TENSORIAL RESOLUTION:A DIRECT TRILINEAR DECOMPOSITION

Modern instrumentation in chemistry routinely generates two-dimensional(second-order)arrays of data.Considering that most analyses need to compare several samples,the analyst ends up with a three-dimensional(third-order)array which is difficult to visualize or interpret with the conventional statisticaltools.Some of these data arrays follow the so-called trilinear model,(?)These trilinear arrays of data are known to have unique factor analysis decompositions which correspondto the true physical factors that form the data,i.e.given the array (?),a unique solution can be found inmany cases for each order X,Y and Z.This is in contrast to the well-known second-order bilinear datafactor analysis,where the abstract solutions obtained are not unique and at best cannot be easilycompared with the underlying physical factors owing to a rotational ambiguity.Trilinear decompositions have had the disadvantage,however,that a non-linear optimization withmany parameters is necessary to reach a least-squares solution.This paper will introduce a method forreducing the problem to a rectangular generalized eigenvalue-eigenvector equation where the eigenbectorsare the contravariant form(pseudo-inverse)of the actual factors.It is shown that the method works wellwhen the factors are linearly independent in at least two orders(e.g.X_(jr),and Y_(jr) are full rank matrices).Finally,it is shown how trilinear decompositions relate to multicomponent calibration,curve resolutionand chemical analysis.     

Zonation and Evolution of a Rocky Bottom Hydroid Community*

Abstract. .The distribution in space and time of a hydroid community on a vertical rocky substratum is studied from the surface to 20 m depth. The role of the major environmental factors (light, water movement, temperature) in determining the zonation is evaluated. The results are compared with the main zonation models of Mediterranean benthos. The autecology of the collected species is briefly described.     

AN IMPROVED ALGORITHM FOR THE GENERALIZED RANK ANNIHILATION METHOD

An improved algorithm for the generalized rank annihilation method(GRAM)is presented.GRAM isa method for multicomponent calibration using two-dimensional instruments,such as GC-MS.In thispaper an orthonormal base is first computed and used to project the calibration and unknown sampleresponse matrices into a lower-dimensional subspace.The resulting generalized eigenproblem is thensolved using the QZ algorithm.The result of these improvements is that GRAM is computationally morestable,particularly in the case where the calibration sample contains chemical constituents not present inthe unknown sample and the unknown contains constituents not present in the calibration(the mostgeneral case).