Once extracted, these features were processed by Linear Dis-
criminant Analysis (a pre-classi fi ed method), using a commercial
package (Multisens Analyser, JLM Innovation, Germany). The
method initially involved assigning a disease-state class to each
sample and extracting a set of individual features from the re-
sponses of the sensors to it. These features were then ranked by a
control algorithm to maximise the separation between samples of
different class while minimising spread within classes and com-
bined into one or more discriminant functions (bound by the
constraint of the number of classes). The general equation for
discriminant function g(x) is illustrated below in Eq. (4), where w i
and x i are respectively the weights and values of the ‘i th ′ extracted
feature (ranging from 1 to d), and w 0 is a constant threshold
weight. Classi fi cations were made both for comparing the data of
all three groups, as well as for a two-group CRC and IBS compar-
ison that more accurately simulates a clinical situation where
a patient arrives complaining of symptoms linked to lower GI
disease.