Ar r Ar2 2w x w x w x14 N 76r35 Ar r HO2 2

regression. Comparison of the first five optimalfactors and PLS test results among different oper-ating parameters and different data sets are shownin Tables 11 and 12. Except for the absence ofstandards, the key factors were not very differentfrom those for the standard solution. Consistentwith the standard solution, the best PLS resultoccurred when using only intensity-ratios fortraining and using stepwise regression as a pre-filter. The accuracy was approximately 90%. It isalso interesting to see that the results for the C4.5algorithm are better than those for the standard

solution. As shown in Table 13, a reasonableaccuracy for the C4.5 decision tree, approximately84%, is obtained with the total data set.

5. Conclusion

In this work, both a warning diagnosis proce-dure for ICP-MS and a new technique for devel-oping such procedures have been presented. Theresults can be used to create software that woulduse the intensity measurements of certain masses

Table 8Comparison of the first five key factors for liquid flow rate and power among different data sets

Raw data set Ratio-only data set Total data set

For liquid flow rate209 30 56 30 56w x w x w x w x w xBi NO, CO, N H, N r ArO, ArNH NO, CO, N H, N r ArO, ArNH2 2 2 280 80 32 80 32w x w x w x w x w xAr Ar r O , NOH, NO Ar r O , NOH, NO2 2 2 2 219 15 39 209w x w x w x w xH O, OH, H O N r ArH Bi3 2138 80 56 44 209w x w x w x w x w xBa Ar r ArO, ArNH N O, CO r Bi2 2 232 209 138 30 209w x w x w x w x w xO , NOH, NO Bi r Ba NO, CO, N H, N r Bi2 2 2

For power56 14 38 56w x w x w x w xArO, ArNH N r Ar ArO, ArNH17 56 33 17w x w x w x w xOH, NH , O ArO, ArNH r HO , NOH, O , NO OH, NH , O3 2 2 3209 80 18 138 19w x w x w x w x w xBi Ar r H O, NH , O, OH Ba r H O, OH, H O2 2 4 3 235 30 33 19 80w x w x w x w x w xHO NO, CO, N H, N r HO , NOH, O , NO H O, OH, H O r Ar2 2 2 2 2 3 2 2115 80 33 138 33w x w x w x w x w xIn Ar r HO , NOH, O , NO Ba r HO , NOH, O , NO2 2 2 2 2


Table 9aComparison of PLS test results for the standard solution with different data sets and alternative modeling methods

raw,wo raw,ws rat,wo rat,ws tot,wo tot,wsŽ . Ž . Ž . Ž . Ž . Ž .% % % % % %

Gas 96.0 94.4 96.0 95.2 96.0 95.2Liquid 63.7 64.5 74.2 85.5 75.0 80.6Power 94.4 92.7 95.2 96.0 95.2 89.5

aAbbre¨iations: raw, raw data set; rat, ratio-only data set; tot, total data set; wo, without stepwise regression; ws, with stepwiseregression.

to alert operators to problems, ensuring a betterquality analysis. It appears that instrument degra-dation can be predicted with a success rate ofgreater than 90% in both normal and warm-upstages for ICP-MS.

ŽDuring normal operation i.e. after a warm-up.period , spectral information from only four Ar-

based species in blank solutions, i.e. deionizedwater and 0.5% nitric acid, appears to be capableof successfully identifying instrument malfunction

Table 10Comparison of C4.5 results for standard solution

Data sets

Raw Ratios Total

Variables 30 870 900Decision tree size 29 31 21

Ž . Ž . Ž . Ž .Training errors % 2.8 5r180 1.7 3r180 1.7 3r180Ž . Ž . Ž . Ž .Test errors % 25.0 31r124 20.2 25r124 22.6 28r124

Table 11Ž .Comparison of the first five key factors for nebulizer gas flow rate, liquid flow rate and power among different data sets blank data

Raw data set Ratio-only data set Total data set

For nebulizer gas flow rate37 19 32w Ž . x w x w xH O H O , ArH H O, OH, H O r O , NOH, NO3 2 3 2 212 32 33w x w x w xC O , NOH, NO r HO , NOH, O , NO2 2 280 80 18w x w x w xAr Ar r H O, NH , O, OH2 2 2 456 76 30w x w x w xArO, ArNH Ar r NO, N H, CO, N2 2 230 37 34w x w Ž . x w xNO, N H, CO, N H O H O , ArH r H O , O , HO , NOH2 2 3 2 2 2 2 2

For liquid flow rate80 30 56w x w x w xAr NO, CO, N H, N r ArO, ArNH2 2 233 80 32w x w x w xHO , NOH, O , NO Ar r O , NOH, NO2 2 2 230 15 39w x w x w xNO, N H, CO, N N r ArH2 212 80 56w x w x w xC Ar r ArO, ArNH23 19 17w x w x w xH H O, OH, H O r OH, NH , O3 3 2 3

For power56 14 38 56w x w x w x w xArO, ArNH N r Ar ArO, ArNH17 56 33 17w x w x w x w xOH, NH , O ArO, ArNH r HO , NOH, O , NO OH, NH , O3 2 2 376 80 18 80 18w x w x w x w x w xAr Ar r H O, NH , O, OH Ar r H O, NH , O, OH2 2 2 4 2 2 415 30 33 56 33w x w x w x w x w xN NO, CO, N H, N r HO , NOH, O , NO ArO, ArNH r HO , NOH, O , NO2 2 2 2 2 234 w x 80 w x 33w x 19w x 80 w xH O , O , HO , NOH Ar r HO , NOH, O , NO H O, OH, H O r Ar2 2 2 2 2 2 2 3 2 2


Table 12aComparison of PLS test results for ‘blank data’ with different data sets and alternative modeling methods

raw,wo raw,ws rat,wo rat,ws tot,wo tot,wsŽ . Ž . Ž . Ž . Ž . Ž .% % % % % %

Gas 89.5 87.9 98.4 99.2 98.4 96.8Liquid 58.1 58.9 75.0 82.3 75.0 82.3Power 88.7 87.9 95.2 96.8 95.2 90.3

aAbbre¨iations: raw, raw data set; rat, ratio-only data set; tot, total data set; wo, without stepwise regression; ws, with stepwiseregression.

Table 13Comparison of C4.5 results for ‘blank data’

Data sets

Raw Ratios Total

Variables 26 650 676Decision tree size 28 35 31

Ž . Ž . Ž . Ž .Training errors % 2.2 4r180 2.2 4r180 1.7 3r180Ž . Ž . Ž . Ž .Test errors % 22.6 28r124 21.8 27r124 16.1 20r124

using a decision tree generated by the C4.5 induc-tion algorithm.

In the warm-up stage, because of fluctuationsin the spectra, predictions of the C4.5 inductionengine were not as good as during the normalworking stage. Multivariate calibration methodsgave better predictions and a reasonable explana-tion for the models. Due to its capability of over-coming significant noise in a system, PLS had anoverall prediction accuracy of 90%. Meanwhile,stepwise regression selected the optimal variablesubsets from the large number of variables pro-duced with increased dimensionality techniques.The results indicate that the blank solutions canbe used as the monitoring solution.

It should be pointed out that while the method-ology to develop a prediction model is quite gen-eral, the actual model developed depends on theinstrument. That means, for a new instrument ora new accessory, the model development process,as shown in Fig. 1, should be performed to build anew prediction model. It would take approxi-mately 2 h to collect data and -30 s to run theprogram to update the model. This might also berequired after changing certain components, e.g.the sampler or skimmer cones, nebulizer or spraychamber, etc. In addition, if very different sets of

conditions are considered ‘normal’, then the sys-tem would have to retrained around those condi-tions.

Acknowledgements

The authors wish to thank the Natural Sciencesand Engineering Research Council of CanadaŽ .NSERC , the National Research Council of

Ž .Canada NRC and SCIEX Canada for their fi-nancial support under the Strategic Grants Part-nership Program, and Michael Rybak, JanHamier, Beth Brown, Will Long, and David Burnsfor valuable discussions.

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