  A generalization of Gy's theory for the variance of the fundamental sampling
error is reviewed. Practical situations where the generalized model potentially
leads to more accurate variance estimates are identified as: clustering of
particles, differences in densities or sizes of the particles or repulsive
inter-particle forces. Two general approaches for estimating an input parameter
for the generalized model are discussed. The first approach consists of
modelling based on physical properties of particles such as size, density and
electrostatic forces between particles. The second approach uses image analysis
of actual samples. Further research into both methods is proposed and a
suggestion is made to use line-intercept sampling combined with Markov Chain
modelling in the second approach.
  It is concluded that although, at the moment, it is too early for a routine
application of the generalized theory, the generalization has the potential of
providing more accurate variance estimates than are possible in the theory of
Gy. Therefore, further research into the development and expansion of the
generalized theory is worthwhile.
