A mere glance at the contents of any of the conferences organised by ISEMA (International Society for Electromagnetic Aquametry) [1] shows that the measurement and control of water (its quantity and states) in materials is a very wide and active field. That water lends itself to study in this way is because of its very dominant dispersive dielectric properties and an unusually large dipole moment for such a small molecule (1.84 Debye units). At room temperature the dispersion is centred on ∼ 12.5GHz and the real part of the relative permittivity at its upper and lower frequency extremes is ∼ 4.3 and 80 respectively [2]. The complex dielectric properties characteristic of this dispersion are the properties that are measured and correlated with whatever aspect of the water content is of interest. Historically much of the work was carried out at one or two frequencies, mostly in X-band where the dielectric loss is at a maximum, but the advent of time domain reflectometry (TDR) [3] for broadband dielectric measurements in the microwave region eventually led to such measurements being made using open ended coaxial sensors [4], although a greater potential of such measurements was only realised later by the authors. The use of such sensors freed the experimenter from the difficult task of defining the sample shape by means of a sample cell; the measurements still required however that the sensor be in contact with the sample. As a true frequency domain UWB application, dielectric measurements of foodstuffs over a wide range of frequencies (100MHz to 20GHz) were made using network analysers and such coaxial sensors [5, 6]. Drawing on the experience in other chemometric applications such as NIR (near infra-red spectroscopy) [7], the dielectric spectra obtained were subjected to various multivariate analyses (PCR (principal component regression), PLSR (partial least squares regression), and ANNs (artificial neural networks both linear and non-linear)). Such analyses both compress the data into orthogonal factors and extract from those factors the best information to predict the composition of the foodstuffs. In such analysis the important variables are, not so much the dielectric properties at each sampled frequency, but rather the shape of the spectrum. In effect the data reduction provides suitable shape descriptors, which are in the case of foods, very dependent on the water content and its interaction with other