3.3 PLS-DA Analysis
The PLS-DA models were developed for the three microorganisms using the
two optimal pre-processing algorithms determined with the PCA models.
Therefore, PLS-DA models were made using SG1+SNV and SNV+SG2,
respectively. The best factor for each PLS-DA model was found by
comparing the classification parameters. Next, the results from both
pre-processing methods are compared.
The optimal results for Ml were obtained with SNV+SG2, Sewith SG1+SNV, and Sa with SNV+SG2 from this analysis. ForMl , the PLS-DA model discriminates all samples perfectly with a
%T of 100%, %F of 100%, SEN of 1.0, ξ of 1.0, and MCC of 1.0 for
both pre-processing methods. Furthermore, the Ml PLS-DA model
classifies Sa without difficulty, while the Ml samples are close
to the threshold value of zero. Se appears close to the
threshold, indicating slight difficulty; however, the samples are
farther from the threshold than Ml . For Se, each group has a
larger distance to the threshold than the Ml model; however, the
model classifies two samples incorrectly from two different classes.
Table 2 indicates that SNV+SG2 has better results than SG1+SNV for Se
with a %T of 98.4%, %F of 1.6%, SEN of 1.0, ξ of 1.0, and MCC of
1.0. The PLS-DA models for Sa discriminate all samples perfectly with
the same results as Ml , as shown in Table 2. Visualizing the
predicted values vs. the number of samples shows that Sa has
better classification than Ml and Se due to the values
being far from the threshold and the clusters having a more compact
structure. On the other hand, if separating all three microorganisms is
desired, the Ml PLS-DA model with SNV+SG2 shows the best results
since all the clusters do not overlap.
(A)