MULTIVARIATE DETECTION LIMITS ESTIMATORS

Abstract The theoretical development of the concept of detection limit in multicomponent systems has only begun very recently and its practical applications are limited to a few specific techniques such as emission spectrometry (ICP-OES) or high performance liquid chromatography (HPLC-DAD). This paper critically reviews the theoretical approaches advanced up to the present, describes the hypotheses and theoretical backgrounds on which they are based and discusses the advantages and limitations of the different techniques and derived estimators. The connection with the derivation of the detection limits based on the var(cun) variance associated to the predicted response of an individual observation in regression theory is introduced and finally, some suggestions as to potential areas of interest for future development are envisaged.

[1]  H. R. Keller,et al.  Heuristic evolving latent projections: resolving two-way multicomponent data. 2. Detection and resolution of minor constituents , 1992 .

[2]  N. Cressie Limits of detection , 1994 .

[3]  Andre. Hubaux,et al.  Decision and detection limits for calibration curves , 1970 .

[4]  A. Lorber,et al.  Curve resolution and figures of merit estimation for determination of trace elements in geological materials by inductively coupled plasma atomic emission spectrometry , 1987 .

[5]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[6]  C. Andrew. Clayton,et al.  Detection limits with specified assurance probabilities , 1987 .

[7]  M. Deaton,et al.  Response Surfaces: Designs and Analyses , 1989 .

[8]  Evaluation of detection limit estimators , 1988 .

[9]  L. A. Currie,et al.  LIMITS FOR QUALITATIVE DETECTION AND QUANTITATIVE DETERMINATION. APPLICATION TO RADIOCHEMISTRY. , 1968 .

[10]  Norman R. Draper,et al.  Applied regression analysis (2. ed.) , 1981, Wiley series in probability and mathematical statistics.

[11]  Yizeng Liang,et al.  Heuristic evolving latent projections: resolving two-way multicomponent data. 1. Selectivity, latent-projective graph, datascope, local rank, and unique resolution , 1992 .

[12]  Terje V. Karstang,et al.  Estimation of prediction error for samples within the calibration range , 1992 .

[13]  A. Lorber Error propagation and figures of merit for quantification by solving matrix equations , 1986 .

[14]  Avraham Lorber,et al.  The effect of interferences and calbiration design on accuracy: Implications for sensor and sample selection , 1988 .

[15]  D. Massart Chemometrics: A Textbook , 1988 .

[16]  Agnar Höskuldsson,et al.  Determination of a multivariate detection limit and local chemical rank by designing a non‐parametric test from the zero‐component regions , 1993 .

[17]  M. C. Ortiz,et al.  DETARCHI: A program for detection limits with specified assurance probabilities and characteristic curves of detection , 1994 .

[18]  Avraham Lorber,et al.  Estimation of prediction error for multivariate calibration , 1988 .

[19]  T. Næs,et al.  Principal component regression in NIR analysis: Viewpoints, background details and selection of components , 1988 .

[20]  Multivariate detection limits for selected ion monitoring gas chromatography — mass spectrometry , 1988 .

[21]  B. Kowalski,et al.  Chemical piezoelectric sensor and sensor array characterization , 1986 .

[22]  Anita Singh,et al.  Multivariate decision and detection limits , 1993 .

[23]  E. V. Thomas,et al.  COMPARISON OF MULTIVARIATE CALIBRATION METHODS FOR QUANTITATIVE SPECTRAL ANALYSIS , 1990 .