A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Analytical Terms :

  • A

      Accuracy “Closeness of agreement between the result of a measurement and a true valueof the measurand”. Analysis (of a sample) Investigation of a sample to identify —> Quotation from ANAL and/or determine (an) analyte(s) or as- CHEM [1975] at the end of the say a material. Analyte “The chemical entity being investigated (qualitatively or quantitatively)”. Analytical function (evaluation function) Inverse of the calibration function, x = f—'(y), describing the dependence of 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 links of a given analytical technique with par- ticular excitation and detection. Analytical procedure “Set of operations, described specifically, used in the performance of particular analytical measurements according to a given method”. Analytical process Logic sequence of objects linked by gen- eral analytical standard operations. Analytical result Analytical value attributed to a measur- and, obtained by measurement and com- pleted by information on the uncertainty of measurement. Analytical quantity “Particular quantity subject to analytical measurement”. Analytical technique “Generic analytical application of a sci- entific principle”. According to PRITCHARD et al. [2001] ISO 3524-1 [1993] — Fig. 7.1 ISO 3524-1 [1993] PRITCHARD et al. [2001] — Fig. 7.1 —> Fig. 2.1 According to ISO 3524-1 [1993] —> 8.1 According to ISO 3524-1 [1993] — Measurand According to PRITCHARD et al. [2001] — Fig. 7.1 Assay Determination of how much of a sample is the material indicated by the name. Analytical value Magnitude of an analytical quantity, x, measured at test samples on the one hand and given for reference samples used for calibration on the other hand. — Quotation from ANAL CHEM [1975] at the end of the Glossary — eg. analysis of ores

  • B

      Background (instrumental background, background signal) Instrumental background is the null sig- nal, obtained in the absence of any analyte- or interference-derived signal. Baseline “Summation of the instrumental back- ground plus signals in the analyte (peak) region of interest due to interfering species”. Bias “The difference between the expectation of the test results and an accepted refer- ence value”. Blank A value, yg, obtained my measuring a blank 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 (analyte blank). — IUPAC [1995]; CurRIE [1999] —> Background may be set to zero, on the average, for certain instruments IUPAC [1995]; Currig [1999] According to GAT VIII [1997] — IUPAC [1995]; CurRIE [1999] — Background — Baseline — Chemical blank Blank measurement Procedure by which a measured value is obtained with a sample in that the analyte of interest is intentionally absent. Blank sample A sample whose analyte concentration is below the limit of decision of the analyt- ical procedure being used.

  • C

      alibration Set of operations that establish, under specified conditions, the relationship be- tween values of quantities indicated by a measuring system and the corresponding values of quantities represented by a ma- terial both in form of reference materials and samples. In a wider sense, calibra- tion represents a set of operations that establish relationships between quanti- ties in the sample domain with quantities in the signal domain, viz y = f(x) and z= f(Q). Calibration function Equation for the estimation of the values of a measuring quantity from given val- ues of a analytical quantity. The calibra- tion function may be known a priori by natural laws or estimated experimentally by means of calibration samples. The calibration function represents that segment of the response function that is chosen for estimating the analytical value of an unknown sample. According to PRITCHARD et al. [2001]; TayLor [1987]; SHARAF et al. [1986] > According to PRITCHARD et al. Blank sample [2001] => bbibidbd Lilt Blank measurement ISO 3524-1 [1993] GAT IV [1996] PRITCHARD et al. [2001] IUPAC [1998] Sect. 6.1 Sample domain Signal domain PRITCHARD et al. [2001] IUPAC [1998] SHARAF et al. [1986] Sensitivity Response function 287 Calibration samples Set of samples characterized by accurate —> Pritcuarn et al. [2001] and precise values of the measurand. In —> Reference material a concrete case, calibration samples may —* Certified reference material be portions of (certified) reference mate- rials, in-house reference materials (labo- ratory standard samples), or spiked sam- ples, and, in addition, blank samples. Certified reference material (CRM) “A reference material, accompanied by a __1SO 3524-1 [1993] certificate, one or more of whose prop- — GAT IV [1996] erty values are certified by a procedure —* Pritcuarp et al. [2001] which establishes traceability to an accu- rate realization of the unit in which the property values are expressed, and for which each certified value is accompa- nied by an uncertainty at a stated level of confidence”. Chemical blank (analyte blank) “Blank which arises from contamination JUPAC [1995], Currie [1999] from the reagents, sampling procedure, or sample preparation steps which corre- spond to the very analyte being sought”. Coefficient 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. Confidence interval (CI) Statistical interval, e.g., of a mean, Y, — Sect. 7.5 cnf) =y+ AY cup which express the — Critical value uncertainty of measured values. CIs are ~” Prediction interval applied for significance tests and to es- tablish quantities for limit values (CV). Conventional true value “Value attributed to a particular quantity 1SO 3534-1 [1993] and accepted, sometimes by convention, — True value as having an uncertainty appropriate for a given purpose”. Correlation Stochastic relationship between random variables in such a way that one depends on the other. The degree of relationship may be estimated by the correlation co- efficient. Correlation coefficient The correlation coefficient, ty, is given —> Sect. 6.1.3 by the covariance of two random vari- X_ The correlation coefficient ables x and y, cov(x, y) = Sx, divided by _ is not of any relevance in cal- the standard deviations s, and s,, see Eq. ibration, as a rule. This is be- (6.3). The correlation coefficient becomes ‘#™S¢ only the measured value Ty = 0 if there is no relationship between 1s a Tancom vaniaie ane, in d d —-+1 if there exist a contrast, the analytical value is x and y, and ry = i xi : d inistic d d a fixed value and not selected stringent deterministic dependence. randomly Correlation matrix Matrix formed by a set of correlation co- — Eqs. (6.4) and (8.14) efficients related to m variables in multi- variate data sets, R = (x,,x,)- It is relevant in multicomponent analysis. Critical value (CV) Limit in the signal domain, esti- Eurtica and Danzer [2006]; mated from the average blank plus Currie [1999] its uncertainty, generally according —* Sect. 7.5 toy =V—,+UG%g,), in analytical —7 Fig 78 chemistry frequently according to ~~ Decision limit Ye = Vpz + 35pr- If the critical value is exceeded, the respective analyte is reliably detected (except for a remaining risk of error a). Therefore, the CV stands for the guarantee of presence of an analyte. Cross sensitivity (partial sensitivity) Dependence of the measured value (sig- nal intensity), y4, from other constituents than the analyte A, present in the mea- suring sample, quantitatively expressed by the respective partial differential quo- tient.

  • D

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

  • E

      Evaluation function (analytical function) Inverse of the calibration function, x = f—'(y), describing the dependence of the analytical values from the mea- sured values, being so the basis of an- alytical evaluation.

  • H

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

  • I

      Identification Recognizing of (an) unknown con- stituent(s) in an analytical test sample. In contrast, by qualitative analysis it is tested whether (a) known constituent(s) are present or absent. Imprecision A quantitative term to describe the (lack —>» IUPAC [1995]; CurRIE of) “precision” of an analytical procedure [1999] (e.g. by standard deviation). —> Precision — Imprecision of analytical results, see Sect. 7.1 — Standard deviation Inaccuracy A quantitative term to describe the (lack —>» IUPAC [1995]; CurRIE of) accuracy of an analytical procedure [1999] which comprises the imprecision andthe —* Accuracy bias. xX Inaccuracy should not be confused with uncertainty, see IUPAC [1994a] Inhomogeneity “Term used to describe situations where the analyte is unevenly distributed through the sample matrix”. The degree of inhomogeneity may be characterized quantitatively by Eq. (2.9) the value of which becomes negative with the transition from homogeneity to inho- mogeneity. Interlaboratory study “A study in which several laboratories measure a quantity in one or more iden- tical portions of homogeneous, stable materials under documented conditions, the results of which are compiled into a single report”. According to the evaluation types, it is differentiated between: (1) Method-performance studies. (2) Laboratory-performance studies. (3) Material-certification studies. — Pritcuarp et al. [2001] — Sect. 2.1 X The term inhomogeneity should not be confused with heterogeneity IUPAC [1994b] — A minimum of five labora- tories should be used to provide meaningful statistical conclu- sions from interlaboratory stud- ies — Sect. 8.2.4

  • L

      Limit of decision (“30-limit of detection”) The analytical value (e.g. the concen- tration) that corresponds to the critical value. The limit of decision is of minor importance in analytical chemistry be- cause the detection at this level of con- centration succeeds only in 50% of all cases. Limit of detection (LD) The analytical value, xzp, that always produce a signal which can be distin- guished from the blank (except for a re- maining risk of error f). LD is the limit in the sample domain (an- alyte domain). It characterizes analytical procedures, in particular with regard to the limit concentration that can be de- tected. Therefore, the LD stands for the guarantee of absence of an analyte. EHRLICH and DANZER [2006]; IUPAC [1995]; Currig [1999] — Sect. 7.5 — Fig. 7.7 —> Critical value — Detection limit XX The decision limit should not be used as a_ perfor- mance characteristic of analyt- ical methods and also not as a limit of guarantee of an analyte EHRLICH and DANZER [2006]; IUPAC [1995]; Currig [1999] — Sect. 7.5 — Fig. 7.7 — Critical value Limit of determination — Limit of quantitation Limit of quantitation (LQ) An analytical value, xg, above which quantitative 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 = x,q/Ax;q, the limit of quantifi- cation can be estimated by Eqs. (7.48) and (7.49). Linear dynamic range The range of concentration in which the response varies linearly with the analyte concentration. EHRLICH and DANZER [2006]; IUPAC [1995]; Currig [1999] — Precision — For factual reasons, the limit of quantification cannot be lower than the limit of detection — The declaration of preci- sion must always be given be- cause it is an inherent compo- nent of LQ — SuararF et al. [1986] Linearity Ability of an analytical method to give a response which depends linearly on the analyte concentration.

  • M

      Matrix All of the constituents of a sample except the analyte. The matrix is the carrier of the analyte. Matrix effect Influence of one or more matrix con- stituent(s) on the analyte under study. Matrix influences may affect the analyte signal directly by interferences or indi- rectly by signal depression or amplifica- tion. Measurand “Particular quantity subject to measure- ment”. Measured result Measured value, obtained by measure- ment and completed by information on the uncertainty of measurement. Measured value “Outcome of an analytical measurement” or “value attributed to a measurand”. A measured value is a “Magnitude of a measuring quantity generally expressed as a unit of measurement multiplied by a number”. Measuring quantity “Attribute of a phenomenon ... that may be distinguished qualitatively and deter- mined quantitatively”. — Pritcuarp et al. [2001] — SuararF et al. [1986] — IUPAC OranceE Book — [1997, 2000] — Analyte —> Sect. 3.5 — Egs. (3.12)-G.14); (3.16); (3.17) ISO 3524-1 [1993] — Sect. 8.1 — Measured value — Uncertainty 1SO 3524-1 [1993] IUPAC [1995]; Currig [1999] — Measuring quantity ISO 3524-1 [1993] Measuring sample Sample that is directly introduced into analytical measurement. A measured sample is created from a test sample by conversion into a measurable form by means of a procedure of sample prepa- ration. Metrology “Science of measurement”. Monitoring Continuous or repeated observation, measurement, and evaluation of a pro- cess in a certain field of application (e.g., environmental surveillance, health checking, foodstuff inspection, quality assurance in manufacturing), according to given schedules in space and time. Simultaneous determination of several analytes (species) by means of a multi- component sensing technique or hyphen- ated techniques.

  • N

      Noise Fluctuations of the baseline- or back- ground record of an (analytical) instru- ment. Noise do not provide meaningful information, on the contrary, it degrades the quality of signals and, therefore their detectability.

  • P

      Population “Finite or infinite 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 p and the standard deviation o. Precision “The closeness of agreement between in- dependent test results obtained under stipulated conditions”. “The precision of a set of results of mea- surements may be quantified as a stan- dard deviation”. Prediction interval (PI) Statistical interval, e.g., of a mean, x, prd(x) = X + AXpq, that express the un- certainty of analytical values which are predicted on the basis of experimental calibration. PIs are applied for signifi- cance tests and to establish quantities for limit values (LD, LQ). Proficiency test “Study of laboratory performance by means of ongoing interlaboratory test comparisons“.

  • Q

      Qualitative analysis Testing whether (a) known constituent(s) are present or absent in test samples. In contrast, identification means recog- nizing of (an) unknown constituent(s) in a test sample. Quantitative analysis Determination of the amount(s) of (an) analyte(s) in a test sample.

  • R

      Random variable A quantity that appears in a random ex- periment. Random variables relate events into a set of values. Range (in the analytical sense) “The interval between the upper and the lower concentration of the analyte in the sample for which it has been determined that the method is applicable”. Range (in the statistical sense) Difference between the greatest and the smallest values of a series of measure- ments. Recalibration Updating of a calibration model in the case that details of the analytical proce- dure are changed. Reference material “A material or substance one or more of whose property values are sufficiently homogeneous and well established to be used for the calibration of an apparatus, the assessment of a measurement method or for assigning values to materials”. Regression Statistical method to model a mathemat- ical equation that describes the relation- ship between random variables (usually x and y). The goal of regression analysis is both modelling and predicting. Regression coefficients (regression parameter) Coefficients of the predictors in a regres- sion model, e.g., a, and b, or ay and by, respectively, in linear regression models. Regression model Mathematical model that describes the relationship between random variables (usually x and y) by means of regres- sion coefficients and their uncertainties as well as uncertainties of model and the prediction. In linear regression there are two differ- ent models: that of the prediction of y from x J =a, + b,x (6.6) and that of the prediction of x from y £=ay+by (6.7) Relative standard deviation (RSD) Standard deviation expressed as a frac- tion of the mean $;.) =s/x. RSD is a dimensionless quantity; sometimes it is multiplied by 100 and expressed as a per- centage. 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 the results of successive measurements of the same measurand carried out under the same conditions of measurement” (Pre- cision under repeatability conditions). Repeatability may be expressed quanti- tatively in terms of suitable dispersion characteristics. Repeatability standard deviation (Srepeat) Experimental standard deviation ob- tained from a series of n measurements under repeatability conditions. Repeatability interval (repeatability limit) A confidence interval representing the maximum permitted difference between two single test results under repeatabil- ity conditions: r= ne . Srepeat Reproducibility (of results of measurements) “Closeness of the agreement between the results of measurements of the same measurand carried out under changed conditions of measurement” (Precision under reproducibility conditions). Reproducibility may be expressed quan- titatively in terms of suitable dispersion characteristics. Reproducibility standard deviation (Srep;o) Experimental standard deviation ob- tained from a series of measurements un- der reproducibility conditions. Reproducibility interval (reproducibility limit) A confidence interval representing the maximum permitted difference between two single test results under repro- ducibility conditions: R= tow 2 * Srepro — Reproducibility conditions are characterized by changing conditions such as: observer, measuring instrument, condi- tions of use, location, time, but applying the same method — Reproducibility standard deviation — Reproducibility interval — Pritcuarp et al. [2001] — The number of measure- ments should be sufficiently large to estimate a represen- tative reproducibility standard deviation — Pritcuarp et al. [2001] — In the given formula, t,_2» is the quantile of the t- distribution (the degrees of freedom v relates to the num- ber of replicates by which Syepro has been estimated) Resolution Process by which a composite signal is — Sect. 6.4.1 split up into individual forms. The reso- — Sect. 7.6 lution can be related to: Gi) Signal overlappings and fine struc- ture (z-scale) Gi) Signals in close succession in time and space Resolution limit The smallest difference Az at which two — Suarar et al. [1986] adjacent signals can be separately ob- —> In case (ii) of resolution of served, i.e., their overlap does not exceed the resolution problem, At and a threshold of 50% of the individual pro- 4! are the crucial parameters files. — Sect. 7.6 Resolution power Ability of an analytical procedure to de- — Suarar et al. [1986] tect signals of small differences as sep- —* Sect. 7.6 arate signals. Resolution power is in- versely proportional to resolution limit, e.g. R= z/Az Response Output of an analytical system as a reac- —>» Stimulus tion to a certain stimulus. —> The output may be an ob- servable or measurable effect Response function Relationship between the response of the — Suarar et al. [1986] analytical system and the amount of ana- —* Calibration function lyte. The overall response function is fre- quently nonlinear. Response variable (dependent variable) — Measuring quantity, measured value Robustness Property of an analytical procedure that indicates insensitivity against changes of known operational parameters on the re- sults of the method and hence its suit- ability for its defined purpose. Round robin test —> interlaboratory study Ruggedness Property of an analytical procedure that indicates insensitivity against changes of known operational variables and in ad- dition any variations (not discovered in intra-laboratory experiments) which may be revealed by inter-laboratory studies.

  • S

      ample (in the analytical sense) Portion of the object under study (the material submitted for analysis). A sample consists of the analyte and the matrix. Sample (in the statistical sense) “Subset of a population that is collected in order to estimate the properties of the underlying population”, e.g., the sample parameters mean X and standard devi- ation s. In the ideal case of representa- tive sampling, the sample parameter fit the parameter of the population p and oO, respectively. — Burns et al. [2005] — ICH [1996] — Robustness may be quan- tified by means of quantities characterizing signal effects — Eq. (7.31) — Burns et al. [2005] —> Ruggedness may be quan- tified by means of quantities characterizing signal effects — Eq. (7.33) — Pritcuarp et al. [2001] —> There are various types of samples within given sampling schemes, e.g., bulk samples > primary samples > gross sam- ples > subsamples > test sam- ples > measuring samples — Fig. 2.4 Sample domain (analyte domain) Field of analytical operation that is char- acterized by samples’ properties such as type of analytes, Q, and their amount, xg. The transition to signal domain is done by calibration and analytical mea- surement. Sampling “Sequence of selective and non-selective operations ending with the selection of one or several test portions submitted to the analytical process in their entirety. Their physical properties (maximum par- ticle size, mass, etc) are specified in the analytical procedure.” Sampling covers sampling (in the narrow sense) and sam- ple reduction. Screening Testing of (a large number of) objects in order to identify those with particu- lar characteristics. Selectivity The extent to which n given analytes can be measured simultaneously by (a least) n sensors (detecting channels) without interferences by other components and, therefore, can be detected and deter- mined independently and undisturbedly. Sensitivity “Change in the response of a measuring instrument divided by the correspond- ing change in the stimulus”. In analytical measurements is this, in fact, the differ- ential quotient of the measured value to the analytical value. — Fig. 2.12 — Signal domain Sensitivity matrix (Matrix of partial sensitivities) Matrix that contains all the sensitivities and cross sensitivities of a multicompo- nent (multidetector) analytical system. Signal “Response of a device (usually an instru- ment or a module of an instrument) to certain stimuli”. A signal is characterized by at least three parameters: position, in- tensity, and width (symmetry, shape). Signal domain (response domain) Field of analytical operation that is char- acterized by signal properties such as sig- nal position, z, and signal intensity, y,. The transition to sample domain is done by analytical evaluation (signal decod- ing). Signal function Record of signal intensity in dependence of the signal position over a certain range of the z-scale: y = f(z). Signal-to-noise ratio Measure of the precision of signal mea- surement, expressed mostly by the ratio of the net signal value to a noise param- eter (standard deviation or peak-to-peak distance). Specificity The extent to which one individual ana- lyte can be measured undisturbedly in a real sample by a specific reagent, a par- ticular sensor or a comparable specific measuring system. — Kaiser [1972]; Danzer [2001] — Sect. 7.2 — Eq. (7.17) — Cross sensitivity SHARAF et al. [1986] — Sect. 3.3 — Fig. 3.6 — Fig. 2.12 — Sample domain — Sect. 7.1, Fig. 7.2 — Eas. (7.1)-(7.6) — Kaiser [1972]; Danzer [2001] — Sect. 7.3 — Eq. (7.26) — Selectivity X Specificity should not be merged with selectivity Specimen Fraction of a lot (batch sample) taken without respecting the rules for sampling correctness or under unknown condi- tions. Standard deviation (SD) Dispersion parameter for the distribution of measured values, sy, or analytical re- sults, s,, for a given sample or the pop- ulation, oy and oy. The SD is the square root of the variance. Standard error The term “standard error” is not ex- plicitly introduced. It is used sometimes (a) synonymously for standard deviation and (b) for the residual standard devia- tion in modelling and calibration. Stimulus Property of an analytical system to pro- duce a response of an observation- or measuring system. Rousing effect of an analyte 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”. Process of analyzing the sample to rec- ognize (an) analyte(s) and/or determine the amount(s) of (an) analyte(s). Gy [1992] — IUPAC [1995]; CurRIE [1999] — Sacus [1992] — Drxon and Massey [1969] — Sect. 4.1.2 — Eas. (4.12)-(4.14) — Sacus [1992] — FRANK and ToDESCHINI [1994] > The term standard error should be avoided EPA [2001] — Pritcuarp et al. [2001] — Fig. 7.1 — Pritcuarp et al. [2001] — Quotation from ANAL CHEM [1975] at the end of the Glossary

  • T

      Total sensitivity (total multicomponent sensitivity) Sensitivity of a multicomponent analysis. In the simplest case it is given by the determinant of the sensitivity matrix. Traceability “The property of a result of measurement whereby it can be related to appropri- ate standards, generally international or national standards, through an unbroken chain of comparisons”. Trackability “The property of a result of a measure- ment whereby the result can be uniquely related to the sample”. True value “Value consistent with the definition of a given particular quantity” and “value which characterizes a quantity perfectly defined in the conditions which exist when that quantity is considered”. Trueness “Closeness of agreement between the av- erage value obtained from a large series of test results and an accepted reference value”. Trueness has been referred to as “accu- racy of the mean”. — SuararF et al. [1986] — Massart et al. [1988] — Sect. 7.2 — Eqs. (7.18)-(7.20) GAT I [1996] —> ISO 3435-1 [1993] —> All the standards used should have stated uncertainties GAT I [1996] ISO 3534-1 [1993] GAT III [1996] — Conventional true value — Sect. 7.1 IUPAC ORANGE Book [1997, 2000] —> COoDEX ALIMENTARIUS COMMISSION [1997] — Sect. 7.1.3

  • U

      Uncertainty of measurement “Parameter, associated with the result of a measurement, that characterizes the dispersion of the values that could rea- sonably be attributed to the measurand”. The uncertainty should combine both statistical and non-statistical contribu- tions to the variation of the measured values which may occur in all steps of the analytical process. Uncertainty of an analytical result Interval, e.g., of a mean, U(x), that ex- press the uncertainty of analytical values considering statistical and non-statistical variations within the measurement pro- cess plus uncertainties of experimental calibration.

  • V

      alidation (of an analytical method) “Process by which it is established, by laboratory studies, that the performance characteristics of the method meet the requirements for the intended analytical applications”. Variable “Characteristic of an object that may take on any value from a specified set”. ISO 3524-1 [1993] EURACHEM [1995] GAT I [1996] — The uncertainty of mea- surement may be expressed by the 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) — Sect. 4.2 — Eq. (4.32) 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 FRANK and TODESCHINI [1994] —> There are several types of variables, e.g., categorical, dependent and independent, experimental and_ theoretical, manifest and latent, random, standardized variables Variance Dispersion parameter for the distribution — IUPAC [1995]; Currir of measured values, Sy» or analytical re- [1999] . — Sacus [1992]

  • W

      Working range —> Range (in the analytical sense)