
™hman, J., Geladi, P., & Wold, S. (1990). Residual bilinearization. Part 2: application to HPLCdiode array data and comparison with rank annihilation factor analysis. J.Chemom., 4, 135–146.
Abstract: The PLSresidual bilinearization (PLSRBL) approach to background correction presented in part 1 of this work is demonstrated with an example from HPLC with diode array detection. Data are also evaluated with generalized RAFA and results are compared. threeway matrix
Keywords: analysis; annihilation; Array; Array Detection; Background; Background Correction; bilinearization; comparison; correction; data; detection; diode array; Diode Array Detection; DiodeArray; DiodeArrayDetection; Example; factor; factor analysis; generalized; Hplc; Matrix; Part; Part 2; Rafa; Rank; rank annihilation; Residual; Residual Bilinearization; three way; threeway; threeway; Work


™hman, J., Geladi, P., & Wold, S. (1990). Residual bilinearization. Part 1: theory and algorithms. J.Chemom., 4, 79–90.
Abstract: When using hyphenated methods in analytical chemistry, data obtained for each sample are given as a matrix. When a regression equation is set up between an unknown sample and a calibration set the residual is a matrix, R. The regression equation usually solved by least squares on R. If the sample contains some constituent not calibrated for, this approach is not valid. In this paper an algorithm is presented which partition R into one matrix of low rank corresponding to the unknown constituents, and one random noise matrix to which the least squares restrictions are applied. Properties and possible applications of the algorithm are also discussed. In part 2 is an example from HPLC with DAD and the results are compared with generalized RAFA. PLS, threeway matrix, GRAFA
Keywords: Algorithm; algorithms; analytical; analytical chemistry; AnalyticalChemistry; applications; bilinearization; calibration; calibration set; chemistry; Constituents; Dad; data; Equation; Example; generalized; Grafa; Hplc; least; Least square; least squares; LeastSquares; Matrix; method; methods; Noise; Paper; Part; Part 2; Partition; Pls; Property; Rafa; random; Rank; regression; Residual; Residual Bilinearization; sample; set; squares; theory; three way; threeway; threeway


™hman, J., Lindberg, W., & Wold, S. (1988). Data reduction of bilinear matrices prior to calibration. Anal.Chem., 60, 2756–2760.
Abstract: A PLS calibration model is used for quantitative analysis ot bilinear data. In order to reduce the number of independent variables (X block) of the PLS model, the first few principal components for each chromatogram were calculated and the resulting scores were used as X variables. The actual number of principal components was determined by crossvalidation. Application of the method is demonstrated by using both simulated and real data, the latter an analysis of phenanthrene and anthracene mixtures by HPLC with an UVDAD. Results show good predictive properties and stability compared with normal PLS
Keywords: analysis; Bilinear; bilinear data; calibration; Calibration Model; component; components; cross validation; crossvalidation; crossvalidation; data; data reduction; Hplc; Matrices; method; Mixture; mixtures; model; normal; Number; number of principal components; Order; Pls; PLS calibration; Pls1; principal; principal component; principal components; Property; Quantitative; quantitative analysis; QuantitativeAnalysis; Reduction; score; Scores; Stability; Variable; variables; Wa


Zwanziger, H. W., Einax, J., & Geiss, S. (1997). Chemometrics in environmental analysis. Weinheim ; Cambridge : VCH Verlagsgesellschaft.
Keywords: analysis; Chemistry,Analytic – Mathematics; Chemistry,Analytic – Statistical methods; chemometric; chemometrics; Environmental; environmental analysis; Environmental chemistry – Mathematics; Environmental chemistry – Statistical methods; EnvironmentalAnalysis


Zwally, H. J., & Giovinetto, M. B. (1997). Annual sea level variability induced by changes in sea ice extent and accumulation on ice sheets: an assessment based on remotely sensed data. Surveys In Geophysics, 18(23), 327–340.
Abstract: Changes of mean annual net accumulation at the surface on the grounded ice sheets of East Antarctica, West Antarctica and Greenland in response to variations in sea ice extent are estimated using gridpoint values 100 km apart. The data bases are assembled principally by bilinear interpolation of remotely sensed brightness temperature (Nimbus5 ESMR, Nimbus7 SMMR), surface temperature (Nimbus7 THIR), and surface elevation (ERS1 radar altimeter). These data, complemented by field data where remotely sensed data are not available, are used in multivariate analyses in which mean annual accumulation (derived from firn emissivity) is the dependent variable; the independent variables are latitude, surface elevation, mean annual surface temperature, and mean annual distance to open ocean (as a source of energy and moisture). The last is the shortest distance measured between a grid point and the mean annual position of the 10% sea ice concentration boundary, and is used as an index of changes in sea ice extent as well as of mean concentration. Stepwise correlation analyses indicate that variations in sea ice extent of +/50 km would lead to changes in accumulation inversely of +/4% on East Antarctica, +/10% on West Antarctica, and +/4% on Greenland. These results are compared with those obtained in a previous study using visually interpolated values from contoured compilations of field data; they substantiate the findings for the Antarctic ice sheets (+/4% on East Antarctica, +/9% in West Antarctica), and suggest a reduction by one half of the probable change of accumulation on Greenland (from +/8%). The results also suggest a reduction of the combined contribution to sea level variability to +/0.19 mm a(1) (from +/0.22 mm/a)
Keywords: Antarctica; Base; Bases; Bilinear; Boundary; Compilation; concentration; correlation; data; Distance; East Antarctica; estimated; Field; Greenland; Grid; index; Indexes; interpolation; Lead; Level; Mean; Moisture; multivariate; multivariate analyses; Ocean; Open; Point; Position; Radar; Reduction; Sea; SeaIce; Stepwise; studies; Surface; temperature; value; Values; Variability; Variable; variables; Variation; West


