CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...
CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...
CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...
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5.5 Conclusion<br />
In order to verify the generalisation ability of the implemented multivariate analysis<br />
pipeline, the constructed statistical tools were tested upon three new individual<br />
real-world case studies featuring different types of data (beef fillets, minced beef,<br />
pork), which have been analysed using a variety of instruments (FTIR, HPLC, Raman<br />
and e-nose) under different temperatures and packaging. By analysing the new case<br />
studies, we were able to show that significant results can be obtained on further<br />
datasets. In addition, by a direct comparison of their results, some noteworthy<br />
conclusions were drawn.<br />
HPLC proved to be the best instrumental technique for the chosen application of<br />
assessing meat freshness. Standalone HPLC data, both prior and after the application<br />
of PCA, consistently demonstrated the highest percentages of correctly classified<br />
samples (%CC). Thus, we can conclude that the provided HPLC data contained<br />
abundances of several specific chemical compounds associated with and denoting<br />
spoilage. Conversely, the FTIR, Raman and e-nose data were the measurements of<br />
raw sensors with no prior feature selection or mapping to specific compounds. Also, it<br />
is obvious that the HPLC data present higher classification accuracies for kernelbased<br />
(RBF) SVMs. On the contrary, the overall accuracies of the simple<br />
spectroscopic data by FTIR are clearly profiting by the application of linear<br />
classifiers, and especially by the application of traditional chemometric methods such<br />
as PLS-DA. In this instance, the nonlinear mapping by RBF SVMs has been found<br />
unfit. Finally, the e-nose data did not demonstrate any discriminative information, and<br />
its classification results proved to be statistically non-significant.<br />
As far as the integrated datasets are concerned, CPCA was consistently found to be a<br />
better data fusion technique than GPA. More specifically, CPCA clearly improves the<br />
outcome of the integration by combining the strongest features of the initial datasets,<br />
while GPA appears to be dominated by the weakest experimental technique.<br />
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