  Brillouin imaging relies on the reliable extraction of subtle spectral
information from hyperspectral datasets. To date, the mainstream practice has
been using line fitting of spectral features to retrieve the average peak shift
and linewidth parameters. Good results, however, depend heavily on sufficient
SNR and may not be applicable in complex samples that consist of spectral
mixtures. In this work, we thus propose the use of various multivariate
algorithms that can be used to perform supervised or unsupervised analysis of
the hyperspectral data, with which we explore advanced image analysis
applications, namely unmixing, classification and segmentation in a phantom and
live cells. The resulting images are shown to provide more contrast and detail,
and obtained on a timescale $10^2$ faster than fitting. The estimated spectral
parameters are consistent with those calculated from pure fitting.
