  Civilizations have tried to make drinking water safe to consume for thousands
of years. The process of determining water contaminants has evolved with the
complexity of the contaminants due to pesticides and heavy metals. The routine
procedure to determine water safety is to use targeted analysis which searches
for specific substances from some known list; however, we do not explicitly
know which substances should be on this list. Before experimentally determining
which substances are contaminants, how do we answer the sampling problem of
identifying all the substances in the water? Here, we present an approach that
builds on the work of Jaanus Liigand et al., which used non-targeted analysis
that conducts a broader search on the sample to develop a random-forest
regression model, to predict the names of all the substances in a sample, as
well as their respective concentrations[1]. This work utilizes techniques from
dimensionality reduction and linear decompositions to present a more accurate
model using data from the European Massbank Metabolome Library to produce a
global list of chemicals that researchers can then identify and test for when
purifying water.
