THE AMT APPROACH IN CHEMOMETRICS - FIRST FORAYS

The 'Angle Measure Technique' (AMT) was introduced in 1994 by Robert A ndrle as a new method for characterizing the complexity of geomorphic lines. AMT was proposed as an alternative to fractal analysis (in whic h the statistical measure of the complexity of a feature, e.g. an angu lar line, is assumed constant over the range of scales of measurement) for this purpose. Instead, AMT was designed to delineate changes in c omplexity of a geomorphic feature as a function of scale. In this pape r we induct this approach into chemometrics and give several didactic and application examples. Initially it is instructive to view AMT as a n analogy to Fourier transformation, but only concerning the way that AMT spectra can be used in further practical data analysis. The AMT ap proach has profound implications for analysis of both 1D and 2D measur ement series in which 'noise' is dominant. AMT characterizes the noise part as well as quasi-periodic phenomena of a measurement series in a novel fashion as a function of a scale factor s. AMT derives complexi ty spectra which can often be used directly in furthering other specif ic data analytic objectives, e.g. as X-input for multivariate calibrat ion or for interpretative purposes. AMT in fact creates a new domain o f general data analysis, the scale domain, which complements the time and frequency domains of signal analysis. We here develop AMT so as to be able to work on any general 'measurement series', including, but f ar from restricted to, time series, image analysis and process chemome trics. A software program for generic AMT analysis has been developed, with which we have begun a series of forays into chemometric applicat ions, some of which are delineated here in order to appreciate the pot ential of AMT. We also illustrate the method with a detailed example f rom food science, namely AMT spectra derived from textured bread image ry, which can be well calibrated with respect to sensory attributes (p roduct volume, porosity). This type of application will be of great va lue in product and process optimization (certainly not only in food sc ience). This example serves as an exemplar for direct at-line imaging for general quality or process control and automation, i.e. non-invasi ve on-line or at-line process analysis. We have further developed the original AMT concept in several ways, notably by a 'mean-difference Y' complexity measure and an augmented standard deviation addition as we ll as 'automated' X/Y-axis scalings. A central issue in interpretative AMT analysis relates to 'optimal scaling' (Y-axis and/or X-axis scali ng). We have only barely begun approaching this complex issue, but AMT analysis would appear not to be fatally hampered even if not optimall y scaled. For comparative studies the scaling is irrelevant. (C) 1996 by John Wiley & Son, Ltd.