Glossary of Analytical Terms - Home - Springer978-3-540-35990...Glossary of Analytical Terms...

Glossary of Analytical Terms

Globalization in science, technology and economy causes, amongst otherthings, problems in derivation, adaptation and acceptance of technical termsin general, and in Analytical Chemistry, too. Both vague and contradictorydeˇnitions have been developed and established over the years. Therefore,in the last decade the efforts have been increased to harmonize the use ofanalytical terms. In particular international organisations like ISO, IUPACrender outstanding services to the ˇxing and harmonization of essentialanalytical terms. But also additional activities have been done in this ˇeld,e.g., by the Royal Society of Chemistry, the EURACHEM Education andTraining Working Group and the Federation of European Chemical Societies(FECS), Division on Analytical Chemistry (DAC) as well as by publicationsin form of books (e.g., Prichard et al. [2001]) and other publications, e.g.the series \Glossary of Analytical Terms" (GAT) in the journal Accreditationand Quality Assurance, see GAT I to X [1996-1998], where multilingual termsare given, and Holcombe [1999/2000].

In the following, deˇnitions of essential analytical terms are compiled, ifpossible on the basis of international agreements. Attached are sparse refer-ences and cross-references. The symbols, being used here, means:! \seealso"(cross-references to terms, additional references as well as paragraphs,chapters, equations and ˇgures of this book), and � is a warning notice.

Accuracy

\Closeness of agreement between the re-sult of a measurement and a true valueof the measurand".

ISO 3534-1 [1993]! Sect. 7.1! Trueness� Do not confuse withPrecision

Analysis (of a sample)

Investigation of a sample to identifyand/or determine (an) analyte(s) or as-say a material.

! Quotation from AnalChem [1975] at the end of theGlossary

284 Glossary of Analytical Terms

Analyte

\The chemical entity being investigated(qualitatively or quantitatively)".

According to Pritchard et al.[2001]

Analytical function (evaluation function)

Inverse of the calibration function,x = f �1(y ), describing the dependenceof the analytical values from the mea-sured values.

Analytical method

\Logical sequence of operations, de-scribed generally, used in the perfor-mance of measurements", e.g., the linksof a given analytical technique with par-ticular excitation and detection.

ISO 3524-1 [1993]! Fig. 7.1

Analytical procedure

\Set of operations, described speciˇcally,used in the performance of particularanalytical measurements according to agiven method".

ISO 3524-1 [1993]Pritchard et al. [2001]! Fig. 7.1

Analytical process

Logic sequence of objects linked by gen-eral analytical standard operations.

! Fig. 2.1

Analytical result

Analytical value attributed to a measur-and, obtained by measurement and com-pleted by information on the uncertaintyof measurement.

According to ISO 3524-1 [1993]! 8.1

Analytical quantity

\Particular quantity subject to analyticalmeasurement".

According to ISO 3524-1 [1993]! Measurand

Analytical technique

\Generic analytical application of a sci-entiˇc principle".

According to Pritchard et al.[2001]! Fig. 7.1

Glossary of Analytical Terms 285

Assay

Determination of how much of a sampleis the material indicated by the name.

! Quotation from AnalChem [1975] at the end of theGlossary! e.g. analysis of ores

Analytical value

Magnitude of an analytical quantity, x,measured at test samples on the one handand given for reference samples used forcalibration on the other hand.

Background (instrumental background, background signal)

Instrumental background is the null sig-nal, obtained in the absence of anyanalyte- or interference-derived signal.

! IUPAC [1995]; Currie[1999]! Background may be set tozero, on the average, for certaininstruments

Baseline

\Summation of the instrumental back-ground plus signals in the analyte (peak)region of interest due to interferingspecies".

IUPAC [1995]; Currie [1999]

Bias

\The difference between the expectationof the test results and an accepted refer-ence value".

According to GAT VIII [1997]

Blank

A value, yB , obtained my measuring ablank sample (in calibration, the inter-cept of the calibration curve is consid-ered to be equal to the blank).Blanks may be differentiated into instru-mental blank (background and baseline,respectively) and chemical blank (analyteblank).

! IUPAC [1995]; Currie[1999]! Background! Baseline! Chemical blank


Blank measurement

Procedure by which a measured value isobtained with a sample in that the analyteof interest is intentionally absent.

According to Pritchard et al.[2001]; Taylor [1987]; Sharafet al. [1986]! Blank sample

Blank sample

A sample whose analyte concentration isbelow the limit of decision of the analyt-ical procedure being used.

According to Pritchard et al.[2001]! Blank measurement

Calibration

Set of operations that establish, underspeciˇed conditions, the relationship be-tween values of quantities indicated by ameasuring system and the correspondingvalues of quantities represented by a ma-terial both in form of reference materialsand samples. In a wider sense, calibra-tion represents a set of operations thatestablish relationships between quanti-ties in the sample domain with quantitiesin the signal domain, viz y = f (x) andz = f (Q).

! ISO 3524-1 [1993]! GAT IV [1996]! Pritchard et al. [2001]! IUPAC [1998]! Sect. 6.1! Sample domain! Signal domain

Calibration function

Equation for the estimation of the valuesof a measuring quantity from given val-ues of a analytical quantity. The calibra-tion function may be known a priori bynatural laws or estimated experimentallyby means of calibration samples.The calibration function represents thatsegment of the response function that ischosen for estimating the analytical valueof an unknown sample.

! Pritchard et al. [2001]! IUPAC [1998]! Sharaf et al. [1986]! Sensitivity! Response function


Calibration samples

Set of samples characterized by accurateand precise values of the measurand. Ina concrete case, calibration samples maybe portions of (certiˇed) reference mate-rials, in-house reference materials (labo-ratory standard samples), or spiked sam-ples, and, in addition, blank samples.

! Pritchard et al. [2001]! Reference material! Certiˇed reference material

Certiˇed reference material (CRM)

\A reference material, accompanied by acertiˇcate, one or more of whose prop-erty values are certiˇed by a procedurewhich establishes traceability to an accu-rate realization of the unit in which theproperty values are expressed, and forwhich each certiˇed value is accompa-nied by an uncertainty at a stated levelof conˇdence".

ISO 3524-1 [1993]! GAT IV [1996]! Pritchard et al. [2001]

Chemical blank (analyte blank)

\Blank which arises from contaminationfrom the reagents, sampling procedure,or sample preparation steps which corre-spond to the very analyte being sought".

IUPAC [1995], Currie [1999]

Coefˇcient of variation: The term is not recommended by IUPAC;! Relative standard deviation

Concentration domain! Sample domain

One of the dimensions of the sample do-main.

Conˇdence interval (CI)

Statistical interval, e.g., of a mean, y ,cnf(y ) = y ˙ �y cnf, which express theuncertainty of measured values. CIs areapplied for signiˇcance tests and to es-tablish quantities for limit values (CV).

! Sect. 7.5! Critical value! Prediction interval


Conventional true value

\Value attributed to a particular quantityand accepted, sometimes by convention,as having an uncertainty appropriate fora given purpose".

ISO 3534-1 [1993]! True value

Correlation

Stochastic relationship between randomvariables in such a way that one dependson the other. The degree of relationshipmay be estimated by the correlation co-efˇcient.

Correlation coefˇcient

The correlation coefˇcient, rxy, is givenby the covariance of two random vari-ables x and y , cov(x; y ) = sxy, divided bythe standard deviations sx and sy , see Eq.(6.3). The correlation coefˇcient becomesrxy = 0 if there is no relationship betweenx and y , and rxy = ˙1 if there exist astringent deterministic dependence.

! Sect. 6.1.3� The correlation coefˇcientis not of any relevance in cal-ibration, as a rule. This is be-cause only the measured valueis a random variable and, incontrast, the analytical value isa ˇxed value and not selectedrandomly

Correlation matrix

Matrix formed by a set of correlation co-efˇcients related to m variables in multi-variate data sets, R = (rxi ;xj ). It is relevantin multicomponent analysis.

! Eqs. (6.4) and (8.14)

Critical value (CV)

Limit in the signal domain, esti-mated from the average blank plusits uncertainty, generally accordingto yc = y BL + U (y BL), in analyticalchemistry frequently according toyc = y BL + 3sBL. If the critical valueis exceeded, the respective analyte isreliably detected (except for a remainingrisk of error ˛). Therefore, the CVstands for the guarantee of presence ofan analyte.

Ehrlich and Danzer [2006];Currie [1999]! Sect. 7.5! Fig. 7.8! Decision limit


Cross sensitivity (partial sensitivity)

Dependence of the measured value (sig-nal intensity), yA, from other constituentsthan the analyte A, present in the mea-suring sample, quantitatively expressedby the respective partial differential quo-tient.

! Kaiser [1972]; Danzer[2001]! Sect. 7.2! Eq. (3.11)! Sensitivity! Sensitivity matrix! Total sensitivity

Determination

Analysis of a sample to estimate quanti-tatively the amount (content, concentra-tion) of (an) analyte(s).

! Quotation from AnalChem [1975] at the end of theGlossary

Evaluation function (analytical function)

Inverse of the calibration function,x = f �1(y ), describing the dependenceof the analytical values from the mea-sured values, being so the basis of an-alytical evaluation.

Homogeneity

A qualitative term used to describethat the analyte is uniformly distributedthrough the sample. The degree of homo-geneity may also be characterized quan-titatively as a result of a statistical test.

! Pritchard et al. [2001]! Inhomogeneity! Sect. 2.1! Eq. (2.9)

Hyphenated techniques

Coupling of two (or more) separate an-alytical techniques via appropriate inter-faces and computer with the goal to ob-tain faster a higher amount of informa-tion on the subject under investigation.

! Hirschfeld [1980]! Kellner et al. [1998]! By hyphenating analyticalmethods, the dimension of ana-lytical information will be in-creased (usually by one)! Sect. 3.4

Identiˇcation

Recognizing of (an) unknown con-stituent(s) in an analytical test sample.In contrast, by qualitative analysis it istested whether (a) known constituent(s)are present or absent.

! Sect. 9.3


Imprecision

A quantitative term to describe the (lackof) \precision" of an analytical procedure(e.g. by standard deviation).

! IUPAC [1995]; Currie[1999]! Precision! Imprecision of analyticalresults, see Sect. 7.1! Standard deviation

Inaccuracy

A quantitative term to describe the (lackof) accuracy of an analytical procedurewhich comprises the imprecision and thebias.

! IUPAC [1995]; Currie[1999]! Accuracy� Inaccuracy should not beconfused with uncertainty, seeIUPAC [1994a]

Inhomogeneity

\Term used to describe situations wherethe analyte is unevenly distributedthrough the sample matrix".The degree of inhomogeneity may becharacterized quantitatively by Eq. (2.9)the value of which becomes negative withthe transition from homogeneity to inho-mogeneity.

! Pritchard et al. [2001]! Sect. 2.1� The term inhomogeneityshould not be confused withheterogeneity

Interlaboratory study

\A study in which several laboratoriesmeasure a quantity in one or more iden-tical portions of homogeneous, stablematerials under documented conditions,the results of which are compiled into asingle report".According to the evaluation types, it isdifferentiated between:(1) Method-performance studies.(2) Laboratory-performance studies.(3) Material-certiˇcation studies.

IUPAC [1994b]! A minimum of ˇve labora-tories should be used to providemeaningful statistical conclu-sions from interlaboratory stud-ies! Sect. 8.2.4


Limit of decision (\3� -limit of detection")

The analytical value (e.g. the concen-tration) that corresponds to the criticalvalue. The limit of decision is of minorimportance in analytical chemistry be-cause the detection at this level of con-centration succeeds only in 50% of allcases.

Ehrlich and Danzer [2006];IUPAC [1995]; Currie [1999]! Sect. 7.5! Fig. 7.7! Critical value! Detection limit� The decision limit shouldnot be used as a perfor-mance characteristic of analyt-ical methods and also not as alimit of guarantee of an analyte

Limit of detection (LD)

The analytical value, xLD , that alwaysproduce a signal which can be distin-guished from the blank (except for a re-maining risk of error ˇ).LD is the limit in the sample domain (an-alyte domain). It characterizes analyticalprocedures, in particular with regard tothe limit concentration that can be de-tected. Therefore, the LD stands for theguarantee of absence of an analyte.

Ehrlich and Danzer [2006];IUPAC [1995]; Currie [1999]! Sect. 7.5! Fig. 7.7! Critical value

Limit of determination! Limit of quantitation

Limit of quantitation (LQ)

An analytical value, xLQ , above whichquantitative determinations are possi-ble with a given minimum precision.The condition on precision must be de-clared in each case. For a given preci-sion k = xLQ=�xLQ , the limit of quantiˇ-cation can be estimated by Eqs. (7.48)and (7.49).

Ehrlich and Danzer [2006];IUPAC [1995]; Currie [1999]! Precision! For factual reasons, thelimit of quantiˇcation cannot belower than the limit of detection! The declaration of preci-sion must always be given be-cause it is an inherent compo-nent of LQ

Linear dynamic range

The range of concentration in which theresponse varies linearly with the analyteconcentration.

! Sharaf et al. [1986]


Linearity

Ability of an analytical method to give aresponse which depends linearly on theanalyte concentration.

! Pritchard et al. [2001]! Sharaf et al. [1986]

Matrix

All of the constituents of a sample exceptthe analyte. The matrix is the carrier ofthe analyte.

! IUPAC Orange Book! [1997, 2000]! Analyte

Matrix effect

In�uence of one or more matrix con-stituent(s) on the analyte under study.Matrix in�uences may affect the analytesignal directly by interferences or indi-rectly by signal depression or ampliˇca-tion.

! Sect. 3.5! Eqs. (3.12){(3.14); (3.16);(3.17)

Measurand

\Particular quantity subject to measure-ment".

ISO 3524-1 [1993]

Measured result

Measured value, obtained by measure-ment and completed by information onthe uncertainty of measurement.

! Sect. 8.1! Measured value! Uncertainty

Measured value

\Outcome of an analytical measurement"or \value attributed to a measurand".A measured value is a \Magnitude of ameasuring quantity generally expressedas a unit of measurement multiplied bya number".

ISO 3524-1 [1993]IUPAC [1995]; Currie [1999]! Measuring quantity

Measuring quantity

\Attribute of a phenomenon . . . that maybe distinguished qualitatively and deter-mined quantitatively".

ISO 3524-1 [1993]


Measuring sample

Sample that is directly introduced intoanalytical measurement. A measuredsample is created from a test sample byconversion into a measurable form bymeans of a procedure of sample prepa-ration.

! Sect. 2.2

Metrology

\Science of measurement". ISO 3524-1 [1993]

Monitoring

Continuous or repeated observation,measurement, and evaluation of a pro-cess in a certain ˇeld of application(e.g., environmental surveillance, healthchecking, foodstuff inspection, qualityassurance in manufacturing), accordingto given schedules in space and time.

Multicomponent analysis (multispecies analysis)

Simultaneous determination of severalanalytes (species) by means of a multi-component sensing technique or hyphen-ated techniques.

! IUPAC [1995]; Currie[1999]! IUPAC [2004]

Noise

Fluctuations of the baseline- or back-ground record of an (analytical) instru-ment. Noise do not provide meaningfulinformation, on the contrary, it degradesthe quality of signals and, therefore theirdetectability.

! Background! Baseline! Signal-to-noise ratio, R/N,is a measure of the quality ofsignals! Sect. 7.5! Figs. 7.9B and 7.10

Population

\Finite or inˇnite set of individuals (ob-jects, items). A population implicitly con-tains all the useful information for cal-culating the true values of the popula-tion parameters", e.g., the mean � andthe standard deviation � .

Frank and Todeschini [1994]! Sample (in the statisticalsense)


Precision

\The closeness of agreement between in-dependent test results obtained understipulated conditions".\The precision of a set of results of mea-surements may be quantiˇed as a stan-dard deviation".

ISO 3524-1 [1993]; GAT II [1996]! Kaiser and Specker[1956]� In fact, standard deviationcharacterizes imprecision! Sect. 7.1.1; Eqs. (7.8) and(7.9)

Prediction interval (PI)

Statistical interval, e.g., of a mean, x,prd(x) = x ˙ �xprd, that express the un-certainty of analytical values which arepredicted on the basis of experimentalcalibration. PIs are applied for signiˇ-cance tests and to establish quantities forlimit values (LD, LQ).

! Sect. 7.5! Limit of detection! Limit of quantiˇcation! Conˇdence interval

Proˇciency test

\Study of laboratory performance bymeans of ongoing interlaboratory testcomparisons\.

ISO Guide 33 [1989]Pritchard et al. [2001]! Interlaboratory study

Qualitative analysis

Testing whether (a) known constituent(s)are present or absent in test samples.In contrast, identiˇcation means recog-nizing of (an) unknown constituent(s) ina test sample.

! Sect. 9.3

Quantitative analysis

Determination of the amount(s) of (an)analyte(s) in a test sample.

! Assay

Random variable

A quantity that appears in a random ex-periment. Random variables relate eventsinto a set of values.

! Sachs [1992]! Frank and Todeschini[1994]


Range (in the analytical sense)

\The interval between the upper and thelower concentration of the analyte in thesample for which it has been determinedthat the method is applicable".

Pritchard et al. [2001]

Range (in the statistical sense)

Difference between the greatest and thesmallest values of a series of measure-ments.

! Pritchard et al. [2001]! Sect. 4.3.2

Recalibration

Updating of a calibration model in thecase that details of the analytical proce-dure are changed.

! Standard operating proce-dure

Reference material

\A material or substance one or moreof whose property values are sufˇcientlyhomogeneous and well established to beused for the calibration of an apparatus,the assessment of a measurement methodor for assigning values to materials".

ISO 3524-1 [1993]! GAT IV [1996]! Pritchard et al. [2001]

Regression

Statistical method to model a mathemat-ical equation that describes the relation-ship between random variables (usuallyx and y ). The goal of regression analysisis both modelling and predicting.

! Sachs [1992]! Frank and Todeschini[1994]! Regression model! Sect. 6.1.3

Regression coefˇcients (regression parameter)

Coefˇcients of the predictors in a regres-sion model, e.g., ax and bx or ay and by ,respectively, in linear regression models.

! Eqs. (6.8) to (6.10)


Regression model

Mathematical model that describes therelationship between random variables(usually x and y ) by means of regres-sion coefˇcients and their uncertaintiesas well as uncertainties of model and theprediction.In linear regression there are two differ-ent models:that of the prediction of y from x

y = ax + bxx (6.6)and that of the prediction of x from y

x = ay + by y (6.7)

! Sachs [1992]! Frank and Todeschini[1994]! Sect. 6.1.3� Eq. (6.7) is not the inverseof Eq. (6.6), viz

x = ay + by y /=y � axbx

though Eq. (6.7) approximatesto Eq. (6.6) in the same degreeas the correlation coefˇcient rxyapproximates to 1

Relative standard deviation (RSD)

Standard deviation expressed as a frac-tion of the mean srel = s=x. RSD is adimensionless quantity; sometimes it ismultiplied by 100 and expressed as a per-centage.

� The use of the term \co-efˇcient of variation" (\varia-tion coefˇcient") is not recom-mended by IUPAC! IUPAC Orange Book [1997,2000]

Reliability

A qualitative term that covers preci-sion and accuracy as well as robustness(ruggedness).

Repeatability (of results of measurements)

\Closeness of the agreement between theresults of successive measurements of thesame measurand carried out under thesame conditions of measurement" (Pre-cision under repeatability conditions).Repeatability may be expressed quanti-tatively in terms of suitable dispersioncharacteristics.

ISO 3524-1 [1993]! GAT I [1996]! Sect. 7.1.3! Repeatability conditionsinclude the same measurementprocedure, the same observer,the same measuring instrument,used under the same conditions,the same location, and repeti-tion over a short period of time! Repeatability standard de-viation! Repeatability interval


Repeatability standard deviation (srepeat)

Experimental standard deviation ob-tained from a series of n measurementsunder repeatability conditions.

! Pritchard et al. [2001]! The number of measure-ments should be about n = 10

Repeatability interval (repeatability limit)

A conˇdence interval representing themaximum permitted difference betweentwo single test results under repeatabil-ity conditions:

r = t1�˛;�p

2 � srepeat

! Pritchard et al. [2001]In the given formula, t1�˛;� isthe quantile of the respectivet-distribution (the degrees offreedom � relates to the num-ber of replicates by which srepeat

has been estimated)

Reproducibility (of results of measurements)

\Closeness of the agreement between theresults of measurements of the samemeasurand carried out under changedconditions of measurement" (Precisionunder reproducibility conditions).Reproducibility may be expressed quan-titatively in terms of suitable dispersioncharacteristics.

ISO 3524-1 [1993]! GAT I [1996]! Sect. 7.1.3! Reproducibility conditionsare characterized by changingconditions such as: observer,measuring instrument, condi-tions of use, location, time, butapplying the same method! Reproducibility standarddeviation! Reproducibility interval

Reproducibility standard deviation (srepro)

Experimental standard deviation ob-tained from a series of measurements un-der reproducibility conditions.

! Pritchard et al. [2001]! The number of measure-ments should be sufˇcientlylarge to estimate a represen-tative reproducibility standarddeviation

Reproducibility interval (reproducibility limit)

A conˇdence interval representing themaximum permitted difference betweentwo single test results under repro-ducibility conditions:

R = t1�˛;�p

2 � srepro

! Pritchard et al. [2001]! In the given formula, t1�˛;�is the quantile of the t-distribution (the degrees offreedom � relates to the num-ber of replicates by which srepro

has been estimated)


Resolution

Process by which a composite signal issplit up into individual forms. The reso-lution can be related to:(i) Signal overlappings and ˇne struc-

ture (z-scale)(ii) Signals in close succession in time

and space

! Sect. 6.4.1! Sect. 7.6

Resolution limit

The smallest difference �z at which twoadjacent signals can be separately ob-served, i.e., their overlap does not exceeda threshold of 50% of the individual pro-ˇles.

! Sharaf et al. [1986]! In case (ii) of resolution ofthe resolution problem, �t and�l are the crucial parameters! Sect. 7.6

Resolution power

Ability of an analytical procedure to de-tect signals of small differences as sep-arate signals. Resolution power is in-versely proportional to resolution limit,e.g., R = z=�z

! Sharaf et al. [1986]! Sect. 7.6

Response

Output of an analytical system as a reac-tion to a certain stimulus.

! Stimulus! The output may be an ob-servable or measurable effect

Response function

Relationship between the response of theanalytical system and the amount of ana-lyte. The overall response function is fre-quently nonlinear.

! Sharaf et al. [1986]! Calibration function

Response variable (dependent variable)! Measuring quantity, measured value


Robustness

Property of an analytical procedure thatindicates insensitivity against changes ofknown operational parameters on the re-sults of the method and hence its suit-ability for its deˇned purpose.

! Burns et al. [2005]! ICH [1996]! Robustness may be quan-tiˇed by means of quantitiescharacterizing signal effects! Eq. (7.31)

Round robin test! interlaboratory study

Ruggedness

Property of an analytical procedure thatindicates insensitivity against changes ofknown operational variables and in ad-dition any variations (not discovered inintra-laboratory experiments) which maybe revealed by inter-laboratory studies.

! Burns et al. [2005]! Ruggedness may be quan-tiˇed by means of quantitiescharacterizing signal effects! Eq. (7.33)

Sample (in the analytical sense)

Portion of the object under study (thematerial submitted for analysis).A sample consists of the analyte and thematrix.

! Pritchard et al. [2001]! There are various types ofsamples within given samplingschemes, e.g., bulk samples >primary samples > gross sam-ples > subsamples > test sam-ples > measuring samples! Fig. 2.4

Sample (in the statistical sense)

\Subset of a population that is collectedin order to estimate the properties of theunderlying population", e.g., the sampleparameters mean x and standard devi-ation s. In the ideal case of representa-tive sampling, the sample parameter ˇtthe parameter of the population � and� , respectively.

Frank and Todeschini [1994]


Sample domain (analyte domain)

Field of analytical operation that is char-acterized by samples' properties such astype of analytes, Q, and their amount,xQ . The transition to signal domain isdone by calibration and analytical mea-surement.

! Fig. 2.12! Signal domain

Sampling

\Sequence of selective and non-selectiveoperations ending with the selection ofone or several test portions submitted tothe analytical process in their entirety.Their physical properties (maximum par-ticle size, mass, etc) are speciˇed in theanalytical procedure." Sampling coverssampling (in the narrow sense) and sam-ple reduction.

! Gy [1992]! Sect. 2.1! Fig. 2.4

Screening

Testing of (a large number of) objectsin order to identify those with particu-lar characteristics.

! Sect. 1.2! Fig. 1.5

Selectivity

The extent to which n given analytes canbe measured simultaneously by (a least)n sensors (detecting channels) withoutinterferences by other components and,therefore, can be detected and deter-mined independently and undisturbedly.

! Kaiser [1972]; Danzer[2001]! Sect. 7.3! Eq. (7.24)� Selectivity should not bemerged with speciˇcity: selectiv-ity relates to multicomponentanalysis and speciˇcity to singlecomponent analysis

Sensitivity

\Change in the response of a measuringinstrument divided by the correspond-ing change in the stimulus". In analyticalmeasurements is this, in fact, the differ-ential quotient of the measured value tothe analytical value.

ISO 3435-1 [1993]; GAT VII[1997]! Sect. 7.2! Eq. (7.12)� Sensitivity should not beconfused with limit of detection


Sensitivity matrix (Matrix of partial sensitivities)

Matrix that contains all the sensitivitiesand cross sensitivities of a multicompo-nent (multidetector) analytical system.

! Kaiser [1972]; Danzer[2001]! Sect. 7.2! Eq. (7.17)! Cross sensitivity

Signal

\Response of a device (usually an instru-ment or a module of an instrument) tocertain stimuli". A signal is characterizedby at least three parameters: position, in-tensity, and width (symmetry, shape).

Sharaf et al. [1986]! Sect. 3.3! Fig. 3.6

Signal domain (response domain)

Field of analytical operation that is char-acterized by signal properties such as sig-nal position, z, and signal intensity, yz .The transition to sample domain is doneby analytical evaluation (signal decod-ing).

! Fig. 2.12! Sample domain

Signal function

Record of signal intensity in dependenceof the signal position over a certain rangeof the z-scale: y = f (z).

Signal-to-noise ratio

Measure of the precision of signal mea-surement, expressed mostly by the ratioof the net signal value to a noise param-eter (standard deviation or peak-to-peakdistance).

! Sect. 7.1, Fig. 7.2! Eqs. (7.1){(7.6)

Speciˇcity

The extent to which one individual ana-lyte can be measured undisturbedly in areal sample by a speciˇc reagent, a par-ticular sensor or a comparable speciˇcmeasuring system.

! Kaiser [1972]; Danzer[2001]! Sect. 7.3! Eq. (7.26)! Selectivity� Speciˇcity should not bemerged with selectivity


Specimen

Fraction of a lot (batch sample) takenwithout respecting the rules for samplingcorrectness or under unknown condi-tions.

Gy [1992]

Standard deviation (SD)

Dispersion parameter for the distributionof measured values, sy , or analytical re-sults, sx , for a given sample or the pop-ulation, �y and �x . The SD is the squareroot of the variance.

! IUPAC [1995]; Currie[1999]! Sachs [1992]! Dixon and Massey [1969]! Sect. 4.1.2! Eqs. (4.12){(4.14)

Standard error

The term \standard error" is not ex-plicitly introduced. It is used sometimes(a) synonymously for standard deviationand (b) for the residual standard devia-tion in modelling and calibration.

! Sachs [1992]! Frank and Todeschini[1994]� The term standard errorshould be avoided

Stimulus

Property of an analytical system to pro-duce a response of an observation- ormeasuring system. Rousing effect of ananalyte that can be characterized quali-tatively and quantitatively.

Standard operating procedure (SOP)

\A set of written instructions that docu-ment a routine or repetitive activity fol-lowed by an organization".

EPA [2001]! Pritchard et al. [2001]! Fig. 7.1

Test

Process of analyzing the sample to rec-ognize (an) analyte(s) and/or determinethe amount(s) of (an) analyte(s).

! Pritchard et al. [2001]! Quotation from AnalChem [1975] at the end of theGlossary


Total sensitivity (total multicomponent sensitivity)

Sensitivity of a multicomponent analysis.In the simplest case it is given by thedeterminant of the sensitivity matrix.

! Sharaf et al. [1986]! Massart et al. [1988]! Sect. 7.2! Eqs. (7.18){(7.20)

Traceability

\The property of a result of measurementwhereby it can be related to appropri-ate standards, generally international ornational standards, through an unbrokenchain of comparisons".

GAT I [1996]! ISO 3435-1 [1993]! All the standards usedshould have stated uncertainties

Trackability

\The property of a result of a measure-ment whereby the result can be uniquelyrelated to the sample".

GAT I [1996]

True value

\Value consistent with the deˇnition ofa given particular quantity" and \valuewhich characterizes a quantity perfectlydeˇned in the conditions which existwhen that quantity is considered".

ISO 3534-1 [1993]GAT III [1996]! Conventional true value! Sect. 7.1

Trueness

\Closeness of agreement between the av-erage value obtained from a large seriesof test results and an accepted referencevalue".Trueness has been referred to as \accu-racy of the mean".

IUPAC Orange Book [1997,2000]! Codex alimentariuscommission [1997]! Sect. 7.1.3


Uncertainty of measurement

\Parameter, associated with the result ofa measurement, that characterizes thedispersion of the values that could rea-sonably be attributed to the measurand".The uncertainty should combine bothstatistical and non-statistical contribu-tions to the variation of the measuredvalues which may occur in all steps ofthe analytical process.

ISO 3524-1 [1993]EURACHEM [1995]GAT I [1996]! The uncertainty of mea-surement may be expressed bythe combined or extended un-certainty, u(y ) or U (y ), respec-tively! Sect. 4.2! Eqs. (4.25), (4.26) and(4.29) to (4.32)

Uncertainty of an analytical result

Interval, e.g., of a mean, U (x), that ex-press the uncertainty of analytical valuesconsidering statistical and non-statisticalvariations within the measurement pro-cess plus uncertainties of experimentalcalibration.

! Sect. 4.2! Eq. (4.32)

Validation (of an analytical method)

\Process by which it is established, bylaboratory studies, that the performancecharacteristics of the method meet therequirements for the intended analyticalapplications".

USP XXII < 1225 > [1990]Wegscheider [1996]EURACHEM [1998]! Typical performance char-acteristics that should be con-sidered in the validation are:precision, accuracy, limit of de-tection, limit of quantitation,selectivity, range, linearity, ro-bustness, ruggedness

Variable

\Characteristic of an object that may takeon any value from a speciˇed set".

Frank and Todeschini[1994]! There are several typesof variables, e.g., categorical,dependent and independent,experimental and theoretical,manifest and latent, random,standardized variables

References 305

Variance

Dispersion parameter for the distributionof measured values, s2

y , or analytical re-sults, s2

x , for a given sample or the popu-lation, � 2

y and � 2x . Statistically deˇned as

the second moment about the mean.

! IUPAC [1995]; Currie[1999]! Sachs [1992]! Dixon and Massey [1969]! Sect. 4.1.2! Eqs. (4.8); (4.10)

Working range! Range (in the analytical sense)

Analysis, Identiˇcation, Determination, and Assay (quoted from AnalChem [1975])

\While most chemists probably realize the difference between the termsanalyze, identify, and determine, they are frequently careless when usingthem. Most frequently the term analysis is used when determination ismeant.

A study of the nomenclature problem indicates that only samples areanalyzed; elements, ions, and compounds are identiˇed or determined.The difˇculty occurs when the sample is nominally an element or com-pound (of unknown purity). `Analysis of . . . ' (an element or compound)must be understood to mean the identiˇcation or determination of im-purities. When the intent is to determine how much of such a sample isthe material indicated by the name, assay is the proper word."

References

Anal Chem (1975), published in several issues amongst the Instructions for Authors,e.g., 47:2527

Burns DT, Danzer K, Townshend A (2005) IUPAC, Analytical Chemistry Division. Use ofthe terms \Robust" and \Rugged" and the associated characteristics of \Robustness" and\Ruggedness" in descriptions of analytical procedures. Draft 2005

Codex Alimentarius Commission (1997) ALINORM 97/23A, Appendix III: Analyticalterminology for codex use

Currie LA (1999) Nomenclature in evaluation of analytical methods including detectionand quantiˇcation capabilities (IUPAC Recommendations 1995). Anal Chim Acta 391:105

Danzer, K (2001) Selectivity and speciˇcity in analytical chemistry. General considerationsand attempt of a deˇnition and quantiˇcation. Fresenius J Anal Chem 369:397

Dixon WJ, Massey, FJ (1969) Introduction to statistical analysis. McGraw-Hill, New York

Ehrlich, G, Danzer, K (2006) Nachweisverm�ogen von Analysenverfahren. Objektive Be-wertung und Ergebnisinterpretation. Springer, Berlin Heidelberg New York

306 References

EPA (2001) Environmental Protection Agency. Requirements for quality assurance projectplans for environmental data operations (EPA QA R/5)

EURACHEM (1995) Quantifying uncertainty in analytical measurement. Teddington

EURACHEM (1998) The ˇtness for purpose of analytical methods. Teddington

Frank IE, Todeschini R (1994) The data analysis handbook. Elsevier, Amsterdam

GAT: Glossary of analytical terms. Series in the journal \Accreditation and Quality As-surance"

I. (1996) 1:41 (Fleming J, Neidhart B, Tausch C, Wegscheider W)

II. (1996) 1:87 (Fleming J, Albus H, Neidhart B, Wegscheider W)

III. (1996) 1:135 (Fleming J, Neidhart B, Albus H, Wegscheider W)

IV. (1996) 1:190 (Fleming J, Albus H, Neidhart B, Wegscheider W)

V. (1996) 1:233 (Fleming J, Albus H, Neidhart B, Wegscheider W)

VI. (1996) 1:277 (Fleming J, Albus H, Neidhart B, Wegscheider W)

VII. (1997) 2:51 (Fleming J, Albus H, Neidhart B, Wegscheider W)

VIII. (1997) 2:160 (Fleming J, Albus H, Neidhart B, Wegscheider W)

IX. (1997) 2:348 (Prichard E, Albus H, Neidhart B, Wegscheider W)

X. (1998) 3:171 (Prichard E, Albus H, Neidhart B, Wegscheider W)

Gy PM (1992) Sampling of heterogeneous and dynamic material systems: theories ofheterogeneity, sampling and homogenizing. Elsevier, Amsterdam

Hirschfeld T (1980) The hy-phen-ated methods. Anal Chem 52(2):297A

Holcombe D (1999/2000) Alphabetical index of deˇned terms and where they can befound. Accr Qual Assur Part I: A{F 4:525; Part II: G{Q 5:77; Part III: R{Z 5:159

ICH (1996) ICH Topic Q2B (ICH Harmonised Tripartite Guideline). Validation of ana-lytical methods: methodology (CPMP/ICH/281/95), ICH, London

ISO 3534-1 (1993) International Organization for Standardization (BIPM, IEC, IFCC, ISO,IUPAC, IUPAP, OIML), International vocabulary of basic and general terms in metrology.Geneva

ISO Guide 33 (1989) International Organization for Standardization, Use of certiˇedreference materials. Genova

IUPAC (1994a) Analytical Chemistry Division, Commission on Analytical Nomenclature:Recommendations for the presentation of results of chemical analysis. Prepared for pub-lication by LA Currie and G Svehla. Pure Appl Chem 66:595

IUPAC (1994b) Analytical Chemistry Division, Commission on Analytical Nomenclature,Commission on General Aspects of Analytical Chemistry: Nomenclature of interlabora-tory analytical studies. Prepared for publication by W Horwitz. Pure Appl Chem 66:1903

IUPAC (1995) Analytical Chemistry Division, Commission on General Aspects of Ana-lytical Chemistry: Nomenclature in evaluation of analytical methods including detectionand quantiˇcation capabilities (IUPAC Recommendations 1995). Prepared for publicationby LA Currie. Pure Appl Chem 67:1699

References 307

IUPAC (1998) Analytical Chemistry Division, Commission on General Aspects of Analyti-cal Chemistry: Guidelines for calibration in analytical chemistry. Prepared for publicationby K Danzer, LA Currie. Pure Appl Chem 70:993

IUPAC (2004) Analytical Chemistry Division: Guidelines for calibration in analyticalchemistry. Part 2. Multispecies calibration (IUPAC Technical Report). Prepared for pub-lication by K Danzer, M Otto, LA Currie. Pure Appl Chem 76:1215

IUPAC Orange Book (1997, 2000){ printed version: Compendium of Analytical Nomenclature (Deˇnitive Rules 1997), 3rdedn. Edited by J Incz�edy, JT Lengyiel, AM Ure, A Geleneser, A Hulanicki. Blackwell,Oxford, 1997{ web version (from 2000 on): www.iupac.org/publications/analytical compendium/

Kaiser H, Specker H (1956) Bewertung und Vergleich von Analysenmethoden. FreseniusZ Anal Chem 149:46

Kaiser H (1972) Zur Deˇnition von Selektivit�at, Speziˇt�at und Empˇndlichkeit von Ana-lysenverfahren. Fresenius Z Anal Chem 260:252

Kellner R, Mermet J-M, Otto M, Widmer HM (Eds) (1998) Analytical chemistry. Theapproved text to the FECS curriculum analytical chemistry. Wiley-VCH, Weinheim, NewYork

Massart DL, Vandeginste BGM, Deming SN, Michotte Y, Kaufman L (1988) Chemometrics:a textbook. Elsevier, Amsterdam

Prichard, E, Green, J, Houlgate, P, Miller, J, Newman, E, Phillips, G, Rowley, A (2001)Analytical measurement terminology { handbook of terms used in quality assurance ofanalytical measurement. LGC, Teddington, Royal Society of Chemistry, Cambridge

Sachs L (1992) Angewandte Statistik 7th edn. Springer, Berlin Heidelberg New York

Sharaf MA, Illman DL, Kowalski BR (1986) Chemometrics. Wiley, New York

Taylor JK (1987) Quality assurance of chemical measurements. Lewis Publishers, Chelsea,MI

USP XXII < 1225 > (1990), United States Pharmacopoeia, Validation of compendialmethods

Wegscheider W (1996) Validation of analytical methods. Chap 6 in: G�unzler H (ed)Accreditation and quality assurance in analytical chemistry. Springer, Berlin HeidelbergNew York

Index

Absolute measurement 35, 124Acceptance function 93, 94Accumulation 52Accuracy 141, 183, 184, 283Activation function 166, 168Alternative hypothesis 79Analysis 283, 305Analysis of variance 19, 21, 83, 101, 199

one-way 102, 103three-way 104{107two-way 21, 103{105

Analyte 23, 284Analytical chemistry

deˇnition 1, 3development 2ˇeld of operation 9progress 5repertoire 5

Analytical datadimensionality 59

Analytical evaluation 28, 36Analytical function 33, 59, 284, 289Analytical image 59, 250, 252Analytical information 28

dimensionality 26Analytical measurement 27, 28, 31Analytical method 284Analytical procedure 284Analytical process 13, 14, 284Analytical quantity 32, 124, 284Analytical resolving power 209, 275Analytical result 36, 217, 218, 284Analytical sciences 4Analytical signal 2, 4, 44, 101

appearance 46conversion 47detection 47

generation 46genesis 45, 46time characteristics 45

Analytical technique 178, 284Analytical trinity 5Analytical value 14, 218, 285Andrews plot 245ANOVA: analysis of variance 19, 21, 83,

101, 199Artiˇcial intelligence 246Artiˇcial neural networks 117, 120, 165,

248Assay 285, 305At-line analysis 15Attribute testing 92Autocorrelation 21, 52Autocorrelation function 22, 51

Background 52, 207, 285Background noise 208Backpropagation networks 167, 248Baseline 285Benninghoven matrix 30Bias 66, 142, 184, 285Blank 35, 63, 125, 202, 285Blank correction 75Blank measurement 286Blank sample 286Bootstrapping 149Boxplots 243Box-Wilson optimization method 115Bulk analysis 6

Calibration 28, 67, 123{125, 286by artiˇcial neural networks 165individual three-dimensional 147linear 130orthogonal 140

310 Index

Calibration function 29, 34, 35, 124, 126,127, 186, 286

Calibration model 125general three-dimensional 126linear 130

Calibration points 125Calibration samples 125, 287Categorical quantities 28Cause-effect diagrams 76Certiˇed reference material 68, 125, 141,

148, 287Chemical blank 287Chemical information 43Chemical measurement process 13, 43,

44Chemical shift 60Chemometrics 4Chernoff face 245Classiˇcation 229Classiˇcation methods 235Cluster analysis 229, 231, 234, 246Clustering

hierarchical 232Coding 28, 29, 44, 123Combined signal 48Combined uncertainty 36, 76, 218Comparison

of standard deviations 82of measured values 82

Composite sample 16Concentration function 16{18Concentration proˇle 250, 277Concordancy rate 87Condition number 159, 162Conˇdence band 136Conˇdence interval 66, 72, 73, 127, 134,

136, 147, 218, 287Conˇdence limit 73, 93Control limit 96Control parameters 98Conventional true value 183, 225, 288Convolution 30Correlation 22, 288Correlation analysis 127Correlation coefˇcient 50, 77, 128, 129,

288Correlation matrix 128, 129, 231, 288Correlation time 22Covariance 75, 77

Covariance matrix 128, 129, 231Coverage factor 78, 218Critical level 224Critical limit 224Critical sample mass 19, 20Critical signal-to-noise ratio 207Critical value 90, 201, 202, 204, 209, 220,

288Cross sensitivity 61, 188, 193, 289Cross validation 163Cross-correlation function 50Cusum control 97

Data bases 36Data evaluation 67Data interpretation 229Data matrix 229Data processing 253Decoding 29, 44, 123Deˇnitive measurement 35, 124Dendrogram 232, 246Density function 234Depth resolving power 211, 278Detection capability 206Detection limit 63, 207Detection power 206Determination 289, 305Deviation

additive 67multiplicative 67

Digital ˇltering 52Dimensionality 53, 56, 58Dimensionality reduction 229Dimensions

chemical 53measuring 53physical 53statistical 53

Direct calibration 158Direct imaging 253Discriminant function 230, 235Discriminant variable 230Dispersion factor 74Distribution analysis 6Dynamic analysis 6

Eigenanalysis 229Eigenvalue 159, 231Eigenvector 231Elastic interactions 27

Index 311

Element analysis 29Elemental images 277Encoding 28Enrichment 24Error of the ˇrst kind 87, 91Error of the second kind 79, 87, 91Error propagation 67, 75Euclidean distance 230Evaluation 123Evaluation function 34, 49, 126, 127, 284,

289Exceeding limit 224Experimental calibration 124Experimental design 62, 108, 162Expert system 36, 246Extended uncertainty 77, 203, 218Extraction 24

Factor analysis 229, 231, 239Factor extraction 240Factor loadings 239Factor rotation 241Factor scores 239Factorial design

complete 109, 114fractional 111partial 111two-level 109

False negative rate 87False positive rate 87False-positive 79Fourier transformation 50

two-dimensional 254, 255Frequency domain 50F-test 18{20, 81, 102, 104Fuzzy set theory 37, 150

Genetic algorithms 117, 118, 120, 246,250

Geometrical mean 73, 74, 218, 219Geometrical resolving power 277Global optimization 116, 118Global optimum 113, 116, 118Gradient analysis 21Grid experiments 114Gross sample 16

Hat matrix 163Heterogeneity 16, 17Heteroscedasticity 132, 137

Hidden layer 165Histograms 243, 244Homogeneity 16{18, 20, 104, 289Homogeneity value 20Homoscedasticity 129, 131, 143Hyphenated techniques 25, 289Hypothesis testing 62

Icon plot 244Identiˇcation 7, 34, 124, 274, 289, 305Image analysis 253, 276Image information 277Image processing 253, 255, 256Imprecision 183, 290In-line analysis 14, 15Inaccuracy 184, 269, 290Indirect calibration 150, 157, 158Inelastic interactions 27In�uence factor 60, 61, 101Information 43

one-dimensional 55Information amount 26, 47, 274, 278

potential 275Information capacity 280Information content 266, 267, 270, 271,

273of qualitative analysis 267, 268

Information density 280Information efˇciency 279, 280Information �ow 279, 280Information performance 279, 280Information process 43, 44Information proˇtability 279, 280Inhomogeneity 16, 17, 20, 290Inhomogeneity variance 18Input layer 165Inspection analysis 54Interferences 62, 101, 154, 189Interlaboratory comparisons 62Interlaboratory study 179, 195, 227, 228,

273, 290Interquantile ranges 74Intrinsic energy 27, 30Inverse calibration 157, 159

k nearest neighbours 238Kinetic energy 28Kohonen network 249

Laboratory information managementsystems 248

312 Index

Laboratory performance study 227Laboratory sample 15Laboratory standards 125Latent information 28, 43Latent variable 160Lateral proˇles 277Lateral resolving power 210, 211, 277Latin square design 108Learning rate 167Least squares

linear 137ordinary 131{134orthogonal 132weighted 132

Least trimmed squares 145Level of signiˇcance 218Leverage points 144Library search 36Limit of decision 291Limit of detection 6, 201, 204, 205, 220,

291multivariate 164

Limit of determination 206, 291Limit of discrimination 88Limit of quantiˇcation 201, 206, 220Limit of quantitation 291Limit of speciˇcation 88, 89Limit values 201LIMS 248Line analysis 58Linear calibration 130, 134Linear discriminant analysis 238Linear dynamic range 291Linearity 142, 292Local analysis 6Local frequency 18Local optimum 116

Mass resolving power 210Material certiˇcation study 227Matrix 23, 60, 61, 292Matrix effect 112, 292Matrix in�uence 148Matrix of partial sensitivities 188Mean (mean value) 69, 71, 98Measurand 124, 292Measured result 292Measured value 14, 292Measuring function 29, 47Measuring quantity 292

Measuring sample 14, 23, 24, 26, 27, 147,293

Median 74, 218Median absolute deviation 74Median statistics 145Method performance study 227Metrology 4, 293Micro analysis 6Minimal spanning tree 246Missing data

completion 222, 223Monitoring 21, 293Moving averages 223Multi-range calibration 151Multi-stage techniques 25Multicollinearities 156, 160Multicomponent analysis 6, 155, 189,

273, 293Multicomponent calibration 155, 157Multicomponent sensitivity 188, 189Multifactorial design 62, 108Multilayer perceptron 166Multisignal calibration 152, 154Multisignal evaluation 187Multispecies analysis 293Multivariate analytical images 256Multivariate calibration 157, 162

uncertainty 159validation 162

Multivariate discriminant function 235Multivariate distances 230Multivariate image 254Multivariate similarity 232Multivariate variance and discriminant

analysis 229, 231, 235

Nano analysis 6Natural computation 116Natural design 116Net function 166Neural networks 246Neuron 165, 167Noise 52, 293Noise function 52Nonlinear calibration 134Nonlinear calibration functions 152Normal distribution 69, 70

logarithmic 73Null hypothesis 79, 102

Index 313

Off-line analysis 14On-line analysis 14, 15Optimization 111, 112Optimum 114Order of instruments 59Output function 167Output layer 165

Partial least squares 152, 160Partial sensitivity 61, 289Pattern recognition 21, 236Peak-to-peak noise 181Performance characteristic curve 89Performance characteristics 177Performance parameters 5Pixel 277Plausibility 267Poisson distribution 74Population 293Potential function 234Power spectrum 51Precision 63, 65, 141, 142, 162, 184, 294

of a complete analytical technique180

of a measuring system 180of an analytical procedure 179, 184of an analytical result 180, 184of analytical methods 179of analytical techniques 179of calibration 142of trace analyses 182

Precision analysis 6Prediction band 136Prediction interval 127, 137, 180, 218,

294Prediction rate 88Predictive residual sum of squares 163Prevalency 87Primary sample 16Principal component analysis 140, 152,

160, 187, 229, 231, 239Principal component plot 241Principal component regression 160Probability 86

conditional 86Process analysis 6, 58, 276Process sampling 21Proˇciency test 228, 294Propagation function 166

Qualitative analysis 7, 34, 58, 124, 294Quality assurance 90, 91, 273Quality control chart 96Quality criteria 90Quantitative analysis 58, 124, 294

Radial basis function nets 168Random deviations 65{67Random samples 69Random variable 294Random variations 69Range 98, 295Recalibration 295Recovery 68Recovery function 68, 126, 127, 141, 164Recovery rate 98Recovery study 141Reduction of dimensionality 230Redundancy 275Reference calibration 133Reference data 36Reference material 225, 295Reference measurements 35

direct 124indirect 124

Reference method 133Reference spectra 36, 49Reference value 225, 226Regression 127, 295Regression analysis 21, 127, 138Regression coefˇcient 129, 295Regression model 130, 296Rejection function 93, 94Relative standard deviation 72, 296Reliability 296Repeatability 65, 69, 179, 296Repeatability standard deviation 297Reproducibility 65, 179, 297Reproducibility interval 297Reproducibility standard deviation 297Resampling techniques 204Residual error 104Residual standard deviation 135Resolution 298Resolution limit 298Resolution power 209, 298Response domain 301Response function 298Response surface 113, 114, 117Response variable 298

314 Index

Reverse calibration 157Robust calibration 133, 134, 144, 146Robust regression 144Robustness 63, 112, 195, 196, 198, 299Root mean standard error of prediction

171Round robin test 299Ruggedness 63, 195, 197, 198, 299

Sample 299Sample domain 28, 29, 32, 33, 201, 300Sample preparation 23, 24, 67

chemical 24Sample pretreatment

physical 24Sampling 15, 67, 300

representativeness 19Sampling theorem 253Scanning 253Scattering factor 74Scatterplot matrix 243, 244Schematic plot 243, 244Score images 256Screening 9, 85, 271, 300Screening limit 88Screening test 85, 86, 89Selectivity 159, 162, 189{193, 195, 300Semivariogram analysis 23Sensitivity 35, 61, 63, 125, 185, 186, 191,

199, 217, 300Sensitivity coefˇcient 35Sensitivity factor 124Sensitivity functions 152Sensitivity matrix 158, 188, 192, 301Separation 24Sequential analysis 93, 94Signal 43, 301

ˇne structure 52, 60form 48hidden 46latent 45manifest 45pattern 48shape 52

Signal averaging 52Signal background 51Signal domain 28, 29, 32, 33, 47, 201, 301Signal function 30, 33, 44, 45, 47, 48,

50{52, 58, 180, 207, 301three-dimensional 55

two-dimensional 54Signal generation 14, 43, 60Signal half width 47, 51, 209Signal intensity 28, 50, 51, 60, 61Signal model 62Signal parameters 51, 60Signal position 28, 36, 50, 51, 60Signal recordings 49Signal resolving power 47, 209Signal validation 44Signal-to-noise ratio 52, 180{182, 184,

207, 208, 301Signiˇcance level 203Simplex optimization 115, 117Simulated annealing 117Slope 125Soft modeling methods 160Spatial interpolation 223Spatial resolving power 210, 212Species images 250Speciˇcity 63, 189{193, 195, 301Speciˇcity function 195Specimen 302Spectra interpretation 248Spectral resolving power 210Spline function 151Spline interpolation 223Standard 34Standard addition method 133, 146, 147Standard additions 68Standard deviation 69, 72, 74, 75, 98, 302

of noise 181Standard error 302Standard error of prediction 162Standard normal distribution 70Standard operating procedure 180, 302Standard samples 141Standard value 90Standardized data 230Standards 125Stimulus 302Stochastic process 22, 23Stochastic sampling 21Structure analysis 7, 9, 29, 124Subsample 16Supervised learning 235Surface analysis 58Surface resolving power 211, 277Systematic deviations 66, 67

Index 315

Target value 90, 95, 98t-distribution 70Temporal resolution power 211Test 302

for normal distribution 80for outliers 81for trend 80statistical 78

Test efˇciency 87Test kits 86Test portion 16Test sample 15, 16, 24, 26Test spectra 49Theory of fractals 59Thresholds 201Time domain 50Time resolving power 276Time series analysis 21Tolerance limits 92Total sensitivity 303Trace analysis 6Traceability 125, 126, 303Trackability 303Trans-line analysis 15Transfer function 166, 167Trend surface analysis 223True negative rate 86True positive rate 86True signal function 30, 52True value 66, 183, 303Trueness 164, 183, 184, 269, 303t-test

generalized 83

Ultra trace analysis 6

Uncertainty 17, 18, 36, 65, 67, 76, 204,205, 269

at critical value 204at limit of detection 205at limit of quantiˇcation 205of an analytical result 304

Uncertainty interval 66, 78, 202, 217, 225,227

unsymmetrical 219Uncertainty of measurement 75, 180,

304Uncertainty of sampling 19Unsupervised pattern recognition 231

Validation 68, 90, 140, 190, 304Validation function 141Variable 304Variable testing 92Variance 71, 102, 104, 305

common 240residual 240true 240unique 240

Variance function 144Variance-covariance matrix 231Volume resolving power 211, 278Voxel 278

Warning limit 96Wavelength selection 120Weighted calibration 144Working range 305

z-scores 228

Glossary of Analytical Terms - Home - Springer978-3-540-35990...Glossary of Analytical Terms...

Documents

Transcript of Glossary of Analytical Terms - Home - Springer978-3-540-35990...Glossary of Analytical Terms...